commit 31ec984482a49047e625d84d75ea828665c7c59b
parent be084ce91b81503c5bb3978eb760b34fdb050e9b
Author: andrewlaack-collab <andrew.laack@imbue.com>
Date: Mon, 2 Feb 2026 18:24:25 +0000
Refactoring for simple install (#6)
Diffstat:
180 files changed, 12676 insertions(+), 18622 deletions(-)
diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md
@@ -4,10 +4,20 @@ Vet is a library and CLI tool for verifying code quality and correctness.
## Installation
-From the repository root:
+**From git (pip/uv):**
```bash
-uv sync --project vet
+pip install git+https://github.com/imbue-ai/vet.git
+# or
+uv pip install git+https://github.com/imbue-ai/vet.git
+```
+
+**For development (from repository root):**
+
+```bash
+uv sync
+# or
+pip install -e .
```
## Usage
@@ -86,7 +96,7 @@ By default, `vet` runs all the registered issue identifiers and outputs all the
If you want to add a new issue identifier, you need to:
-1. Implement the `IssueIdentifier` protocol from `imbue_tools.repo_utils.data_types`.
+1. Implement the `IssueIdentifier` protocol from `vet.imbue_tools.repo_utils.data_types`.
2. Register the new issue identifier by adding it to `IDENTIFIERS` in `vet.issue_identifiers.registry`.
Based on your needs, instead of the above, you can also extend one of the existing batched zero-shot issue identifiers:
@@ -102,7 +112,7 @@ Refer to the source code for more details.
When creating a new entrypoint into vet, you must call `ensure_core_log_levels_configured()` to register the custom log levels used throughout the codebase.
```python
-from imbue_core.log_utils import ensure_core_log_levels_configured
+from vet.imbue_core.log_utils import ensure_core_log_levels_configured
ensure_core_log_levels_configured()
```
diff --git a/README.md b/README.md
@@ -10,6 +10,12 @@ It reviews git diffs, and optionally an agent's conversation history, to find is
pip install verify-everything
```
+Or install directly from git:
+
+```bash
+pip install git+https://github.com/imbue-ai/vet.git
+```
+
## Quickstart
Run Vet in the current repo:
diff --git a/imbue_core/README.md b/imbue_core/README.md
@@ -1,3 +0,0 @@
-# Imbue Core
-
-Utilities for `imbue-desktop`. This package is not meant to be installed on its own.
diff --git a/imbue_core/imbue_core/agents/agent_api/api.py b/imbue_core/imbue_core/agents/agent_api/api.py
@@ -1,59 +0,0 @@
-from __future__ import annotations
-
-from contextlib import contextmanager
-from functools import singledispatch
-from pathlib import Path
-from typing import Any
-from typing import ContextManager
-from typing import Iterator
-
-from imbue_core.agents.agent_api.claude.client import ClaudeCodeClient
-from imbue_core.agents.agent_api.claude.data_types import ClaudeCodeOptions
-from imbue_core.agents.agent_api.client import AgentClient
-from imbue_core.agents.agent_api.client import AgentOptionsT
-from imbue_core.agents.agent_api.client import CachedAgentClient
-from imbue_core.agents.agent_api.codex.client import CodexClient
-from imbue_core.agents.agent_api.codex.data_types import CodexOptions
-from imbue_core.agents.agent_api.data_types import AgentOptions
-
-
-@singledispatch
-def _build_client_from_options(
- options: AgentOptions,
-) -> ContextManager[AgentClient[Any]]:
- """Return a context manager that builds an AgentClient for the given options."""
- raise ValueError(f"Unsupported agent options type: {type(options).__name__}")
-
-
-@_build_client_from_options.register
-def _(options: ClaudeCodeOptions) -> ContextManager[AgentClient[ClaudeCodeOptions]]:
- return ClaudeCodeClient.build(options)
-
-
-@_build_client_from_options.register
-def _(options: CodexOptions) -> ContextManager[AgentClient[CodexOptions]]:
- return CodexClient.build(options)
-
-
-@contextmanager
-def get_agent_client(
- *,
- options: AgentOptionsT,
- cache_path: Path | None = None,
-) -> Iterator[AgentClient[AgentOptionsT]]:
- """Build and manage the lifecycle of an AgentClient based on the provided options.
-
- Args:
- options: AgentOptions instance describing which agent to run.
- cache_path: Optional path to use for caching agent interactions.
-
- Yields:
- An AgentClient (or CachedAgentClient) bound to the selected agent implementation.
- """
-
- with _build_client_from_options(options) as client:
- if cache_path is None:
- yield client
- return
-
- yield CachedAgentClient(client, cache_path)
diff --git a/imbue_core/imbue_core/agents/agent_api/cache_utils.py b/imbue_core/imbue_core/agents/agent_api/cache_utils.py
@@ -1,36 +0,0 @@
-import hashlib
-from pathlib import Path
-
-from imbue_core.agents.agent_api.data_types import AgentOptions
-from imbue_core.agents.agent_api.interaction import AgentInteractionRecord
-from imbue_core.caching import get_cache
-
-
-def _create_cache_key(prompt: str, options: AgentOptions) -> str:
- """Create a cache key for the given prompt and options."""
- return hashlib.md5(f"{prompt} | {options.model_dump_json() if options else ''}".encode()).hexdigest()
-
-
-def check_cache(cache_path: Path, prompt: str, options: AgentOptions) -> AgentInteractionRecord | None:
- """Check the cache for the given prompt and options."""
- cache_key = _create_cache_key(prompt, options)
- cache = get_cache(cache_path)
-
- with cache:
- value = cache.get(cache_key)
-
- if value is None:
- return None
- assert isinstance(value, str), f"Got value of type {type(value)} from cache, expected str"
- return AgentInteractionRecord.model_validate_json(value)
-
-
-def update_cache(
- agent_interaction: AgentInteractionRecord,
- cache_dir: Path,
-) -> None:
- """Save an agent interaction record to the cache."""
- cache = get_cache(cache_dir)
- cache_key = _create_cache_key(agent_interaction.prompt, agent_interaction.options)
- with cache:
- cache.set(cache_key, agent_interaction.model_dump_json())
diff --git a/imbue_core/imbue_core/agents/agent_api/claude/client.py b/imbue_core/imbue_core/agents/agent_api/claude/client.py
@@ -1,191 +0,0 @@
-import json
-import shutil
-import tempfile
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Generator
-from typing import Iterator
-from typing import Self
-
-from loguru import logger
-
-from imbue_core.agents.agent_api.claude.data_types import ClaudeCodeOptions
-from imbue_core.agents.agent_api.claude.message_parser import parse_claude_message
-from imbue_core.agents.agent_api.client import RealAgentClient
-from imbue_core.agents.agent_api.data_types import AgentMessage
-from imbue_core.agents.agent_api.data_types import AgentResultMessage
-from imbue_core.agents.agent_api.errors import AgentCLINotFoundError
-from imbue_core.agents.agent_api.transport import AgentSubprocessCLITransport
-from imbue_core.agents.agent_api.transport import AgentSubprocessCLITransportOptions
-from imbue_core.agents.agent_api.transport import AgentTransport
-
-
-class ClaudeCodeClient(RealAgentClient[ClaudeCodeOptions]):
- """Claude Code client implementation.
-
- Most callers should obtain an instance through `get_agent_client(options=ClaudeCodeOptions(...))`,
- which takes care of building and tearing down the underlying CLI transport.
-
- Example:
- ```python
- with get_agent_client(options=ClaudeCodeOptions()) as client:
- for message in client.process_query(prompt="Hello"):
- print(message)
- ```
- """
-
- def __init__(self, options: ClaudeCodeOptions, transport: AgentTransport) -> None:
- super().__init__(options)
- self._transport = transport
-
- @classmethod
- @contextmanager
- def build(cls, options: ClaudeCodeOptions) -> Generator[Self, None, None]:
- cmd = cls._build_cli_cmd(options)
- with AgentSubprocessCLITransport.build(
- AgentSubprocessCLITransportOptions(
- cmd=cmd,
- cwd=options.cwd,
- extra_env_vars={"CLAUDE_CODE_ENTRYPOINT": "sdk-py"},
- )
- ) as transport:
- yield cls(options=options, transport=transport)
-
- def process_query(self, prompt: str) -> Iterator[AgentMessage]:
- logger.trace(
- "{client_name}: calling agent with prompt={prompt}",
- client_name=type(self).__name__,
- prompt=prompt,
- )
- # Claude code expects "User message" objects as inputs
- self._transport.send_request(
- [
- {
- "type": "user",
- "message": {
- "role": "user",
- "content": [{"type": "text", "text": prompt}],
- },
- }
- ],
- self._options,
- )
-
- for data in self._transport.receive_messages():
- logger.trace(
- "{client_name}: received raw JSON message={data}",
- client_name=type(self).__name__,
- data=data,
- )
-
- message = parse_claude_message(data)
- if message:
- yield message
-
- if isinstance(message, AgentResultMessage):
- break
-
- logger.trace(
- "{client_name}: finished calling agent with prompt={prompt}",
- client_name=type(self).__name__,
- prompt=prompt,
- )
-
- @staticmethod
- def _find_cli() -> str:
- """Find Claude Code CLI binary."""
- cli = shutil.which("claude")
- if cli:
- return cli
-
- locations = [
- # TODO: Document what these do. Does the path to claude inside the container need to be here?
- Path("/imbue_addons/bin/claude"),
- Path.home() / ".npm-global/bin/claude",
- Path("/usr/local/bin/claude"),
- Path.home() / ".local/bin/claude",
- Path.home() / "node_modules/.bin/claude",
- Path.home() / ".yarn/bin/claude",
- ]
-
- for path in locations:
- if path.exists() and path.is_file():
- return str(path)
-
- node_installed = shutil.which("node") is not None
-
- if not node_installed:
- raise AgentCLINotFoundError(
- "\n".join(
- [
- "Claude Code requires Node.js, which is not installed.",
- "Install Node.js from: https://nodejs.org/",
- "\nAfter installing Node.js, install Claude Code:",
- " npm install -g @anthropic-ai/claude-code",
- ]
- )
- )
-
- raise AgentCLINotFoundError(
- "\n".join(
- [
- "Claude Code not found. Install with:",
- " npm install -g @anthropic-ai/claude-code",
- "\nIf already installed locally, try:",
- ' export PATH="$HOME/node_modules/.bin:$PATH"',
- ]
- )
- )
-
- @classmethod
- def _build_cli_cmd(cls, options: ClaudeCodeOptions) -> list[str]:
- """Build CLI command with arguments."""
- if options.is_cached:
- # in this case, the cmd should never be used
- cmd = ["CACHED_CLAUDE_CODE_EXEC_PLACEHOLDER"]
- return cmd
- cli_path = str(options.cli_path) if options.cli_path is not None else cls._find_cli()
- cmd = [
- cli_path,
- "--output-format",
- "stream-json",
- "--input-format",
- "stream-json",
- "--verbose",
- ]
- cmd.extend(cls._build_cli_args(options))
- return cmd
-
- @staticmethod
- def _build_cli_args(options: ClaudeCodeOptions) -> list[str]:
- args = []
- if options.system_prompt:
- args.extend(["--system-prompt", options.system_prompt])
-
- if options.append_system_prompt:
- args.extend(["--append-system-prompt", options.append_system_prompt])
-
- if options.model:
- args.extend(["--model", options.model])
-
- if options.permission_prompt_tool_name:
- args.extend(["--permission-prompt-tool", options.permission_prompt_tool_name])
-
- if options.permission_mode:
- args.extend(["--permission-mode", options.permission_mode])
-
- if options.continue_conversation:
- args.append("--continue")
-
- if options.resume:
- args.extend(["--resume", options.resume])
-
- if options.mcp_servers:
- mcp_config_file = tempfile.NamedTemporaryFile(delete=False, suffix=".json")
- mcp_config_file.write(
- json.dumps({"mcpServers": {k: v.model_dump() for k, v in options.mcp_servers.items()}}).encode("utf-8")
- )
- args.extend(["--mcp-config", mcp_config_file.name])
-
- args.append("--print")
- return args
diff --git a/imbue_core/imbue_core/agents/agent_api/claude/data_types.py b/imbue_core/imbue_core/agents/agent_api/claude/data_types.py
@@ -1,79 +0,0 @@
-from pathlib import Path
-from typing import Literal
-
-from pydantic import Field
-
-from imbue_core.agents.agent_api.data_types import AgentOptions
-from imbue_core.agents.agent_api.data_types import AgentToolName
-from imbue_core.pydantic_serialization import SerializableModel
-
-ClaudePermissionMode = Literal["plan", "default", "acceptEdits", "bypassPermissions"]
-
-
-class ClaudeMcpStdioServerConfig(SerializableModel):
- """MCP stdio server configuration."""
-
- type: Literal["stdio"] = "stdio"
- command: str
- args: list[str] = Field(default_factory=list)
- env: dict[str, str] = Field(default_factory=dict)
-
-
-class ClaudeMcpHttpServerConfig(SerializableModel):
- """MCP HTTP server configuration."""
-
- type: Literal["http"] = "http"
- url: str
- headers: dict[str, str] | None = None
-
-
-ClaudeMcpServerConfig = ClaudeMcpStdioServerConfig | ClaudeMcpHttpServerConfig
-
-
-class ClaudeCodeOptions(AgentOptions):
- """Query options for Claude SDK."""
-
- object_type: Literal["ClaudeCodeOptions"] = "ClaudeCodeOptions"
-
- allowed_tools: list[str] = Field(default_factory=list)
- max_thinking_tokens: int = 8000
- system_prompt: str | None = None
- append_system_prompt: str | None = None
- mcp_tools: list[str] = Field(default_factory=list)
- mcp_servers: dict[str, ClaudeMcpServerConfig] = Field(default_factory=dict)
- permission_mode: ClaudePermissionMode | None = None
- continue_conversation: bool = False
- resume: str | None = None
- max_turns: int | None = None
- disallowed_tools: list[str] = Field(default_factory=list)
- model: str | None = None
- permission_prompt_tool_name: str | None = None
- # Optional override for the Claude CLI path
- cli_path: Path | None = None
- is_cached: bool = False
-
-
-CLAUDE_TOOLS = (
- AgentToolName.READ,
- AgentToolName.WRITE,
- AgentToolName.EDIT,
- AgentToolName.MULTI_EDIT,
- AgentToolName.GLOB,
- AgentToolName.NOTEBOOK_READ,
- AgentToolName.NOTEBOOK_EDIT,
- AgentToolName.LS,
- AgentToolName.GREP,
- AgentToolName.BASH,
- AgentToolName.BASH_OUTPUT,
- AgentToolName.KILL_SHELL,
- AgentToolName.WEB_SEARCH,
- AgentToolName.WEB_FETCH,
- AgentToolName.TASK,
- AgentToolName.TODO_READ,
- AgentToolName.TODO_WRITE,
- AgentToolName.SLASH_COMMAND,
- AgentToolName.EXIT_PLAN_MODE,
- AgentToolName.MCP_TOOL,
- AgentToolName.LIST_MCP_RESOURCES,
- AgentToolName.READ_MCP_RESOURCE,
-)
diff --git a/imbue_core/imbue_core/agents/agent_api/claude/message_parser.py b/imbue_core/imbue_core/agents/agent_api/claude/message_parser.py
@@ -1,117 +0,0 @@
-from typing import Any
-from typing import assert_never
-
-from imbue_core.agents.agent_api.data_types import AgentAssistantMessage
-from imbue_core.agents.agent_api.data_types import AgentContentBlock
-from imbue_core.agents.agent_api.data_types import AgentMessage
-from imbue_core.agents.agent_api.data_types import AgentResultMessage
-from imbue_core.agents.agent_api.data_types import AgentSystemEventType
-from imbue_core.agents.agent_api.data_types import AgentSystemMessage
-from imbue_core.agents.agent_api.data_types import AgentTextBlock
-from imbue_core.agents.agent_api.data_types import AgentThinkingBlock
-from imbue_core.agents.agent_api.data_types import AgentToolResultBlock
-from imbue_core.agents.agent_api.data_types import AgentToolUseBlock
-from imbue_core.agents.agent_api.data_types import AgentUsage
-from imbue_core.agents.agent_api.data_types import AgentUserMessage
-
-
-def parse_claude_message(data: dict[str, Any]) -> AgentMessage | None:
- """Parse message from CLI output using unified types.
-
- Reference:
- https://github.com/anthropics/claude-agent-sdk-python/blob/main/src/claude_agent_sdk/_internal/message_parser.py
- https://docs.claude.com/en/api/agent-sdk/typescript#sdkmessage
- https://docs.claude.com/en/api/agent-sdk/python#message-types
- """
-
- match data["type"]:
- case "user":
- return AgentUserMessage(content=parse_claude_content_blocks(data), original_message=data)
-
- case "assistant":
- return AgentAssistantMessage(content=parse_claude_content_blocks(data), original_message=data)
-
- case "system":
- # Normalize system event types
- event_type = parse_claude_system_event_type(data.get("subtype", ""))
- return AgentSystemMessage(
- event_type=event_type,
- session_id=data.get("session_id"),
- error=data.get("error"),
- original_message=data,
- )
-
- case "result":
- # Build normalized usage
- usage = None
- raw_usage = data.get("usage")
- if raw_usage or data.get("total_cost_usd"):
- usage = AgentUsage(
- input_tokens=raw_usage.get("input_tokens") if raw_usage else None,
- output_tokens=raw_usage.get("output_tokens") if raw_usage else None,
- cached_tokens=(raw_usage.get("cache_read_input_tokens") if raw_usage else None),
- total_tokens=(
- raw_usage.get("input_tokens", 0) + raw_usage.get("output_tokens", 0) if raw_usage else None
- ),
- total_cost_usd=data.get("total_cost_usd"),
- )
-
- return AgentResultMessage(
- session_id=data["session_id"],
- is_error=data["is_error"],
- duration_ms=data.get("duration_ms"),
- api_duration_ms=data.get("duration_api_ms"),
- num_turns=data.get("num_turns"),
- usage=usage,
- result=data.get("result"),
- error=data.get("error") if data["is_error"] else None,
- original_message=data,
- )
-
- case _ as unreachable:
- assert_never(unreachable)
-
-
-def parse_claude_system_event_type(subtype: str) -> AgentSystemEventType:
- """Parse Claude system event subtype to unified event type."""
- subtype_lower = subtype.lower()
-
- # TODO add other system event types as we find them
- # basically the documentattion doesn't mention any other system event types
- # other than init AFAIKT
- if "init" in subtype_lower:
- return AgentSystemEventType.SESSION_STARTED
- else:
- return AgentSystemEventType.OTHER
-
-
-def parse_claude_content_blocks(data: dict[str, Any]) -> list[AgentContentBlock]:
- return [parse_claude_content_block(block) for block in data["message"]["content"]]
-
-
-def parse_claude_content_block(block: dict[str, Any]) -> AgentContentBlock:
- """Parse content block from CLI output using unified types."""
-
- match block["type"]:
- case "text":
- return AgentTextBlock(text=block["text"])
-
- case "thinking":
- # Claude Code thinking blocks
- return AgentThinkingBlock(
- content=block.get("thinking", ""),
- thinking_tokens=block.get("thinking_tokens"),
- )
-
- case "tool_use":
- return AgentToolUseBlock(id=block["id"], name=block["name"], input=block["input"])
-
- case "tool_result":
- return AgentToolResultBlock(
- tool_use_id=block["tool_use_id"],
- content=block.get("content"),
- is_error=block.get("is_error"),
- )
-
- case _ as unreachable:
- assert_never(unreachable)
diff --git a/imbue_core/imbue_core/agents/agent_api/client.py b/imbue_core/imbue_core/agents/agent_api/client.py
@@ -1,89 +0,0 @@
-import abc
-from pathlib import Path
-from typing import Generic
-from typing import Iterator
-from typing import TypeVar
-
-from imbue_core.agents.agent_api.cache_utils import check_cache
-from imbue_core.agents.agent_api.cache_utils import update_cache
-from imbue_core.agents.agent_api.data_types import AgentMessage
-from imbue_core.agents.agent_api.data_types import AgentOptions
-from imbue_core.agents.agent_api.interaction import AgentInteraction
-from imbue_core.agents.agent_api.interaction import AgentInteractionRecord
-
-AgentOptionsT = TypeVar("AgentOptionsT", bound=AgentOptions)
-
-
-class AgentClient(abc.ABC, Generic[AgentOptionsT]):
- """Base code agent client interface.
-
- This client defines the interface for launching and interacting with a coding agent (e.g., ClaudeCode, Codex, etc.)
-
- Clients are usually created through `get_agent_client`, which selects the right concrete implementation
- and manages any transports. Direct subclasses only need to implement `process_query`.
- """
-
- def __init__(self, options: AgentOptionsT) -> None:
- self._options = options
-
- @abc.abstractmethod
- def process_query(self, prompt: str) -> Iterator[AgentMessage]:
- """Call the underlying agent to process a query."""
-
-
-class RealAgentClient(AgentClient[AgentOptionsT]):
- """Agent client that is not cached or dummy; it runs real commands."""
-
- @staticmethod
- @abc.abstractmethod
- def _find_cli() -> str:
- """Find the CLI binary for the agent."""
-
- @classmethod
- @abc.abstractmethod
- def _build_cli_cmd(cls, options: AgentOptionsT) -> list[str]:
- """Build the CLI command for the agent."""
-
- @staticmethod
- @abc.abstractmethod
- def _build_cli_args(options: AgentOptionsT) -> list[str]:
- """Build the CLI arguments for the agent."""
-
-
-class CachedAgentClient(AgentClient[AgentOptionsT]):
- """Cached agent client implementation.
-
- This client is a wrapper around an agent client that caches the agent responses.
- """
-
- def __init__(self, client: AgentClient[AgentOptionsT], cache_path: Path) -> None:
- super().__init__(client._options)
- self._client = client
- self._cache_path = cache_path
-
- def process_query(self, prompt: str) -> Iterator[AgentMessage]:
- cache_path = self._cache_path
- if cache_path is not None:
- cache_record = check_cache(cache_path, prompt, self._client._options)
- if cache_record is not None:
- for message in cache_record.messages:
- yield message
- return
-
- agent_interaction = AgentInteraction(prompt, self._client._options)
- for message in self._client.process_query(prompt):
- agent_interaction.put(message)
- yield message
-
- # NOTE we only cache full interactions given the 'process_query' method is called till
- # the generator is exhausted.
- # This means that if the generator is not exhausted, the cache will not be updated.
- # If we do want a way to still cache interactions, even if we early exit the generator,
- # then we could use a separate thread to get the agent response and cache it in the background.
- agent_interaction_record = AgentInteractionRecord.from_agent_interaction(agent_interaction)
- update_cache(agent_interaction_record, cache_path)
-
- @property
- def client(self) -> AgentClient[AgentOptionsT]:
- """Get the underlying client."""
- return self._client
diff --git a/imbue_core/imbue_core/agents/agent_api/codex/client.py b/imbue_core/imbue_core/agents/agent_api/codex/client.py
@@ -1,166 +0,0 @@
-import json
-import shutil
-from contextlib import contextmanager
-from pathlib import Path
-from typing import Generator
-from typing import Iterator
-from typing import Self
-
-from loguru import logger
-
-from imbue_core.agents.agent_api.client import RealAgentClient
-from imbue_core.agents.agent_api.codex.data_types import CodexOptions
-from imbue_core.agents.agent_api.codex.message_parser import parse_codex_event
-from imbue_core.agents.agent_api.data_types import AgentMessage
-from imbue_core.agents.agent_api.data_types import AgentSystemMessage
-from imbue_core.agents.agent_api.errors import AgentCLINotFoundError
-from imbue_core.agents.agent_api.transport import AgentSubprocessCLITransport
-from imbue_core.agents.agent_api.transport import AgentSubprocessCLITransportOptions
-
-
-class CodexClient(RealAgentClient[CodexOptions]):
- """Codex CLI client implementation."""
-
- def __init__(self, options: CodexOptions) -> None:
- super().__init__(options=options)
- self._session_id: str | None = options.resume_session_id
-
- @classmethod
- @contextmanager
- def build(cls, options: CodexOptions) -> Generator[Self, None, None]:
- yield cls(options=options)
-
- def process_query(self, prompt: str) -> Iterator[AgentMessage]:
- logger.trace(
- "{client_name}: calling agent with prompt={prompt}",
- client_name=type(self).__name__,
- prompt=prompt,
- )
-
- # NOTE: (2025-11-20) Codex CLI does not support streaming inputs, and only supports using codex CLI via
- # non-interactive mode, where each call is a new process.
- # So here we just create a new transport for each call, and handle things like resuming the session as
- # needed.
- options = self._options
- if self._session_id is not None and self._session_id != self._options.resume_session_id:
- # Inject the current session id into the options before building the command
- options = self._options.model_copy(update={"resume_session_id": self._session_id})
- cmd = self._build_cli_cmd(options)
- with AgentSubprocessCLITransport.build(
- AgentSubprocessCLITransportOptions(cmd=[*cmd, prompt], cwd=options.cwd)
- ) as transport:
- transport.send_request([prompt], options)
-
- thread_id: str | None = None
- for data in transport.receive_messages():
- logger.trace(
- "{client_name}: received raw JSON message={data}",
- client_name=type(self).__name__,
- data=data,
- )
-
- message = parse_codex_event(data, thread_id)
- if message:
- if isinstance(message, AgentSystemMessage):
- thread_id = message.session_id
- # Store the new session id for subsequent calls to process_query on this client
- self._session_id = message.session_id
-
- yield message
-
- logger.trace(
- "{client_name}: finished calling agent with prompt={prompt}",
- client_name=type(self).__name__,
- prompt=prompt,
- )
-
- @staticmethod
- def _find_cli() -> str:
- """Find Codex CLI binary."""
- cli = shutil.which("codex")
- if cli:
- return cli
-
- locations = [
- Path("/usr/local/bin/codex"),
- Path.home() / ".local/bin/codex",
- Path.home() / "node_modules/.bin/codex",
- Path.home() / ".npm-global/bin/codex",
- ]
-
- for path in locations:
- if path.exists() and path.is_file():
- return str(path)
-
- node_installed = shutil.which("node") is not None
- npm_installed = shutil.which("npm") is not None
-
- if not node_installed or not npm_installed:
- raise AgentCLINotFoundError(
- "\n".join(
- [
- "Codex CLI requires Node.js and npm, which may not be installed.",
- "Install Node.js from: https://nodejs.org/",
- "\nAfter installing Node.js, install Codex CLI:",
- " npm install -g @openai/codex",
- ]
- )
- )
-
- raise AgentCLINotFoundError(
- "\n".join(
- [
- "Codex CLI not found. Install with:",
- " npm install -g @openai/codex",
- "\nOr via Homebrew:",
- " brew install codex",
- "\nIf already installed locally, try:",
- ' export PATH="$HOME/node_modules/.bin:$PATH"',
- ]
- )
- )
-
- @classmethod
- def _build_cli_cmd(cls, options: CodexOptions) -> list[str]:
- """Build CLI command with arguments."""
- if options.is_cached:
- # in this case, the cmd should never be used
- cmd = ["CACHED_CODEX_EXEC_PLACEHOLDER"]
- return cmd
- cli_path = str(options.cli_path) if options.cli_path is not None else cls._find_cli()
- cmd = [cli_path, "exec"]
- cmd.extend(cls._build_cli_args(options))
- return cmd
-
- @staticmethod
- def _build_cli_args(options: CodexOptions) -> list[str]:
- args = []
- # Permissions flags
- if options.approval_mode:
- args.extend(["-c", f"'approval_mode={options.approval_mode}'"])
- if options.sandbox_mode:
- args.extend(["--sandbox", options.sandbox_mode])
- if options.approval_policy:
- args.extend(["-c", f"'approval={options.approval_policy}'"])
-
- # JSON streaming output
- args.append("--json")
-
- # Model selection
- if options.model:
- args.extend(["--model", options.model])
-
- # Skip git repo check
- if options.skip_git_repo_check:
- args.append("--skip-git-repo-check")
-
- # Output schema for structured output
- if options.output_schema:
- args.extend(["--output-schema", json.dumps(options.output_schema)])
-
- # Session resumption
- if options.resume_last:
- args.extend(["resume", "--last"])
- elif options.resume_session_id:
- args.extend(["resume", options.resume_session_id])
- return args
diff --git a/imbue_core/imbue_core/agents/agent_api/codex/data_types.py b/imbue_core/imbue_core/agents/agent_api/codex/data_types.py
@@ -1,241 +0,0 @@
-"""Data types for Codex agent integration."""
-
-from pathlib import Path
-from typing import Annotated
-from typing import Any
-from typing import Literal
-
-from pydantic import Field
-from pydantic import Tag
-
-from imbue_core.agents.agent_api.data_types import AgentOptions
-from imbue_core.agents.agent_api.data_types import AgentToolName
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_core.pydantic_serialization import build_discriminator
-
-# https://developers.openai.com/codex/cli/features#approval-modes
-CodexApprovalMode = Literal["auto", "read-only", "full-access"] | None
-
-# https://developers.openai.com/codex/cli/reference, --sandbox options
-CodexSandboxMode = Literal["read-only", "workspace-write", "danger-full-access"] | None
-
-# https://developers.openai.com/codex/cli/reference, --ask-for-approval options
-CodexApprovalPolicy = Literal["untrusted", "on-failure", "on-request", "never"] | None
-
-
-class CodexOptions(AgentOptions):
- """Options for Codex CLI execution."""
-
- object_type: Literal["CodexOptions"] = "CodexOptions"
-
- approval_mode: CodexApprovalMode = None
- sandbox_mode: CodexSandboxMode = None
- approval_policy: CodexApprovalPolicy = None
- model: str | None = None
- system_prompt: str | None = None
- image_paths: list[Path] = Field(default_factory=list)
- skip_git_repo_check: bool = False
- output_schema: dict[str, Any] | None = None
- # Session management
- resume_session_id: str | None = None
- resume_last: bool = False
- thread_id: str | None = None
- # Optional override for the Codex CLI path
- cli_path: Path | None = None
- is_cached: bool = False
-
-
-# Codex item types
-# Ref: https://github.com/openai/codex/blob/main/sdk/typescript/src/items.ts
-# Ref: https://github.com/openai/codex/blob/main/codex-rs/exec/src/exec_events.rs
-
-
-# The status of a command execution.
-CommandExecutionStatus = Literal["in_progress", "completed", "failed"]
-
-
-class CodexCommandExecutionItem(SerializableModel):
- type: Literal["command_execution"] = "command_execution"
- id: str
- command: str
- aggregated_output: str
- exit_code: int | None = None
- status: CommandExecutionStatus
-
-
-# Indicates the type of the file change.
-PatchChangeKind = Literal["add", "delete", "update"]
-
-
-class CodexFileUpdateChange(SerializableModel):
- path: str
- kind: PatchChangeKind
-
-
-# The status of a file change.
-PatchApplyStatus = Literal["completed", "failed"]
-
-
-class CodexFileChangeItem(SerializableModel):
- type: Literal["file_change"] = "file_change"
- id: str
- changes: list[CodexFileUpdateChange]
- status: PatchApplyStatus
-
-
-# The status of an MCP tool call.
-McpToolCallStatus = Literal["in_progress", "completed", "failed"]
-
-
-class CodexMcpToolCallItem(SerializableModel):
- type: Literal["mcp_tool_call"] = "mcp_tool_call"
- id: str
- server: str
- tool: str
- status: McpToolCallStatus
-
-
-class CodexAgentMessageItem(SerializableModel):
- type: Literal["agent_message"] = "agent_message"
- id: str
- text: str
-
-
-class CodexReasoningItem(SerializableModel):
- type: Literal["reasoning"] = "reasoning"
- id: str
- text: str
-
-
-class CodexWebSearchItem(SerializableModel):
- type: Literal["web_search"] = "web_search"
- id: str
- query: str
-
-
-class CodexErrorItem(SerializableModel):
- type: Literal["error"] = "error"
- id: str
- message: str
-
-
-class CodexTodoItem(SerializableModel):
- text: str
- completed: bool
-
-
-class CodexTodoListItem(SerializableModel):
- type: Literal["todo_list"] = "todo_list"
- id: str
- items: list[CodexTodoItem]
-
-
-# Canonical union of thread items and their type-specific payloads.
-CodexThreadItemUnion = Annotated[
- (
- Annotated[CodexAgentMessageItem, Tag("agent_message")]
- | Annotated[CodexReasoningItem, Tag("reasoning")]
- | Annotated[CodexCommandExecutionItem, Tag("command_execution")]
- | Annotated[CodexFileChangeItem, Tag("file_change")]
- | Annotated[CodexMcpToolCallItem, Tag("mcp_tool_call")]
- | Annotated[CodexWebSearchItem, Tag("web_search")]
- | Annotated[CodexTodoListItem, Tag("todo_list")]
- | Annotated[CodexErrorItem, Tag("error")]
- ),
- build_discriminator("type"),
-]
-
-
-# Codex (JSONL) event stream models
-# Ref:https://github.com/openai/codex/blob/main/sdk/typescript/src/events.ts
-
-
-class CodexThreadStartedEvent(SerializableModel):
- type: Literal["thread.started"] = "thread.started"
- thread_id: str
-
-
-class CodexTurnStartedEvent(SerializableModel):
- type: Literal["turn.started"] = "turn.started"
-
-
-class CodexUsage(SerializableModel):
- input_tokens: int
- cached_input_tokens: int
- output_tokens: int
-
-
-class CodexTurnCompletedEvent(SerializableModel):
- type: Literal["turn.completed"] = "turn.completed"
- usage: CodexUsage
-
-
-class CodexThreadError(SerializableModel):
- message: str
-
-
-class CodexTurnFailedEvent(SerializableModel):
- type: Literal["turn.failed"] = "turn.failed"
- error: CodexThreadError
-
-
-class CodexItemStartedEvent(SerializableModel):
- type: Literal["item.started"] = "item.started"
- item: CodexThreadItemUnion
-
-
-class CodexItemUpdatedEvent(SerializableModel):
- type: Literal["item.updated"] = "item.updated"
- item: CodexThreadItemUnion
-
-
-class CodexItemCompletedEvent(SerializableModel):
- type: Literal["item.completed"] = "item.completed"
- item: CodexThreadItemUnion
-
-
-class CodexThreadErrorEvent(SerializableModel):
- type: Literal["error"] = "error"
- message: str
-
-
-CodexThreadEvent = Annotated[
- (
- Annotated[CodexThreadStartedEvent, Tag("thread.started")]
- | Annotated[CodexTurnStartedEvent, Tag("turn.started")]
- | Annotated[CodexTurnCompletedEvent, Tag("turn.completed")]
- | Annotated[CodexTurnFailedEvent, Tag("turn.failed")]
- | Annotated[CodexItemStartedEvent, Tag("item.started")]
- | Annotated[CodexItemUpdatedEvent, Tag("item.updated")]
- | Annotated[CodexItemCompletedEvent, Tag("item.completed")]
- | Annotated[CodexThreadErrorEvent, Tag("error")]
- ),
- build_discriminator("type"),
-]
-
-# TODO: some of these might not actually be valid for codex!
-CODEX_TOOLS = (
- AgentToolName.AGENT,
- AgentToolName.BASH,
- AgentToolName.EDIT,
- AgentToolName.GLOB,
- AgentToolName.GREP,
- AgentToolName.LS,
- AgentToolName.MULTI_EDIT,
- AgentToolName.NOTEBOOK_EDIT,
- AgentToolName.NOTEBOOK_READ,
- AgentToolName.READ,
- AgentToolName.TODO_READ,
- AgentToolName.TODO_WRITE,
- AgentToolName.WEB_FETCH,
- AgentToolName.WEB_SEARCH,
- AgentToolName.WRITE,
- AgentToolName.COMPUTER,
- AgentToolName.MEMORY,
- AgentToolName.OTHER,
- AgentToolName.CODE_EXECUTION,
- AgentToolName.BASH_CODE_EXECUTION,
- AgentToolName.TEXT_EDITOR_CODE_EXECUTION,
- AgentToolName.COMMAND_EXECUTION,
- AgentToolName.FILE_CHANGE,
-)
diff --git a/imbue_core/imbue_core/agents/agent_api/codex/message_parser.py b/imbue_core/imbue_core/agents/agent_api/codex/message_parser.py
@@ -1,218 +0,0 @@
-from typing import Any
-from typing import assert_never
-
-from pydantic import TypeAdapter
-
-from imbue_core.agents.agent_api.codex.data_types import CodexAgentMessageItem
-from imbue_core.agents.agent_api.codex.data_types import CodexCommandExecutionItem
-from imbue_core.agents.agent_api.codex.data_types import CodexErrorItem
-from imbue_core.agents.agent_api.codex.data_types import CodexFileChangeItem
-from imbue_core.agents.agent_api.codex.data_types import CodexItemCompletedEvent
-from imbue_core.agents.agent_api.codex.data_types import CodexItemStartedEvent
-from imbue_core.agents.agent_api.codex.data_types import CodexItemUpdatedEvent
-from imbue_core.agents.agent_api.codex.data_types import CodexMcpToolCallItem
-from imbue_core.agents.agent_api.codex.data_types import CodexReasoningItem
-from imbue_core.agents.agent_api.codex.data_types import CodexThreadErrorEvent
-from imbue_core.agents.agent_api.codex.data_types import CodexThreadEvent
-from imbue_core.agents.agent_api.codex.data_types import CodexThreadItemUnion
-from imbue_core.agents.agent_api.codex.data_types import CodexThreadStartedEvent
-from imbue_core.agents.agent_api.codex.data_types import CodexTodoListItem
-from imbue_core.agents.agent_api.codex.data_types import CodexTurnCompletedEvent
-from imbue_core.agents.agent_api.codex.data_types import CodexTurnFailedEvent
-from imbue_core.agents.agent_api.codex.data_types import CodexTurnStartedEvent
-from imbue_core.agents.agent_api.codex.data_types import CodexWebSearchItem
-from imbue_core.agents.agent_api.data_types import AgentAssistantMessage
-from imbue_core.agents.agent_api.data_types import AgentContentBlock
-from imbue_core.agents.agent_api.data_types import AgentMessage
-from imbue_core.agents.agent_api.data_types import AgentResultMessage
-from imbue_core.agents.agent_api.data_types import AgentSystemEventType
-from imbue_core.agents.agent_api.data_types import AgentSystemMessage
-from imbue_core.agents.agent_api.data_types import AgentTextBlock
-from imbue_core.agents.agent_api.data_types import AgentThinkingBlock
-from imbue_core.agents.agent_api.data_types import AgentToolResultBlock
-from imbue_core.agents.agent_api.data_types import AgentToolUseBlock
-from imbue_core.agents.agent_api.data_types import AgentUsage
-
-
-def parse_codex_event(data: dict[str, Any], thread_id: str | None = None) -> AgentMessage | None:
- """Parse Codex event into unified message.
-
- Reference:
- https://github.com/openai/codex/blob/main/docs/exec.md
- https://github.com/openai/codex/blob/main/sdk/typescript/src/events.ts
- """
- codex_event = TypeAdapter(CodexThreadEvent).validate_python(data)
- match codex_event:
- case CodexThreadStartedEvent():
- return AgentSystemMessage(
- event_type=AgentSystemEventType.SESSION_STARTED,
- session_id=codex_event.thread_id,
- original_message=data,
- )
-
- case CodexTurnStartedEvent():
- # Turn started within a thread. Nothing to do
- return None
-
- case CodexTurnCompletedEvent():
- assert thread_id is not None, "thread_id is required for turn.completed event"
- usage = AgentUsage(
- input_tokens=codex_event.usage.input_tokens,
- output_tokens=codex_event.usage.output_tokens,
- cached_tokens=codex_event.usage.cached_input_tokens,
- total_tokens=codex_event.usage.input_tokens + codex_event.usage.output_tokens,
- )
- return AgentResultMessage(
- session_id=thread_id,
- is_error=False,
- usage=usage,
- original_message=data,
- )
-
- case CodexTurnFailedEvent():
- assert thread_id is not None, "thread_id is required for turn.failed event"
- return AgentResultMessage(
- session_id=thread_id,
- is_error=True,
- error=codex_event.error.message,
- usage=None,
- original_message=data,
- )
-
- case CodexItemStartedEvent():
- content_blocks = parse_codex_item(codex_event.item)
- return AgentAssistantMessage(content=content_blocks, original_message=data)
-
- case CodexItemUpdatedEvent():
- # Intermediate item, don't return anything
- return None
-
- case CodexItemCompletedEvent():
- content_blocks = parse_codex_item(codex_event.item)
- return AgentAssistantMessage(content=content_blocks, original_message=data)
-
- case CodexThreadErrorEvent():
- return AgentResultMessage(
- session_id=thread_id or "",
- is_error=True,
- error=codex_event.message,
- usage=None,
- original_message=data,
- )
- case _ as unreachable:
- assert_never(unreachable)
-
-
-def parse_codex_item(
- item_data: dict[str, Any] | CodexThreadItemUnion,
-) -> list[AgentContentBlock]:
- """Parse Codex item into unified content blocks.
-
- Refs:
- https://github.com/openai/codex/blob/main/sdk/typescript/src/items.ts
- """
- if isinstance(item_data, dict):
- codex_item = TypeAdapter(CodexThreadItemUnion).validate_python(item_data)
- else:
- codex_item = item_data
-
- match codex_item:
- case CodexAgentMessageItem():
- return [AgentTextBlock(text=codex_item.text)]
-
- case CodexReasoningItem():
- return [AgentThinkingBlock(content=codex_item.text)]
-
- case CodexErrorItem():
- return [AgentTextBlock(text=f"[Error: {codex_item.message}]")]
-
- case CodexCommandExecutionItem():
- if codex_item.status == "in_progress":
- return [
- AgentToolUseBlock(
- id=codex_item.id,
- name=codex_item.type,
- input={"command": codex_item.command},
- )
- ]
- return [
- AgentToolResultBlock(
- tool_use_id=codex_item.id,
- content=codex_item.aggregated_output,
- exit_code=codex_item.exit_code,
- is_error=codex_item.exit_code != 0,
- )
- ]
-
- case CodexFileChangeItem():
- is_error = codex_item.status == "failed"
- return [
- AgentToolUseBlock(
- id=codex_item.id,
- name=codex_item.type,
- input={"changes": [change.model_dump() for change in codex_item.changes]},
- ),
- AgentToolResultBlock(
- tool_use_id=codex_item.id,
- content=[change.model_dump() for change in codex_item.changes],
- is_error=is_error,
- exit_code=-1 if is_error else 0,
- ),
- ]
-
- case CodexMcpToolCallItem():
- if codex_item.status == "in_progress":
- return [
- AgentToolUseBlock(
- id=codex_item.id,
- name=codex_item.type,
- input={"server": codex_item.server, "tool": codex_item.tool},
- )
- ]
- # NOTE: currently (24-oct-2025) the MCP tool call item is not really well defined
- # it does not have a result field or anything. So for now, we just return the server and tool as the content.
- return [
- AgentToolResultBlock(
- tool_use_id=codex_item.id,
- content=[{"server": codex_item.server, "tool": codex_item.tool}],
- is_error=codex_item.status == "failed",
- exit_code=-1 if codex_item.status == "failed" else 0,
- )
- ]
-
- case CodexWebSearchItem():
- # NOTE: currently (24-oct-2025) the web search item is not really well defined
- # i.e. it only has a query field, and no other fields like results, progress, etc.
- # so for now, so that each tool use has a matching result, we just return the query as the content.
- return [
- AgentToolUseBlock(
- id=codex_item.id,
- name=codex_item.type,
- input={"query": codex_item.query},
- ),
- AgentToolResultBlock(
- tool_use_id=codex_item.id,
- content=codex_item.query,
- # No error reported for web search
- is_error=False,
- exit_code=0,
- ),
- ]
-
- case CodexTodoListItem():
- return [
- AgentToolUseBlock(
- id=codex_item.id,
- name=codex_item.type,
- input={"todos": [item.model_dump() for item in codex_item.items]},
- ),
- AgentToolResultBlock(
- tool_use_id=codex_item.id,
- content=[item.model_dump() for item in codex_item.items],
- is_error=False,
- exit_code=0,
- ),
- ]
-
- case _ as unreachable:
- assert_never(unreachable)
diff --git a/imbue_core/imbue_core/agents/agent_api/data_types.py b/imbue_core/imbue_core/agents/agent_api/data_types.py
@@ -1,252 +0,0 @@
-import enum
-from pathlib import Path
-from typing import Annotated
-from typing import Any
-from typing import Literal
-
-from pydantic import Field
-from pydantic import Tag
-
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_core.pydantic_serialization import build_discriminator
-
-AgentPermissionMode = Literal["default", "acceptEdits", "bypassPermissions"]
-
-
-class AgentToolName(enum.StrEnum):
- """Enumeration of all known coding agent tools across Claude Code and Codex.
-
- This is a superset of tools available across different coding agents.
- Not all tools are available in all agents.
- """
-
- # File operations
- READ = "Read"
- WRITE = "Write"
- EDIT = "Edit"
- MULTI_EDIT = "MultiEdit"
- GLOB = "Glob"
- NOTEBOOK_READ = "NotebookRead"
- NOTEBOOK_EDIT = "NotebookEdit"
- LS = "LS"
-
- # Search operations
- GREP = "Grep"
-
- # Execution tools
- BASH = "Bash"
- BASH_OUTPUT = "BashOutput"
- KILL_SHELL = "KillShell"
-
- # Web operations
- WEB_SEARCH = "WebSearch"
- WEB_FETCH = "WebFetch"
-
- # Agent orchestration
- TASK = "Task"
- TODO_READ = "TodoRead"
- TODO_WRITE = "TodoWrite"
- SLASH_COMMAND = "SlashCommand"
- EXIT_PLAN_MODE = "exit_plan_mode"
-
- # MCP tools
- MCP_TOOL = "mcp_tool" # Generic MCP tool prefix
- LIST_MCP_RESOURCES = "ListMcpResourcesTool"
- READ_MCP_RESOURCE = "ReadMcpResourceTool"
-
- # Code execution
- CODE_EXECUTION = "code_execution"
- BASH_CODE_EXECUTION = "bash_code_execution"
- TEXT_EDITOR_CODE_EXECUTION = "text_editor_code_execution"
-
- # Codex-specific operations
- COMMAND_EXECUTION = "command_execution" # Codex's command execution
- FILE_CHANGE = "file_change" # Codex's file change operation
-
- # Other tools
- AGENT = "Agent"
- COMPUTER = "computer" # Computer use capability
- MEMORY = "memory" # Memory storage
- OTHER = "other" # Catch-all for unknown/custom tools
-
-
-# TODO: these are not, in the strict sense, read-only; perhaps we should have finer gradations
-READ_ONLY_TOOLS = (
- AgentToolName.TASK,
- AgentToolName.READ,
- AgentToolName.GLOB,
- AgentToolName.GREP,
- AgentToolName.LS,
- AgentToolName.BASH,
- AgentToolName.NOTEBOOK_READ,
- AgentToolName.TODO_READ,
- AgentToolName.TODO_WRITE,
- AgentToolName.WEB_FETCH,
- AgentToolName.WEB_SEARCH,
-)
-
-
-# Content block types
-class AgentTextBlock(SerializableModel):
- """Text content block.
-
- Represents plain text output from the agent.
- """
-
- text: str
-
-
-class AgentThinkingBlock(SerializableModel):
- """Agent's internal reasoning/thinking block.
-
- Represents the agent's thought process or reasoning, which may be hidden
- from the end user in some interfaces.
- """
-
- content: str
- thinking_tokens: int | None = Field(default=None, description="Number of tokens used for thinking")
-
-
-class AgentToolUseBlock(SerializableModel):
- """Tool invocation request.
-
- Represents a request from the agent to use a specific tool.
- """
-
- id: str
- name: AgentToolName | str # allow str for flexibility
- input: dict[str, Any]
-
-
-class AgentToolResultBlock(SerializableModel):
- """Tool execution result.
-
- Represents the result of executing a tool, which is fed back to the agent.
- """
-
- tool_use_id: str
- content: str | list[dict[str, Any]] | None = None
- is_error: bool | None = None
- exit_code: int | None = Field(default=None, description="Exit code for command executions")
-
-
-AgentContentBlock = AgentTextBlock | AgentThinkingBlock | AgentToolUseBlock | AgentToolResultBlock
-
-
-class AgentSystemEventType(enum.StrEnum):
- """System event types
-
- Super set of system event types across all agents.
- """
-
- SESSION_STARTED = "session_started"
- SESSION_RESUMED = "session_resumed"
- TURN_STARTED = "turn_started"
- TURN_COMPLETED = "turn_completed"
- TURN_FAILED = "turn_failed"
- # For agent-specific events that don't fit into the above categories
- OTHER = "other"
-
-
-# Message types (`type` field is required for serialization)
-class AgentUserMessage(SerializableModel):
- """User message.
-
- Represents input from the user to the agent.
- """
-
- object_type: Literal["AgentUserMessage"] = "AgentUserMessage"
- content: str | list[AgentContentBlock]
- original_message: dict[str, Any] | None = Field(default=None, description="Original agent-specific message data")
-
-
-class AgentAssistantMessage(SerializableModel):
- """Assistant message with content blocks.
-
- Represents output from the agent, which may include text, thinking,
- tool uses, and tool results.
- """
-
- object_type: Literal["AgentAssistantMessage"] = "AgentAssistantMessage"
- content: list[AgentContentBlock]
- original_message: dict[str, Any] | None = Field(default=None, description="Original agent-specific message data")
-
-
-class AgentSystemMessage(SerializableModel):
- """System message with normalized event data.
-
- Represents lifecycle events from the agent session (e.g., turn started,
- turn completed, session started).
- """
-
- object_type: Literal["AgentSystemMessage"] = "AgentSystemMessage"
- event_type: AgentSystemEventType
- session_id: str | None = Field(default=None, description="Session/thread identifier")
- error: str | None = Field(default=None, description="Error message for failed events")
- original_message: dict[str, Any] | None = Field(default=None, description="Original agent-specific message data")
-
-
-class AgentUsage(SerializableModel):
- """Normalized usage tracking across agents.
-
- Tracks token usage and costs in a unified format.
- """
-
- input_tokens: int | None = Field(default=None, description="Input/prompt tokens consumed")
- output_tokens: int | None = Field(default=None, description="Output/completion tokens generated")
- cached_tokens: int | None = Field(default=None, description="Cached input tokens reused")
- total_tokens: int | None = Field(default=None, description="Total tokens (input + output)")
- thinking_tokens: int | None = Field(default=None, description="Tokens used for extended thinking")
- total_cost_usd: float | None = Field(default=None, description="Estimated cost in USD")
-
-
-class AgentResultMessage(SerializableModel):
- """Result message with cost and usage information.
-
- Represents the final result of an agent session, including timing,
- usage statistics, and success/error status.
- """
-
- object_type: Literal["AgentResultMessage"] = "AgentResultMessage"
- session_id: str
- is_error: bool
- duration_ms: int | None = Field(default=None, description="Total duration in milliseconds")
- api_duration_ms: int | None = Field(default=None, description="API call duration in milliseconds")
- num_turns: int | None = Field(default=None, description="Number of conversation turns")
- usage: AgentUsage | None = Field(default=None, description="Token usage and cost information")
- result: str | None = Field(default=None, description="Final result or output from the agent")
- error: str | None = Field(default=None, description="Error message if is_error=True")
- original_message: dict[str, Any] | None = Field(default=None, description="Original agent-specific message data")
-
-
-AgentMessage = AgentUserMessage | AgentAssistantMessage | AgentSystemMessage | AgentResultMessage
-AgentMessageUnion = Annotated[
- Annotated[AgentUserMessage, Tag("AgentUserMessage")]
- | Annotated[AgentAssistantMessage, Tag("AgentAssistantMessage")]
- | Annotated[AgentSystemMessage, Tag("AgentSystemMessage")]
- | Annotated[AgentResultMessage, Tag("AgentResultMessage")],
- build_discriminator(),
-]
-
-
-class ToolUseRecord(SerializableModel):
- """A record of a tool use."""
-
- request_message: AgentToolUseBlock
- result_message: AgentToolResultBlock
-
- @property
- def tool_name(self) -> str:
- """The name of the tool used."""
- return self.request_message.name
-
- @property
- def tool_input(self) -> dict[str, Any]:
- """The input to the tool."""
- return self.request_message.input
-
-
-class AgentOptions(SerializableModel):
- """Parent class for all agent options."""
-
- cwd: str | Path | None = None
diff --git a/imbue_core/imbue_core/agents/agent_api/interaction.py b/imbue_core/imbue_core/agents/agent_api/interaction.py
@@ -1,96 +0,0 @@
-from typing import Sequence
-
-from imbue_core.agents.agent_api.data_types import AgentAssistantMessage
-from imbue_core.agents.agent_api.data_types import AgentMessage
-from imbue_core.agents.agent_api.data_types import AgentOptions
-from imbue_core.agents.agent_api.data_types import AgentToolResultBlock
-from imbue_core.agents.agent_api.data_types import AgentToolUseBlock
-from imbue_core.agents.agent_api.data_types import AgentUserMessage
-from imbue_core.agents.agent_api.data_types import ToolUseRecord
-from imbue_core.pydantic_serialization import SerializableModel
-
-
-class AgentInteraction:
- """A class for tracking an ongoing interaction with an agent.
-
- Note that this class is not thread-safe.
- """
-
- def __init__(self, prompt: str, options: AgentOptions) -> None:
- self.prompt = prompt
- self.options = options
- self.messages: list[AgentMessage] = []
- self.tool_use_records: list[ToolUseRecord] = []
- self._unresolved_tool_use_requests: list[AgentToolUseBlock] = []
-
- def put(self, message: AgentMessage) -> None:
- self.messages.append(message)
-
- if isinstance(message, AgentAssistantMessage):
- for assistant_content_block in message.content:
- if isinstance(assistant_content_block, AgentToolUseBlock):
- self._unresolved_tool_use_requests.append(assistant_content_block)
- elif isinstance(message, AgentUserMessage) and isinstance(message.content, list):
- for content_block in message.content:
- if isinstance(content_block, AgentToolResultBlock):
- remaining_unresolved_requests = []
- for request in self._unresolved_tool_use_requests:
- if request.id == content_block.tool_use_id:
- self.tool_use_records.append(
- ToolUseRecord(
- request_message=request,
- result_message=content_block,
- )
- )
- else:
- remaining_unresolved_requests.append(request)
- self._unresolved_tool_use_requests = remaining_unresolved_requests
-
- def find_tool_use_record_by_command(self, command: str, by_most_recent: bool = True) -> ToolUseRecord | None:
- """Look for tool use request and result messages by the tool command.
-
- If by_most_recent is True, the records are searched in reverse order (most recent first).
- """
- return _find_tool_use_record_by_command(self.tool_use_records, command, by_most_recent)
-
-
-class AgentInteractionRecord(SerializableModel):
- """A serializable record of a completed agent interaction.
-
- This is meant to be used for storing a completed log in a database or cache.
- """
-
- prompt: str
- options: AgentOptions
- messages: tuple[AgentMessage, ...]
- tool_use_records: tuple[ToolUseRecord, ...]
-
- @classmethod
- def from_agent_interaction(cls, agent_interaction: AgentInteraction) -> "AgentInteractionRecord":
- return cls(
- prompt=agent_interaction.prompt,
- options=agent_interaction.options,
- messages=tuple(agent_interaction.messages),
- tool_use_records=tuple(agent_interaction.tool_use_records),
- )
-
- def find_tool_use_record_by_command(self, command: str, by_most_recent: bool = True) -> ToolUseRecord | None:
- """Look for tool use request and result messages by the tool command.
-
- If by_most_recent is True, the records are searched in reverse order (most recent first).
- """
- return _find_tool_use_record_by_command(self.tool_use_records, command, by_most_recent)
-
-
-def _find_tool_use_record_by_command(
- tool_use_records: Sequence[ToolUseRecord], command: str, reverse: bool = True
-) -> ToolUseRecord | None:
- """Look for tool use request and result messages by the tool command.
-
- If reverse is True, the records are searched in reverse order (most recent first).
- """
- for record in reversed(tool_use_records) if reverse else tool_use_records:
- tool_input = record.tool_input
- if "command" in tool_input and tool_input["command"] == command:
- return record
- return None
diff --git a/imbue_core/imbue_core/agents/agent_api/transport.py b/imbue_core/imbue_core/agents/agent_api/transport.py
@@ -1,176 +0,0 @@
-import json
-import os
-import subprocess
-import threading
-from abc import ABC
-from abc import abstractmethod
-from contextlib import contextmanager
-from pathlib import Path
-from subprocess import PIPE
-from typing import Any
-from typing import ContextManager
-from typing import Generator
-from typing import Generic
-from typing import Iterable
-from typing import Iterator
-from typing import Self
-from typing import Sequence
-from typing import TypeVar
-
-from imbue_core.agents.agent_api.data_types import AgentOptions
-from imbue_core.agents.agent_api.errors import AgentCLIConnectionError
-from imbue_core.agents.agent_api.errors import (
- AgentCLIJSONDecodeError as SDKJSONDecodeError,
-)
-from imbue_core.agents.agent_api.errors import AgentCLINotFoundError
-from imbue_core.agents.agent_api.errors import AgentProcessError
-from imbue_core.pydantic_serialization import SerializableModel
-
-TransportOptionsT = TypeVar("TransportOptionsT", bound=SerializableModel)
-
-
-class AgentTransport(ABC, Generic[TransportOptionsT]):
- """Abstract transport for Agent communication."""
-
- @classmethod
- @abstractmethod
- def build(cls, options: TransportOptionsT) -> ContextManager[Self]:
- """Build a transport from options.
-
- This is the main entry point for building a transport and managing its lifecycle.
- """
-
- @abstractmethod
- def send_request(self, messages: list[Any], agent_options: AgentOptions) -> None:
- """Send request to underlying agent via transport."""
-
- @abstractmethod
- def receive_messages(self) -> Iterator[dict[str, Any]]:
- """Receive messages from underlying agent via transport."""
-
- @abstractmethod
- def is_connected(self) -> bool:
- """Check if transport is connected."""
-
-
-class AgentSubprocessCLITransportOptions(SerializableModel):
- """Options for AgentSubprocessCLITransport."""
-
- cmd: Sequence[str]
- cwd: str | Path | None = None
- extra_env_vars: dict[str, str] | None = None
-
-
-class AgentSubprocessCLITransport(AgentTransport[AgentSubprocessCLITransportOptions]):
- """Subprocess transport using Coding Agent via a CLI."""
-
- def __init__(
- self,
- popen: subprocess.Popen[str],
- ) -> None:
- self._process = popen
- self._stdin_stream = popen.stdin
- self._stdout_stream = popen.stdout
- self._stderr_stream = popen.stderr
-
- @classmethod
- @contextmanager
- def build(cls, options: AgentSubprocessCLITransportOptions) -> Generator[Self, None, None]:
- extra_env_vars = options.extra_env_vars or {}
- try:
- popen = subprocess.Popen(
- options.cmd,
- stdin=PIPE,
- stdout=PIPE,
- stderr=PIPE,
- cwd=options.cwd,
- env={**os.environ, **extra_env_vars},
- # ensure output is line buffered
- bufsize=1,
- text=True,
- encoding="utf-8",
- )
- except FileNotFoundError as e:
- raise AgentCLINotFoundError(f"Agent CLI not found for: cmd={options.cmd}") from e
- except Exception as e:
- raise AgentCLIConnectionError(f"Failed to start Agent CLI via cmd={options.cmd}: {e}") from e
-
- try:
- yield cls(popen)
- finally:
- # Make sure to terminate the process if it is still running, and clean up the streams
- if popen.poll() is None:
- try:
- popen.terminate()
- popen.wait(timeout=5.0)
- except subprocess.TimeoutExpired:
- popen.kill()
- popen.wait(timeout=5.0)
- popen.stdout and popen.stdout.close()
- popen.stderr and popen.stderr.close()
- popen.stdin and popen.stdin.close()
-
- def send_request(self, messages: Iterable[dict[str, Any] | str], agent_options: AgentOptions) -> None:
- process = self._process
- stdin_stream = self._stdin_stream
- if not process or not stdin_stream:
- raise AgentCLIConnectionError("Not connected")
-
- for message in messages:
- stdin_stream.write(json.dumps(message) + "\n")
- stdin_stream.flush()
-
- def _read_stderr(self, output_buffer: list[str]) -> None:
- """Read stderr in background."""
- stderr_stream = self._stderr_stream
- if stderr_stream:
- try:
- for line in stderr_stream:
- output_buffer.append(line.strip())
- except subprocess.SubprocessError:
- pass
-
- def receive_messages(self) -> Iterator[dict[str, Any]]:
- process = self._process
- stdout_stream = self._stdout_stream
- if not process or not stdout_stream:
- raise AgentCLIConnectionError("Not connected")
-
- stderr_lines: list[str] = []
- stderr_read_thread = threading.Thread(target=self._read_stderr, args=(stderr_lines,))
- stderr_read_thread.start()
-
- try:
- for line in stdout_stream:
- line_str = line.strip()
- if not line_str:
- continue
-
- try:
- data = json.loads(line_str)
- try:
- yield data
- except GeneratorExit:
- # Handle generator cleanup gracefully
- return
- except json.JSONDecodeError as e:
- if line_str.startswith("{") or line_str.startswith("["):
- raise SDKJSONDecodeError(line_str, e) from e
- continue
-
- except subprocess.SubprocessError:
- pass
-
- process.wait()
- if process.returncode is not None and process.returncode != 0:
- stderr_output = "\n".join(stderr_lines)
- if stderr_output and "error" in stderr_output.lower():
- raise AgentProcessError(
- "CLI process failed",
- exit_code=process.returncode,
- stderr=stderr_output,
- )
-
- def is_connected(self) -> bool:
- process = self._process
- return process is not None and process.returncode is None
diff --git a/imbue_core/imbue_core/agents/configs.py b/imbue_core/imbue_core/agents/configs.py
@@ -1,123 +0,0 @@
-from pathlib import Path
-from typing import Any
-from typing import assert_never
-
-from imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
-from imbue_core.agents.llm_apis.anthropic_api import count_anthropic_tokens
-from imbue_core.agents.llm_apis.common import get_model_max_context_length
-from imbue_core.agents.llm_apis.constants import approximate_token_count
-from imbue_core.agents.llm_apis.data_types import ModelStr
-from imbue_core.agents.llm_apis.mock_api import MY_MOCK_MODEL_INFO
-from imbue_core.agents.llm_apis.openai_api import OpenAIModelName
-from imbue_core.agents.llm_apis.openai_api import count_openai_tokens
-from imbue_core.language_model_mode import LanguageModelMode
-from imbue_core.pydantic_serialization import SerializableModel
-
-
-class LanguageModelGenerationConfig(SerializableModel):
- model_name: ModelStr = OpenAIModelName.GPT_4O_2024_08_06
- # this should almost always be None (you dont want to save your cache path into the hammer invocation data!)
- cache_path: Path | None = None
- count_tokens_cache_path: Path | None = None
- is_prompt_debugging_enabled: bool = False
-
- # If true, the LLM API will cache the inputs to the LLM call as well as the outputs, which makes prompt diffing easier.
- is_caching_inputs: bool = False
-
- # if this is set, the LLM API will return ONLY cached responses
- is_running_offline: bool = False
-
- # if set, the LLM API will return log probabilities for the output tokens (if supported by the model)
- is_using_logprobs: bool = False
-
- # Retry configuration
- retry_jitter_factor: float = 0.5
-
- def model_post_init(self, __context: Any) -> None:
- super().model_post_init(__context)
- # FIXME: do proper validation
- if self.cache_path is None and self.is_caching_inputs:
- raise ValueError("cache_path must be provided if is_caching_inputs is True")
-
- def count_tokens(self, text: str) -> int:
- """Count tokens in the given text using the model's tokenizer."""
- if self.model_name in (v for v in OpenAIModelName):
- return count_openai_tokens(text, self.model_name)
- if self.model_name in (v for v in AnthropicModelName):
- return count_anthropic_tokens(text)
- return approximate_token_count(text)
-
- def get_max_context_length(self) -> int:
- """Get the maximum context length for this model."""
- return get_model_max_context_length(self.model_name)
-
- def is_custom_model(self) -> bool:
- """Return True if this is a custom/user-defined model.
-
- Custom models use approximate token counting since there's no mechanism
- for defining a tokenizer for them.
- """
- return False
-
-
-class OpenAICompatibleModelConfig(LanguageModelGenerationConfig):
- """Configuration for custom models using OpenAI-compatible APIs (e.g., Ollama, local LLMs)."""
-
- custom_base_url: str
- custom_api_key_env: str
- custom_context_window: int
- custom_max_output_tokens: int
-
- def count_tokens(self, text: str) -> int:
- """Count tokens using approximation since we don't have access to the model's tokenizer."""
- return approximate_token_count(text)
-
- def get_max_context_length(self) -> int:
- """Get the maximum context length for this model."""
- return self.custom_context_window
-
- def is_custom_model(self) -> bool:
- """Return True if this is a custom/user-defined model.
-
- Custom models use approximate token counting since there's no mechanism
- for defining a tokenizer for them.
- """
- # TODO: Support custom tokenizers with custom models.
- return True
-
-
-class MockedLanguageModelGenerationConfig(LanguageModelGenerationConfig):
- model_name: ModelStr = MY_MOCK_MODEL_INFO.model_name
- is_running_offline: bool = True
- mock_responses_path: Path
-
-
-def create_safe_llm_config(
- llm_name: ModelStr, mode: LanguageModelMode, cache_path: Path | None = None
-) -> LanguageModelGenerationConfig:
- match mode:
- case LanguageModelMode.LIVE:
- assert cache_path is None
- language_model_config = LanguageModelGenerationConfig(model_name=llm_name)
- case LanguageModelMode.OFFLINE:
- assert cache_path is not None
- language_model_config = LanguageModelGenerationConfig(
- model_name=llm_name,
- is_running_offline=True,
- is_caching_inputs=True,
- cache_path=cache_path,
- )
- case LanguageModelMode.UPDATE_SNAPSHOT:
- assert cache_path is not None
- language_model_config = LanguageModelGenerationConfig(
- model_name=llm_name, is_caching_inputs=True, cache_path=cache_path
- )
- case LanguageModelMode.MOCKED:
- assert cache_path is not None
- language_model_config = MockedLanguageModelGenerationConfig(
- model_name=llm_name, mock_responses_path=cache_path
- )
- case _ as unreachable:
- assert_never(unreachable) # pyre-ignore[6]: pyre doesn't understand enums
- assert False # because pyre doesn't really understand assert_never, either
- return language_model_config
diff --git a/imbue_core/imbue_core/agents/llm_apis/anthropic_api.py b/imbue_core/imbue_core/agents/llm_apis/anthropic_api.py
@@ -1,818 +0,0 @@
-import enum
-import inspect
-from contextlib import contextmanager
-from functools import lru_cache
-from pathlib import Path
-from types import FrameType
-from typing import AsyncGenerator
-from typing import Final
-from typing import Iterator
-
-import anthropic
-import httpx
-from anthropic._types import NOT_GIVEN
-from anthropic.types import CacheControlEphemeralParam
-from anthropic.types import MessageParam
-from anthropic.types import TextBlockParam
-from loguru import logger
-from pydantic.functional_validators import field_validator
-import tiktoken
-
-from imbue_core.agents.llm_apis.anthropic_data_types import AnthropicCachingInfo
-from imbue_core.agents.llm_apis.anthropic_data_types import AnthropicModelInfo
-from imbue_core.agents.llm_apis.api_utils import convert_prompt_to_messages
-from imbue_core.agents.llm_apis.api_utils import (
- create_costed_language_model_response_for_single_result,
-)
-from imbue_core.agents.llm_apis.data_types import CachedCountTokensResponse
-from imbue_core.agents.llm_apis.data_types import CachingInfo
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import CountTokensInputs
-from imbue_core.agents.llm_apis.data_types import CountTokensResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
-from imbue_core.agents.llm_apis.data_types import ResponseStopReason
-from imbue_core.agents.llm_apis.errors import BadAPIRequestError
-from imbue_core.agents.llm_apis.errors import LanguageModelInvalidModelNameError
-from imbue_core.agents.llm_apis.errors import MissingAPIKeyError
-from imbue_core.agents.llm_apis.errors import NewSeedRetriableLanguageModelError
-from imbue_core.agents.llm_apis.errors import SafelyRetriableTransientLanguageModelError
-from imbue_core.agents.llm_apis.errors import TransientLanguageModelError
-from imbue_core.agents.llm_apis.errors import UnsetCachePathError
-from imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
-from imbue_core.agents.llm_apis.models import ModelInfo
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamStartEvent
-from imbue_core.async_monkey_patches import log_exception
-from imbue_core.caching import AsyncCache
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.frozen_utils import FrozenMapping
-from imbue_core.itertools import only
-from imbue_core.nested_evolver import assign
-from imbue_core.nested_evolver import chill
-from imbue_core.nested_evolver import evolver
-from imbue_core.secrets_utils import get_secret
-
-
-class AnthropicModelName(enum.StrEnum):
- CLAUDE_3_HAIKU_2024_03_07 = "claude-3-haiku-20240307"
- CLAUDE_3_OPUS_2024_02_29 = "claude-3-opus-20240229"
- CLAUDE_3_5_SONNET_2024_06_20 = "claude-3-5-sonnet-20240620"
- CLAUDE_3_5_SONNET_2024_10_22 = "claude-3-5-sonnet-20241022"
- CLAUDE_3_5_HAIKU_2024_10_22 = "claude-3-5-haiku-20241022"
- CLAUDE_3_7_SONNET_2025_02_19 = "claude-3-7-sonnet-20250219"
- CLAUDE_4_OPUS_2025_05_14 = "claude-opus-4-20250514"
- CLAUDE_4_1_OPUS_2025_08_05 = "claude-opus-4-1-20250805"
- CLAUDE_4_SONNET_2025_05_14 = "claude-sonnet-4-20250514"
- CLAUDE_4_5_SONNET_2025_09_29 = "claude-sonnet-4-5-20250929"
- CLAUDE_4_5_HAIKU_2025_10_01 = "claude-haiku-4-5-20251001"
- CLAUDE_4_5_OPUS_2025_11_01 = "claude-opus-4-5-20251101"
- # the same as above but with the token limit and cost per token for the 1M token limit
- # TODO: combine these and add ability for token costs to be nonlinear
- # FIXME: this is an exception where the model name is not the same as the model name in the API
- CLAUDE_4_SONNET_2025_05_14_LONG = "claude-sonnet-4-20250514-long"
- CLAUDE_4_5_SONNET_2025_09_29_LONG = "claude-sonnet-4-5-20250929-long"
-
- # the following are 'retired' and are no longer available: https://docs.claude.com/en/docs/about-claude/model-deprecations
- # CLAUDE_2_1 = "claude-2.1"
- # CLAUDE_2 = "claude-2"
- # CLAUDE_3_SONNET_2024_02_29 = "claude-3-sonnet-20240229"
-
-
-# Basic info is available at https://docs.anthropic.com/claude/reference/models
-# Rate limits for Anthropic models are available on our dashboard: https://console.anthropic.com/settings/limits
-# (we have a custom plan, so the public docs don't reflect our actual rate limits)
-# Prompt caching pricing is available at https://docs.claude.com/en/docs/build-with-claude/prompt-caching#pricing
-# NOTE: as of 2025-06-04, there are some models that don't have rate limits set in our dashboard
-ANTHROPIC_MODEL_INFO_BY_NAME: FrozenMapping[AnthropicModelName, ModelInfo] = FrozenDict(
- {
- AnthropicModelName.CLAUDE_3_HAIKU_2024_03_07: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_3_HAIKU_2024_03_07,
- cost_per_input_token=0.25 / 1_000_000,
- cost_per_output_token=1.25 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=4096,
- rate_limit_req=4000 / 60, # 4000 RPM = 66.67 RPS
- rate_limit_tok=4_000_000 / 60,
- rate_limit_output_tok=800_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=0.3 / 1_000_000,
- cost_per_1h_cache_write_token=0.5 / 1_000_000,
- cost_per_cache_read_token=0.03 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_3_OPUS_2024_02_29: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_3_OPUS_2024_02_29,
- cost_per_input_token=15.00 / 1_000_000,
- cost_per_output_token=75.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=4096,
- rate_limit_req=4000 / 60, # 4000 RPM = 66.67 RPS
- rate_limit_tok=1_000_000 / 60,
- rate_limit_output_tok=150_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=18.75 / 1_000_000,
- cost_per_1h_cache_write_token=30 / 1_000_000,
- cost_per_cache_read_token=1.5 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_3_5_SONNET_2024_06_20: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_3_5_SONNET_2024_06_20,
- cost_per_input_token=3.00 / 1_000_000,
- cost_per_output_token=15.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=4096,
- rate_limit_req=5000 / 60, # 5000 RPM = 83.33 RPS
- rate_limit_tok=8_000_000 / 60,
- rate_limit_output_tok=1_600_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=3.75 / 1_000_000,
- cost_per_1h_cache_write_token=6 / 1_000_000,
- cost_per_cache_read_token=0.3 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_3_5_SONNET_2024_10_22: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_3_5_SONNET_2024_10_22,
- cost_per_input_token=3.00 / 1_000_000,
- cost_per_output_token=15.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=8192,
- rate_limit_req=5000 / 60, # 5000 RPM = 83.33 RPS
- rate_limit_tok=8_000_000 / 60,
- rate_limit_output_tok=400_000 / 60,
- ),
- AnthropicModelName.CLAUDE_3_5_HAIKU_2024_10_22: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_3_5_HAIKU_2024_10_22,
- cost_per_input_token=1.00 / 1_000_000,
- cost_per_output_token=5.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=8192,
- rate_limit_req=4000 / 60, # 4000 RPM = 66.67 RPS
- rate_limit_tok=4_000_000 / 60,
- rate_limit_output_tok=800_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=1 / 1_000_000,
- cost_per_1h_cache_write_token=1.6 / 1_000_000,
- cost_per_cache_read_token=0.08 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_3_7_SONNET_2025_02_19: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_3_7_SONNET_2025_02_19,
- cost_per_input_token=3.00 / 1_000_000,
- cost_per_output_token=15.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=8192,
- rate_limit_req=None, # Currently no limit set in our dashboard
- rate_limit_tok=2_000_000 / 60,
- rate_limit_output_tok=400_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=3.75 / 1_000_000,
- cost_per_1h_cache_write_token=6 / 1_000_000,
- cost_per_cache_read_token=0.3 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_4_OPUS_2025_05_14: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_4_OPUS_2025_05_14,
- cost_per_input_token=15.00 / 1_000_000,
- cost_per_output_token=75.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=32_000,
- rate_limit_req=4000 / 60,
- rate_limit_tok=2_000_000 / 60,
- rate_limit_output_tok=400_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=18.75 / 1_000_000,
- cost_per_1h_cache_write_token=30 / 1_000_000,
- cost_per_cache_read_token=1.5 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_4_1_OPUS_2025_08_05: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_4_1_OPUS_2025_08_05,
- cost_per_input_token=15.00 / 1_000_000,
- cost_per_output_token=75.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=32_000,
- rate_limit_req=4000 / 60,
- rate_limit_tok=2_000_000 / 60,
- rate_limit_output_tok=400_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=18.75 / 1_000_000,
- cost_per_1h_cache_write_token=30 / 1_000_000,
- cost_per_cache_read_token=1.5 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_4_5_OPUS_2025_11_01: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_4_5_OPUS_2025_11_01,
- cost_per_input_token=5.00 / 1_000_000,
- cost_per_output_token=25.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=64_000,
- rate_limit_req=4000 / 60,
- rate_limit_tok=2_000_000 / 60,
- rate_limit_output_tok=400_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=6.25 / 1_000_000,
- cost_per_1h_cache_write_token=10 / 1_000_000,
- cost_per_cache_read_token=0.5 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_4_SONNET_2025_05_14: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_4_SONNET_2025_05_14,
- cost_per_input_token=3.00 / 1_000_000,
- cost_per_output_token=15.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=64_000,
- rate_limit_req=None, # Currently no limit set in our dashboard
- rate_limit_tok=2_000_000 / 60,
- rate_limit_output_tok=400_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=3.75 / 1_000_000,
- cost_per_1h_cache_write_token=6 / 1_000_000,
- cost_per_cache_read_token=0.3 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29,
- cost_per_input_token=3.00 / 1_000_000,
- cost_per_output_token=15.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=64_000,
- rate_limit_req=None, # Currently no limit set in our dashboard
- rate_limit_tok=2_000_000 / 60,
- rate_limit_output_tok=400_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=3.75 / 1_000_000,
- cost_per_1h_cache_write_token=6 / 1_000_000,
- cost_per_cache_read_token=0.3 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_4_5_HAIKU_2025_10_01: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_4_5_HAIKU_2025_10_01,
- cost_per_input_token=1.00 / 1_000_000,
- cost_per_output_token=5.00 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=64_000,
- rate_limit_req=4_000 / 60,
- rate_limit_tok=4_000_000 / 60,
- rate_limit_output_tok=800_000 / 60,
- provider_specific_info=AnthropicModelInfo(
- cost_per_5m_cache_write_token=1.25 / 1_000_000,
- cost_per_1h_cache_write_token=2.0 / 1_000_000,
- cost_per_cache_read_token=0.1 / 1_000_000,
- ),
- ),
- AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG,
- # the first 200_000 input tokens use the rates above, and the next up to 800_000 use the rate 6.0 / 1_000_000.
- # thus the maximum average cost per input token is (3.0 * 200_000 + 6.0 * 800_000) / 1_000_000 = 5.4 per 1_000_000.
- # (all output tokens may be past 200_000 input tokens, so the max average cost there is just the cost for tokens after 200_000)
- cost_per_input_token=5.40 / 1_000_000,
- cost_per_output_token=22.50 / 1_000_000,
- max_input_tokens=1_000_000,
- max_output_tokens=64_000,
- rate_limit_req=None, # Currently no limit set in our dashboard
- rate_limit_tok=1_000_000 / 60, # <-- yeah they let us have one (1) 1M request per minute
- rate_limit_output_tok=200_000 / 60,
- ),
- AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG: ModelInfo(
- model_name=AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG,
- # the first 200_000 input tokens use the rates above, and the next up to 800_000 use the rate 6.0 / 1_000_000.
- # thus the maximum average cost per input token is (3.0 * 200_000 + 6.0 * 800_000) / 1_000_000 = 5.4 per 1_000_000.
- # (all output tokens may be past 200_000 input tokens, so the max average cost there is just the cost for tokens after 200_000)
- cost_per_input_token=5.40 / 1_000_000,
- cost_per_output_token=22.50 / 1_000_000,
- max_input_tokens=1_000_000,
- max_output_tokens=64_000,
- rate_limit_req=None, # Currently no limit set in our dashboard
- rate_limit_tok=1_000_000 / 60, # <-- yeah they let us have one (1) 1M request per minute
- rate_limit_output_tok=200_000 / 60,
- ),
- }
-)
-
-
-_ROLE_TO_ANTHROPIC_ROLE: Final[FrozenMapping[str, str]] = FrozenDict(
- {
- "HUMAN": "user",
- "ASSISTANT": "assistant",
- "USER": "user",
- "USER_CACHED": "user",
- "SYSTEM": "system",
- "SYSTEM_CACHED": "system",
- }
-)
-
-_ANTHROPIC_STOP_REASON_TO_STOP_REASON: Final[FrozenMapping[str, ResponseStopReason]] = FrozenDict(
- {
- "end_turn": ResponseStopReason.END_TURN,
- "max_tokens": ResponseStopReason.MAX_TOKENS,
- "stop_sequence": ResponseStopReason.STOP_SEQUENCE,
- "refusal": ResponseStopReason.CONTENT_FILTER,
- }
-)
-
-_ANTHROPIC_BETA_PROMPT_CACHING = "prompt-caching-2024-07-31"
-_ANTHROPIC_BETA_OAUTH = "oauth-2025-04-20"
-
-
-@lru_cache(maxsize=1)
-def get_anthropic_tokenizer() -> tiktoken.Encoding:
- """Use cl100k_base encoding as an approximation for Claude tokenization.
-
- Modern Anthropic SDK does not expose a tokenizer directly and instead
- relies on API calls to count tokens. Using that implementation would
- put HTTP calls in our `count_tokens` implementation which would be tricky
- as the method would have to be async or block the event loop.
-
- Instead, we use tiktoken's cl100k_base encoding (used by GPT-4) as a
- reasonable approximation. This allows us to count tokens without making
- HTTP requests at the cost of slightly inaccurate token counts.
- """
- return tiktoken.get_encoding("cl100k_base")
-
-
-def count_anthropic_tokens(text: str) -> int:
- return int(len(get_anthropic_tokenizer().encode(text)) * 1.1)
-
-
-SystemMessageParam = TextBlockParam
-
-
-def _convert_prompt_to_anthropic_messages(
- prompt: str,
-) -> tuple[list[MessageParam], list[SystemMessageParam] | None]:
- """Converts a prompt into list of non-system (user/assistant) messages and the optional system prompt."""
- non_system_messages = []
- system_messages = []
- for msg in convert_prompt_to_messages(prompt, is_cache_role_preserved=True):
- role = _ROLE_TO_ANTHROPIC_ROLE[msg.role]
- if msg.role == "SYSTEM_CACHED":
- system_messages.append(
- {
- "type": "text",
- "text": msg.content,
- "cache_control": {"type": "ephemeral"},
- },
- )
- elif role == "system":
- system_messages.append(
- {
- "type": "text",
- "text": msg.content,
- },
- )
- elif role == "USER_CACHED":
- non_system_messages.append(
- MessageParam( # pyre-fixme[28]: MessageParam doesn't have cache_control
- content=msg.content,
- role="user",
- cache_control=CacheControlEphemeralParam(type="ephemeral"),
- )
- )
- else:
- non_system_messages.append(MessageParam(content=msg.content, role=role)) # type: ignore
-
- if len(system_messages) > 1:
- logger.debug("system_messages: {}", system_messages)
- raise ValueError(f"Anthropic API supports only 0 or 1 system message; got {len(system_messages)}.")
-
- if len(non_system_messages) == 0:
- system_messages = None
-
- return non_system_messages, system_messages
-
-
-@contextmanager
-def _anthropic_exception_manager() -> Iterator[None]:
- """Simple context manager for parsing Anthropic API exceptions."""
- # ref
- try:
- yield
- except anthropic.InternalServerError as e:
- # this can be caused by either malformed requests or transient errors, so play it safe and retry
- raise TransientLanguageModelError(str(e)) from e
- except anthropic.BadRequestError as e:
- logger.info("BadAPIRequestError {e}", e=e)
- raise BadAPIRequestError(str(e)) from e
- except TypeError as e:
- logger.info("Type error calling Anthropic API: {e}", e=e)
- raise BadAPIRequestError(str(e)) from e
- except anthropic.APIConnectionError as e:
- raise TransientLanguageModelError(str(e)) from e
- except anthropic.RateLimitError as e:
- extra_header_keys = [x for x in e.response.headers.keys() if x.startswith("anthropic-")]
- extra_data = ", ".join([f"{key}={e.response.headers[key]}" for key in extra_header_keys])
- extra_info = f"Rate limit data: {extra_data}"
- raise TransientLanguageModelError(extra_info) from e
- except anthropic.APIStatusError as e:
- if "overloaded_error" in str(e):
- raise SafelyRetriableTransientLanguageModelError(str(e)) from e
- if "internal server error" in str(e).lower():
- raise SafelyRetriableTransientLanguageModelError(str(e)) from e
- # this happens when anthropic provides us open source code and then feels bad about it
- # anthropic.APIStatusError: {'type': 'error', 'error': {'details': None, 'type': 'invalid_request_error', 'message': 'Output blocked by content filtering policy'}}
- if e.message == "Output blocked by content filtering policy":
- raise NewSeedRetriableLanguageModelError(e)
- logger.info(str(e))
- if e.status_code == 409 or e.status_code >= 500:
- raise TransientLanguageModelError(str(e)) from e
- raise
- except httpx.RemoteProtocolError as e:
- logger.info(str(e))
- raise TransientLanguageModelError("httpx.RemoteProtocolError") from e
- except (BadAPIRequestError, TransientLanguageModelError, MissingAPIKeyError):
- # we already raised this error ourselves earlier, so we don't need to mark it as unknown
- raise
- except Exception as e:
- # we catch TransientLanguageModelError later to retry it, but we still want to log it so it's not silent
- log_exception(
- e,
- "Failed to generate output from Anthropic, unknown error of type {type_name}",
- type_name=type(e).__name__,
- )
- raise TransientLanguageModelError("Unknown error") from e
-
-
-class MissingCachingInfoError(Exception):
- pass
-
-
-class AnthropicAPI(LanguageModelAPI):
- model_name: AnthropicModelName = AnthropicModelName.CLAUDE_4_SONNET_2025_05_14
- is_conversational: bool = True
-
- # Anthropic specific args
- # unclear what the timeout ought to be actually, set to 1 minute for now because their default of 10 minutes seems insane
- timeout: float = 60.0
- max_retries: int = 0
- count_tokens_cache_path: Path | None = None
-
- @field_validator("model_name") # pyre-ignore[56]: pyre doesn't understand pydantic
- @classmethod
- def validate_model_name(cls, v: str) -> str:
- if v not in ANTHROPIC_MODEL_INFO_BY_NAME:
- raise LanguageModelInvalidModelNameError(v, cls.__name__, list(ANTHROPIC_MODEL_INFO_BY_NAME))
- return v
-
- @property
- def model_info(self) -> ModelInfo:
- return ANTHROPIC_MODEL_INFO_BY_NAME[self.model_name]
-
- def _get_sync_client(self) -> anthropic.Anthropic:
- api_key, auth_token = _get_api_key_or_auth_token()
- if api_key:
- return anthropic.Anthropic(api_key=api_key)
- else:
- return anthropic.Anthropic(
- auth_token=auth_token,
- default_headers={"anthropic-beta": _ANTHROPIC_BETA_OAUTH},
- )
-
- def _get_client(self) -> anthropic.AsyncAnthropic:
- api_key, auth_token = _get_api_key_or_auth_token()
- if api_key:
- return anthropic.AsyncAnthropic(
- api_key=api_key,
- max_retries=self.max_retries,
- timeout=self.timeout,
- default_headers={"anthropic-beta": _ANTHROPIC_BETA_PROMPT_CACHING},
- )
- else:
- return anthropic.AsyncAnthropic(
- auth_token=auth_token,
- max_retries=self.max_retries,
- timeout=self.timeout,
- default_headers={"anthropic-beta": f"{_ANTHROPIC_BETA_PROMPT_CACHING},{_ANTHROPIC_BETA_OAUTH}"},
- )
-
- async def _call_api(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- network_failure_count: int = 0,
- ) -> CostedLanguageModelResponse:
- assert (
- params.count == 1
- ), "Anthropic API only supports count=1. It is possible to hack around this by using a for loop, but doesn't seem worth it right now."
-
- non_system_messages, system_messages = _convert_prompt_to_anthropic_messages(prompt)
-
- with _anthropic_exception_manager():
- async with self._get_client() as client:
- if params.max_tokens is None:
- # NOTE: anthropic's API REQUIRES you to provide this, if you don't pass it in we just set it to the maximum possible
-
- # use the evolver method of updating instead
- # params.max_tokens = self.model_info.max_output_tokens
- param_with_max_tokens_evolver = evolver(params)
- assign(
- param_with_max_tokens_evolver.max_tokens,
- lambda: self.model_info.max_output_tokens,
- )
- params = chill(param_with_max_tokens_evolver)
- assert params.max_tokens is not None, "max_tokens must be provided for Anthropic API"
-
- if self.model_name in (
- AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG,
- AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG,
- ):
- # FIXME: Fix this once this is no longer beta or as this becomes required for more models
- # Map the name back to the actual model name for the API call
- if self.model_name == AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG:
- model_name = AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29
- elif self.model_name == AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG:
- model_name = AnthropicModelName.CLAUDE_4_SONNET_2025_05_14
- else:
- assert False, "unreachable"
- api_result = await client.beta.messages.create(
- messages=non_system_messages,
- stop_sequences=([params.stop] if params.stop is not None else NOT_GIVEN),
- model=model_name,
- temperature=params.temperature,
- system=prepend_claude_code_system_prompt(system_messages),
- max_tokens=params.max_tokens,
- betas=["context-1m-2025-08-07"],
- )
- detailed_caching_data = AnthropicCachingInfo(
- written_5m=api_result.usage.cache_creation.ephemeral_5m_input_tokens,
- written_1h=api_result.usage.cache_creation.ephemeral_1h_input_tokens,
- )
- else:
- api_result = await client.messages.create(
- messages=non_system_messages,
- stop_sequences=([params.stop] if params.stop is not None else NOT_GIVEN),
- model=self.model_name,
- temperature=params.temperature,
- system=prepend_claude_code_system_prompt(system_messages),
- max_tokens=params.max_tokens,
- )
- detailed_caching_data = AnthropicCachingInfo(
- written_5m=api_result.usage.cache_creation.ephemeral_5m_input_tokens,
- written_1h=api_result.usage.cache_creation.ephemeral_1h_input_tokens,
- )
- text = only(api_result.content).text
- if api_result.stop_reason:
- stop_reason = _ANTHROPIC_STOP_REASON_TO_STOP_REASON.get(
- str(api_result.stop_reason), ResponseStopReason.NONE
- )
- else:
- stop_reason = ResponseStopReason.NONE
- if params.stop and stop_reason == ResponseStopReason.STOP_SEQUENCE:
- text += params.stop
- logger.trace(text)
-
- prompt_tokens = api_result.usage.input_tokens
- completion_tokens = api_result.usage.output_tokens # type: ignore
- caching_info = CachingInfo(
- read_from_cache=api_result.usage.cache_read_input_tokens,
- provider_specific_data=detailed_caching_data,
- )
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens, caching_info)
- logger.trace("Dollars used: {dollars_used}", dollars_used=dollars_used)
-
- return create_costed_language_model_response_for_single_result(
- text=text,
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- stop_reason=stop_reason,
- network_failure_count=network_failure_count,
- dollars_used=dollars_used,
- caching_info=caching_info,
- )
-
- async def _get_api_stream(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- non_system_messages, system_messages = _convert_prompt_to_anthropic_messages(prompt)
- with _anthropic_exception_manager():
- async with self._get_client() as client:
- yield LanguageModelStreamStartEvent()
-
- # NOTE: anthropic's API REQUIRES you to provide this, if you don't pass it in we just set it to the maximum possible
- max_tokens = params.max_tokens if params.max_tokens is not None else self.model_info.max_output_tokens
- assert max_tokens is not None, "max_tokens must be provided for Anthropic API"
-
- if self.model_name in (
- AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG,
- AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG,
- ):
- # FIXME: Fix this once this is no longer beta or as this becomes required for more models
- # Map the name back to the actual model name for the API call
- if self.model_name == AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG:
- model_name = AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29
- elif self.model_name == AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG:
- model_name = AnthropicModelName.CLAUDE_4_SONNET_2025_05_14
- else:
- assert False, "unreachable"
- stream_fn = lambda **kwargs: client.beta.messages.stream(**kwargs, betas=["context-1m-2025-08-07"])
- cache_info_maker = lambda api_result: AnthropicCachingInfo(
- written_5m=api_result.usage.cache_creation.ephemeral_5m_input_tokens,
- written_1h=api_result.usage.cache_creation.ephemeral_1h_input_tokens,
- )
- else:
- model_name = self.model_name
- stream_fn = lambda **kwargs: client.messages.stream(**kwargs)
- cache_info_maker = lambda api_result: AnthropicCachingInfo(
- written_5m=api_result.usage.cache_creation.ephemeral_5m_input_tokens,
- written_1h=api_result.usage.cache_creation.ephemeral_1h_input_tokens,
- )
- async with stream_fn(
- max_tokens=max_tokens,
- messages=non_system_messages,
- model=model_name,
- stop_sequences=([params.stop] if params.stop is not None else NOT_GIVEN),
- system=system_messages or NOT_GIVEN,
- temperature=params.temperature,
- ) as stream:
- async for text_delta in stream.text_stream:
- yield LanguageModelStreamDeltaEvent(delta=text_delta)
-
- final_message = await stream.get_final_message()
- text = only(final_message.content).text
- stop_reason = (
- final_message.stop_reason if final_message.stop_reason is not None else ResponseStopReason.NONE
- )
- if params.stop and stop_reason == ResponseStopReason.STOP_SEQUENCE:
- yield LanguageModelStreamDeltaEvent(delta=params.stop)
- text += params.stop
- logger.trace(text)
-
- prompt_tokens = final_message.usage.input_tokens
- # useful to confirm that the cache is actually being hit
- logger.debug(
- "Used this many cached read tokens: {cached_tokens}",
- cached_tokens=final_message.usage.cache_read_input_tokens,
- )
- completion_tokens = final_message.usage.output_tokens
- caching_info = CachingInfo(
- read_from_cache=final_message.usage.cache_read_input_tokens,
- provider_specific_data=cache_info_maker(final_message),
- )
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens, caching_info)
-
- if final_message.stop_reason:
- stop_reason = _ANTHROPIC_STOP_REASON_TO_STOP_REASON.get(
- str(final_message.stop_reason), ResponseStopReason.NONE
- )
- else:
- stop_reason = ResponseStopReason.NONE
-
- logger.trace("Dollars used: {dollars_used}", dollars_used=dollars_used)
- yield LanguageModelStreamEndEvent(
- usage=LanguageModelResponseUsage(
- prompt_tokens_used=prompt_tokens,
- completion_tokens_used=completion_tokens,
- dollars_used=dollars_used,
- caching_info=caching_info,
- ),
- stop_reason=stop_reason,
- )
-
- def count_tokens(self, text: str) -> int:
- return count_anthropic_tokens(text)
-
- def get_count_tokens_response_cache(self) -> AsyncCache[CachedCountTokensResponse]:
- if self.count_tokens_cache_path is None:
- raise UnsetCachePathError()
- return AsyncCache(self.count_tokens_cache_path, CachedCountTokensResponse)
-
- async def check_count_tokens_cache(self, cache_key: str) -> CountTokensResponse | None:
- return await self.check_cache_core(self.get_count_tokens_response_cache, cache_key)
-
- async def _get_from_count_tokens_cache(
- self, frame: FrameType | None
- ) -> tuple[str | None, CountTokensResponse | None]:
- return await self._get_from_cache_core(frame, lambda cr: cr, self.check_count_tokens_cache)
-
- async def count_tokens_api(self, prompt: str, is_caching_enabled: bool) -> int:
- """
- Call the count tokens API. This API is free, so we don't ensure resource limits before calling it.
- There are rate limits though: https://docs.anthropic.com/en/docs/build-with-claude/token-counting#pricing-and-rate-limits
- """
-
- self.assert_caching_enabled_if_offline(is_caching_enabled)
-
- frame: FrameType | None = None
- if is_caching_enabled:
- frame = inspect.currentframe()
-
- cache_key: str | None = None
- if is_caching_enabled:
- cache_key, cached_response = await self._get_from_count_tokens_cache(frame)
-
- if cached_response is not None:
- return cached_response.input_tokens
-
- self.assert_not_offline_if_cache_miss(prompt)
-
- non_system_messages, system_messages = _convert_prompt_to_anthropic_messages(prompt)
-
- with _anthropic_exception_manager():
- async with self._get_client() as client:
- raw_response = await client.messages.count_tokens(
- model=self.model_info.model_name,
- messages=non_system_messages,
- system=system_messages,
- )
-
- response = CountTokensResponse(input_tokens=raw_response.input_tokens)
- result = CachedCountTokensResponse(
- response=response,
- inputs=(
- CountTokensInputs(model=self.model_info.model_name, prompt=prompt)
- if self.is_caching_inputs
- else None
- ),
- )
-
- if is_caching_enabled:
- assert cache_key is not None
- async with self.get_count_tokens_response_cache() as cache:
- await cache.set(cache_key, result)
-
- return response.input_tokens
-
- def calculate_cost(
- self,
- prompt_tokens: int,
- completion_tokens: int,
- caching_info: CachingInfo | None = None,
- ) -> float:
- try:
- # find the cost for the prompt, broken down into cache writes and regular input tokens
-
- # if we don't have the caching info, use the basic cost model (we catch the error below)
- if (
- caching_info is None
- or caching_info.provider_specific_data is None
- or self.model_info.provider_specific_info is None
- ):
- raise MissingCachingInfoError(
- f"Missing required info for more precise cost estimates; caching info: {caching_info}, model info: {self.model_info.provider_specific_info}"
- )
- anthropic_caching_usage = caching_info.provider_specific_data
- assert isinstance(anthropic_caching_usage, AnthropicCachingInfo), "Expected AnthropicCachingInfo"
- anthropic_caching_rates = self.model_info.provider_specific_info
- assert isinstance(anthropic_caching_rates, AnthropicModelInfo), "Expected AnthropicModelInfo"
- cache_write_5m_tokens = anthropic_caching_usage.written_5m
- cache_write_1h_tokens = anthropic_caching_usage.written_1h
- cache_read_tokens = caching_info.read_from_cache
- regular_input_tokens = prompt_tokens - cache_write_5m_tokens - cache_write_1h_tokens
-
- input_cost = (
- cache_write_5m_tokens * anthropic_caching_rates.cost_per_5m_cache_write_token
- + cache_write_1h_tokens * anthropic_caching_rates.cost_per_1h_cache_write_token
- + cache_read_tokens * anthropic_caching_rates.cost_per_cache_read_token
- + regular_input_tokens * self.model_info.cost_per_input_token
- )
-
- output_cost = completion_tokens * self.model_info.cost_per_output_token
-
- return input_cost + output_cost
-
- except MissingCachingInfoError as e:
- logger.info("{}; using basic cost model", e)
- return self.basic_calculate_cost(prompt_tokens, completion_tokens)
-
-
-def _get_api_key_or_auth_token() -> tuple[str | None, str | None]:
- api_key = get_secret("ANTHROPIC_API_KEY")
- auth_token = get_secret("ANTHROPIC_AUTH_TOKEN")
- if not api_key and not auth_token:
- raise MissingAPIKeyError("Neither ANTHROPIC_API_KEY nor ANTHROPIC_AUTH_TOKEN environment variable is set")
- return api_key, auth_token
-
-
-_CLAUDE_CODE_SYSTEM_PROMPT = TextBlockParam(
- type="text",
- text="You are Claude Code, Anthropic's official CLI for Claude.",
- cache_control=CacheControlEphemeralParam(type="ephemeral"),
-)
-
-
-def prepend_claude_code_system_prompt(
- system_prompt: str | list[TextBlockParam] | None,
-) -> list[TextBlockParam]:
- """Prepends the system prompt used by Claude Code.
-
- When using the Claude API through Claude Pro/Max subscriptions,
- the Claude API requires this particular system prompt to be set;
- otherwise the request will fail.
-
- For simplicity and consistency,
- we always do this even when it's not strictly required,
- (like when using the Claude API through API keys).
- """
- if not system_prompt:
- return [_CLAUDE_CODE_SYSTEM_PROMPT]
- elif isinstance(system_prompt, str):
- return [
- _CLAUDE_CODE_SYSTEM_PROMPT,
- TextBlockParam(type="text", text=system_prompt),
- ]
- else:
- return [_CLAUDE_CODE_SYSTEM_PROMPT] + system_prompt
diff --git a/imbue_core/imbue_core/agents/llm_apis/anthropic_data_types.py b/imbue_core/imbue_core/agents/llm_apis/anthropic_data_types.py
@@ -1,15 +0,0 @@
-from imbue_core.pydantic_serialization import SerializableModel
-
-
-class AnthropicModelInfo(SerializableModel):
- object_type: str = "AnthropicModelInfo"
- cost_per_5m_cache_write_token: float
- cost_per_1h_cache_write_token: float
- cost_per_cache_read_token: float
-
-
-class AnthropicCachingInfo(SerializableModel):
- object_type: str = "AnthropicCachingInfo"
- # record info on cache writes for 5 minute and 1 hour durations
- written_5m: int
- written_1h: int
diff --git a/imbue_core/imbue_core/agents/llm_apis/api_utils.py b/imbue_core/imbue_core/agents/llm_apis/api_utils.py
@@ -1,110 +0,0 @@
-from typing import Final
-from typing import Iterable
-
-from loguru import logger
-
-from imbue_core.agents.llm_apis.data_types import CachingInfo
-from imbue_core.agents.llm_apis.data_types import ConversationMessage
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseWithThoughts
-from imbue_core.agents.llm_apis.data_types import ResponseStopReason
-from imbue_core.agents.llm_apis.data_types import ThoughtResponse
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.frozen_utils import FrozenMapping
-
-_ROLE_TO_OPENAI_ROLE: Final[FrozenMapping] = FrozenDict(
- {
- "HUMAN": "user",
- "ASSISTANT": "assistant",
- "SYSTEM": "system",
- "USER": "user",
- "SYSTEM_CACHED": "system",
- "USER_CACHED": "user",
- }
-)
-
-
-def convert_prompt_to_messages(prompt: str, is_cache_role_preserved: bool = False) -> tuple[ConversationMessage, ...]:
- messages = []
- for raw_message in convert_prompt_to_openai_messages(prompt, is_cache_role_preserved):
- messages.append(ConversationMessage(role=raw_message["role"].upper(), content=raw_message["content"]))
- return tuple(messages)
-
-
-def convert_messages_to_prompt_template(messages: Iterable[ConversationMessage]) -> str:
- return "\n".join(f"[ROLE={message.role.upper()}]\n{message.content}" for message in messages)
-
-
-def create_costed_language_model_response_for_single_result(
- text: str,
- prompt_tokens: int,
- completion_tokens: int,
- stop_reason: ResponseStopReason,
- network_failure_count: int,
- dollars_used: float,
- thoughts: ThoughtResponse | None = None,
- caching_info: CachingInfo | None = None,
-) -> CostedLanguageModelResponse:
- logger.trace("dollars used: {}", dollars_used)
- logger.trace("completion_tokens_used used: {}", completion_tokens)
- if thoughts is None:
- result = LanguageModelResponse(
- text=text,
- token_count=completion_tokens + prompt_tokens,
- stop_reason=stop_reason,
- network_failure_count=network_failure_count,
- )
- else:
- result = LanguageModelResponseWithThoughts(
- text=text,
- token_count=completion_tokens + prompt_tokens,
- stop_reason=stop_reason,
- network_failure_count=network_failure_count,
- thoughts=thoughts,
- )
-
- return CostedLanguageModelResponse(
- usage=LanguageModelResponseUsage(
- prompt_tokens_used=prompt_tokens,
- completion_tokens_used=completion_tokens,
- dollars_used=dollars_used,
- caching_info=caching_info,
- ),
- responses=(result,),
- )
-
-
-# FIXME: we should make sure that all our LLM providers use the same function here, some clean up is required
-def convert_prompt_to_openai_messages(prompt: str, is_cache_role_preserved: bool = False) -> list[dict[str, str]]:
- prompt = prompt.lstrip()
- assert prompt.startswith("[ROLE=")
- prompt = prompt.replace("[ROLE=", "", 1)
- chunks = prompt.split("\n[ROLE=")
- messages: list[dict[str, str]] = []
- for chunk in chunks:
- lines = chunk.split("\n")
- role = lines[0].strip().rstrip("]")
- assert role in (
- "HUMAN",
- "ASSISTANT",
- "USER",
- "SYSTEM",
- "SYSTEM_CACHED",
- "USER_CACHED",
- ), f"Unknown role {role} in prompt {prompt}"
- lines.pop(0)
- if role == "HUMAN":
- role = "USER"
- if len(messages) > 0:
- messages[-1]["content"] = messages[-1]["content"] + "\n"
- content = "\n".join(lines)
- content = content.rstrip()
- fixed_role = _ROLE_TO_OPENAI_ROLE[role]
- if is_cache_role_preserved and role == "SYSTEM_CACHED":
- fixed_role = "SYSTEM_CACHED"
- elif is_cache_role_preserved and role == "USER_CACHED":
- fixed_role = "USER_CACHED"
- messages.append({"role": fixed_role, "content": content})
- return messages
diff --git a/imbue_core/imbue_core/agents/llm_apis/build_apis.py b/imbue_core/imbue_core/agents/llm_apis/build_apis.py
@@ -1,128 +0,0 @@
-from pathlib import Path
-
-from imbue_core.agents.configs import LanguageModelGenerationConfig
-from imbue_core.agents.configs import MockedLanguageModelGenerationConfig
-from imbue_core.agents.configs import OpenAICompatibleModelConfig
-from imbue_core.agents.llm_apis.anthropic_api import AnthropicAPI
-from imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
-from imbue_core.agents.llm_apis.constants import approximate_token_count
-from imbue_core.agents.llm_apis.gemini_api import GeminiAPI
-from imbue_core.agents.llm_apis.gemini_api import GeminiModelName
-from imbue_core.agents.llm_apis.groq_api import GroqChatAPI
-from imbue_core.agents.llm_apis.groq_api import GroqSupportedModelName
-from imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
-from imbue_core.agents.llm_apis.mock_api import FileBasedLanguageModelMock
-from imbue_core.agents.llm_apis.mock_api import MockModelName
-from imbue_core.agents.llm_apis.openai_api import OpenAIChatAPI
-from imbue_core.agents.llm_apis.openai_api import OpenAIModelName
-from imbue_core.agents.llm_apis.openai_compatible_api import OpenAICompatibleAPI
-from imbue_core.agents.llm_apis.together_api import TogetherAIModelName
-from imbue_core.agents.llm_apis.together_api import TogetherAPI
-
-
-def build_language_model_from_config(
- config: LanguageModelGenerationConfig,
-) -> LanguageModelAPI:
- if isinstance(config, MockedLanguageModelGenerationConfig):
- return FileBasedLanguageModelMock(cache_path=config.mock_responses_path)
-
- if isinstance(config, OpenAICompatibleModelConfig):
- return OpenAICompatibleAPI(
- model_name=config.model_name,
- base_url=config.custom_base_url,
- api_key_env=config.custom_api_key_env,
- context_window=config.custom_context_window,
- max_output_tokens=config.custom_max_output_tokens,
- cache_path=config.cache_path,
- is_caching_inputs=config.is_caching_inputs,
- is_running_offline=config.is_running_offline,
- is_conversational=True,
- retry_jitter_factor=config.retry_jitter_factor,
- )
-
- if config.model_name in (v for v in MockModelName):
- return FileBasedLanguageModelMock(cache_path=config.cache_path)
- if config.model_name in (v for v in OpenAIModelName):
- return OpenAIChatAPI(
- model_name=config.model_name,
- cache_path=config.cache_path,
- is_caching_inputs=config.is_caching_inputs,
- is_running_offline=config.is_running_offline,
- is_conversational=True,
- is_using_logprobs=config.is_using_logprobs,
- retry_jitter_factor=config.retry_jitter_factor,
- )
- if config.model_name in (v for v in GroqSupportedModelName):
- return GroqChatAPI(
- model_name=config.model_name,
- cache_path=config.cache_path,
- is_caching_inputs=config.is_caching_inputs,
- is_running_offline=config.is_running_offline,
- is_conversational=True,
- is_using_logprobs=config.is_using_logprobs,
- retry_jitter_factor=config.retry_jitter_factor,
- )
- if config.model_name in (v for v in AnthropicModelName):
- return AnthropicAPI(
- model_name=config.model_name,
- cache_path=config.cache_path,
- count_tokens_cache_path=config.count_tokens_cache_path,
- is_caching_inputs=config.is_caching_inputs,
- is_running_offline=config.is_running_offline,
- is_conversational=True,
- is_using_logprobs=config.is_using_logprobs,
- retry_jitter_factor=config.retry_jitter_factor,
- )
- if config.model_name in (v for v in TogetherAIModelName):
- return TogetherAPI(
- model_name=config.model_name,
- cache_path=config.cache_path,
- # count tokens is not supported for Together API
- # count_tokens_cache_path=config.count_tokens_cache_path,
- is_caching_inputs=config.is_caching_inputs,
- is_running_offline=config.is_running_offline,
- is_conversational=True,
- is_using_logprobs=config.is_using_logprobs,
- retry_jitter_factor=config.retry_jitter_factor,
- )
- if config.model_name in (v for v in GeminiModelName):
- return GeminiAPI(
- model_name=config.model_name,
- cache_path=config.cache_path,
- count_tokens_cache_path=config.count_tokens_cache_path,
- is_caching_inputs=config.is_caching_inputs,
- is_running_offline=config.is_running_offline,
- is_conversational=True,
- is_using_logprobs=config.is_using_logprobs,
- retry_jitter_factor=config.retry_jitter_factor,
- )
- # if config.model_name in MISTRAL_CHAT_MODEL_NAMES:
- # return MistralChatAPI(
- # model_name=config.model_name,
- # cache_path=config.cache_path,
- # is_conversational=True,
- # )
-
- raise NotImplementedError(f"{config.model_name} not supported by LanguageModelAPI")
-
-
-def build_language_model_by_name(
- model_name: str,
- cache_path: Path | None = None,
- is_caching_inputs: bool = False,
- is_using_logprobs: bool = False,
-) -> LanguageModelAPI:
- config = LanguageModelGenerationConfig(
- model_name=model_name,
- cache_path=cache_path,
- is_caching_inputs=is_caching_inputs,
- is_using_logprobs=is_using_logprobs,
- )
- return build_language_model_from_config(config)
-
-
-def get_token_count_for_text_and_model(text: str, model_name: str) -> int:
- try:
- return build_language_model_by_name(model_name).count_tokens(text)
- except NotImplementedError:
- return approximate_token_count(text)
diff --git a/imbue_core/imbue_core/agents/llm_apis/common.py b/imbue_core/imbue_core/agents/llm_apis/common.py
@@ -1,73 +0,0 @@
-from imbue_core.agents.llm_apis.anthropic_api import ANTHROPIC_MODEL_INFO_BY_NAME
-from imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
-from imbue_core.agents.llm_apis.gemini_api import GEMINI_MODEL_INFO_BY_NAME
-from imbue_core.agents.llm_apis.gemini_api import GeminiModelName
-from imbue_core.agents.llm_apis.groq_api import GroqSupportedModelName
-from imbue_core.agents.llm_apis.groq_api import get_model_info as get_groq_model_info
-from imbue_core.agents.llm_apis.mock_api import MY_MOCK_MODEL_INFO
-from imbue_core.agents.llm_apis.models import ModelInfo
-from imbue_core.agents.llm_apis.openai_api import OpenAIModelName
-from imbue_core.agents.llm_apis.openai_api import (
- get_model_info as get_openai_model_info,
-)
-from imbue_core.agents.llm_apis.together_api import TOGETHERAI_MODEL_INFO_BY_NAME
-from imbue_core.agents.llm_apis.together_api import TogetherAIModelName
-
-ModelName = AnthropicModelName | OpenAIModelName | GroqSupportedModelName | TogetherAIModelName | GeminiModelName
-
-
-def get_model_info_from_name(model_name: str) -> ModelInfo:
- if model_name == MY_MOCK_MODEL_INFO.model_name:
- return MY_MOCK_MODEL_INFO
- if model_name in (v for v in AnthropicModelName):
- return ANTHROPIC_MODEL_INFO_BY_NAME[AnthropicModelName(model_name)]
- elif model_name in (v for v in OpenAIModelName):
- return get_openai_model_info(OpenAIModelName(model_name))
- elif model_name in (v for v in GroqSupportedModelName):
- return get_groq_model_info(GroqSupportedModelName(model_name))
- elif model_name in (v for v in TogetherAIModelName):
- return TOGETHERAI_MODEL_INFO_BY_NAME[TogetherAIModelName(model_name)]
- elif model_name in (v for v in GeminiModelName):
- return GEMINI_MODEL_INFO_BY_NAME[GeminiModelName(model_name)]
- else:
- raise Exception(f"Unknown model: {model_name}")
-
-
-def get_model_max_context_length(model_name: str) -> int:
- model_info = get_model_info_from_name(model_name)
- return model_info.max_input_tokens
-
-
-def get_model_max_output_tokens(model_name: str) -> int:
- model_info = get_model_info_from_name(model_name)
- if model_info.max_output_tokens is None:
- raise ValueError(f"Model {model_name} does not have max_output_tokens defined")
- return model_info.max_output_tokens
-
-
-def get_all_model_names() -> list[str]:
- names = []
- names.extend(list(v for v in AnthropicModelName))
- names.extend(list(v for v in OpenAIModelName))
- names.extend(list(v for v in GroqSupportedModelName))
- names.extend(list(v for v in TogetherAIModelName))
- names.extend(list(v for v in GeminiModelName))
- return names
-
-
-def get_formatted_model_name(model_name: str) -> str:
- """Get a nicely formatted model name.
-
- Does things like removing generic prefixes like 'models/' and forward slashes (which can interfere with file names).
-
- Some examples:
-
- - `models/gemini-1.5-flash-001` -> `gemini-1.5-flash-001`
- - 'groq/llama-3.3-70b-versatile' -> 'groq-llama-3.3-70b-versatile'
- - 'claude-3-5-haiku-20241022' -> 'claude-3-5-haiku-20241022'
- - 'together/google/gemma-2-27b-it' -> 'together-google-gemma-2-27b-it'
-
- """
- if model_name.startswith("models/"):
- model_name = model_name[len("models/") :]
- return model_name.replace("/", "-")
diff --git a/imbue_core/imbue_core/agents/llm_apis/data_types.py b/imbue_core/imbue_core/agents/llm_apis/data_types.py
@@ -1,214 +0,0 @@
-import datetime
-import enum
-import math
-from abc import ABC
-from typing import Any
-from typing import Generic
-from typing import TypeVar
-
-import attr
-from pydantic import ValidationInfo
-from pydantic import field_validator
-
-from imbue_core.agents.llm_apis.union_data_types import ProviderSpecificCachingInfoUnion
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_core.serialization_types import Serializable
-from imbue_core.time_utils import get_current_time
-
-__all__ = [
- "CachedCostedLanguageModelResponse",
- "ConversationMessage",
- "CostedLanguageModelResponse",
- "LanguageModelCompleteInputs",
- "LanguageModelResponse",
- "LanguageModelResponseUsage",
- "LanguageModelResponseWithLogits",
- "LanguageModelResponseWithThoughts",
- "LanguageModelStreamInputs",
- "ModelStr",
- "ResponseStopReason",
- "TokenProbability",
-]
-
-
-class ConversationMessage(SerializableModel):
- role: str
- content: str
-
-
-class ResponseStopReason(enum.StrEnum):
- END_TURN = "end_turn"
- MAX_TOKENS = "max_tokens"
- STOP_SEQUENCE = "stop_sequence"
- ERROR = "error"
- NONE = "none"
- # TODO: We aren't handling any of the below, we should likely error in these cases
- CONTENT_FILTER = "content_filter"
- TOOL_CALLS = "tool_calls"
- FUNCTION_CALL = "function_call"
-
- def response_not_finished(self) -> bool:
- return self in {self.CONTENT_FILTER, self.MAX_TOKENS, self.ERROR}
-
-
-class TokenProbability(SerializableModel):
- token: str
- log_probability: float
- is_stop: bool
-
- @property
- def probability(self) -> float:
- return math.exp(self.log_probability)
-
-
-class ModelResponse(ABC):
- pass
-
-
-class ThoughtResponse(SerializableModel, ModelResponse):
- text: str
- completion_tokens: int
-
-
-@attr.s(auto_attribs=True, frozen=True)
-class LanguageModelResponse(Serializable, ModelResponse):
- text: str
- token_count: int
- stop_reason: ResponseStopReason
- network_failure_count: int
-
- def get_token_probability_sequence(
- self,
- ) -> tuple[tuple[TokenProbability, ...], ...] | None:
- return None
-
-
-@attr.s(auto_attribs=True, frozen=True)
-class LanguageModelResponseWithThoughts(LanguageModelResponse):
- thoughts: ThoughtResponse | None = None
-
-
-@attr.s(auto_attribs=True, frozen=True)
-class LanguageModelResponseWithLogits(LanguageModelResponse):
- # guarantees that the first in each sequence was the one that was selected.
- # the inner sequence are *not* guaranteed to be the same length, nor are they guaranteed to be sorted
- token_probabilities: tuple[tuple[TokenProbability, ...], ...]
-
- def get_token_probability_sequence(
- self,
- ) -> tuple[tuple[TokenProbability, ...], ...] | None:
- return self.token_probabilities
-
-
-@attr.s(auto_attribs=True, frozen=True)
-class CountTokensResponse(Serializable, ModelResponse):
- input_tokens: int
- cached_content_token_count: int | None = None
-
-
-class CachingInfo(SerializableModel):
- read_from_cache: int
-
- # this should contain info that's not the same between providers. e.g. anthropic requires explicit cache writes with 5m or 1h duration,
- # whereas openai does automatic prompt caching at no extra cost; so, we store cache write info here
- provider_specific_data: ProviderSpecificCachingInfoUnion | None = None
-
-
-class LanguageModelResponseUsage(SerializableModel):
- prompt_tokens_used: int
- completion_tokens_used: int
- dollars_used: float
- caching_info: CachingInfo | None = None
-
-
-@attr.s(auto_attribs=True, frozen=True)
-class CostedLanguageModelResponse(Serializable, ModelResponse):
- usage: LanguageModelResponseUsage
- responses: tuple[LanguageModelResponse, ...]
-
-
-class ThinkConfig(SerializableModel):
- # watch out: at least for gemini, this is a soft limit!
- max_tokens: int | None = None
- output_thinking: bool = False
-
-
-class LanguageModelGenerationParams(SerializableModel):
- """Parameters for a single API call to an LLM. Excludes things that you don't want a default for, e.g. the prompt."""
-
- temperature: float = 0.2
- count: int = 1
- max_tokens: int | None = None
- stop: str | None = None
- # specifically to allow generating new responses even when using caching
- seed: int | None = None
- thinking: ThinkConfig | None = None
-
-
-class ModelInputs(SerializableModel, ABC):
- """Base class for inputs to an LLM API call."""
-
-
-class LanguageModelCompleteInputs(ModelInputs):
- """Used to serialize the inputs for an LLM complete call."""
-
- prompt: str
- params: LanguageModelGenerationParams
- network_failure_count: int
-
-
-class LanguageModelStreamInputs(ModelInputs):
- """Used to serialize the inputs for an LLM stream call."""
-
- prompt: str
- params: LanguageModelGenerationParams
-
-
-class CountTokensInputs(ModelInputs):
- """Used to serialize the inputs for a token count call."""
-
- model: str
- prompt: str
-
-
-InputsT = TypeVar("InputsT", bound=ModelInputs)
-ModelResponseT = TypeVar("ModelResponseT", bound=ModelResponse)
-
-
-@attr.s(auto_attribs=True, frozen=True)
-class CachedCostedModelResponse(Serializable, Generic[InputsT, ModelResponseT]):
- response: ModelResponseT | None = None
- error: str | None = None
-
- # The timestamp is used to order cache entries when checking them in unit tests.
- timestamp: datetime.datetime = attr.ib(factory=get_current_time)
-
- # Cache entries are keyed based on an MD5 hash of the inputs to prevent the cache from growing too large.
- # Here, we optionally store the inputs to the request.
- # This is useful for unit tests to highlight changes in the prompt and other parts of the request.
- # But since it can grow very large, we don't always store this information.
- inputs: InputsT | None = None
-
- @field_validator("response", "error")
- def validate_response_or_error(cls, v: Any, info: ValidationInfo) -> Any:
- if "response" in info.data and "error" in info.data:
- if not ((info.data["response"] is None) ^ (info.data["error"] is None)):
- raise ValueError("Must provide exactly one of response or error")
- return v
-
-
-class CachedCostedLanguageModelResponse(
- CachedCostedModelResponse[
- LanguageModelCompleteInputs | LanguageModelStreamInputs,
- CostedLanguageModelResponse,
- ]
-):
- pass
-
-
-class CachedCountTokensResponse(CachedCostedModelResponse[CountTokensInputs, CountTokensResponse]):
- pass
-
-
-# to allow type checking to work when model names are passed as strings
-ModelStr = str
diff --git a/imbue_core/imbue_core/agents/llm_apis/gemini_api.py b/imbue_core/imbue_core/agents/llm_apis/gemini_api.py
@@ -1,526 +0,0 @@
-import enum
-import inspect
-from contextlib import contextmanager
-from pathlib import Path
-from types import FrameType
-from typing import AsyncGenerator
-from typing import Callable
-from typing import Final
-from typing import Iterable
-from typing import Iterator
-from typing import TypeVar
-
-import google.genai as genai
-import httpx
-from google.genai.errors import APIError
-from google.genai.types import BlockedReason
-from google.genai.types import ContentListUnion
-from google.genai.types import ContentUnion
-from google.genai.types import FinishReason
-from google.genai.types import GenerateContentConfig
-from google.genai.types import GenerateContentResponse
-from google.genai.types import HarmProbability
-from google.genai.types import ModelContent
-from google.genai.types import Part
-from google.genai.types import ThinkingConfig
-from google.genai.types import UserContent
-from loguru import logger
-from pydantic.functional_validators import field_validator
-
-from imbue_core.agents.llm_apis.api_utils import convert_prompt_to_messages
-from imbue_core.agents.llm_apis.api_utils import (
- create_costed_language_model_response_for_single_result,
-)
-from imbue_core.agents.llm_apis.data_types import CachedCountTokensResponse
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import CountTokensInputs
-from imbue_core.agents.llm_apis.data_types import CountTokensResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.agents.llm_apis.data_types import ResponseStopReason
-from imbue_core.agents.llm_apis.data_types import ThoughtResponse
-from imbue_core.agents.llm_apis.errors import BadAPIRequestError
-from imbue_core.agents.llm_apis.errors import LanguageModelInvalidModelNameError
-from imbue_core.agents.llm_apis.errors import MissingAPIKeyError
-from imbue_core.agents.llm_apis.errors import TransientLanguageModelError
-from imbue_core.agents.llm_apis.errors import UnsetCachePathError
-from imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
-from imbue_core.agents.llm_apis.models import ModelInfo
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
-from imbue_core.async_monkey_patches import log_exception
-from imbue_core.caching import AsyncCache
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.frozen_utils import FrozenMapping
-from imbue_core.itertools import only
-from imbue_core.secrets_utils import get_secret
-
-
-class GeminiModelName(enum.StrEnum):
- GEMINI_1_0_PRO = "models/gemini-1.0-pro-001"
- GEMINI_1_5_FLASH = "models/gemini-1.5-flash-001"
- GEMINI_1_5_PRO = "models/gemini-1.5-pro-001"
- GEMINI_1_5_PRO_2 = "models/gemini-1.5-pro-002"
- GEMINI_1_5_FLASH_2 = "models/gemini-1.5-flash-002"
- GEMINI_2_0_FLASH = "models/gemini-2.0-flash-001"
- GEMINI_2_5_FLASH = "models/gemini-2.5-flash"
- GEMINI_2_5_FLASH_LITE_PREVIEW = "models/gemini-2.5-flash-lite-preview-06-17"
-
-
-# Rate limits for Google Gemini models based on published API documentation
-# Reference: https://ai.google.dev/gemini-api/docs/rate-limits#tier-3
-# Using Tier 3 rate limits
-
-GEMINI_MODEL_INFO_BY_NAME: FrozenMapping[GeminiModelName, ModelInfo] = FrozenDict(
- {
- # https://ai.google.dev/gemini-api/docs/models/gemini
- # https://ai.google.dev/pricing
- # For pricing there are different rates depending on context/prompt size, so below we use the most
- # expensive value. Note that this only kicks in at 128k tokens, the cost for most prompts is 2x lower
- GeminiModelName.GEMINI_1_0_PRO: ModelInfo(
- model_name="models/gemini-1.0-pro-001",
- cost_per_input_token=0.5 / 1_000_000,
- cost_per_output_token=1.5 / 1_000_000,
- max_input_tokens=30_720,
- max_output_tokens=2048,
- rate_limit_req=2000 / 60, # 2000 RPM = 33.33 RPS
- ),
- GeminiModelName.GEMINI_1_5_FLASH: ModelInfo(
- model_name="models/gemini-1.5-flash-001",
- cost_per_input_token=0.15 / 1_000_000,
- cost_per_output_token=0.60 / 1_000_000,
- max_input_tokens=1_048_576,
- max_output_tokens=8192,
- rate_limit_req=30000 / 60, # 30000 RPM = 500.00 RPS
- ),
- GeminiModelName.GEMINI_1_5_FLASH_2: ModelInfo(
- model_name="models/gemini-1.5-flash-002",
- cost_per_input_token=0.15 / 1_000_000,
- cost_per_output_token=0.60 / 1_000_000,
- max_input_tokens=1_048_576,
- max_output_tokens=8192,
- rate_limit_req=30000 / 60, # 30000 RPM = 500.00 RPS
- ),
- GeminiModelName.GEMINI_1_5_PRO: ModelInfo(
- model_name="models/gemini-1.5-pro-001",
- cost_per_input_token=2.5 / 1_000_000,
- cost_per_output_token=10.0 / 1_000_000,
- max_input_tokens=2_097_152,
- max_output_tokens=8192,
- rate_limit_req=4000 / 60, # 4000 RPM = 66.67 RPS
- ),
- GeminiModelName.GEMINI_1_5_PRO_2: ModelInfo(
- model_name="models/gemini-1.5-pro-002",
- cost_per_input_token=2.5 / 1_000_000,
- cost_per_output_token=10.0 / 1_000_000,
- max_input_tokens=2_097_152,
- max_output_tokens=8192,
- rate_limit_req=4000 / 60, # 4000 RPM = 66.67 RPS
- ),
- GeminiModelName.GEMINI_2_0_FLASH: ModelInfo(
- model_name="models/gemini-2.0-flash-001",
- cost_per_input_token=0.1 / 1_000_000,
- cost_per_output_token=0.4 / 1_000_000,
- max_input_tokens=1_048_576,
- max_output_tokens=8192,
- rate_limit_req=30000 / 60, # 30000 RPM = 500.00 RPS
- ),
- GeminiModelName.GEMINI_2_5_FLASH: ModelInfo(
- model_name="models/gemini-2.5-flash",
- cost_per_input_token=0.3 / 1_000_000,
- cost_per_output_token=2.5 / 1_000_000,
- max_input_tokens=1_048_576,
- max_output_tokens=65536,
- rate_limit_req=10_000 / 60, # 10000 RPM = 166.67 RPS
- rate_limit_tok=8_000_000 / 60, # 8,000,000 TPM = 133,333.33 TPS
- max_thinking_budget=24576,
- ),
- GeminiModelName.GEMINI_2_5_FLASH_LITE_PREVIEW: ModelInfo(
- model_name="models/gemini-2.5-flash-lite-preview-06-17",
- cost_per_input_token=0.1 / 1_000_000,
- cost_per_output_token=0.4 / 1_000_000,
- max_input_tokens=1_000_000,
- max_output_tokens=64_000,
- # these are the tier 2 rate limits. the above claims that we're on tier 3, but i've never actually seen that
- rate_limit_req=10_000 / 60,
- rate_limit_tok=10_000_000 / 60,
- # rate_limit_req=30_000 / 60, # 30000 RPM = 500.00 RPS
- # rate_limit_tok=30_000_000 / 60, # 30,000,000 TPM = 500,000 TPS
- max_thinking_budget=24_576,
- ),
- }
-)
-
-
-_ROLE_TO_GEMINI_ROLE: Final[FrozenMapping[str, str]] = FrozenDict(
- {
- "HUMAN": "user",
- "ASSISTANT": "model",
- "USER": "user",
- "SYSTEM": "user",
- }
-)
-
-NO_SIMPLE_TEXT_ERROR = "".join(
- [
- "The `response.text` quick accessor only works for ",
- "simple (single-`Part`) text responses. This response is not simple text.",
- "Use the `result.parts` accessor or the full ",
- "`result.candidates[index].content.parts` lookup ",
- "instead.",
- ]
-)
-
-_GEMINI_STOP_REASON_TO_STOP_REASON: Final[FrozenMapping[FinishReason, ResponseStopReason]] = FrozenDict(
- {
- # Gemini treats stop due to natural stop point and provided stop sequence the same
- FinishReason.STOP: ResponseStopReason.END_TURN,
- FinishReason.MAX_TOKENS: ResponseStopReason.MAX_TOKENS,
- FinishReason.SAFETY: ResponseStopReason.CONTENT_FILTER,
- # Recitation means the content was flagged for being memorized, i.e. the LLM just
- # copied data from the training data (@johnny at least that's how I understood the docs)
- # https://ai.google.dev/api/generate-content#FinishReason
- FinishReason.RECITATION: ResponseStopReason.CONTENT_FILTER,
- FinishReason.OTHER: ResponseStopReason.NONE,
- FinishReason.FINISH_REASON_UNSPECIFIED: ResponseStopReason.NONE,
- }
-)
-
-T = TypeVar("T")
-
-
-def only_and_not_none(iterable: Iterable[T] | None) -> T:
- in_value = iterable if iterable is not None else []
- return only(in_value)
-
-
-def _is_flagged_as_unsafe(api_result: GenerateContentResponse) -> bool:
- if api_result.prompt_feedback is None:
- return False
- block_reason = api_result.prompt_feedback.block_reason
- if block_reason == BlockedReason.SAFETY:
- return True
- candidate = only_and_not_none(api_result.candidates)
- if candidate.finish_reason == FinishReason.SAFETY:
- return True
- if candidate.finish_reason == FinishReason.OTHER and any(
- rating.probability != HarmProbability.NEGLIGIBLE for rating in (candidate.safety_ratings or [])
- ):
- return True
- return False
-
-
-def _is_flagged_as_recitation(api_result: GenerateContentResponse) -> bool:
- candidate = only_and_not_none(api_result.candidates)
- finish_reason = candidate.finish_reason
- if finish_reason == FinishReason.RECITATION:
- return True
- return False
-
-
-def role_to_content(role: str, parts: list[Part]) -> ContentUnion:
- match role:
- case "user":
- return UserContent(parts=parts)
- case "model":
- return ModelContent(parts=parts)
- case _:
- raise BadAPIRequestError(f"Invalid role: {role}")
-
-
-def convert_prompt_to_gemini_messages(prompt: str) -> ContentListUnion:
- messages: list[ContentUnion] = []
- parts = []
- last_role = None
- for message in convert_prompt_to_messages(prompt):
- role = _ROLE_TO_GEMINI_ROLE[message.role]
- parts.append(Part(text=f"\n{message.content}"))
- if last_role != role and last_role is not None:
- messages.append(role_to_content(last_role, parts))
- parts = []
- last_role = role
- if len(parts) > 0:
- assert last_role is not None
- messages.append(role_to_content(last_role, parts))
- return messages
-
-
-@contextmanager
-def _gemini_exception_manager() -> Iterator[None]:
- """Simple context manager for parsing gemini API exceptions."""
- # TODO probably some exceptions missing here. The google.ai docs/code is annoying to parse
- try:
- yield
- except AssertionError as e:
- logger.info("The Gemini prompt is invalid.")
- raise BadAPIRequestError(str(e)) from e
- except APIError as e:
- logger.info("Gemini failed to generate content.")
- raise BadAPIRequestError(str(e)) from e
- except ValueError as e:
- logger.info("Gemini did not return a simple text response.")
- raise BadAPIRequestError(str(e)) from e
- except AttributeError as e:
- logger.info("There is an error with the Gemini prompt or processing code: {}.", str(e))
- raise BadAPIRequestError(str(e)) from e
- except httpx.RemoteProtocolError as e:
- logger.info(str(e))
- raise TransientLanguageModelError("httpx.RemoteProtocolError") from e
- except (BadAPIRequestError, TransientLanguageModelError, MissingAPIKeyError):
- # we already raised this error ourselves earlier, so we don't need to mark it as unknown
- raise
- except Exception as e:
- # we catch TransientLanguageModelError later to retry it, but we still want to log it so it's not silent
- log_exception(
- e,
- "Failed to generate output from Gemini, unknown error of type {type_name}",
- type_name=type(e).__name__,
- )
- raise TransientLanguageModelError("Unknown error") from e
-
-
-R = TypeVar("R")
-
-
-def fmap(fn: Callable[[T], R], values: T | None) -> R | None:
- if values is None:
- return None
- return fn(values)
-
-
-class GeminiAPI(LanguageModelAPI):
- model_name: GeminiModelName = GeminiModelName.GEMINI_1_5_FLASH
- is_conversational: bool = True
-
- count_tokens_cache_path: Path | None = None
-
- @field_validator("model_name") # pyre-ignore[56]: pyre doesn't understand pydantic
- @classmethod
- def validate_model_name(cls, v: str) -> str:
- if v not in GEMINI_MODEL_INFO_BY_NAME:
- raise LanguageModelInvalidModelNameError(v, cls.__name__, list(GEMINI_MODEL_INFO_BY_NAME))
- return v
-
- @property
- def model_info(self) -> ModelInfo:
- return GEMINI_MODEL_INFO_BY_NAME[self.model_name]
-
- def _get_client(self) -> genai.Client:
- api_key = get_secret("GOOGLE_API_KEY")
- if not api_key:
- raise MissingAPIKeyError("GOOGLE_API_KEY environment variable is not set")
- return genai.Client(api_key=api_key)
-
- async def _call_api(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- network_failure_count: int = 0,
- ) -> CostedLanguageModelResponse:
- # TODO: check if this is still true
- assert params.count == 1, "Gemini only supports a single completion"
- messages = convert_prompt_to_gemini_messages(prompt)
- with _gemini_exception_manager():
- client = self._get_client()
- generation_config = GenerateContentConfig(
- temperature=params.temperature,
- candidate_count=params.count,
- stop_sequences=fmap(lambda x: [x], params.stop),
- max_output_tokens=params.max_tokens,
- thinking_config=fmap(
- lambda thinking: ThinkingConfig(
- thinking_budget=thinking.max_tokens,
- include_thoughts=thinking.output_thinking,
- ),
- params.thinking,
- ),
- )
-
- api_result: GenerateContentResponse = await client.aio.models.generate_content(
- model=self.model_info.model_name,
- contents=messages,
- config=generation_config,
- )
-
- prompt_tokens = self.count_tokens(prompt)
-
- if (
- api_result.prompt_feedback is not None
- and api_result.prompt_feedback.block_reason is not None
- and api_result.prompt_feedback.block_reason != BlockedReason.BLOCKED_REASON_UNSPECIFIED
- ):
- logger.info(
- f"Gemini blocked output: {messages=}, {api_result.prompt_feedback.block_reason=}, {api_result.prompt_feedback.safety_ratings=}"
- )
- return create_costed_language_model_response_for_single_result(
- text="",
- prompt_tokens=prompt_tokens,
- completion_tokens=0,
- stop_reason=ResponseStopReason.NONE,
- network_failure_count=network_failure_count,
- dollars_used=self.calculate_cost(prompt_tokens, 0), # guestimate of cost,
- )
-
- if _is_flagged_as_unsafe(api_result) or _is_flagged_as_recitation(api_result):
- block_reason = fmap(lambda x: x.block_reason, api_result.prompt_feedback)
- safety_ratings = (
- api_result.prompt_feedback.safety_ratings if api_result.prompt_feedback is not None else None
- )
- logger.info(
- "Gemini flagged output: block_reason={block_reason}, safety_ratings={safety_ratings}",
- block_reason=block_reason,
- safety_ratings=safety_ratings,
- )
- return create_costed_language_model_response_for_single_result(
- text="",
- prompt_tokens=prompt_tokens,
- completion_tokens=0,
- stop_reason=ResponseStopReason.CONTENT_FILTER,
- network_failure_count=network_failure_count,
- dollars_used=self.calculate_cost(prompt_tokens, 0),
- )
-
- candidate = only_and_not_none(api_result.candidates)
- finish_reason = candidate.finish_reason
- parsed_finish_reason = (
- _GEMINI_STOP_REASON_TO_STOP_REASON[finish_reason]
- if finish_reason is not None
- else ResponseStopReason.NONE
- )
-
- if finish_reason not in [FinishReason.MAX_TOKENS, FinishReason.STOP]:
- block_reason = fmap(lambda x: x.block_reason, api_result.prompt_feedback)
- safety_ratings = fmap(lambda x: x.safety_ratings, api_result.prompt_feedback)
- logger.info(
- f"Gemini did not return a simple text response, {block_reason=}, {safety_ratings=}, {finish_reason=}, {candidate.safety_ratings=}"
- )
- return create_costed_language_model_response_for_single_result(
- text="",
- prompt_tokens=prompt_tokens,
- completion_tokens=0,
- stop_reason=parsed_finish_reason,
- network_failure_count=network_failure_count,
- dollars_used=self.calculate_cost(prompt_tokens, 0),
- )
-
- text = api_result.text
-
- thoughts_list = fmap(
- lambda content: fmap(
- lambda parts: [part.text for part in parts if part.thought],
- content.parts,
- ),
- candidate.content,
- )
- if not thoughts_list:
- thoughts = None
- else:
- thoughts = only(thoughts_list)
-
- if text is None:
- if finish_reason == FinishReason.MAX_TOKENS and generation_config.thinking_config is not None:
- raise BadAPIRequestError(
- "Gemini ran out of tokens while thinking and did not return a text response"
- )
- logger.info("Non-simple-text response: {}", api_result)
- raise BadAPIRequestError("Gemini did not return a simple text response (text is None)")
-
- prompt_tokens = (
- api_result.usage_metadata.prompt_token_count
- if api_result.usage_metadata is not None and api_result.usage_metadata.prompt_token_count is not None
- else self.count_tokens(prompt)
- )
-
- thought_tokens = (
- api_result.usage_metadata.thoughts_token_count
- if api_result.usage_metadata is not None and api_result.usage_metadata.thoughts_token_count is not None
- else 0
- )
-
- output_tokens = (
- api_result.usage_metadata.candidates_token_count
- if api_result.usage_metadata is not None
- and api_result.usage_metadata.candidates_token_count is not None
- else self.count_tokens(text)
- )
-
- completion_tokens = output_tokens + thought_tokens
-
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
- logger.trace(text)
- logger.trace("Dollars used: {}", dollars_used)
- return create_costed_language_model_response_for_single_result(
- text=text,
- prompt_tokens=prompt_tokens,
- completion_tokens=completion_tokens,
- stop_reason=parsed_finish_reason,
- network_failure_count=network_failure_count,
- dollars_used=dollars_used,
- thoughts=fmap(
- lambda x: ThoughtResponse(text=x, completion_tokens=thought_tokens),
- thoughts,
- ),
- )
-
- def _get_api_stream(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- # TODO Implement streaming support (?)
- raise NotImplementedError()
-
- # TODO: these are the same as in anthropic_api.py. it might be good to refactor so that both AnthropicAPI and GeminiAPI inherit from a class LanguageModelAPIWithCountTokens which has these methods
- def get_count_tokens_response_cache(self) -> AsyncCache[CachedCountTokensResponse]:
- if self.count_tokens_cache_path is None:
- raise UnsetCachePathError()
- return AsyncCache(self.count_tokens_cache_path, CachedCountTokensResponse)
-
- async def check_count_tokens_cache(self, cache_key: str) -> CountTokensResponse | None:
- return await self.check_cache_core(self.get_count_tokens_response_cache, cache_key)
-
- async def _get_from_count_tokens_cache(
- self, frame: FrameType | None
- ) -> tuple[str | None, CountTokensResponse | None]:
- return await self._get_from_cache_core(frame, lambda cr: cr, self.check_count_tokens_cache)
-
- async def count_tokens_api(self, text: str, is_caching_enabled: bool) -> int | None:
- """Call the count_tokens api to get a definitive token count. May be fragile and is definitely slow."""
-
- self.assert_caching_enabled_if_offline(is_caching_enabled)
-
- frame: FrameType | None = None
- if is_caching_enabled:
- frame = inspect.currentframe()
-
- cache_key: str | None = None
- if is_caching_enabled:
- cache_key, cached_response = await self._get_from_count_tokens_cache(frame)
-
- if cached_response is not None:
- return cached_response.input_tokens
-
- self.assert_not_offline_if_cache_miss(text)
-
- with _gemini_exception_manager():
- client = self._get_client()
- response = client.models.count_tokens(model=self.model_info.model_name, contents=text)
-
- total_tokens = response.total_tokens
- if total_tokens is None:
- raise TransientLanguageModelError("Gemini did not return a valid token count")
-
- result = CachedCountTokensResponse(
- response=CountTokensResponse(
- input_tokens=total_tokens,
- cached_content_token_count=response.cached_content_token_count,
- ),
- inputs=(
- CountTokensInputs(model=self.model_info.model_name, prompt=text) if self.is_caching_inputs else None
- ),
- )
-
- if is_caching_enabled:
- assert cache_key is not None
- async with self.get_count_tokens_response_cache() as cache:
- await cache.set(cache_key, result)
-
- return total_tokens
diff --git a/imbue_core/imbue_core/agents/llm_apis/groq_api.py b/imbue_core/imbue_core/agents/llm_apis/groq_api.py
@@ -1,357 +0,0 @@
-import asyncio
-import enum
-import math
-from contextlib import contextmanager
-from typing import AsyncGenerator
-from typing import Final
-from typing import Iterator
-from typing import Mapping
-
-import httpx
-from groq import APIConnectionError
-from groq import APIError
-from groq import AsyncGroq
-from groq import AsyncStream
-from groq import BadRequestError
-from groq import RateLimitError
-from groq.types.chat import ChatCompletion
-from loguru import logger
-from pydantic.functional_validators import field_validator
-
-from imbue_core.agents.llm_apis.api_utils import convert_prompt_to_openai_messages
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
-from imbue_core.agents.llm_apis.data_types import ResponseStopReason
-from imbue_core.agents.llm_apis.errors import BadAPIRequestError
-from imbue_core.agents.llm_apis.errors import LanguageModelInvalidModelNameError
-from imbue_core.agents.llm_apis.errors import MissingAPIKeyError
-from imbue_core.agents.llm_apis.errors import PromptTooLongError
-from imbue_core.agents.llm_apis.errors import TransientLanguageModelError
-from imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
-from imbue_core.agents.llm_apis.models import ModelInfo
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamStartEvent
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.frozen_utils import FrozenMapping
-from imbue_core.itertools import only
-from imbue_core.secrets_utils import get_secret
-
-# note: we require that these model versions are explicit, just like the rest of our dependencies
-# the reason is that these models are actually now mostly deterministic, and it is much easier to debug if we know what model was used
-# also, there's no need to troll yourself by wondering why results have improved (or gotten worse) when you dont realized that the version has shifted under you
-# if you want to use an upgraded model, just upgrade the model to the key displayed on the website
-# please do NOT set these back to the generic model names!
-
-
-# TODO: there are likely more models to add
-class GroqSupportedModelName(enum.StrEnum):
- GROQ_GEMMA2_9B_IT = "groq/gemma2-9b-it"
- GROQ_LLAMA3_70B_8192 = "groq/llama3-70b-8192"
- GROQ_LLAMA3_8B_8192 = "groq/llama3-8b-8192"
- GROQ_LLAMA_3_3_70B_SPECDEC = "groq/llama-3.3-70b-specdec"
- GROQ_MIXTRAL_8X7B_32768 = "groq/mixtral-8x7b-32768"
- GROQ_LLAMA_3_3_70B_VERSATILE = "groq/llama-3.3-70b-versatile"
- GROQ_LLAMA_3_1_8B_INSTANT = "groq/llama-3.1-8b-instant"
- GROQ_LLAMA_3_2_1B_PREVIEW = "groq/llama-3.2-1b-preview"
- GROQ_LLAMA_3_2_3B_PREVIEW = "groq/llama-3.2-3b-preview"
-
-
-# Rate limits for Groq models based on custom rate limits for our organization.
-# See here https://console.groq.com/dashboard/limits (requires login, use your Google account)
-
-GROQ_MODEL_INFO_BY_NAME: FrozenMapping[GroqSupportedModelName, ModelInfo] = FrozenDict(
- {
- GroqSupportedModelName.GROQ_GEMMA2_9B_IT: ModelInfo(
- model_name=str(GroqSupportedModelName.GROQ_GEMMA2_9B_IT),
- cost_per_input_token=0.20 / 1_000_000,
- cost_per_output_token=0.20 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
- ),
- GroqSupportedModelName.GROQ_LLAMA3_70B_8192: ModelInfo(
- model_name=str(GroqSupportedModelName.GROQ_LLAMA3_70B_8192),
- cost_per_input_token=0.59 / 1_000_000,
- cost_per_output_token=0.79 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
- ),
- GroqSupportedModelName.GROQ_LLAMA3_8B_8192: ModelInfo(
- model_name=str(GroqSupportedModelName.GROQ_LLAMA3_8B_8192),
- cost_per_input_token=0.05 / 1_000_000,
- cost_per_output_token=0.08 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
- ),
- GroqSupportedModelName.GROQ_LLAMA_3_3_70B_SPECDEC: ModelInfo(
- model_name=str(GroqSupportedModelName.GROQ_LLAMA_3_3_70B_SPECDEC),
- cost_per_input_token=0.59 / 1_000_000,
- cost_per_output_token=0.99 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
- ),
- GroqSupportedModelName.GROQ_MIXTRAL_8X7B_32768: ModelInfo(
- model_name=str(GroqSupportedModelName.GROQ_MIXTRAL_8X7B_32768),
- cost_per_input_token=0.24 / 1_000_000,
- cost_per_output_token=0.24 / 1_000_000,
- max_input_tokens=32768,
- max_output_tokens=None,
- rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
- ),
- GroqSupportedModelName.GROQ_LLAMA_3_3_70B_VERSATILE: ModelInfo(
- model_name=str(GroqSupportedModelName.GROQ_LLAMA_3_3_70B_VERSATILE),
- cost_per_input_token=0.59 / 1_000_000,
- cost_per_output_token=0.79 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=None,
- rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
- ),
- GroqSupportedModelName.GROQ_LLAMA_3_1_8B_INSTANT: ModelInfo(
- model_name=str(GroqSupportedModelName.GROQ_LLAMA_3_1_8B_INSTANT),
- cost_per_input_token=0.05 / 1_000_000,
- cost_per_output_token=0.08 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=None,
- rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
- ),
- GroqSupportedModelName.GROQ_LLAMA_3_2_1B_PREVIEW: ModelInfo(
- model_name=str(GroqSupportedModelName.GROQ_LLAMA_3_2_1B_PREVIEW),
- cost_per_input_token=0.04 / 1_000_000,
- cost_per_output_token=0.04 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=None,
- rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
- ),
- GroqSupportedModelName.GROQ_LLAMA_3_2_3B_PREVIEW: ModelInfo(
- model_name=str(GroqSupportedModelName.GROQ_LLAMA_3_2_3B_PREVIEW),
- cost_per_input_token=0.06 / 1_000_000,
- cost_per_output_token=0.06 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=None,
- rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
- ),
- }
-)
-
-
-def get_model_info(model_name: GroqSupportedModelName) -> ModelInfo:
- return GROQ_MODEL_INFO_BY_NAME[model_name]
-
-
-_CAPACITY_SEMAPHOR_BY_MODEL_NAME: Mapping[str, asyncio.Semaphore] = {
- GroqSupportedModelName.GROQ_GEMMA2_9B_IT: asyncio.Semaphore(100),
- GroqSupportedModelName.GROQ_LLAMA3_70B_8192: asyncio.Semaphore(100),
- GroqSupportedModelName.GROQ_LLAMA3_8B_8192: asyncio.Semaphore(100),
- GroqSupportedModelName.GROQ_LLAMA_3_3_70B_SPECDEC: asyncio.Semaphore(100),
- GroqSupportedModelName.GROQ_MIXTRAL_8X7B_32768: asyncio.Semaphore(100),
- GroqSupportedModelName.GROQ_LLAMA_3_3_70B_VERSATILE: asyncio.Semaphore(100),
- GroqSupportedModelName.GROQ_LLAMA_3_1_8B_INSTANT: asyncio.Semaphore(100),
- GroqSupportedModelName.GROQ_LLAMA_3_2_1B_PREVIEW: asyncio.Semaphore(100),
- GroqSupportedModelName.GROQ_LLAMA_3_2_3B_PREVIEW: asyncio.Semaphore(100),
-}
-
-
-def _get_capacity_semaphor(model_name: str) -> asyncio.Semaphore:
- return _CAPACITY_SEMAPHOR_BY_MODEL_NAME[model_name]
-
-
-# ref: https://github.com/groq/groq-python/blob/b74ce9e301115520c744e18425653a4c783cb6f5/src/groq/types/chat/chat_completion_chunk.py#L86
-_GROQ_STOP_REASON_TO_STOP_REASON: Final[FrozenMapping[str, ResponseStopReason]] = FrozenDict(
- {
- # Groq copies OpenAI and treats stop due to natural stop point and provided stop sequence the same
- "stop": ResponseStopReason.END_TURN,
- "length": ResponseStopReason.MAX_TOKENS,
- "tool_calls": ResponseStopReason.TOOL_CALLS,
- "function_call": ResponseStopReason.FUNCTION_CALL,
- "content_filter": ResponseStopReason.CONTENT_FILTER,
- }
-)
-
-
-@contextmanager
-def _groq_exception_manager() -> Iterator[None]:
- """Simple context manager for parsing groq exceptions mostly based on how we parse OpenAI API exceptions."""
- try:
- yield
- except BadRequestError as e:
- logger.info("BadAPIRequestError {}", e)
- raise BadAPIRequestError(str(e)) from e
- except APIConnectionError as e:
- logger.info("Rate limited? Received APIConnectionError {}", e)
- raise TransientLanguageModelError("APIConnectionError") from e
- except RateLimitError as e:
- logger.info("Rate limited? {}", e)
- raise TransientLanguageModelError("RateLimitError") from e
- except httpx.RemoteProtocolError as e:
- logger.info("{}", e)
- raise TransientLanguageModelError("httpx.RemoteProtocolError") from e
- except APIError as e:
- if e.body["code"] == "context_length_exceeded": # type: ignore
- # TODO: eventually fix elsewhere, since this doesn't actually give you any information in the body...
- raise PromptTooLongError(prompt_len=1, max_prompt_len=1)
- raise TransientLanguageModelError("APIError") from e
-
-
-class GroqChatAPI(LanguageModelAPI):
- model_name: GroqSupportedModelName = GroqSupportedModelName.GROQ_LLAMA3_8B_8192
- is_conversational: bool = True
- presence_penalty: float = 0.0
- # this shouldn't really ever even be used, but just in case
- stop_token_log_probability: float = math.log(0.9999)
-
- @field_validator("model_name") # pyre-ignore[56]: pyre doesn't understand pydantic
- @classmethod
- def validate_model_name(cls, v: str) -> str:
- if v not in GROQ_MODEL_INFO_BY_NAME:
- raise LanguageModelInvalidModelNameError(v, cls.__name__, list(GROQ_MODEL_INFO_BY_NAME))
- return v
-
- @property
- def model_info(self) -> ModelInfo:
- return GROQ_MODEL_INFO_BY_NAME[self.model_name]
-
- @property
- def external_model_name(self) -> str:
- return self.model_name.replace("groq/", "")
-
- def _get_client(self) -> AsyncGroq:
- api_key = get_secret("GROQ_API_KEY")
- if not api_key:
- raise MissingAPIKeyError("GROQ_API_KEY environment variable is not set")
- return AsyncGroq(api_key=api_key)
-
- async def _call_api(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- network_failure_count: int = 0,
- ) -> CostedLanguageModelResponse:
- with _groq_exception_manager():
- messages = convert_prompt_to_openai_messages(prompt)
- client = self._get_client()
- async with _get_capacity_semaphor(self.model_name):
- # logger.info("Open requests: {}", semaphor._value)
- api_result = await client.chat.completions.create(
- model=self.external_model_name,
- messages=messages, # type: ignore
- max_tokens=params.max_tokens,
- n=params.count,
- temperature=params.temperature,
- stop=params.stop,
- logprobs=False,
- seed=params.seed,
- stream=False,
- presence_penalty=self.presence_penalty,
- )
- assert isinstance(api_result, ChatCompletion)
-
- results = []
- for data in api_result.choices:
- assert data.message.content is not None
-
- assert data.logprobs is not None and data.logprobs.content is not None
- text = data.message.content
-
- stop_reason = _GROQ_STOP_REASON_TO_STOP_REASON[str(data.finish_reason)]
-
- # Note, like OpenAI, Groq treats end turn and stop sequence the same
- # Here we assume it is stop sequence if user has specified a stop sequence
- if params.stop is not None and stop_reason == ResponseStopReason.END_TURN:
- text += params.stop
- result = LanguageModelResponse(
- text=text,
- token_count=0,
- stop_reason=stop_reason,
- network_failure_count=network_failure_count,
- )
- results.append(result)
-
- logger.trace("text: " + results[0].text)
- if api_result.usage is not None:
- completion_tokens = api_result.usage.completion_tokens
- prompt_tokens = api_result.usage.prompt_tokens
- else:
- completion_tokens = 0
- prompt_tokens = 0
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
- logger.trace("dollars used: {}", dollars_used)
- return CostedLanguageModelResponse(
- usage=LanguageModelResponseUsage(
- prompt_tokens_used=prompt_tokens,
- completion_tokens_used=completion_tokens,
- dollars_used=dollars_used,
- ),
- responses=tuple(results),
- )
-
- async def _get_api_stream(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- with _groq_exception_manager():
- messages = convert_prompt_to_openai_messages(prompt)
- client = self._get_client()
- async with _get_capacity_semaphor(self.model_name):
- api_result = await client.chat.completions.create(
- model=self.external_model_name,
- messages=messages, # type: ignore
- max_tokens=params.max_tokens,
- n=1,
- temperature=params.temperature,
- stop=params.stop,
- logprobs=False,
- seed=params.seed,
- stream=True,
- # This field is currently unsupported by the groq API
- # stream_options={"include_usage": True},
- presence_penalty=self.presence_penalty,
- )
- assert isinstance(api_result, AsyncStream)
- logger.info("API response status code: {}", api_result.response.status_code)
-
- yield LanguageModelStreamStartEvent()
-
- usage = None
- finish_reason: str | None = None
- async for chunk in api_result:
- if chunk.choices:
- assert len(chunk.choices) == 1, "Currently only count=1 supported for streaming API."
- data = only(chunk.choices)
- delta = data.delta.content
- if delta is not None:
- yield LanguageModelStreamDeltaEvent(delta=delta)
- if data.finish_reason:
- finish_reason = str(data.finish_reason)
-
- stop_reason = _GROQ_STOP_REASON_TO_STOP_REASON[str(finish_reason)]
- # Note, Open API treats end turn and stop sequence the same TODO: check if groq is the same
- # Here we assume it is stop sequence if user has specified a stop sequence
- if params.stop is not None and stop_reason == ResponseStopReason.END_TURN:
- yield LanguageModelStreamDeltaEvent(delta=params.stop)
-
- if usage is not None:
- completion_tokens = usage.completion_tokens
- prompt_tokens = usage.prompt_tokens
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
- else:
- completion_tokens = -1
- prompt_tokens = -1
- dollars_used = -1
- logger.trace("dollars used: {}", dollars_used)
-
- yield LanguageModelStreamEndEvent(
- usage=LanguageModelResponseUsage(
- prompt_tokens_used=prompt_tokens,
- completion_tokens_used=completion_tokens,
- dollars_used=dollars_used,
- ),
- stop_reason=stop_reason,
- )
diff --git a/imbue_core/imbue_core/agents/llm_apis/language_model_api.py b/imbue_core/imbue_core/agents/llm_apis/language_model_api.py
@@ -1,547 +0,0 @@
-import abc
-import asyncio
-import contextvars
-import hashlib
-import inspect
-import os
-import random
-from pathlib import Path
-from types import FrameType
-from typing import AsyncGenerator
-from typing import Awaitable
-from typing import Callable
-from typing import TypeVar
-from typing import final
-from uuid import UUID
-from uuid import uuid4
-
-import anyio
-from loguru import logger
-
-from imbue_core.agents.llm_apis.constants import approximate_token_count
-from imbue_core.agents.llm_apis.data_types import CachedCostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import CachedCostedModelResponse
-from imbue_core.agents.llm_apis.data_types import CachingInfo
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import CountTokensResponse
-from imbue_core.agents.llm_apis.data_types import InputsT
-from imbue_core.agents.llm_apis.data_types import LanguageModelCompleteInputs
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelStreamInputs
-from imbue_core.agents.llm_apis.data_types import ModelResponseT
-from imbue_core.agents.llm_apis.errors import LanguageModelRetryLimitError
-from imbue_core.agents.llm_apis.errors import PromptTooLongError
-from imbue_core.agents.llm_apis.errors import TransientLanguageModelError
-from imbue_core.agents.llm_apis.errors import UnsetCachePathError
-from imbue_core.agents.llm_apis.models import ModelInfo
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamCallback
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
-from imbue_core.agents.llm_apis.stream import PromptDebuggingCallback
-from imbue_core.agents.llm_apis.stream import SettleSpendCallback
-from imbue_core.agents.llm_apis.stream import StreamedLanguageModelResponse
-from imbue_core.agents.llm_apis.stream import UpdateCacheCallback
-from imbue_core.agents.llm_apis.stream import get_cached_response_stream
-from imbue_core.agents.primitives.resource_limits import PaymentAuthorization
-from imbue_core.agents.primitives.resource_limits import get_global_resource_limits
-from imbue_core.async_utils import sync
-from imbue_core.caching import AsyncCache
-from imbue_core.cattrs_serialization import serialize_to_json
-from imbue_core.pydantic_serialization import MutableModel
-
-# Context variable to disable caching.
-IS_LLM_CACHING_DISABLED_GLOBALLY = contextvars.ContextVar("is_llm_caching_disabled_globally", default=False)
-# Context variable for injecting a default seed which will become part of the LLM cache key.
-LLM_GLOBAL_DEFAULT_SEED = contextvars.ContextVar("llm_global_default_seed", default=0)
-
-# Maximum number of retries for network failures.
-MAX_RETRIES = 5
-
-
-EXCLUDED_CACHE_KEY_ARGS = ["self", "is_caching_enabled", "call_id"]
-
-
-def _create_base_cache_key_from_frame(frame: FrameType) -> str:
- """Create a cache key from the args of a function by passing its frame."""
- args, _, _, values = inspect.getargvalues(frame)
- return "|".join(f"{arg}={values[arg]}" for arg in args if arg not in EXCLUDED_CACHE_KEY_ARGS)
-
-
-CostedResponseT = TypeVar("CostedResponseT", bound=CostedLanguageModelResponse | CountTokensResponse)
-FinalResponseT = TypeVar(
- "FinalResponseT",
- bound=CostedLanguageModelResponse | StreamedLanguageModelResponse | CountTokensResponse,
-)
-
-
-class LanguageModelAPI(abc.ABC, MutableModel):
- model_name: str
- cache_path: Path | None
- is_caching_inputs: bool = False
- is_running_offline: bool = False
- is_conversational: bool = False
- is_using_logprobs: bool = False
-
- # retry/timeout values
- retry_sleep_time: float = 2.0
- retry_backoff_factor: float = 3.0
- retry_jitter_factor: float = 0.5
-
- # TODO: Consider storing the model_config here as well.
-
- @property
- @abc.abstractmethod
- def model_info(self) -> ModelInfo: ...
-
- def get_response_cache(self) -> AsyncCache[CachedCostedLanguageModelResponse]:
- if self.cache_path is None:
- raise UnsetCachePathError()
- return AsyncCache(self.cache_path, CachedCostedLanguageModelResponse)
-
- def _create_cache_key(self, base_key: str) -> str:
- object_cache_attributes = self.model_dump(
- exclude={
- "cache_path",
- "count_tokens_cache_path",
- "base_url",
- "api_key_env",
- "context_window",
- "max_output_tokens",
- }
- )
- # have to reset the offline key to the same value so that that doesnt invalidate the cache
- object_cache_attributes["is_running_offline"] = True
- object_cache_attributes["__name__"] = self.__class__.__name__
- base_key_md5 = hashlib.md5(base_key.encode()).hexdigest()
- object_cache_attributes["__request_key_md5__"] = base_key_md5
- return serialize_to_json(object_cache_attributes)
-
- async def check_cache_core(
- self,
- cache_getter: Callable[[], AsyncCache[CachedCostedModelResponse[InputsT, ModelResponseT]]],
- cache_key: str,
- ) -> ModelResponseT | None:
- async with cache_getter() as cache:
- cached_result = await cache.get(cache_key)
-
- if cached_result is not None:
- if cached_result.error:
- if cached_result.error.startswith(PromptTooLongError.__name__):
- raise PromptTooLongError.from_string(cached_result.error)
- raise Exception(f"Unknown cached result error type: {cached_result.error}")
- assert cached_result.response is not None
- return cached_result.response
- return None
-
- async def check_cache(self, cache_key: str) -> CostedLanguageModelResponse | None:
- return await self.check_cache_core(self.get_response_cache, cache_key)
-
- async def _get_auth(self, prompt: str, max_tokens: int | None) -> PaymentAuthorization | None:
- global_resource_limits = get_global_resource_limits()
- if global_resource_limits is not None:
- prompt_tokens = self.count_tokens(prompt)
- completion_tokens = max_tokens if max_tokens is not None else self.get_max_completion_size_in_tokens()
- upper_bound_cost_estimate = self.estimate_cost(prompt_tokens, completion_tokens)
- assert global_resource_limits is not None
- auth: PaymentAuthorization = await global_resource_limits.authorize_spend(
- upper_bound_cost_estimate,
- debug_info={
- "model_name": self.model_name,
- "prompt_tokens": prompt_tokens,
- "completion_tokens": completion_tokens,
- },
- )
- return auth
-
- if "PYTEST_CURRENT_TEST" not in os.environ:
- logger.warning(
- "You are trying to call a language model with no global resource limits set. That is a bad idea because the spend will not be restricted, and you may end up accidentally spending much more than you expected."
- )
- return None
-
- async def _settle_spend(self, auth: PaymentAuthorization, dollars_used: float) -> None:
- global_resource_limits = get_global_resource_limits()
- assert global_resource_limits is not None
- await global_resource_limits.settle_spend(auth, dollars_used)
- return None
-
- def assert_caching_enabled_if_offline(self, is_caching_enabled: bool) -> None:
- if self.is_running_offline:
- assert is_caching_enabled, "Caching must be enabled when running offline"
-
- def assert_not_offline_if_cache_miss(self, prompt: str) -> None:
- max_n_chars = 50
- prompt_stub = prompt[:max_n_chars] + ("..." if len(prompt) > max_n_chars else "")
- assert (
- not self.is_running_offline
- ), f"Running offline but did not have a cached response for this query! Prompt: {prompt_stub}"
-
- async def complete(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- is_caching_enabled: bool = True,
- ) -> tuple[LanguageModelResponse, ...]:
- call_id = uuid4()
- logger.trace(
- "[{call_id}] Calling complete with params: {params} and is_caching_enabled={is_caching_enabled} and {prompt}",
- call_id=call_id,
- params=params,
- is_caching_enabled=is_caching_enabled,
- prompt=prompt[:40],
- )
- if _complete_concurrency_hook_fn is not None:
- await _complete_concurrency_hook_fn(self)
-
- is_caching_enabled_with_override = is_caching_enabled and not IS_LLM_CACHING_DISABLED_GLOBALLY.get()
- if params.seed is None:
- params = params.evolve(params.ref().seed, LLM_GLOBAL_DEFAULT_SEED.get())
-
- return await self._complete(
- prompt,
- params,
- is_caching_enabled=is_caching_enabled_with_override,
- call_id=call_id,
- )
-
- complete_sync = sync(complete)
-
- async def complete_with_usage(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- is_caching_enabled: bool = True,
- ) -> CostedLanguageModelResponse:
- call_id = uuid4()
- if _complete_concurrency_hook_fn is not None:
- await _complete_concurrency_hook_fn(self)
-
- is_caching_enabled_with_override = is_caching_enabled and not IS_LLM_CACHING_DISABLED_GLOBALLY.get()
- if params.seed is None:
- params = params.evolve(params.ref().seed, LLM_GLOBAL_DEFAULT_SEED.get())
-
- return await self._complete_with_usage(
- prompt,
- params,
- is_caching_enabled=is_caching_enabled_with_override,
- call_id=call_id,
- )
-
- complete_with_usage_sync = sync(complete_with_usage)
-
- async def _complete_with_usage(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- is_caching_enabled: bool,
- call_id: UUID,
- ) -> CostedLanguageModelResponse:
- self._warn_if_no_stop_condition_and_not_conversational(params)
- self.assert_caching_enabled_if_offline(is_caching_enabled)
-
- frame: FrameType | None = None
- if is_caching_enabled:
- frame = inspect.currentframe()
-
- costed_response_to_output: Callable[[CostedLanguageModelResponse], CostedLanguageModelResponse] = lambda cr: cr
-
- cache_key: str | None = None
- if is_caching_enabled:
- cache_key, cached_response = await self._get_from_cache(frame, costed_response_to_output)
-
- if cached_response is not None:
- return cached_response
-
- self.assert_not_offline_if_cache_miss(prompt)
-
- auth = await self._get_auth(prompt, params.max_tokens)
-
- sleep_time = self.retry_sleep_time
- last_error_msg: str | None = None
- for network_failure_count in range(MAX_RETRIES):
- try:
- api_inputs = LanguageModelCompleteInputs(
- prompt=prompt,
- params=params,
- network_failure_count=network_failure_count,
- )
- response = await self._call_api_one_arg(api_inputs)
- if is_caching_enabled:
- assert cache_key is not None
- result = CachedCostedLanguageModelResponse(
- response=response,
- inputs=api_inputs if self.is_caching_inputs else None,
- )
- async with self.get_response_cache() as cache:
- await cache.set(cache_key, result)
-
- if auth is not None:
- await self._settle_spend(auth, response.usage.dollars_used)
-
- return response
-
- except PromptTooLongError as e:
- logger.trace(
- "[{call_id}] Prompt too long error in model {model_name}",
- call_id=call_id,
- model_name=self.model_name,
- )
- if is_caching_enabled:
- assert cache_key is not None
- async with self.get_response_cache() as cache:
- await cache.set(
- cache_key,
- CachedCostedLanguageModelResponse(error=e.to_string()),
- )
- raise
- except TransientLanguageModelError as e:
- last_error_msg = str(e)
- if network_failure_count < MAX_RETRIES - 1:
- if self.retry_jitter_factor > 0:
- max_jitter = sleep_time * self.retry_jitter_factor
- sleep_time += random.uniform(-max_jitter / 2, max_jitter / 2)
- logger.debug(
- f"Transient language model error ({str(e)}) in model {self.model_name}, retrying with sleep time {sleep_time} seconds..."
- )
- await asyncio.sleep(sleep_time)
- sleep_time *= self.retry_backoff_factor
- raise LanguageModelRetryLimitError(last_error_msg or "Unknown error (this should not happen)")
-
- async def _complete(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- is_caching_enabled: bool,
- call_id: UUID,
- ) -> tuple[LanguageModelResponse, ...]:
- # Delegate to _complete_with_usage and extract just the responses
- # May have more than count responses cached, so just return first count responses
- costed_response = await self._complete_with_usage(prompt, params, is_caching_enabled, call_id)
- return costed_response.responses[: params.count]
-
- @final
- async def _call_api_one_arg(self, api_inputs: LanguageModelCompleteInputs) -> CostedLanguageModelResponse:
- """Delegates to the abstract method _call_api, which must be implemented by subclasses."""
- return await self._call_api(
- prompt=api_inputs.prompt,
- params=api_inputs.params,
- network_failure_count=api_inputs.network_failure_count,
- )
-
- @abc.abstractmethod
- async def _call_api(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- # this is used to track how many times we've retried due to network failures, since we want the return type to contain that information
- network_failure_count: int = 0,
- ) -> CostedLanguageModelResponse:
- """If defined, the stop sequence should be part of the sequence (if it was actually generated)"""
-
- async def stream(
- self,
- prompt: str,
- is_caching_enabled: bool = True,
- params: LanguageModelGenerationParams = LanguageModelGenerationParams(),
- ) -> StreamedLanguageModelResponse:
- if params.seed is None:
- params = params.evolve(params.ref().seed, LLM_GLOBAL_DEFAULT_SEED.get())
-
- is_caching_enabled_with_override = is_caching_enabled and not IS_LLM_CACHING_DISABLED_GLOBALLY.get()
-
- return await self._stream(
- prompt=prompt,
- is_caching_enabled=is_caching_enabled_with_override,
- params=params,
- )
-
- async def _stream(
- self,
- prompt: str,
- is_caching_enabled: bool,
- params: LanguageModelGenerationParams,
- ) -> StreamedLanguageModelResponse:
- assert params.count == 1, "Stream API currently only supports count=1 due to limitations of some APIs."
-
- self._warn_if_no_stop_condition_and_not_conversational(params)
- self.assert_caching_enabled_if_offline(is_caching_enabled)
-
- frame: FrameType | None = None
- if is_caching_enabled:
- frame = inspect.currentframe()
-
- # Note it's technically possible multiple responses cached for given prompt (e.g. from call to complete())
- # for now we just return first one
- costed_response_to_output = lambda cr: StreamedLanguageModelResponse(
- get_cached_response_stream(cr),
- network_failure_count=0,
- completion_callbacks=(),
- )
- cache_key: str | None = None
- if is_caching_enabled:
- cache_key, cached_response = await self._get_from_cache(frame, costed_response_to_output)
-
- if cached_response is not None:
- return cached_response
-
- self.assert_not_offline_if_cache_miss(prompt)
-
- auth = await self._get_auth(prompt, params.max_tokens)
-
- sleep_time = self.retry_sleep_time
- last_error_msg: str | None = None
- for network_failure_count in range(MAX_RETRIES):
- # Loop until success or an exception is raised
- try:
- api_inputs = LanguageModelStreamInputs(prompt=prompt, params=params)
- api_stream = await self._get_api_stream_one_arg(api_inputs)
- callbacks: list[LanguageModelStreamCallback] = []
- if is_caching_enabled:
- assert cache_key is not None
- cache = self.get_response_cache()
- callbacks.append(
- UpdateCacheCallback(
- key=cache_key,
- cache=cache,
- api_inputs=api_inputs if self.is_caching_inputs else None,
- )
- )
- llm_debug_output_folder = os.getenv("LLM_DEBUG_PATH", None)
- if llm_debug_output_folder is not None:
- output_path = anyio.Path(llm_debug_output_folder) / f"{uuid4()}.txt"
- # write out the prompt (helps with debugging so we can see when things blow up)
- await output_path.write_text(prompt)
- # overwrite the file with the prompt and completion when done
- callbacks.append(PromptDebuggingCallback(prompt=prompt, output_path=output_path))
-
- if auth is not None:
- callbacks.append(SettleSpendCallback(auth=auth))
-
- return StreamedLanguageModelResponse(
- api_stream,
- network_failure_count=network_failure_count,
- completion_callbacks=callbacks,
- )
-
- except PromptTooLongError as e:
- if is_caching_enabled:
- assert cache_key is not None
- async with self.get_response_cache() as cache:
- await cache.set(
- cache_key,
- CachedCostedLanguageModelResponse(error=e.to_string()),
- )
- raise
- except TransientLanguageModelError as e:
- last_error_msg = str(e)
- if network_failure_count < MAX_RETRIES - 1:
- if self.retry_jitter_factor > 0:
- sleep_time += random.uniform(0, sleep_time * self.retry_jitter_factor)
- logger.debug(
- f"Transient language model error ({str(e)}) in model {self.model_name}, retrying with sleep time {sleep_time} seconds..."
- )
- await asyncio.sleep(sleep_time)
- sleep_time *= self.retry_backoff_factor
- raise LanguageModelRetryLimitError(last_error_msg or "Unknown error (this should not happen)")
-
- @final
- async def _get_api_stream_one_arg(
- self, api_inputs: LanguageModelStreamInputs
- ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- """Delegates to the abstract method _get_api_stream, which must be implemented by subclasses."""
- return self._get_api_stream(prompt=api_inputs.prompt, params=api_inputs.params)
-
- @abc.abstractmethod
- def _get_api_stream(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- """If defined, the stop sequence should be part of the sequence (if it was actually generated)"""
-
- def _warn_if_no_stop_condition_and_not_conversational(self, params: LanguageModelGenerationParams) -> None:
- if (params.stop is None and params.max_tokens is None) and not self.is_conversational:
- logger.debug(
- "Did not specify either `max_tokens` or `stop`, and this is not a conversational model. The completion will go until the entire context window is filled. Preferably you don't do this, because it is fairly inefficient."
- )
-
- async def _get_from_cache_core(
- self,
- frame: FrameType | None,
- costed_response_to_output: Callable[[CostedResponseT], FinalResponseT],
- cache_checker: Callable[[str], Awaitable[CostedResponseT | None]],
- ) -> tuple[str | None, FinalResponseT | None]:
- cache_key: str | None
-
- cache_key, costed_response = await self._get_costed_response_from_frame_core(cache_checker, frame)
-
- if costed_response is not None:
- return cache_key, costed_response_to_output(costed_response)
- return cache_key, None
-
- async def _get_from_cache(
- self,
- frame: FrameType | None,
- costed_response_to_output: Callable[[CostedLanguageModelResponse], FinalResponseT],
- ) -> tuple[str | None, FinalResponseT | None]:
- return await self._get_from_cache_core(frame, costed_response_to_output, self.check_cache)
-
- async def _get_costed_response_from_frame_core(
- self,
- cache_checker: Callable[[str], Awaitable[CostedResponseT | None]],
- frame: FrameType | None,
- ) -> tuple[str, CostedResponseT | None]:
- assert frame is not None
- cache_key = self._create_cache_key(_create_base_cache_key_from_frame(frame))
- costed_response = await cache_checker(cache_key)
- return cache_key, costed_response
-
- def count_tokens(self, text: str) -> int:
- # this is VERY approximate, but many of the child models have nothing, so...
- return approximate_token_count(text)
-
- def basic_calculate_cost(self, prompt_tokens: int, completion_tokens: int) -> float:
- return (
- prompt_tokens * self.model_info.cost_per_input_token
- + completion_tokens * self.model_info.cost_per_output_token
- )
-
- def estimate_cost(self, prompt_tokens: int, completion_tokens: int) -> float:
- """Estimate the cost of a request before it has been made. Doesn't use any caching info."""
- return self.basic_calculate_cost(prompt_tokens, completion_tokens)
-
- def calculate_cost(
- self,
- prompt_tokens: int,
- completion_tokens: int,
- caching_info: CachingInfo | None = None,
- ) -> float:
- """Overridden by subclasses which have more complex cost calculations, such as if caching is used."""
- logger.info(
- f"no calculate_cost implemented for {self.model_name}; using basic_calculate_cost",
- model_name=self.model_name,
- )
- return self.basic_calculate_cost(prompt_tokens, completion_tokens)
-
- def get_max_completion_size_in_tokens(self) -> int:
- if self.model_info.max_output_tokens is not None:
- return self.model_info.max_output_tokens
- # assume max output is just the context window size
- return self.model_info.max_input_tokens
-
- def get_max_prompt_size_in_tokens(self) -> int:
- return self.model_info.max_input_tokens
-
- def get_context_window_size_in_tokens(self) -> int:
- return self.get_max_completion_size_in_tokens() + self.get_max_prompt_size_in_tokens()
-
-
-COMPLETE_CONCURRENCY_HOOK_FN = Callable[[LanguageModelAPI], Awaitable[None]] | None
-_complete_concurrency_hook_fn: COMPLETE_CONCURRENCY_HOOK_FN = None
-
-
-def set_language_model_api_complete_concurrency_hook(
- hook_fn: COMPLETE_CONCURRENCY_HOOK_FN,
-) -> None:
- global _complete_concurrency_hook_fn
- _complete_concurrency_hook_fn = hook_fn
diff --git a/imbue_core/imbue_core/agents/llm_apis/mock_api.py b/imbue_core/imbue_core/agents/llm_apis/mock_api.py
@@ -1,193 +0,0 @@
-import asyncio
-import enum
-from pathlib import Path
-from typing import AsyncGenerator
-
-import toml
-from loguru import logger
-from pydantic.fields import Field
-from pydantic.functional_validators import field_validator
-
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseWithLogits
-from imbue_core.agents.llm_apis.data_types import ResponseStopReason
-from imbue_core.agents.llm_apis.data_types import TokenProbability
-from imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
-from imbue_core.agents.llm_apis.models import ModelInfo
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
-from imbue_core.itertools import only
-from imbue_core.pydantic_serialization import MutableModel
-
-
-class MockModelName(enum.StrEnum):
- MOCK_MODEL = "my-mock-model"
-
-
-MY_MOCK_MODEL_INFO = ModelInfo(
- model_name=MockModelName.MOCK_MODEL,
- cost_per_input_token=0.0 / 1_000_000,
- cost_per_output_token=0.0 / 1_000_000,
- max_input_tokens=32_768,
- max_output_tokens=None,
-)
-
-
-class Stats(MutableModel):
- complete_calls: int = 0
-
-
-class LanguageModelMock(LanguageModelAPI):
- model_name: str = MY_MOCK_MODEL_INFO.model_name
- cache_path: Path | None = None
- # FIXME: can't have a mutable class inside a frozen pydantic model
- stats: Stats = Field(default_factory=Stats)
-
- @property
- def model_info(self) -> ModelInfo:
- return MY_MOCK_MODEL_INFO
-
- async def complete(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- is_caching_enabled: bool = True,
- ) -> tuple[LanguageModelResponse, ...]:
- raise NotImplementedError()
-
- def _get_token_probabilities(self, response_text: str) -> tuple[tuple[TokenProbability, ...], ...]:
- return tuple(
- (TokenProbability(token=pseudo_token, log_probability=0.0, is_stop=False),)
- for pseudo_token in response_text.split()
- )
-
- async def _call_api(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- network_failure_count: int = 0,
- ) -> CostedLanguageModelResponse:
- raise NotImplementedError()
-
- def _get_api_stream(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- # TODO Implement streaming support (?)
- raise NotImplementedError()
-
-
-MOCK_STREAM_SLEEP_TIME = 5.0
-
-
-class FileBasedLanguageModelMock(LanguageModelMock):
- """
- A mock LLM API that reads responses from a toml file.
- The response can either be identified using the toml key or a prompt which is part of the toml dictionary.
- """
-
- calls: int = 0
-
- @field_validator("cache_path") # pyre-ignore[56]: pyre doesn't understand pydantic
- @classmethod
- def validate_cache_path(cls, v: Path | None) -> Path | None:
- if v is None:
- raise ValueError("Mock responses file path is not set.")
- if not v.exists():
- raise ValueError(f"Mock responses file {v} does not exist.")
- if not v.suffix == ".toml":
- raise ValueError(f"Mock responses file {v} is not a toml file.")
- return v
-
- def _get_user_message_from_prompt(self, prompt: str) -> str:
- user_prompt = prompt.rsplit("[ROLE=USER]", 1)[-1].strip()
- return user_prompt
-
- def get_single_response(self, prompt: str) -> str:
- return only(self.get_parts_of_response(prompt))
-
- def get_parts_of_response(self, prompt: str) -> tuple[str, ...]:
- """
- Support both of the following possible identifiers:
- [identifier]
- prompt = "user message here"
- [[identifier.responses]]
- text = "response"
- [[identifier.responses]]
- text = "response2"
-
- [identifier]
- prompt = "user message here"
- response = "response"
- """
- # this is checked during validation but i guess the type checker doesn't see it
- assert self.cache_path is not None
- # TODO: should we try something that is not toml? toml formatting is a little annoying
- toml_dict = toml.load(self.cache_path)
- # TODO: currently the identifier is the last user message, because the entire prompt is really long
- # if we need to support the same user message with different responses, expand this, maybe chat history?
- identifier = self._get_user_message_from_prompt(prompt)
- logger.info("Getting response for identifier: {} from {}", identifier, toml_dict)
- toml_item = toml_dict.get(identifier, None)
- if toml_item is None:
- for toml_key, response in toml_dict.items():
- if "prompt" in response:
- if response["prompt"] == identifier:
- toml_item = response
- break
- if toml_item is None:
- raise KeyError(f"No response found for the given identifier {identifier}")
-
- if "responses" in toml_item:
- responses = toml_item["responses"]
- if isinstance(responses, list):
- return tuple(r["text"] for r in responses if isinstance(r, dict) and "text" in r)
- raise ValueError(f"Expected 'responses' to be a list of tables in section '{identifier}'")
-
- if "response" in toml_item:
- return (str(toml_item["response"]),)
-
- raise ValueError(f"No valid response or responses found for identifier '{identifier}'")
-
- async def complete(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- is_caching_enabled: bool = True,
- ) -> tuple[LanguageModelResponse, ...]:
- response = self.get_single_response(prompt)
- self.stats.complete_calls += 1
- token_probabilities = self._get_token_probabilities(response)
- return (
- LanguageModelResponseWithLogits(
- text=response,
- token_count=len(token_probabilities),
- stop_reason=ResponseStopReason.NONE,
- network_failure_count=0,
- token_probabilities=token_probabilities,
- ),
- )
-
- async def _get_api_stream(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- responses = self.get_parts_of_response(prompt)
- self.stats.complete_calls += 1
- if len(responses) == 1:
- response = responses[0]
- yield LanguageModelStreamDeltaEvent(delta=response)
- else:
- for response in responses:
- yield LanguageModelStreamDeltaEvent(delta=response)
- await asyncio.sleep(MOCK_STREAM_SLEEP_TIME)
- yield LanguageModelStreamEndEvent(
- usage=LanguageModelResponseUsage(prompt_tokens_used=0, completion_tokens_used=0, dollars_used=0),
- stop_reason=ResponseStopReason.NONE,
- )
diff --git a/imbue_core/imbue_core/agents/llm_apis/models.py b/imbue_core/imbue_core/agents/llm_apis/models.py
@@ -1,17 +0,0 @@
-from imbue_core.agents.llm_apis.union_data_types import ProviderSpecificModelInfoUnion
-from imbue_core.pydantic_serialization import SerializableModel
-
-
-class ModelInfo(SerializableModel):
- model_name: str
- cost_per_input_token: float
- cost_per_output_token: float
- max_input_tokens: int
- max_output_tokens: int | None
- # requests per second
- rate_limit_req: float | None = None
- # tokens per second
- rate_limit_tok: float | None = None
- rate_limit_output_tok: float | None = None
- max_thinking_budget: int | None = None
- provider_specific_info: ProviderSpecificModelInfoUnion | None = None
diff --git a/imbue_core/imbue_core/agents/llm_apis/openai_api.py b/imbue_core/imbue_core/agents/llm_apis/openai_api.py
@@ -1,654 +0,0 @@
-import asyncio
-import enum
-import re
-from collections import defaultdict
-from contextlib import contextmanager
-from functools import lru_cache
-from typing import AsyncGenerator
-from typing import Iterator
-from typing import Mapping
-
-import httpx
-import tiktoken
-from loguru import logger
-from openai import AsyncStream
-from openai import InternalServerError
-from openai import NOT_GIVEN
-from openai import NotGiven
-from openai._client import AsyncOpenAI
-from openai._exceptions import APIConnectionError
-from openai._exceptions import BadRequestError
-from openai._exceptions import RateLimitError
-from openai.types.chat import ChatCompletion
-from pydantic.functional_validators import field_validator
-
-from imbue_core.agents.llm_apis.api_utils import convert_prompt_to_openai_messages
-from imbue_core.agents.llm_apis.data_types import CachingInfo
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseWithLogits
-from imbue_core.agents.llm_apis.data_types import ResponseStopReason
-from imbue_core.agents.llm_apis.data_types import TokenProbability
-from imbue_core.agents.llm_apis.errors import BadAPIRequestError
-from imbue_core.agents.llm_apis.errors import LanguageModelInvalidModelNameError
-from imbue_core.agents.llm_apis.errors import MissingAPIKeyError
-from imbue_core.agents.llm_apis.errors import PromptTooLongError
-from imbue_core.agents.llm_apis.errors import TransientLanguageModelError
-from imbue_core.agents.llm_apis.models import ModelInfo
-from imbue_core.agents.llm_apis.openai_compatible_api import OpenAICompatibleAPI
-from imbue_core.agents.llm_apis.openai_compatible_api import (
- _OPENAI_COMPATIBLE_STOP_REASON_TO_STOP_REASON,
-)
-from imbue_core.agents.llm_apis.openai_data_types import OpenAICachingInfo
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamStartEvent
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.frozen_utils import FrozenMapping
-from imbue_core.itertools import only
-from imbue_core.secrets_utils import get_secret
-
-# note: we require that these model versions are explicit, just like the rest of our dependencies
-# the reason is that these models are actually now mostly deterministic, and it is much easier to debug if we know what model was used
-# also, there's no need to troll yourself by wondering why results have improved (or gotten worse) when you dont realized that the version has shifted under you
-# if you want to use an upgraded model, just upgrade the model to the key displayed here: https://platform.openai.com/docs/models/overview
-# please do NOT set these back to the generic model names!
-
-FINE_TUNED_GPT4O_MINI_2024_07_18_PREFIX = "ft:gpt-4o-mini-2024-07-18"
-FINE_TUNED_GPT4O_2024_08_06_PREFIX = "ft:gpt-4o-2024-08-06"
-
-
-class OpenAIModelName(enum.StrEnum):
- GPT_3_5_TURBO = "gpt-3.5-turbo-0125"
- GPT_4_0613 = "gpt-4-0613"
- GPT_4_1106_PREVIEW = "gpt-4-1106-preview"
- GPT_4_0125_PREVIEW = "gpt-4-0125-preview"
- GPT_4_TURBO_2024_04_09 = "gpt-4-turbo-2024-04-09"
- GPT_4O_2024_05_13 = "gpt-4o-2024-05-13"
- GPT_4O_2024_08_06 = "gpt-4o-2024-08-06"
- GPT_4O_MINI_2024_07_18 = "gpt-4o-mini-2024-07-18"
- O1_2024_12_17 = "o1-2024-12-17"
- GPT_4_1_2025_04_14 = "gpt-4.1-2025-04-14"
- GPT_4_1_MINI_2025_04_14 = "gpt-4.1-mini-2025-04-14"
- GPT_4_1_NANO_2025_04_14 = "gpt-4.1-nano-2025-04-14"
- O3_2025_04_16 = "o3-2025-04-16"
- O3_MINI_2025_01_31 = "o3-mini-2025-01-31"
- O4_MINI_2025_04_16 = "o4-mini-2025-04-16"
- GPT_5_2025_08_07 = "gpt-5-2025-08-07"
- GPT_5_MINI_2025_08_07 = "gpt-5-mini-2025-08-07"
- GPT_5_NANO_2025_08_07 = "gpt-5-nano-2025-08-07"
- GPT_5_1_2025_11_13 = "gpt-5.1-2025-11-13"
-
-
-# Using Tier 5 rate limits
-# https://platform.openai.com/settings/organization/limits
-
-OPENAI_MODEL_INFO_BY_NAME: FrozenMapping[OpenAIModelName, ModelInfo] = FrozenDict(
- {
- OpenAIModelName.GPT_3_5_TURBO: ModelInfo(
- model_name=str(OpenAIModelName.GPT_3_5_TURBO),
- cost_per_input_token=0.5 / 1_000_000,
- cost_per_output_token=1.5 / 1_000_000,
- max_input_tokens=16_385,
- max_output_tokens=4096,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
- ),
- OpenAIModelName.GPT_4_0613: ModelInfo(
- model_name=str(OpenAIModelName.GPT_4_0613),
- cost_per_input_token=30.0 / 1_000_000,
- cost_per_output_token=60.0 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=8192,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
- ),
- OpenAIModelName.GPT_4_1106_PREVIEW: ModelInfo( # Cannot find this model
- model_name=str(OpenAIModelName.GPT_4_1106_PREVIEW),
- cost_per_input_token=10.0 / 1_000_000,
- cost_per_output_token=30.0 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=4096,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
- ),
- OpenAIModelName.GPT_4_0125_PREVIEW: ModelInfo(
- model_name=str(OpenAIModelName.GPT_4_0125_PREVIEW),
- cost_per_input_token=10.0 / 1_000_000,
- cost_per_output_token=30.0 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=4096,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
- ),
- OpenAIModelName.GPT_4_TURBO_2024_04_09: ModelInfo(
- model_name=str(OpenAIModelName.GPT_4_TURBO_2024_04_09),
- cost_per_input_token=10.0 / 1_000_000,
- cost_per_output_token=30.0 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=4096,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
- ),
- OpenAIModelName.GPT_4O_2024_05_13: ModelInfo(
- model_name=str(OpenAIModelName.GPT_4O_2024_05_13),
- cost_per_input_token=5.0 / 1_000_000,
- cost_per_output_token=15.0 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=4096,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
- ),
- OpenAIModelName.GPT_4O_2024_08_06: ModelInfo(
- model_name=str(OpenAIModelName.GPT_4O_2024_08_06),
- cost_per_input_token=2.5 / 1_000_000,
- cost_per_output_token=10.0 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=16_384,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
- ),
- OpenAIModelName.GPT_4O_MINI_2024_07_18: ModelInfo(
- model_name=str(OpenAIModelName.GPT_4O_MINI_2024_07_18),
- cost_per_input_token=0.15 / 1_000_000,
- cost_per_output_token=0.60 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=16_384,
- rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
- ),
- OpenAIModelName.O1_2024_12_17: ModelInfo(
- model_name=str(OpenAIModelName.O1_2024_12_17),
- cost_per_input_token=15 / 1_000_000,
- cost_per_output_token=60 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=100_000,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
- ),
- OpenAIModelName.GPT_4_1_2025_04_14: ModelInfo(
- model_name=str(OpenAIModelName.GPT_4_1_2025_04_14),
- cost_per_input_token=2 / 1_000_000,
- cost_per_output_token=8 / 1_000_000,
- max_input_tokens=1_047_576,
- max_output_tokens=32_768,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
- ),
- OpenAIModelName.GPT_4_1_MINI_2025_04_14: ModelInfo(
- model_name=str(OpenAIModelName.GPT_4_1_MINI_2025_04_14),
- cost_per_input_token=0.4 / 1_000_000,
- cost_per_output_token=1.6 / 1_000_000,
- max_input_tokens=1_047_576,
- max_output_tokens=32_768,
- rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
- ),
- OpenAIModelName.GPT_4_1_NANO_2025_04_14: ModelInfo(
- model_name=str(OpenAIModelName.GPT_4_1_NANO_2025_04_14),
- cost_per_input_token=0.1 / 1_000_000,
- cost_per_output_token=0.4 / 1_000_000,
- max_input_tokens=1_047_576,
- max_output_tokens=32_768,
- rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
- ),
- OpenAIModelName.O4_MINI_2025_04_16: ModelInfo(
- model_name=str(OpenAIModelName.O4_MINI_2025_04_16),
- cost_per_input_token=1.1 / 1_000_000,
- cost_per_output_token=4.4 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=100_000,
- rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
- ),
- OpenAIModelName.O3_2025_04_16: ModelInfo(
- model_name=str(OpenAIModelName.O3_2025_04_16),
- cost_per_input_token=10 / 1_000_000,
- cost_per_output_token=40 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=100_000,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
- ),
- OpenAIModelName.O3_MINI_2025_01_31: ModelInfo(
- model_name=str(OpenAIModelName.O3_MINI_2025_01_31),
- cost_per_input_token=1.1 / 1_000_000,
- cost_per_output_token=4.4 / 1_000_000,
- max_input_tokens=200_000,
- max_output_tokens=100_000,
- rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
- ),
- OpenAIModelName.GPT_5_2025_08_07: ModelInfo(
- model_name=str(OpenAIModelName.GPT_5_2025_08_07),
- cost_per_input_token=1.25 / 1_000_000,
- cost_per_output_token=10 / 1_000_000,
- max_input_tokens=400_000,
- max_output_tokens=128_000,
- rate_limit_req=15000 / 60, # 15000 RPM = 250 RPS
- ),
- OpenAIModelName.GPT_5_MINI_2025_08_07: ModelInfo(
- model_name=str(OpenAIModelName.GPT_5_MINI_2025_08_07),
- cost_per_input_token=0.25 / 1_000_000,
- cost_per_output_token=2.00 / 1_000_000,
- max_input_tokens=400_000,
- max_output_tokens=128_000,
- rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
- ),
- OpenAIModelName.GPT_5_NANO_2025_08_07: ModelInfo(
- model_name=str(OpenAIModelName.GPT_5_NANO_2025_08_07),
- cost_per_input_token=0.05 / 1_000_000,
- cost_per_output_token=0.40 / 1_000_000,
- max_input_tokens=400_000,
- max_output_tokens=128_000,
- rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
- ),
- OpenAIModelName.GPT_5_1_2025_11_13: ModelInfo(
- model_name=str(OpenAIModelName.GPT_5_1_2025_11_13),
- cost_per_input_token=1.25 / 1_000_000,
- cost_per_output_token=10 / 1_000_000,
- max_input_tokens=400_000,
- max_output_tokens=128_000,
- rate_limit_req=15000 / 60, # 15000 RPM = 250 RPS
- ),
- }
-)
-
-
-# Pricing for fine-tuned models taken from here: https://platform.openai.com/docs/pricing
-def get_model_info(model_name: OpenAIModelName) -> ModelInfo:
- # Check for the family of fine-tuned models.
- if model_name.startswith(FINE_TUNED_GPT4O_MINI_2024_07_18_PREFIX):
- return ModelInfo(
- model_name=str(model_name),
- cost_per_input_token=0.3 / 1_000_000,
- cost_per_output_token=1.2 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=16_384,
- rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS (same as base model)
- )
- if model_name.startswith(FINE_TUNED_GPT4O_2024_08_06_PREFIX):
- return ModelInfo(
- model_name=str(model_name),
- cost_per_input_token=3.75 / 1_000_000,
- cost_per_output_token=15.0 / 1_000_000,
- max_input_tokens=128_000,
- max_output_tokens=16_384,
- rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS (same as base model)
- )
- # Otherwise, return the model info for the base model.
- return OPENAI_MODEL_INFO_BY_NAME[model_name]
-
-
-_CAPACITY_SEMAPHOR_BY_MODEL_NAME: Mapping[OpenAIModelName, asyncio.Semaphore] = defaultdict(
- lambda: asyncio.Semaphore(20),
- {
- OpenAIModelName.GPT_3_5_TURBO: asyncio.Semaphore(100),
- OpenAIModelName.GPT_4_0613: asyncio.Semaphore(60),
- OpenAIModelName.GPT_4_1_NANO_2025_04_14: asyncio.Semaphore(80),
- },
-)
-
-
-def _get_capacity_semaphor(model_name: OpenAIModelName) -> asyncio.Semaphore:
- # Fine-tuned models share rate limits with the base model.
- if model_name.startswith(FINE_TUNED_GPT4O_MINI_2024_07_18_PREFIX):
- model_name = OpenAIModelName.GPT_4O_MINI_2024_07_18
- elif model_name.startswith(FINE_TUNED_GPT4O_2024_08_06_PREFIX):
- model_name = OpenAIModelName.GPT_4O_2024_08_06
- return _CAPACITY_SEMAPHOR_BY_MODEL_NAME[model_name]
-
-
-def is_openai_reasoning_model(model_name: str) -> bool:
- return model_name in (
- OpenAIModelName.O1_2024_12_17,
- OpenAIModelName.O4_MINI_2025_04_16,
- OpenAIModelName.O3_2025_04_16,
- OpenAIModelName.O3_MINI_2025_01_31,
- OpenAIModelName.GPT_5_2025_08_07,
- OpenAIModelName.GPT_5_MINI_2025_08_07,
- OpenAIModelName.GPT_5_NANO_2025_08_07,
- )
-
-
-def is_fine_tuned_openai_model(model_name: OpenAIModelName) -> bool:
- return model_name.value.startswith(FINE_TUNED_GPT4O_MINI_2024_07_18_PREFIX) or model_name.value.startswith(
- FINE_TUNED_GPT4O_2024_08_06_PREFIX
- )
-
-
-_OPENAI_COMPLETION_ERROR_PATTERN = re.compile(
- r".*This model's maximum context length is (\d+) tokens, however you requested (\d+) tokens \((\d+) in your prompt; (\d+) for the completion\). Please reduce your prompt; or completion length.*"
-)
-
-_OPENAI_STOP_REASON_TO_STOP_REASON = _OPENAI_COMPATIBLE_STOP_REASON_TO_STOP_REASON
-
-
-@lru_cache(maxsize=1)
-def get_openai_tokenizer(model_name: str) -> tiktoken.Encoding:
- """Get the appropriate tiktoken tokenizer for an OpenAI model.
-
- Args:
- model_name: The OpenAI model name (e.g., "gpt-4o-2024-08-06").
-
- Returns:
- The tiktoken Encoding for the model.
- """
- if model_name.startswith("gpt-4"):
- fixed_model_name = "gpt-4"
- elif model_name.startswith("gpt-3.5"):
- fixed_model_name = "gpt-3.5"
- else:
- # Just default to `gpt-4o` for now, since this seems to be the most recent tokenizer
- # and we are only using it for estimating token usage
- fixed_model_name = "gpt-4o"
- return tiktoken.encoding_for_model(fixed_model_name)
-
-
-def count_openai_tokens(text: str, model_name: str) -> int:
- return len(get_openai_tokenizer(model_name).encode(text))
-
-
-@contextmanager
-def _openai_exception_manager() -> Iterator[None]:
- """Simple context manager for parsing OpenAI API exceptions."""
- try:
- yield
- except BadRequestError as e:
- error_text_match = _OPENAI_COMPLETION_ERROR_PATTERN.search(str(e))
- if error_text_match is not None:
- max_prompt_len = int(error_text_match.group(1))
- prompt_len = int(error_text_match.group(2))
- logger.debug(
- "PromptTooLongError max_prompt_len={max_prompt_len} prompt_len={prompt_len}",
- max_prompt_len=max_prompt_len,
- prompt_len=prompt_len,
- )
- raise PromptTooLongError(prompt_len, max_prompt_len) from e
- logger.debug("BadAPIRequestError {e}", e=e)
- raise BadAPIRequestError(str(e)) from e
- except APIConnectionError as e:
- logger.debug("Rate limited? Received APIConnectionError {e}", e=e)
- raise TransientLanguageModelError("APIConnectionError") from e
- except RateLimitError as e:
- if e.code == "insufficient_quota":
- raise
- logger.debug("Rate limited? {e}", e=e)
- raise TransientLanguageModelError("RateLimitError") from e
- except httpx.RemoteProtocolError as e:
- logger.debug("httpx.RemoteProtocolError {e}", e=e)
- raise TransientLanguageModelError("httpx.RemoteProtocolError") from e
- except InternalServerError as e:
- logger.debug("InternalServerError {e}", e=e)
- raise TransientLanguageModelError("InternalServerError") from e
-
-
-class OpenAIChatAPI(OpenAICompatibleAPI):
- model_name: OpenAIModelName = OpenAIModelName.GPT_4O_MINI_2024_07_18
-
- @field_validator("model_name") # pyre-ignore[56]: pyre doesn't understand pydantic
- @classmethod
- def validate_model_name(cls, v: str) -> str:
- if v not in OPENAI_MODEL_INFO_BY_NAME:
- raise LanguageModelInvalidModelNameError(v, cls.__name__, list(OPENAI_MODEL_INFO_BY_NAME))
- return v
-
- @property
- def model_info(self) -> ModelInfo:
- return get_model_info(self.model_name)
-
- def _get_client(self) -> AsyncOpenAI:
- api_key = get_secret("OPENAI_API_KEY")
- if not api_key:
- raise MissingAPIKeyError("OPENAI_API_KEY environment variable is not set")
- return AsyncOpenAI( # pyre-ignore[16]: pyre doesn't understand the auto-generated openai._client
- api_key=api_key
- )
-
- async def _call_api(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- network_failure_count: int = 0,
- ) -> CostedLanguageModelResponse:
- messages = convert_prompt_to_openai_messages(prompt)
- with _openai_exception_manager():
- client = self._get_client()
-
- is_reasoning_model = is_openai_reasoning_model(self.model_name)
-
- top_logprobs: NotGiven | int
- if self.is_using_logprobs:
- assert not is_reasoning_model, "Logprobs are not supported for reasoning models."
- top_logprobs = 5
- else:
- top_logprobs = NOT_GIVEN
-
- temperature: NotGiven | float = NOT_GIVEN if is_reasoning_model else params.temperature
-
- async with _get_capacity_semaphor(self.model_name):
- api_result = await client.chat.completions.create(
- model=self.model_name,
- messages=messages, # type: ignore
- max_completion_tokens=params.max_tokens,
- n=params.count,
- temperature=temperature,
- stream=False,
- seed=params.seed,
- stop=params.stop,
- presence_penalty=self.presence_penalty,
- logprobs=self.is_using_logprobs,
- top_logprobs=top_logprobs,
- )
- assert isinstance(api_result, ChatCompletion)
-
- usage = api_result.usage
- if usage is not None:
- completion_tokens = usage.completion_tokens
- prompt_tokens = usage.prompt_tokens
- cached_tokens = (
- usage.prompt_tokens_details.cached_tokens if usage.prompt_tokens_details is not None else 0
- ) or 0
- caching_info = CachingInfo(
- read_from_cache=cached_tokens,
- provider_specific_data=OpenAICachingInfo(),
- )
- else:
- completion_tokens = 0
- prompt_tokens = self.count_tokens(prompt)
- cached_tokens = None
- caching_info = None
-
- results: tuple[LanguageModelResponse | LanguageModelResponseWithLogits, ...]
- if self.is_using_logprobs:
- results = self._parse_response_with_logprobs(
- api_result,
- prompt_tokens=prompt_tokens,
- stop=params.stop,
- network_failure_count=network_failure_count,
- )
- else:
- results = self._parse_response_without_logprobs(
- api_result,
- prompt_tokens=prompt_tokens,
- stop=params.stop,
- network_failure_count=network_failure_count,
- )
-
- logger.trace("text: {text}", text=results[0].text)
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
- logger.trace("dollars used: {dollars_used}", dollars_used=dollars_used)
- return CostedLanguageModelResponse(
- usage=LanguageModelResponseUsage(
- prompt_tokens_used=prompt_tokens,
- completion_tokens_used=completion_tokens,
- dollars_used=dollars_used,
- caching_info=caching_info,
- ),
- responses=tuple(results),
- )
-
- async def _get_api_stream(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- messages = convert_prompt_to_openai_messages(prompt)
- with _openai_exception_manager():
- client = self._get_client()
-
- is_reasoning_model = is_openai_reasoning_model(self.model_name)
- temperature: NotGiven | float = NOT_GIVEN if is_reasoning_model else params.temperature
-
- async with _get_capacity_semaphor(self.model_name):
- api_result = await client.chat.completions.create(
- model=self.model_name,
- messages=messages, # type: ignore
- max_completion_tokens=params.max_tokens,
- n=1,
- temperature=temperature,
- stop=params.stop,
- seed=params.seed,
- stream=True,
- stream_options={"include_usage": True},
- presence_penalty=self.presence_penalty,
- logprobs=False, # not used when streaming
- top_logprobs=NOT_GIVEN, # only allowed when logprobs=True
- )
- assert isinstance(api_result, AsyncStream)
-
- yield LanguageModelStreamStartEvent()
-
- usage = None
- finish_reason: str | None = None
- async for chunk in api_result:
- if hasattr(chunk, "usage") and chunk.usage is not None:
- # final chunk containing usage info after all streaming is done
- usage = chunk.usage
- continue
-
- if chunk.choices:
- assert len(chunk.choices) == 1, "Currently only count=1 supported for streaming API."
- data = only(chunk.choices)
- delta = data.delta.content
- if delta is not None:
- yield LanguageModelStreamDeltaEvent(delta=delta)
- if data.finish_reason:
- finish_reason = str(data.finish_reason)
-
- stop_reason = _OPENAI_STOP_REASON_TO_STOP_REASON[str(finish_reason)]
- # Note, OpenAI API treats end turn and stop sequence the same
- # Here we assume it is stop sequence if user has specified a stop sequence
- if params.stop is not None and stop_reason == ResponseStopReason.END_TURN:
- yield LanguageModelStreamDeltaEvent(delta=params.stop)
-
- if usage is not None:
- completion_tokens = usage.completion_tokens
- prompt_tokens = usage.prompt_tokens
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
- cached_tokens = usage.prompt_tokens_details.cached_tokens
- logger.trace(
- "Used this many cached read tokens: {cached_tokens}",
- cached_tokens=cached_tokens,
- )
- caching_info = CachingInfo(
- read_from_cache=cached_tokens,
- provider_specific_data=OpenAICachingInfo(),
- )
- else:
- completion_tokens = -1
- prompt_tokens = -1
- dollars_used = -1
- caching_info = None
- logger.trace("dollars used: {dollars_used}", dollars_used=dollars_used)
-
- yield LanguageModelStreamEndEvent(
- usage=LanguageModelResponseUsage(
- prompt_tokens_used=prompt_tokens,
- completion_tokens_used=completion_tokens,
- dollars_used=dollars_used,
- caching_info=caching_info,
- ),
- stop_reason=stop_reason,
- )
-
- def count_tokens(self, text: str) -> int:
- return count_openai_tokens(text, self.model_name)
-
- def _parse_response_without_logprobs(
- self,
- response: ChatCompletion,
- prompt_tokens: int,
- stop: str | None,
- network_failure_count: int,
- ) -> tuple[LanguageModelResponse, ...]:
- results = []
- for data in response.choices:
- assert data.message.content is not None
- text = data.message.content
- token_count = self.count_tokens(text) + prompt_tokens
- stop_reason = _OPENAI_STOP_REASON_TO_STOP_REASON[str(data.finish_reason)]
- # Note, OpenAI API treats end turn and stop sequence the same
- # Here we assume it is stop sequence if user has specified a stop sequence
- if stop is not None and stop_reason == ResponseStopReason.END_TURN:
- text += stop
- result = LanguageModelResponse(
- text=text,
- token_count=token_count,
- stop_reason=stop_reason,
- network_failure_count=network_failure_count,
- )
- results.append(result)
- return tuple(results)
-
- def _parse_response_with_logprobs(
- self,
- response: ChatCompletion,
- prompt_tokens: int,
- stop: str | None,
- network_failure_count: int,
- ) -> tuple[LanguageModelResponseWithLogits, ...]:
- results = []
- for data in response.choices:
- assert data.message.content is not None
- logprobs = data.logprobs
- assert logprobs is not None
- logprobs_content = logprobs.content
- assert logprobs_content is not None
- text = data.message.content
-
- token_probabilities = []
- for logprob_token_entry in logprobs_content:
- top_logprobs = logprob_token_entry.top_logprobs
- top_entries = [
- TokenProbability(
- token=top_logprob_obj.token,
- log_probability=top_logprob_obj.logprob,
- is_stop=False,
- )
- for top_logprob_obj in top_logprobs
- ]
- selected_entry = TokenProbability(
- token=logprob_token_entry.token,
- log_probability=logprob_token_entry.logprob,
- is_stop=False,
- )
- if selected_entry in top_entries:
- top_entries.remove(selected_entry)
- token_probabilities.append(tuple([selected_entry] + top_entries))
-
- stop_reason = _OPENAI_STOP_REASON_TO_STOP_REASON[str(data.finish_reason)]
-
- # Note, OpenAI API treats end turn and stop sequence the same
- # Here we assume it is stop sequence if user has specified a stop sequence
- if stop is not None and stop_reason == ResponseStopReason.END_TURN:
- text += stop
- token_probabilities.append(
- tuple(
- [
- TokenProbability(
- token=stop,
- log_probability=self.stop_token_log_probability,
- is_stop=True,
- )
- ]
- )
- )
- result = LanguageModelResponseWithLogits(
- text=text,
- token_probabilities=tuple(token_probabilities),
- token_count=len(logprobs_content) + prompt_tokens,
- stop_reason=stop_reason,
- network_failure_count=network_failure_count,
- )
- results.append(result)
- return tuple(results)
diff --git a/imbue_core/imbue_core/agents/llm_apis/openai_compatible_api.py b/imbue_core/imbue_core/agents/llm_apis/openai_compatible_api.py
@@ -1,286 +0,0 @@
-import math
-from contextlib import contextmanager
-from typing import AsyncGenerator
-from typing import Iterator
-
-import httpx
-from loguru import logger
-from openai import AsyncOpenAI
-from openai import AsyncStream
-from openai import InternalServerError
-from openai import NotGiven
-from openai._exceptions import APIConnectionError
-from openai._exceptions import BadRequestError
-from openai._exceptions import RateLimitError
-from openai.types.chat import ChatCompletion
-
-from imbue_core.agents.llm_apis.api_utils import convert_prompt_to_openai_messages
-from imbue_core.agents.llm_apis.constants import approximate_token_count
-from imbue_core.agents.llm_apis.data_types import CachingInfo
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
-from imbue_core.agents.llm_apis.data_types import ResponseStopReason
-from imbue_core.agents.llm_apis.errors import BadAPIRequestError
-from imbue_core.agents.llm_apis.errors import PromptTooLongError
-from imbue_core.agents.llm_apis.errors import TransientLanguageModelError
-from imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
-from imbue_core.agents.llm_apis.models import ModelInfo
-from imbue_core.agents.llm_apis.openai_data_types import OpenAICachingInfo
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamStartEvent
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.frozen_utils import FrozenMapping
-from imbue_core.itertools import only
-from imbue_core.secrets_utils import get_secret
-
-_OPENAI_COMPATIBLE_STOP_REASON_TO_STOP_REASON: FrozenMapping[str, ResponseStopReason] = FrozenDict(
- {
- "stop": ResponseStopReason.END_TURN,
- "length": ResponseStopReason.MAX_TOKENS,
- "tool_calls": ResponseStopReason.TOOL_CALLS,
- "function_call": ResponseStopReason.FUNCTION_CALL,
- "content_filter": ResponseStopReason.CONTENT_FILTER,
- "None": ResponseStopReason.NONE,
- }
-)
-
-
-# TODO: Should the pre-defined OpenAI model class inherit from this?
-class OpenAICompatibleAPI(LanguageModelAPI):
- model_name: str
- base_url: str = "https://api.openai.com/v1"
- api_key_env: str = "OPENAI_API_KEY"
- context_window: int | None = None
- max_output_tokens: int | None = None
- is_conversational: bool = True
- presence_penalty: float = 0.0
- # this shouldn't really ever even be used, but just in case
- stop_token_log_probability: float = math.log(0.9999)
-
- @property
- def model_info(self) -> ModelInfo:
- if self.context_window is None or self.max_output_tokens is None:
- raise ValueError("Must provide context_window and max_output_tokens, or subclass must override model_info")
- return ModelInfo(
- model_name=self.model_name,
- cost_per_input_token=0.0,
- cost_per_output_token=0.0,
- max_input_tokens=self.context_window,
- max_output_tokens=self.max_output_tokens,
- rate_limit_req=None,
- )
-
- def _get_client(self) -> AsyncOpenAI:
- api_key = get_secret(self.api_key_env) if self.api_key_env else ""
- if not api_key:
- api_key = "not-required"
- logger.debug("API key not set, attempting to use API without key.")
-
- return AsyncOpenAI(
- api_key=api_key,
- base_url=self.base_url,
- )
-
- @contextmanager
- def _exception_handler(self, prompt: str) -> Iterator[None]:
- try:
- yield
- except BadRequestError as e:
- if e.code == "context_length_exceeded":
- prompt_len = self.count_tokens(prompt)
- max_prompt_len = self.model_info.max_input_tokens
- logger.debug(
- "PromptTooLongError max_prompt_len={max_prompt_len} prompt_len={prompt_len}",
- max_prompt_len=max_prompt_len,
- prompt_len=prompt_len,
- )
- raise PromptTooLongError(prompt_len, max_prompt_len) from e
- logger.debug("BadAPIRequestError {e}", e=e)
- raise BadAPIRequestError(str(e)) from e
- except APIConnectionError as e:
- logger.debug("API connection error: {e}", e=e)
- raise TransientLanguageModelError("APIConnectionError") from e
- except RateLimitError as e:
- if e.code == "insufficient_quota":
- raise
- logger.debug("Rate limited: {e}", e=e)
- raise TransientLanguageModelError("RateLimitError") from e
- except httpx.RemoteProtocolError as e:
- logger.debug("httpx.RemoteProtocolError {e}", e=e)
- raise TransientLanguageModelError("httpx.RemoteProtocolError") from e
- except InternalServerError as e:
- logger.debug("InternalServerError {e}", e=e)
- raise TransientLanguageModelError("InternalServerError") from e
-
- async def _call_api(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- network_failure_count: int = 0,
- ) -> CostedLanguageModelResponse:
- messages = convert_prompt_to_openai_messages(prompt)
-
- with self._exception_handler(prompt):
- client = self._get_client()
-
- temperature: NotGiven | float = params.temperature
-
- api_result = await client.chat.completions.create(
- model=self.model_name,
- messages=messages,
- max_completion_tokens=params.max_tokens,
- n=params.count,
- temperature=temperature,
- stream=False,
- seed=params.seed,
- stop=params.stop,
- presence_penalty=self.presence_penalty,
- )
- assert isinstance(api_result, ChatCompletion)
-
- usage = api_result.usage
- if usage is not None:
- completion_tokens = usage.completion_tokens
- prompt_tokens = usage.prompt_tokens
- cached_tokens = (
- usage.prompt_tokens_details.cached_tokens if usage.prompt_tokens_details is not None else 0
- ) or 0
- caching_info = CachingInfo(
- read_from_cache=cached_tokens,
- provider_specific_data=OpenAICachingInfo(),
- )
- else:
- completion_tokens = 0
- prompt_tokens = self.count_tokens(prompt)
- cached_tokens = None
- caching_info = None
-
- results = self._parse_response(
- api_result,
- prompt_tokens=prompt_tokens,
- stop=params.stop,
- network_failure_count=network_failure_count,
- )
-
- logger.trace("text: {text}", text=results[0].text)
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
- logger.trace("dollars used: {dollars_used}", dollars_used=dollars_used)
-
- return CostedLanguageModelResponse(
- usage=LanguageModelResponseUsage(
- prompt_tokens_used=prompt_tokens,
- completion_tokens_used=completion_tokens,
- dollars_used=dollars_used,
- caching_info=caching_info,
- ),
- responses=tuple(results),
- )
-
- async def _get_api_stream(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- messages = convert_prompt_to_openai_messages(prompt)
-
- with self._exception_handler(prompt):
- client = self._get_client()
-
- temperature: NotGiven | float = params.temperature
-
- api_result = await client.chat.completions.create(
- model=self.model_name,
- messages=messages,
- max_completion_tokens=params.max_tokens,
- n=1,
- temperature=temperature,
- stop=params.stop,
- seed=params.seed,
- stream=True,
- stream_options={"include_usage": True},
- presence_penalty=self.presence_penalty,
- )
- assert isinstance(api_result, AsyncStream)
-
- yield LanguageModelStreamStartEvent()
-
- usage = None
- finish_reason: str | None = None
- async for chunk in api_result:
- if hasattr(chunk, "usage") and chunk.usage is not None:
- usage = chunk.usage
- continue
-
- if chunk.choices:
- assert len(chunk.choices) == 1, "Currently only count=1 supported for streaming API."
- data = only(chunk.choices)
- delta = data.delta.content
- if delta is not None:
- yield LanguageModelStreamDeltaEvent(delta=delta)
- if data.finish_reason:
- finish_reason = str(data.finish_reason)
-
- stop_reason = _OPENAI_COMPATIBLE_STOP_REASON_TO_STOP_REASON.get(str(finish_reason), ResponseStopReason.NONE)
- if params.stop is not None and stop_reason == ResponseStopReason.END_TURN:
- yield LanguageModelStreamDeltaEvent(delta=params.stop)
-
- if usage is not None:
- completion_tokens = usage.completion_tokens
- prompt_tokens = usage.prompt_tokens
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
- cached_tokens = (
- usage.prompt_tokens_details.cached_tokens if usage.prompt_tokens_details is not None else 0
- ) or 0
- caching_info = CachingInfo(
- read_from_cache=cached_tokens,
- provider_specific_data=OpenAICachingInfo(),
- )
- else:
- completion_tokens = -1
- prompt_tokens = -1
- dollars_used = -1
- caching_info = None
- logger.trace("dollars used: {dollars_used}", dollars_used=dollars_used)
-
- yield LanguageModelStreamEndEvent(
- usage=LanguageModelResponseUsage(
- prompt_tokens_used=prompt_tokens,
- completion_tokens_used=completion_tokens,
- dollars_used=dollars_used,
- caching_info=caching_info,
- ),
- stop_reason=stop_reason,
- )
-
- def count_tokens(self, text: str) -> int:
- return approximate_token_count(text)
-
- def _parse_response(
- self,
- response: ChatCompletion,
- prompt_tokens: int,
- stop: str | None,
- network_failure_count: int,
- ) -> tuple[LanguageModelResponse, ...]:
- results = []
- for data in response.choices:
- assert data.message.content is not None
- text = data.message.content
- token_count = self.count_tokens(text) + prompt_tokens
- stop_reason = _OPENAI_COMPATIBLE_STOP_REASON_TO_STOP_REASON.get(
- str(data.finish_reason), ResponseStopReason.NONE
- )
- if stop is not None and stop_reason == ResponseStopReason.END_TURN:
- text += stop
- result = LanguageModelResponse(
- text=text,
- token_count=token_count,
- stop_reason=stop_reason,
- network_failure_count=network_failure_count,
- )
- results.append(result)
- return tuple(results)
diff --git a/imbue_core/imbue_core/agents/llm_apis/openai_data_types.py b/imbue_core/imbue_core/agents/llm_apis/openai_data_types.py
@@ -1,13 +0,0 @@
-from imbue_core.pydantic_serialization import SerializableModel
-
-
-class OpenAIModelInfo(SerializableModel):
- """Currently there isn't any model info specific to OpenAI"""
-
- object_type: str = "OpenAIModelInfo"
-
-
-class OpenAICachingInfo(SerializableModel):
- """Currently there isn't any caching info specific to OpenAI"""
-
- object_type: str = "OpenAICachingInfo"
diff --git a/imbue_core/imbue_core/agents/llm_apis/stream.py b/imbue_core/imbue_core/agents/llm_apis/stream.py
@@ -1,168 +0,0 @@
-import abc
-import asyncio
-from collections.abc import AsyncIterator
-from contextlib import aclosing
-from typing import Any
-from typing import AsyncGenerator
-from typing import Sequence
-
-import anyio
-
-from imbue_core.agents.llm_apis.data_types import CachedCostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
-from imbue_core.agents.llm_apis.data_types import LanguageModelStreamInputs
-from imbue_core.agents.llm_apis.data_types import ModelResponse
-from imbue_core.agents.llm_apis.data_types import ResponseStopReason
-from imbue_core.agents.primitives.resource_limits import PaymentAuthorization
-from imbue_core.agents.primitives.resource_limits import get_global_resource_limits
-from imbue_core.caching import AsyncCache
-from imbue_core.pydantic_serialization import SerializableModel
-
-
-class LanguageModelStreamStartEvent(SerializableModel):
- pass
-
-
-class LanguageModelStreamDeltaEvent(SerializableModel):
- delta: str
- # TODO add per delta token count (if there is a demand)
- # TODO add per delta logprobs (if there is a demand)
-
-
-class LanguageModelStreamEndEvent(SerializableModel):
- usage: LanguageModelResponseUsage
- stop_reason: ResponseStopReason
-
-
-LanguageModelStreamEvent = LanguageModelStreamStartEvent | LanguageModelStreamDeltaEvent | LanguageModelStreamEndEvent
-
-
-class LanguageModelStreamCallback(abc.ABC, SerializableModel):
- @abc.abstractmethod
- async def __call__(self, response: CostedLanguageModelResponse) -> None: ...
-
-
-class UpdateCacheCallback(LanguageModelStreamCallback):
- key: str
- cache: AsyncCache[CachedCostedLanguageModelResponse]
- api_inputs: LanguageModelStreamInputs | None
-
- async def __call__(self, response: CostedLanguageModelResponse) -> None:
- async with self.cache:
- await self.cache.set(
- self.key,
- CachedCostedLanguageModelResponse(response=response, inputs=self.api_inputs),
- )
-
-
-class PromptDebuggingCallback(LanguageModelStreamCallback):
- prompt: str
- output_path: anyio.Path
-
- async def __call__(self, response: CostedLanguageModelResponse) -> None:
- await self.output_path.write_text(self.prompt + response.responses[0].text)
-
-
-class SettleSpendCallback(LanguageModelStreamCallback):
- auth: PaymentAuthorization
-
- async def __call__(self, response: CostedLanguageModelResponse) -> None:
- dollars_used = response.usage.dollars_used
- global_resource_limits = get_global_resource_limits()
- assert global_resource_limits is not None
- await global_resource_limits.settle_spend(self.auth, dollars_used)
- return None
-
-
-async def consume_async_iterator(iterator: AsyncIterator[Any]) -> None:
- async for _ in iterator:
- ...
-
-
-async def get_cached_response_stream(
- response: CostedLanguageModelResponse,
-) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- """Simple stream that return cached response in a single go.
-
- Implemented here so user get's a consistent interface from stream().
- """
- yield LanguageModelStreamStartEvent()
- yield LanguageModelStreamDeltaEvent(delta=response.responses[0].text)
- yield LanguageModelStreamEndEvent(usage=response.usage, stop_reason=response.responses[0].stop_reason)
-
-
-class StreamedLanguageModelResponse(ModelResponse):
- """A stream of LanguageModel API events."""
-
- text_stream: AsyncIterator[str]
-
- def __init__(
- self,
- # Note event_stream is AsyncGenerator as it supports aclose for better cleanup (unlike AsyncIterator)
- event_stream: AsyncGenerator[LanguageModelStreamEvent, None],
- network_failure_count: int,
- completion_callbacks: Sequence[LanguageModelStreamCallback] = (),
- ) -> None:
- # the underlying stream coming from the API
- self._event_stream = event_stream
- self.text_stream = self._stream_text()
- self._final_message_snapshot: LanguageModelResponse | None = None
-
- self._completion_callbacks = completion_callbacks
- # this is propogated to final message
- self._network_failure_count = network_failure_count
- self.stop_reason: ResponseStopReason | None = None
-
- async def get_final_message(self) -> LanguageModelResponse:
- # wait until final message
- await consume_async_iterator(self._event_stream)
- assert self._final_message_snapshot is not None
- return self._final_message_snapshot
-
- async def __aiter__(self) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- # iterator of events, with handling of shutdown
- async with aclosing(self._event_stream) as event_stream:
- deltas: list[str] = []
- async for event in event_stream:
- if isinstance(event, LanguageModelStreamStartEvent):
- # Need nested if statement here for outer if-elif-else to correctly filter for unknown event types
- if len(deltas) > 0 or self._final_message_snapshot is not None:
- raise RuntimeError("Start event should be the first event in stream.")
- elif isinstance(event, LanguageModelStreamDeltaEvent):
- deltas.append(event.delta)
- elif isinstance(event, LanguageModelStreamEndEvent):
- self.stop_reason = event.stop_reason
- self._final_message_snapshot = LanguageModelResponse(
- text="".join(deltas),
- token_count=(0 if event.usage is None else event.usage.completion_tokens_used),
- stop_reason=event.stop_reason,
- network_failure_count=self._network_failure_count,
- )
- if self._completion_callbacks is not None:
- costed_response = CostedLanguageModelResponse(
- usage=event.usage, responses=(self._final_message_snapshot,)
- )
- await asyncio.gather(*[callback(costed_response) for callback in self._completion_callbacks])
- else:
- raise ValueError(f"Unknown or Unexpected StreamEvent type {type(event)}.")
-
- yield event
-
- async def _stream_text(self) -> AsyncIterator[str]:
- # iterator of text delta
- async for event in self:
- if isinstance(event, LanguageModelStreamDeltaEvent):
- yield event.delta
-
- async def __aenter__(self) -> "StreamedLanguageModelResponse":
- return self
-
- async def __aexit__(self, *exc: Any) -> None:
- await self.aclose()
-
- async def aclose(self) -> None:
- """Close iterator."""
- # clean up underlying stream (currently not sure how to do this/if we need to do this)
- await self._event_stream.aclose()
diff --git a/imbue_core/imbue_core/agents/llm_apis/together_api.py b/imbue_core/imbue_core/agents/llm_apis/together_api.py
@@ -1,611 +0,0 @@
-import asyncio
-import enum
-import math
-from collections import defaultdict
-from contextlib import contextmanager
-from typing import AsyncGenerator
-from typing import Final
-from typing import Iterable
-from typing import Iterator
-from typing import Mapping
-from typing import cast
-
-from loguru import logger
-from pydantic.functional_validators import field_validator
-from together import AsyncTogether
-from together.abstract.api_requestor import APIRequestor
-from together.abstract.api_requestor import AioHTTPSession
-from together.error import APIConnectionError
-from together.error import APIError
-from together.error import AuthenticationError
-from together.error import InvalidRequestError
-from together.error import RateLimitError
-from together.error import ServiceUnavailableError
-from together.together_response import TogetherResponse
-from together.types import TogetherRequest
-from together.types.chat_completions import ChatCompletionResponse
-
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseWithLogits
-from imbue_core.agents.llm_apis.data_types import ResponseStopReason
-from imbue_core.agents.llm_apis.data_types import TokenProbability
-from imbue_core.agents.llm_apis.errors import BadAPIRequestError
-from imbue_core.agents.llm_apis.errors import LanguageModelInvalidModelNameError
-from imbue_core.agents.llm_apis.errors import MissingAPIKeyError
-from imbue_core.agents.llm_apis.errors import TransientLanguageModelError
-from imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
-from imbue_core.agents.llm_apis.models import ModelInfo
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
-from imbue_core.agents.llm_apis.stream import LanguageModelStreamStartEvent
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.frozen_utils import FrozenMapping
-from imbue_core.itertools import only
-from imbue_core.secrets_utils import get_secret
-
-
-# This function is monkeypatched as the original method does not catch BaseExceptions and asyncio.CancelledErrors are BaseExceptions
-async def arequest(
- self: APIRequestor,
- options: TogetherRequest,
- stream: bool = False,
- request_timeout: float | tuple[float, float] | None = None,
-) -> tuple[TogetherResponse | AsyncGenerator[TogetherResponse, None], bool, str | None]:
- ctx = AioHTTPSession()
- session = await ctx.__aenter__()
- result = None
- try:
- result = await self.arequest_raw(
- options,
- session,
- request_timeout=request_timeout,
- )
- resp, got_stream = await self._interpret_async_response(result, stream)
- except BaseException:
- # Close the request before exiting session context.
- if result is not None:
- result.release()
- await ctx.__aexit__(None, None, None)
- raise
- if got_stream:
-
- async def wrap_resp() -> AsyncGenerator[TogetherResponse, None]:
- assert isinstance(resp, AsyncGenerator)
- try:
- async for r in resp:
- yield r
- finally:
- # Close the request before exiting session context. Important to do it here
- # as if stream is not fully exhausted, we need to close the request nevertheless.
- result.release()
- await ctx.__aexit__(None, None, None)
-
- return wrap_resp(), got_stream, self.api_key
- else:
- # Close the request before exiting session context.
- result.release()
- await ctx.__aexit__(None, None, None)
- return resp, got_stream, self.api_key
-
-
-APIRequestor.arequest = arequest # pyre-fixme[8]: pyre is confused about this
-
-
-class TogetherAIModelName(enum.StrEnum):
- GOOGLE_GEMMA_2_27B_IT = "together/google/gemma-2-27b-it"
- GOOGLE_GEMMA_2_9B_IT = "together/google/gemma-2-9b-it"
- GOOGLE_GEMMA_2B_IT = "together/google/gemma-2b-it"
- META_LLAMA_3_2_3B_INSTRUCT_TURBO = "together/meta-llama/Llama-3.2-3B-Instruct-Turbo"
- META_LLAMA_3_3_70B_INSTRUCT_TURBO = "together/meta-llama/Llama-3.3-70B-Instruct-Turbo"
- META_LLAMA_3_70B_CHAT_HF = "together/meta-llama/Llama-3-70b-chat-hf"
- META_LLAMA_3_8B_CHAT_HF = "together/meta-llama/Llama-3-8b-chat-hf"
- META_LLAMA_3_1_405B_INSTRUCT_TURBO = "together/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo"
- META_LLAMA_3_1_70B_INSTRUCT_TURBO = "together/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo"
- META_LLAMA_3_1_8B_INSTRUCT_TURBO = "together/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"
- META_LLAMA_3_70B_INSTRUCT_LITE = "together/meta-llama/Meta-Llama-3-70B-Instruct-Lite"
- META_LLAMA_3_70B_INSTRUCT_TURBO = "together/meta-llama/Meta-Llama-3-70B-Instruct-Turbo"
- META_LLAMA_3_8B_INSTRUCT_LITE = "together/meta-llama/Meta-Llama-3-8B-Instruct-Lite"
- META_LLAMA_3_8B_INSTRUCT_TURBO = "together/meta-llama/Meta-Llama-3-8B-Instruct-Turbo"
- MISTRALAI_MISTRAL_7B_INSTRUCT_V0_1 = "together/mistralai/Mistral-7B-Instruct-v0.1"
- MISTRALAI_MISTRAL_7B_INSTRUCT_V0_2 = "together/mistralai/Mistral-7B-Instruct-v0.2"
- MISTRALAI_MISTRAL_7B_INSTRUCT_V0_3 = "together/mistralai/Mistral-7B-Instruct-v0.3"
- MISTRALAI_MIXTRAL_8X22B_INSTRUCT_V0_1 = "together/mistralai/Mixtral-8x22B-Instruct-v0.1"
- MISTRALAI_MIXTRAL_8X7B_INSTRUCT_V0_1 = "together/mistralai/Mixtral-8x7B-Instruct-v0.1"
- NOUSRESEARCH_NOUS_HERMES_2_MIXTRAL_8X7B_DPO = "together/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO"
- DEEPSEEK_R1 = "together/deepseek-ai/DeepSeek-R1"
- OPENAI_GPT_OSS_20B = "together/openai/gpt-oss-20b"
- OPENAI_GPT_OSS_120B = "together/openai/gpt-oss-120b"
- # TOGETHERCOMPUTER_LLAMA_3_8B_CHAT_HF_INT4 = "together/togethercomputer/Llama-3-8b-chat-hf-int4"
- # TOGETHERCOMPUTER_LLAMA_3_8B_CHAT_HF_INT8 = "together/togethercomputer/Llama-3-8b-chat-hf-int8"
-
-
-# Rate limits for Together AI models based on published API documentation
-# Reference: https://docs.together.ai/docs/rate-limits
-# Using Tier 5 rate limits (6,000 RPM)
-
-TOGETHERAI_MODEL_INFO_BY_NAME: FrozenMapping[TogetherAIModelName, ModelInfo] = FrozenDict(
- {
- # ref https://docs.together.ai/docs/chat-models
- # pricing ref https://www.together.ai/pricing
- TogetherAIModelName.GOOGLE_GEMMA_2_27B_IT: ModelInfo(
- model_name=str(TogetherAIModelName.GOOGLE_GEMMA_2_27B_IT),
- cost_per_input_token=0.8 / 1_000_000,
- cost_per_output_token=0.8 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.GOOGLE_GEMMA_2_9B_IT: ModelInfo(
- model_name=str(TogetherAIModelName.GOOGLE_GEMMA_2_9B_IT),
- cost_per_input_token=0.3 / 1_000_000,
- cost_per_output_token=0.3 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.GOOGLE_GEMMA_2B_IT: ModelInfo(
- model_name=str(TogetherAIModelName.GOOGLE_GEMMA_2B_IT),
- cost_per_input_token=0.1 / 1_000_000,
- cost_per_output_token=0.1 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_2_3B_INSTRUCT_TURBO: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_2_3B_INSTRUCT_TURBO),
- cost_per_input_token=0.06 / 1_000_000,
- cost_per_output_token=0.06 / 1_000_000,
- max_input_tokens=131072,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_3_70B_INSTRUCT_TURBO: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_3_70B_INSTRUCT_TURBO),
- cost_per_input_token=0.88 / 1_000_000,
- cost_per_output_token=0.88 / 1_000_000,
- max_input_tokens=131072,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_70B_CHAT_HF: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_70B_CHAT_HF),
- cost_per_input_token=0.88 / 1_000_000,
- cost_per_output_token=0.88 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_8B_CHAT_HF: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_8B_CHAT_HF),
- cost_per_input_token=0.2 / 1_000_000,
- cost_per_output_token=0.2 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_1_405B_INSTRUCT_TURBO: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_1_405B_INSTRUCT_TURBO),
- cost_per_input_token=3.5 / 1_000_000,
- cost_per_output_token=3.5 / 1_000_000,
- max_input_tokens=130815,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_1_70B_INSTRUCT_TURBO: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_1_70B_INSTRUCT_TURBO),
- cost_per_input_token=0.88 / 1_000_000,
- cost_per_output_token=0.88 / 1_000_000,
- max_input_tokens=131072,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_1_8B_INSTRUCT_TURBO: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_1_8B_INSTRUCT_TURBO),
- cost_per_input_token=0.18 / 1_000_000,
- cost_per_output_token=0.18 / 1_000_000,
- max_input_tokens=131072,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_70B_INSTRUCT_LITE: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_70B_INSTRUCT_LITE),
- cost_per_input_token=0.54 / 1_000_000,
- cost_per_output_token=0.54 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_70B_INSTRUCT_TURBO: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_70B_INSTRUCT_TURBO),
- cost_per_input_token=0.88 / 1_000_000,
- cost_per_output_token=0.88 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_8B_INSTRUCT_LITE: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_8B_INSTRUCT_LITE),
- cost_per_input_token=0.1 / 1_000_000,
- cost_per_output_token=0.1 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.META_LLAMA_3_8B_INSTRUCT_TURBO: ModelInfo(
- model_name=str(TogetherAIModelName.META_LLAMA_3_8B_INSTRUCT_TURBO),
- cost_per_input_token=0.18 / 1_000_000,
- cost_per_output_token=0.18 / 1_000_000,
- max_input_tokens=8192,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_1: ModelInfo(
- model_name=str(TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_1),
- cost_per_input_token=0.2 / 1_000_000,
- cost_per_output_token=0.2 / 1_000_000,
- max_input_tokens=32768,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_2: ModelInfo(
- model_name=str(TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_2),
- cost_per_input_token=0.2 / 1_000_000,
- cost_per_output_token=0.2 / 1_000_000,
- max_input_tokens=32768,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_3: ModelInfo(
- model_name=str(TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_3),
- cost_per_input_token=0.2 / 1_000_000,
- cost_per_output_token=0.2 / 1_000_000,
- max_input_tokens=32768,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.MISTRALAI_MIXTRAL_8X22B_INSTRUCT_V0_1: ModelInfo(
- model_name=str(TogetherAIModelName.MISTRALAI_MIXTRAL_8X22B_INSTRUCT_V0_1),
- cost_per_input_token=1.2 / 1_000_000,
- cost_per_output_token=1.2 / 1_000_000,
- max_input_tokens=65536,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.MISTRALAI_MIXTRAL_8X7B_INSTRUCT_V0_1: ModelInfo(
- model_name=str(TogetherAIModelName.MISTRALAI_MIXTRAL_8X7B_INSTRUCT_V0_1),
- cost_per_input_token=0.6 / 1_000_000,
- cost_per_output_token=0.6 / 1_000_000,
- max_input_tokens=32768,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.NOUSRESEARCH_NOUS_HERMES_2_MIXTRAL_8X7B_DPO: ModelInfo(
- model_name=str(TogetherAIModelName.NOUSRESEARCH_NOUS_HERMES_2_MIXTRAL_8X7B_DPO),
- cost_per_input_token=0.6 / 1_000_000,
- cost_per_output_token=0.6 / 1_000_000,
- max_input_tokens=32768,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.DEEPSEEK_R1: ModelInfo(
- model_name=str(TogetherAIModelName.DEEPSEEK_R1),
- cost_per_input_token=3.0 / 1_000_000,
- cost_per_output_token=7.0 / 1_000_000,
- max_input_tokens=32768,
- max_output_tokens=None,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.OPENAI_GPT_OSS_20B: ModelInfo(
- model_name=str(TogetherAIModelName.OPENAI_GPT_OSS_20B),
- cost_per_input_token=0.00 / 1_000_000,
- cost_per_output_token=0.00 / 1_000_000,
- max_input_tokens=131_072,
- max_output_tokens=131_072,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- TogetherAIModelName.OPENAI_GPT_OSS_120B: ModelInfo(
- model_name=str(TogetherAIModelName.OPENAI_GPT_OSS_120B),
- cost_per_input_token=0.00 / 1_000_000,
- cost_per_output_token=0.00 / 1_000_000,
- max_input_tokens=131_072,
- max_output_tokens=131_072,
- rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
- ),
- }
-)
-
-
-def _default_capacity_semaphor() -> asyncio.Semaphore:
- return asyncio.Semaphore(100)
-
-
-_CAPACITY_SEMAPHOR_BY_MODEL_NAME: Mapping[str, asyncio.Semaphore] = defaultdict(_default_capacity_semaphor)
-
-
-_ROLE_TO_TOGETHERAI_ROLE: Final[FrozenMapping] = FrozenDict(
- {
- "HUMAN": "user",
- "ASSISTANT": "assistant",
- "SYSTEM": "system",
- "SYSTEM_CACHED": "system",
- "USER": "user",
- "USER_CACHED": "user",
- }
-)
-
-# ref: https://github.com/togethercomputer/together-python/blob/main/src/together/types/common.py#L13
-_TOGETHERAI_STOP_REASON_TO_STOP_REASON: Final[FrozenMapping[str, ResponseStopReason]] = FrozenDict(
- {
- "length": ResponseStopReason.MAX_TOKENS,
- # This is a little sketchy, we treat them the same since we don't know which models emit stop sequence reasons
- # Since we don't want to break downstream applications that may require ending in the stop sequence
- # This is similar to how openai models are treated
- "stop": ResponseStopReason.END_TURN,
- "eos": ResponseStopReason.END_TURN,
- "tool_calls": ResponseStopReason.TOOL_CALLS,
- "error": ResponseStopReason.ERROR,
- "None": ResponseStopReason.NONE,
- }
-)
-
-
-def convert_prompt_to_together_messages(prompt: str) -> list[dict[str, str]]:
- prompt = prompt.lstrip()
- assert prompt.endswith("\n[ROLE=ASSISTANT]\n"), "prompt must end with [ROLE=ASSISTANT], prompt=\n" + prompt
- prompt = "".join(prompt.rsplit("\n[ROLE=ASSISTANT]\n", 1))
- assert prompt.startswith("[ROLE=")
- prompt = prompt.replace("[ROLE=", "", 1)
- chunks = prompt.split("\n[ROLE=")
- messages: list[dict[str, str]] = []
- for chunk in chunks:
- lines = chunk.split("\n")
- role = lines[0].strip().rstrip("]")
- assert role in (
- "HUMAN",
- "ASSISTANT",
- "USER",
- "SYSTEM",
- "SYSTEM_CACHED",
- "USER_CACHED",
- ), f"Unknown role {role} in prompt {prompt}"
- lines.pop(0)
- if len(messages) > 0:
- messages[-1]["content"] = messages[-1]["content"] + "\n"
- messages.append({"role": _ROLE_TO_TOGETHERAI_ROLE[role], "content": "\n".join(lines)})
- return messages
-
-
-@contextmanager
-def _together_exception_manager() -> Iterator[None]:
- """Simple context manager for parsing TogetherAI API exceptions."""
- # ref https://github.com/togethercomputer/together-python/blob/main/src/together/abstract/api_requestor.py#L332
- try:
- yield
- except RateLimitError as e:
- logger.info("Rate limited? {}", e)
- raise TransientLanguageModelError("RateLimitError") from e
- except InvalidRequestError as e:
- logger.info("BadAPIRequestError {}", e)
- raise BadAPIRequestError(str(e)) from e
- except AuthenticationError as e:
- logger.info("API Authentication error {}", e)
- raise
- except APIError as e:
- logger.info("Received APIError {}", e)
- raise
- except ServiceUnavailableError as e:
- logger.info("Received ServiceUnavailableError {}", e)
- raise TransientLanguageModelError("ServiceUnavailableError") from e
- except APIConnectionError as e:
- logger.info("Received APIConnectionError {}", e)
- raise TransientLanguageModelError("APIConnectionError") from e
- # Note, the together python SDK uses aiohttp under the hood
- # but takes care of parsing the main aiohttp exceptions into together API exceptions
- # ref https://github.com/togethercomputer/together-python/blob/main/src/together/abstract/api_requestor.py#L554
-
-
-class TogetherAPI(LanguageModelAPI):
- model_name: TogetherAIModelName = TogetherAIModelName.META_LLAMA_3_1_8B_INSTRUCT_TURBO
- is_conversational: bool = True
- presence_penalty: float = 0.0
- # this shouldn't really ever even be used, but just in case
- stop_token_log_probability: float = math.log(0.9999)
-
- @field_validator("model_name") # pyre-ignore[56]: pyre doesn't understand pydantic
- @classmethod
- def validate_model_name(cls, v: str) -> str:
- if v not in TOGETHERAI_MODEL_INFO_BY_NAME:
- raise LanguageModelInvalidModelNameError(v, cls.__name__, list(TOGETHERAI_MODEL_INFO_BY_NAME))
- return v
-
- @property
- def model_info(self) -> ModelInfo:
- return TOGETHERAI_MODEL_INFO_BY_NAME[self.model_name]
-
- @property
- def external_model_name(self) -> str:
- return self.model_name.replace("together/", "")
-
- def _get_client(self) -> AsyncTogether:
- api_key = get_secret("TOGETHER_API_KEY")
- if not api_key:
- raise MissingAPIKeyError("TOGETHER_API_KEY environment variable is not set")
- return AsyncTogether(api_key=api_key)
-
- async def _call_api(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- network_failure_count: int = 0,
- ) -> CostedLanguageModelResponse:
- if params.max_tokens is None:
- logger.debug(
- "Togetherai API breaks if `max_tokens` not specified. Defaulting to `max_tokens=512`, make sure to specify this if you want something different."
- )
- params.max_tokens = 512
-
- with _together_exception_manager():
- messages = convert_prompt_to_together_messages(prompt)
- client = self._get_client()
- async with _CAPACITY_SEMAPHOR_BY_MODEL_NAME[self.model_name]:
- # ref: https://github.com/togethercomputer/together-python/blob/main/src/together/resources/chat/completions.py#L153
- api_result = await client.chat.completions.create(
- model=self.external_model_name,
- messages=messages,
- max_tokens=params.max_tokens,
- stop=[params.stop] if params.stop else None,
- temperature=params.temperature,
- top_k=5,
- presence_penalty=self.presence_penalty,
- stream=False,
- logprobs=True,
- n=params.count,
- # currently don't specify this since tokenizer may change between models
- logit_bias=None,
- )
- assert isinstance(api_result, ChatCompletionResponse)
-
- results = []
- choices = api_result.choices
- assert choices is not None
- for data in choices:
- message = data.message
- assert message is not None
- text = message.content
- assert text is not None
- assert isinstance(text, str) # TODO: this is suspicious
-
- # TogetherAI only provides the logprob for the selected token
- logprobs = data.logprobs
- assert logprobs is not None
- tokens = logprobs.tokens
- token_logprobs = logprobs.token_logprobs
- assert tokens is not None and token_logprobs is not None
- assert all(token is not None for token in tokens)
- assert all(logprob is not None for logprob in token_logprobs)
- tokens = cast(Iterable[str], tokens)
- token_logprobs = cast(Iterable[float], token_logprobs)
- token_probabilities = [
- (TokenProbability(token=token, log_probability=logprob, is_stop=False),)
- for token, logprob in zip(tokens, token_logprobs)
- ]
-
- if data.finish_reason:
- stop_reason = _TOGETHERAI_STOP_REASON_TO_STOP_REASON[data.finish_reason.value]
- else:
- stop_reason = ResponseStopReason.NONE
- stop = params.stop
- if stop is not None and stop_reason == ResponseStopReason.END_TURN:
- text += stop
- token_probabilities.append(
- (
- TokenProbability(
- token=stop,
- log_probability=self.stop_token_log_probability,
- is_stop=True,
- ),
- )
- )
- result = LanguageModelResponseWithLogits(
- text=text,
- token_probabilities=tuple(token_probabilities),
- token_count=len(token_probabilities),
- stop_reason=stop_reason,
- network_failure_count=network_failure_count,
- )
- results.append(result)
-
- logger.trace("text: " + results[0].text)
- if api_result.usage is not None:
- completion_tokens = api_result.usage.completion_tokens
- prompt_tokens = api_result.usage.prompt_tokens
- else:
- completion_tokens = 0
- prompt_tokens = 0
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
- logger.trace("dollars used: {}", dollars_used)
- return CostedLanguageModelResponse(
- usage=LanguageModelResponseUsage(
- prompt_tokens_used=prompt_tokens,
- completion_tokens_used=completion_tokens,
- dollars_used=dollars_used,
- ),
- responses=tuple(results),
- )
-
- async def _get_api_stream(
- self,
- prompt: str,
- params: LanguageModelGenerationParams,
- ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
- if params.max_tokens is None:
- logger.debug(
- "Togetherai API breaks if `max_tokens` not specified. Defaulting to `max_tokens=512`, make sure to specify this if you want something different."
- )
- params.max_tokens = 512
-
- with _together_exception_manager():
- messages = convert_prompt_to_together_messages(prompt)
- client = self._get_client()
- async with _CAPACITY_SEMAPHOR_BY_MODEL_NAME[self.model_name]:
- # ref: https://github.com/togethercomputer/together-python/blob/main/src/together/resources/chat/completions.py#L153
- api_result = await client.chat.completions.create(
- model=self.external_model_name,
- messages=messages,
- max_tokens=params.max_tokens,
- stop=[params.stop] if params.stop else None,
- temperature=params.temperature,
- top_k=5,
- presence_penalty=self.presence_penalty,
- stream=True,
- # currently we don't support logprobs with streaming
- logprobs=False,
- n=1,
- # currently don't specify this since tokenizer may change between models
- logit_bias=None,
- )
- assert isinstance(api_result, AsyncGenerator)
-
- yield LanguageModelStreamStartEvent()
-
- usage = None
- finish_reason: str | None = None
- async for chunk in api_result:
- if chunk.usage:
- usage = chunk.usage
-
- if chunk.finish_reason:
- finish_reason = chunk.finish_reason.value
-
- chunk_choices = chunk.choices
- if chunk_choices:
- assert len(chunk_choices) == 1, "Currently only count=1 supported for streaming API."
- delta = only(chunk_choices).delta
- if delta and delta.content:
- yield LanguageModelStreamDeltaEvent(delta=delta.content)
-
- stop_reason = _TOGETHERAI_STOP_REASON_TO_STOP_REASON[str(finish_reason)]
-
- if params.stop is not None and stop_reason == ResponseStopReason.END_TURN:
- yield LanguageModelStreamDeltaEvent(delta=params.stop)
-
- if usage is not None:
- completion_tokens = usage.completion_tokens
- prompt_tokens = usage.prompt_tokens
- else:
- completion_tokens = 0
- prompt_tokens = 0
- dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
- logger.trace("dollars used: {}", dollars_used)
-
- yield LanguageModelStreamEndEvent(
- usage=LanguageModelResponseUsage(
- prompt_tokens_used=prompt_tokens,
- completion_tokens_used=completion_tokens,
- dollars_used=dollars_used,
- ),
- stop_reason=stop_reason,
- )
diff --git a/imbue_core/imbue_core/agents/llm_apis/union_data_types.py b/imbue_core/imbue_core/agents/llm_apis/union_data_types.py
@@ -1,20 +0,0 @@
-from typing import Annotated
-
-from pydantic import Tag
-
-from imbue_core.agents.llm_apis.anthropic_data_types import AnthropicCachingInfo
-from imbue_core.agents.llm_apis.anthropic_data_types import AnthropicModelInfo
-from imbue_core.agents.llm_apis.openai_data_types import OpenAICachingInfo
-from imbue_core.agents.llm_apis.openai_data_types import OpenAIModelInfo
-from imbue_core.pydantic_serialization import build_discriminator
-
-ProviderSpecificModelInfoUnion = Annotated[
- Annotated[AnthropicModelInfo, Tag("AnthropicModelInfo")] | Annotated[OpenAIModelInfo, Tag("OpenAIModelInfo")],
- build_discriminator(),
-]
-
-ProviderSpecificCachingInfoUnion = Annotated[
- Annotated[AnthropicCachingInfo, Tag("AnthropicCachingInfo")]
- | Annotated[OpenAICachingInfo, Tag("OpenAICachingInfo")],
- build_discriminator(),
-]
diff --git a/imbue_core/imbue_core/agents/primitives/resource_limits.py b/imbue_core/imbue_core/agents/primitives/resource_limits.py
@@ -1,485 +0,0 @@
-import asyncio
-import datetime
-import os
-from asyncio import CancelledError
-from asyncio import Task
-from asyncio import TaskGroup
-from typing import Any
-from typing import Callable
-from typing import Coroutine
-from typing import Optional
-from uuid import uuid4
-
-import attr
-from loguru import logger
-
-from imbue_core.agents.primitives.errors import DollarLimitExceeded
-from imbue_core.agents.primitives.errors import MaximumSpendExceeded
-from imbue_core.async_monkey_patches import safe_cancel
-from imbue_core.itertools import first
-from imbue_core.serialization_types import Serializable
-from imbue_core.time_utils import get_current_time
-
-# TODO: someday in the future we can be smarter about this...
-_AUTH_PAYMENT_TIMEOUT_SECONDS = 60 * 60 * 24
-_ONE_HOUR_IN_SECONDS = 60 * 60
-
-
-class InvalidResourceLimitsError(Exception):
- pass
-
-
-class AuthorizationInvalidated(Exception):
- pass
-
-
-@attr.s(auto_attribs=True, frozen=True)
-class PaymentAuthorization:
- dollars: float
- authorization_id: str
- authorized_at: datetime.datetime
-
-
-@attr.s(auto_attribs=True, frozen=True)
-class ResourceLimitState(Serializable):
- hard_cap_dollars: float
- hard_cap_seconds: float
- warn_cap_dollars: float
- warn_cap_seconds: float
- dollars_per_hour: float | None
-
- @classmethod
- def build_for_increase(
- cls,
- hard_cap_dollars: float = 0.001,
- hard_cap_seconds: float = 0.001,
- warn_cap_dollars: float = 0.001,
- warn_cap_seconds: float = 0.001,
- dollars_per_hour: float | None = None,
- ) -> "ResourceLimitState":
- return cls(
- hard_cap_dollars=hard_cap_dollars,
- hard_cap_seconds=hard_cap_seconds,
- warn_cap_dollars=warn_cap_dollars,
- warn_cap_seconds=warn_cap_seconds,
- dollars_per_hour=dollars_per_hour,
- )
-
-
-def _float_or_none(value: str | None) -> float | None:
- if value is None:
- return None
- return float(value)
-
-
-@attr.s(auto_attribs=True)
-class ResourceLimits:
- hard_cap_dollars: float
- hard_cap_seconds: float
- warn_cap_dollars: float
- warn_cap_seconds: float
- # note that setting this effectively caps the size of any given authorization request to this quantity as well
- # (since it is impossible to spend less than $X per hour if you are spending $X+1 in total)
- # in such a case, MaximumSpendExceeded will be raised
- dollars_per_hour: float | None = None
- parent_limits: Optional["ResourceLimits"] = None
- dollars_spent: float = 0.0
- save_spend_callback: Callable[[float], Coroutine[Any, Any, None]] | None = None
- open_authorizations: dict[str, PaymentAuthorization] = attr.ib(factory=dict)
- recent_spend_events: list[PaymentAuthorization] = attr.ib(factory=list)
- # ensure that only a single spend is being authorized at once
- spend_lock: asyncio.Lock = attr.ib(factory=asyncio.Lock)
- # prevent us from mutating our state from multiple coroutines at once
- state_lock: asyncio.Lock = attr.ib(factory=asyncio.Lock)
- # triggered when the limits are updated
- limits_updated_event: asyncio.Event = attr.ib(factory=asyncio.Event)
- # triggered when a settlement happens
- next_settlement_event: asyncio.Event = attr.ib(factory=asyncio.Event)
-
- @classmethod
- def build(
- cls,
- *,
- max_dollars: float | None = None,
- max_seconds: float | None = None,
- warn_fraction: float | None = None,
- dollars_per_hour: float | None = None,
- ) -> "ResourceLimits":
- if max_dollars is None and "DEFAULT_MAX_HAMMER_DOLLARS" in os.environ:
- max_dollars = _float_or_none(os.getenv("DEFAULT_MAX_HAMMER_DOLLARS"))
- assert max_dollars != float("inf"), "max_dollars must be finite"
- if max_dollars is None:
- max_dollars = 0.0
- assert max_dollars >= 0, "max_dollars must be non-negative"
- if max_seconds is None and "DEFAULT_MAX_HAMMER_SECONDS" in os.environ:
- max_seconds = _float_or_none(os.getenv("DEFAULT_MAX_HAMMER_SECONDS"))
- if max_seconds is None:
- max_seconds = float("inf")
- assert max_seconds >= 0, "max_seconds must be non-negative"
- if warn_fraction is None:
- # TODO: is it DEFAULT_WARN_FRACTION or DEFAULT_HAMMER_WARN_FRACTION?
- if "DEFAULT_WARN_FRACTION" in os.environ:
- warn_fraction = _float_or_none(os.getenv("DEFAULT_HAMMER_WARN_FRACTION"))
- assert warn_fraction is not None
- else:
- warn_fraction = 0.25
- if dollars_per_hour is None and "DEFAULT_DOLLARS_PER_HOUR" in os.environ:
- dollars_per_hour = _float_or_none(os.getenv("DEFAULT_DOLLARS_PER_HOUR"))
- result = cls(
- hard_cap_dollars=max_dollars,
- hard_cap_seconds=max_seconds,
- warn_cap_dollars=max_dollars * warn_fraction,
- warn_cap_seconds=max_seconds * warn_fraction,
- dollars_per_hour=dollars_per_hour,
- )
- # check that we're not currently in a hammer, otherwise should be calling create_restricted_limits instead
- try:
- if get_global_resource_limits() is not None:
- # If you encounter this, it probably means you're trying to mix hammer and non-hammer resource limiting.
- # For simplicity and correctness, try to avoid that.
- raise InvalidResourceLimitsError(
- "Should not create a new ResourceLimits while global limits are in place. Instead, call create_restricted_limits"
- )
- except RuntimeError:
- pass
- return result
-
- def create_restricted_limits(
- self,
- *,
- max_dollars: float | None = None,
- max_seconds: float | None = None,
- warn_fraction: float | None = None,
- hard_cap_dollars: float | None = None,
- hard_cap_seconds: float | None = None,
- warn_cap_dollars: float | None = None,
- warn_cap_seconds: float | None = None,
- dollars_per_hour: float | None = None,
- ) -> "ResourceLimits":
- if max_dollars is not None:
- assert hard_cap_dollars is None, "Cannot specify both max_dollars and hard_cap_dollars"
- hard_cap_dollars = max_dollars
- if warn_fraction is not None:
- assert warn_cap_dollars is None, "Cannot specify both warn_fraction and warn_cap_dollars"
- warn_cap_dollars = max_dollars * warn_fraction
-
- if max_seconds is not None:
- assert hard_cap_seconds is None, "Cannot specify both max_seconds and hard_cap_seconds"
- hard_cap_seconds = max_seconds
- if warn_fraction is not None:
- assert warn_cap_seconds is None, "Cannot specify both warn_fraction and warn_cap_seconds"
- warn_cap_seconds = max_seconds * warn_fraction
-
- if hard_cap_dollars is not None:
- hard_cap_dollars = min(hard_cap_dollars, self.hard_cap_dollars)
- if hard_cap_seconds is not None:
- hard_cap_seconds = min(hard_cap_seconds, self.hard_cap_seconds)
- if warn_cap_dollars is not None:
- warn_cap_dollars = min(warn_cap_dollars, self.warn_cap_dollars)
- if warn_cap_seconds is not None:
- warn_cap_seconds = min(warn_cap_seconds, self.warn_cap_seconds)
- if dollars_per_hour is not None and self.dollars_per_hour is not None:
- dollars_per_hour = min(dollars_per_hour, self.dollars_per_hour)
- return ResourceLimits(
- hard_cap_dollars=hard_cap_dollars or self.hard_cap_dollars,
- hard_cap_seconds=hard_cap_seconds or self.hard_cap_seconds,
- warn_cap_dollars=warn_cap_dollars or self.warn_cap_dollars,
- warn_cap_seconds=warn_cap_seconds or self.warn_cap_seconds,
- dollars_per_hour=dollars_per_hour or self.dollars_per_hour,
- parent_limits=self,
- )
-
- def _get_excessive_spend_message(self, dollars: float, debug_info: Any = None) -> str:
- msg = f"Tried to spend {dollars} but only {self.hard_cap_dollars - self.dollars_spent} left (of {self.hard_cap_dollars})."
- if self.hard_cap_dollars == 0:
- msg += " You might want to set DEFAULT_MAX_HAMMER_DOLLARS to something non-zero"
- if debug_info is not None:
- msg += f"\nDebug info: {debug_info}"
- return msg
-
- async def authorize_spend(self, dollars: float, debug_info: Any | None = None) -> PaymentAuthorization:
- # note that we purposefully lock here, even though we are potentially waiting inside the loop below.
- # the reason for this is that otherwise a large transaction could be starved by a series of smaller transactions
- # this is annoying to reason about, so this makes it FIFO instead (though potentially at the cost of having to
- # wait for a while if you are near the limit and there are smaller transactions that could have made it through)
- async with self.spend_lock:
- await self._clear_old_authorizations()
-
- if self.dollars_spent + dollars > self.hard_cap_dollars:
- raise DollarLimitExceeded(self._get_excessive_spend_message(dollars, debug_info))
-
- # if we have outstanding authorizations that mean that this transaction would put us over the limit, wait
- while (await self.get_dollars_authorized_and_spent()) + dollars > self.hard_cap_dollars and (
- await self.get_dollars_currently_authorized()
- ) > 0:
- await self.next_settlement_event.wait()
-
- # now that some authorizations have settled and we're done waiting, have to check again if this would put us over
- if self.dollars_spent + dollars > self.hard_cap_dollars:
- raise DollarLimitExceeded(self._get_excessive_spend_message(dollars))
-
- dollars_per_hour = self.dollars_per_hour
- if dollars_per_hour is not None:
- if dollars > dollars_per_hour:
- raise MaximumSpendExceeded(
- f"Tried to spend ${dollars} but only ${dollars_per_hour} / hr allowed, which caps the total spend"
- )
-
- # wait until the spend rate is low enough
- while (await self.get_dollars_authorized_and_spent_in_the_last_hour()) + dollars > dollars_per_hour:
- oldest_event = first(
- sorted(
- [x for x in self.recent_spend_events],
- key=lambda x: x.authorized_at,
- )
- )
- if oldest_event is None:
- break
- time_since_oldest_event = (get_current_time() - oldest_event.authorized_at).total_seconds()
- time_until_next_event_expires = _ONE_HOUR_IN_SECONDS - time_since_oldest_event
- logger.debug(
- f"Waiting until spend rate has subsided (currently at {(await self.get_dollars_authorized_and_spent_in_the_last_hour())} / hr)"
- )
- waiting_task = asyncio.create_task(self._wait_until_updated())
- try:
- await asyncio.wait_for(waiting_task, timeout=time_until_next_event_expires + 0.01)
- except TimeoutError:
- pass
- await self._clear_old_authorizations()
-
- assert (await self.get_dollars_authorized_and_spent_in_the_last_hour()) + dollars <= dollars_per_hour
-
- async with self.state_lock:
- if self.parent_limits is None:
- auth = PaymentAuthorization(
- dollars=dollars,
- authorization_id=uuid4().hex,
- authorized_at=get_current_time(),
- )
- else:
- auth = await self.parent_limits.authorize_spend(dollars)
- self.open_authorizations[auth.authorization_id] = auth
- return auth
-
- async def settle_spend(self, authorization: PaymentAuthorization, dollars: float) -> None:
- async with self.state_lock:
- await self._clear_old_authorizations(_is_already_locked=True)
-
- if self.parent_limits is not None:
- await self.parent_limits.settle_spend(authorization, dollars)
-
- is_threshold_exceeded_by_this_transaction = (
- self.dollars_spent < self.warn_cap_dollars <= self.dollars_spent + dollars
- )
-
- if authorization.authorization_id not in self.open_authorizations:
- raise AuthorizationInvalidated(
- f"Authorization {authorization.authorization_id} has timed out or already been settled"
- )
- del self.open_authorizations[authorization.authorization_id]
- self.recent_spend_events.append(authorization)
- self.dollars_spent += dollars
- assert self.save_spend_callback is not None, "Should have been initialized by now"
- await self.save_spend_callback(self.dollars_spent)
-
- # notify anything waiting on the next settlement
- self.next_settlement_event.set()
- self.next_settlement_event.clear()
-
- logger.trace(
- "Settled spend of {}, remaining: {}",
- dollars,
- self.hard_cap_dollars - self.dollars_spent,
- )
-
- if is_threshold_exceeded_by_this_transaction:
- await self._warn(f"Spent ${self.dollars_spent} already (will be stopped at ${self.hard_cap_dollars})")
-
- # TODO: make a more configurable warning system, right now just logs
- async def _warn(self, message: str) -> None:
- logger.warning(message)
-
- async def _clear_old_authorizations(self, _is_already_locked: bool = False) -> None:
- if not _is_already_locked:
- await self.state_lock.acquire()
- try:
- now = get_current_time()
- self.open_authorizations = {
- k: v
- for k, v in self.open_authorizations.items()
- if (now - v.authorized_at).total_seconds() < _AUTH_PAYMENT_TIMEOUT_SECONDS
- }
- self.recent_spend_events = [
- x for x in self.recent_spend_events if (now - x.authorized_at).total_seconds() < _ONE_HOUR_IN_SECONDS
- ]
- finally:
- if not _is_already_locked:
- self.state_lock.release()
-
- async def get_dollars_currently_authorized(self) -> float:
- async with self.state_lock:
- return sum(x.dollars for x in self.open_authorizations.values())
-
- async def get_dollars_authorized_and_spent(self) -> float:
- async with self.state_lock:
- dollars_currently_authorized = float(sum(x.dollars for x in self.open_authorizations.values()))
- return dollars_currently_authorized + self.dollars_spent
-
- async def get_dollars_authorized_and_spent_in_the_last_hour(self) -> float:
- async with self.state_lock:
- dollars_currently_authorized = float(sum(x.dollars for x in self.open_authorizations.values()))
- return dollars_currently_authorized + sum(x.dollars for x in self.recent_spend_events)
-
- async def bump_limits(self, limits: ResourceLimitState) -> ResourceLimitState:
- """
- Can only raise limits. This makes it easier for hammers to reason about how much they will be able to spend.
-
- If we were to allow reducing limits, we'd need to be quite careful to update existing timers, and to check
- conditions at the end of the wait loop in authorize_spend as well.
- """
- if self.parent_limits is None:
- assert limits.hard_cap_dollars < float("inf"), "Cannot unlimit spend for the top-level hammer"
-
- async with self.state_lock:
- self.hard_cap_dollars = max(self.hard_cap_dollars, limits.hard_cap_dollars)
- self.hard_cap_seconds = max(self.hard_cap_seconds, limits.hard_cap_seconds)
- self.warn_cap_dollars = max(self.warn_cap_dollars, limits.warn_cap_dollars)
- self.warn_cap_seconds = max(self.warn_cap_seconds, limits.warn_cap_seconds)
- if self.dollars_per_hour is None:
- self.dollars_per_hour = limits.dollars_per_hour
- else:
- if limits.dollars_per_hour is not None and limits.dollars_per_hour > self.dollars_per_hour:
- self.dollars_per_hour = limits.dollars_per_hour
-
- # notify anything waiting in case we just bumped what they were waiting on
- if self.dollars_per_hour and limits.dollars_per_hour is not None:
- self.limits_updated_event.set()
- self.limits_updated_event.clear()
-
- return ResourceLimitState(
- hard_cap_dollars=self.hard_cap_dollars,
- hard_cap_seconds=self.hard_cap_seconds,
- warn_cap_dollars=self.warn_cap_dollars,
- warn_cap_seconds=self.warn_cap_seconds,
- dollars_per_hour=self.dollars_per_hour,
- )
-
- def resume(self, dollars: float, limits: ResourceLimitState) -> None:
- self.hard_cap_dollars = limits.hard_cap_dollars
- self.hard_cap_seconds = limits.hard_cap_seconds
- self.warn_cap_dollars = limits.warn_cap_dollars
- self.warn_cap_seconds = limits.warn_cap_seconds
- self.dollars_per_hour = limits.dollars_per_hour
- self.dollars_spent = dollars
-
- if self.dollars_spent > self.hard_cap_dollars:
- raise DollarLimitExceeded(
- f"Have already spent {self.dollars_spent} dollars (more than the hard cap of {self.hard_cap_dollars})"
- )
-
- async def _wait_until_updated(self) -> None:
- await self.limits_updated_event.wait()
-
-
-@attr.s(auto_attribs=True)
-class HammerTimer:
- limits: ResourceLimits
- timer_started_at: datetime.datetime
- timer_task: Task | None = None
- is_timeout_warning_issued: bool = False
-
- # TODO: need to save whether or not we warned about the time so that we dont warn again when resuming
- async def on_hammer_started(self, task_group: TaskGroup, callback: Callable[[], Coroutine[Any, Any, None]]) -> None:
- seconds_ago = (get_current_time() - self.timer_started_at).total_seconds()
- possible_wait_times = [x - seconds_ago for x in [self.limits.hard_cap_seconds, self.limits.warn_cap_seconds]]
- positive_wait_times = [x for x in possible_wait_times if x > 0]
- if len(positive_wait_times) == 0:
- await callback()
- return
- time_until_limit_check = min(positive_wait_times)
- self.timer_task = task_group.create_task(self._timeout_after(time_until_limit_check, callback))
-
- async def on_hammer_stopped(self) -> None:
- # cancel the timer (if still running
- timer_task = self.timer_task
- if timer_task:
- safe_cancel(timer_task)
- try:
- await timer_task
- except CancelledError:
- pass
- self.timer_task = None
-
- async def _timeout_after(self, seconds: float, callback: Callable[[], Coroutine[Any, Any, None]]) -> None:
- while True:
- await asyncio.sleep(seconds)
-
- # re-schedule ourselves for the next check if the limits have been updated, or we were just here for a warning
- time_since_started = (get_current_time() - self.timer_started_at).total_seconds()
- if time_since_started < self.limits.hard_cap_seconds:
- # emit a warning if necessary
- if time_since_started > self.limits.warn_cap_seconds and not self.is_timeout_warning_issued:
- self.is_timeout_warning_issued = True
- await self.limits._warn(
- f"Taking longer than expected ({seconds} sec so far, will be killed at {self.limits.hard_cap_seconds}"
- )
- # figure out how long to sleep for
- seconds = min(
- [
- self.limits.hard_cap_seconds - time_since_started,
- self.limits.warn_cap_seconds - time_since_started,
- ]
- )
- continue
-
- # if we're still here, we've timed out
- await callback()
- return
-
-
-# Even if you don't use hammers, you can still benefit from having global resource limits.
-# (When in hammer-less context, this global variable is checked by LanguageModelAPI. Only the financial limits are enforced.)
-
-_GLOBAL_RESOURCE_LIMITS: ResourceLimits | None = None
-
-
-def ensure_global_resource_limits(
- *,
- max_dollars: float | None = None,
- max_seconds: float | None = None,
- warn_fraction: float | None = None,
- dollars_per_hour: float | None = None,
- reset_if_already_set: bool = False,
-) -> None:
- global _GLOBAL_RESOURCE_LIMITS
- if _GLOBAL_RESOURCE_LIMITS is None or reset_if_already_set:
- _GLOBAL_RESOURCE_LIMITS = ResourceLimits.build(
- max_dollars=max_dollars,
- max_seconds=max_seconds,
- warn_fraction=warn_fraction,
- dollars_per_hour=dollars_per_hour,
- )
- _GLOBAL_RESOURCE_LIMITS.save_spend_callback = _dummy_save_spend_callback
-
-
-def get_global_resource_limits() -> ResourceLimits | None:
- return _GLOBAL_RESOURCE_LIMITS
-
-
-async def _dummy_save_spend_callback(dollars: float) -> None:
- pass
-
-
-async def get_global_resource_limits_summary() -> str:
- if _GLOBAL_RESOURCE_LIMITS is None:
- return "No global resource limits"
- output = [
- "Global resource limits summary:",
- ]
- max_dollars = _GLOBAL_RESOURCE_LIMITS.hard_cap_dollars
- amount_spent = await _GLOBAL_RESOURCE_LIMITS.get_dollars_authorized_and_spent()
- amount_remaining = max_dollars - amount_spent
- output.append(f"- Max dollars: ${max_dollars:.4f}")
- output.append(f"- Amount spent: ${amount_spent:.4f}")
- output.append(f"- Amount remaining: ${amount_remaining:.4f}")
- return "\n".join(output)
diff --git a/imbue_core/imbue_core/async_monkey_patches.py b/imbue_core/imbue_core/async_monkey_patches.py
@@ -1,343 +0,0 @@
-import asyncio
-import sys
-import traceback
-from asyncio import Future
-from types import TracebackType
-from typing import Any
-from typing import Sequence
-
-from loguru import logger
-
-from imbue_core.errors import ExpectedError
-
-_IS_SHUTTING_DOWN = False
-
-
-def notify_task_groups_of_shutdown() -> None:
- global _IS_SHUTTING_DOWN
- _IS_SHUTTING_DOWN = True
-
-
-class PropagatingTaskGroup(asyncio.TaskGroup):
- """Improves over TaskGroup by ensuring that cancelation messages are actually propagated"""
-
- def __init__(self) -> None:
- # deferring the import in case this doesn't get used
- pass
-
- python_version: sys._version_info = sys.version_info
- if python_version[:2] != (3, 11) and python_version[:2] != (3, 12):
- raise RuntimeError(
- f"Python version 3.11 or 3.12 is required. You are using {python_version.major}.{python_version.minor}"
- )
- super().__init__()
- self._entered = False
- self._exiting = False
- self._aborting = False
- self._loop = None
- self._parent_task: asyncio.Task | None = None
- self._parent_cancel_requested = False
- self._tasks: set[asyncio.Task] = set()
- self._errors: list[BaseException] = []
- self._base_error: BaseException | None = None
- self._on_completed_fut: Future[Any] | None = None
- self._original_message: str | None = None
-
- async def __aexit__(
- self,
- et: type[BaseException] | None,
- exc: BaseException | None,
- tb: TracebackType | None,
- ) -> None:
- try:
- return await self._aexit(et, exc)
- finally:
- # Exceptions are heavy objects that can have object
- # cycles (bad for GC); let's not keep a reference to
- # a bunch of them. It would be nicer to use a try/finally
- # in __aexit__ directly but that introduced some diff noise
- self._parent_task = None
- self._errors = [] # Clear the list instead of setting to None
- self._base_error = None
- exc = None
-
- async def _aexit(self, et: type[BaseException] | None, exc: BaseException | None) -> None:
- self._exiting = True
-
- if exc is not None and self._is_base_error(exc) and self._base_error is None: # type: ignore
- self._base_error = exc
-
- propagate_cancellation_error = exc if et is asyncio.exceptions.CancelledError else None
- if self._parent_cancel_requested:
- # If this flag is set we *must* call uncancel().
- assert self._parent_task
- if self._parent_task.uncancel() == 0:
- # If there are no pending cancellations left,
- # don't propagate CancelledError.
- propagate_cancellation_error = None
-
- if et is not None:
- if not self._aborting:
- # Our parent task is being cancelled:
- #
- # async with TaskGroup() as g:
- # g.create_task(...)
- # await ... # <- CancelledError
- #
- # or there's an exception in "async with":
- #
- # async with TaskGroup() as g:
- # g.create_task(...)
- # 1 / 0
- assert exc is not None
- self._abort_and_propagate(exc)
-
- # We use while-loop here because "self._on_completed_fut"
- # can be cancelled multiple times if our parent task
- # is being cancelled repeatedly (or even once, when
- # our own cancellation is already in progress)
- while self._tasks:
- if self._on_completed_fut is None:
- assert self._loop
- self._on_completed_fut = self._loop.create_future()
-
- try:
- await self._on_completed_fut
- except asyncio.exceptions.CancelledError as ex:
- if not self._aborting:
- # Our parent task is being cancelled:
- #
- # async def wrapper():
- # async with TaskGroup() as g:
- # g.create_task(foo)
- #
- # "wrapper" is being cancelled while "foo" is
- # still running.
- propagate_cancellation_error = ex
- self._abort_and_propagate(ex)
-
- self._on_completed_fut = None
-
- assert not self._tasks
-
- if self._base_error is not None:
- try:
- raise self._base_error
- finally:
- exc = None
-
- # Propagate CancelledError if there is one, except if there
- # are other errors -- those have priority.
- try:
- if propagate_cancellation_error and not self._errors:
- try:
- raise propagate_cancellation_error
- finally:
- exc = None
- finally:
- propagate_cancellation_error = None
-
- if et is not None and et is not asyncio.exceptions.CancelledError:
- assert exc is not None
- self._errors.append(exc)
-
- if self._errors:
- try:
- raise BaseExceptionGroup(
- "unhandled errors in a TaskGroup: see earlier in logs for causal error!",
- self._errors,
- ) from None
- finally:
- exc = None
-
- def _abort(self) -> None:
- raise Exception("Please call _abort_and_propagate instead")
-
- def _abort_and_propagate(self, exc: BaseException) -> None:
- global _IS_SHUTTING_DOWN
- self._aborting = True
-
- if self._original_message is None:
- if isinstance(exc, asyncio.exceptions.CancelledError) and len(exc.args) > 0:
- self._original_message = "TaskGroup canceled because:\n" + exc.args[0]
- else:
- if not isinstance(exc, asyncio.exceptions.CancelledError):
- if not _IS_SHUTTING_DOWN and not isinstance(exc, ExpectedError):
- log_exception(
- exc,
- "Emergency print of error that caused task group to die:",
- )
- self._original_message = f"TaskGroup died because: {type(exc).__name__}: {exc}\n" + "".join(
- traceback.extract_tb(exc.__traceback__).format()
- )
-
- for t in self._tasks:
- if not t.done():
- t.cancel(self._original_message)
-
- def _on_task_done(self, task: asyncio.Task) -> None:
- self._tasks.discard(task)
-
- on_completed_fut = self._on_completed_fut
- if on_completed_fut is not None and not self._tasks:
- if not on_completed_fut.done():
- on_completed_fut.set_result(True)
-
- if task.cancelled():
- return
-
- exc = task.exception()
- if exc is None:
- return
-
- self._errors.append(exc)
- if self._is_base_error(exc) and self._base_error is None: # type: ignore
- self._base_error = exc
-
- parent_task = self._parent_task
- assert parent_task
- if parent_task.done():
- # Not sure if this case is possible, but we want to handle
- # it anyways.
- assert self._loop
- self._loop.call_exception_handler(
- {
- "message": f"Task {task!r} has errored out but its parent task {parent_task} is already completed",
- "exception": exc,
- "task": task,
- }
- )
- return
-
- if not self._aborting and not self._parent_cancel_requested:
- # If parent task *is not* being cancelled, it means that we want
- # to manually cancel it to abort whatever is being run right now
- # in the TaskGroup. But we want to mark parent task as
- # "not cancelled" later in __aexit__. Example situation that
- # we need to handle:
- #
- # async def foo():
- # try:
- # async with TaskGroup() as g:
- # g.create_task(crash_soon())
- # await something # <- this needs to be canceled
- # # by the TaskGroup, e.g.
- # # foo() needs to be cancelled
- # except Exception:
- # # Ignore any exceptions raised in the TaskGroup
- # pass
- # await something_else # this line has to be called
- # # after TaskGroup is finished.
- self._abort_and_propagate(exc)
- self._parent_cancel_requested = True
- parent_task.cancel(self._original_message)
-
-
-def safe_cancel(task: asyncio.Task, msg: str | None = None) -> None:
- """
- NOTE: this is probably not what you want! See safe_cancel_and_wait_for_cleanup below for the more common use case.
-
- Cancels a task in a way that preserves information about who canceled it.
-
- Without using this, it is super obnoxious to figure out why your function is being canceled --
- you just get a CancelledError with no traceback.
-
- We try to ensure that *all* of our tasks are canceled in this way, which makes debugging much easier.
-
- Even safe_cancel_and_wait_for_cleanup will cancel in this way. The only difference is that that function
- also waits for the task to actually be canceled. Otherwise, cancellation just enqueues a cancellation.
-
- Note also that cancellation is never guaranteed -- all it does is raise a CancelledError in the task.
- This is why it is so important to never swallow those errors!
- """
- task.is_being_canceled_by_us = True # type: ignore
- message = f"Task canceled by: \n {''.join(traceback.format_stack()[:-1])}"
- if msg:
- message += f"\nOriginal message: {msg}"
-
- task.cancel(message)
-
-
-async def safe_cancel_and_wait_for_cleanup(
- task: asyncio.Task,
- msg: str | None = None,
- exception_types_to_ignore: Sequence[type[BaseException]] = (),
-) -> None:
- """
- Convenience function for calling safe_cancel_multiple_and_wait_for_cleanup with a single task.
-
- See safe_cancel_multiple_and_wait_for_cleanup for docs.
- """
- await safe_cancel_multiple_and_wait_for_cleanup([task], msg, exception_types_to_ignore)
-
-
-async def safe_cancel_multiple_and_wait_for_cleanup(
- tasks: Sequence[asyncio.Task],
- msg: str | None = None,
- exception_types_to_ignore: Sequence[type[BaseException]] = (),
-) -> None:
- """
- Calls safe_cancel (see docs above) on each task in tasks, then waits for them to be done.
-
- Note that you can pass in a list of exception types to ignore.
- This is important for suppressing exceptions from third party libraries.
- You should probably make a constant in your project that lists these exceptions.
-
- We cannot simply suppress all BaseExceptions here because you really don't want to do that for things like signals and OutOfMemoryError
- """
- for task in tasks:
- safe_cancel(task, msg)
- # if you really want something to be canceled, you need to wait for it to be done
- # https://docs.python.org/3/library/asyncio-task.html#asyncio.Task.cancel
- results = await asyncio.gather(*tasks, return_exceptions=True)
- exceptions = []
- for result in results:
- if isinstance(result, BaseException):
- if isinstance(result, asyncio.CancelledError):
- pass
- elif isinstance(result, ExceptionGroup):
- exceptions.extend(_filter_exception_group(result))
- else:
- exceptions.append(result) # type: ignore
- # cannot do this because the task may have just finished
- # assert (
- # False
- # ), f"While cancelling async task and waiting for cleanup, expected None or CancelledError, got `{type(x)}: {x}`"
-
- filtered_exceptions = []
- for exception in exceptions:
- if not any(isinstance(exception, exception_type) for exception_type in exception_types_to_ignore):
- filtered_exceptions.append(exception)
-
- if len(filtered_exceptions) == 1:
- raise filtered_exceptions[0]
- elif len(filtered_exceptions) > 1:
- raise ExceptionGroup("Multiple exceptions in task group while canceling", filtered_exceptions)
-
-
-def _filter_exception_group(exc_group: ExceptionGroup) -> list[Exception]:
- """Recursively extract exceptions from ExceptionGroups (ignoring canceled errors)."""
- result = []
- for exc in exc_group.exceptions:
- if isinstance(exc, asyncio.CancelledError):
- continue
- elif isinstance(exc, ExceptionGroup):
- result.extend(_filter_exception_group(exc))
- else:
- result.append(exc)
- return result
-
-
-def log_exception(
- exc: BaseException,
- message: str,
- *args: Any,
- **kwargs: Any,
-) -> None:
- """Log an exception with its traceback to stderr via loguru."""
- logger.opt(exception=exc).error(message, *args, **kwargs)
-
-
-def apply() -> None:
- asyncio.TaskGroup = PropagatingTaskGroup # type: ignore
- asyncio.taskgroups.TaskGroup = PropagatingTaskGroup # type: ignore
diff --git a/imbue_core/imbue_core/caching.py b/imbue_core/imbue_core/caching.py
@@ -1,183 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-from functools import lru_cache
-from pathlib import Path
-from types import TracebackType
-from typing import Generic
-from typing import Self
-from typing import Sequence
-from typing import TypeVar
-
-from diskcache import Cache
-from diskcache import JSONDisk
-
-from imbue_core.cattrs_serialization import deserialize_from_json
-from imbue_core.cattrs_serialization import serialize_to_json
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.frozen_utils import FrozenMapping
-
-ValueType = TypeVar("ValueType", covariant=True)
-
-
-class AsyncCacheInterface(Generic[ValueType]):
- async def __aenter__(self) -> Self:
- raise NotImplementedError()
-
- async def __aexit__(
- self,
- exc_type: type[BaseException] | None,
- exc_val: BaseException | None,
- exc_tb: TracebackType | None,
- ) -> None:
- raise NotImplementedError()
-
- async def set(
- self,
- key: str,
- # pyre-fixme[46]: ValueType is covariant
- value: ValueType,
- expire: int | None = None,
- read: bool = False,
- tag: str | None = None,
- retry: bool = False,
- ) -> bool:
- raise NotImplementedError()
-
- async def get(
- self,
- key: str,
- default: ValueType | None = None,
- read: bool = False,
- expire_time: bool = False,
- tag: bool = False,
- retry: bool = False,
- ) -> ValueType | None:
- raise NotImplementedError()
-
- async def get_all(
- self,
- keys: Sequence[str],
- default: ValueType | None = None,
- read: bool = False,
- expire_time: bool = False,
- tag: bool = False,
- retry: bool = False,
- ) -> FrozenMapping[str, ValueType | None]:
- raise NotImplementedError()
-
- async def get_all_keys(self, reverse: bool = False) -> tuple[str, ...]:
- raise NotImplementedError()
-
-
-class AsyncCache(AsyncCacheInterface[ValueType], Generic[ValueType]):
- def __init__(self, path: Path, value_cls: type[ValueType]) -> None:
- self.path = path
- self.value_cls = value_cls
- self.cache: Cache | None = None
-
- async def _build_cache(self) -> Cache:
- loop = asyncio.get_running_loop()
- # pyre-ignore[6]: pyre doesn't like the lru cache here
- return await loop.run_in_executor(None, get_cache, self.path)
-
- async def __aenter__(self) -> Self:
- loop = asyncio.get_running_loop()
- cache = await self._build_cache()
- self.cache = cache
- await loop.run_in_executor(None, cache.__enter__)
- return self
-
- async def __aexit__(
- self,
- exc_type: type[BaseException] | None,
- exc_val: BaseException | None,
- exc_tb: TracebackType | None,
- ) -> None:
- loop = asyncio.get_running_loop()
- cache = self.cache
- assert cache is not None
- result = await loop.run_in_executor(
- None, cache.__exit__, exc_type, exc_val, exc_tb
- )
- self.cache = None
- return result
-
- async def set(
- self,
- key: str,
- # pyre-fixme[46]: ValueType is covariant
- value: ValueType,
- expire: int | None = None,
- read: bool = False,
- tag: str | None = None,
- retry: bool = False,
- ) -> bool:
- cache = self.cache
- assert cache is not None
- loop = asyncio.get_running_loop()
- assert isinstance(value, self.value_cls), (
- f"Expected {self.value_cls}, got {type(value)}"
- )
- serialized_value = serialize_to_json(value)
- return await loop.run_in_executor(
- None, cache.set, key, serialized_value, expire, read, tag, retry
- )
-
- async def get(
- self,
- key: str,
- default: ValueType | None = None,
- read: bool = False,
- expire_time: bool = False,
- tag: bool = False,
- retry: bool = False,
- ) -> ValueType | None:
- cache = self.cache
- assert cache is not None
- loop = asyncio.get_running_loop()
- value = await loop.run_in_executor(
- None, cache.get, key, None, read, expire_time, tag, retry
- )
- if value is None:
- return default
- deserialized_value = deserialize_from_json(value)
- assert isinstance(deserialized_value, self.value_cls), (
- f"Expected {self.value_cls}, got {type(deserialized_value)}"
- )
- return deserialized_value
-
- # TODO: this is not smart implementation, but at least it will be possible to optimize later without refactoring
- async def get_all(
- self,
- keys: Sequence[str],
- default: ValueType | None = None,
- read: bool = False,
- expire_time: bool = False,
- tag: bool = False,
- retry: bool = False,
- ) -> FrozenMapping[str, ValueType | None]:
- tasks = {}
- for key in keys:
- tasks[key] = self.get(key, default, read, expire_time, tag, retry)
- results = await asyncio.gather(*tasks.values())
- return FrozenDict(zip(tasks.keys(), results))
-
- # TODO: might be nice to get iterkeys back someday, but whatever for now, too annoying to get the sync/async right
- async def get_all_keys(self, reverse: bool = False) -> tuple[str, ...]:
- cache = self.cache
- assert cache is not None
- loop = asyncio.get_running_loop()
- return tuple(await loop.run_in_executor(None, cache.iterkeys, reverse))
-
-
-@lru_cache
-def get_cache(data_path: Path) -> Cache:
- # not sure if the size limit applies when eviction is none, but ~64GB should be enough for now
- return Cache(
- str(data_path),
- disk=JSONDisk,
- disk_compress_level=0,
- eviction_policy="none",
- size_limit=2**36,
- )
diff --git a/imbue_core/imbue_core/cattrs_serialization.py b/imbue_core/imbue_core/cattrs_serialization.py
@@ -1,991 +0,0 @@
-import abc
-import asyncio
-import base64
-import builtins
-import datetime
-import functools
-import importlib
-import inspect
-import json
-from decimal import Decimal
-from enum import Enum
-from functools import cached_property
-from functools import lru_cache
-from functools import partial
-from pathlib import Path
-from pathlib import PosixPath
-from types import NoneType
-from types import UnionType
-from typing import Any
-from typing import Callable
-from typing import ForwardRef
-from typing import Hashable
-from typing import Mapping
-from typing import TypeVar
-from typing import Union
-from typing import cast
-from typing import get_origin
-from uuid import UUID
-
-import anyio
-import attr
-from cachetools import LRUCache
-from cattrs import Converter
-from cattrs._compat import is_generic
-from cattrs.gen import make_dict_unstructure_fn
-from cattrs.gen import override
-from httpx import URL
-from humps import camelize # pyre-ignore[21]: pyre doesn't understand this import
-from pydantic import BaseModel
-from pydantic_core import PydanticUndefined
-
-from imbue_core.errors import ImbueError
-from imbue_core.fixed_traceback import FixedTraceback
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.frozen_utils import FrozenMapping
-from imbue_core.serialization import SerializedException
-from imbue_core.serialization_types import Serializable
-
-T = TypeVar("T")
-TYPE_KEY = "__type"
-EXCEPTION_KEY = "__exception"
-
-# LABELS for marking attributes with special handling
-DONT_SERIALIZE_METADATA_KEY = "_imbue_dont_serialize"
-DONT_SERIALIZE = {DONT_SERIALIZE_METADATA_KEY: True}
-SERIALIZE_WITH_DEFAULT_KEY = "_imbue_serialize_with_default"
-SERIALIZE_WITH_DEFAULT = {SERIALIZE_WITH_DEFAULT_KEY: True}
-
-SERIALIZABLE_PROPERTY_KEY = "_imbue_is_serializable_property"
-CACHED_SERIALIZABLE_PROPERTY_KEY = "_imbue_is_cached_serializable_property"
-
-
-##########################################################################################
-# UTILITY FUNCTIONS
-##########################################################################################
-
-
-def _safe_issubclass(t1: type, t2: type) -> bool:
- return inspect.isclass(t1) and issubclass(t1, t2)
-
-
-def _is_frozen_mapping_type(t: type) -> bool:
- return _safe_issubclass(get_origin(t) or t, FrozenMapping)
-
-
-def _is_mapping_type(t: type) -> bool:
- return _safe_issubclass(get_origin(t) or t, Mapping)
-
-
-_ALLOWED_SPECIAL_MAPPING_TYPES = (LRUCache,)
-
-
-def _is_special_mapping_type(t: type) -> bool:
- return t in _ALLOWED_SPECIAL_MAPPING_TYPES
-
-
-def _is_str_type_special_mapping_type(t: str) -> bool:
- return t in [_type_to_string(t, fully_qualified=True) for t in _ALLOWED_SPECIAL_MAPPING_TYPES]
-
-
-def _is_obj_supported_primitive(obj: Any) -> bool:
- return type(obj) in {bool, int, float, str, NoneType}
-
-
-def _type_to_string(t: type, fully_qualified: bool) -> str:
- name = t.__name__
- if fully_qualified:
- return f"{t.__module__}.{name}"
- else:
- return name
-
-
-def _type_from_string(type_str: str) -> Any:
- if "[" in type_str:
- class_details, _ = type_str.split("[", 1)
- else:
- class_details = type_str
- if "." in class_details:
- module_path, class_name = class_details.rsplit(".", 1)
- module = importlib.import_module(module_path)
- else:
- class_name = class_details
- module = builtins
- result = getattr(module, class_name)
- return result
-
-
-def get_serializable_properties(obj: Any) -> dict[str, Any]:
- members = inspect.getmembers(type(obj))
- marked_members = {}
- for name, member in members:
- if is_serializable_property(member):
- marked_members[name] = getattr(obj, name)
- return marked_members
-
-
-def is_serializable_property(func: Callable) -> bool:
- return getattr(func, CACHED_SERIALIZABLE_PROPERTY_KEY, False) or (
- isinstance(func, property) and getattr(func.fget, SERIALIZABLE_PROPERTY_KEY, False)
- )
-
-
-def cached_serializable_property(func: Callable[..., T]) -> cached_property[T]:
- property_to_return = cached_property(func)
- setattr(property_to_return, CACHED_SERIALIZABLE_PROPERTY_KEY, True)
- return property_to_return
-
-
-def serializable_property(func: Callable[..., T]) -> property:
- property_to_return = func
- # NOTE: this will be stored in the fget attribute of the property, which is also the function
- # we are decorating, so we must check in `func.fget` to see if the property is serializable.
- # We need to do it this way because we cannot set the attribute on the property object/wrapper
- # itself, because of the way the inbuilt `property` decorator works.
- setattr(property_to_return, SERIALIZABLE_PROPERTY_KEY, True)
- return property(property_to_return)
-
-
-def get_dont_serialize_member_names_of_type(obj_type: type) -> list[str]:
- if not attr.has(obj_type):
- return []
- return [field.name for field in attr.fields(obj_type) if field.metadata.get(DONT_SERIALIZE_METADATA_KEY, False)]
-
-
-def get_serialize_with_default_member_names_of_type(
- obj_type: type,
-) -> Mapping[str, Any]:
- if _safe_issubclass(obj_type, BaseModel):
- model_fields = getattr(obj_type, "model_fields", {})
- return {
- name: None if field.default == PydanticUndefined else field.default for name, field in model_fields.items()
- }
- if not attr.has(obj_type):
- return {}
- return {
- field.name: None if field.default == attr.NOTHING else field.default
- for field in attr.fields(obj_type)
- if field.metadata.get(SERIALIZE_WITH_DEFAULT_KEY, False)
- }
-
-
-def get_dont_serialize_member_names(obj: Any) -> list[str]:
- if not attr.has(obj):
- return []
- members = inspect.getmembers(obj)
- marked_members = []
- for name, _ in members:
- if is_dont_serialize_member(obj, name):
- marked_members.append(name)
- return marked_members
-
-
-def is_dont_serialize_member(obj: Any, member_name: str) -> bool:
- if not attr.has(obj):
- return False
- for field in attr.fields(obj.__class__): # type: ignore
- if field.name == member_name:
- return bool(field.metadata.get(DONT_SERIALIZE_METADATA_KEY, False))
- return False
-
-
-class SerializationError(ImbueError):
- """Raised when we encounter problems related to Serialization or Deserialization."""
-
-
-def _to_json_dumpable_object_without_type_keys(data: Any) -> Any:
- if isinstance(data, dict):
- if data.get(TYPE_KEY, "") in {
- _type_to_string(PosixPath, fully_qualified=True),
- _type_to_string(Path, fully_qualified=True),
- _type_to_string(UUID, fully_qualified=True),
- }:
- return data["value"]
- else:
- return {
- key: _to_json_dumpable_object_without_type_keys(value) for key, value in data.items() if key != TYPE_KEY
- }
- elif isinstance(data, list):
- return [_to_json_dumpable_object_without_type_keys(item) for item in data]
- elif _is_obj_supported_primitive(data):
- return data
- else:
- return str(data)
-
-
-def _camelize_keys_which_represent_python_names(data: Any) -> Any:
- """Converts JSON-style objects to use camel case keys.
-
- Takes a JSON-style object produced by CONVERTER.structure and returns the same object with certain
- keys converted to camel case. Camel cases keys which are derived from names of Python attributes and properties.
- Does not camel-case keys which were keys of dictionaries before serialization.
-
- See cattrs_serialization_test.test_camel_casing for an example.
- """
- if isinstance(data, dict):
- if TYPE_KEY not in data or issubclass(_type_from_string(data[TYPE_KEY]), Mapping):
- return {key: _camelize_keys_which_represent_python_names(value) for key, value in data.items()}
- else:
- # pyre-ignore[16]: pyre doesn't understand the import of camelize
- return {camelize(key): _camelize_keys_which_represent_python_names(value) for key, value in data.items()}
- elif isinstance(data, list):
- return [_camelize_keys_which_represent_python_names(item) for item in data]
- else:
- return data
-
-
-##########################################################################################
-# CLASS-SPECIFIC HOOKS
-##########################################################################################
-
-
-class _ShouldDeserialize:
- pass
-
-
-# FIXME: Types such as LRUCache will always serialize without errors since they inherit from Mapping but will not deserialize correctly.
-# We should either document this behavior or change it so that the serialization fails if the type is not supported.
-def _serialize_mapping_to_json_dict(data: Mapping, converter: Converter) -> Any:
- assert _is_mapping_type(type(data)), f"Attempted to serialize object of type {type(data)} as a mapping."
- return {str(converter.unstructure(k)): converter.unstructure(v) for k, v in data.items()}
-
-
-def _serialize_mapping(data: Mapping, converter: Converter) -> Any:
- assert _is_mapping_type(type(data)), f"Attempted to serialize object of type {type(data)} as a mapping."
- entries = [(converter.unstructure(k), converter.unstructure(v)) for k, v in data.items()]
- return {
- TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
- "__entries": entries,
- }
-
-
-def _deserialize_special_mapping_types(data: dict, type_key: str) -> Mapping:
- if type_key == _type_to_string(LRUCache, fully_qualified=True):
- # FIXME: We're not serializing the object correctly and so the deserialization is hacky
- obj: LRUCache = LRUCache(maxsize=10000)
- return obj
- else:
- raise ValueError(f"Unsupported type {type_key}")
-
-
-def _deserialize_mapping(data: dict, mapping_type: type, converter: Converter) -> Mapping:
- if TYPE_KEY in data and _is_str_type_special_mapping_type(data[TYPE_KEY]):
- return _deserialize_special_mapping_types(data, data[TYPE_KEY])
-
- out = {}
- if "__entries" in data:
- entries = data["__entries"]
- else:
- # We keep this branch for backwards compatibility with mappings serialized as dictionaries.
- # We do not support Yasoo's DictWithSerializedKeys -- those will need to be migrated to the new format.
- if TYPE_KEY in data:
- del data[TYPE_KEY]
- entries = data.items()
-
- for k, v in entries:
- out[converter.structure(k, _ShouldDeserialize)] = converter.structure(v, _ShouldDeserialize)
-
- if _is_frozen_mapping_type(mapping_type):
- return FrozenDict(out)
- return out
-
-
-def _serialize_frozen_set(data: frozenset, converter: Converter) -> dict:
- assert type(data) is frozenset, f"Attempted to serialize object of type {type(data)} as a frozenset."
- value = converter.unstructure(data, unstructure_as=list)
- return {"value": value, TYPE_KEY: _type_to_string(type(data), fully_qualified=True)}
-
-
-def _deserialize_frozen_set(data: dict, _: type, converter: Converter) -> frozenset:
- return frozenset(converter.structure(data["value"], list))
-
-
-def _serialize_uuid(data: UUID) -> dict:
- if type(data) is UUID:
- return {
- "value": data.hex,
- TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
- }
- elif type(data) is str:
- return {"value": data, TYPE_KEY: _type_to_string(UUID, fully_qualified=True)}
- else:
- raise TypeError("Tried to serialize " + str(data) + ", which is neither a string nor a UUID, as a UUID.")
-
-
-def _deserialize_uuid(data: dict[str, str] | str, _: type) -> UUID:
- if isinstance(data, dict):
- return UUID(data["value"])
- elif isinstance(data, str):
- return UUID(data)
- else:
- raise TypeError("Tried to deserialize something which is neither a string nor a dictionary, as a UUID.")
-
-
-def _serialize_tuple(data: tuple, converter: Converter) -> dict:
- assert type(data) is tuple, f"Attempted to serialize object of type {type(data)} as a tuple."
- return {
- "value": [converter.unstructure(x) for x in data],
- TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
- }
-
-
-def _deserialize_tuple(data: dict, _: type, converter: Converter) -> tuple:
- return tuple(converter.structure(x, _ShouldDeserialize) for x in data["value"])
-
-
-def _serialize_url(data: URL) -> dict:
- assert type(data) is URL, f"Tried to serialize {data} which is not a URL."
- return {
- "value": str(data),
- TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
- }
-
-
-def _deserialize_url(data: dict, _: type) -> URL:
- return URL(data["value"])
-
-
-def _serialize_decimal(data: Decimal) -> dict:
- assert type(data) is Decimal, f"Attempted to serialize object of type {type(data)} as a Decimal."
- return {
- "value": str(data),
- TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
- }
-
-
-def _deserialize_decimal(data: dict, _: type) -> Decimal:
- return Decimal(data["value"])
-
-
-def _serialize_traceback(data: FixedTraceback) -> dict:
- assert _safe_issubclass(
- type(data), FixedTraceback
- ), f"Attempted to serialize object of type {type(data)} as a traceback."
- return {
- "value": data.to_dict(),
- TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
- }
-
-
-def _deserialize_traceback(data: dict, _: type) -> FixedTraceback:
- return FixedTraceback.from_dict(data["value"])
-
-
-def _serialize_path(data: Path) -> dict:
- assert _safe_issubclass(type(data), Path), f"Attempted to serialize an object of type {type(data)} as a Path."
- return {
- "value": str(data),
- TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
- }
-
-
-def _deserialize_path(data: Any, _: type) -> Path:
- if type(data) is dict:
- return Path(data["value"])
- return Path(data)
-
-
-def _serialize_anyio_path(data: anyio.Path) -> dict:
- assert _safe_issubclass(type(data), anyio.Path), f"Attempted to serialize an object of type {type(data)} as a Path."
- return {
- "value": str(data),
- TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
- }
-
-
-def _deserialize_anyio_path(data: Any, _: type) -> anyio.Path:
- if type(data) is dict:
- return anyio.Path(data["value"])
- return anyio.Path(data)
-
-
-def _serialize_datetime(data: datetime.datetime) -> dict:
- assert _safe_issubclass(
- type(data), datetime.datetime
- ), f"Attempted to serialize object of type {type(data)} as a datetime."
- return {
- TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
- "time": data.astimezone(datetime.timezone.utc).timestamp(),
- "tzaware": data.tzinfo is not None,
- }
-
-
-def _deserialize_datetime(data: dict, _: type) -> datetime.datetime:
- return datetime.datetime.fromtimestamp(data["time"], datetime.timezone.utc if data.get("tzaware", None) else None)
-
-
-def _serialize_bytes(data: bytes) -> dict:
- assert type(data) is bytes, f"Attempted to serialize object of type {type(data)} as bytes."
- return {
- TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
- # use ascii since base64 guarantees ascii characters only
- "value": base64.b64encode(data).decode("ascii"),
- }
-
-
-def _deserialize_bytes(data: dict, _: type) -> bytes:
- return base64.b64decode(data["value"])
-
-
-def _is_forward_ref(t: type) -> bool:
- return isinstance(t, ForwardRef)
-
-
-def _serialize_forward_ref(data: Any, converter: Converter) -> Any:
- return converter.unstructure(data, unstructure_as=type(data))
-
-
-def _deserialize_forward_ref(data: Any, _: type, converter: Converter) -> Any:
- # TODO: think of a way to evaluate the ForwardRef _, to improve type safety.
- # Once we do that, we can swap out the evaluated type for ShouldDeserialize
- # and enforce that we're getting an object of the correct type.
- return _deserialize_serialized_object(data, _ShouldDeserialize, converter)
-
-
-def _is_union_type(t: type) -> bool:
- origin = get_origin(t)
- return origin is Union or origin is UnionType
-
-
-def _deserialize_union_type(data: Any, type_of_data: type, converter: Converter) -> Any:
- return converter.structure(data, _ShouldDeserialize)
-
-
-def _serialize_enum(data: Enum, converter: Converter) -> Any:
- assert inspect.isclass(type(data)) and issubclass(
- type(data), Enum
- ), f"Attempted to serialize object of type {type(data)} as an Enum."
- return converter._unstructure_enum(data)
-
-
-def _deserialize_enum(data: dict[str, str] | str, t: type) -> Any:
- # We include this complicated logic to preserve backwards compatibility with old JSON that was
- # serialized by Yasoo. Yasoo serialized enums by converting them into the form
- # {"__type": "...", "value": "..."}. Strangely, Yasoo converted this dictionary into a string
- # whenever an enum value occurred as a dictionary key, but did not convert it into a string
- # when it occurred anywhere else. Hence we need to handle enums that are represented by
- # dictionaries, stringified dictionaries, and strings.
-
- assert _safe_issubclass(t, Enum)
-
- if isinstance(data, str):
- try:
- # This is the case where data is an enum value, serialized by Cattrs.
- return t(data)
- except ValueError:
- # This is the case where data is a stringified dictionary, serialized by Yasoo.
- data_as_dict = json.loads(data)
- return t[data_as_dict["value"]] # type: ignore
- else:
- # This is the case where data is a dictionary, serialized by Yasoo.
- return t[data["value"]] # type: ignore
-
-
-##########################################################################################
-# TYPE KEY LOGIC
-##########################################################################################
-
-
-class _AvoidTypeKeyLogic:
- pass
-
-
-@lru_cache
-def flag_to_ignore_type_key_hooks(t: type) -> type:
- class GivenTypeFlaggedToAvoidTypeKeyLogic(t, _AvoidTypeKeyLogic):
- pass
-
- GivenTypeFlaggedToAvoidTypeKeyLogic.__name__ = t.__name__
- GivenTypeFlaggedToAvoidTypeKeyLogic.__qualname__ = t.__qualname__
-
- # pyre-fixme[16]: pyre doesn't understand dynamically created classes
- return GivenTypeFlaggedToAvoidTypeKeyLogic
-
-
-def get_pydantic_model_attributes(model: BaseModel) -> dict[str, Any]:
- # This is a hack to dump only the top level but also avoid dumping any properties
- attributes = getattr(type(model), "model_fields", {})
- return {a: getattr(model, a) for a in attributes}
-
-
-# These two factory functions produce the functions for serializing attr classes.
-# Only one of them should be registered at a time, depending on whether we are including
-# do-not-serialize fields in the serialization.
-def _serialize_attr_class_factory(cls: type, converter: Converter) -> Callable[[Any], Any]:
- return make_dict_unstructure_fn(cls, converter)
-
-
-def _serialize_attr_class_without_dont_serialize_fields(
- cls: type, converter: Converter, is_camel_case: bool
-) -> Callable[[Any], Any]:
- members_to_omit = get_dont_serialize_member_names_of_type(cls)
- omit_kwargs = {name: override(omit=True) for name in members_to_omit}
- return make_dict_unstructure_fn(cls, converter, **omit_kwargs) # type: ignore
-
-
-def _serialize_with_type_key(data: Any, converter: Converter, for_javascript: bool = False) -> Any:
- type_of_data = type(data)
-
- if _is_obj_supported_primitive(data) or isinstance(data, list) or isinstance(data, tuple):
- # This means that data was annotated as a Serializable, but it is a primitive or a tuple.
- return converter.unstructure(data, unstructure_as=type_of_data)
-
- type_of_data_with_typekey_already_added = flag_to_ignore_type_key_hooks(type_of_data) # type: ignore
-
- # This is a hack which is necessary because cattrs does not work well with Protocols.
- # Protocols are generic classes, but they don't have __orig_bases__, which cattrs
- # assumes them to have.
- if is_generic(type_of_data_with_typekey_already_added):
- old_orig_bases = getattr(type_of_data_with_typekey_already_added, "__orig_bases__", ())
- setattr(type_of_data_with_typekey_already_added, "__orig_bases__", old_orig_bases)
-
- if isinstance(data, BaseModel):
- # This is a shortcut: when you encounter a Pydantic model, just use Pydantic serialization.
- # NOTE: currently we don't support `DONT_SERIALIZE` fields in pydantic models.
- # so we just serialize all fields.
- unstructured = data.model_dump(by_alias=for_javascript, mode="json")
- else:
- unstructured = converter.unstructure(data, unstructure_as=type_of_data_with_typekey_already_added)
-
- assert isinstance(unstructured, dict)
-
- if for_javascript:
- unstructured.update({k: converter.unstructure(v) for k, v in get_serializable_properties(data).items()})
-
- return {
- TYPE_KEY: _type_to_string(type_of_data, fully_qualified=True),
- **unstructured,
- }
-
-
-# This is the predicate used in the factory functions above, so they trigger for serializable and attr classes
-# that have had their type key logic handled.
-def _should_serialize_without_type_key(t: type) -> bool:
- is_serializable_class = _safe_issubclass(t, Serializable) or attr.has(t) or _safe_issubclass(t, BaseModel)
- return is_serializable_class and _safe_issubclass(t, _AvoidTypeKeyLogic)
-
-
-def _should_add_type_key(t: type) -> bool:
- is_serializable_class = _safe_issubclass(t, Serializable) or attr.has(t) or _safe_issubclass(t, BaseModel)
- return is_serializable_class and not _safe_issubclass(t, _AvoidTypeKeyLogic)
-
-
-def _deserialize_serialized_object(data: Any, type_of_data: type, converter: Converter) -> Any:
- if isinstance(data, list):
- # Data is a list of objects.
- return converter.structure(data, list[_ShouldDeserialize])
- elif not isinstance(data, Mapping):
- # Data is a primitive, like an integer or a string.
- return converter.structure(data, type(data))
- else:
- # Data is a dictionary with a type key, representing an attrs object, a Pydantic model, or a Mapping
- return _deserialize_using_type_marker(data, type_of_data, converter)
-
-
-def _should_deserialize_with_type_key_logic(t: type) -> bool:
- is_type_that_should_be_deserialized = (
- attr.has(t)
- or _safe_issubclass(t, Serializable)
- or _safe_issubclass(t, _ShouldDeserialize)
- or t is Hashable
- or _is_mapping_type(t)
- or _safe_issubclass(t, BaseModel)
- )
- should_avoid_type_key_logic = _safe_issubclass(t, _AvoidTypeKeyLogic) or _safe_issubclass(
- get_origin(t) or NoneType, _AvoidTypeKeyLogic
- )
- return is_type_that_should_be_deserialized and not should_avoid_type_key_logic
-
-
-def deserialized_object_violates_target_type(obj: Any, target_type: type) -> bool:
- if target_type is _ShouldDeserialize or target_type is Serializable:
- return False
- if type(target_type) is TypeVar:
- # We're not really able to check if the object is an instance of a type that's behind a TypeVar.
- return False
- return not isinstance(obj, get_origin(target_type) or target_type)
-
-
-# Note that expected_type_based_on_annotations may be much more vague than the actual type of the object.
-# For example: it may be Serializable, when the object is supposed to be
-# deserialized as a HammerResult. We get the real type from the "__type" key.
-def _deserialize_using_type_marker(
- obj: Mapping[Any, Any],
- expected_type_based_on_annotations: type[T],
- converter: Converter,
-) -> T:
- if TYPE_KEY in obj:
- type_of_obj = _type_from_string(obj[TYPE_KEY])
- else:
- type_of_obj = expected_type_based_on_annotations
-
- if _is_special_mapping_type(type_of_obj):
- pass
- elif _is_frozen_mapping_type(type_of_obj):
- obj.pop(TYPE_KEY, None) # type: ignore
- type_of_obj = FrozenMapping[_ShouldDeserialize, _ShouldDeserialize]
- elif _is_mapping_type(type_of_obj):
- obj.pop(TYPE_KEY, None) # type: ignore
- type_of_obj = dict[_ShouldDeserialize, _ShouldDeserialize]
- elif _safe_issubclass(type_of_obj, BaseModel):
- assert isinstance(obj, dict)
- obj.pop(TYPE_KEY, None)
- return cast(T, type_of_obj.model_validate(obj))
- elif not attr.has(type_of_obj):
- # This happens when there is a primitive object which is annotated as Serializable.
- return converter.structure(obj, type_of_obj) # type: ignore
-
- # By mixing in the "avoid type key logic" class, force cattrs to do its normal behavior.
- ret: T = converter.structure(obj, flag_to_ignore_type_key_hooks(type_of_obj))
-
- if inspect.isclass(type_of_obj):
- # Upcast the result so that it has the correct type again, without the mixin.
- object.__setattr__(ret, "__class__", type_of_obj)
-
- if deserialized_object_violates_target_type(ret, expected_type_based_on_annotations):
- raise TypeError(
- f"Tried to deserialize into type {expected_type_based_on_annotations}, but got object of type {type(ret)}"
- )
-
- return ret
-
-
-def _resolve_default(default: Any) -> Any:
- if isinstance(default, attr.Factory): # type: ignore
- return default.factory()
- return default
-
-
-def _serialize_with_defaults(cls: type, converter: Converter) -> Callable[[Any], Any]:
- # Handle a pydantic model
- if _safe_issubclass(cls, BaseModel):
- return lambda x: {k: converter.unstructure(v) for k, v in get_pydantic_model_attributes(x).items()}
-
- members_with_defaults = get_serialize_with_default_member_names_of_type(cls)
- overriden_kwargs = {
- name: override(unstruct_hook=(lambda _, value=_resolve_default(default): value)) # type: ignore
- for name, default in members_with_defaults.items()
- }
- return make_dict_unstructure_fn(cls, converter, **overriden_kwargs) # type: ignore
-
-
-def _should_serialize_as_serialized_exception(t: type) -> bool:
- return (
- _safe_issubclass(get_origin(t) or t, BaseException) and not attr.has(t) and not _safe_issubclass(t, BaseModel)
- )
-
-
-##########################################################################################
-# CONVERTER FACTORY
-##########################################################################################
-
-
-class _ConverterFactory:
- """Factory for creating converters with different configurations.
-
- e.g. for serializing to javascript, or python, or to include do-not-serialize fields.
- """
-
- def build_base_converter(self) -> Converter:
- # Builds of new base converter object, which registers all the hooks that are common to all converters.
- # The idea being that all new converters start from this base and then override hooks they need to change
- # NOTE: we need to generate a new converter object for each independent concrete converter (as opposed to
- # using converter.copy()) since we use partial functions/closures and this way we ensure the function is
- # being called with the correct converter object.
- converter = Converter()
-
- converter.register_structure_hook_func(_is_mapping_type, partial(_deserialize_mapping, converter=converter))
- # serialization of mapping types depends on the specific converter so is done in the get_converter factory method
-
- converter.register_unstructure_hook(frozenset, partial(_serialize_frozen_set, converter=converter))
- converter.register_structure_hook(frozenset, partial(_deserialize_frozen_set, converter=converter))
-
- converter.register_unstructure_hook(UUID, _serialize_uuid)
- converter.register_structure_hook(UUID, _deserialize_uuid)
-
- converter.register_unstructure_hook(URL, _serialize_url)
- converter.register_structure_hook(URL, _deserialize_url)
-
- converter.register_unstructure_hook(Decimal, _serialize_decimal)
- converter.register_structure_hook(Decimal, _deserialize_decimal)
-
- converter.register_unstructure_hook(FixedTraceback, _serialize_traceback)
- converter.register_structure_hook(FixedTraceback, _deserialize_traceback)
-
- converter.register_unstructure_hook(Path, _serialize_path)
- converter.register_structure_hook(Path, _deserialize_path)
-
- converter.register_unstructure_hook(anyio.Path, _serialize_anyio_path)
- converter.register_structure_hook(anyio.Path, _deserialize_anyio_path)
-
- converter.register_unstructure_hook(datetime.datetime, _serialize_datetime)
- converter.register_structure_hook(datetime.datetime, _deserialize_datetime)
-
- converter.register_unstructure_hook(bytes, _serialize_bytes)
- converter.register_structure_hook(bytes, _deserialize_bytes)
-
- converter.register_unstructure_hook(PosixPath, _serialize_path)
- converter.register_structure_hook(PosixPath, _deserialize_path)
-
- converter.register_unstructure_hook_func(_is_forward_ref, partial(_serialize_forward_ref, converter=converter))
- converter.register_structure_hook_func(_is_forward_ref, partial(_deserialize_forward_ref, converter=converter))
-
- converter.register_structure_hook_func(_is_union_type, partial(_deserialize_union_type, converter=converter))
-
- converter.register_structure_hook(NoneType, lambda data, _: None)
-
- converter.register_unstructure_hook(Enum, partial(_serialize_enum, converter=converter))
- converter.register_structure_hook(Enum, _deserialize_enum)
-
- converter.register_unstructure_hook_func(
- _should_serialize_as_serialized_exception,
- lambda e: serialize_to_dict(
- SerializedException.build(e),
- use_defaults_for_unserializable_fields=True,
- ),
- )
-
- converter.register_structure_hook_func(
- _should_deserialize_with_type_key_logic,
- partial(_deserialize_serialized_object, converter=converter),
- )
-
- converter.register_structure_hook_func(
- lambda t: isinstance(t, TypeVar),
- partial(_deserialize_serialized_object, converter=converter),
- )
-
- return converter
-
- def get_converter_with_defaults(self, converter: Converter) -> Converter:
- converter.register_unstructure_hook(asyncio.Lock, lambda _: None)
- converter.register_structure_hook(asyncio.Lock, lambda data, _: asyncio.Lock())
-
- converter.register_unstructure_hook(asyncio.Task, lambda _: None)
- converter.register_structure_hook(asyncio.Task, lambda data, _: None)
-
- converter.register_unstructure_hook(asyncio.Queue, lambda _: None)
- converter.register_structure_hook(asyncio.Queue, lambda data, _: None)
-
- converter.register_unstructure_hook(asyncio.Event, lambda _: None)
- converter.register_structure_hook(asyncio.Event, lambda data, _: None)
-
- converter.register_unstructure_hook(asyncio.Semaphore, lambda _: None)
- converter.register_structure_hook(asyncio.Semaphore, lambda data, _: None)
-
- converter.register_unstructure_hook(abc.ABCMeta, lambda _: None)
- converter.register_structure_hook(abc.ABCMeta, lambda data, _: None)
- converter.register_unstructure_hook_factory(
- _should_serialize_without_type_key,
- partial(_serialize_with_defaults, converter=converter),
- )
-
- return converter
-
- @functools.cache
- def get_converter(
- self,
- for_javascript: bool = False,
- exclude_dont_serialize_fields: bool = False,
- use_defaults_for_unserializable_fields: bool = False,
- ) -> Converter:
- """Returns a converter with the given configuration.
-
- The result of this method is cached, so subsequent calls with the same arguments will return the same converter.
- """
- assert not (
- exclude_dont_serialize_fields and use_defaults_for_unserializable_fields
- ), f"Expected exactly one flag to be set, got {exclude_dont_serialize_fields=}, {use_defaults_for_unserializable_fields=}"
-
- converter = self.build_base_converter()
- if for_javascript:
- converter.register_unstructure_hook_func(
- _is_mapping_type,
- partial(_serialize_mapping_to_json_dict, converter=converter),
- )
- else:
- converter.register_unstructure_hook_func(_is_mapping_type, partial(_serialize_mapping, converter=converter))
- converter.register_unstructure_hook(tuple, partial(_serialize_tuple, converter=converter))
- converter.register_structure_hook(tuple, partial(_deserialize_tuple, converter=converter))
-
- if exclude_dont_serialize_fields:
- converter.register_unstructure_hook_factory(
- _should_serialize_without_type_key,
- partial(
- _serialize_attr_class_without_dont_serialize_fields,
- converter=converter,
- is_camel_case=for_javascript,
- ),
- )
- else:
- converter.register_unstructure_hook_factory(
- _should_serialize_without_type_key,
- partial(_serialize_attr_class_factory, converter=converter),
- )
- if use_defaults_for_unserializable_fields:
- converter = self.get_converter_with_defaults(converter)
-
- converter.register_unstructure_hook_func(
- _should_add_type_key,
- partial(
- _serialize_with_type_key,
- converter=converter,
- for_javascript=for_javascript,
- ),
- )
-
- return converter
-
-
-CONVERTER_FACTORY = _ConverterFactory()
-
-
-##########################################################################################
-# ENTRY POINTS
-##########################################################################################
-
-
-def _serialize_to_json_dumpable_object(
- obj: Any,
- is_reversible: bool = True,
- for_javascript: bool = False,
- exclude_dont_serialize_fields: bool = False,
- use_defaults_for_unserializable_fields: bool = False,
-) -> Any:
- if exclude_dont_serialize_fields:
- # Check and raise error to make it clear to the caller that the object cannot be deserialized.
- # This is a sanity check, to make it easier to debug when using do-not-serialize fields.
- # NOTE: this will only catch cases where non-serializable fields are in obj, but not cases where
- # the non-serializable fields are in nested objects, checking for the nested case is a little complicated
- # so we don't do it basically.
- assert (
- not is_reversible
- ), "Cannot deserialize object when excluding do-not-serialize fields (i.e. when `exclude_dont_serialize_fields=True`). If you want to serialize an object and exclude do-not-serialize fields, make sure to set `is_reversible=False`."
-
- if use_defaults_for_unserializable_fields:
- # The point of the use_defaults_for_unserializable_fields flag is to make it possible to serialize objects
- # and then recreate them later even if certain fields are not fully saved. We never want to use this flag
- # with `is_reversible=False` since we won't know the type to be able to recreate the object.
- assert is_reversible, "Cannot restructure inputs if is_reversible=False"
-
- # TODO: this is a hack to make it possible to serialize ExecutionContexts for class methods.
- # This lets us serialize ExecutionContexts for calls to class methods without serializing the class itself.
- # The long-term solution is to write a custom hook that can serialize type objects.
- if type(obj) is dict and "__class__" in obj:
- del obj["__class__"]
-
- converter = CONVERTER_FACTORY.get_converter(
- for_javascript=for_javascript,
- exclude_dont_serialize_fields=exclude_dont_serialize_fields,
- use_defaults_for_unserializable_fields=use_defaults_for_unserializable_fields,
- )
-
- dict_result = converter.unstructure(obj)
- if for_javascript:
- dict_result = _camelize_keys_which_represent_python_names(dict_result)
-
- if not is_reversible:
- return _to_json_dumpable_object_without_type_keys(dict_result)
-
- return dict_result
-
-
-def serialize_to_dict(
- obj: Any,
- is_reversible: bool = True,
- for_javascript: bool = False,
- exclude_dont_serialize_fields: bool = False,
- use_defaults_for_unserializable_fields: bool = False,
-) -> dict[str, Any]:
- """Serialize to a python dict."""
- return cast(
- dict[str, Any],
- _serialize_to_json_dumpable_object(
- obj,
- is_reversible=is_reversible,
- for_javascript=for_javascript,
- exclude_dont_serialize_fields=exclude_dont_serialize_fields,
- use_defaults_for_unserializable_fields=use_defaults_for_unserializable_fields,
- ),
- )
-
-
-def serialize_to_json(
- obj: Any,
- indent: int | None = None,
- sort_keys: bool = False,
- is_reversible: bool = True,
- for_javascript: bool = False,
- exclude_dont_serialize_fields: bool = False,
- use_defaults_for_unserializable_fields: bool = False,
-) -> str:
- """Serialize an object to a JSON string.
-
- This is the main serialization entrypoint.
-
- `is_reversible` controls whether we enforce that the result can be deserialized. In some cases we don't care about
- reversibility, e.g. when serializing data for a frontend we often don't care whether we can deserialize.
-
- `for_javascript` controls whether we use camelCase for keys that originally were Python identifiers.
-
- `exclude_dont_serialize_fields` controls whether we include do-not-serialize fields in the serialization.
- If this is `False` then any attr class fields marked with as don't serialize, e.g. with `attr.ib(metadata=DONT_SERIALIZE)`,
- will still be included in the serialization. If this is `True` then they will be excluded, however this also means that
- the result will not be reversible (and thus the caller will have to set `is_reversible=False`).
-
- `use_defaults_for_unserializable_fields` controls whether we fill fields that cannot be serialized with their default values.
- IMPORTANT: If you use this flag, data may be discarded during deserialization.
- The goal is to be able to deserialize fields to the original type without caring about the data contained.
- Default value choices (guided by crafty serialization requirements):
- - Fields that are marked with attr.ib(metadata=SERIALIZE_WITH_DEFAULT) have the following default values:
- - Fields that are marked with `attr.ib(default=...)` or `attr.ib(factory=...)` use their default values.
- - Fields that do not have a default value are filled with None.
- - Asyncio objects are filled with None.
- - Exceptions are replaced with a string representation
- """
- try:
- unstructured = _serialize_to_json_dumpable_object(
- obj,
- is_reversible=is_reversible,
- for_javascript=for_javascript,
- exclude_dont_serialize_fields=exclude_dont_serialize_fields,
- use_defaults_for_unserializable_fields=use_defaults_for_unserializable_fields,
- )
- return json.dumps(unstructured, indent=indent, sort_keys=sort_keys)
- except Exception as e:
- raise SerializationError(str(e)) from e
-
-
-def deserialize_from_json(
- data: str,
- for_javascript: bool = False,
- exclude_dont_serialize_fields: bool = False,
- use_defaults_for_unserializable_fields: bool = False,
-) -> Any:
- try:
- converter = CONVERTER_FACTORY.get_converter(
- for_javascript=for_javascript,
- exclude_dont_serialize_fields=exclude_dont_serialize_fields,
- use_defaults_for_unserializable_fields=use_defaults_for_unserializable_fields,
- )
- return _deserialize_serialized_object(json.loads(data), _ShouldDeserialize, converter=converter)
- except Exception as e:
- raise SerializationError(str(e)) from e
-
-
-def deserialize_from_dict(
- data: dict[str, Any],
- as_type: type = _ShouldDeserialize,
- for_javascript: bool = False,
- exclude_dont_serialize_fields: bool = False,
- use_defaults_for_unserializable_fields: bool = False,
-) -> Any:
- try:
- converter = CONVERTER_FACTORY.get_converter(
- for_javascript=for_javascript,
- exclude_dont_serialize_fields=exclude_dont_serialize_fields,
- use_defaults_for_unserializable_fields=use_defaults_for_unserializable_fields,
- )
- return _deserialize_using_type_marker(data, as_type, converter=converter)
- except Exception as e:
- raise SerializationError(str(e)) from e
diff --git a/imbue_core/imbue_core/data_types.py b/imbue_core/imbue_core/data_types.py
@@ -1,317 +0,0 @@
-"""
-Interfaces and data types for the issue identification system.
-
-We foresee two basic kinds of issue identifiers:
-
- 1. Heavily specialized ones.
- - Those would typically only check for a single well-defined issue.
- - The user only wants to know if a specific thing is wrong.
- 2. General ones.
- - They would still have a certain focus but they would typically check for a broader range of issues.
- - We wouldn't know the whole range of possible issues in advance.
- - Here, the user asks for up to N most problematic issues of the given kind in a given scope.
-
-Issue identifiers can either be created:
- - by implementing the IssueIdentifier protocol in an arbitrary way
- - or by leaning on a common LLM-based zero-shot classifier
- - This has the advantage of being more efficient.
- - We can ask for a (set of) score(s) on various metrics / error types for a list of scopes.
- - These computations can basically all be batched together into a single call to the LLM.
- - NOTE: as of writing this, this hasn't been implemented yet.
-
-What follows is a list of possible issues we may eventually want to identify:
-
-
-- docstrings / documentation / tests / constraints / validation
- - missing
- - outdated
- - ambiguous
- - poorly written / duplicated
- - conflicting
- - insufficient
-- assumptions
- - unstated
- - violated
- - conflicting
-- possible race condition
-- overly complex code
-- code in need of refactoring
-- project layout in need of refactoring
-- duplicated code
-- brittle logic
-- use of state (at all, where unnecessary, where needless)
-- gross inefficiency
-- caching (the presence of, at all)
-- bad/confusing/unclear naming
-- forbidden stylistic patterns (that cannot be caught by ratchets)
-- poorly handled edge cases
-- just plain ol bugs, overall correctness, etc
-- missing test coverage (not line based, but more meaning based, esp around integration tests)
-- disagreeing ensembles
-- missing features / implementations / etc
-- refactoring elements that were missed
-- invocation outputs that seem suspect
-- overly broad types
-- misunderstanding the users mental model
-- architectural flaws
-- general critiques
-- better alternatives
-- reinvented wheels / places where some library or external service should have been used
-- mutated globals / global state / imported globals
-- any unnecessary complexity
-
-There are also things we explicitly don't want to catch with this system:
-
-- runtime errors (when running a main script or tests)
-- most ratchet errors
-- anything caught by an existing tool (typing, pylint, tests, etc)
-- errors during the deployment process itself
-- errors when building the image
-- errors about packaging, installation, dependencies, etc
-- errors collected from production
-
-"""
-
-from enum import StrEnum
-from typing import Literal
-
-from pydantic import Field
-
-from imbue_core.common import generate_id
-from imbue_core.pydantic_serialization import SerializableModel
-
-# Define semantics for the normalized confidence and severity scores.
-CONFIDENCE_CERTAINLY_FINE = (0.0, 0.2)
-CONFIDENCE_RATHER_FINE = (0.2, 0.4)
-CONFIDENCE_NOT_SURE = (0.4, 0.6)
-CONFIDENCE_RATHER_PROBLEMATIC = (0.6, 0.8)
-CONFIDENCE_CERTAINLY_PROBLEMATIC = (0.8, 1.0)
-
-
-class ConfidenceScore(SerializableModel):
- """
- A score for the confidence in the issue / error detection.
-
- - The raw score is the score as output by the underlying model.
- - The normalized score is rescaled in such a way that the interval between 0 and 1 maps to the defined confidence levels.
-
- """
-
- raw: float
- normalized: float
-
-
-class SeverityScore(SerializableModel):
- """
- A score for the severity of the issue / error.
-
- - The raw score is the score as potentially output by the underlying model.
- - The normalized score is rescaled in such a way that the interval between 0 and 1 maps to the defined severity levels.
-
- """
-
- raw: float
- normalized: float
-
-
-class LineRange(SerializableModel):
- start: int
- end: int
-
- def __lt__(self, other: "LineRange") -> bool:
- if self.start != other.start:
- return self.start < other.start
- return self.end < other.end
-
- @classmethod
- def build_from_substring(cls, file_contents: str, substring: str) -> tuple["LineRange", ...]:
- """
- Convert a substring in a file to a tuple of LineRange instances.
-
- Each LineRange instance corresponds to a single occurrence of the substring in the file.
- (Except when multiple occurences are on the same line, in which case only one LineRange is
- created to represent them).
-
- LineRanges are returned in the order they appear in the file.
-
- In case the substring can't be found, an empty tuple is returned.
-
- """
-
- line_ranges = set()
- offset_chars = 0
- offset_lines = 0
- while True:
- cut_contents = file_contents[offset_chars:]
- start_index = cut_contents.find(substring)
- if start_index == -1:
- break
- end_index = start_index + len(substring)
- line_start = offset_lines + cut_contents.count("\n", 0, start_index)
- line_end = offset_lines + cut_contents.count("\n", 0, end_index)
- offset_chars += end_index
- offset_lines = line_end
- line_ranges.add(LineRange(start=line_start, end=line_end))
- return tuple(sorted(line_ranges))
-
-
-class AgenticPhase(StrEnum):
- """Phases of agentic analysis."""
-
- ISSUE_IDENTIFICATION = "issue_identification"
- COLLATION = "collation"
- FILTRATION = "filtration"
- DEDUPLICATION = "deduplication"
-
-
-class IssueIdentifierType(StrEnum):
- BATCHED_COMMIT_CHECK = "batched_commit_check"
- CORRECTNESS_COMMIT_CLASSIFIER = "correctness_commit_classifier"
- AGENTIC_ISSUE_IDENTIFIER = "agentic_issue_identifier"
- CONVERSATION_HISTORY_IDENTIFIER = "conversation_history_issue_identifier"
-
-
-class IssueCode(StrEnum):
- """
- A code for the type of issue / error detected.
-
- The code can either correspond something very specific (e.g. "ambiguous_docstring")
- or to something more general (e.g. "function_implementation").
-
- The latter case would be used as an "umbrella" code in cases we don't know what exactly comes out of an issue verifier.
-
- """
-
- # Verifier-based.
- INCORRECT_FUNCTION_IMPLEMENTATION = "incorrect_function_implementation"
-
- # Batched file checks
- INEFFICIENT_CODE = "inefficient_code"
- BAD_NAMING = "bad_naming"
- POOR_DOCSTRING = "poor_docstring"
- RACE_CONDITION = "race_condition"
- HARDCODED_SECRET = "hardcoded_secret"
- DUPLICATE_CODE = "duplicate_code"
- UNUSED_CODE = "unused_code"
- COMMIT_MESSAGE_MISMATCH = "commit_message_mismatch"
-
- # Batched commit checks
- INCOMPLETE_INTEGRATION_WITH_EXISTING_CODE = "incomplete_integration_with_existing_code"
- DOCUMENTATION_IMPLEMENTATION_MISMATCH = "documentation_implementation_mismatch"
- USER_REQUEST_ARTIFACTS_LEFT_IN_CODE = "user_request_artifacts_left_in_code"
- POOR_NAMING = "poor_naming"
- REPETITIVE_OR_DUPLICATE_CODE = "repetitive_or_duplicate_code"
- REFACTORING_NEEDED = "refactoring_needed"
- TEST_COVERAGE = "test_coverage"
- RESOURCE_LEAKAGE = "resource_leakage"
- DEPENDENCY_MANAGEMENT = "dependency_management"
- INSECURE_CODE = "insecure_code"
- CORRECTNESS_SYNTAX_ISSUES = "correctness_syntax_issues"
- FAILS_SILENTLY = "fails_silently"
- INSTRUCTION_FILE_DISOBEYED = "instruction_file_disobeyed"
- ABSTRACTION_VIOLATION = "abstraction_violation"
-
- # Correctness commit classifier
- LOGIC_ERROR = "logic_error"
- RUNTIME_ERROR_RISK = "runtime_error_risk"
- INCORRECT_ALGORITHM = "incorrect_algorithm"
- ERROR_HANDLING_MISSING = "error_handling_missing"
- ASYNC_CORRECTNESS = "async_correctness"
- TYPE_SAFETY_VIOLATION = "type_safety_violation"
-
- # Conversation history identifier
- MISLEADING_BEHAVIOR = "misleading_behavior"
- INSTRUCTION_TO_SAVE = "instruction_to_save"
-
- # Github dataset, not yet implemented in commit checks
- MISMATCHED_CODE_PATTERNS = "mismatched_code_patterns"
-
- # Issue code for flagging suggested improvements or new features, as opposed to actual issues
- SUGGESTED_IMPROVEMENT = "suggested_improvement"
-
- # Catchall
- MISCELLANEOUS = "miscellaneous"
- ALL_CODE_ISSUES = "all_code_issues"
-
- # Deprecated
- _DEPRECATED_LLM_ARTIFACTS_LEFT_IN_CODE = "llm_artifacts_left_in_code"
-
-
-class IssueLocation(SerializableModel):
- """A location in a file."""
-
- line_start: int
- line_end: int
- filename: str | None = None
- # The scope of the issue. Usually the qualified name of the function that the issue is located in.
- # If the issue is part of a class definition (and not limited to a particular method),
- # the name of the class. If the issue is at the global file level, None.
- scope: str | None = None
-
-
-IssueID = str
-
-
-class IdentifiedVerifyIssue(SerializableModel):
- """An identified code issue / error."""
-
- issue_id: IssueID | None = Field(default_factory=generate_id)
- code: IssueCode
- description: str
- severity_score: SeverityScore
- location: tuple[IssueLocation, ...] = Field(default_factory=tuple)
- confidence_score: ConfidenceScore | None = None
- fix: str | None = None
- violating_instruction: str | None = None
- violating_instruction_location: IssueLocation | None = None
- # TODO: remove these fields
-
- # An issue is fundamentally fixable if we can change the implementation to make the issue go away.
- # (An example of a non-fixable issue is a nonsensical commit message - changing the implementation doesn't help here.)
- # - iffv something is not fixable why would we want to report it?
- is_fixable: bool = True
-
-
-class InvocationInfo(SerializableModel):
- """Information about an LLM invocation including token usage, timing, and cost. Populate whichever fields are available."""
-
- input_tokens: int | None = None
- cache_creation_input_tokens: int | None = None
- cache_read_input_tokens: int | None = None
- total_input_tokens: int | None = None
- output_tokens: int | None = None
- duration_ms: float | None = None
- cost: float | None = None
- num_turns: int | None = None
-
-
-class IssueIdentificationLLMResponseMetadata(SerializableModel):
- """Configuration metadata for LLM responses."""
-
- type: Literal["IssueIdentificationLLMResponseMetadata", "IssueIdentificationLLMResponseConfig"] = (
- "IssueIdentificationLLMResponseMetadata"
- )
- agentic_phase: AgenticPhase | None = None
- issue_type: IssueCode | None = None
- identifier_name: str | None = None
- issue_ids: tuple[IssueID] | None = None
-
-
-class LLMResponse(SerializableModel):
- metadata: IssueIdentificationLLMResponseMetadata # Make this a union if there are other types of LLM responses
- raw_response: tuple[str, ...]
- invocation_info: InvocationInfo | None = None
-
- # Deprecated fields
- config: IssueIdentificationLLMResponseMetadata | None = Field(default=None, deprecated=True)
-
-
-class IssueIdentificationDebugInfo(SerializableModel):
- llm_responses: tuple[LLMResponse, ...]
-
-
-class IssueIdentifierResult(SerializableModel):
- """Container for an identified issue along with the LLM responses that generated it."""
-
- issue: IdentifiedVerifyIssue
- passes_filtration: bool = True
diff --git a/imbue_core/imbue_core/itertools.py b/imbue_core/imbue_core/itertools.py
@@ -1,52 +0,0 @@
-import contextlib
-import itertools
-from typing import Generator
-from typing import Iterable
-from typing import Sequence
-from typing import TypeVar
-
-from imbue_core.errors import ImbueError
-
-T = TypeVar("T")
-
-
-class ImbueItertoolsValueError(ImbueError, ValueError):
- """This value error is thrown when the assumptions of the itertools module are violated."""
-
-
-def flatten(iterable: Iterable[Iterable[T]]) -> list[T]:
- return list(itertools.chain.from_iterable(iterable))
-
-
-def remove_none(data: Iterable[T | None]) -> list[T]:
- return [x for x in data if x is not None]
-
-
-def only(x: Iterable[T]) -> T:
- try:
- (value,) = x
- except ValueError as e:
- message = "Expected exactly one value"
- if isinstance(x, Sequence):
- with contextlib.suppress():
- message += f" but got {len(x)} {x[:3]=}"
- raise ImbueItertoolsValueError(message) from e
-
- return value
-
-
-def first(iterable: Iterable[T]) -> T | None:
- return next(iter(iterable), None)
-
-
-# TODO replace with itertools.batched when we can require Python 3.12+
-def generate_chunks(iterable: Iterable[T], chunk_size: int) -> Generator[tuple[T, ...], None, None]:
- """Yield successive n-sized chunks from any iterable"""
- chunk = []
- for item in iterable:
- chunk.append(item)
- if len(chunk) == chunk_size:
- yield tuple(chunk)
- chunk = []
- if len(chunk) > 0:
- yield tuple(chunk)
diff --git a/imbue_core/imbue_core/nested_evolver.py b/imbue_core/imbue_core/nested_evolver.py
@@ -1,220 +0,0 @@
-"""Evolver uses duck-typing to give the appearance of editing a frozen, nested structure of attrs classes and tuples, recording changes in a way that they can be applied to generate a newly frozen instance.
-
-One of the design goals is that mypy, autocomplete, and automatic refactoring work for the assignments made into these nested structures.
-
-If you make changes here and then the tests fail with:
-```
-E RecursionError: maximum recursion depth exceeded
-!!! Recursion detected (same locals & position)
-```
-It's possible that you're accidentally invoking `Evolver.something_undefined` and that's causing the infinite recursion.
-Mypy cannot catch this when it thinks the type is an `Evolver` because the `Evolver` class has a `__getattr__` method that makes it look like any attribute access could be valid.
-"""
-
-import threading
-from typing import Any
-from typing import Callable
-from typing import Generic
-from typing import TypeVar
-from typing import cast
-
-import attr
-from pydantic import BaseModel
-
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.pydantic_utils import model_update
-
-_T = TypeVar("_T")
-
-_threading_local = threading.local()
-
-
-def evolver(obj: _T) -> _T:
- """Creates a wrapper around an immutable attrs object, tuple, or FrozenDict that records potentially nested attribute assignments.
-
- The return type is our first white lie to the type system.
- """
- result = _Evolver[_T](obj)
- # The cast is a little white lie to the type system to make type-checking, autocomplete, and refactoring work.
- return cast(_T, result)
-
-
-def assign(dest: _T, src: Callable[[], _T]) -> None:
- """Since mypy would complain about assignments to frozen attrs fields, use this function to make assignments.
-
- The only reason src is a `Callable[[], _T]` instead of just `_T` is that it makes type checking signal attempts to assign
- the wrong type to the field. Surprisingly, just using `(dest: _T, src: _T)` doesn't cause mypy to complain about type mismatch.
- """
- assert isinstance(dest, _Evolver) # Tricked you, type system!
- dest_evolver: _Evolver[_T] = cast(_Evolver[_T], dest)
- dest_evolver.assign(src())
-
-
-def chill(evolver: _T) -> _T:
- """Produces a new frozen instance with the recorded changes applied.
-
- The name `chill` is a play on the fact that original input was frozen, and we are now re-freezing it.
- """
- assert isinstance(evolver, _Evolver) # Tricked you, type system!
- cast_evolver = cast(_Evolver[_T], evolver)
- return cast_evolver.chill()
-
-
-class _RegularValue:
- regular_value: Any
-
- def __init__(self, value: Any) -> None:
- self.regular_value = value
-
-
-class _AttrValue:
- attr_value: Any
- child_evolver_by_name: dict[str, "_Evolver[Any]"]
-
- def __init__(self, value: Any) -> None:
- self.attr_value = value
- self.child_evolver_by_name = {}
-
-
-class _PydanticModelValue:
- pydantic_model_value: Any
- child_evolver_by_name: dict[str, "_Evolver[Any]"]
-
- def __init__(self, value: Any) -> None:
- self.pydantic_model_value = value
- self.child_evolver_by_name = {}
-
-
-class _TupleValue:
- tuple_evolvers: list["_Evolver[Any]"]
-
- def __init__(self, value: tuple[Any, ...]) -> None:
- # It may be premature to create evolvers for all the elements of the tuple, but it's easier.
- self.tuple_evolvers = [evolver(item) for item in value]
-
-
-class _FrozenDictValue:
- frozen_dict_evolvers: dict[Any, "_Evolver[Any]"]
-
- def __init__(self, value: dict[Any, Any]) -> None:
- # It may be premature to create evolvers for all the elements of dict, but it's easier.
- self.frozen_dict_evolvers = {k: evolver(v) for k, v in value.items()}
-
-
-class _Evolver(Generic[_T]):
- # pyre-ignore[13]: pyre is confused by the trickery here
- _value: (
- _RegularValue
- | _AttrValue
- | _TupleValue
- | _FrozenDictValue
- | _PydanticModelValue
- )
-
- def __init__(self, initial_value: _T) -> None:
- super().__init__()
- self.assign(initial_value)
-
- def assign(self, new_value: _T) -> None:
- """Assign a new value to this Evolver, recording a change to the frozen structure to be later applied during `chill()`."""
-
- if attr.has(type(new_value)):
- self._value = _AttrValue(new_value)
- elif isinstance(new_value, BaseModel):
- self._value = _PydanticModelValue(new_value)
- elif isinstance(new_value, tuple):
- self._value = _TupleValue(new_value)
- elif isinstance(new_value, FrozenDict):
- self._value = _FrozenDictValue(new_value)
- else:
- self._value = _RegularValue(new_value)
-
- def __getattr__(self, item: str) -> "_Evolver[Any]":
- """Access Evolvers for nested members of a frozen attrs object."""
- try:
- value = self._value
- if isinstance(value, _AttrValue):
- if item not in value.child_evolver_by_name:
- child_obj = getattr(value.attr_value, item)
- result = evolver(child_obj)
- assert isinstance(result, _Evolver), (
- "Expose a lie to the type system."
- )
- value.child_evolver_by_name[item] = result
- return value.child_evolver_by_name[item]
- elif isinstance(value, _PydanticModelValue):
- if item not in value.child_evolver_by_name:
- child_obj = getattr(value.pydantic_model_value, item)
- result = evolver(child_obj)
- assert isinstance(result, _Evolver), (
- "Expose a lie to the type system."
- )
- value.child_evolver_by_name[item] = result
- return value.child_evolver_by_name[item]
- raise TypeError(
- f"You're trying to access field {item=} on an object of {type(value)=} that doesn't have that field (should have been a mypy error)."
- )
- except BaseException as e:
- if hasattr(_threading_local, "evolved_obj"):
- # pyre-ignore[16]: pyre is suspicious of the trickery here
- if getattr(_threading_local, "evolved_obj") == self:
- delattr(_threading_local, "evolved_obj")
- raise e
-
- # TODO: It wouldn't be terribly difficult to support "appending" to the tuple as well, by appending to this list.
- def __getitem__(self, key: Any) -> "_Evolver[Any]":
- """Access Evolvers for the elements of a tuple or dict."""
- value = self._value
- if isinstance(value, _TupleValue):
- assert isinstance(key, int)
- return value.tuple_evolvers[key]
- elif isinstance(value, _FrozenDictValue):
- if key not in value.frozen_dict_evolvers:
- # Presumably we're going to evolver_assign to this very soon.
- cast(_FrozenDictValue, self._value).frozen_dict_evolvers[key] = (
- _Evolver(_RegularValue(None))
- )
- return cast(_FrozenDictValue, self._value).frozen_dict_evolvers[key]
- raise TypeError(
- f"You're using [square_brackets] access {key=} on an object of {type(self._value)=} that doesn't support this (should have been a mypy error)."
- )
-
- def chill(self) -> _T:
- """Recursively apply the recorded changes to the original object and return a new frozen instance."""
- if isinstance(self._value, _AttrValue):
- new_children: dict[str, Any] = {
- name: chill(child)
- for name, child in self._value.child_evolver_by_name.items()
- }
- assert attr.has(self._value.attr_value.__class__)
- return cast(
- _T,
- attr.evolve(
- cast(Any, cast(_AttrValue, self._value).attr_value), **new_children
- ),
- )
- elif isinstance(self._value, _PydanticModelValue):
- return cast(
- _T,
- model_update(
- self._value.pydantic_model_value,
- update={
- name: chill(child)
- for name, child in self._value.child_evolver_by_name.items()
- },
- ),
- )
- elif isinstance(self._value, _TupleValue):
- return cast(
- _T, tuple(evolver.chill() for evolver in self._value.tuple_evolvers)
- )
- elif isinstance(self._value, _RegularValue):
- return cast(_T, self._value.regular_value)
- elif isinstance(self._value, _FrozenDictValue):
- return cast(
- _T,
- FrozenDict(
- {k: v.chill() for k, v in self._value.frozen_dict_evolvers.items()}
- ),
- )
- raise ValueError(f"This Evolver has no value to evolve, {type(self._value)=}.")
diff --git a/imbue_core/imbue_core/pydantic_serialization.py b/imbue_core/imbue_core/pydantic_serialization.py
@@ -1,118 +0,0 @@
-import threading
-from typing import Any
-from typing import TypeVar
-from typing import cast
-
-from pydantic import BaseModel
-from pydantic import ConfigDict
-from pydantic import Discriminator
-from pydantic.alias_generators import to_camel
-
-from imbue_core.nested_evolver import _Evolver
-from imbue_core.nested_evolver import chill
-from imbue_core.nested_evolver import evolver
-from imbue_core.serialization_types import Serializable
-
-T = TypeVar("T", bound=BaseModel)
-V = TypeVar("V")
-
-_threading_local = threading.local()
-
-
-class EvolvableModel:
- # pyre-ignore[47]: pyre is not so easily tricked
- def evolve(self: T, attribute: V, new_value: V) -> T:
- # pyre-ignore[16]: pyre doesn't know about evolved_obj
- assert _threading_local.evolved_obj is not None, (
- ".ref() must be called before evolve"
- )
-
- assert isinstance(attribute, _Evolver) # Tricked you, type system!
- dest_evolver: _Evolver[T] = cast(_Evolver[T], attribute)
- dest_evolver.assign(new_value)
-
- result = chill(_threading_local.evolved_obj)
- _threading_local.evolved_obj = None
- return result
-
- # pyre-ignore[47]: pyre is not so easily tricked
- def ref(self: T) -> T:
- # pyre-ignore[16]: pyre doesn't know about evolved_obj
- _threading_local.evolved_obj = evolver(self)
- return _threading_local.evolved_obj
-
-
-class MutableModel(BaseModel):
- """
- The base class for any internal data that strictly must be mutable. Should be used sparingly.
- """
-
- model_config = ConfigDict(
- frozen=False,
- extra="forbid",
- # FIXME: go back to preventing arbitrary types once we're done converting
- # arbitrary_types_allowed=False,
- arbitrary_types_allowed=True,
- )
-
-
-class SerializableModel(EvolvableModel, BaseModel, Serializable):
- """
- The base class for all data that can be serialized to/from JSON.
- """
-
- model_config = ConfigDict(
- frozen=True,
- ser_json_bytes="base64",
- val_json_bytes="base64",
- alias_generator=to_camel,
- validate_by_alias=True,
- validate_by_name=True,
- # any extra values will end up in the __pydantic_extra__ field
- # this is effectively required for backwards compatibility
- # IMPORTANT: note that, by default, we clear this below! These types are ONLY for backwards compatibility
- extra="allow",
- # this is also effectively required for backwards compatibility
- arbitrary_types_allowed=True,
- )
-
- # this is a place where we might way to do any backwards compatibility related logic
- def model_post_init(self, __context: Any) -> None:
- pydantic_extra = self.__pydantic_extra__
- assert pydantic_extra is not None
- pydantic_extra.clear()
-
-
-# this is mostly here for the default cases.
-# When you want to upgrade a model (and keep it backwards compatible), you can make a custom discriminator
-# (eg, that looks for the old type name or converts the old class names)
-def build_discriminator(
- field_name: str = "object_type",
- additional_types_and_string_representations: tuple[tuple[type, str], ...] = (),
-) -> Discriminator:
- """
- Build a discriminator function for a Pydantic model.
-
- Args:
- field_name (str): The name of the field to use as the discriminator.
- additional_types_and_string_representations (Tuple[Tuple[Type, str], ...]): Register additional types to the discriminator.
-
- Returns:
- Callable[[T | dict], str]: A function that takes an instance of T or a dictionary and returns the value of the
- specified field.
- """
-
- def discriminator(obj: T | dict) -> str:
- for (
- model_type,
- string_representation,
- ) in additional_types_and_string_representations:
- if isinstance(obj, model_type):
- return string_representation
- if isinstance(obj, dict):
- if field_name not in obj:
- return obj[to_camel(field_name)]
- return obj[field_name]
- return getattr(obj, field_name)
-
- return Discriminator(discriminator=discriminator)
diff --git a/imbue_core/imbue_core/serialization.py b/imbue_core/imbue_core/serialization.py
@@ -1,424 +0,0 @@
-import builtins
-import datetime
-import json
-from enum import Enum
-from functools import cached_property
-from importlib import import_module
-from importlib.metadata import version
-from pathlib import PosixPath
-from traceback import format_tb
-from types import TracebackType
-from typing import Any
-from typing import Hashable
-from typing import Iterable
-from typing import Mapping
-from typing import TypeVar
-from typing import cast
-from uuid import UUID
-
-from loguru import logger
-from typing_extensions import TypeAliasType
-from yasoo import Deserializer
-from yasoo import Serializer
-from yasoo.constants import ENUM_VALUE_KEY
-from yasoo.objects import DictWithSerializedKeys
-from yasoo.serialization import _convert_to_json_serializable
-from yasoo.utils import get_fields
-from yasoo.utils import is_obj_supported_primitive
-from yasoo.utils import normalize_type
-from yasoo.utils import resolve_types
-
-from imbue_core.fixed_traceback import FixedTraceback
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_core.serialization_types import Serializable
-
-assert version("yasoo") == "0.12.6", (
- "This code was written for yasoo 0.12.6 and requires inheriting / monkeypatching the deserializer, so you probably don't want to use any other version without fixing TupleDeserializer"
-)
-
-T = TypeVar("T", bound=Hashable)
-
-
-class TupleDeserializer(Deserializer):
- def _deserialize(
- self,
- data: bool | int | float | str | list[Any] | dict[str, Any] | None,
- obj_type: type[T] | None,
- type_key: str | None,
- allow_extra_fields: bool,
- external_globals: dict[str, Any],
- ignore_custom_deserializer: bool = False,
- ) -> object:
- all_globals = dict(globals())
- all_globals.update(external_globals)
- if is_obj_supported_primitive(data):
- return data
- if isinstance(data, list):
- list_types = self._get_list_types(obj_type, data)
- return tuple(
- [
- self._deserialize(d, t, type_key, allow_extra_fields, all_globals)
- for t, d in list_types
- ]
- )
-
- assert isinstance(data, dict), f"Expected a dict, but got {type(data)}"
-
- # load wrapped primitives
- if type_key is not None:
- type_data = data.get(type_key, None)
-
- if (
- type_data is not None
- and type_data.startswith("builtins.")
- and type_data != "builtins.dict"
- ):
- return data["value"]
-
- # TODO: we need to potentially handle `builtins.dict`
- # if type_key is not None:
- # type_data = data.get(type_key, None)
- #
- # # TODO: serialization currently breaks with builtin.dicts and dicts with non-string keys
- # if type_data == "builtins.dict":
- # raise NotImplementedError(
- # "Only `FrozenMapping` is supported for dict serialization/deserialization, call `freeze_mapping` on your dict before serializing"
- # )
- # if type_data is not None and type_data.startswith("builtins.") and type_data != "builtins.dict":
- # return data["value"]
-
- # TODO: remove this hack. Many of our sqlite files (search s3_sqlite_path) have FrozenDicts
- if (
- isinstance(type_key, str)
- and data.get(type_key, None) == "flax.core.frozen_dict.FrozenDict"
- ):
- data[type_key] = "imbue_core.frozen_utils.FrozenMapping"
- # we deliberately pass in a `None` type_key sometimes, which results in just returning obj_type
- obj_type = self._get_object_type(
- obj_type, data, type_key, all_globals
- ) # pyre-ignore[6]
- if type_key in data:
- data.pop(type_key)
- real_type, generic_args = normalize_type(obj_type, all_globals)
- if external_globals and isinstance(real_type, type):
- bases = {real_type}
- while bases:
- all_globals.update((b.__name__, b) for b in bases)
- bases = {ancestor for b in bases for ancestor in b.__bases__}
-
- if not ignore_custom_deserializer:
- deserialization_method = self._custom_deserializers.get(
- obj_type, self._custom_deserializers.get(real_type)
- )
- if deserialization_method:
- return deserialization_method(data)
- for base_class, method in self._inheritance_deserializers.items():
- if issubclass(real_type, base_class):
- return method(data, real_type)
-
- key_type = None
- try:
- # pyre-fixme[6]: obj_type needs to be Hashable, but pyre isn't sure that it is
- fields = {f.name: f for f in get_fields(obj_type)}
- except TypeError:
- if obj_type is FixedTraceback:
- return FixedTraceback.from_dict(data["value"])
- if issubclass(real_type, Enum):
- value = data[ENUM_VALUE_KEY]
- if isinstance(value, str):
- try:
- return real_type[value]
- except KeyError:
- for e in real_type:
- if e.name.lower() == value.lower():
- return e
- return real_type(value)
- # TODO: serialization currently breaks with builtin.dicts and dicts with non-string keys
- # if you have weird keys in your dict this branch won't be hit and your object won't be properly deserialized
- elif issubclass(real_type, Mapping):
- key_type = generic_args[0] if generic_args else None
- if self._is_mapping_dict_with_serialized_keys(key_type, data):
- # pyre-fixme[9]: obj_type needs to be Hashable, but pyre doesn't realize that type[DictWithSerializedKeys] is ok
- obj_type = DictWithSerializedKeys
- # pyre-fixme[6]: arg of get_fields needs to be Hashable, but pyre doesn't realize that type[DictWithSerializedKeys] is ok
- fields = {f.name: f for f in get_fields(DictWithSerializedKeys)}
- value_type = generic_args[1] if generic_args else Any
- fields["data"].field_type = dict[str, value_type] # type: ignore
- else:
- return self._load_mapping(
- data,
- real_type,
- generic_args,
- type_key,
- allow_extra_fields,
- all_globals,
- )
- elif issubclass(real_type, Iterable):
- # If we got here it means data is not a list, so obj_type came from the data itself and is safe to use
- return self._load_iterable(
- data, obj_type, type_key, allow_extra_fields, all_globals
- )
- elif real_type != obj_type:
- return self._deserialize(
- data, real_type, type_key, allow_extra_fields, external_globals
- )
- else:
- raise
-
- self._check_for_missing_fields(data, fields, obj_type)
- self._check_for_extraneous_fields(data, fields, obj_type, allow_extra_fields)
- self._load_inner_fields(data, fields, type_key, allow_extra_fields, all_globals)
- if obj_type is DictWithSerializedKeys:
- return self._load_dict_with_serialized_keys(
- obj_type(**data), key_type, type_key, allow_extra_fields, all_globals
- )
- kwargs = {k: v for k, v in data.items() if fields[k].init}
- assert obj_type is not None
- result = obj_type(**kwargs)
- for k, v in data.items():
- if k not in kwargs:
- setattr(result, k, v)
- return result
-
-
-# TODO: probably a good idea to ensure that all dicts are frozen as well...
-class FrozenSerializer(Serializer):
- def __init__(
- self, force_serialization: bool, allow_unsafe_list_serialization: bool = False
- ) -> None:
- super().__init__()
- self._force_serialization = force_serialization
- self._allow_unsafe_list_serialization = allow_unsafe_list_serialization
-
- def _serialize_iterable(
- self,
- obj: Iterable[object],
- type_key: Any,
- fully_qualified_types: Any,
- preserve_iterable_types: Any,
- stringify_dict_keys: Any,
- ) -> list[object]:
- if isinstance(obj, list):
- if self._allow_unsafe_list_serialization:
- logger.info("Converting list to tuple for serialization: {}", obj)
- obj = tuple(obj)
- else:
- raise Exception(
- f"Lists are not allowed for serialization. Use tuples instead. Current iterable: {obj}"
- )
- assert isinstance(obj, (tuple, frozenset, bytes)), (
- f"All iterables should be tuples or frozenset. Received {obj}"
- )
- return cast(
- list[object],
- tuple(
- self._serialize(
- item,
- type_key,
- fully_qualified_types,
- preserve_iterable_types,
- stringify_dict_keys,
- )
- for item in obj
- ),
- )
-
- # overriding this method just to get some better error messages out--previously it would just "type error" and
- # moan about things like int64 not being serializable, which is fine, but it is nicer if the key is included
- def serialize(
- self,
- obj: Any,
- type_key: str | None = "__type",
- fully_qualified_types: bool = True,
- preserve_iterable_types: bool = False,
- stringify_dict_keys: bool = True,
- globals: dict[str, Any] | None = None,
- ) -> bool | int | float | str | list | dict[str, Any] | None:
- try:
- if is_obj_supported_primitive(obj):
- return obj # type: ignore
-
- if globals:
- self._custom_serializers = resolve_types(
- self._custom_serializers, globals
- ) # type: ignore
-
- result = self._serialize(
- obj,
- type_key,
- fully_qualified_types,
- preserve_iterable_types,
- stringify_dict_keys,
- inner=False,
- )
- try:
- result = _convert_to_json_serializable(result)
- except TypeError:
- _convert_to_json_serializable_with_better_errors(result)
- assert False, "previous method should have raised..."
- return result # type: ignore
- except Exception:
- if self._force_serialization:
- return repr(obj)
- else:
- raise
-
-
-JsonTypeAlias = TypeAliasType(
- "JsonTypeAlias",
- "dict[str, JsonTypeAlias] | list[JsonTypeAlias] | str | int | float | bool | None",
-)
-
-
-class SerializedException(SerializableModel):
- """A serializable dataclass that represents an exception"""
-
- exception: str
- args: "tuple[SerializedException | JsonTypeAlias, ...]" # pyre-ignore[11]: pyre doesn't like TypeAliasType
- traceback_dict: JsonTypeAlias
-
- @classmethod
- def build(
- cls, exception: BaseException, traceback: TracebackType | None = None
- ) -> "SerializedException":
- if traceback is None:
- traceback = exception.__traceback__
- assert traceback is not None, " ".join(
- (
- "No traceback deriveable or as a concrete argument!",
- f"You probably want to convert_to_serialized_exception in your except clause: {exception=}",
- )
- )
- return SerializedException( # pyre-fixme[28]: pyre doesn't understand pydantic
- exception=get_fully_qualified_name_for_error(exception),
- args=tuple(
- _convert_serialized_exception_args(x, traceback) for x in exception.args
- ),
- traceback_dict=FixedTraceback.from_tb(traceback).as_dict(),
- )
-
-
-def _convert_serialized_exception_args(
- error: Serializable, traceback: TracebackType | None = None
-) -> JsonTypeAlias:
- if isinstance(error, BaseException):
- return SerializedException.build(error, traceback=traceback)
- elif isinstance(error, (list, tuple)):
- return tuple(_convert_serialized_exception_args(x, traceback) for x in error)
- return error
-
-
-def get_fully_qualified_name_for_error(e: BaseException) -> str:
- if e.__class__.__module__ == "builtins":
- return e.__class__.__name__
- return f"{e.__class__.__module__}.{e.__class__.__name__}"
-
-
-def _convert_to_json_serializable_with_better_errors(
- obj: Any, path: str = ""
-) -> int | float | str | list | dict | None:
- if is_obj_supported_primitive(obj):
- return obj # type: ignore
- if isinstance(obj, Mapping):
- return {
- key: _convert_to_json_serializable_with_better_errors(
- value, f"{path}.{key}"
- )
- for key, value in obj.items()
- }
- if isinstance(obj, Iterable):
- return [
- _convert_to_json_serializable_with_better_errors(item, f"{path}[{i}]")
- for i, item in enumerate(obj)
- ]
- raise TypeError(
- f'Found object of type "{type(obj).__name__}" at {path} which cannot be serialized'
- )
-
-
-SERIALIZER = FrozenSerializer(
- force_serialization=False, allow_unsafe_list_serialization=False
-)
-DESERIALIZER = TupleDeserializer()
-
-# note: you cannot change this without changing other calls to yasoo, this is its default
-TYPE_KEY = "__type"
-
-
-class SerializationError(Exception):
- pass
-
-
-@SERIALIZER.register()
-def serialize_frozen_set(data: frozenset) -> dict:
- value = SERIALIZER.serialize(tuple(data))
- return {"value": value}
-
-
-@DESERIALIZER.register()
-def deserialize_frozen_set(data: dict) -> frozenset:
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-
-
-@SERIALIZER.register()
-def serialize_uuid(data: UUID) -> dict:
- return {"value": data.hex}
-
-
-@DESERIALIZER.register()
-def deserialize_uuid(data: dict) -> UUID:
- return UUID(data["value"])
-
-
-@SERIALIZER.register()
-def serialize_traceback(data: FixedTraceback) -> dict:
- return {"value": data.to_dict()}
-
-
-@DESERIALIZER.register()
-def deserialize_traceback(data: dict) -> FixedTraceback:
- return FixedTraceback.from_dict(data["value"])
-
-
-@SERIALIZER.register()
-def serialize_posix_path(data: PosixPath) -> dict:
- return {"value": str(data)}
-
-
-@DESERIALIZER.register()
-def deserialize_posix_path(data: dict) -> PosixPath:
- return PosixPath(data["value"])
-
-
-@SERIALIZER.register()
-def serialize_datetime(data: datetime.datetime) -> dict:
- return {
- "time": data.astimezone(datetime.timezone.utc).timestamp(),
- "tzaware": data.tzinfo is not None,
- "__type": "datetime.datetime",
- }
-
-
-@DESERIALIZER.register()
-def deserialize_datetime(data: dict) -> datetime.datetime:
- return datetime.datetime.fromtimestamp(
- data["time"], datetime.timezone.utc if data.get("tzaware", None) else None
- )
-
-
-def serialize_to_json(
- obj: Any, indent: int | None = None, sort_keys: bool = False
-) -> str:
- try:
- return json.dumps(SERIALIZER.serialize(obj), indent=indent, sort_keys=sort_keys)
- except Exception as e:
- raise SerializationError(str(e)) from e
-
-
-def deserialize_from_json(data: str) -> Any:
- try:
- return DESERIALIZER.deserialize(
- json.loads(data)
- ) # pyre-ignore[20]: pyre doesn't understand deserialize
- except Exception as e:
- raise SerializationError(str(e)) from e
diff --git a/imbue_core/pyproject.toml b/imbue_core/pyproject.toml
@@ -1,66 +0,0 @@
-[build-system]
-requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "imbue-core"
-version = "0.0.9"
-description = "Utilities for imbue-desktop."
-readme = "README.md"
-authors = [
- { name="Imbue", email="imbue@imbue.com" },
-]
-license = "MIT"
-dependencies = [
- "anyio",
- "attrs",
- "boto3>=1.38.27",
- "cachetools",
- "cattrs",
- "diskcache>=5.6.3",
- "grpclib>=0.4.7",
- "httpx",
- "inline-snapshot",
- "loguru",
- "pathspec",
- "prometheus-client>=0.20.0",
- "pydantic-settings",
- "pydantic>=2.11.4",
- "pygit2>=1.18.0",
- "pylint==3.2.6",
- "pygments>=2.0.0",
- "pyhumps",
- "pytest",
- "pytest-asyncio",
- "pytest-mock",
- "syrupy",
- "python-gitlab>=4.5.0",
- "tblib==2.0.0", # pinning because Hammers code relies on "get_locals"
- "tenacity>=8.2.2",
- "toml",
- "traceback-with-variables>=2.2.0",
- "typeid-python",
- "yasoo",
-
- "anthropic~=0.54", # forcing a newer version that is not reliant on old httpx
- "openai>=1.79.0",
- "tiktoken",
- "together",
- "groq>=0.18.0",
- "google-genai>=1.26.0",
-]
-requires-python = ">=3.11,<3.13"
-
-[project.urls]
-"homepage" = "https://imbue.com/"
-
-[tool.setuptools.package-data]
-"imbue" = ["py.typed"]
-
-[tool.setuptools.packages.find]
-include = ["imbue_core*"]
-
-[dependency-groups]
-dev = [
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-]
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diff --git a/imbue_tools/README.md b/imbue_tools/README.md
@@ -1,9 +0,0 @@
-# Purpose
-Shared functionality for CLI tools like vet.
-
-# Contents
-- formatting git repos as LLM input
-
-# Excluded files list
-We currently maintain a hard-coded list of filename patterns for which we'll only ever include the file name, rather than the files' contents, in the LLM input.
-This list is maintained in the EXCLUSIONS_PATHSPEC constant in imbue_tools/imbue_tools/repo_utils/context_prefix.py .
diff --git a/imbue_tools/imbue_tools/get_conversation_history/get_conversation_history.py b/imbue_tools/imbue_tools/get_conversation_history/get_conversation_history.py
@@ -1,70 +0,0 @@
-import json
-from typing import assert_never
-
-from loguru import logger
-from pydantic import TypeAdapter
-from pydantic import ValidationError
-
-from vet_types.chat_state import ContentBlockTypes
-from vet_types.messages import ChatInputUserMessage
-from vet_types.messages import ConversationMessageUnion
-from vet_types.messages import ResponseBlockAgentMessage
-
-
-class ConversationLoadingError(Exception):
- pass
-
-
-# === formatting for prompt ===
-
-
-def delete_unnecessary_content_block_fields(block: ContentBlockTypes) -> str:
- """Returns the content as a json-serialized string without the fields that we don't want to include in the prompt"""
- fields_to_remove = {"id"}
- return block.model_dump_json(exclude=fields_to_remove)
-
-
-def delete_unnecessary_conversation_message_fields(
- message: ConversationMessageUnion,
-) -> str:
- """Returns the message as a json-serialized string without the fields that we don't want to include in the prompt"""
- general_fields_to_remove = {"message_id", "source", "approximate_creation_time"}
- match message:
- case ChatInputUserMessage():
- # remove the 'files' field if it's empty
- fields_to_remove = general_fields_to_remove | {"model_name"} | {"files"} if not message.files else set()
- return message.model_dump_json(exclude=fields_to_remove)
- case ResponseBlockAgentMessage():
- fields_to_remove = general_fields_to_remove | {"assistant_message_id"}
- return json.dumps(
- message.model_dump(mode="json", exclude=fields_to_remove)
- | {"content": [delete_unnecessary_content_block_fields(block) for block in message.content]}
- )
- case _ as unreachable:
- assert_never(unreachable)
-
-
-def format_conversation_history_for_prompt(
- conversation_history: tuple[ConversationMessageUnion, ...],
-) -> str:
- formatted_messages = [delete_unnecessary_conversation_message_fields(message) for message in conversation_history]
- return "\n".join(message for message in formatted_messages if message is not None)
-
-
-# === loading from file ===
-
-
-def parse_conversation_history(
- conversation_str: str,
-) -> tuple[ConversationMessageUnion, ...]:
- """Load a jsonl string into a list of conversation messages"""
- messages = []
- for line in conversation_str.strip().splitlines():
- try:
- # deserialize the message with pydantic
- message: ConversationMessageUnion = TypeAdapter(ConversationMessageUnion).validate_json(line)
- except ValidationError:
- logger.info("Skipping malformed history line {}", line)
- continue
- messages.append(message)
- return tuple(messages)
diff --git a/imbue_tools/imbue_tools/get_conversation_history/input_data_types.py b/imbue_tools/imbue_tools/get_conversation_history/input_data_types.py
@@ -1,72 +0,0 @@
-from typing import Self
-from typing import TypeVar
-
-from pydantic import model_validator
-
-from imbue_core.pydantic_serialization import SerializableModel
-from vet_types.messages import ConversationMessageUnion
-
-
-class IdentifierInputsMissingError(Exception):
- pass
-
-
-class IdentifierInputs(SerializableModel):
- # goal (for now, commit message) and diff to check
- maybe_goal: str | None = None
- maybe_diff: str | None = None
-
- # whole files to check
- maybe_files: tuple[str, ...] | None = None
-
- # conversation history to check
- maybe_conversation_history: tuple[ConversationMessageUnion, ...] | None = None
-
-
-class CommitInputs(IdentifierInputs):
- # pyre-ignore[56]: pyre's stubs don't match pydantic v2 decorator signatures
- @model_validator(mode="after")
- def validate_goal_not_none(self) -> Self:
- if self.maybe_goal is None:
- raise IdentifierInputsMissingError("goal cannot be None for CommitInputs")
- return self
-
- # pyre-ignore[56]: pyre's stubs don't match pydantic v2 decorator signatures
- @model_validator(mode="after")
- def validate_diff_not_none(self) -> Self:
- if self.maybe_diff is None:
- raise IdentifierInputsMissingError("goal cannot be None for CommitInputs")
- return self
-
- @property
- def goal(self) -> str:
- assert self.maybe_goal is not None
- return self.maybe_goal
-
- @property
- def diff(self) -> str:
- assert self.maybe_diff is not None
- return self.maybe_diff
-
-
-class ConversationInputs(IdentifierInputs):
- # pyre-ignore[56]: pyre's stubs don't match pydantic v2 decorator signatures
- @model_validator(mode="after")
- def validate_conversation_history_not_none(self) -> Self:
- if self.maybe_conversation_history is None:
- raise IdentifierInputsMissingError("conversation_history is required for conversation inputs")
- return self
-
- @property
- def conversation_history(self) -> tuple[ConversationMessageUnion, ...]:
- assert self.maybe_conversation_history is not None
- return self.maybe_conversation_history
-
-
-SpecificIdentifierInputsType = TypeVar("SpecificIdentifierInputsType", bound=IdentifierInputs)
-
-
-def to_specific_inputs_type(
- identifier_inputs: IdentifierInputs, to_type: type[SpecificIdentifierInputsType]
-) -> SpecificIdentifierInputsType:
- return to_type(**identifier_inputs.model_dump())
diff --git a/imbue_tools/imbue_tools/llm_output_parsing/parse_model_json_response.py b/imbue_tools/imbue_tools/llm_output_parsing/parse_model_json_response.py
@@ -1,51 +0,0 @@
-import json
-import re
-from typing import TypeVar
-
-from pydantic import ValidationError
-
-from imbue_core.async_monkey_patches import log_exception
-from imbue_core.pydantic_serialization import SerializableModel
-
-
-def parse_json_block_from_response_text(response_text: str) -> str:
- """Clean markdown formatting and extra content from LLM response."""
- response_text = response_text.strip()
- # Parse content between first ```json and ``` block
- json_start_line = re.search(r"^.*?```json\s*", response_text, flags=re.MULTILINE)
- if json_start_line:
- response_text = response_text[json_start_line.end() :]
- json_end_line = re.search(r"```\s*$", response_text, flags=re.MULTILINE)
- if json_end_line:
- response_text = response_text[: json_end_line.start()]
- return response_text.strip()
-
-
-ResponseSchema = TypeVar("ResponseSchema", bound=SerializableModel)
-
-
-class ResponseParsingError(Exception):
- pass
-
-
-def parse_model_json_response(response_text: str, result_type: type[ResponseSchema]) -> ResponseSchema:
- """Parse a JSON response from the LLM into a Pydantic model."""
- cleaned_response = parse_json_block_from_response_text(response_text)
- try:
- return result_type.model_validate_json(cleaned_response)
- except json.JSONDecodeError as e:
- log_exception(
- e,
- "Response is not valid JSON.\nraw_response: {response_text}\ncleaned_response: {cleaned_response}",
- response_text=response_text,
- cleaned_response=cleaned_response,
- )
- raise ResponseParsingError(str(e)) from e
- except ValidationError as e:
- log_exception(
- e,
- "Response does not match the expected schema.\nraw_response: {response_text}\ncleaned_response: {cleaned_response}",
- response_text=response_text,
- cleaned_response=cleaned_response,
- )
- raise ResponseParsingError(str(e)) from e
diff --git a/imbue_tools/imbue_tools/repo_utils/context_prefix.py b/imbue_tools/imbue_tools/repo_utils/context_prefix.py
@@ -1,613 +0,0 @@
-import functools
-from enum import StrEnum
-from pathlib import Path
-from typing import Any
-from typing import Iterable
-from typing import Mapping
-from typing import assert_never
-
-from loguru import logger
-from pydantic import BaseModel
-from pydantic import ConfigDict
-
-from imbue_core.agents.configs import LanguageModelGenerationConfig
-from imbue_core.async_monkey_patches import log_exception
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_tools.repo_utils.context_utils import escape_prompt_markers
-from imbue_tools.repo_utils.context_utils import maybe_get_file_path_from_qualified_name
-from imbue_tools.repo_utils.data_types import FileContextUnion
-from imbue_tools.repo_utils.errors import ContextLengthExceededError
-from imbue_tools.repo_utils.file_system import InMemoryFileSystem
-from imbue_tools.repo_utils.python_imports import QualifiedName
-from imbue_tools.repo_utils.python_imports import STANDARD_LIBRARIES
-from imbue_tools.repo_utils.python_imports import get_global_imports
-from imbue_tools.repo_utils.subrepo_formatting import BaseFilenamePattern
-from imbue_tools.repo_utils.subrepo_formatting import ContextFormatStyle
-from imbue_tools.repo_utils.subrepo_formatting import ExactFilenamePattern
-from imbue_tools.repo_utils.subrepo_formatting import FilenamePattern
-from imbue_tools.repo_utils.subrepo_formatting import IntersectionFilenamePattern
-from imbue_tools.repo_utils.subrepo_formatting import NegatedFilenamePattern
-from imbue_tools.repo_utils.subrepo_formatting import REPO_CONTEXT_TEMPLATE
-from imbue_tools.repo_utils.subrepo_formatting import SubrepoContextMatchers
-from imbue_tools.repo_utils.subrepo_formatting import UnionFilenamePattern
-from imbue_tools.repo_utils.subrepo_formatting import compute_file_context_format_styles
-from imbue_tools.repo_utils.subrepo_formatting import format_subrepo_context
-from imbue_tools.repo_utils.subrepo_formatting import (
- parse_subrepo_context_matchers_from_toml,
-)
-
-
-class SubrepoContext(SerializableModel):
- repo_context_files: tuple[FileContextUnion, ...]
- subrepo_context_strategy_label: str
-
-
-class SubrepoContextWithFormattedContext(SubrepoContext):
- formatted_repo_context: str
-
-
-def is_qualified_name_from_stdlib(qualified_name: QualifiedName) -> bool:
- return qualified_name.top_level_name.value in STANDARD_LIBRARIES
-
-
-def get_immediate_first_party_import_paths_for_python_file(
- current_file_path: str, full_repo_contents_map: InMemoryFileSystem
-) -> set[str] | None:
- file_contents = full_repo_contents_map.get_text(current_file_path)
- if not file_contents or not current_file_path.endswith(".py"):
- return None
-
- try:
- global_imports = get_global_imports(file_contents)
- except SyntaxError as e:
- log_exception(
- e,
- "Failed to parse imports for {current_file_path}",
- current_file_path=current_file_path,
- )
- return None
-
- parent_names: set[QualifiedName] = set()
- for import_ in global_imports:
- parent_name = import_.qualified_name.parent_name
- parent_names.add(parent_name)
-
- imported_file_paths = set()
- all_file_paths = [Path(x) for x in full_repo_contents_map.text_files.keys()]
- for parent_name in parent_names:
- other_file_path = maybe_get_file_path_from_qualified_name(parent_name, all_file_paths)
- # if this doesn't exist it's likely not a first party import so we can ignore it
- if not other_file_path or is_qualified_name_from_stdlib(parent_name):
- continue
- imported_file_paths.add(str(other_file_path))
-
- return imported_file_paths
-
-
-FULL_REPO_PATHSPEC = BaseFilenamePattern.from_lines(["/**"])
-DOC_FILE_EXTENSIONS = [".md", ".txt"]
-DOC_PATHSPEC = BaseFilenamePattern.from_lines([f"**/*{ext}" for ext in DOC_FILE_EXTENSIONS])
-
-# Common files that we want to exclude since they can be large and are of low signal for issue identification.
-EXCLUSIONS_PATHSPEC = BaseFilenamePattern.from_lines(["uv.lock", "**/__snapshots__/**"])
-
-
-def escape_gitignore_pattern(path: str) -> str:
- """
- Escape a path into a GitIgnore pattern that matches exactly the path.
-
- GitWildMatchPattern assigns special meaning to the following characters, which need to be escaped:
- `*`, `?`, `[`, `]` and `\\`.
- At the beginning of a line, we additionally need to escape leading `#` and `!` characters,
- and at the end of a line we need to escape trailing ` ` (space) characters.
- For simplicity, we simply escape these characters everywhere, which should still work correctly.
- """
- return (
- path.replace("\\", "\\\\")
- .replace("*", "\\*")
- .replace("?", "\\?")
- .replace("[", "\\[")
- .replace("]", "\\]")
- .replace("#", "\\#")
- .replace("!", "\\!")
- .replace(" ", "\\ ")
- )
-
-
-def first_level_files_along_paths(file_paths: Iterable[str]) -> FilenamePattern:
- """
- Create a pathspec that matches all files along the given paths, but doesn't match adjacent directories.
- """
- # for each level in the path, we create an IntersectionFilenamePattern
- # which has one branch that matches everything starting with that path,
- # and another branch which matches everything except subdirectories starting with that path
- # then we OR these all together as a UnionFilenamePattern
- sorted_file_paths = sorted(file_paths)
- file_patterns = []
- for file_path in sorted_file_paths:
- for parent in Path(file_path).parents:
- escaped_parent = Path(escape_gitignore_pattern(str(parent)))
- match_all = BaseFilenamePattern.from_lines([str("/" / escaped_parent / "*")])
- match_except_subdirectories = NegatedFilenamePattern.build_from_positive_pattern(
- BaseFilenamePattern.from_lines([str("/" / escaped_parent / "*/*")])
- )
- file_patterns.append(IntersectionFilenamePattern(specs=(match_all, match_except_subdirectories)))
- return UnionFilenamePattern(specs=tuple(file_patterns))
-
-
-# cache this since it's reused across strategies
-@functools.lru_cache(maxsize=5)
-def make_docs_pathspec_along_paths(file_paths: frozenset[str]) -> FilenamePattern:
- """
- Create a pathspec that matches documentation files (.md, .txt) along each parent folder of the given file paths.
- """
- return IntersectionFilenamePattern(specs=(DOC_PATHSPEC, first_level_files_along_paths(file_paths=file_paths)))
-
-
-INSTRUCTIONS_PATHSPEC = BaseFilenamePattern.from_lines(["**/.claude.md", "**/CLAUDE.md", "**/AGENTS.md"])
-
-
-@functools.lru_cache(maxsize=5)
-def make_relevant_files_pathspec(file_paths: frozenset[str]) -> FilenamePattern:
- """
- Create a pathspec that matches the given file paths.
- """
- return ExactFilenamePattern(filenames=tuple(sorted(file_paths)))
-
-
-# cache this since it's reused across strategies
-@functools.lru_cache(maxsize=5)
-def make_instructions_pathspec_along_paths(
- file_paths: frozenset[str],
-) -> FilenamePattern:
- """
- Create a pathspec that matches instruction files (e.g. .claude.md, CLAUDE.md, AGENTS.md) along each parent folder of the given file paths.
-
- Should match a strict subset of make_docs_pathspec_along_paths.
- """
- return IntersectionFilenamePattern(
- specs=(
- INSTRUCTIONS_PATHSPEC,
- first_level_files_along_paths(file_paths=file_paths),
- )
- )
-
-
-# cache this since it's reused across strategies
-@functools.lru_cache(maxsize=5)
-def make_imports_pathspec_for_paths(
- file_paths: frozenset[str], full_repo_contents: InMemoryFileSystem
-) -> FilenamePattern:
- """
- Create a pathspec that matches Python files that are imported by the given file paths.
- """
- full_repo_python_file_contents_map = InMemoryFileSystem.build(
- {k: v for k, v in full_repo_contents.files.items() if k.endswith(".py")}
- )
-
- imported_file_paths = set()
- for file_path in file_paths:
- if file_path.endswith(".py"):
- # Include first party imports
- imported_paths = get_immediate_first_party_import_paths_for_python_file(
- file_path, full_repo_python_file_contents_map
- )
- if imported_paths:
- imported_file_paths.update(imported_paths)
-
- return ExactFilenamePattern(filenames=tuple(sorted(imported_file_paths)))
-
-
-class SubrepoContextStrategy(BaseModel):
- model_config = ConfigDict(frozen=True, arbitrary_types_allowed=True)
- label: str
- matchers: SubrepoContextMatchers
-
-
-class SubrepoContextStrategyType(StrEnum):
- # defaults if we have relevant files
- FULL_REPO_CONTENTS = "full repo contents"
- RELEVANT_WHOLE_FILES_IMPORTS_DOCS_AND_ELSEWHERE_FILENAME = (
- "relevant files + immediate imports + docs along relevant paths + filenames elsewhere"
- )
- RELEVANT_WHOLE_FILES_IMPORTS_DOCS = "relevant files + immediate imports + docs along relevant paths"
- RELEVANT_WHOLE_FILES_AND_RELEVANT_STUBBIFIED_IMPORTS_DOCS = (
- "relevant files + stubbified imports + docs along relevant paths"
- )
- RELEVANT_WHOLE_FILES_DOCS = "relevant files + docs along relevant paths"
- RELEVANT_WHOLE_FILES_INSTRUCTIONS = "relevant files + agent instructions along relevant paths"
- RELEVANT_WHOLE_FILES = "relevant files"
- RELEVANT_STUBBIFIED_FILES = "relevant stubbified files"
- NOTHING = "nothing"
-
- # defaults if we don't have relevant files (missing FULL_REPO_CONTENTS and NOTHING because they're already listed for if we do have relevant files)
- WHOLE_DOCS_AND_OTHERWISE_FILENAMES = "docs + filenames elsewhere"
- WHOLE_INSTRUCTIONS_AND_OTHERWISE_FILENAMES = "agent instructions + filenames elsewhere"
- WHOLE_INSTRUCTIONS = "agent instructions"
-
- # defaults for providing instruction files if we have relevant files
- WHOLE_DOCS = "docs"
- WHOLE_INSTRUCTIONS_AND_RELEVANT_DOCS = "agent instructions + relevant docs"
- RELEVANT_DOCS = "relevant docs"
- RELEVANT_INSTRUCTIONS = "relevant agent instructions"
-
- # custom
- CUSTOM = "custom"
-
-
-class StrategyMode(StrEnum):
- REGULAR = "regular"
- DOCS = "docs"
-
-
-class AvailableInfoMode(StrEnum):
- YES_FILES = "yes_files"
- NO_FILES = "no_files"
-
-
-DEFAULT_STRATEGY_TYPES: dict[tuple[StrategyMode, AvailableInfoMode], tuple[SubrepoContextStrategyType, ...]] = {
- (StrategyMode.REGULAR, AvailableInfoMode.YES_FILES): (
- SubrepoContextStrategyType.FULL_REPO_CONTENTS,
- SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_IMPORTS_DOCS_AND_ELSEWHERE_FILENAME,
- SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_IMPORTS_DOCS,
- SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_AND_RELEVANT_STUBBIFIED_IMPORTS_DOCS,
- SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_DOCS,
- SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_INSTRUCTIONS,
- SubrepoContextStrategyType.RELEVANT_WHOLE_FILES,
- SubrepoContextStrategyType.RELEVANT_STUBBIFIED_FILES,
- SubrepoContextStrategyType.NOTHING,
- ),
- (StrategyMode.REGULAR, AvailableInfoMode.NO_FILES): (
- SubrepoContextStrategyType.FULL_REPO_CONTENTS,
- SubrepoContextStrategyType.WHOLE_DOCS_AND_OTHERWISE_FILENAMES,
- SubrepoContextStrategyType.WHOLE_INSTRUCTIONS_AND_OTHERWISE_FILENAMES,
- SubrepoContextStrategyType.WHOLE_INSTRUCTIONS,
- SubrepoContextStrategyType.NOTHING,
- ),
- (StrategyMode.DOCS, AvailableInfoMode.YES_FILES): (
- SubrepoContextStrategyType.WHOLE_DOCS,
- SubrepoContextStrategyType.WHOLE_INSTRUCTIONS_AND_RELEVANT_DOCS,
- SubrepoContextStrategyType.RELEVANT_DOCS,
- SubrepoContextStrategyType.RELEVANT_INSTRUCTIONS,
- # these don't have `nothing` strategies because having some user instructions is crucial for
- # the issue identifier which uses this, whereas the others can do ok with just a diff
- ),
- (StrategyMode.DOCS, AvailableInfoMode.NO_FILES): (
- SubrepoContextStrategyType.WHOLE_DOCS,
- SubrepoContextStrategyType.WHOLE_INSTRUCTIONS,
- ),
-}
-
-
-def build_strategy(
- strategy_type: SubrepoContextStrategyType,
- full_repo_contents: InMemoryFileSystem,
- relevant_file_paths: frozenset[str] | None,
-) -> SubrepoContextStrategy:
- match strategy_type:
- case SubrepoContextStrategyType.FULL_REPO_CONTENTS as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=((ContextFormatStyle.FULL_FILE, FULL_REPO_PATHSPEC),),
- )
- case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_IMPORTS_DOCS_AND_ELSEWHERE_FILENAME as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (
- ContextFormatStyle.FULL_FILE,
- make_relevant_files_pathspec(relevant_file_paths),
- ),
- (
- ContextFormatStyle.FULL_FILE,
- make_imports_pathspec_for_paths(relevant_file_paths, full_repo_contents),
- ),
- (
- ContextFormatStyle.FULL_FILE,
- make_docs_pathspec_along_paths(relevant_file_paths),
- ),
- (ContextFormatStyle.FILENAME_ONLY, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_IMPORTS_DOCS as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (
- ContextFormatStyle.FULL_FILE,
- make_relevant_files_pathspec(relevant_file_paths),
- ),
- (
- ContextFormatStyle.FULL_FILE,
- make_imports_pathspec_for_paths(relevant_file_paths, full_repo_contents),
- ),
- (
- ContextFormatStyle.FULL_FILE,
- make_docs_pathspec_along_paths(relevant_file_paths),
- ),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_AND_RELEVANT_STUBBIFIED_IMPORTS_DOCS as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (
- ContextFormatStyle.FULL_FILE,
- make_relevant_files_pathspec(relevant_file_paths),
- ),
- (
- ContextFormatStyle.STUB,
- make_imports_pathspec_for_paths(relevant_file_paths, full_repo_contents),
- ),
- (
- ContextFormatStyle.FULL_FILE,
- make_docs_pathspec_along_paths(relevant_file_paths),
- ),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_DOCS as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (
- ContextFormatStyle.FULL_FILE,
- make_relevant_files_pathspec(relevant_file_paths),
- ),
- (
- ContextFormatStyle.FULL_FILE,
- make_docs_pathspec_along_paths(relevant_file_paths),
- ),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_INSTRUCTIONS as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (
- ContextFormatStyle.FULL_FILE,
- make_relevant_files_pathspec(relevant_file_paths),
- ),
- (
- ContextFormatStyle.FULL_FILE,
- make_instructions_pathspec_along_paths(relevant_file_paths),
- ),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (
- ContextFormatStyle.FULL_FILE,
- make_relevant_files_pathspec(relevant_file_paths),
- ),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.RELEVANT_STUBBIFIED_FILES as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (
- ContextFormatStyle.STUB,
- make_relevant_files_pathspec(relevant_file_paths),
- ),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.NOTHING as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=((ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),),
- )
-
- case SubrepoContextStrategyType.WHOLE_DOCS_AND_OTHERWISE_FILENAMES as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (ContextFormatStyle.FULL_FILE, DOC_PATHSPEC),
- (ContextFormatStyle.FILENAME_ONLY, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.WHOLE_INSTRUCTIONS_AND_OTHERWISE_FILENAMES as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (ContextFormatStyle.FULL_FILE, INSTRUCTIONS_PATHSPEC),
- (ContextFormatStyle.FILENAME_ONLY, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.WHOLE_INSTRUCTIONS as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (ContextFormatStyle.FULL_FILE, INSTRUCTIONS_PATHSPEC),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
-
- case SubrepoContextStrategyType.WHOLE_DOCS as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (ContextFormatStyle.FULL_FILE, DOC_PATHSPEC),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.WHOLE_INSTRUCTIONS_AND_RELEVANT_DOCS as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (ContextFormatStyle.FULL_FILE, INSTRUCTIONS_PATHSPEC),
- (
- ContextFormatStyle.FULL_FILE,
- make_docs_pathspec_along_paths(relevant_file_paths),
- ),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.RELEVANT_DOCS as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (
- ContextFormatStyle.FULL_FILE,
- make_docs_pathspec_along_paths(relevant_file_paths),
- ),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
- case SubrepoContextStrategyType.RELEVANT_INSTRUCTIONS as s:
- return SubrepoContextStrategy(
- label=s,
- matchers=(
- (
- ContextFormatStyle.FULL_FILE,
- make_instructions_pathspec_along_paths(relevant_file_paths),
- ),
- (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
- ),
- )
- case _ as unreachable:
- assert_never(unreachable) # pyre-ignore[6]: pyre doesn't understand enums
-
-
-def generate_subrepo_strategies(
- mode: StrategyMode,
- full_repo_contents: InMemoryFileSystem,
- relevant_file_paths: frozenset[str] | None = None,
-) -> list[SubrepoContextStrategy]:
- available_info = AvailableInfoMode.YES_FILES if relevant_file_paths else AvailableInfoMode.NO_FILES
- return [
- build_strategy(strategy_type, full_repo_contents, relevant_file_paths)
- for strategy_type in DEFAULT_STRATEGY_TYPES[(mode, available_info)]
- ]
-
-
-def select_desired_subrepo_strategies(
- full_repo_contents: InMemoryFileSystem,
- relevant_file_paths: frozenset[str] | None = None,
- subrepo_context_config: str | None = None,
- strategy_types_to_try: tuple[SubrepoContextStrategyType] | None = None,
- strategy_mode: StrategyMode | None = None, # if no config option is set, defaults to StrategyMode.REGULAR
-) -> list[SubrepoContextStrategy]:
- num_ways_config_was_set = sum(
- 1 for v in [subrepo_context_config, strategy_types_to_try, strategy_mode] if v is not None
- )
- if num_ways_config_was_set > 1:
- assert False, "Can only specify one of subrepo_context_config, strategy_types_to_try, and strategy_mode"
-
- if subrepo_context_config is not None:
- # An explicit subrepo context config was provided. Use it exclusively.
- subrepo_context_matchers = parse_subrepo_context_matchers_from_toml(subrepo_context_config)
- return [
- SubrepoContextStrategy(
- label=SubrepoContextStrategyType.CUSTOM,
- matchers=subrepo_context_matchers,
- )
- ]
- elif strategy_types_to_try is not None:
- return [
- build_strategy(strategy_type, full_repo_contents, relevant_file_paths)
- for strategy_type in strategy_types_to_try
- ]
- else:
- strategy_mode_to_use = strategy_mode if strategy_mode is not None else StrategyMode.REGULAR
- return generate_subrepo_strategies(
- strategy_mode_to_use,
- full_repo_contents=full_repo_contents,
- relevant_file_paths=relevant_file_paths,
- )
-
-
-# Caching results because this function is quite expensive. We compose multiple repo_context prefixes, and
-# also have to tokenize them to check their respective lengths. Both of these operations are expensive,
-@functools.lru_cache(maxsize=10)
-def get_repo_context(
- model_config: LanguageModelGenerationConfig,
- full_repo_contents: InMemoryFileSystem,
- # how many tokens to reserve for additional prompt messages and output
- tokens_to_reserve: int,
- relevant_file_paths: frozenset[str] | None = None,
- subrepo_context_config: str | None = None,
- strategy_types_to_try: tuple[SubrepoContextStrategyType] | None = None,
- strategy_mode: StrategyMode | None = None, # if no config option is set, defaults to StrategyMode.REGULAR
- template: str = REPO_CONTEXT_TEMPLATE,
-) -> SubrepoContextWithFormattedContext:
- """
- Make sure to try pass the same `full_repo_contents` when making multiple similar calls.
- Ordering of the dict is relevant for caching.
- """
- subrepo_context_strategies_to_try = select_desired_subrepo_strategies(
- full_repo_contents,
- relevant_file_paths,
- subrepo_context_config,
- strategy_types_to_try,
- strategy_mode,
- )
-
- last_context_length_exceeded_error: ContextLengthExceededError | None = None
- for subrepo_context_strategy in subrepo_context_strategies_to_try:
- try:
- path_to_format_style = compute_file_context_format_styles(
- file_paths=full_repo_contents.text_files.keys(),
- subrepo_context_matchers=subrepo_context_strategy.matchers,
- exclusions=EXCLUSIONS_PATHSPEC,
- )
- repo_context_str, repo_context_files = format_subrepo_context(
- full_repo_contents=full_repo_contents.text_files,
- model_config=model_config,
- path_to_format_style=path_to_format_style,
- tokens_to_reserve=tokens_to_reserve,
- template=template,
- )
- logger.info("Selected subrepo context strategy: {}", subrepo_context_strategy.label)
-
- if subrepo_context_strategy.label == SubrepoContextStrategyType.NOTHING:
- # log an error if we have to use the NOTHING strategy, but still proceed with the call
- logger.error("Selected NOTHING subrepo context strategy; hopefully this doesn't happen too often!")
-
- return SubrepoContextWithFormattedContext(
- formatted_repo_context=repo_context_str,
- repo_context_files=repo_context_files,
- subrepo_context_strategy_label=subrepo_context_strategy.label,
- )
- except ContextLengthExceededError as e:
- last_context_length_exceeded_error = e
-
- # We have exhausted all subrepo context strategies, and none of them worked.
- assert last_context_length_exceeded_error is not None
- raise last_context_length_exceeded_error from last_context_length_exceeded_error
-
-
-# TODO: why not just render this here?
-def create_context_prompt_prefix(repo_context: str) -> tuple[str, Mapping[str, Any]]:
- """Create a message that provides context about the repo contents."""
- cached_prefix_template = """[ROLE=SYSTEM_CACHED]
-You are a detail-oriented, expert software developer.
-
-Your goal is to help the user develop a particular commit to make a change to their program.
-
-{{repo_context}}
-
-{% if recent_git_history -%}
-
-As additional context, here are some of the most recent changes made to the codebase (the output of `git log` and diffs for each of those commits):
-
-```
-{{recent_git_history}}
-```
-{% endif -%}
-"""
-
- return (
- cached_prefix_template,
- dict(
- repo_context=escape_prompt_markers(repo_context),
- recent_git_history=None,
- ),
- )
diff --git a/imbue_tools/imbue_tools/repo_utils/context_retrieval.py b/imbue_tools/imbue_tools/repo_utils/context_retrieval.py
@@ -1,121 +0,0 @@
-import threading
-import time
-from pathlib import Path
-from typing import Generator
-
-import pygit2
-from loguru import logger
-from pygit2.enums import ObjectType
-from pygit2.repository import Repository
-
-from imbue_core.async_utils import make_async
-from imbue_tools.repo_utils.diff_utils import apply_diffs_to_files
-from imbue_tools.repo_utils.file_system import FileContents
-from imbue_tools.repo_utils.file_system import InMemoryFileSystem
-from imbue_tools.repo_utils.file_system import SymlinkContents
-
-
-class RepoContextManagerError(Exception):
- pass
-
-
-class RepoContextManager:
- """A manager for handling retrieval of files, etc from the repo."""
-
- def __init__(self, repo_path: Path, project_name: str) -> None:
- self.project_name = project_name
- self.repo_path = repo_path
- self._repo = Repository(path=str(repo_path))
-
- # We need the sync lock due to pygit2 being synchronous.
- # It is mostly used for the blob data cache, but also for the repo contents by git hash cache.
- self._lock = threading.Lock()
-
- @classmethod
- def build(cls, repo_path: Path) -> "RepoContextManager":
- try:
- # make sure we are in a git repo
- Repository(path=str(repo_path))
- except pygit2.GitError as e:
- raise RepoContextManagerError(f"Failed to initialize git repo at {repo_path}") from e
-
- repo_context_manager = cls(repo_path=repo_path, project_name=repo_path.name)
- return repo_context_manager
-
- async def get_full_repo_contents_at_repo_state(self, git_hash: str, diff: str) -> InMemoryFileSystem:
- final_contents = await self.get_full_repo_contents_at_commit(git_hash)
- final_contents = await apply_diffs_to_files(final_contents, (diff,))
- return final_contents
-
- def get_full_repo_contents_at_commit_sync(self, git_hash: str) -> InMemoryFileSystem:
- # NOTE: most of the time we want to get the contents at a repo state, not a git hash.
- # Call get_full_repo_contents_at_repo_state instead in that case.
- with self._lock:
- start_time = time.perf_counter()
-
- # Assert against use of HEAD specifically because there could be some existing code
- # that uses it, and we want to catch that. It would fail below as well with a KeyError,
- # but this assert makes the exception message more explicit.
- assert git_hash != "HEAD", "Only proper commit hashes are supported, not HEAD"
- commit = self._repo[git_hash]
- assert isinstance(commit, pygit2.Commit), f"Expected a pygit2.Commit, got {type(commit)}"
-
- full_repo_contents = self._read_blobs_from_commit(commit)
-
- end_time = time.perf_counter()
- logger.debug(
- "Loaded full repo contents for git hash {git_hash} in {duration:.2f} seconds",
- git_hash=git_hash,
- duration=end_time - start_time,
- )
- return full_repo_contents
-
- @make_async
- def get_full_repo_contents_at_commit(self, git_hash: str) -> InMemoryFileSystem:
- return self.get_full_repo_contents_at_commit_sync(git_hash)
-
- def _read_blobs_from_commit(self, commit: pygit2.Commit) -> InMemoryFileSystem:
- """Read all blobs in a given commit."""
- file_system_dict: dict[str, FileContents] = {}
-
- for path, blob in self._list_blobs_from_tree(commit.tree, skip_binary=False, skip_symlinks=False):
- if blob.filemode == 0o120000:
- # Blob is a symbolic link. Its contents in git represent the target path.
- file_system_dict[path] = SymlinkContents(target_path=blob.data.decode("utf-8"))
- else:
- file_system_dict[path] = blob.data
- return InMemoryFileSystem.build(file_system_dict)
-
- def _list_blobs_from_tree(
- self, tree: pygit2.Tree, skip_binary: bool, skip_symlinks: bool
- ) -> Generator[tuple[str, pygit2.Blob], None, None]:
- """Recursively list all blobs in a tree, including its subtrees."""
- assert self._lock.locked()
- for entry in tree:
- if entry.type == ObjectType.BLOB:
- assert isinstance(entry, pygit2.Blob)
- if skip_binary and entry.is_binary:
- continue
- if skip_symlinks and entry.filemode == 0o120000:
- continue
-
- blob_path = entry.name
- assert blob_path is not None
- yield blob_path, entry
-
- elif entry.type == ObjectType.TREE:
- assert isinstance(entry, pygit2.Tree)
- # Recurse into a subtree (folder)
- sub_tree = self._repo[entry.id]
- assert isinstance(sub_tree, pygit2.Tree)
- for sub_path, sub_blob in self._list_blobs_from_tree(
- sub_tree, skip_binary=skip_binary, skip_symlinks=skip_symlinks
- ):
- yield f"{entry.name}/{sub_path}", sub_blob
-
- elif entry.type == ObjectType.COMMIT:
- # A COMMIT object indicates a submodule, which we do not traverse for the time being.
- logger.info("Skipping submodule in repo context: {}", entry.name)
-
- else:
- raise ValueError(f"Unexpected entry type in git tree: {entry.type}")
diff --git a/imbue_tools/imbue_tools/repo_utils/context_utils.py b/imbue_tools/imbue_tools/repo_utils/context_utils.py
@@ -1,52 +0,0 @@
-from pathlib import Path
-from typing import Iterable
-
-from imbue_tools.repo_utils.python_imports import QualifiedName
-
-
-def escape_prompt_markers(text: str) -> str:
- markers = [
- "[ROLE=ASSISTANT]",
- "[ROLE=USER]",
- "[ROLE=USER_CACHED]",
- "[ROLE=SYSTEM]",
- "[ROLE=SYSTEM_CACHED]",
- "[ROLE=HUMAN]",
- ]
- for marker in markers:
- text = text.replace(marker, f"[{marker}]")
- return text
-
-
-def escape_all_jinja_variables(text: str) -> str:
- return "{% raw %}" + text + "{% endraw %}"
-
-
-def does_relative_path_match_target_path_suffix(target_path: Path, relative_file_path: Path) -> bool:
- """
- Checks if the parts of a relative path match the suffix of a target path.
- """
- possible_parts = relative_file_path.parts
- target_parts = target_path.parts
-
- if len(possible_parts) > len(target_parts):
- return False
-
- for i in range(1, len(possible_parts) + 1):
- if possible_parts[-i] != target_parts[-i]:
- return False
- return True
-
-
-def maybe_get_file_path_from_qualified_name(
- qualified_name: QualifiedName, all_file_paths: Iterable[Path]
-) -> Path | None:
- """
- Tries to find the file path that corresponds to qualified name. This requires the qualified name to be a file in the repo.
- """
- possible_relative_file_path = qualified_name.to_path()
- # NOTE: it's possible to make this faster by doing some upfront computation
- for target_file_path in all_file_paths:
- if does_relative_path_match_target_path_suffix(target_file_path, possible_relative_file_path):
- return target_file_path
- return None
diff --git a/imbue_tools/imbue_tools/repo_utils/data_types.py b/imbue_tools/imbue_tools/repo_utils/data_types.py
@@ -1,51 +0,0 @@
-from abc import ABC
-from abc import abstractmethod
-from typing import Annotated
-
-from pydantic import Tag
-
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_core.pydantic_serialization import build_discriminator
-
-
-class FileContext(ABC, SerializableModel):
- object_type: str
- path: str
-
- @abstractmethod
- def format_for_agent(self) -> str:
- pass
-
-
-class FullFileContext(FileContext):
- object_type: str = "FullFileContext"
- path: str
- contents: str = "RAW FILE CONTENTS"
-
- def format_for_agent(self) -> str:
- return f"<FILE>\n<PATH>\n{self.path}\n</PATH>\n<CONTENTS>\n{self.contents}\n</CONTENTS>\n</FILE>\n\n"
-
-
-class FilenameContext(FileContext):
- object_type: str = "FilenameContext"
- path: str
-
- def format_for_agent(self) -> str:
- return f"<FILE>\n<PATH>\n{self.path}\n</PATH>\n</FILE>\n\n"
-
-
-class StubFileContext(FileContext):
- object_type: str = "StubFileContext"
- path: str
- stub: str
-
- def format_for_agent(self) -> str:
- return f"<FILE>\n<PATH>\n{self.path}\n</PATH>\n<STUBIFIED_CONTENTS>\n{self.stub}\n</STUBIFIED_CONTENTS>\n</FILE>\n\n"
-
-
-FileContextUnion = Annotated[
- Annotated[FullFileContext, Tag("FullFileContext")]
- | Annotated[FilenameContext, Tag("FilenameContext")]
- | Annotated[StubFileContext, Tag("StubFileContext")],
- build_discriminator(),
-]
diff --git a/imbue_tools/imbue_tools/repo_utils/diff_utils.py b/imbue_tools/imbue_tools/repo_utils/diff_utils.py
@@ -1,96 +0,0 @@
-import re
-import subprocess
-import tempfile
-from pathlib import Path
-
-import pygit2
-from async_lru import alru_cache # type: ignore[undefined-attribute]: pyre on modal has an issue with this
-from loguru import logger
-
-from imbue_tools.repo_utils.errors import DiffApplicationError
-from imbue_tools.repo_utils.file_system import FileContents
-from imbue_tools.repo_utils.file_system import InMemoryFileSystem
-from imbue_tools.repo_utils.file_system import SymlinkContents
-from imbue_tools.repo_utils.file_system_utils import (
- create_initial_placeholder_commit_for_dir,
-)
-from imbue_tools.repo_utils.file_system_utils import (
- temporary_local_dir_from_in_memory_file_system,
-)
-
-
-@alru_cache
-async def apply_diffs_to_files(file_contents: InMemoryFileSystem, diff_strings: tuple[str, ...]) -> InMemoryFileSystem:
- # Have to do this wrapping and unwrapping into dicts to allow @alru_cache to work
- files_with_diffs = file_contents
- for diff_string in diff_strings:
- files_with_diffs = await _apply_diff_to_files(file_contents=files_with_diffs, diff_string=diff_string)
- return files_with_diffs
-
-
-async def _apply_diff_to_files(file_contents: InMemoryFileSystem, diff_string: str) -> InMemoryFileSystem:
- if diff_string.strip() == "":
- return file_contents
-
- file_pattern = re.compile(r"^diff --git a/(.+?) b/(.+)$", re.MULTILINE)
- matches = file_pattern.findall(diff_string)
-
- relevant_file_contents_dict = {}
- for match in matches:
- assert len(match) == 2
- for file_path in match:
- contents = file_contents.get(file_path, None)
- if contents is not None:
- relevant_file_contents_dict[file_path] = contents
-
- async with temporary_local_dir_from_in_memory_file_system(
- InMemoryFileSystem.build(relevant_file_contents_dict)
- ) as temp_repo_dir:
- repo = pygit2.init_repository(temp_repo_dir, bare=False)
- create_initial_placeholder_commit_for_dir(repo)
-
- with tempfile.NamedTemporaryFile(delete=False) as temp_patch_file:
- temp_patch_file.write(diff_string.encode("utf-8"))
- temp_patch_file.flush()
- patch_file_path = temp_patch_file.name
-
- try:
- result = subprocess.run(
- ("git", "apply", "--verbose", patch_file_path),
- cwd=temp_repo_dir,
- capture_output=True,
- text=True,
- timeout=10.0,
- check=True,
- )
- except Exception as e:
- logger.trace("Unable to apply patch: {error}", error=e)
- raise DiffApplicationError from e
-
- try:
- updated_file_contents = _read_file_contents_from_dir_without_git(temp_repo_dir)
- except Exception as e:
- raise DiffApplicationError from e
-
- combined_file_contents_dict = dict(updated_file_contents.files)
- for file_path, contents in file_contents.files.items():
- if file_path not in relevant_file_contents_dict:
- combined_file_contents_dict[file_path] = contents
-
- return InMemoryFileSystem.build(combined_file_contents_dict)
-
-
-def _read_file_contents_from_dir_without_git(dir_path_str: str) -> InMemoryFileSystem:
- file_system_dict: dict[str, FileContents] = {}
- for file_path in Path(dir_path_str).rglob("*"):
- if ".git" in file_path.parts:
- continue
- if file_path.is_symlink():
- relative_path = str(file_path.relative_to(dir_path_str))
- target_path = str(file_path.readlink())
- file_system_dict[relative_path] = SymlinkContents(target_path=target_path)
- elif file_path.is_file():
- relative_path = str(file_path.relative_to(dir_path_str))
- with open(file_path, "rb") as file:
- file_system_dict[relative_path] = file.read()
- return InMemoryFileSystem.build(file_system_dict)
diff --git a/imbue_tools/imbue_tools/repo_utils/file_system.py b/imbue_tools/imbue_tools/repo_utils/file_system.py
@@ -1,71 +0,0 @@
-from typing import Generator
-from typing import Mapping
-
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.frozen_utils import deep_freeze_mapping
-from imbue_core.pydantic_serialization import SerializableModel
-
-
-class SymlinkContents(SerializableModel):
- """A special type to represent a symbolic link in a file system."""
-
- target_path: str
-
- # Need to make SymlinkContents non-Iterable, or else deep_freeze_mapping will convert this to a tuple in InMemoryFileSystem.build.
- __iter__ = None # type: ignore
-
-
-DecodedTextFileContents = str
-FileContents = bytes | SymlinkContents
-
-
-class InMemoryFileSystem(SerializableModel):
- """
- A simple representation of in-memory file system. Can contain both text and binary files.
- """
-
- # Mapping from file path to contents
- files: FrozenDict[str, FileContents]
- # Only text files, decoded as UTF-8. Excludes symlinks and binary files.
- text_files: FrozenDict[str, DecodedTextFileContents]
-
- @classmethod
- def build(cls, files: Mapping[str, FileContents]) -> "InMemoryFileSystem":
- sorted_files = {k: v for k, v in sorted(files.items())}
- sorted_decoded_files: dict[str, DecodedTextFileContents | None] = {
- k: _try_decode_file_contents(c) for k, c in sorted_files.items()
- }
- sorted_text_files: dict[str, DecodedTextFileContents] = {
- k: c for k, c in sorted_decoded_files.items() if c is not None
- }
- return cls(
- files=deep_freeze_mapping(sorted_files),
- text_files=deep_freeze_mapping(sorted_text_files),
- )
-
- def get(self, file_path: str, default: FileContents | None = None) -> FileContents | None:
- if file_path in self.files:
- return self.files[file_path]
- return default
-
- def get_text(
- self, file_path: str, default: DecodedTextFileContents | None = None
- ) -> DecodedTextFileContents | None:
- """Get a the contents of a text file as a string. Returns `default` if the file does not exist, is a symlink, or is a binary file."""
- if file_path in self.text_files:
- return self.text_files[file_path]
- return default
-
- def __iter__(self) -> Generator[tuple[str, FileContents], None, None]:
- return (file for file in self.files.items())
-
-
-def _try_decode_file_contents(contents: FileContents) -> DecodedTextFileContents | None:
- if isinstance(contents, SymlinkContents):
- return None
- else:
- assert isinstance(contents, bytes)
- try:
- return contents.decode("utf-8")
- except UnicodeDecodeError:
- return None
diff --git a/imbue_tools/imbue_tools/repo_utils/file_system_utils.py b/imbue_tools/imbue_tools/repo_utils/file_system_utils.py
@@ -1,70 +0,0 @@
-import asyncio
-import tempfile
-from contextlib import asynccontextmanager
-from pathlib import Path
-from typing import AsyncGenerator
-from typing import cast
-
-import anyio
-import pygit2
-from loguru import logger
-
-from imbue_tools.repo_utils.file_system import FileContents
-from imbue_tools.repo_utils.file_system import InMemoryFileSystem
-from imbue_tools.repo_utils.file_system import SymlinkContents
-
-
-async def write_file_contents_to_dir(file_contents: InMemoryFileSystem, dir_path_str: str) -> None:
- dir_path = Path(dir_path_str)
- tasks = [
- asyncio.create_task(_write_single_file_to_dir(dir_path / file_path, content))
- for file_path, content in file_contents.files.items()
- ]
- await asyncio.gather(*tasks)
-
-
-async def _write_single_file_to_dir(full_path: Path, content: FileContents) -> None:
- await anyio.to_thread.run_sync(_write_file_sync, full_path, content)
-
-
-def _write_file_sync(full_path: Path, content: FileContents) -> None:
- full_path.parent.mkdir(parents=True, exist_ok=True)
- if isinstance(content, bytes):
- full_path.write_bytes(content)
- elif isinstance(content, SymlinkContents):
- full_path.symlink_to(content.target_path)
- else:
- logger.error(
- "Tried to write contents that were neither bytes nor SymlinkContents: {content}",
- content=content,
- )
-
-
-@asynccontextmanager
-async def temporary_local_dir_from_in_memory_file_system(
- file_contents: InMemoryFileSystem,
-) -> AsyncGenerator[str, None]:
- with tempfile.TemporaryDirectory() as temp_dir:
- await write_file_contents_to_dir(file_contents, temp_dir)
- yield temp_dir
-
-
-def create_initial_placeholder_commit_for_dir(repo: pygit2.Repository) -> pygit2.Commit:
- # pyre-ignore[16]: pyre doesn't understand the inheritance of Repository from BaseRepository
- repo_index = repo.index
- repo_index.add_all()
- repo_index.write()
- tree = repo_index.write_tree()
- signature = pygit2.Signature("placeholder", "placeholder@example.com")
-
- commit_oid = repo.create_commit(
- "refs/heads/master",
- signature,
- signature,
- "placeholder commit for diff utils",
- tree,
- [],
- )
- # pyre-ignore[16]: pyre doesn't understand the inheritance of Repository from BaseRepository
- commit = repo.get(commit_oid)
- return cast(pygit2.Commit, commit)
diff --git a/imbue_tools/imbue_tools/repo_utils/project_context.py b/imbue_tools/imbue_tools/repo_utils/project_context.py
@@ -1,234 +0,0 @@
-from functools import cached_property
-from functools import lru_cache
-from pathlib import Path
-from typing import Annotated
-from typing import Self
-
-import jinja2
-from pydantic import Tag
-from pydantic import computed_field
-
-from imbue_core.agents.configs import LanguageModelGenerationConfig
-from imbue_core.agents.configs import OpenAICompatibleModelConfig
-from imbue_core.async_utils import sync
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_core.pydantic_serialization import build_discriminator
-from imbue_tools.repo_utils.context_prefix import StrategyMode
-from imbue_tools.repo_utils.context_prefix import SubrepoContext
-from imbue_tools.repo_utils.context_prefix import SubrepoContextWithFormattedContext
-from imbue_tools.repo_utils.context_prefix import create_context_prompt_prefix
-from imbue_tools.repo_utils.context_prefix import get_repo_context
-from imbue_tools.repo_utils.context_retrieval import RepoContextManager
-from imbue_tools.repo_utils.file_system import InMemoryFileSystem
-from imbue_tools.repo_utils.subrepo_formatting import (
- REPO_CONTEXT_TEMPLATE_WITH_NO_MENTION_OF_DIFF,
-)
-
-
-@lru_cache
-def _get_repo_context_manager_for_repo_path(repo_path: Path) -> RepoContextManager:
- """
- Wrapper around RepoContextManager.build() to cache the resulting repo context manager.
-
- Internally, the RepoContextManager object will itself cache the repo contents.
- """
- return RepoContextManager.build(repo_path)
-
-
-class BaseProjectContext(SerializableModel):
- """
- Holds the context of the checked project including all its files.
-
- For LLM-based issue identifiers, no matter the scope, we want to use a fixed prompt prefix to leverage caching.
- We use the stable cached_prompt_prefix for that purpose.
-
- """
-
- object_type: str = "BaseProjectContext"
-
- file_contents_by_path: FrozenDict[str, str]
- cached_prompt_prefix: str
- # 0 - n most recent commits, with the most recent one being the first.
- # The state of the project (file_contents_by_path) is the state after the most recent commit.
-
- subrepo_context: SubrepoContext | None = None
- instruction_context: SubrepoContext | None = None
- repo_path: Path | None = None
-
- def get_file_contents(self, file_path: str) -> str | None:
- return self.file_contents_by_path.get(file_path)
-
- def get_computed_contexts(
- self,
- ) -> tuple[SubrepoContext | None, SubrepoContext | None]:
- """To match usage for LazyProjectContext; all fields are always computed because this isn't lazy"""
- return self.subrepo_context, self.instruction_context
-
-
-class LazyProjectContext(SerializableModel):
- object_type: str = "LazyProjectContext"
-
- base_commit: str
- diff: str
- language_model_name: str
- repo_path: Path
- tokens_to_reserve: int
-
- # Optional context window override. If not provided, the model's default context window
- # will be looked up from the model registry (which fails for unknown models).
- context_window: int | None = None
-
- # If True, this is a custom/user-defined model (uses approximate token counting).
- is_custom_model: bool = False
-
- def get_file_contents(self, file_path: str) -> str | None:
- return self.file_contents_by_path.get(file_path)
-
- @classmethod
- def build(
- cls,
- base_commit: str,
- diff: str,
- language_model_name: str,
- repo_path: Path,
- # How many tokens to keep for the vet specific prompt and any output tokens.
- tokens_to_reserve: int,
- context_window: int | None = None,
- is_custom_model: bool = False,
- ) -> Self:
- return cls(
- base_commit=base_commit,
- diff=diff,
- language_model_name=language_model_name,
- repo_path=repo_path,
- tokens_to_reserve=tokens_to_reserve,
- context_window=context_window,
- is_custom_model=is_custom_model,
- )
-
- # The fields are computed and cached because they are quite expensive to compute.
- # We compose multiple repo_context prefixes, and also have to tokenize them to check their respective lengths.
- # Both of these operations are expensive
-
- @computed_field
- @cached_property
- def repo_context_manager(self) -> RepoContextManager:
- return _get_repo_context_manager_for_repo_path(self.repo_path)
-
- @computed_field
- @cached_property
- def original_content_by_path(self) -> InMemoryFileSystem:
- original_content_by_path = sync(self.repo_context_manager.get_full_repo_contents_at_commit)(self.base_commit)
- return original_content_by_path
-
- @computed_field
- @cached_property
- def content_by_path(self) -> InMemoryFileSystem:
- if self.diff:
- return sync(self.repo_context_manager.get_full_repo_contents_at_repo_state)(self.base_commit, self.diff)
- else:
- return self.original_content_by_path
-
- @computed_field
- @property
- def file_contents_by_path(self) -> FrozenDict[str, str]:
- return self.content_by_path.text_files
-
- @computed_field
- @cached_property
- def cached_prompt_prefix(self) -> str:
- prompt_prefix_template, prompt_prefix_params = create_context_prompt_prefix(
- repo_context=self.subrepo_context.formatted_repo_context,
- )
- env = jinja2.Environment(undefined=jinja2.StrictUndefined)
- jinja_template = env.from_string(prompt_prefix_template)
- cached_prompt_prefix = jinja_template.render(**prompt_prefix_params)
- return cached_prompt_prefix
-
- @computed_field
- @cached_property
- def modified_file_paths(self) -> frozenset[str]:
- modified_file_paths = []
- for file_path in self.content_by_path.files.keys():
- if self.content_by_path.get(file_path) != self.original_content_by_path.get(file_path):
- modified_file_paths.append(file_path)
- return frozenset(modified_file_paths)
-
- def _create_model_config(self) -> LanguageModelGenerationConfig:
- """Create the appropriate model config for context building.
-
- For custom models (is_custom_model=True), creates an OpenAICompatibleModelConfig
- that uses approximate token counting and the specified context window.
-
- For known models, creates a standard LanguageModelGenerationConfig that uses
- the model registry for token counting and context window lookup.
- """
- if self.is_custom_model:
- if self.context_window is None:
- raise ValueError(
- "context_window must be provided when is_custom_model=True "
- + "(custom models don't have a known context window)"
- )
- return OpenAICompatibleModelConfig(
- model_name=self.language_model_name,
- custom_base_url="", # Not used for context building
- custom_api_key_env="", # Not used for context building
- custom_context_window=self.context_window,
- custom_max_output_tokens=0, # Not used for context building
- )
- else:
- return LanguageModelGenerationConfig(model_name=self.language_model_name)
-
- @computed_field
- @cached_property
- def subrepo_context(self) -> SubrepoContextWithFormattedContext:
- model_config = self._create_model_config()
-
- subrepo_context = get_repo_context(
- full_repo_contents=self.content_by_path,
- model_config=model_config,
- relevant_file_paths=self.modified_file_paths,
- tokens_to_reserve=self.tokens_to_reserve,
- )
- return subrepo_context
-
- def to_base_project_context(self) -> BaseProjectContext:
- return BaseProjectContext(
- file_contents_by_path=self.file_contents_by_path,
- cached_prompt_prefix=self.cached_prompt_prefix,
- subrepo_context=self.subrepo_context,
- instruction_context=self.instruction_context,
- repo_path=self.repo_path,
- )
-
- @computed_field
- @cached_property
- def instruction_context(self) -> SubrepoContextWithFormattedContext:
- model_config = self._create_model_config()
- return get_repo_context(
- model_config=model_config,
- full_repo_contents=self.content_by_path,
- relevant_file_paths=None,
- tokens_to_reserve=self.tokens_to_reserve,
- strategy_mode=StrategyMode.DOCS,
- template=REPO_CONTEXT_TEMPLATE_WITH_NO_MENTION_OF_DIFF,
- )
-
- def get_computed_contexts(
- self,
- ) -> tuple[
- SubrepoContextWithFormattedContext | None,
- SubrepoContextWithFormattedContext | None,
- ]:
- """Returns subrepo context and instruction context, but only if they have already been computed; those that haven't been computed are None"""
- # checking for presence in __dict__ does not trigger computation
- subrepo_context = self.subrepo_context if "subrepo_context" in self.__dict__ else None
- instruction_context = self.instruction_context if "instruction_context" in self.__dict__ else None
- return subrepo_context, instruction_context
-
-
-ProjectContext = Annotated[
- Annotated[BaseProjectContext, Tag("BaseProjectContext")] | LazyProjectContext,
- build_discriminator(),
-]
diff --git a/imbue_tools/imbue_tools/repo_utils/python_imports.py b/imbue_tools/imbue_tools/repo_utils/python_imports.py
@@ -1,130 +0,0 @@
-import ast
-import sys
-from pathlib import Path
-
-from imbue_core.pydantic_serialization import SerializableModel
-
-STANDARD_LIBRARIES: frozenset[str] = sys.stdlib_module_names | frozenset(sys.builtin_module_names)
-
-
-class QualifiedName(SerializableModel):
- """A qualified name like 'foo.bar.baz'."""
-
- value: str
-
- @property
- def top_level_name(self) -> "QualifiedName":
- """Return the top-level module name (e.g., 'foo' from 'foo.bar.baz')."""
- return QualifiedName(value=self.value.split(".", maxsplit=1)[0])
-
- @property
- def parent_name(self) -> "QualifiedName":
- """Return the parent module name (e.g., 'foo.bar' from 'foo.bar.baz')."""
- return QualifiedName(value=self.value.rsplit(".", maxsplit=1)[0])
-
- def to_path(self) -> Path:
- """Convert qualified name to a file path (e.g., 'foo.bar' -> 'foo/bar.py')."""
- return Path(self.value.replace(".", "/") + ".py")
-
-
-class Import(SerializableModel):
- """Represents a single import statement."""
-
- source: str
- alias: str | None
- qualified_name: QualifiedName
-
-
-def _collect_global_imports(node: ast.AST, imports: list[Import]) -> None:
- """
- Recursively collect import statements at global scope.
-
- Stops recursing into function and class definitions since imports
- inside those are not at global scope.
-
- Args:
- node: The AST node to process
- imports: List to accumulate found imports
- """
- if isinstance(node, ast.Import):
- # Handle: import foo, bar
- # Handle: import foo as bar
- for alias in node.names:
- if alias.asname:
- source = f"import {alias.name} as {alias.asname}"
- alias_name = alias.asname
- else:
- source = f"import {alias.name}"
- alias_name = None
- imports.append(
- Import(
- source=source,
- alias=alias_name,
- qualified_name=QualifiedName(value=alias.name),
- )
- )
- elif isinstance(node, ast.ImportFrom):
- # Handle: from foo import bar, baz
- module = node.module or ""
- if node.names[0].name == "*":
- # from foo import *
- source = f"from {module} import *"
- imports.append(
- Import(
- source=source,
- alias=None,
- qualified_name=QualifiedName(value=f"{module}.*"),
- )
- )
- else:
- for alias in node.names:
- if module:
- full_name = f"{module}.{alias.name}"
- else:
- # relative import like: from . import foo
- full_name = alias.name
-
- if alias.asname:
- source = f"from {module} import {alias.name} as {alias.asname}"
- alias_name = alias.asname
- else:
- source = f"from {module} import {alias.name}"
- alias_name = None
-
- imports.append(
- Import(
- source=source,
- alias=alias_name,
- qualified_name=QualifiedName(value=full_name),
- )
- )
-
- # Don't recurse into function or class definitions - imports there are not global
- if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef, ast.Lambda)):
- return
-
- # Recurse into child nodes
- for child in ast.iter_child_nodes(node):
- _collect_global_imports(child, imports)
-
-
-def get_global_imports(source_code: str) -> tuple[Import, ...]:
- """
- Extract all global imports from Python source code.
-
- This includes imports at module level, as well as imports inside conditionals
- or other control flow structures at the top level (not inside functions or classes).
-
- Args:
- source_code: Python source code as a string
-
- Returns:
- Tuple of Import objects representing all imports in the file
-
- Raises:
- SyntaxError: If the source code cannot be parsed
- """
- tree = ast.parse(source_code)
- imports: list[Import] = []
- _collect_global_imports(tree, imports)
- return tuple(imports)
diff --git a/imbue_tools/imbue_tools/repo_utils/subrepo_formatting.py b/imbue_tools/imbue_tools/repo_utils/subrepo_formatting.py
@@ -1,345 +0,0 @@
-import functools
-from enum import Enum
-from typing import Annotated
-from typing import Iterable
-from typing import Mapping
-from typing import Self
-from typing import assert_never
-
-import jinja2
-from pathspec import GitIgnoreSpec
-from pydantic import Tag
-
-from imbue_core.agents.configs import LanguageModelGenerationConfig
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_core.pydantic_serialization import build_discriminator
-from imbue_tools.repo_utils.context_utils import escape_all_jinja_variables
-from imbue_tools.repo_utils.context_utils import escape_prompt_markers
-from imbue_tools.repo_utils.data_types import FileContext
-from imbue_tools.repo_utils.data_types import FileContextUnion
-from imbue_tools.repo_utils.data_types import FilenameContext
-from imbue_tools.repo_utils.data_types import FullFileContext
-from imbue_tools.repo_utils.data_types import StubFileContext
-from imbue_tools.repo_utils.errors import ContextLengthExceededError
-from imbue_tools.repo_utils.stubify_file import stubify_code_file
-
-
-class ContextFormatStyle(Enum):
- FULL_FILE = "FULL_FILE"
- STUB = "STUB"
- FILENAME_ONLY = "FILENAME_ONLY"
- HIDDEN = "HIDDEN"
-
-
-class BaseFilenamePattern(SerializableModel):
- """
- Extends the functionality of `GitIgnoreSpec` to be serializable.
- """
-
- object_type: str = "BaseFilenamePattern"
- lines: tuple[str, ...]
-
- @functools.cached_property
- def git_ignore_spec(self) -> GitIgnoreSpec:
- return GitIgnoreSpec.from_lines(self.lines)
-
- @classmethod
- def from_lines(cls, lines: Iterable[str]) -> Self:
- return cls(lines=tuple(sorted(lines)))
-
- def match_file(self, file: str) -> bool:
- return self.git_ignore_spec.match_file(file)
-
-
-class NegatedFilenamePattern(BaseFilenamePattern):
- """Matches everything except the files matched by the base pattern."""
-
- object_type: str = "NegatedFilenamePattern"
-
- def match_file(self, file: str) -> bool:
- return not self.git_ignore_spec.match_file(file)
-
- @classmethod
- def build_from_positive_pattern(cls, positive_pattern: BaseFilenamePattern) -> Self:
- return cls(lines=positive_pattern.lines)
-
-
-class IntersectionFilenamePattern(SerializableModel):
- object_type: str = "IntersectionFilenamePattern"
- specs: tuple["FilenamePattern", ...]
-
- def match_file(self, file: str) -> bool:
- return all(spec.match_file(file) for spec in self.specs)
-
-
-class UnionFilenamePattern(SerializableModel):
- object_type: str = "UnionFilenamePattern"
- specs: tuple["FilenamePattern", ...]
-
- def match_file(self, file: str) -> bool:
- return any(spec.match_file(file) for spec in self.specs)
-
-
-class ExactFilenamePattern(SerializableModel):
- """
- Similar to a BaseFilenamePattern, but more efficient thanks to the use of a hash set.
- However, it only supports exact filename matches and no patterns.
-
- O(1) matching over n filenames, instead of O(n) with BaseFilenamePattern.
- """
-
- object_type: str = "ExactFilenamePattern"
- # Will match these exact filenames.
- # We store this as a tuple instead of frozenset to have deterministic ordering. Helps with snapshot tests.
- filenames: tuple[str, ...]
-
- def match_file(self, file: str) -> bool:
- return file in self.filenames_set
-
- @functools.cached_property
- def filenames_set(self) -> set[str]:
- return set(self.filenames)
-
-
-FilenamePattern = Annotated[
- Annotated[BaseFilenamePattern, Tag("BaseFilenamePattern")]
- | Annotated[NegatedFilenamePattern, Tag("NegatedFilenamePattern")]
- | Annotated[IntersectionFilenamePattern, Tag("IntersectionFilenamePattern")]
- | Annotated[UnionFilenamePattern, Tag("UnionFilenamePattern")]
- | Annotated[ExactFilenamePattern, Tag("ExactFilenamePattern")],
- build_discriminator(),
-]
-
-
-SubrepoContextMatchers = tuple[tuple[ContextFormatStyle, FilenamePattern], ...]
-
-
-@functools.lru_cache(maxsize=100)
-def stubify_file_contents_cached(path: str, contents: str) -> str:
- # TODO: there's various flags here we could try
- # TODO: we may want an option to suppress comments, which end up being a large percent of the lines
- if path.endswith(".py"):
- return stubify_code_file(path, contents, keep_indent=True)
- else:
- # For non-Python files, maintain the full contents for now.
- return contents
-
-
-def stubify_and_format_for_agent_context(path: str, contents: str | None) -> StubFileContext:
- contents_to_use = contents if contents is not None else "RAW FILE CONTENTS"
- stub = stubify_file_contents_cached(path=path, contents=contents_to_use)
- return StubFileContext(path=path, stub=stub)
-
-
-def format_filename_only_for_agent_context(path: str) -> FilenameContext:
- return FilenameContext(path=path)
-
-
-def format_full_file_for_agent_context(path: str, contents: str) -> FullFileContext:
- return FullFileContext(path=path, contents=contents)
-
-
-BASE_REPO_CONTEXT_TEMPLATE = """
-<REPO_CONTEXT>
-{{repo_context}}
-</REPO_CONTEXT>
-"""
-
-REPO_CONTEXT_TEMPLATE_WITH_NO_MENTION_OF_DIFF = (
- """
-{% if not is_shortened %}For context, here are the contents of all files currently in the project:
-{% else %}The project's repository is too large to show in full, so we have chosen a useful subset for your context.
-Files in the repository may be shown in either full, stubified, or filename-only form{% if has_hidden_files %}, or they may be hidden entirely{% endif %}. Here are the contents of some of the files in the repository:{% endif %}
-"""
- + BASE_REPO_CONTEXT_TEMPLATE
-)
-
-REPO_CONTEXT_TEMPLATE = (
- REPO_CONTEXT_TEMPLATE_WITH_NO_MENTION_OF_DIFF
- + """
-
-If any files have been changed, the changes will be described next. Otherwise, you can assume this is the current state of the project.
-"""
-)
-
-
-def format_file_for_agent_context(
- path: str, contents: str, format_style: ContextFormatStyle
-) -> FileContextUnion | None:
- if format_style == ContextFormatStyle.FULL_FILE:
- return format_full_file_for_agent_context(path, contents)
- elif format_style == ContextFormatStyle.STUB:
- return stubify_and_format_for_agent_context(path, contents)
- elif format_style == ContextFormatStyle.FILENAME_ONLY:
- return format_filename_only_for_agent_context(path)
- elif format_style == ContextFormatStyle.HIDDEN:
- return None
- else:
- assert_never(format_style) # pyre-ignore[6]: pyre doesn't understand enums
-
-
-@functools.lru_cache(maxsize=20)
-def parse_subrepo_context_matchers_from_toml(
- subrepo_context_config_toml: str,
-) -> SubrepoContextMatchers:
- current_mode: ContextFormatStyle
- matchers = []
- for line in subrepo_context_config_toml.splitlines():
- line = line.strip()
- if not line:
- continue
- if line.startswith("["):
- current_mode = ContextFormatStyle[line.replace("[", "").replace("]", "").upper()] # type: ignore
- continue
-
- exclude_spec = BaseFilenamePattern.from_lines([line])
- matchers.append((current_mode, exclude_spec))
- return tuple(matchers)
-
-
-def compute_file_format_style(
- file_path: str,
- subrepo_context_matchers: SubrepoContextMatchers,
- exclusions: FilenamePattern | None = None,
-) -> ContextFormatStyle:
- for matcher_format, matcher in subrepo_context_matchers:
- if matcher.match_file(file_path):
- if exclusions and matcher_format != ContextFormatStyle.HIDDEN and exclusions.match_file(file_path):
- # Exclusions override any non-hidden format and downgrade it to FILENAME_ONLY.
- return ContextFormatStyle.FILENAME_ONLY
- # Return the first match
- return matcher_format
- return ContextFormatStyle.HIDDEN
-
-
-def compute_file_context_format_styles(
- file_paths: Iterable[str],
- subrepo_context_matchers: SubrepoContextMatchers,
- exclusions: FilenamePattern | None = None,
-) -> Mapping[str, ContextFormatStyle]:
- return {
- file_path: compute_file_format_style(file_path, subrepo_context_matchers, exclusions)
- for file_path in file_paths
- }
-
-
-def get_estimated_lower_bound_token_count_for_text_and_model(text: str, model_name: str) -> int:
- # A factor of 1/4.5 appears to be a reasonable empirical estimate for current models.
- # We use a slighly smaller factor (1/5) to give more of a lower bound estimate.
- return round(len(text) / 5)
-
-
-def format_all_for_agent(repo_contents: tuple[FileContext, ...]) -> dict[str, str]:
- return {contents.path: contents.format_for_agent() for contents in repo_contents}
-
-
-def format_subrepo(formatted_repo_contents: Mapping[str, str]) -> str:
- repo_context_str = "".join([contents for contents in formatted_repo_contents.values() if contents is not None])
- return escape_all_jinja_variables(escape_prompt_markers(repo_context_str))
-
-
-def formatted_subrepo_to_prompt(
- repo_context_str: str, is_shortened: bool, has_hidden_files: bool, template: str
-) -> str:
- env = jinja2.Environment(undefined=jinja2.StrictUndefined)
- jinja_template = env.from_string(template)
- repo_context_prompt = jinja_template.render(
- repo_context=repo_context_str,
- is_shortened=is_shortened,
- has_hidden_files=has_hidden_files,
- )
- return repo_context_prompt
-
-
-def format_subrepo_context_into_filecontexts(
- full_repo_contents: Mapping[str, str],
- path_to_format_style: Mapping[str, ContextFormatStyle],
-) -> tuple[FileContextUnion, ...]:
- repo_contents = tuple(
- format_file_for_agent_context(path, contents, path_to_format_style[path])
- for path, contents in full_repo_contents.items()
- )
- repo_contents_with_hidden_removed = tuple(contents for contents in repo_contents if contents is not None)
- return repo_contents_with_hidden_removed
-
-
-def build_context_from_filecontexts(
- repo_contents_with_hidden_removed: tuple[FileContextUnion, ...],
- model_config: LanguageModelGenerationConfig,
- # how many tokens to reserve for additional prompt messages and output
- tokens_to_reserve: int,
- template: str = REPO_CONTEXT_TEMPLATE,
- path_to_format_style: Mapping[str, ContextFormatStyle] | None = None,
-) -> str:
- """
- Returns the repo contents formatted according to the format styles as a string.
- Includes (at least if the default template is used) an explanation at the beginning
- saying that these are the contents of the repo, potentially truncated or hidden.
-
- If there are no repo contents, returns an empty string.
- """
-
- if not repo_contents_with_hidden_removed:
- return ""
-
- max_context_length = model_config.get_max_context_length()
- available_tokens = max_context_length - tokens_to_reserve
-
- formatted_repo_contents_with_hidden_removed = format_all_for_agent(repo_contents_with_hidden_removed)
-
- repo_context_str = format_subrepo(formatted_repo_contents_with_hidden_removed)
-
- if model_config.is_custom_model():
- # For custom models, approximate_token_count is already fast, so skip the estimation step.
- full_repo_context_token_count = model_config.count_tokens(repo_context_str)
- else:
- # First use an estimation of the token count to see if we are likely below the maximum length. Then
- # double-check with the exact token count.
- # We do this because getting the exact token count is quite slow.
- estimated_full_repo_context_token_count = get_estimated_lower_bound_token_count_for_text_and_model(
- repo_context_str, model_config.model_name
- )
- if estimated_full_repo_context_token_count > available_tokens:
- raise ContextLengthExceededError(
- f"Estimated context has size {estimated_full_repo_context_token_count}; available tokens {available_tokens}"
- )
- full_repo_context_token_count = model_config.count_tokens(repo_context_str)
- if full_repo_context_token_count > available_tokens:
- raise ContextLengthExceededError(
- f"Context has size {full_repo_context_token_count}; available tokens {available_tokens}"
- )
-
- if path_to_format_style is None:
- path_to_format_style = {
- contents.path: ContextFormatStyle.FULL_FILE for contents in repo_contents_with_hidden_removed
- }
-
- is_shortened = any([style != ContextFormatStyle.FULL_FILE for style in path_to_format_style.values()])
- has_hidden_files = any([style == ContextFormatStyle.HIDDEN for style in path_to_format_style.values()])
-
- repo_context_prompt = formatted_subrepo_to_prompt(
- repo_context_str=repo_context_str,
- is_shortened=is_shortened,
- has_hidden_files=has_hidden_files,
- template=template,
- )
- return repo_context_prompt
-
-
-def format_subrepo_context(
- full_repo_contents: Mapping[str, str],
- path_to_format_style: Mapping[str, ContextFormatStyle],
- model_config: LanguageModelGenerationConfig,
- # how many tokens to reserve for additional prompt messages and output
- tokens_to_reserve: int,
- template: str = REPO_CONTEXT_TEMPLATE,
-) -> tuple[str, tuple[FileContextUnion, ...]]:
- repo_contents_tuple = format_subrepo_context_into_filecontexts(full_repo_contents, path_to_format_style)
- repo_context_str = build_context_from_filecontexts(
- repo_contents_tuple,
- model_config,
- tokens_to_reserve,
- template,
- path_to_format_style,
- )
- return repo_context_str, repo_contents_tuple
diff --git a/imbue_tools/imbue_tools/types/vet_config.py b/imbue_tools/imbue_tools/types/vet_config.py
@@ -1,100 +0,0 @@
-from pathlib import Path
-
-from imbue_core.agents.configs import LanguageModelGenerationConfig
-from imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
-from imbue_core.data_types import IssueCode
-from imbue_core.pydantic_serialization import SerializableModel
-
-DEFAULT_CONFIDENCE_THRESHOLD = 0.8
-
-
-class VetConfig(SerializableModel):
- """Configuration for the vet system."""
-
- # If none, all registered identifiers are used.
- # Otherwise, only the identifiers in this tuple are used.
- enabled_identifiers: tuple[str, ...] | None = None
-
- # Issue identifiers that are disabled are never used.
- disabled_identifiers: tuple[str, ...] | None = None
-
- # Similar to the above, but for reporting specific types of issues.
- # (Use the values from the vet.data_types.IssueCode enum.)
- enabled_issue_codes: tuple[IssueCode, ...] | None = None
- disabled_issue_codes: tuple[IssueCode, ...] | None = ()
-
- # Todo: Different models for different issue identifiers
- language_model_generation_config: LanguageModelGenerationConfig = LanguageModelGenerationConfig(
- model_name=AnthropicModelName.CLAUDE_4_5_HAIKU_2025_10_01
- )
- max_identifier_spend_dollars: float = 5.0
- max_output_tokens: int = 20000
- enable_parallel_agentic_issue_identification: bool = False
- max_identify_workers: int | None = None
- temperature: float = 0.5
-
- # If True, apply an additional LLM-based filtering stage, where each identified issue is evaluated
- # according to a number of quality criteria. Only issues that pass the evaluation are returned.
- filter_issues: bool = True
- filter_issues_through_llm_evaluator: bool = True
- filter_issues_below_confidence: float | None = DEFAULT_CONFIDENCE_THRESHOLD
-
- enable_deduplication: bool = True
- enable_collation: bool = True
-
- # If True, we attempt to cache the full prompts including specific inputs with the LLM provider.
- # There can be an additional cost for such a cache write, but it can help save cost in evaluation
- # contexts (such as black_box_evals) where the same inputs are being evaluated multiple times.
- cache_full_prompt: bool = False
-
- extra_context: str | None = None
-
- @classmethod
- def build(
- cls,
- language_model_name: str | None = None,
- language_model_cache_path: Path | None = None,
- enabled_identifiers: tuple[str, ...] | None = None,
- enable_parallel_agentic_issue_identification: bool = False,
- max_identify_workers: int | None = None,
- filter_issues: bool = True,
- filter_issues_below_confidence: float | None = DEFAULT_CONFIDENCE_THRESHOLD,
- enable_deduplication: bool = True,
- enable_collation: bool = True,
- enabled_issue_codes: tuple[IssueCode, ...] | None = None,
- temperature: float = 0.5,
- retry_jitter_factor: float = 0.0,
- cache_full_prompt: bool = False,
- ) -> "VetConfig":
- if not language_model_name:
- language_model_name = AnthropicModelName.CLAUDE_4_5_HAIKU_2025_10_01
- language_model_generation_config = LanguageModelGenerationConfig(
- model_name=language_model_name,
- cache_path=language_model_cache_path,
- retry_jitter_factor=retry_jitter_factor,
- )
- return cls(
- language_model_generation_config=language_model_generation_config,
- enabled_identifiers=enabled_identifiers,
- enable_parallel_agentic_issue_identification=enable_parallel_agentic_issue_identification,
- max_identify_workers=max_identify_workers,
- filter_issues=filter_issues,
- filter_issues_below_confidence=filter_issues_below_confidence,
- enable_deduplication=enable_deduplication,
- enable_collation=enable_collation,
- enabled_issue_codes=enabled_issue_codes,
- temperature=temperature,
- cache_full_prompt=cache_full_prompt,
- )
-
-
-def get_enabled_issue_codes(config: VetConfig) -> set[IssueCode]:
- all_issue_code_values = {item.value for item in IssueCode}
- explicitly_enabled = config.enabled_issue_codes or tuple()
- explicitly_disabled = config.disabled_issue_codes or tuple()
- for code in explicitly_enabled + explicitly_disabled:
- if code not in all_issue_code_values:
- raise ValueError(f"Bad config: unknown issue code: {code}")
- possibly_enabled_values = set(explicitly_enabled) if len(explicitly_enabled) > 0 else set(v for v in IssueCode)
- disabled_values = set(explicitly_disabled)
- return possibly_enabled_values - disabled_values
diff --git a/imbue_tools/imbue_tools/util_prompts/goal_from_conversation.py b/imbue_tools/imbue_tools/util_prompts/goal_from_conversation.py
@@ -1,64 +0,0 @@
-import jinja2
-
-from imbue_core.agents.configs import LanguageModelGenerationConfig
-from imbue_core.agents.llm_apis.build_apis import build_language_model_from_config
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.itertools import only
-from vet_types.messages import ConversationMessageUnion
-from imbue_tools.get_conversation_history.get_conversation_history import (
- format_conversation_history_for_prompt,
-)
-from imbue_tools.util_prompts.conversation_prefix import CONVERSATION_PREFIX_TEMPLATE
-
-# TODO: see how this does on actual examples where the agent did something other than what the user asked for
-PROMPT_TEMPLATE = (
- CONVERSATION_PREFIX_TEMPLATE
- + """
-[ROLE=USER]
-What is the user's goal based on the preceding conversation?
-Pay attention only to what the user asks for, not what the agent does.
-Respond with a brief description of the goal--a few sentences at most.
-The goal should be listed as an imperative; for example "Implement XYZ" rather than "The user's goal is to implement XYZ".
-Do not include any reasoning or other text in your response.
-"""
-)
-
-# should be totally sufficient for a goal that's only supposed to be a few sentences
-MAX_OUTPUT_TOKENS = 500
-
-GOAL_GENERATION_DEFAULT_PARAMS = LanguageModelGenerationParams(temperature=0.0, max_tokens=MAX_OUTPUT_TOKENS)
-
-
-def prompt_for_getting_goal_from_conversation(
- conversation_history: tuple[ConversationMessageUnion, ...],
-) -> str:
- env = jinja2.Environment(undefined=jinja2.StrictUndefined)
- jinja_template = env.from_string(PROMPT_TEMPLATE)
- return jinja_template.render(conversation_history=format_conversation_history_for_prompt(conversation_history))
-
-
-def get_goal_from_conversation_with_usage(
- conversation_history: tuple[ConversationMessageUnion, ...],
- language_model_generation_config: LanguageModelGenerationConfig,
-) -> CostedLanguageModelResponse:
- """Query an LLM with the conversation history to get the user's goal, and include usage info in the response."""
- language_model = build_language_model_from_config(language_model_generation_config)
- prompt = prompt_for_getting_goal_from_conversation(conversation_history)
- costed_response = language_model.complete_with_usage_sync(
- prompt,
- params=GOAL_GENERATION_DEFAULT_PARAMS,
- is_caching_enabled=language_model.cache_path is not None,
- )
- return costed_response
-
-
-def get_goal_from_conversation(
- conversation_history: tuple[ConversationMessageUnion, ...],
- language_model_generation_config: LanguageModelGenerationConfig,
-) -> str:
- """Query an LLM with the conversation history to get the user's goal."""
- response = only(
- get_goal_from_conversation_with_usage(conversation_history, language_model_generation_config).responses
- )
- return response.text
diff --git a/imbue_tools/pyproject.toml b/imbue_tools/pyproject.toml
@@ -1,43 +0,0 @@
-[build-system]
-requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "imbue-tools"
-version = "0.1.0"
-description = "Utils for imbue-cli tools"
-readme = "README.md"
-dependencies = [
- "anyio",
- "async_lru",
- "attrs",
- "imbue_core",
- "jinja2",
- "libcst",
- "loguru",
- "psycopg[binary]",
- "pydantic",
- "pydantic-settings",
- "pygit2",
- "pytest",
- "pytest-asyncio",
- "python-gitlab",
- "requests",
- "syrupy",
-]
-requires-python = ">=3.11"
-
-[project.optional-dependencies]
-test = [
- "vet",
-]
-
-[tool.setuptools]
-package-data.imbue_tools = ["py.typed"]
-
-[tool.setuptools.packages.find]
-include = ["imbue_tools*"]
-
-[tool.uv.sources]
-imbue_core = { path = "../imbue_core", editable = true }
-vet = { path = "..", editable = true }
diff --git a/imbue_tools/uv.lock b/imbue_tools/uv.lock
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diff --git a/pyproject.toml b/pyproject.toml
@@ -7,20 +7,55 @@ name = "vet"
version = "0.1.0"
readme = "README.md"
dependencies = [
+ # Original vet dependencies
"aiohttp>=3.8.0",
"click",
- "imbue_core",
- "imbue_tools",
"jinja2",
"loguru",
- "pydantic",
+ "pydantic>=2.11.4",
"pygments>=2.0.0",
"pytest",
"syrupy",
"together>=1.5.35",
- "vet_types",
+
+ # From imbue_core
+ "anyio",
+ "attrs",
+ "boto3>=1.38.27",
+ "cachetools",
+ "cattrs",
+ "diskcache>=5.6.3",
+ "grpclib>=0.4.7",
+ "httpx",
+ "inline-snapshot",
+ "pathspec",
+ "prometheus-client>=0.20.0",
+ "pydantic-settings",
+ "pygit2>=1.18.0",
+ "pylint==3.2.6",
+ "pyhumps",
+ "pytest-asyncio",
+ "pytest-mock",
+ "python-gitlab>=4.5.0",
+ "tblib==2.0.0",
+ "tenacity>=8.2.2",
+ "toml",
+ "traceback-with-variables>=2.2.0",
+ "typeid-python",
+ "yasoo",
+ "anthropic~=0.54",
+ "openai>=1.79.0",
+ "tiktoken",
+ "groq>=0.18.0",
+ "google-genai>=1.26.0",
+
+ # From imbue_tools
+ "async_lru",
+ "libcst",
+ "psycopg[binary]",
+ "requests",
]
-requires-python = ">=3.11"
+requires-python = ">=3.11,<3.13"
[project.scripts]
vet = "vet.cli.main:main"
@@ -31,14 +66,10 @@ package-data.vet = ["py.typed"]
[tool.setuptools.packages.find]
include = ["vet*"]
-[tool.uv.sources]
-imbue_core = { path = "./imbue_core", editable = true }
-imbue_tools = { path = "./imbue_tools", editable = true }
-vet_types = { path = "./vet_types", editable = true }
-
[dependency-groups]
dev = [
"black",
+ "moto>=4.1.12",
]
[tool.black]
diff --git a/uv.lock b/uv.lock
@@ -1,10 +1,8 @@
version = 1
revision = 3
-requires-python = ">=3.11"
+requires-python = ">=3.11, <3.13"
resolution-markers = [
- "python_full_version >= '3.14'",
- "python_full_version == '3.13.*'",
- "python_full_version == '3.12.*'",
+ "python_full_version >= '3.12'",
"python_full_version < '3.12'",
]
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diff --git a/vet/api.py b/vet/api.py
@@ -8,17 +8,17 @@ from pathlib import Path
from loguru import logger
-from imbue_core.data_types import IdentifiedVerifyIssue
-from imbue_core.data_types import IssueIdentificationDebugInfo
-from vet_types.messages import ConversationMessageUnion
-from imbue_tools.get_conversation_history.get_conversation_history import (
+from vet.imbue_core.data_types import IdentifiedVerifyIssue
+from vet.imbue_core.data_types import IssueIdentificationDebugInfo
+from vet.vet_types.messages import ConversationMessageUnion
+from vet.imbue_tools.get_conversation_history.get_conversation_history import (
ConversationLoadingError,
)
-from imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
-from imbue_tools.repo_utils.project_context import LazyProjectContext
-from imbue_tools.repo_utils.project_context import ProjectContext
-from imbue_tools.types.vet_config import VetConfig
-from imbue_tools.util_prompts.goal_from_conversation import get_goal_from_conversation
+from vet.imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
+from vet.imbue_tools.repo_utils.project_context import LazyProjectContext
+from vet.imbue_tools.repo_utils.project_context import ProjectContext
+from vet.imbue_tools.types.vet_config import VetConfig
+from vet.imbue_tools.util_prompts.goal_from_conversation import get_goal_from_conversation
from vet.issue_identifiers import registry
from vet.issue_identifiers.utils import ReturnCapturingGenerator
from vet.repo_utils import VET_MAX_PROMPT_TOKENS
diff --git a/vet/cli/config/loader.py b/vet/cli/config/loader.py
@@ -6,9 +6,9 @@ from pathlib import Path
from pydantic import ValidationError
-from imbue_core.agents.configs import LanguageModelGenerationConfig
-from imbue_core.agents.configs import OpenAICompatibleModelConfig
-from imbue_core.agents.llm_apis.common import get_model_max_output_tokens
+from vet.imbue_core.agents.configs import LanguageModelGenerationConfig
+from vet.imbue_core.agents.configs import OpenAICompatibleModelConfig
+from vet.imbue_core.agents.llm_apis.common import get_model_max_output_tokens
from vet.cli.config.cli_config_schema import CliConfigPreset
from vet.cli.config.cli_config_schema import merge_presets
from vet.cli.config.cli_config_schema import parse_cli_config_from_dict
diff --git a/vet/cli/main.py b/vet/cli/main.py
@@ -11,11 +11,11 @@ from pathlib import Path
from loguru import logger
-from imbue_core.data_types import IssueCode
-from imbue_tools.get_conversation_history.get_conversation_history import (
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_tools.get_conversation_history.get_conversation_history import (
parse_conversation_history,
)
-from imbue_tools.types.vet_config import VetConfig
+from vet.imbue_tools.types.vet_config import VetConfig
from vet.api import find_issues
from vet.cli.config.cli_config_schema import CLI_DEFAULTS
from vet.cli.config.cli_config_schema import CliConfigPreset
diff --git a/vet/cli/models.py b/vet/cli/models.py
@@ -1,11 +1,11 @@
from __future__ import annotations
-from imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
-from imbue_core.agents.llm_apis.common import get_all_model_names
-from imbue_core.agents.llm_apis.gemini_api import GeminiModelName
-from imbue_core.agents.llm_apis.groq_api import GroqSupportedModelName
-from imbue_core.agents.llm_apis.openai_api import OpenAIModelName
-from imbue_core.agents.llm_apis.together_api import TogetherAIModelName
+from vet.imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
+from vet.imbue_core.agents.llm_apis.common import get_all_model_names
+from vet.imbue_core.agents.llm_apis.gemini_api import GeminiModelName
+from vet.imbue_core.agents.llm_apis.groq_api import GroqSupportedModelName
+from vet.imbue_core.agents.llm_apis.openai_api import OpenAIModelName
+from vet.imbue_core.agents.llm_apis.together_api import TogetherAIModelName
from vet.cli.config.loader import get_models_by_provider_from_config
from vet.cli.config.loader import get_user_defined_model_ids
from vet.cli.config.schema import ModelsConfig
diff --git a/vet/conftest.py b/vet/conftest.py
@@ -3,8 +3,8 @@ from typing import Generator
import pytest
-from imbue_core.async_monkey_patches_test import explode_on_error # noqa: F401
-from imbue_core.test_repo_utils import make_simple_test_git_repo
+from vet.imbue_core.async_monkey_patches_test import explode_on_error # noqa: F401
+from vet.imbue_core.test_repo_utils import make_simple_test_git_repo
simple_test_git_repo = pytest.fixture(make_simple_test_git_repo)
diff --git a/vet/formatters.py b/vet/formatters.py
@@ -2,7 +2,7 @@ from __future__ import annotations
from pydantic import BaseModel
-from imbue_core.data_types import IdentifiedVerifyIssue
+from vet.imbue_core.data_types import IdentifiedVerifyIssue
OUTPUT_FORMATS = ["text", "json"]
diff --git a/vet/git.py b/vet/git.py
@@ -9,7 +9,7 @@ from typing import Sequence
import anyio
from loguru import logger
-from imbue_core.async_monkey_patches import log_exception
+from vet.imbue_core.async_monkey_patches import log_exception
from vet.errors import RunCommandError
# Flexible path type alias
diff --git a/imbue_core/imbue_core/__init__.py b/vet/imbue_core/__init__.py
diff --git a/imbue_core/imbue_core/agents/__init__.py b/vet/imbue_core/agents/__init__.py
diff --git a/imbue_core/imbue_core/agents/agent_api/__init__.py b/vet/imbue_core/agents/agent_api/__init__.py
diff --git a/vet/imbue_core/agents/agent_api/api.py b/vet/imbue_core/agents/agent_api/api.py
@@ -0,0 +1,59 @@
+from __future__ import annotations
+
+from contextlib import contextmanager
+from functools import singledispatch
+from pathlib import Path
+from typing import Any
+from typing import ContextManager
+from typing import Iterator
+
+from vet.imbue_core.agents.agent_api.claude.client import ClaudeCodeClient
+from vet.imbue_core.agents.agent_api.claude.data_types import ClaudeCodeOptions
+from vet.imbue_core.agents.agent_api.client import AgentClient
+from vet.imbue_core.agents.agent_api.client import AgentOptionsT
+from vet.imbue_core.agents.agent_api.client import CachedAgentClient
+from vet.imbue_core.agents.agent_api.codex.client import CodexClient
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexOptions
+from vet.imbue_core.agents.agent_api.data_types import AgentOptions
+
+
+@singledispatch
+def _build_client_from_options(
+ options: AgentOptions,
+) -> ContextManager[AgentClient[Any]]:
+ """Return a context manager that builds an AgentClient for the given options."""
+ raise ValueError(f"Unsupported agent options type: {type(options).__name__}")
+
+
+@_build_client_from_options.register
+def _(options: ClaudeCodeOptions) -> ContextManager[AgentClient[ClaudeCodeOptions]]:
+ return ClaudeCodeClient.build(options)
+
+
+@_build_client_from_options.register
+def _(options: CodexOptions) -> ContextManager[AgentClient[CodexOptions]]:
+ return CodexClient.build(options)
+
+
+@contextmanager
+def get_agent_client(
+ *,
+ options: AgentOptionsT,
+ cache_path: Path | None = None,
+) -> Iterator[AgentClient[AgentOptionsT]]:
+ """Build and manage the lifecycle of an AgentClient based on the provided options.
+
+ Args:
+ options: AgentOptions instance describing which agent to run.
+ cache_path: Optional path to use for caching agent interactions.
+
+ Yields:
+ An AgentClient (or CachedAgentClient) bound to the selected agent implementation.
+ """
+
+ with _build_client_from_options(options) as client:
+ if cache_path is None:
+ yield client
+ return
+
+ yield CachedAgentClient(client, cache_path)
diff --git a/vet/imbue_core/agents/agent_api/cache_utils.py b/vet/imbue_core/agents/agent_api/cache_utils.py
@@ -0,0 +1,36 @@
+import hashlib
+from pathlib import Path
+
+from vet.imbue_core.agents.agent_api.data_types import AgentOptions
+from vet.imbue_core.agents.agent_api.interaction import AgentInteractionRecord
+from vet.imbue_core.caching import get_cache
+
+
+def _create_cache_key(prompt: str, options: AgentOptions) -> str:
+ """Create a cache key for the given prompt and options."""
+ return hashlib.md5(f"{prompt} | {options.model_dump_json() if options else ''}".encode()).hexdigest()
+
+
+def check_cache(cache_path: Path, prompt: str, options: AgentOptions) -> AgentInteractionRecord | None:
+ """Check the cache for the given prompt and options."""
+ cache_key = _create_cache_key(prompt, options)
+ cache = get_cache(cache_path)
+
+ with cache:
+ value = cache.get(cache_key)
+
+ if value is None:
+ return None
+ assert isinstance(value, str), f"Got value of type {type(value)} from cache, expected str"
+ return AgentInteractionRecord.model_validate_json(value)
+
+
+def update_cache(
+ agent_interaction: AgentInteractionRecord,
+ cache_dir: Path,
+) -> None:
+ """Save an agent interaction record to the cache."""
+ cache = get_cache(cache_dir)
+ cache_key = _create_cache_key(agent_interaction.prompt, agent_interaction.options)
+ with cache:
+ cache.set(cache_key, agent_interaction.model_dump_json())
diff --git a/imbue_core/imbue_core/agents/agent_api/claude/__init__.py b/vet/imbue_core/agents/agent_api/claude/__init__.py
diff --git a/vet/imbue_core/agents/agent_api/claude/client.py b/vet/imbue_core/agents/agent_api/claude/client.py
@@ -0,0 +1,191 @@
+import json
+import shutil
+import tempfile
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Generator
+from typing import Iterator
+from typing import Self
+
+from loguru import logger
+
+from vet.imbue_core.agents.agent_api.claude.data_types import ClaudeCodeOptions
+from vet.imbue_core.agents.agent_api.claude.message_parser import parse_claude_message
+from vet.imbue_core.agents.agent_api.client import RealAgentClient
+from vet.imbue_core.agents.agent_api.data_types import AgentMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentResultMessage
+from vet.imbue_core.agents.agent_api.errors import AgentCLINotFoundError
+from vet.imbue_core.agents.agent_api.transport import AgentSubprocessCLITransport
+from vet.imbue_core.agents.agent_api.transport import AgentSubprocessCLITransportOptions
+from vet.imbue_core.agents.agent_api.transport import AgentTransport
+
+
+class ClaudeCodeClient(RealAgentClient[ClaudeCodeOptions]):
+ """Claude Code client implementation.
+
+ Most callers should obtain an instance through `get_agent_client(options=ClaudeCodeOptions(...))`,
+ which takes care of building and tearing down the underlying CLI transport.
+
+ Example:
+ ```python
+ with get_agent_client(options=ClaudeCodeOptions()) as client:
+ for message in client.process_query(prompt="Hello"):
+ print(message)
+ ```
+ """
+
+ def __init__(self, options: ClaudeCodeOptions, transport: AgentTransport) -> None:
+ super().__init__(options)
+ self._transport = transport
+
+ @classmethod
+ @contextmanager
+ def build(cls, options: ClaudeCodeOptions) -> Generator[Self, None, None]:
+ cmd = cls._build_cli_cmd(options)
+ with AgentSubprocessCLITransport.build(
+ AgentSubprocessCLITransportOptions(
+ cmd=cmd,
+ cwd=options.cwd,
+ extra_env_vars={"CLAUDE_CODE_ENTRYPOINT": "sdk-py"},
+ )
+ ) as transport:
+ yield cls(options=options, transport=transport)
+
+ def process_query(self, prompt: str) -> Iterator[AgentMessage]:
+ logger.trace(
+ "{client_name}: calling agent with prompt={prompt}",
+ client_name=type(self).__name__,
+ prompt=prompt,
+ )
+ # Claude code expects "User message" objects as inputs
+ self._transport.send_request(
+ [
+ {
+ "type": "user",
+ "message": {
+ "role": "user",
+ "content": [{"type": "text", "text": prompt}],
+ },
+ }
+ ],
+ self._options,
+ )
+
+ for data in self._transport.receive_messages():
+ logger.trace(
+ "{client_name}: received raw JSON message={data}",
+ client_name=type(self).__name__,
+ data=data,
+ )
+
+ message = parse_claude_message(data)
+ if message:
+ yield message
+
+ if isinstance(message, AgentResultMessage):
+ break
+
+ logger.trace(
+ "{client_name}: finished calling agent with prompt={prompt}",
+ client_name=type(self).__name__,
+ prompt=prompt,
+ )
+
+ @staticmethod
+ def _find_cli() -> str:
+ """Find Claude Code CLI binary."""
+ cli = shutil.which("claude")
+ if cli:
+ return cli
+
+ locations = [
+ # TODO: Document what these do. Does the path to claude inside the container need to be here?
+ Path("/imbue_addons/bin/claude"),
+ Path.home() / ".npm-global/bin/claude",
+ Path("/usr/local/bin/claude"),
+ Path.home() / ".local/bin/claude",
+ Path.home() / "node_modules/.bin/claude",
+ Path.home() / ".yarn/bin/claude",
+ ]
+
+ for path in locations:
+ if path.exists() and path.is_file():
+ return str(path)
+
+ node_installed = shutil.which("node") is not None
+
+ if not node_installed:
+ raise AgentCLINotFoundError(
+ "\n".join(
+ [
+ "Claude Code requires Node.js, which is not installed.",
+ "Install Node.js from: https://nodejs.org/",
+ "\nAfter installing Node.js, install Claude Code:",
+ " npm install -g @anthropic-ai/claude-code",
+ ]
+ )
+ )
+
+ raise AgentCLINotFoundError(
+ "\n".join(
+ [
+ "Claude Code not found. Install with:",
+ " npm install -g @anthropic-ai/claude-code",
+ "\nIf already installed locally, try:",
+ ' export PATH="$HOME/node_modules/.bin:$PATH"',
+ ]
+ )
+ )
+
+ @classmethod
+ def _build_cli_cmd(cls, options: ClaudeCodeOptions) -> list[str]:
+ """Build CLI command with arguments."""
+ if options.is_cached:
+ # in this case, the cmd should never be used
+ cmd = ["CACHED_CLAUDE_CODE_EXEC_PLACEHOLDER"]
+ return cmd
+ cli_path = str(options.cli_path) if options.cli_path is not None else cls._find_cli()
+ cmd = [
+ cli_path,
+ "--output-format",
+ "stream-json",
+ "--input-format",
+ "stream-json",
+ "--verbose",
+ ]
+ cmd.extend(cls._build_cli_args(options))
+ return cmd
+
+ @staticmethod
+ def _build_cli_args(options: ClaudeCodeOptions) -> list[str]:
+ args = []
+ if options.system_prompt:
+ args.extend(["--system-prompt", options.system_prompt])
+
+ if options.append_system_prompt:
+ args.extend(["--append-system-prompt", options.append_system_prompt])
+
+ if options.model:
+ args.extend(["--model", options.model])
+
+ if options.permission_prompt_tool_name:
+ args.extend(["--permission-prompt-tool", options.permission_prompt_tool_name])
+
+ if options.permission_mode:
+ args.extend(["--permission-mode", options.permission_mode])
+
+ if options.continue_conversation:
+ args.append("--continue")
+
+ if options.resume:
+ args.extend(["--resume", options.resume])
+
+ if options.mcp_servers:
+ mcp_config_file = tempfile.NamedTemporaryFile(delete=False, suffix=".json")
+ mcp_config_file.write(
+ json.dumps({"mcpServers": {k: v.model_dump() for k, v in options.mcp_servers.items()}}).encode("utf-8")
+ )
+ args.extend(["--mcp-config", mcp_config_file.name])
+
+ args.append("--print")
+ return args
diff --git a/vet/imbue_core/agents/agent_api/claude/data_types.py b/vet/imbue_core/agents/agent_api/claude/data_types.py
@@ -0,0 +1,79 @@
+from pathlib import Path
+from typing import Literal
+
+from pydantic import Field
+
+from vet.imbue_core.agents.agent_api.data_types import AgentOptions
+from vet.imbue_core.agents.agent_api.data_types import AgentToolName
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+ClaudePermissionMode = Literal["plan", "default", "acceptEdits", "bypassPermissions"]
+
+
+class ClaudeMcpStdioServerConfig(SerializableModel):
+ """MCP stdio server configuration."""
+
+ type: Literal["stdio"] = "stdio"
+ command: str
+ args: list[str] = Field(default_factory=list)
+ env: dict[str, str] = Field(default_factory=dict)
+
+
+class ClaudeMcpHttpServerConfig(SerializableModel):
+ """MCP HTTP server configuration."""
+
+ type: Literal["http"] = "http"
+ url: str
+ headers: dict[str, str] | None = None
+
+
+ClaudeMcpServerConfig = ClaudeMcpStdioServerConfig | ClaudeMcpHttpServerConfig
+
+
+class ClaudeCodeOptions(AgentOptions):
+ """Query options for Claude SDK."""
+
+ object_type: Literal["ClaudeCodeOptions"] = "ClaudeCodeOptions"
+
+ allowed_tools: list[str] = Field(default_factory=list)
+ max_thinking_tokens: int = 8000
+ system_prompt: str | None = None
+ append_system_prompt: str | None = None
+ mcp_tools: list[str] = Field(default_factory=list)
+ mcp_servers: dict[str, ClaudeMcpServerConfig] = Field(default_factory=dict)
+ permission_mode: ClaudePermissionMode | None = None
+ continue_conversation: bool = False
+ resume: str | None = None
+ max_turns: int | None = None
+ disallowed_tools: list[str] = Field(default_factory=list)
+ model: str | None = None
+ permission_prompt_tool_name: str | None = None
+ # Optional override for the Claude CLI path
+ cli_path: Path | None = None
+ is_cached: bool = False
+
+
+CLAUDE_TOOLS = (
+ AgentToolName.READ,
+ AgentToolName.WRITE,
+ AgentToolName.EDIT,
+ AgentToolName.MULTI_EDIT,
+ AgentToolName.GLOB,
+ AgentToolName.NOTEBOOK_READ,
+ AgentToolName.NOTEBOOK_EDIT,
+ AgentToolName.LS,
+ AgentToolName.GREP,
+ AgentToolName.BASH,
+ AgentToolName.BASH_OUTPUT,
+ AgentToolName.KILL_SHELL,
+ AgentToolName.WEB_SEARCH,
+ AgentToolName.WEB_FETCH,
+ AgentToolName.TASK,
+ AgentToolName.TODO_READ,
+ AgentToolName.TODO_WRITE,
+ AgentToolName.SLASH_COMMAND,
+ AgentToolName.EXIT_PLAN_MODE,
+ AgentToolName.MCP_TOOL,
+ AgentToolName.LIST_MCP_RESOURCES,
+ AgentToolName.READ_MCP_RESOURCE,
+)
diff --git a/vet/imbue_core/agents/agent_api/claude/message_parser.py b/vet/imbue_core/agents/agent_api/claude/message_parser.py
@@ -0,0 +1,117 @@
+from typing import Any
+from typing import assert_never
+
+from vet.imbue_core.agents.agent_api.data_types import AgentAssistantMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentContentBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentResultMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentSystemEventType
+from vet.imbue_core.agents.agent_api.data_types import AgentSystemMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentTextBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentThinkingBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentToolResultBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentToolUseBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentUsage
+from vet.imbue_core.agents.agent_api.data_types import AgentUserMessage
+
+
+def parse_claude_message(data: dict[str, Any]) -> AgentMessage | None:
+ """Parse message from CLI output using unified types.
+
+ Reference:
+ https://github.com/anthropics/claude-agent-sdk-python/blob/main/src/claude_agent_sdk/_internal/message_parser.py
+ https://docs.claude.com/en/api/agent-sdk/typescript#sdkmessage
+ https://docs.claude.com/en/api/agent-sdk/python#message-types
+ """
+
+ match data["type"]:
+ case "user":
+ return AgentUserMessage(content=parse_claude_content_blocks(data), original_message=data)
+
+ case "assistant":
+ return AgentAssistantMessage(content=parse_claude_content_blocks(data), original_message=data)
+
+ case "system":
+ # Normalize system event types
+ event_type = parse_claude_system_event_type(data.get("subtype", ""))
+ return AgentSystemMessage(
+ event_type=event_type,
+ session_id=data.get("session_id"),
+ error=data.get("error"),
+ original_message=data,
+ )
+
+ case "result":
+ # Build normalized usage
+ usage = None
+ raw_usage = data.get("usage")
+ if raw_usage or data.get("total_cost_usd"):
+ usage = AgentUsage(
+ input_tokens=raw_usage.get("input_tokens") if raw_usage else None,
+ output_tokens=raw_usage.get("output_tokens") if raw_usage else None,
+ cached_tokens=(raw_usage.get("cache_read_input_tokens") if raw_usage else None),
+ total_tokens=(
+ raw_usage.get("input_tokens", 0) + raw_usage.get("output_tokens", 0) if raw_usage else None
+ ),
+ total_cost_usd=data.get("total_cost_usd"),
+ )
+
+ return AgentResultMessage(
+ session_id=data["session_id"],
+ is_error=data["is_error"],
+ duration_ms=data.get("duration_ms"),
+ api_duration_ms=data.get("duration_api_ms"),
+ num_turns=data.get("num_turns"),
+ usage=usage,
+ result=data.get("result"),
+ error=data.get("error") if data["is_error"] else None,
+ original_message=data,
+ )
+
+ case _ as unreachable:
+ assert_never(unreachable)
+
+
+def parse_claude_system_event_type(subtype: str) -> AgentSystemEventType:
+ """Parse Claude system event subtype to unified event type."""
+ subtype_lower = subtype.lower()
+
+ # TODO add other system event types as we find them
+ # basically the documentattion doesn't mention any other system event types
+ # other than init AFAIKT
+ if "init" in subtype_lower:
+ return AgentSystemEventType.SESSION_STARTED
+ else:
+ return AgentSystemEventType.OTHER
+
+
+def parse_claude_content_blocks(data: dict[str, Any]) -> list[AgentContentBlock]:
+ return [parse_claude_content_block(block) for block in data["message"]["content"]]
+
+
+def parse_claude_content_block(block: dict[str, Any]) -> AgentContentBlock:
+ """Parse content block from CLI output using unified types."""
+
+ match block["type"]:
+ case "text":
+ return AgentTextBlock(text=block["text"])
+
+ case "thinking":
+ # Claude Code thinking blocks
+ return AgentThinkingBlock(
+ content=block.get("thinking", ""),
+ thinking_tokens=block.get("thinking_tokens"),
+ )
+
+ case "tool_use":
+ return AgentToolUseBlock(id=block["id"], name=block["name"], input=block["input"])
+
+ case "tool_result":
+ return AgentToolResultBlock(
+ tool_use_id=block["tool_use_id"],
+ content=block.get("content"),
+ is_error=block.get("is_error"),
+ )
+
+ case _ as unreachable:
+ assert_never(unreachable)
diff --git a/vet/imbue_core/agents/agent_api/client.py b/vet/imbue_core/agents/agent_api/client.py
@@ -0,0 +1,89 @@
+import abc
+from pathlib import Path
+from typing import Generic
+from typing import Iterator
+from typing import TypeVar
+
+from vet.imbue_core.agents.agent_api.cache_utils import check_cache
+from vet.imbue_core.agents.agent_api.cache_utils import update_cache
+from vet.imbue_core.agents.agent_api.data_types import AgentMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentOptions
+from vet.imbue_core.agents.agent_api.interaction import AgentInteraction
+from vet.imbue_core.agents.agent_api.interaction import AgentInteractionRecord
+
+AgentOptionsT = TypeVar("AgentOptionsT", bound=AgentOptions)
+
+
+class AgentClient(abc.ABC, Generic[AgentOptionsT]):
+ """Base code agent client interface.
+
+ This client defines the interface for launching and interacting with a coding agent (e.g., ClaudeCode, Codex, etc.)
+
+ Clients are usually created through `get_agent_client`, which selects the right concrete implementation
+ and manages any transports. Direct subclasses only need to implement `process_query`.
+ """
+
+ def __init__(self, options: AgentOptionsT) -> None:
+ self._options = options
+
+ @abc.abstractmethod
+ def process_query(self, prompt: str) -> Iterator[AgentMessage]:
+ """Call the underlying agent to process a query."""
+
+
+class RealAgentClient(AgentClient[AgentOptionsT]):
+ """Agent client that is not cached or dummy; it runs real commands."""
+
+ @staticmethod
+ @abc.abstractmethod
+ def _find_cli() -> str:
+ """Find the CLI binary for the agent."""
+
+ @classmethod
+ @abc.abstractmethod
+ def _build_cli_cmd(cls, options: AgentOptionsT) -> list[str]:
+ """Build the CLI command for the agent."""
+
+ @staticmethod
+ @abc.abstractmethod
+ def _build_cli_args(options: AgentOptionsT) -> list[str]:
+ """Build the CLI arguments for the agent."""
+
+
+class CachedAgentClient(AgentClient[AgentOptionsT]):
+ """Cached agent client implementation.
+
+ This client is a wrapper around an agent client that caches the agent responses.
+ """
+
+ def __init__(self, client: AgentClient[AgentOptionsT], cache_path: Path) -> None:
+ super().__init__(client._options)
+ self._client = client
+ self._cache_path = cache_path
+
+ def process_query(self, prompt: str) -> Iterator[AgentMessage]:
+ cache_path = self._cache_path
+ if cache_path is not None:
+ cache_record = check_cache(cache_path, prompt, self._client._options)
+ if cache_record is not None:
+ for message in cache_record.messages:
+ yield message
+ return
+
+ agent_interaction = AgentInteraction(prompt, self._client._options)
+ for message in self._client.process_query(prompt):
+ agent_interaction.put(message)
+ yield message
+
+ # NOTE we only cache full interactions given the 'process_query' method is called till
+ # the generator is exhausted.
+ # This means that if the generator is not exhausted, the cache will not be updated.
+ # If we do want a way to still cache interactions, even if we early exit the generator,
+ # then we could use a separate thread to get the agent response and cache it in the background.
+ agent_interaction_record = AgentInteractionRecord.from_agent_interaction(agent_interaction)
+ update_cache(agent_interaction_record, cache_path)
+
+ @property
+ def client(self) -> AgentClient[AgentOptionsT]:
+ """Get the underlying client."""
+ return self._client
diff --git a/imbue_core/imbue_core/agents/agent_api/codex/__init__.py b/vet/imbue_core/agents/agent_api/codex/__init__.py
diff --git a/vet/imbue_core/agents/agent_api/codex/client.py b/vet/imbue_core/agents/agent_api/codex/client.py
@@ -0,0 +1,166 @@
+import json
+import shutil
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Generator
+from typing import Iterator
+from typing import Self
+
+from loguru import logger
+
+from vet.imbue_core.agents.agent_api.client import RealAgentClient
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexOptions
+from vet.imbue_core.agents.agent_api.codex.message_parser import parse_codex_event
+from vet.imbue_core.agents.agent_api.data_types import AgentMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentSystemMessage
+from vet.imbue_core.agents.agent_api.errors import AgentCLINotFoundError
+from vet.imbue_core.agents.agent_api.transport import AgentSubprocessCLITransport
+from vet.imbue_core.agents.agent_api.transport import AgentSubprocessCLITransportOptions
+
+
+class CodexClient(RealAgentClient[CodexOptions]):
+ """Codex CLI client implementation."""
+
+ def __init__(self, options: CodexOptions) -> None:
+ super().__init__(options=options)
+ self._session_id: str | None = options.resume_session_id
+
+ @classmethod
+ @contextmanager
+ def build(cls, options: CodexOptions) -> Generator[Self, None, None]:
+ yield cls(options=options)
+
+ def process_query(self, prompt: str) -> Iterator[AgentMessage]:
+ logger.trace(
+ "{client_name}: calling agent with prompt={prompt}",
+ client_name=type(self).__name__,
+ prompt=prompt,
+ )
+
+ # NOTE: (2025-11-20) Codex CLI does not support streaming inputs, and only supports using codex CLI via
+ # non-interactive mode, where each call is a new process.
+ # So here we just create a new transport for each call, and handle things like resuming the session as
+ # needed.
+ options = self._options
+ if self._session_id is not None and self._session_id != self._options.resume_session_id:
+ # Inject the current session id into the options before building the command
+ options = self._options.model_copy(update={"resume_session_id": self._session_id})
+ cmd = self._build_cli_cmd(options)
+ with AgentSubprocessCLITransport.build(
+ AgentSubprocessCLITransportOptions(cmd=[*cmd, prompt], cwd=options.cwd)
+ ) as transport:
+ transport.send_request([prompt], options)
+
+ thread_id: str | None = None
+ for data in transport.receive_messages():
+ logger.trace(
+ "{client_name}: received raw JSON message={data}",
+ client_name=type(self).__name__,
+ data=data,
+ )
+
+ message = parse_codex_event(data, thread_id)
+ if message:
+ if isinstance(message, AgentSystemMessage):
+ thread_id = message.session_id
+ # Store the new session id for subsequent calls to process_query on this client
+ self._session_id = message.session_id
+
+ yield message
+
+ logger.trace(
+ "{client_name}: finished calling agent with prompt={prompt}",
+ client_name=type(self).__name__,
+ prompt=prompt,
+ )
+
+ @staticmethod
+ def _find_cli() -> str:
+ """Find Codex CLI binary."""
+ cli = shutil.which("codex")
+ if cli:
+ return cli
+
+ locations = [
+ Path("/usr/local/bin/codex"),
+ Path.home() / ".local/bin/codex",
+ Path.home() / "node_modules/.bin/codex",
+ Path.home() / ".npm-global/bin/codex",
+ ]
+
+ for path in locations:
+ if path.exists() and path.is_file():
+ return str(path)
+
+ node_installed = shutil.which("node") is not None
+ npm_installed = shutil.which("npm") is not None
+
+ if not node_installed or not npm_installed:
+ raise AgentCLINotFoundError(
+ "\n".join(
+ [
+ "Codex CLI requires Node.js and npm, which may not be installed.",
+ "Install Node.js from: https://nodejs.org/",
+ "\nAfter installing Node.js, install Codex CLI:",
+ " npm install -g @openai/codex",
+ ]
+ )
+ )
+
+ raise AgentCLINotFoundError(
+ "\n".join(
+ [
+ "Codex CLI not found. Install with:",
+ " npm install -g @openai/codex",
+ "\nOr via Homebrew:",
+ " brew install codex",
+ "\nIf already installed locally, try:",
+ ' export PATH="$HOME/node_modules/.bin:$PATH"',
+ ]
+ )
+ )
+
+ @classmethod
+ def _build_cli_cmd(cls, options: CodexOptions) -> list[str]:
+ """Build CLI command with arguments."""
+ if options.is_cached:
+ # in this case, the cmd should never be used
+ cmd = ["CACHED_CODEX_EXEC_PLACEHOLDER"]
+ return cmd
+ cli_path = str(options.cli_path) if options.cli_path is not None else cls._find_cli()
+ cmd = [cli_path, "exec"]
+ cmd.extend(cls._build_cli_args(options))
+ return cmd
+
+ @staticmethod
+ def _build_cli_args(options: CodexOptions) -> list[str]:
+ args = []
+ # Permissions flags
+ if options.approval_mode:
+ args.extend(["-c", f"'approval_mode={options.approval_mode}'"])
+ if options.sandbox_mode:
+ args.extend(["--sandbox", options.sandbox_mode])
+ if options.approval_policy:
+ args.extend(["-c", f"'approval={options.approval_policy}'"])
+
+ # JSON streaming output
+ args.append("--json")
+
+ # Model selection
+ if options.model:
+ args.extend(["--model", options.model])
+
+ # Skip git repo check
+ if options.skip_git_repo_check:
+ args.append("--skip-git-repo-check")
+
+ # Output schema for structured output
+ if options.output_schema:
+ args.extend(["--output-schema", json.dumps(options.output_schema)])
+
+ # Session resumption
+ if options.resume_last:
+ args.extend(["resume", "--last"])
+ elif options.resume_session_id:
+ args.extend(["resume", options.resume_session_id])
+ return args
diff --git a/vet/imbue_core/agents/agent_api/codex/data_types.py b/vet/imbue_core/agents/agent_api/codex/data_types.py
@@ -0,0 +1,241 @@
+"""Data types for Codex agent integration."""
+
+from pathlib import Path
+from typing import Annotated
+from typing import Any
+from typing import Literal
+
+from pydantic import Field
+from pydantic import Tag
+
+from vet.imbue_core.agents.agent_api.data_types import AgentOptions
+from vet.imbue_core.agents.agent_api.data_types import AgentToolName
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_core.pydantic_serialization import build_discriminator
+
+# https://developers.openai.com/codex/cli/features#approval-modes
+CodexApprovalMode = Literal["auto", "read-only", "full-access"] | None
+
+# https://developers.openai.com/codex/cli/reference, --sandbox options
+CodexSandboxMode = Literal["read-only", "workspace-write", "danger-full-access"] | None
+
+# https://developers.openai.com/codex/cli/reference, --ask-for-approval options
+CodexApprovalPolicy = Literal["untrusted", "on-failure", "on-request", "never"] | None
+
+
+class CodexOptions(AgentOptions):
+ """Options for Codex CLI execution."""
+
+ object_type: Literal["CodexOptions"] = "CodexOptions"
+
+ approval_mode: CodexApprovalMode = None
+ sandbox_mode: CodexSandboxMode = None
+ approval_policy: CodexApprovalPolicy = None
+ model: str | None = None
+ system_prompt: str | None = None
+ image_paths: list[Path] = Field(default_factory=list)
+ skip_git_repo_check: bool = False
+ output_schema: dict[str, Any] | None = None
+ # Session management
+ resume_session_id: str | None = None
+ resume_last: bool = False
+ thread_id: str | None = None
+ # Optional override for the Codex CLI path
+ cli_path: Path | None = None
+ is_cached: bool = False
+
+
+# Codex item types
+# Ref: https://github.com/openai/codex/blob/main/sdk/typescript/src/items.ts
+# Ref: https://github.com/openai/codex/blob/main/codex-rs/exec/src/exec_events.rs
+
+
+# The status of a command execution.
+CommandExecutionStatus = Literal["in_progress", "completed", "failed"]
+
+
+class CodexCommandExecutionItem(SerializableModel):
+ type: Literal["command_execution"] = "command_execution"
+ id: str
+ command: str
+ aggregated_output: str
+ exit_code: int | None = None
+ status: CommandExecutionStatus
+
+
+# Indicates the type of the file change.
+PatchChangeKind = Literal["add", "delete", "update"]
+
+
+class CodexFileUpdateChange(SerializableModel):
+ path: str
+ kind: PatchChangeKind
+
+
+# The status of a file change.
+PatchApplyStatus = Literal["completed", "failed"]
+
+
+class CodexFileChangeItem(SerializableModel):
+ type: Literal["file_change"] = "file_change"
+ id: str
+ changes: list[CodexFileUpdateChange]
+ status: PatchApplyStatus
+
+
+# The status of an MCP tool call.
+McpToolCallStatus = Literal["in_progress", "completed", "failed"]
+
+
+class CodexMcpToolCallItem(SerializableModel):
+ type: Literal["mcp_tool_call"] = "mcp_tool_call"
+ id: str
+ server: str
+ tool: str
+ status: McpToolCallStatus
+
+
+class CodexAgentMessageItem(SerializableModel):
+ type: Literal["agent_message"] = "agent_message"
+ id: str
+ text: str
+
+
+class CodexReasoningItem(SerializableModel):
+ type: Literal["reasoning"] = "reasoning"
+ id: str
+ text: str
+
+
+class CodexWebSearchItem(SerializableModel):
+ type: Literal["web_search"] = "web_search"
+ id: str
+ query: str
+
+
+class CodexErrorItem(SerializableModel):
+ type: Literal["error"] = "error"
+ id: str
+ message: str
+
+
+class CodexTodoItem(SerializableModel):
+ text: str
+ completed: bool
+
+
+class CodexTodoListItem(SerializableModel):
+ type: Literal["todo_list"] = "todo_list"
+ id: str
+ items: list[CodexTodoItem]
+
+
+# Canonical union of thread items and their type-specific payloads.
+CodexThreadItemUnion = Annotated[
+ (
+ Annotated[CodexAgentMessageItem, Tag("agent_message")]
+ | Annotated[CodexReasoningItem, Tag("reasoning")]
+ | Annotated[CodexCommandExecutionItem, Tag("command_execution")]
+ | Annotated[CodexFileChangeItem, Tag("file_change")]
+ | Annotated[CodexMcpToolCallItem, Tag("mcp_tool_call")]
+ | Annotated[CodexWebSearchItem, Tag("web_search")]
+ | Annotated[CodexTodoListItem, Tag("todo_list")]
+ | Annotated[CodexErrorItem, Tag("error")]
+ ),
+ build_discriminator("type"),
+]
+
+
+# Codex (JSONL) event stream models
+# Ref:https://github.com/openai/codex/blob/main/sdk/typescript/src/events.ts
+
+
+class CodexThreadStartedEvent(SerializableModel):
+ type: Literal["thread.started"] = "thread.started"
+ thread_id: str
+
+
+class CodexTurnStartedEvent(SerializableModel):
+ type: Literal["turn.started"] = "turn.started"
+
+
+class CodexUsage(SerializableModel):
+ input_tokens: int
+ cached_input_tokens: int
+ output_tokens: int
+
+
+class CodexTurnCompletedEvent(SerializableModel):
+ type: Literal["turn.completed"] = "turn.completed"
+ usage: CodexUsage
+
+
+class CodexThreadError(SerializableModel):
+ message: str
+
+
+class CodexTurnFailedEvent(SerializableModel):
+ type: Literal["turn.failed"] = "turn.failed"
+ error: CodexThreadError
+
+
+class CodexItemStartedEvent(SerializableModel):
+ type: Literal["item.started"] = "item.started"
+ item: CodexThreadItemUnion
+
+
+class CodexItemUpdatedEvent(SerializableModel):
+ type: Literal["item.updated"] = "item.updated"
+ item: CodexThreadItemUnion
+
+
+class CodexItemCompletedEvent(SerializableModel):
+ type: Literal["item.completed"] = "item.completed"
+ item: CodexThreadItemUnion
+
+
+class CodexThreadErrorEvent(SerializableModel):
+ type: Literal["error"] = "error"
+ message: str
+
+
+CodexThreadEvent = Annotated[
+ (
+ Annotated[CodexThreadStartedEvent, Tag("thread.started")]
+ | Annotated[CodexTurnStartedEvent, Tag("turn.started")]
+ | Annotated[CodexTurnCompletedEvent, Tag("turn.completed")]
+ | Annotated[CodexTurnFailedEvent, Tag("turn.failed")]
+ | Annotated[CodexItemStartedEvent, Tag("item.started")]
+ | Annotated[CodexItemUpdatedEvent, Tag("item.updated")]
+ | Annotated[CodexItemCompletedEvent, Tag("item.completed")]
+ | Annotated[CodexThreadErrorEvent, Tag("error")]
+ ),
+ build_discriminator("type"),
+]
+
+# TODO: some of these might not actually be valid for codex!
+CODEX_TOOLS = (
+ AgentToolName.AGENT,
+ AgentToolName.BASH,
+ AgentToolName.EDIT,
+ AgentToolName.GLOB,
+ AgentToolName.GREP,
+ AgentToolName.LS,
+ AgentToolName.MULTI_EDIT,
+ AgentToolName.NOTEBOOK_EDIT,
+ AgentToolName.NOTEBOOK_READ,
+ AgentToolName.READ,
+ AgentToolName.TODO_READ,
+ AgentToolName.TODO_WRITE,
+ AgentToolName.WEB_FETCH,
+ AgentToolName.WEB_SEARCH,
+ AgentToolName.WRITE,
+ AgentToolName.COMPUTER,
+ AgentToolName.MEMORY,
+ AgentToolName.OTHER,
+ AgentToolName.CODE_EXECUTION,
+ AgentToolName.BASH_CODE_EXECUTION,
+ AgentToolName.TEXT_EDITOR_CODE_EXECUTION,
+ AgentToolName.COMMAND_EXECUTION,
+ AgentToolName.FILE_CHANGE,
+)
diff --git a/vet/imbue_core/agents/agent_api/codex/message_parser.py b/vet/imbue_core/agents/agent_api/codex/message_parser.py
@@ -0,0 +1,218 @@
+from typing import Any
+from typing import assert_never
+
+from pydantic import TypeAdapter
+
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexAgentMessageItem
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexCommandExecutionItem
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexErrorItem
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexFileChangeItem
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexItemCompletedEvent
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexItemStartedEvent
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexItemUpdatedEvent
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexMcpToolCallItem
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexReasoningItem
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexThreadErrorEvent
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexThreadEvent
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexThreadItemUnion
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexThreadStartedEvent
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexTodoListItem
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexTurnCompletedEvent
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexTurnFailedEvent
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexTurnStartedEvent
+from vet.imbue_core.agents.agent_api.codex.data_types import CodexWebSearchItem
+from vet.imbue_core.agents.agent_api.data_types import AgentAssistantMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentContentBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentResultMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentSystemEventType
+from vet.imbue_core.agents.agent_api.data_types import AgentSystemMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentTextBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentThinkingBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentToolResultBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentToolUseBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentUsage
+
+
+def parse_codex_event(data: dict[str, Any], thread_id: str | None = None) -> AgentMessage | None:
+ """Parse Codex event into unified message.
+
+ Reference:
+ https://github.com/openai/codex/blob/main/docs/exec.md
+ https://github.com/openai/codex/blob/main/sdk/typescript/src/events.ts
+ """
+ codex_event = TypeAdapter(CodexThreadEvent).validate_python(data)
+ match codex_event:
+ case CodexThreadStartedEvent():
+ return AgentSystemMessage(
+ event_type=AgentSystemEventType.SESSION_STARTED,
+ session_id=codex_event.thread_id,
+ original_message=data,
+ )
+
+ case CodexTurnStartedEvent():
+ # Turn started within a thread. Nothing to do
+ return None
+
+ case CodexTurnCompletedEvent():
+ assert thread_id is not None, "thread_id is required for turn.completed event"
+ usage = AgentUsage(
+ input_tokens=codex_event.usage.input_tokens,
+ output_tokens=codex_event.usage.output_tokens,
+ cached_tokens=codex_event.usage.cached_input_tokens,
+ total_tokens=codex_event.usage.input_tokens + codex_event.usage.output_tokens,
+ )
+ return AgentResultMessage(
+ session_id=thread_id,
+ is_error=False,
+ usage=usage,
+ original_message=data,
+ )
+
+ case CodexTurnFailedEvent():
+ assert thread_id is not None, "thread_id is required for turn.failed event"
+ return AgentResultMessage(
+ session_id=thread_id,
+ is_error=True,
+ error=codex_event.error.message,
+ usage=None,
+ original_message=data,
+ )
+
+ case CodexItemStartedEvent():
+ content_blocks = parse_codex_item(codex_event.item)
+ return AgentAssistantMessage(content=content_blocks, original_message=data)
+
+ case CodexItemUpdatedEvent():
+ # Intermediate item, don't return anything
+ return None
+
+ case CodexItemCompletedEvent():
+ content_blocks = parse_codex_item(codex_event.item)
+ return AgentAssistantMessage(content=content_blocks, original_message=data)
+
+ case CodexThreadErrorEvent():
+ return AgentResultMessage(
+ session_id=thread_id or "",
+ is_error=True,
+ error=codex_event.message,
+ usage=None,
+ original_message=data,
+ )
+ case _ as unreachable:
+ assert_never(unreachable)
+
+
+def parse_codex_item(
+ item_data: dict[str, Any] | CodexThreadItemUnion,
+) -> list[AgentContentBlock]:
+ """Parse Codex item into unified content blocks.
+
+ Refs:
+ https://github.com/openai/codex/blob/main/sdk/typescript/src/items.ts
+ """
+ if isinstance(item_data, dict):
+ codex_item = TypeAdapter(CodexThreadItemUnion).validate_python(item_data)
+ else:
+ codex_item = item_data
+
+ match codex_item:
+ case CodexAgentMessageItem():
+ return [AgentTextBlock(text=codex_item.text)]
+
+ case CodexReasoningItem():
+ return [AgentThinkingBlock(content=codex_item.text)]
+
+ case CodexErrorItem():
+ return [AgentTextBlock(text=f"[Error: {codex_item.message}]")]
+
+ case CodexCommandExecutionItem():
+ if codex_item.status == "in_progress":
+ return [
+ AgentToolUseBlock(
+ id=codex_item.id,
+ name=codex_item.type,
+ input={"command": codex_item.command},
+ )
+ ]
+ return [
+ AgentToolResultBlock(
+ tool_use_id=codex_item.id,
+ content=codex_item.aggregated_output,
+ exit_code=codex_item.exit_code,
+ is_error=codex_item.exit_code != 0,
+ )
+ ]
+
+ case CodexFileChangeItem():
+ is_error = codex_item.status == "failed"
+ return [
+ AgentToolUseBlock(
+ id=codex_item.id,
+ name=codex_item.type,
+ input={"changes": [change.model_dump() for change in codex_item.changes]},
+ ),
+ AgentToolResultBlock(
+ tool_use_id=codex_item.id,
+ content=[change.model_dump() for change in codex_item.changes],
+ is_error=is_error,
+ exit_code=-1 if is_error else 0,
+ ),
+ ]
+
+ case CodexMcpToolCallItem():
+ if codex_item.status == "in_progress":
+ return [
+ AgentToolUseBlock(
+ id=codex_item.id,
+ name=codex_item.type,
+ input={"server": codex_item.server, "tool": codex_item.tool},
+ )
+ ]
+ # NOTE: currently (24-oct-2025) the MCP tool call item is not really well defined
+ # it does not have a result field or anything. So for now, we just return the server and tool as the content.
+ return [
+ AgentToolResultBlock(
+ tool_use_id=codex_item.id,
+ content=[{"server": codex_item.server, "tool": codex_item.tool}],
+ is_error=codex_item.status == "failed",
+ exit_code=-1 if codex_item.status == "failed" else 0,
+ )
+ ]
+
+ case CodexWebSearchItem():
+ # NOTE: currently (24-oct-2025) the web search item is not really well defined
+ # i.e. it only has a query field, and no other fields like results, progress, etc.
+ # so for now, so that each tool use has a matching result, we just return the query as the content.
+ return [
+ AgentToolUseBlock(
+ id=codex_item.id,
+ name=codex_item.type,
+ input={"query": codex_item.query},
+ ),
+ AgentToolResultBlock(
+ tool_use_id=codex_item.id,
+ content=codex_item.query,
+ # No error reported for web search
+ is_error=False,
+ exit_code=0,
+ ),
+ ]
+
+ case CodexTodoListItem():
+ return [
+ AgentToolUseBlock(
+ id=codex_item.id,
+ name=codex_item.type,
+ input={"todos": [item.model_dump() for item in codex_item.items]},
+ ),
+ AgentToolResultBlock(
+ tool_use_id=codex_item.id,
+ content=[item.model_dump() for item in codex_item.items],
+ is_error=False,
+ exit_code=0,
+ ),
+ ]
+
+ case _ as unreachable:
+ assert_never(unreachable)
diff --git a/vet/imbue_core/agents/agent_api/data_types.py b/vet/imbue_core/agents/agent_api/data_types.py
@@ -0,0 +1,252 @@
+import enum
+from pathlib import Path
+from typing import Annotated
+from typing import Any
+from typing import Literal
+
+from pydantic import Field
+from pydantic import Tag
+
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_core.pydantic_serialization import build_discriminator
+
+AgentPermissionMode = Literal["default", "acceptEdits", "bypassPermissions"]
+
+
+class AgentToolName(enum.StrEnum):
+ """Enumeration of all known coding agent tools across Claude Code and Codex.
+
+ This is a superset of tools available across different coding agents.
+ Not all tools are available in all agents.
+ """
+
+ # File operations
+ READ = "Read"
+ WRITE = "Write"
+ EDIT = "Edit"
+ MULTI_EDIT = "MultiEdit"
+ GLOB = "Glob"
+ NOTEBOOK_READ = "NotebookRead"
+ NOTEBOOK_EDIT = "NotebookEdit"
+ LS = "LS"
+
+ # Search operations
+ GREP = "Grep"
+
+ # Execution tools
+ BASH = "Bash"
+ BASH_OUTPUT = "BashOutput"
+ KILL_SHELL = "KillShell"
+
+ # Web operations
+ WEB_SEARCH = "WebSearch"
+ WEB_FETCH = "WebFetch"
+
+ # Agent orchestration
+ TASK = "Task"
+ TODO_READ = "TodoRead"
+ TODO_WRITE = "TodoWrite"
+ SLASH_COMMAND = "SlashCommand"
+ EXIT_PLAN_MODE = "exit_plan_mode"
+
+ # MCP tools
+ MCP_TOOL = "mcp_tool" # Generic MCP tool prefix
+ LIST_MCP_RESOURCES = "ListMcpResourcesTool"
+ READ_MCP_RESOURCE = "ReadMcpResourceTool"
+
+ # Code execution
+ CODE_EXECUTION = "code_execution"
+ BASH_CODE_EXECUTION = "bash_code_execution"
+ TEXT_EDITOR_CODE_EXECUTION = "text_editor_code_execution"
+
+ # Codex-specific operations
+ COMMAND_EXECUTION = "command_execution" # Codex's command execution
+ FILE_CHANGE = "file_change" # Codex's file change operation
+
+ # Other tools
+ AGENT = "Agent"
+ COMPUTER = "computer" # Computer use capability
+ MEMORY = "memory" # Memory storage
+ OTHER = "other" # Catch-all for unknown/custom tools
+
+
+# TODO: these are not, in the strict sense, read-only; perhaps we should have finer gradations
+READ_ONLY_TOOLS = (
+ AgentToolName.TASK,
+ AgentToolName.READ,
+ AgentToolName.GLOB,
+ AgentToolName.GREP,
+ AgentToolName.LS,
+ AgentToolName.BASH,
+ AgentToolName.NOTEBOOK_READ,
+ AgentToolName.TODO_READ,
+ AgentToolName.TODO_WRITE,
+ AgentToolName.WEB_FETCH,
+ AgentToolName.WEB_SEARCH,
+)
+
+
+# Content block types
+class AgentTextBlock(SerializableModel):
+ """Text content block.
+
+ Represents plain text output from the agent.
+ """
+
+ text: str
+
+
+class AgentThinkingBlock(SerializableModel):
+ """Agent's internal reasoning/thinking block.
+
+ Represents the agent's thought process or reasoning, which may be hidden
+ from the end user in some interfaces.
+ """
+
+ content: str
+ thinking_tokens: int | None = Field(default=None, description="Number of tokens used for thinking")
+
+
+class AgentToolUseBlock(SerializableModel):
+ """Tool invocation request.
+
+ Represents a request from the agent to use a specific tool.
+ """
+
+ id: str
+ name: AgentToolName | str # allow str for flexibility
+ input: dict[str, Any]
+
+
+class AgentToolResultBlock(SerializableModel):
+ """Tool execution result.
+
+ Represents the result of executing a tool, which is fed back to the agent.
+ """
+
+ tool_use_id: str
+ content: str | list[dict[str, Any]] | None = None
+ is_error: bool | None = None
+ exit_code: int | None = Field(default=None, description="Exit code for command executions")
+
+
+AgentContentBlock = AgentTextBlock | AgentThinkingBlock | AgentToolUseBlock | AgentToolResultBlock
+
+
+class AgentSystemEventType(enum.StrEnum):
+ """System event types
+
+ Super set of system event types across all agents.
+ """
+
+ SESSION_STARTED = "session_started"
+ SESSION_RESUMED = "session_resumed"
+ TURN_STARTED = "turn_started"
+ TURN_COMPLETED = "turn_completed"
+ TURN_FAILED = "turn_failed"
+ # For agent-specific events that don't fit into the above categories
+ OTHER = "other"
+
+
+# Message types (`type` field is required for serialization)
+class AgentUserMessage(SerializableModel):
+ """User message.
+
+ Represents input from the user to the agent.
+ """
+
+ object_type: Literal["AgentUserMessage"] = "AgentUserMessage"
+ content: str | list[AgentContentBlock]
+ original_message: dict[str, Any] | None = Field(default=None, description="Original agent-specific message data")
+
+
+class AgentAssistantMessage(SerializableModel):
+ """Assistant message with content blocks.
+
+ Represents output from the agent, which may include text, thinking,
+ tool uses, and tool results.
+ """
+
+ object_type: Literal["AgentAssistantMessage"] = "AgentAssistantMessage"
+ content: list[AgentContentBlock]
+ original_message: dict[str, Any] | None = Field(default=None, description="Original agent-specific message data")
+
+
+class AgentSystemMessage(SerializableModel):
+ """System message with normalized event data.
+
+ Represents lifecycle events from the agent session (e.g., turn started,
+ turn completed, session started).
+ """
+
+ object_type: Literal["AgentSystemMessage"] = "AgentSystemMessage"
+ event_type: AgentSystemEventType
+ session_id: str | None = Field(default=None, description="Session/thread identifier")
+ error: str | None = Field(default=None, description="Error message for failed events")
+ original_message: dict[str, Any] | None = Field(default=None, description="Original agent-specific message data")
+
+
+class AgentUsage(SerializableModel):
+ """Normalized usage tracking across agents.
+
+ Tracks token usage and costs in a unified format.
+ """
+
+ input_tokens: int | None = Field(default=None, description="Input/prompt tokens consumed")
+ output_tokens: int | None = Field(default=None, description="Output/completion tokens generated")
+ cached_tokens: int | None = Field(default=None, description="Cached input tokens reused")
+ total_tokens: int | None = Field(default=None, description="Total tokens (input + output)")
+ thinking_tokens: int | None = Field(default=None, description="Tokens used for extended thinking")
+ total_cost_usd: float | None = Field(default=None, description="Estimated cost in USD")
+
+
+class AgentResultMessage(SerializableModel):
+ """Result message with cost and usage information.
+
+ Represents the final result of an agent session, including timing,
+ usage statistics, and success/error status.
+ """
+
+ object_type: Literal["AgentResultMessage"] = "AgentResultMessage"
+ session_id: str
+ is_error: bool
+ duration_ms: int | None = Field(default=None, description="Total duration in milliseconds")
+ api_duration_ms: int | None = Field(default=None, description="API call duration in milliseconds")
+ num_turns: int | None = Field(default=None, description="Number of conversation turns")
+ usage: AgentUsage | None = Field(default=None, description="Token usage and cost information")
+ result: str | None = Field(default=None, description="Final result or output from the agent")
+ error: str | None = Field(default=None, description="Error message if is_error=True")
+ original_message: dict[str, Any] | None = Field(default=None, description="Original agent-specific message data")
+
+
+AgentMessage = AgentUserMessage | AgentAssistantMessage | AgentSystemMessage | AgentResultMessage
+AgentMessageUnion = Annotated[
+ Annotated[AgentUserMessage, Tag("AgentUserMessage")]
+ | Annotated[AgentAssistantMessage, Tag("AgentAssistantMessage")]
+ | Annotated[AgentSystemMessage, Tag("AgentSystemMessage")]
+ | Annotated[AgentResultMessage, Tag("AgentResultMessage")],
+ build_discriminator(),
+]
+
+
+class ToolUseRecord(SerializableModel):
+ """A record of a tool use."""
+
+ request_message: AgentToolUseBlock
+ result_message: AgentToolResultBlock
+
+ @property
+ def tool_name(self) -> str:
+ """The name of the tool used."""
+ return self.request_message.name
+
+ @property
+ def tool_input(self) -> dict[str, Any]:
+ """The input to the tool."""
+ return self.request_message.input
+
+
+class AgentOptions(SerializableModel):
+ """Parent class for all agent options."""
+
+ cwd: str | Path | None = None
diff --git a/imbue_core/imbue_core/agents/agent_api/errors.py b/vet/imbue_core/agents/agent_api/errors.py
diff --git a/vet/imbue_core/agents/agent_api/interaction.py b/vet/imbue_core/agents/agent_api/interaction.py
@@ -0,0 +1,96 @@
+from typing import Sequence
+
+from vet.imbue_core.agents.agent_api.data_types import AgentAssistantMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentOptions
+from vet.imbue_core.agents.agent_api.data_types import AgentToolResultBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentToolUseBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentUserMessage
+from vet.imbue_core.agents.agent_api.data_types import ToolUseRecord
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+
+class AgentInteraction:
+ """A class for tracking an ongoing interaction with an agent.
+
+ Note that this class is not thread-safe.
+ """
+
+ def __init__(self, prompt: str, options: AgentOptions) -> None:
+ self.prompt = prompt
+ self.options = options
+ self.messages: list[AgentMessage] = []
+ self.tool_use_records: list[ToolUseRecord] = []
+ self._unresolved_tool_use_requests: list[AgentToolUseBlock] = []
+
+ def put(self, message: AgentMessage) -> None:
+ self.messages.append(message)
+
+ if isinstance(message, AgentAssistantMessage):
+ for assistant_content_block in message.content:
+ if isinstance(assistant_content_block, AgentToolUseBlock):
+ self._unresolved_tool_use_requests.append(assistant_content_block)
+ elif isinstance(message, AgentUserMessage) and isinstance(message.content, list):
+ for content_block in message.content:
+ if isinstance(content_block, AgentToolResultBlock):
+ remaining_unresolved_requests = []
+ for request in self._unresolved_tool_use_requests:
+ if request.id == content_block.tool_use_id:
+ self.tool_use_records.append(
+ ToolUseRecord(
+ request_message=request,
+ result_message=content_block,
+ )
+ )
+ else:
+ remaining_unresolved_requests.append(request)
+ self._unresolved_tool_use_requests = remaining_unresolved_requests
+
+ def find_tool_use_record_by_command(self, command: str, by_most_recent: bool = True) -> ToolUseRecord | None:
+ """Look for tool use request and result messages by the tool command.
+
+ If by_most_recent is True, the records are searched in reverse order (most recent first).
+ """
+ return _find_tool_use_record_by_command(self.tool_use_records, command, by_most_recent)
+
+
+class AgentInteractionRecord(SerializableModel):
+ """A serializable record of a completed agent interaction.
+
+ This is meant to be used for storing a completed log in a database or cache.
+ """
+
+ prompt: str
+ options: AgentOptions
+ messages: tuple[AgentMessage, ...]
+ tool_use_records: tuple[ToolUseRecord, ...]
+
+ @classmethod
+ def from_agent_interaction(cls, agent_interaction: AgentInteraction) -> "AgentInteractionRecord":
+ return cls(
+ prompt=agent_interaction.prompt,
+ options=agent_interaction.options,
+ messages=tuple(agent_interaction.messages),
+ tool_use_records=tuple(agent_interaction.tool_use_records),
+ )
+
+ def find_tool_use_record_by_command(self, command: str, by_most_recent: bool = True) -> ToolUseRecord | None:
+ """Look for tool use request and result messages by the tool command.
+
+ If by_most_recent is True, the records are searched in reverse order (most recent first).
+ """
+ return _find_tool_use_record_by_command(self.tool_use_records, command, by_most_recent)
+
+
+def _find_tool_use_record_by_command(
+ tool_use_records: Sequence[ToolUseRecord], command: str, reverse: bool = True
+) -> ToolUseRecord | None:
+ """Look for tool use request and result messages by the tool command.
+
+ If reverse is True, the records are searched in reverse order (most recent first).
+ """
+ for record in reversed(tool_use_records) if reverse else tool_use_records:
+ tool_input = record.tool_input
+ if "command" in tool_input and tool_input["command"] == command:
+ return record
+ return None
diff --git a/vet/imbue_core/agents/agent_api/transport.py b/vet/imbue_core/agents/agent_api/transport.py
@@ -0,0 +1,176 @@
+import json
+import os
+import subprocess
+import threading
+from abc import ABC
+from abc import abstractmethod
+from contextlib import contextmanager
+from pathlib import Path
+from subprocess import PIPE
+from typing import Any
+from typing import ContextManager
+from typing import Generator
+from typing import Generic
+from typing import Iterable
+from typing import Iterator
+from typing import Self
+from typing import Sequence
+from typing import TypeVar
+
+from vet.imbue_core.agents.agent_api.data_types import AgentOptions
+from vet.imbue_core.agents.agent_api.errors import AgentCLIConnectionError
+from vet.imbue_core.agents.agent_api.errors import (
+ AgentCLIJSONDecodeError as SDKJSONDecodeError,
+)
+from vet.imbue_core.agents.agent_api.errors import AgentCLINotFoundError
+from vet.imbue_core.agents.agent_api.errors import AgentProcessError
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+TransportOptionsT = TypeVar("TransportOptionsT", bound=SerializableModel)
+
+
+class AgentTransport(ABC, Generic[TransportOptionsT]):
+ """Abstract transport for Agent communication."""
+
+ @classmethod
+ @abstractmethod
+ def build(cls, options: TransportOptionsT) -> ContextManager[Self]:
+ """Build a transport from options.
+
+ This is the main entry point for building a transport and managing its lifecycle.
+ """
+
+ @abstractmethod
+ def send_request(self, messages: list[Any], agent_options: AgentOptions) -> None:
+ """Send request to underlying agent via transport."""
+
+ @abstractmethod
+ def receive_messages(self) -> Iterator[dict[str, Any]]:
+ """Receive messages from underlying agent via transport."""
+
+ @abstractmethod
+ def is_connected(self) -> bool:
+ """Check if transport is connected."""
+
+
+class AgentSubprocessCLITransportOptions(SerializableModel):
+ """Options for AgentSubprocessCLITransport."""
+
+ cmd: Sequence[str]
+ cwd: str | Path | None = None
+ extra_env_vars: dict[str, str] | None = None
+
+
+class AgentSubprocessCLITransport(AgentTransport[AgentSubprocessCLITransportOptions]):
+ """Subprocess transport using Coding Agent via a CLI."""
+
+ def __init__(
+ self,
+ popen: subprocess.Popen[str],
+ ) -> None:
+ self._process = popen
+ self._stdin_stream = popen.stdin
+ self._stdout_stream = popen.stdout
+ self._stderr_stream = popen.stderr
+
+ @classmethod
+ @contextmanager
+ def build(cls, options: AgentSubprocessCLITransportOptions) -> Generator[Self, None, None]:
+ extra_env_vars = options.extra_env_vars or {}
+ try:
+ popen = subprocess.Popen(
+ options.cmd,
+ stdin=PIPE,
+ stdout=PIPE,
+ stderr=PIPE,
+ cwd=options.cwd,
+ env={**os.environ, **extra_env_vars},
+ # ensure output is line buffered
+ bufsize=1,
+ text=True,
+ encoding="utf-8",
+ )
+ except FileNotFoundError as e:
+ raise AgentCLINotFoundError(f"Agent CLI not found for: cmd={options.cmd}") from e
+ except Exception as e:
+ raise AgentCLIConnectionError(f"Failed to start Agent CLI via cmd={options.cmd}: {e}") from e
+
+ try:
+ yield cls(popen)
+ finally:
+ # Make sure to terminate the process if it is still running, and clean up the streams
+ if popen.poll() is None:
+ try:
+ popen.terminate()
+ popen.wait(timeout=5.0)
+ except subprocess.TimeoutExpired:
+ popen.kill()
+ popen.wait(timeout=5.0)
+ popen.stdout and popen.stdout.close()
+ popen.stderr and popen.stderr.close()
+ popen.stdin and popen.stdin.close()
+
+ def send_request(self, messages: Iterable[dict[str, Any] | str], agent_options: AgentOptions) -> None:
+ process = self._process
+ stdin_stream = self._stdin_stream
+ if not process or not stdin_stream:
+ raise AgentCLIConnectionError("Not connected")
+
+ for message in messages:
+ stdin_stream.write(json.dumps(message) + "\n")
+ stdin_stream.flush()
+
+ def _read_stderr(self, output_buffer: list[str]) -> None:
+ """Read stderr in background."""
+ stderr_stream = self._stderr_stream
+ if stderr_stream:
+ try:
+ for line in stderr_stream:
+ output_buffer.append(line.strip())
+ except subprocess.SubprocessError:
+ pass
+
+ def receive_messages(self) -> Iterator[dict[str, Any]]:
+ process = self._process
+ stdout_stream = self._stdout_stream
+ if not process or not stdout_stream:
+ raise AgentCLIConnectionError("Not connected")
+
+ stderr_lines: list[str] = []
+ stderr_read_thread = threading.Thread(target=self._read_stderr, args=(stderr_lines,))
+ stderr_read_thread.start()
+
+ try:
+ for line in stdout_stream:
+ line_str = line.strip()
+ if not line_str:
+ continue
+
+ try:
+ data = json.loads(line_str)
+ try:
+ yield data
+ except GeneratorExit:
+ # Handle generator cleanup gracefully
+ return
+ except json.JSONDecodeError as e:
+ if line_str.startswith("{") or line_str.startswith("["):
+ raise SDKJSONDecodeError(line_str, e) from e
+ continue
+
+ except subprocess.SubprocessError:
+ pass
+
+ process.wait()
+ if process.returncode is not None and process.returncode != 0:
+ stderr_output = "\n".join(stderr_lines)
+ if stderr_output and "error" in stderr_output.lower():
+ raise AgentProcessError(
+ "CLI process failed",
+ exit_code=process.returncode,
+ stderr=stderr_output,
+ )
+
+ def is_connected(self) -> bool:
+ process = self._process
+ return process is not None and process.returncode is None
diff --git a/vet/imbue_core/agents/configs.py b/vet/imbue_core/agents/configs.py
@@ -0,0 +1,123 @@
+from pathlib import Path
+from typing import Any
+from typing import assert_never
+
+from vet.imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
+from vet.imbue_core.agents.llm_apis.anthropic_api import count_anthropic_tokens
+from vet.imbue_core.agents.llm_apis.common import get_model_max_context_length
+from vet.imbue_core.agents.llm_apis.constants import approximate_token_count
+from vet.imbue_core.agents.llm_apis.data_types import ModelStr
+from vet.imbue_core.agents.llm_apis.mock_api import MY_MOCK_MODEL_INFO
+from vet.imbue_core.agents.llm_apis.openai_api import OpenAIModelName
+from vet.imbue_core.agents.llm_apis.openai_api import count_openai_tokens
+from vet.imbue_core.language_model_mode import LanguageModelMode
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+
+class LanguageModelGenerationConfig(SerializableModel):
+ model_name: ModelStr = OpenAIModelName.GPT_4O_2024_08_06
+ # this should almost always be None (you dont want to save your cache path into the hammer invocation data!)
+ cache_path: Path | None = None
+ count_tokens_cache_path: Path | None = None
+ is_prompt_debugging_enabled: bool = False
+
+ # If true, the LLM API will cache the inputs to the LLM call as well as the outputs, which makes prompt diffing easier.
+ is_caching_inputs: bool = False
+
+ # if this is set, the LLM API will return ONLY cached responses
+ is_running_offline: bool = False
+
+ # if set, the LLM API will return log probabilities for the output tokens (if supported by the model)
+ is_using_logprobs: bool = False
+
+ # Retry configuration
+ retry_jitter_factor: float = 0.5
+
+ def model_post_init(self, __context: Any) -> None:
+ super().model_post_init(__context)
+ # FIXME: do proper validation
+ if self.cache_path is None and self.is_caching_inputs:
+ raise ValueError("cache_path must be provided if is_caching_inputs is True")
+
+ def count_tokens(self, text: str) -> int:
+ """Count tokens in the given text using the model's tokenizer."""
+ if self.model_name in (v for v in OpenAIModelName):
+ return count_openai_tokens(text, self.model_name)
+ if self.model_name in (v for v in AnthropicModelName):
+ return count_anthropic_tokens(text)
+ return approximate_token_count(text)
+
+ def get_max_context_length(self) -> int:
+ """Get the maximum context length for this model."""
+ return get_model_max_context_length(self.model_name)
+
+ def is_custom_model(self) -> bool:
+ """Return True if this is a custom/user-defined model.
+
+ Custom models use approximate token counting since there's no mechanism
+ for defining a tokenizer for them.
+ """
+ return False
+
+
+class OpenAICompatibleModelConfig(LanguageModelGenerationConfig):
+ """Configuration for custom models using OpenAI-compatible APIs (e.g., Ollama, local LLMs)."""
+
+ custom_base_url: str
+ custom_api_key_env: str
+ custom_context_window: int
+ custom_max_output_tokens: int
+
+ def count_tokens(self, text: str) -> int:
+ """Count tokens using approximation since we don't have access to the model's tokenizer."""
+ return approximate_token_count(text)
+
+ def get_max_context_length(self) -> int:
+ """Get the maximum context length for this model."""
+ return self.custom_context_window
+
+ def is_custom_model(self) -> bool:
+ """Return True if this is a custom/user-defined model.
+
+ Custom models use approximate token counting since there's no mechanism
+ for defining a tokenizer for them.
+ """
+ # TODO: Support custom tokenizers with custom models.
+ return True
+
+
+class MockedLanguageModelGenerationConfig(LanguageModelGenerationConfig):
+ model_name: ModelStr = MY_MOCK_MODEL_INFO.model_name
+ is_running_offline: bool = True
+ mock_responses_path: Path
+
+
+def create_safe_llm_config(
+ llm_name: ModelStr, mode: LanguageModelMode, cache_path: Path | None = None
+) -> LanguageModelGenerationConfig:
+ match mode:
+ case LanguageModelMode.LIVE:
+ assert cache_path is None
+ language_model_config = LanguageModelGenerationConfig(model_name=llm_name)
+ case LanguageModelMode.OFFLINE:
+ assert cache_path is not None
+ language_model_config = LanguageModelGenerationConfig(
+ model_name=llm_name,
+ is_running_offline=True,
+ is_caching_inputs=True,
+ cache_path=cache_path,
+ )
+ case LanguageModelMode.UPDATE_SNAPSHOT:
+ assert cache_path is not None
+ language_model_config = LanguageModelGenerationConfig(
+ model_name=llm_name, is_caching_inputs=True, cache_path=cache_path
+ )
+ case LanguageModelMode.MOCKED:
+ assert cache_path is not None
+ language_model_config = MockedLanguageModelGenerationConfig(
+ model_name=llm_name, mock_responses_path=cache_path
+ )
+ case _ as unreachable:
+ assert_never(unreachable) # pyre-ignore[6]: pyre doesn't understand enums
+ assert False # because pyre doesn't really understand assert_never, either
+ return language_model_config
diff --git a/imbue_core/imbue_core/agents/llm_apis/__init__.py b/vet/imbue_core/agents/llm_apis/__init__.py
diff --git a/vet/imbue_core/agents/llm_apis/anthropic_api.py b/vet/imbue_core/agents/llm_apis/anthropic_api.py
@@ -0,0 +1,818 @@
+import enum
+import inspect
+from contextlib import contextmanager
+from functools import lru_cache
+from pathlib import Path
+from types import FrameType
+from typing import AsyncGenerator
+from typing import Final
+from typing import Iterator
+
+import anthropic
+import httpx
+from anthropic._types import NOT_GIVEN
+from anthropic.types import CacheControlEphemeralParam
+from anthropic.types import MessageParam
+from anthropic.types import TextBlockParam
+from loguru import logger
+from pydantic.functional_validators import field_validator
+import tiktoken
+
+from vet.imbue_core.agents.llm_apis.anthropic_data_types import AnthropicCachingInfo
+from vet.imbue_core.agents.llm_apis.anthropic_data_types import AnthropicModelInfo
+from vet.imbue_core.agents.llm_apis.api_utils import convert_prompt_to_messages
+from vet.imbue_core.agents.llm_apis.api_utils import (
+ create_costed_language_model_response_for_single_result,
+)
+from vet.imbue_core.agents.llm_apis.data_types import CachedCountTokensResponse
+from vet.imbue_core.agents.llm_apis.data_types import CachingInfo
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import CountTokensInputs
+from vet.imbue_core.agents.llm_apis.data_types import CountTokensResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
+from vet.imbue_core.agents.llm_apis.data_types import ResponseStopReason
+from vet.imbue_core.agents.llm_apis.errors import BadAPIRequestError
+from vet.imbue_core.agents.llm_apis.errors import LanguageModelInvalidModelNameError
+from vet.imbue_core.agents.llm_apis.errors import MissingAPIKeyError
+from vet.imbue_core.agents.llm_apis.errors import NewSeedRetriableLanguageModelError
+from vet.imbue_core.agents.llm_apis.errors import SafelyRetriableTransientLanguageModelError
+from vet.imbue_core.agents.llm_apis.errors import TransientLanguageModelError
+from vet.imbue_core.agents.llm_apis.errors import UnsetCachePathError
+from vet.imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
+from vet.imbue_core.agents.llm_apis.models import ModelInfo
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamStartEvent
+from vet.imbue_core.async_monkey_patches import log_exception
+from vet.imbue_core.caching import AsyncCache
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.frozen_utils import FrozenMapping
+from vet.imbue_core.itertools import only
+from vet.imbue_core.nested_evolver import assign
+from vet.imbue_core.nested_evolver import chill
+from vet.imbue_core.nested_evolver import evolver
+from vet.imbue_core.secrets_utils import get_secret
+
+
+class AnthropicModelName(enum.StrEnum):
+ CLAUDE_3_HAIKU_2024_03_07 = "claude-3-haiku-20240307"
+ CLAUDE_3_OPUS_2024_02_29 = "claude-3-opus-20240229"
+ CLAUDE_3_5_SONNET_2024_06_20 = "claude-3-5-sonnet-20240620"
+ CLAUDE_3_5_SONNET_2024_10_22 = "claude-3-5-sonnet-20241022"
+ CLAUDE_3_5_HAIKU_2024_10_22 = "claude-3-5-haiku-20241022"
+ CLAUDE_3_7_SONNET_2025_02_19 = "claude-3-7-sonnet-20250219"
+ CLAUDE_4_OPUS_2025_05_14 = "claude-opus-4-20250514"
+ CLAUDE_4_1_OPUS_2025_08_05 = "claude-opus-4-1-20250805"
+ CLAUDE_4_SONNET_2025_05_14 = "claude-sonnet-4-20250514"
+ CLAUDE_4_5_SONNET_2025_09_29 = "claude-sonnet-4-5-20250929"
+ CLAUDE_4_5_HAIKU_2025_10_01 = "claude-haiku-4-5-20251001"
+ CLAUDE_4_5_OPUS_2025_11_01 = "claude-opus-4-5-20251101"
+ # the same as above but with the token limit and cost per token for the 1M token limit
+ # TODO: combine these and add ability for token costs to be nonlinear
+ # FIXME: this is an exception where the model name is not the same as the model name in the API
+ CLAUDE_4_SONNET_2025_05_14_LONG = "claude-sonnet-4-20250514-long"
+ CLAUDE_4_5_SONNET_2025_09_29_LONG = "claude-sonnet-4-5-20250929-long"
+
+ # the following are 'retired' and are no longer available: https://docs.claude.com/en/docs/about-claude/model-deprecations
+ # CLAUDE_2_1 = "claude-2.1"
+ # CLAUDE_2 = "claude-2"
+ # CLAUDE_3_SONNET_2024_02_29 = "claude-3-sonnet-20240229"
+
+
+# Basic info is available at https://docs.anthropic.com/claude/reference/models
+# Rate limits for Anthropic models are available on our dashboard: https://console.anthropic.com/settings/limits
+# (we have a custom plan, so the public docs don't reflect our actual rate limits)
+# Prompt caching pricing is available at https://docs.claude.com/en/docs/build-with-claude/prompt-caching#pricing
+# NOTE: as of 2025-06-04, there are some models that don't have rate limits set in our dashboard
+ANTHROPIC_MODEL_INFO_BY_NAME: FrozenMapping[AnthropicModelName, ModelInfo] = FrozenDict(
+ {
+ AnthropicModelName.CLAUDE_3_HAIKU_2024_03_07: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_3_HAIKU_2024_03_07,
+ cost_per_input_token=0.25 / 1_000_000,
+ cost_per_output_token=1.25 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=4096,
+ rate_limit_req=4000 / 60, # 4000 RPM = 66.67 RPS
+ rate_limit_tok=4_000_000 / 60,
+ rate_limit_output_tok=800_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=0.3 / 1_000_000,
+ cost_per_1h_cache_write_token=0.5 / 1_000_000,
+ cost_per_cache_read_token=0.03 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_3_OPUS_2024_02_29: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_3_OPUS_2024_02_29,
+ cost_per_input_token=15.00 / 1_000_000,
+ cost_per_output_token=75.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=4096,
+ rate_limit_req=4000 / 60, # 4000 RPM = 66.67 RPS
+ rate_limit_tok=1_000_000 / 60,
+ rate_limit_output_tok=150_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=18.75 / 1_000_000,
+ cost_per_1h_cache_write_token=30 / 1_000_000,
+ cost_per_cache_read_token=1.5 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_3_5_SONNET_2024_06_20: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_3_5_SONNET_2024_06_20,
+ cost_per_input_token=3.00 / 1_000_000,
+ cost_per_output_token=15.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=4096,
+ rate_limit_req=5000 / 60, # 5000 RPM = 83.33 RPS
+ rate_limit_tok=8_000_000 / 60,
+ rate_limit_output_tok=1_600_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=3.75 / 1_000_000,
+ cost_per_1h_cache_write_token=6 / 1_000_000,
+ cost_per_cache_read_token=0.3 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_3_5_SONNET_2024_10_22: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_3_5_SONNET_2024_10_22,
+ cost_per_input_token=3.00 / 1_000_000,
+ cost_per_output_token=15.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=8192,
+ rate_limit_req=5000 / 60, # 5000 RPM = 83.33 RPS
+ rate_limit_tok=8_000_000 / 60,
+ rate_limit_output_tok=400_000 / 60,
+ ),
+ AnthropicModelName.CLAUDE_3_5_HAIKU_2024_10_22: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_3_5_HAIKU_2024_10_22,
+ cost_per_input_token=1.00 / 1_000_000,
+ cost_per_output_token=5.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=8192,
+ rate_limit_req=4000 / 60, # 4000 RPM = 66.67 RPS
+ rate_limit_tok=4_000_000 / 60,
+ rate_limit_output_tok=800_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=1 / 1_000_000,
+ cost_per_1h_cache_write_token=1.6 / 1_000_000,
+ cost_per_cache_read_token=0.08 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_3_7_SONNET_2025_02_19: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_3_7_SONNET_2025_02_19,
+ cost_per_input_token=3.00 / 1_000_000,
+ cost_per_output_token=15.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=8192,
+ rate_limit_req=None, # Currently no limit set in our dashboard
+ rate_limit_tok=2_000_000 / 60,
+ rate_limit_output_tok=400_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=3.75 / 1_000_000,
+ cost_per_1h_cache_write_token=6 / 1_000_000,
+ cost_per_cache_read_token=0.3 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_4_OPUS_2025_05_14: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_4_OPUS_2025_05_14,
+ cost_per_input_token=15.00 / 1_000_000,
+ cost_per_output_token=75.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=32_000,
+ rate_limit_req=4000 / 60,
+ rate_limit_tok=2_000_000 / 60,
+ rate_limit_output_tok=400_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=18.75 / 1_000_000,
+ cost_per_1h_cache_write_token=30 / 1_000_000,
+ cost_per_cache_read_token=1.5 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_4_1_OPUS_2025_08_05: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_4_1_OPUS_2025_08_05,
+ cost_per_input_token=15.00 / 1_000_000,
+ cost_per_output_token=75.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=32_000,
+ rate_limit_req=4000 / 60,
+ rate_limit_tok=2_000_000 / 60,
+ rate_limit_output_tok=400_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=18.75 / 1_000_000,
+ cost_per_1h_cache_write_token=30 / 1_000_000,
+ cost_per_cache_read_token=1.5 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_4_5_OPUS_2025_11_01: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_4_5_OPUS_2025_11_01,
+ cost_per_input_token=5.00 / 1_000_000,
+ cost_per_output_token=25.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=64_000,
+ rate_limit_req=4000 / 60,
+ rate_limit_tok=2_000_000 / 60,
+ rate_limit_output_tok=400_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=6.25 / 1_000_000,
+ cost_per_1h_cache_write_token=10 / 1_000_000,
+ cost_per_cache_read_token=0.5 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_4_SONNET_2025_05_14: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_4_SONNET_2025_05_14,
+ cost_per_input_token=3.00 / 1_000_000,
+ cost_per_output_token=15.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=64_000,
+ rate_limit_req=None, # Currently no limit set in our dashboard
+ rate_limit_tok=2_000_000 / 60,
+ rate_limit_output_tok=400_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=3.75 / 1_000_000,
+ cost_per_1h_cache_write_token=6 / 1_000_000,
+ cost_per_cache_read_token=0.3 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29,
+ cost_per_input_token=3.00 / 1_000_000,
+ cost_per_output_token=15.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=64_000,
+ rate_limit_req=None, # Currently no limit set in our dashboard
+ rate_limit_tok=2_000_000 / 60,
+ rate_limit_output_tok=400_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=3.75 / 1_000_000,
+ cost_per_1h_cache_write_token=6 / 1_000_000,
+ cost_per_cache_read_token=0.3 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_4_5_HAIKU_2025_10_01: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_4_5_HAIKU_2025_10_01,
+ cost_per_input_token=1.00 / 1_000_000,
+ cost_per_output_token=5.00 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=64_000,
+ rate_limit_req=4_000 / 60,
+ rate_limit_tok=4_000_000 / 60,
+ rate_limit_output_tok=800_000 / 60,
+ provider_specific_info=AnthropicModelInfo(
+ cost_per_5m_cache_write_token=1.25 / 1_000_000,
+ cost_per_1h_cache_write_token=2.0 / 1_000_000,
+ cost_per_cache_read_token=0.1 / 1_000_000,
+ ),
+ ),
+ AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG,
+ # the first 200_000 input tokens use the rates above, and the next up to 800_000 use the rate 6.0 / 1_000_000.
+ # thus the maximum average cost per input token is (3.0 * 200_000 + 6.0 * 800_000) / 1_000_000 = 5.4 per 1_000_000.
+ # (all output tokens may be past 200_000 input tokens, so the max average cost there is just the cost for tokens after 200_000)
+ cost_per_input_token=5.40 / 1_000_000,
+ cost_per_output_token=22.50 / 1_000_000,
+ max_input_tokens=1_000_000,
+ max_output_tokens=64_000,
+ rate_limit_req=None, # Currently no limit set in our dashboard
+ rate_limit_tok=1_000_000 / 60, # <-- yeah they let us have one (1) 1M request per minute
+ rate_limit_output_tok=200_000 / 60,
+ ),
+ AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG: ModelInfo(
+ model_name=AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG,
+ # the first 200_000 input tokens use the rates above, and the next up to 800_000 use the rate 6.0 / 1_000_000.
+ # thus the maximum average cost per input token is (3.0 * 200_000 + 6.0 * 800_000) / 1_000_000 = 5.4 per 1_000_000.
+ # (all output tokens may be past 200_000 input tokens, so the max average cost there is just the cost for tokens after 200_000)
+ cost_per_input_token=5.40 / 1_000_000,
+ cost_per_output_token=22.50 / 1_000_000,
+ max_input_tokens=1_000_000,
+ max_output_tokens=64_000,
+ rate_limit_req=None, # Currently no limit set in our dashboard
+ rate_limit_tok=1_000_000 / 60, # <-- yeah they let us have one (1) 1M request per minute
+ rate_limit_output_tok=200_000 / 60,
+ ),
+ }
+)
+
+
+_ROLE_TO_ANTHROPIC_ROLE: Final[FrozenMapping[str, str]] = FrozenDict(
+ {
+ "HUMAN": "user",
+ "ASSISTANT": "assistant",
+ "USER": "user",
+ "USER_CACHED": "user",
+ "SYSTEM": "system",
+ "SYSTEM_CACHED": "system",
+ }
+)
+
+_ANTHROPIC_STOP_REASON_TO_STOP_REASON: Final[FrozenMapping[str, ResponseStopReason]] = FrozenDict(
+ {
+ "end_turn": ResponseStopReason.END_TURN,
+ "max_tokens": ResponseStopReason.MAX_TOKENS,
+ "stop_sequence": ResponseStopReason.STOP_SEQUENCE,
+ "refusal": ResponseStopReason.CONTENT_FILTER,
+ }
+)
+
+_ANTHROPIC_BETA_PROMPT_CACHING = "prompt-caching-2024-07-31"
+_ANTHROPIC_BETA_OAUTH = "oauth-2025-04-20"
+
+
+@lru_cache(maxsize=1)
+def get_anthropic_tokenizer() -> tiktoken.Encoding:
+ """Use cl100k_base encoding as an approximation for Claude tokenization.
+
+ Modern Anthropic SDK does not expose a tokenizer directly and instead
+ relies on API calls to count tokens. Using that implementation would
+ put HTTP calls in our `count_tokens` implementation which would be tricky
+ as the method would have to be async or block the event loop.
+
+ Instead, we use tiktoken's cl100k_base encoding (used by GPT-4) as a
+ reasonable approximation. This allows us to count tokens without making
+ HTTP requests at the cost of slightly inaccurate token counts.
+ """
+ return tiktoken.get_encoding("cl100k_base")
+
+
+def count_anthropic_tokens(text: str) -> int:
+ return int(len(get_anthropic_tokenizer().encode(text)) * 1.1)
+
+
+SystemMessageParam = TextBlockParam
+
+
+def _convert_prompt_to_anthropic_messages(
+ prompt: str,
+) -> tuple[list[MessageParam], list[SystemMessageParam] | None]:
+ """Converts a prompt into list of non-system (user/assistant) messages and the optional system prompt."""
+ non_system_messages = []
+ system_messages = []
+ for msg in convert_prompt_to_messages(prompt, is_cache_role_preserved=True):
+ role = _ROLE_TO_ANTHROPIC_ROLE[msg.role]
+ if msg.role == "SYSTEM_CACHED":
+ system_messages.append(
+ {
+ "type": "text",
+ "text": msg.content,
+ "cache_control": {"type": "ephemeral"},
+ },
+ )
+ elif role == "system":
+ system_messages.append(
+ {
+ "type": "text",
+ "text": msg.content,
+ },
+ )
+ elif role == "USER_CACHED":
+ non_system_messages.append(
+ MessageParam( # pyre-fixme[28]: MessageParam doesn't have cache_control
+ content=msg.content,
+ role="user",
+ cache_control=CacheControlEphemeralParam(type="ephemeral"),
+ )
+ )
+ else:
+ non_system_messages.append(MessageParam(content=msg.content, role=role)) # type: ignore
+
+ if len(system_messages) > 1:
+ logger.debug("system_messages: {}", system_messages)
+ raise ValueError(f"Anthropic API supports only 0 or 1 system message; got {len(system_messages)}.")
+
+ if len(non_system_messages) == 0:
+ system_messages = None
+
+ return non_system_messages, system_messages
+
+
+@contextmanager
+def _anthropic_exception_manager() -> Iterator[None]:
+ """Simple context manager for parsing Anthropic API exceptions."""
+ # ref
+ try:
+ yield
+ except anthropic.InternalServerError as e:
+ # this can be caused by either malformed requests or transient errors, so play it safe and retry
+ raise TransientLanguageModelError(str(e)) from e
+ except anthropic.BadRequestError as e:
+ logger.info("BadAPIRequestError {e}", e=e)
+ raise BadAPIRequestError(str(e)) from e
+ except TypeError as e:
+ logger.info("Type error calling Anthropic API: {e}", e=e)
+ raise BadAPIRequestError(str(e)) from e
+ except anthropic.APIConnectionError as e:
+ raise TransientLanguageModelError(str(e)) from e
+ except anthropic.RateLimitError as e:
+ extra_header_keys = [x for x in e.response.headers.keys() if x.startswith("anthropic-")]
+ extra_data = ", ".join([f"{key}={e.response.headers[key]}" for key in extra_header_keys])
+ extra_info = f"Rate limit data: {extra_data}"
+ raise TransientLanguageModelError(extra_info) from e
+ except anthropic.APIStatusError as e:
+ if "overloaded_error" in str(e):
+ raise SafelyRetriableTransientLanguageModelError(str(e)) from e
+ if "internal server error" in str(e).lower():
+ raise SafelyRetriableTransientLanguageModelError(str(e)) from e
+ # this happens when anthropic provides us open source code and then feels bad about it
+ # anthropic.APIStatusError: {'type': 'error', 'error': {'details': None, 'type': 'invalid_request_error', 'message': 'Output blocked by content filtering policy'}}
+ if e.message == "Output blocked by content filtering policy":
+ raise NewSeedRetriableLanguageModelError(e)
+ logger.info(str(e))
+ if e.status_code == 409 or e.status_code >= 500:
+ raise TransientLanguageModelError(str(e)) from e
+ raise
+ except httpx.RemoteProtocolError as e:
+ logger.info(str(e))
+ raise TransientLanguageModelError("httpx.RemoteProtocolError") from e
+ except (BadAPIRequestError, TransientLanguageModelError, MissingAPIKeyError):
+ # we already raised this error ourselves earlier, so we don't need to mark it as unknown
+ raise
+ except Exception as e:
+ # we catch TransientLanguageModelError later to retry it, but we still want to log it so it's not silent
+ log_exception(
+ e,
+ "Failed to generate output from Anthropic, unknown error of type {type_name}",
+ type_name=type(e).__name__,
+ )
+ raise TransientLanguageModelError("Unknown error") from e
+
+
+class MissingCachingInfoError(Exception):
+ pass
+
+
+class AnthropicAPI(LanguageModelAPI):
+ model_name: AnthropicModelName = AnthropicModelName.CLAUDE_4_SONNET_2025_05_14
+ is_conversational: bool = True
+
+ # Anthropic specific args
+ # unclear what the timeout ought to be actually, set to 1 minute for now because their default of 10 minutes seems insane
+ timeout: float = 60.0
+ max_retries: int = 0
+ count_tokens_cache_path: Path | None = None
+
+ @field_validator("model_name") # pyre-ignore[56]: pyre doesn't understand pydantic
+ @classmethod
+ def validate_model_name(cls, v: str) -> str:
+ if v not in ANTHROPIC_MODEL_INFO_BY_NAME:
+ raise LanguageModelInvalidModelNameError(v, cls.__name__, list(ANTHROPIC_MODEL_INFO_BY_NAME))
+ return v
+
+ @property
+ def model_info(self) -> ModelInfo:
+ return ANTHROPIC_MODEL_INFO_BY_NAME[self.model_name]
+
+ def _get_sync_client(self) -> anthropic.Anthropic:
+ api_key, auth_token = _get_api_key_or_auth_token()
+ if api_key:
+ return anthropic.Anthropic(api_key=api_key)
+ else:
+ return anthropic.Anthropic(
+ auth_token=auth_token,
+ default_headers={"anthropic-beta": _ANTHROPIC_BETA_OAUTH},
+ )
+
+ def _get_client(self) -> anthropic.AsyncAnthropic:
+ api_key, auth_token = _get_api_key_or_auth_token()
+ if api_key:
+ return anthropic.AsyncAnthropic(
+ api_key=api_key,
+ max_retries=self.max_retries,
+ timeout=self.timeout,
+ default_headers={"anthropic-beta": _ANTHROPIC_BETA_PROMPT_CACHING},
+ )
+ else:
+ return anthropic.AsyncAnthropic(
+ auth_token=auth_token,
+ max_retries=self.max_retries,
+ timeout=self.timeout,
+ default_headers={"anthropic-beta": f"{_ANTHROPIC_BETA_PROMPT_CACHING},{_ANTHROPIC_BETA_OAUTH}"},
+ )
+
+ async def _call_api(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ network_failure_count: int = 0,
+ ) -> CostedLanguageModelResponse:
+ assert (
+ params.count == 1
+ ), "Anthropic API only supports count=1. It is possible to hack around this by using a for loop, but doesn't seem worth it right now."
+
+ non_system_messages, system_messages = _convert_prompt_to_anthropic_messages(prompt)
+
+ with _anthropic_exception_manager():
+ async with self._get_client() as client:
+ if params.max_tokens is None:
+ # NOTE: anthropic's API REQUIRES you to provide this, if you don't pass it in we just set it to the maximum possible
+
+ # use the evolver method of updating instead
+ # params.max_tokens = self.model_info.max_output_tokens
+ param_with_max_tokens_evolver = evolver(params)
+ assign(
+ param_with_max_tokens_evolver.max_tokens,
+ lambda: self.model_info.max_output_tokens,
+ )
+ params = chill(param_with_max_tokens_evolver)
+ assert params.max_tokens is not None, "max_tokens must be provided for Anthropic API"
+
+ if self.model_name in (
+ AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG,
+ AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG,
+ ):
+ # FIXME: Fix this once this is no longer beta or as this becomes required for more models
+ # Map the name back to the actual model name for the API call
+ if self.model_name == AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG:
+ model_name = AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29
+ elif self.model_name == AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG:
+ model_name = AnthropicModelName.CLAUDE_4_SONNET_2025_05_14
+ else:
+ assert False, "unreachable"
+ api_result = await client.beta.messages.create(
+ messages=non_system_messages,
+ stop_sequences=([params.stop] if params.stop is not None else NOT_GIVEN),
+ model=model_name,
+ temperature=params.temperature,
+ system=prepend_claude_code_system_prompt(system_messages),
+ max_tokens=params.max_tokens,
+ betas=["context-1m-2025-08-07"],
+ )
+ detailed_caching_data = AnthropicCachingInfo(
+ written_5m=api_result.usage.cache_creation.ephemeral_5m_input_tokens,
+ written_1h=api_result.usage.cache_creation.ephemeral_1h_input_tokens,
+ )
+ else:
+ api_result = await client.messages.create(
+ messages=non_system_messages,
+ stop_sequences=([params.stop] if params.stop is not None else NOT_GIVEN),
+ model=self.model_name,
+ temperature=params.temperature,
+ system=prepend_claude_code_system_prompt(system_messages),
+ max_tokens=params.max_tokens,
+ )
+ detailed_caching_data = AnthropicCachingInfo(
+ written_5m=api_result.usage.cache_creation.ephemeral_5m_input_tokens,
+ written_1h=api_result.usage.cache_creation.ephemeral_1h_input_tokens,
+ )
+ text = only(api_result.content).text
+ if api_result.stop_reason:
+ stop_reason = _ANTHROPIC_STOP_REASON_TO_STOP_REASON.get(
+ str(api_result.stop_reason), ResponseStopReason.NONE
+ )
+ else:
+ stop_reason = ResponseStopReason.NONE
+ if params.stop and stop_reason == ResponseStopReason.STOP_SEQUENCE:
+ text += params.stop
+ logger.trace(text)
+
+ prompt_tokens = api_result.usage.input_tokens
+ completion_tokens = api_result.usage.output_tokens # type: ignore
+ caching_info = CachingInfo(
+ read_from_cache=api_result.usage.cache_read_input_tokens,
+ provider_specific_data=detailed_caching_data,
+ )
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens, caching_info)
+ logger.trace("Dollars used: {dollars_used}", dollars_used=dollars_used)
+
+ return create_costed_language_model_response_for_single_result(
+ text=text,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ stop_reason=stop_reason,
+ network_failure_count=network_failure_count,
+ dollars_used=dollars_used,
+ caching_info=caching_info,
+ )
+
+ async def _get_api_stream(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ non_system_messages, system_messages = _convert_prompt_to_anthropic_messages(prompt)
+ with _anthropic_exception_manager():
+ async with self._get_client() as client:
+ yield LanguageModelStreamStartEvent()
+
+ # NOTE: anthropic's API REQUIRES you to provide this, if you don't pass it in we just set it to the maximum possible
+ max_tokens = params.max_tokens if params.max_tokens is not None else self.model_info.max_output_tokens
+ assert max_tokens is not None, "max_tokens must be provided for Anthropic API"
+
+ if self.model_name in (
+ AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG,
+ AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG,
+ ):
+ # FIXME: Fix this once this is no longer beta or as this becomes required for more models
+ # Map the name back to the actual model name for the API call
+ if self.model_name == AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29_LONG:
+ model_name = AnthropicModelName.CLAUDE_4_5_SONNET_2025_09_29
+ elif self.model_name == AnthropicModelName.CLAUDE_4_SONNET_2025_05_14_LONG:
+ model_name = AnthropicModelName.CLAUDE_4_SONNET_2025_05_14
+ else:
+ assert False, "unreachable"
+ stream_fn = lambda **kwargs: client.beta.messages.stream(**kwargs, betas=["context-1m-2025-08-07"])
+ cache_info_maker = lambda api_result: AnthropicCachingInfo(
+ written_5m=api_result.usage.cache_creation.ephemeral_5m_input_tokens,
+ written_1h=api_result.usage.cache_creation.ephemeral_1h_input_tokens,
+ )
+ else:
+ model_name = self.model_name
+ stream_fn = lambda **kwargs: client.messages.stream(**kwargs)
+ cache_info_maker = lambda api_result: AnthropicCachingInfo(
+ written_5m=api_result.usage.cache_creation.ephemeral_5m_input_tokens,
+ written_1h=api_result.usage.cache_creation.ephemeral_1h_input_tokens,
+ )
+ async with stream_fn(
+ max_tokens=max_tokens,
+ messages=non_system_messages,
+ model=model_name,
+ stop_sequences=([params.stop] if params.stop is not None else NOT_GIVEN),
+ system=system_messages or NOT_GIVEN,
+ temperature=params.temperature,
+ ) as stream:
+ async for text_delta in stream.text_stream:
+ yield LanguageModelStreamDeltaEvent(delta=text_delta)
+
+ final_message = await stream.get_final_message()
+ text = only(final_message.content).text
+ stop_reason = (
+ final_message.stop_reason if final_message.stop_reason is not None else ResponseStopReason.NONE
+ )
+ if params.stop and stop_reason == ResponseStopReason.STOP_SEQUENCE:
+ yield LanguageModelStreamDeltaEvent(delta=params.stop)
+ text += params.stop
+ logger.trace(text)
+
+ prompt_tokens = final_message.usage.input_tokens
+ # useful to confirm that the cache is actually being hit
+ logger.debug(
+ "Used this many cached read tokens: {cached_tokens}",
+ cached_tokens=final_message.usage.cache_read_input_tokens,
+ )
+ completion_tokens = final_message.usage.output_tokens
+ caching_info = CachingInfo(
+ read_from_cache=final_message.usage.cache_read_input_tokens,
+ provider_specific_data=cache_info_maker(final_message),
+ )
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens, caching_info)
+
+ if final_message.stop_reason:
+ stop_reason = _ANTHROPIC_STOP_REASON_TO_STOP_REASON.get(
+ str(final_message.stop_reason), ResponseStopReason.NONE
+ )
+ else:
+ stop_reason = ResponseStopReason.NONE
+
+ logger.trace("Dollars used: {dollars_used}", dollars_used=dollars_used)
+ yield LanguageModelStreamEndEvent(
+ usage=LanguageModelResponseUsage(
+ prompt_tokens_used=prompt_tokens,
+ completion_tokens_used=completion_tokens,
+ dollars_used=dollars_used,
+ caching_info=caching_info,
+ ),
+ stop_reason=stop_reason,
+ )
+
+ def count_tokens(self, text: str) -> int:
+ return count_anthropic_tokens(text)
+
+ def get_count_tokens_response_cache(self) -> AsyncCache[CachedCountTokensResponse]:
+ if self.count_tokens_cache_path is None:
+ raise UnsetCachePathError()
+ return AsyncCache(self.count_tokens_cache_path, CachedCountTokensResponse)
+
+ async def check_count_tokens_cache(self, cache_key: str) -> CountTokensResponse | None:
+ return await self.check_cache_core(self.get_count_tokens_response_cache, cache_key)
+
+ async def _get_from_count_tokens_cache(
+ self, frame: FrameType | None
+ ) -> tuple[str | None, CountTokensResponse | None]:
+ return await self._get_from_cache_core(frame, lambda cr: cr, self.check_count_tokens_cache)
+
+ async def count_tokens_api(self, prompt: str, is_caching_enabled: bool) -> int:
+ """
+ Call the count tokens API. This API is free, so we don't ensure resource limits before calling it.
+ There are rate limits though: https://docs.anthropic.com/en/docs/build-with-claude/token-counting#pricing-and-rate-limits
+ """
+
+ self.assert_caching_enabled_if_offline(is_caching_enabled)
+
+ frame: FrameType | None = None
+ if is_caching_enabled:
+ frame = inspect.currentframe()
+
+ cache_key: str | None = None
+ if is_caching_enabled:
+ cache_key, cached_response = await self._get_from_count_tokens_cache(frame)
+
+ if cached_response is not None:
+ return cached_response.input_tokens
+
+ self.assert_not_offline_if_cache_miss(prompt)
+
+ non_system_messages, system_messages = _convert_prompt_to_anthropic_messages(prompt)
+
+ with _anthropic_exception_manager():
+ async with self._get_client() as client:
+ raw_response = await client.messages.count_tokens(
+ model=self.model_info.model_name,
+ messages=non_system_messages,
+ system=system_messages,
+ )
+
+ response = CountTokensResponse(input_tokens=raw_response.input_tokens)
+ result = CachedCountTokensResponse(
+ response=response,
+ inputs=(
+ CountTokensInputs(model=self.model_info.model_name, prompt=prompt)
+ if self.is_caching_inputs
+ else None
+ ),
+ )
+
+ if is_caching_enabled:
+ assert cache_key is not None
+ async with self.get_count_tokens_response_cache() as cache:
+ await cache.set(cache_key, result)
+
+ return response.input_tokens
+
+ def calculate_cost(
+ self,
+ prompt_tokens: int,
+ completion_tokens: int,
+ caching_info: CachingInfo | None = None,
+ ) -> float:
+ try:
+ # find the cost for the prompt, broken down into cache writes and regular input tokens
+
+ # if we don't have the caching info, use the basic cost model (we catch the error below)
+ if (
+ caching_info is None
+ or caching_info.provider_specific_data is None
+ or self.model_info.provider_specific_info is None
+ ):
+ raise MissingCachingInfoError(
+ f"Missing required info for more precise cost estimates; caching info: {caching_info}, model info: {self.model_info.provider_specific_info}"
+ )
+ anthropic_caching_usage = caching_info.provider_specific_data
+ assert isinstance(anthropic_caching_usage, AnthropicCachingInfo), "Expected AnthropicCachingInfo"
+ anthropic_caching_rates = self.model_info.provider_specific_info
+ assert isinstance(anthropic_caching_rates, AnthropicModelInfo), "Expected AnthropicModelInfo"
+ cache_write_5m_tokens = anthropic_caching_usage.written_5m
+ cache_write_1h_tokens = anthropic_caching_usage.written_1h
+ cache_read_tokens = caching_info.read_from_cache
+ regular_input_tokens = prompt_tokens - cache_write_5m_tokens - cache_write_1h_tokens
+
+ input_cost = (
+ cache_write_5m_tokens * anthropic_caching_rates.cost_per_5m_cache_write_token
+ + cache_write_1h_tokens * anthropic_caching_rates.cost_per_1h_cache_write_token
+ + cache_read_tokens * anthropic_caching_rates.cost_per_cache_read_token
+ + regular_input_tokens * self.model_info.cost_per_input_token
+ )
+
+ output_cost = completion_tokens * self.model_info.cost_per_output_token
+
+ return input_cost + output_cost
+
+ except MissingCachingInfoError as e:
+ logger.info("{}; using basic cost model", e)
+ return self.basic_calculate_cost(prompt_tokens, completion_tokens)
+
+
+def _get_api_key_or_auth_token() -> tuple[str | None, str | None]:
+ api_key = get_secret("ANTHROPIC_API_KEY")
+ auth_token = get_secret("ANTHROPIC_AUTH_TOKEN")
+ if not api_key and not auth_token:
+ raise MissingAPIKeyError("Neither ANTHROPIC_API_KEY nor ANTHROPIC_AUTH_TOKEN environment variable is set")
+ return api_key, auth_token
+
+
+_CLAUDE_CODE_SYSTEM_PROMPT = TextBlockParam(
+ type="text",
+ text="You are Claude Code, Anthropic's official CLI for Claude.",
+ cache_control=CacheControlEphemeralParam(type="ephemeral"),
+)
+
+
+def prepend_claude_code_system_prompt(
+ system_prompt: str | list[TextBlockParam] | None,
+) -> list[TextBlockParam]:
+ """Prepends the system prompt used by Claude Code.
+
+ When using the Claude API through Claude Pro/Max subscriptions,
+ the Claude API requires this particular system prompt to be set;
+ otherwise the request will fail.
+
+ For simplicity and consistency,
+ we always do this even when it's not strictly required,
+ (like when using the Claude API through API keys).
+ """
+ if not system_prompt:
+ return [_CLAUDE_CODE_SYSTEM_PROMPT]
+ elif isinstance(system_prompt, str):
+ return [
+ _CLAUDE_CODE_SYSTEM_PROMPT,
+ TextBlockParam(type="text", text=system_prompt),
+ ]
+ else:
+ return [_CLAUDE_CODE_SYSTEM_PROMPT] + system_prompt
diff --git a/vet/imbue_core/agents/llm_apis/anthropic_data_types.py b/vet/imbue_core/agents/llm_apis/anthropic_data_types.py
@@ -0,0 +1,15 @@
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+
+class AnthropicModelInfo(SerializableModel):
+ object_type: str = "AnthropicModelInfo"
+ cost_per_5m_cache_write_token: float
+ cost_per_1h_cache_write_token: float
+ cost_per_cache_read_token: float
+
+
+class AnthropicCachingInfo(SerializableModel):
+ object_type: str = "AnthropicCachingInfo"
+ # record info on cache writes for 5 minute and 1 hour durations
+ written_5m: int
+ written_1h: int
diff --git a/vet/imbue_core/agents/llm_apis/api_utils.py b/vet/imbue_core/agents/llm_apis/api_utils.py
@@ -0,0 +1,110 @@
+from typing import Final
+from typing import Iterable
+
+from loguru import logger
+
+from vet.imbue_core.agents.llm_apis.data_types import CachingInfo
+from vet.imbue_core.agents.llm_apis.data_types import ConversationMessage
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseWithThoughts
+from vet.imbue_core.agents.llm_apis.data_types import ResponseStopReason
+from vet.imbue_core.agents.llm_apis.data_types import ThoughtResponse
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.frozen_utils import FrozenMapping
+
+_ROLE_TO_OPENAI_ROLE: Final[FrozenMapping] = FrozenDict(
+ {
+ "HUMAN": "user",
+ "ASSISTANT": "assistant",
+ "SYSTEM": "system",
+ "USER": "user",
+ "SYSTEM_CACHED": "system",
+ "USER_CACHED": "user",
+ }
+)
+
+
+def convert_prompt_to_messages(prompt: str, is_cache_role_preserved: bool = False) -> tuple[ConversationMessage, ...]:
+ messages = []
+ for raw_message in convert_prompt_to_openai_messages(prompt, is_cache_role_preserved):
+ messages.append(ConversationMessage(role=raw_message["role"].upper(), content=raw_message["content"]))
+ return tuple(messages)
+
+
+def convert_messages_to_prompt_template(messages: Iterable[ConversationMessage]) -> str:
+ return "\n".join(f"[ROLE={message.role.upper()}]\n{message.content}" for message in messages)
+
+
+def create_costed_language_model_response_for_single_result(
+ text: str,
+ prompt_tokens: int,
+ completion_tokens: int,
+ stop_reason: ResponseStopReason,
+ network_failure_count: int,
+ dollars_used: float,
+ thoughts: ThoughtResponse | None = None,
+ caching_info: CachingInfo | None = None,
+) -> CostedLanguageModelResponse:
+ logger.trace("dollars used: {}", dollars_used)
+ logger.trace("completion_tokens_used used: {}", completion_tokens)
+ if thoughts is None:
+ result = LanguageModelResponse(
+ text=text,
+ token_count=completion_tokens + prompt_tokens,
+ stop_reason=stop_reason,
+ network_failure_count=network_failure_count,
+ )
+ else:
+ result = LanguageModelResponseWithThoughts(
+ text=text,
+ token_count=completion_tokens + prompt_tokens,
+ stop_reason=stop_reason,
+ network_failure_count=network_failure_count,
+ thoughts=thoughts,
+ )
+
+ return CostedLanguageModelResponse(
+ usage=LanguageModelResponseUsage(
+ prompt_tokens_used=prompt_tokens,
+ completion_tokens_used=completion_tokens,
+ dollars_used=dollars_used,
+ caching_info=caching_info,
+ ),
+ responses=(result,),
+ )
+
+
+# FIXME: we should make sure that all our LLM providers use the same function here, some clean up is required
+def convert_prompt_to_openai_messages(prompt: str, is_cache_role_preserved: bool = False) -> list[dict[str, str]]:
+ prompt = prompt.lstrip()
+ assert prompt.startswith("[ROLE=")
+ prompt = prompt.replace("[ROLE=", "", 1)
+ chunks = prompt.split("\n[ROLE=")
+ messages: list[dict[str, str]] = []
+ for chunk in chunks:
+ lines = chunk.split("\n")
+ role = lines[0].strip().rstrip("]")
+ assert role in (
+ "HUMAN",
+ "ASSISTANT",
+ "USER",
+ "SYSTEM",
+ "SYSTEM_CACHED",
+ "USER_CACHED",
+ ), f"Unknown role {role} in prompt {prompt}"
+ lines.pop(0)
+ if role == "HUMAN":
+ role = "USER"
+ if len(messages) > 0:
+ messages[-1]["content"] = messages[-1]["content"] + "\n"
+ content = "\n".join(lines)
+ content = content.rstrip()
+ fixed_role = _ROLE_TO_OPENAI_ROLE[role]
+ if is_cache_role_preserved and role == "SYSTEM_CACHED":
+ fixed_role = "SYSTEM_CACHED"
+ elif is_cache_role_preserved and role == "USER_CACHED":
+ fixed_role = "USER_CACHED"
+ messages.append({"role": fixed_role, "content": content})
+ return messages
diff --git a/vet/imbue_core/agents/llm_apis/build_apis.py b/vet/imbue_core/agents/llm_apis/build_apis.py
@@ -0,0 +1,128 @@
+from pathlib import Path
+
+from vet.imbue_core.agents.configs import LanguageModelGenerationConfig
+from vet.imbue_core.agents.configs import MockedLanguageModelGenerationConfig
+from vet.imbue_core.agents.configs import OpenAICompatibleModelConfig
+from vet.imbue_core.agents.llm_apis.anthropic_api import AnthropicAPI
+from vet.imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
+from vet.imbue_core.agents.llm_apis.constants import approximate_token_count
+from vet.imbue_core.agents.llm_apis.gemini_api import GeminiAPI
+from vet.imbue_core.agents.llm_apis.gemini_api import GeminiModelName
+from vet.imbue_core.agents.llm_apis.groq_api import GroqChatAPI
+from vet.imbue_core.agents.llm_apis.groq_api import GroqSupportedModelName
+from vet.imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
+from vet.imbue_core.agents.llm_apis.mock_api import FileBasedLanguageModelMock
+from vet.imbue_core.agents.llm_apis.mock_api import MockModelName
+from vet.imbue_core.agents.llm_apis.openai_api import OpenAIChatAPI
+from vet.imbue_core.agents.llm_apis.openai_api import OpenAIModelName
+from vet.imbue_core.agents.llm_apis.openai_compatible_api import OpenAICompatibleAPI
+from vet.imbue_core.agents.llm_apis.together_api import TogetherAIModelName
+from vet.imbue_core.agents.llm_apis.together_api import TogetherAPI
+
+
+def build_language_model_from_config(
+ config: LanguageModelGenerationConfig,
+) -> LanguageModelAPI:
+ if isinstance(config, MockedLanguageModelGenerationConfig):
+ return FileBasedLanguageModelMock(cache_path=config.mock_responses_path)
+
+ if isinstance(config, OpenAICompatibleModelConfig):
+ return OpenAICompatibleAPI(
+ model_name=config.model_name,
+ base_url=config.custom_base_url,
+ api_key_env=config.custom_api_key_env,
+ context_window=config.custom_context_window,
+ max_output_tokens=config.custom_max_output_tokens,
+ cache_path=config.cache_path,
+ is_caching_inputs=config.is_caching_inputs,
+ is_running_offline=config.is_running_offline,
+ is_conversational=True,
+ retry_jitter_factor=config.retry_jitter_factor,
+ )
+
+ if config.model_name in (v for v in MockModelName):
+ return FileBasedLanguageModelMock(cache_path=config.cache_path)
+ if config.model_name in (v for v in OpenAIModelName):
+ return OpenAIChatAPI(
+ model_name=config.model_name,
+ cache_path=config.cache_path,
+ is_caching_inputs=config.is_caching_inputs,
+ is_running_offline=config.is_running_offline,
+ is_conversational=True,
+ is_using_logprobs=config.is_using_logprobs,
+ retry_jitter_factor=config.retry_jitter_factor,
+ )
+ if config.model_name in (v for v in GroqSupportedModelName):
+ return GroqChatAPI(
+ model_name=config.model_name,
+ cache_path=config.cache_path,
+ is_caching_inputs=config.is_caching_inputs,
+ is_running_offline=config.is_running_offline,
+ is_conversational=True,
+ is_using_logprobs=config.is_using_logprobs,
+ retry_jitter_factor=config.retry_jitter_factor,
+ )
+ if config.model_name in (v for v in AnthropicModelName):
+ return AnthropicAPI(
+ model_name=config.model_name,
+ cache_path=config.cache_path,
+ count_tokens_cache_path=config.count_tokens_cache_path,
+ is_caching_inputs=config.is_caching_inputs,
+ is_running_offline=config.is_running_offline,
+ is_conversational=True,
+ is_using_logprobs=config.is_using_logprobs,
+ retry_jitter_factor=config.retry_jitter_factor,
+ )
+ if config.model_name in (v for v in TogetherAIModelName):
+ return TogetherAPI(
+ model_name=config.model_name,
+ cache_path=config.cache_path,
+ # count tokens is not supported for Together API
+ # count_tokens_cache_path=config.count_tokens_cache_path,
+ is_caching_inputs=config.is_caching_inputs,
+ is_running_offline=config.is_running_offline,
+ is_conversational=True,
+ is_using_logprobs=config.is_using_logprobs,
+ retry_jitter_factor=config.retry_jitter_factor,
+ )
+ if config.model_name in (v for v in GeminiModelName):
+ return GeminiAPI(
+ model_name=config.model_name,
+ cache_path=config.cache_path,
+ count_tokens_cache_path=config.count_tokens_cache_path,
+ is_caching_inputs=config.is_caching_inputs,
+ is_running_offline=config.is_running_offline,
+ is_conversational=True,
+ is_using_logprobs=config.is_using_logprobs,
+ retry_jitter_factor=config.retry_jitter_factor,
+ )
+ # if config.model_name in MISTRAL_CHAT_MODEL_NAMES:
+ # return MistralChatAPI(
+ # model_name=config.model_name,
+ # cache_path=config.cache_path,
+ # is_conversational=True,
+ # )
+
+ raise NotImplementedError(f"{config.model_name} not supported by LanguageModelAPI")
+
+
+def build_language_model_by_name(
+ model_name: str,
+ cache_path: Path | None = None,
+ is_caching_inputs: bool = False,
+ is_using_logprobs: bool = False,
+) -> LanguageModelAPI:
+ config = LanguageModelGenerationConfig(
+ model_name=model_name,
+ cache_path=cache_path,
+ is_caching_inputs=is_caching_inputs,
+ is_using_logprobs=is_using_logprobs,
+ )
+ return build_language_model_from_config(config)
+
+
+def get_token_count_for_text_and_model(text: str, model_name: str) -> int:
+ try:
+ return build_language_model_by_name(model_name).count_tokens(text)
+ except NotImplementedError:
+ return approximate_token_count(text)
diff --git a/vet/imbue_core/agents/llm_apis/common.py b/vet/imbue_core/agents/llm_apis/common.py
@@ -0,0 +1,73 @@
+from vet.imbue_core.agents.llm_apis.anthropic_api import ANTHROPIC_MODEL_INFO_BY_NAME
+from vet.imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
+from vet.imbue_core.agents.llm_apis.gemini_api import GEMINI_MODEL_INFO_BY_NAME
+from vet.imbue_core.agents.llm_apis.gemini_api import GeminiModelName
+from vet.imbue_core.agents.llm_apis.groq_api import GroqSupportedModelName
+from vet.imbue_core.agents.llm_apis.groq_api import get_model_info as get_groq_model_info
+from vet.imbue_core.agents.llm_apis.mock_api import MY_MOCK_MODEL_INFO
+from vet.imbue_core.agents.llm_apis.models import ModelInfo
+from vet.imbue_core.agents.llm_apis.openai_api import OpenAIModelName
+from vet.imbue_core.agents.llm_apis.openai_api import (
+ get_model_info as get_openai_model_info,
+)
+from vet.imbue_core.agents.llm_apis.together_api import TOGETHERAI_MODEL_INFO_BY_NAME
+from vet.imbue_core.agents.llm_apis.together_api import TogetherAIModelName
+
+ModelName = AnthropicModelName | OpenAIModelName | GroqSupportedModelName | TogetherAIModelName | GeminiModelName
+
+
+def get_model_info_from_name(model_name: str) -> ModelInfo:
+ if model_name == MY_MOCK_MODEL_INFO.model_name:
+ return MY_MOCK_MODEL_INFO
+ if model_name in (v for v in AnthropicModelName):
+ return ANTHROPIC_MODEL_INFO_BY_NAME[AnthropicModelName(model_name)]
+ elif model_name in (v for v in OpenAIModelName):
+ return get_openai_model_info(OpenAIModelName(model_name))
+ elif model_name in (v for v in GroqSupportedModelName):
+ return get_groq_model_info(GroqSupportedModelName(model_name))
+ elif model_name in (v for v in TogetherAIModelName):
+ return TOGETHERAI_MODEL_INFO_BY_NAME[TogetherAIModelName(model_name)]
+ elif model_name in (v for v in GeminiModelName):
+ return GEMINI_MODEL_INFO_BY_NAME[GeminiModelName(model_name)]
+ else:
+ raise Exception(f"Unknown model: {model_name}")
+
+
+def get_model_max_context_length(model_name: str) -> int:
+ model_info = get_model_info_from_name(model_name)
+ return model_info.max_input_tokens
+
+
+def get_model_max_output_tokens(model_name: str) -> int:
+ model_info = get_model_info_from_name(model_name)
+ if model_info.max_output_tokens is None:
+ raise ValueError(f"Model {model_name} does not have max_output_tokens defined")
+ return model_info.max_output_tokens
+
+
+def get_all_model_names() -> list[str]:
+ names = []
+ names.extend(list(v for v in AnthropicModelName))
+ names.extend(list(v for v in OpenAIModelName))
+ names.extend(list(v for v in GroqSupportedModelName))
+ names.extend(list(v for v in TogetherAIModelName))
+ names.extend(list(v for v in GeminiModelName))
+ return names
+
+
+def get_formatted_model_name(model_name: str) -> str:
+ """Get a nicely formatted model name.
+
+ Does things like removing generic prefixes like 'models/' and forward slashes (which can interfere with file names).
+
+ Some examples:
+
+ - `models/gemini-1.5-flash-001` -> `gemini-1.5-flash-001`
+ - 'groq/llama-3.3-70b-versatile' -> 'groq-llama-3.3-70b-versatile'
+ - 'claude-3-5-haiku-20241022' -> 'claude-3-5-haiku-20241022'
+ - 'together/google/gemma-2-27b-it' -> 'together-google-gemma-2-27b-it'
+
+ """
+ if model_name.startswith("models/"):
+ model_name = model_name[len("models/") :]
+ return model_name.replace("/", "-")
diff --git a/imbue_core/imbue_core/agents/llm_apis/constants.py b/vet/imbue_core/agents/llm_apis/constants.py
diff --git a/vet/imbue_core/agents/llm_apis/data_types.py b/vet/imbue_core/agents/llm_apis/data_types.py
@@ -0,0 +1,214 @@
+import datetime
+import enum
+import math
+from abc import ABC
+from typing import Any
+from typing import Generic
+from typing import TypeVar
+
+import attr
+from pydantic import ValidationInfo
+from pydantic import field_validator
+
+from vet.imbue_core.agents.llm_apis.union_data_types import ProviderSpecificCachingInfoUnion
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_core.serialization_types import Serializable
+from vet.imbue_core.time_utils import get_current_time
+
+__all__ = [
+ "CachedCostedLanguageModelResponse",
+ "ConversationMessage",
+ "CostedLanguageModelResponse",
+ "LanguageModelCompleteInputs",
+ "LanguageModelResponse",
+ "LanguageModelResponseUsage",
+ "LanguageModelResponseWithLogits",
+ "LanguageModelResponseWithThoughts",
+ "LanguageModelStreamInputs",
+ "ModelStr",
+ "ResponseStopReason",
+ "TokenProbability",
+]
+
+
+class ConversationMessage(SerializableModel):
+ role: str
+ content: str
+
+
+class ResponseStopReason(enum.StrEnum):
+ END_TURN = "end_turn"
+ MAX_TOKENS = "max_tokens"
+ STOP_SEQUENCE = "stop_sequence"
+ ERROR = "error"
+ NONE = "none"
+ # TODO: We aren't handling any of the below, we should likely error in these cases
+ CONTENT_FILTER = "content_filter"
+ TOOL_CALLS = "tool_calls"
+ FUNCTION_CALL = "function_call"
+
+ def response_not_finished(self) -> bool:
+ return self in {self.CONTENT_FILTER, self.MAX_TOKENS, self.ERROR}
+
+
+class TokenProbability(SerializableModel):
+ token: str
+ log_probability: float
+ is_stop: bool
+
+ @property
+ def probability(self) -> float:
+ return math.exp(self.log_probability)
+
+
+class ModelResponse(ABC):
+ pass
+
+
+class ThoughtResponse(SerializableModel, ModelResponse):
+ text: str
+ completion_tokens: int
+
+
+@attr.s(auto_attribs=True, frozen=True)
+class LanguageModelResponse(Serializable, ModelResponse):
+ text: str
+ token_count: int
+ stop_reason: ResponseStopReason
+ network_failure_count: int
+
+ def get_token_probability_sequence(
+ self,
+ ) -> tuple[tuple[TokenProbability, ...], ...] | None:
+ return None
+
+
+@attr.s(auto_attribs=True, frozen=True)
+class LanguageModelResponseWithThoughts(LanguageModelResponse):
+ thoughts: ThoughtResponse | None = None
+
+
+@attr.s(auto_attribs=True, frozen=True)
+class LanguageModelResponseWithLogits(LanguageModelResponse):
+ # guarantees that the first in each sequence was the one that was selected.
+ # the inner sequence are *not* guaranteed to be the same length, nor are they guaranteed to be sorted
+ token_probabilities: tuple[tuple[TokenProbability, ...], ...]
+
+ def get_token_probability_sequence(
+ self,
+ ) -> tuple[tuple[TokenProbability, ...], ...] | None:
+ return self.token_probabilities
+
+
+@attr.s(auto_attribs=True, frozen=True)
+class CountTokensResponse(Serializable, ModelResponse):
+ input_tokens: int
+ cached_content_token_count: int | None = None
+
+
+class CachingInfo(SerializableModel):
+ read_from_cache: int
+
+ # this should contain info that's not the same between providers. e.g. anthropic requires explicit cache writes with 5m or 1h duration,
+ # whereas openai does automatic prompt caching at no extra cost; so, we store cache write info here
+ provider_specific_data: ProviderSpecificCachingInfoUnion | None = None
+
+
+class LanguageModelResponseUsage(SerializableModel):
+ prompt_tokens_used: int
+ completion_tokens_used: int
+ dollars_used: float
+ caching_info: CachingInfo | None = None
+
+
+@attr.s(auto_attribs=True, frozen=True)
+class CostedLanguageModelResponse(Serializable, ModelResponse):
+ usage: LanguageModelResponseUsage
+ responses: tuple[LanguageModelResponse, ...]
+
+
+class ThinkConfig(SerializableModel):
+ # watch out: at least for gemini, this is a soft limit!
+ max_tokens: int | None = None
+ output_thinking: bool = False
+
+
+class LanguageModelGenerationParams(SerializableModel):
+ """Parameters for a single API call to an LLM. Excludes things that you don't want a default for, e.g. the prompt."""
+
+ temperature: float = 0.2
+ count: int = 1
+ max_tokens: int | None = None
+ stop: str | None = None
+ # specifically to allow generating new responses even when using caching
+ seed: int | None = None
+ thinking: ThinkConfig | None = None
+
+
+class ModelInputs(SerializableModel, ABC):
+ """Base class for inputs to an LLM API call."""
+
+
+class LanguageModelCompleteInputs(ModelInputs):
+ """Used to serialize the inputs for an LLM complete call."""
+
+ prompt: str
+ params: LanguageModelGenerationParams
+ network_failure_count: int
+
+
+class LanguageModelStreamInputs(ModelInputs):
+ """Used to serialize the inputs for an LLM stream call."""
+
+ prompt: str
+ params: LanguageModelGenerationParams
+
+
+class CountTokensInputs(ModelInputs):
+ """Used to serialize the inputs for a token count call."""
+
+ model: str
+ prompt: str
+
+
+InputsT = TypeVar("InputsT", bound=ModelInputs)
+ModelResponseT = TypeVar("ModelResponseT", bound=ModelResponse)
+
+
+@attr.s(auto_attribs=True, frozen=True)
+class CachedCostedModelResponse(Serializable, Generic[InputsT, ModelResponseT]):
+ response: ModelResponseT | None = None
+ error: str | None = None
+
+ # The timestamp is used to order cache entries when checking them in unit tests.
+ timestamp: datetime.datetime = attr.ib(factory=get_current_time)
+
+ # Cache entries are keyed based on an MD5 hash of the inputs to prevent the cache from growing too large.
+ # Here, we optionally store the inputs to the request.
+ # This is useful for unit tests to highlight changes in the prompt and other parts of the request.
+ # But since it can grow very large, we don't always store this information.
+ inputs: InputsT | None = None
+
+ @field_validator("response", "error")
+ def validate_response_or_error(cls, v: Any, info: ValidationInfo) -> Any:
+ if "response" in info.data and "error" in info.data:
+ if not ((info.data["response"] is None) ^ (info.data["error"] is None)):
+ raise ValueError("Must provide exactly one of response or error")
+ return v
+
+
+class CachedCostedLanguageModelResponse(
+ CachedCostedModelResponse[
+ LanguageModelCompleteInputs | LanguageModelStreamInputs,
+ CostedLanguageModelResponse,
+ ]
+):
+ pass
+
+
+class CachedCountTokensResponse(CachedCostedModelResponse[CountTokensInputs, CountTokensResponse]):
+ pass
+
+
+# to allow type checking to work when model names are passed as strings
+ModelStr = str
diff --git a/imbue_core/imbue_core/agents/llm_apis/errors.py b/vet/imbue_core/agents/llm_apis/errors.py
diff --git a/vet/imbue_core/agents/llm_apis/gemini_api.py b/vet/imbue_core/agents/llm_apis/gemini_api.py
@@ -0,0 +1,526 @@
+import enum
+import inspect
+from contextlib import contextmanager
+from pathlib import Path
+from types import FrameType
+from typing import AsyncGenerator
+from typing import Callable
+from typing import Final
+from typing import Iterable
+from typing import Iterator
+from typing import TypeVar
+
+import google.genai as genai
+import httpx
+from google.genai.errors import APIError
+from google.genai.types import BlockedReason
+from google.genai.types import ContentListUnion
+from google.genai.types import ContentUnion
+from google.genai.types import FinishReason
+from google.genai.types import GenerateContentConfig
+from google.genai.types import GenerateContentResponse
+from google.genai.types import HarmProbability
+from google.genai.types import ModelContent
+from google.genai.types import Part
+from google.genai.types import ThinkingConfig
+from google.genai.types import UserContent
+from loguru import logger
+from pydantic.functional_validators import field_validator
+
+from vet.imbue_core.agents.llm_apis.api_utils import convert_prompt_to_messages
+from vet.imbue_core.agents.llm_apis.api_utils import (
+ create_costed_language_model_response_for_single_result,
+)
+from vet.imbue_core.agents.llm_apis.data_types import CachedCountTokensResponse
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import CountTokensInputs
+from vet.imbue_core.agents.llm_apis.data_types import CountTokensResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.agents.llm_apis.data_types import ResponseStopReason
+from vet.imbue_core.agents.llm_apis.data_types import ThoughtResponse
+from vet.imbue_core.agents.llm_apis.errors import BadAPIRequestError
+from vet.imbue_core.agents.llm_apis.errors import LanguageModelInvalidModelNameError
+from vet.imbue_core.agents.llm_apis.errors import MissingAPIKeyError
+from vet.imbue_core.agents.llm_apis.errors import TransientLanguageModelError
+from vet.imbue_core.agents.llm_apis.errors import UnsetCachePathError
+from vet.imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
+from vet.imbue_core.agents.llm_apis.models import ModelInfo
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
+from vet.imbue_core.async_monkey_patches import log_exception
+from vet.imbue_core.caching import AsyncCache
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.frozen_utils import FrozenMapping
+from vet.imbue_core.itertools import only
+from vet.imbue_core.secrets_utils import get_secret
+
+
+class GeminiModelName(enum.StrEnum):
+ GEMINI_1_0_PRO = "models/gemini-1.0-pro-001"
+ GEMINI_1_5_FLASH = "models/gemini-1.5-flash-001"
+ GEMINI_1_5_PRO = "models/gemini-1.5-pro-001"
+ GEMINI_1_5_PRO_2 = "models/gemini-1.5-pro-002"
+ GEMINI_1_5_FLASH_2 = "models/gemini-1.5-flash-002"
+ GEMINI_2_0_FLASH = "models/gemini-2.0-flash-001"
+ GEMINI_2_5_FLASH = "models/gemini-2.5-flash"
+ GEMINI_2_5_FLASH_LITE_PREVIEW = "models/gemini-2.5-flash-lite-preview-06-17"
+
+
+# Rate limits for Google Gemini models based on published API documentation
+# Reference: https://ai.google.dev/gemini-api/docs/rate-limits#tier-3
+# Using Tier 3 rate limits
+
+GEMINI_MODEL_INFO_BY_NAME: FrozenMapping[GeminiModelName, ModelInfo] = FrozenDict(
+ {
+ # https://ai.google.dev/gemini-api/docs/models/gemini
+ # https://ai.google.dev/pricing
+ # For pricing there are different rates depending on context/prompt size, so below we use the most
+ # expensive value. Note that this only kicks in at 128k tokens, the cost for most prompts is 2x lower
+ GeminiModelName.GEMINI_1_0_PRO: ModelInfo(
+ model_name="models/gemini-1.0-pro-001",
+ cost_per_input_token=0.5 / 1_000_000,
+ cost_per_output_token=1.5 / 1_000_000,
+ max_input_tokens=30_720,
+ max_output_tokens=2048,
+ rate_limit_req=2000 / 60, # 2000 RPM = 33.33 RPS
+ ),
+ GeminiModelName.GEMINI_1_5_FLASH: ModelInfo(
+ model_name="models/gemini-1.5-flash-001",
+ cost_per_input_token=0.15 / 1_000_000,
+ cost_per_output_token=0.60 / 1_000_000,
+ max_input_tokens=1_048_576,
+ max_output_tokens=8192,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500.00 RPS
+ ),
+ GeminiModelName.GEMINI_1_5_FLASH_2: ModelInfo(
+ model_name="models/gemini-1.5-flash-002",
+ cost_per_input_token=0.15 / 1_000_000,
+ cost_per_output_token=0.60 / 1_000_000,
+ max_input_tokens=1_048_576,
+ max_output_tokens=8192,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500.00 RPS
+ ),
+ GeminiModelName.GEMINI_1_5_PRO: ModelInfo(
+ model_name="models/gemini-1.5-pro-001",
+ cost_per_input_token=2.5 / 1_000_000,
+ cost_per_output_token=10.0 / 1_000_000,
+ max_input_tokens=2_097_152,
+ max_output_tokens=8192,
+ rate_limit_req=4000 / 60, # 4000 RPM = 66.67 RPS
+ ),
+ GeminiModelName.GEMINI_1_5_PRO_2: ModelInfo(
+ model_name="models/gemini-1.5-pro-002",
+ cost_per_input_token=2.5 / 1_000_000,
+ cost_per_output_token=10.0 / 1_000_000,
+ max_input_tokens=2_097_152,
+ max_output_tokens=8192,
+ rate_limit_req=4000 / 60, # 4000 RPM = 66.67 RPS
+ ),
+ GeminiModelName.GEMINI_2_0_FLASH: ModelInfo(
+ model_name="models/gemini-2.0-flash-001",
+ cost_per_input_token=0.1 / 1_000_000,
+ cost_per_output_token=0.4 / 1_000_000,
+ max_input_tokens=1_048_576,
+ max_output_tokens=8192,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500.00 RPS
+ ),
+ GeminiModelName.GEMINI_2_5_FLASH: ModelInfo(
+ model_name="models/gemini-2.5-flash",
+ cost_per_input_token=0.3 / 1_000_000,
+ cost_per_output_token=2.5 / 1_000_000,
+ max_input_tokens=1_048_576,
+ max_output_tokens=65536,
+ rate_limit_req=10_000 / 60, # 10000 RPM = 166.67 RPS
+ rate_limit_tok=8_000_000 / 60, # 8,000,000 TPM = 133,333.33 TPS
+ max_thinking_budget=24576,
+ ),
+ GeminiModelName.GEMINI_2_5_FLASH_LITE_PREVIEW: ModelInfo(
+ model_name="models/gemini-2.5-flash-lite-preview-06-17",
+ cost_per_input_token=0.1 / 1_000_000,
+ cost_per_output_token=0.4 / 1_000_000,
+ max_input_tokens=1_000_000,
+ max_output_tokens=64_000,
+ # these are the tier 2 rate limits. the above claims that we're on tier 3, but i've never actually seen that
+ rate_limit_req=10_000 / 60,
+ rate_limit_tok=10_000_000 / 60,
+ # rate_limit_req=30_000 / 60, # 30000 RPM = 500.00 RPS
+ # rate_limit_tok=30_000_000 / 60, # 30,000,000 TPM = 500,000 TPS
+ max_thinking_budget=24_576,
+ ),
+ }
+)
+
+
+_ROLE_TO_GEMINI_ROLE: Final[FrozenMapping[str, str]] = FrozenDict(
+ {
+ "HUMAN": "user",
+ "ASSISTANT": "model",
+ "USER": "user",
+ "SYSTEM": "user",
+ }
+)
+
+NO_SIMPLE_TEXT_ERROR = "".join(
+ [
+ "The `response.text` quick accessor only works for ",
+ "simple (single-`Part`) text responses. This response is not simple text.",
+ "Use the `result.parts` accessor or the full ",
+ "`result.candidates[index].content.parts` lookup ",
+ "instead.",
+ ]
+)
+
+_GEMINI_STOP_REASON_TO_STOP_REASON: Final[FrozenMapping[FinishReason, ResponseStopReason]] = FrozenDict(
+ {
+ # Gemini treats stop due to natural stop point and provided stop sequence the same
+ FinishReason.STOP: ResponseStopReason.END_TURN,
+ FinishReason.MAX_TOKENS: ResponseStopReason.MAX_TOKENS,
+ FinishReason.SAFETY: ResponseStopReason.CONTENT_FILTER,
+ # Recitation means the content was flagged for being memorized, i.e. the LLM just
+ # copied data from the training data (@johnny at least that's how I understood the docs)
+ # https://ai.google.dev/api/generate-content#FinishReason
+ FinishReason.RECITATION: ResponseStopReason.CONTENT_FILTER,
+ FinishReason.OTHER: ResponseStopReason.NONE,
+ FinishReason.FINISH_REASON_UNSPECIFIED: ResponseStopReason.NONE,
+ }
+)
+
+T = TypeVar("T")
+
+
+def only_and_not_none(iterable: Iterable[T] | None) -> T:
+ in_value = iterable if iterable is not None else []
+ return only(in_value)
+
+
+def _is_flagged_as_unsafe(api_result: GenerateContentResponse) -> bool:
+ if api_result.prompt_feedback is None:
+ return False
+ block_reason = api_result.prompt_feedback.block_reason
+ if block_reason == BlockedReason.SAFETY:
+ return True
+ candidate = only_and_not_none(api_result.candidates)
+ if candidate.finish_reason == FinishReason.SAFETY:
+ return True
+ if candidate.finish_reason == FinishReason.OTHER and any(
+ rating.probability != HarmProbability.NEGLIGIBLE for rating in (candidate.safety_ratings or [])
+ ):
+ return True
+ return False
+
+
+def _is_flagged_as_recitation(api_result: GenerateContentResponse) -> bool:
+ candidate = only_and_not_none(api_result.candidates)
+ finish_reason = candidate.finish_reason
+ if finish_reason == FinishReason.RECITATION:
+ return True
+ return False
+
+
+def role_to_content(role: str, parts: list[Part]) -> ContentUnion:
+ match role:
+ case "user":
+ return UserContent(parts=parts)
+ case "model":
+ return ModelContent(parts=parts)
+ case _:
+ raise BadAPIRequestError(f"Invalid role: {role}")
+
+
+def convert_prompt_to_gemini_messages(prompt: str) -> ContentListUnion:
+ messages: list[ContentUnion] = []
+ parts = []
+ last_role = None
+ for message in convert_prompt_to_messages(prompt):
+ role = _ROLE_TO_GEMINI_ROLE[message.role]
+ parts.append(Part(text=f"\n{message.content}"))
+ if last_role != role and last_role is not None:
+ messages.append(role_to_content(last_role, parts))
+ parts = []
+ last_role = role
+ if len(parts) > 0:
+ assert last_role is not None
+ messages.append(role_to_content(last_role, parts))
+ return messages
+
+
+@contextmanager
+def _gemini_exception_manager() -> Iterator[None]:
+ """Simple context manager for parsing gemini API exceptions."""
+ # TODO probably some exceptions missing here. The google.ai docs/code is annoying to parse
+ try:
+ yield
+ except AssertionError as e:
+ logger.info("The Gemini prompt is invalid.")
+ raise BadAPIRequestError(str(e)) from e
+ except APIError as e:
+ logger.info("Gemini failed to generate content.")
+ raise BadAPIRequestError(str(e)) from e
+ except ValueError as e:
+ logger.info("Gemini did not return a simple text response.")
+ raise BadAPIRequestError(str(e)) from e
+ except AttributeError as e:
+ logger.info("There is an error with the Gemini prompt or processing code: {}.", str(e))
+ raise BadAPIRequestError(str(e)) from e
+ except httpx.RemoteProtocolError as e:
+ logger.info(str(e))
+ raise TransientLanguageModelError("httpx.RemoteProtocolError") from e
+ except (BadAPIRequestError, TransientLanguageModelError, MissingAPIKeyError):
+ # we already raised this error ourselves earlier, so we don't need to mark it as unknown
+ raise
+ except Exception as e:
+ # we catch TransientLanguageModelError later to retry it, but we still want to log it so it's not silent
+ log_exception(
+ e,
+ "Failed to generate output from Gemini, unknown error of type {type_name}",
+ type_name=type(e).__name__,
+ )
+ raise TransientLanguageModelError("Unknown error") from e
+
+
+R = TypeVar("R")
+
+
+def fmap(fn: Callable[[T], R], values: T | None) -> R | None:
+ if values is None:
+ return None
+ return fn(values)
+
+
+class GeminiAPI(LanguageModelAPI):
+ model_name: GeminiModelName = GeminiModelName.GEMINI_1_5_FLASH
+ is_conversational: bool = True
+
+ count_tokens_cache_path: Path | None = None
+
+ @field_validator("model_name") # pyre-ignore[56]: pyre doesn't understand pydantic
+ @classmethod
+ def validate_model_name(cls, v: str) -> str:
+ if v not in GEMINI_MODEL_INFO_BY_NAME:
+ raise LanguageModelInvalidModelNameError(v, cls.__name__, list(GEMINI_MODEL_INFO_BY_NAME))
+ return v
+
+ @property
+ def model_info(self) -> ModelInfo:
+ return GEMINI_MODEL_INFO_BY_NAME[self.model_name]
+
+ def _get_client(self) -> genai.Client:
+ api_key = get_secret("GOOGLE_API_KEY")
+ if not api_key:
+ raise MissingAPIKeyError("GOOGLE_API_KEY environment variable is not set")
+ return genai.Client(api_key=api_key)
+
+ async def _call_api(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ network_failure_count: int = 0,
+ ) -> CostedLanguageModelResponse:
+ # TODO: check if this is still true
+ assert params.count == 1, "Gemini only supports a single completion"
+ messages = convert_prompt_to_gemini_messages(prompt)
+ with _gemini_exception_manager():
+ client = self._get_client()
+ generation_config = GenerateContentConfig(
+ temperature=params.temperature,
+ candidate_count=params.count,
+ stop_sequences=fmap(lambda x: [x], params.stop),
+ max_output_tokens=params.max_tokens,
+ thinking_config=fmap(
+ lambda thinking: ThinkingConfig(
+ thinking_budget=thinking.max_tokens,
+ include_thoughts=thinking.output_thinking,
+ ),
+ params.thinking,
+ ),
+ )
+
+ api_result: GenerateContentResponse = await client.aio.models.generate_content(
+ model=self.model_info.model_name,
+ contents=messages,
+ config=generation_config,
+ )
+
+ prompt_tokens = self.count_tokens(prompt)
+
+ if (
+ api_result.prompt_feedback is not None
+ and api_result.prompt_feedback.block_reason is not None
+ and api_result.prompt_feedback.block_reason != BlockedReason.BLOCKED_REASON_UNSPECIFIED
+ ):
+ logger.info(
+ f"Gemini blocked output: {messages=}, {api_result.prompt_feedback.block_reason=}, {api_result.prompt_feedback.safety_ratings=}"
+ )
+ return create_costed_language_model_response_for_single_result(
+ text="",
+ prompt_tokens=prompt_tokens,
+ completion_tokens=0,
+ stop_reason=ResponseStopReason.NONE,
+ network_failure_count=network_failure_count,
+ dollars_used=self.calculate_cost(prompt_tokens, 0), # guestimate of cost,
+ )
+
+ if _is_flagged_as_unsafe(api_result) or _is_flagged_as_recitation(api_result):
+ block_reason = fmap(lambda x: x.block_reason, api_result.prompt_feedback)
+ safety_ratings = (
+ api_result.prompt_feedback.safety_ratings if api_result.prompt_feedback is not None else None
+ )
+ logger.info(
+ "Gemini flagged output: block_reason={block_reason}, safety_ratings={safety_ratings}",
+ block_reason=block_reason,
+ safety_ratings=safety_ratings,
+ )
+ return create_costed_language_model_response_for_single_result(
+ text="",
+ prompt_tokens=prompt_tokens,
+ completion_tokens=0,
+ stop_reason=ResponseStopReason.CONTENT_FILTER,
+ network_failure_count=network_failure_count,
+ dollars_used=self.calculate_cost(prompt_tokens, 0),
+ )
+
+ candidate = only_and_not_none(api_result.candidates)
+ finish_reason = candidate.finish_reason
+ parsed_finish_reason = (
+ _GEMINI_STOP_REASON_TO_STOP_REASON[finish_reason]
+ if finish_reason is not None
+ else ResponseStopReason.NONE
+ )
+
+ if finish_reason not in [FinishReason.MAX_TOKENS, FinishReason.STOP]:
+ block_reason = fmap(lambda x: x.block_reason, api_result.prompt_feedback)
+ safety_ratings = fmap(lambda x: x.safety_ratings, api_result.prompt_feedback)
+ logger.info(
+ f"Gemini did not return a simple text response, {block_reason=}, {safety_ratings=}, {finish_reason=}, {candidate.safety_ratings=}"
+ )
+ return create_costed_language_model_response_for_single_result(
+ text="",
+ prompt_tokens=prompt_tokens,
+ completion_tokens=0,
+ stop_reason=parsed_finish_reason,
+ network_failure_count=network_failure_count,
+ dollars_used=self.calculate_cost(prompt_tokens, 0),
+ )
+
+ text = api_result.text
+
+ thoughts_list = fmap(
+ lambda content: fmap(
+ lambda parts: [part.text for part in parts if part.thought],
+ content.parts,
+ ),
+ candidate.content,
+ )
+ if not thoughts_list:
+ thoughts = None
+ else:
+ thoughts = only(thoughts_list)
+
+ if text is None:
+ if finish_reason == FinishReason.MAX_TOKENS and generation_config.thinking_config is not None:
+ raise BadAPIRequestError(
+ "Gemini ran out of tokens while thinking and did not return a text response"
+ )
+ logger.info("Non-simple-text response: {}", api_result)
+ raise BadAPIRequestError("Gemini did not return a simple text response (text is None)")
+
+ prompt_tokens = (
+ api_result.usage_metadata.prompt_token_count
+ if api_result.usage_metadata is not None and api_result.usage_metadata.prompt_token_count is not None
+ else self.count_tokens(prompt)
+ )
+
+ thought_tokens = (
+ api_result.usage_metadata.thoughts_token_count
+ if api_result.usage_metadata is not None and api_result.usage_metadata.thoughts_token_count is not None
+ else 0
+ )
+
+ output_tokens = (
+ api_result.usage_metadata.candidates_token_count
+ if api_result.usage_metadata is not None
+ and api_result.usage_metadata.candidates_token_count is not None
+ else self.count_tokens(text)
+ )
+
+ completion_tokens = output_tokens + thought_tokens
+
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
+ logger.trace(text)
+ logger.trace("Dollars used: {}", dollars_used)
+ return create_costed_language_model_response_for_single_result(
+ text=text,
+ prompt_tokens=prompt_tokens,
+ completion_tokens=completion_tokens,
+ stop_reason=parsed_finish_reason,
+ network_failure_count=network_failure_count,
+ dollars_used=dollars_used,
+ thoughts=fmap(
+ lambda x: ThoughtResponse(text=x, completion_tokens=thought_tokens),
+ thoughts,
+ ),
+ )
+
+ def _get_api_stream(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ # TODO Implement streaming support (?)
+ raise NotImplementedError()
+
+ # TODO: these are the same as in anthropic_api.py. it might be good to refactor so that both AnthropicAPI and GeminiAPI inherit from a class LanguageModelAPIWithCountTokens which has these methods
+ def get_count_tokens_response_cache(self) -> AsyncCache[CachedCountTokensResponse]:
+ if self.count_tokens_cache_path is None:
+ raise UnsetCachePathError()
+ return AsyncCache(self.count_tokens_cache_path, CachedCountTokensResponse)
+
+ async def check_count_tokens_cache(self, cache_key: str) -> CountTokensResponse | None:
+ return await self.check_cache_core(self.get_count_tokens_response_cache, cache_key)
+
+ async def _get_from_count_tokens_cache(
+ self, frame: FrameType | None
+ ) -> tuple[str | None, CountTokensResponse | None]:
+ return await self._get_from_cache_core(frame, lambda cr: cr, self.check_count_tokens_cache)
+
+ async def count_tokens_api(self, text: str, is_caching_enabled: bool) -> int | None:
+ """Call the count_tokens api to get a definitive token count. May be fragile and is definitely slow."""
+
+ self.assert_caching_enabled_if_offline(is_caching_enabled)
+
+ frame: FrameType | None = None
+ if is_caching_enabled:
+ frame = inspect.currentframe()
+
+ cache_key: str | None = None
+ if is_caching_enabled:
+ cache_key, cached_response = await self._get_from_count_tokens_cache(frame)
+
+ if cached_response is not None:
+ return cached_response.input_tokens
+
+ self.assert_not_offline_if_cache_miss(text)
+
+ with _gemini_exception_manager():
+ client = self._get_client()
+ response = client.models.count_tokens(model=self.model_info.model_name, contents=text)
+
+ total_tokens = response.total_tokens
+ if total_tokens is None:
+ raise TransientLanguageModelError("Gemini did not return a valid token count")
+
+ result = CachedCountTokensResponse(
+ response=CountTokensResponse(
+ input_tokens=total_tokens,
+ cached_content_token_count=response.cached_content_token_count,
+ ),
+ inputs=(
+ CountTokensInputs(model=self.model_info.model_name, prompt=text) if self.is_caching_inputs else None
+ ),
+ )
+
+ if is_caching_enabled:
+ assert cache_key is not None
+ async with self.get_count_tokens_response_cache() as cache:
+ await cache.set(cache_key, result)
+
+ return total_tokens
diff --git a/vet/imbue_core/agents/llm_apis/groq_api.py b/vet/imbue_core/agents/llm_apis/groq_api.py
@@ -0,0 +1,357 @@
+import asyncio
+import enum
+import math
+from contextlib import contextmanager
+from typing import AsyncGenerator
+from typing import Final
+from typing import Iterator
+from typing import Mapping
+
+import httpx
+from groq import APIConnectionError
+from groq import APIError
+from groq import AsyncGroq
+from groq import AsyncStream
+from groq import BadRequestError
+from groq import RateLimitError
+from groq.types.chat import ChatCompletion
+from loguru import logger
+from pydantic.functional_validators import field_validator
+
+from vet.imbue_core.agents.llm_apis.api_utils import convert_prompt_to_openai_messages
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
+from vet.imbue_core.agents.llm_apis.data_types import ResponseStopReason
+from vet.imbue_core.agents.llm_apis.errors import BadAPIRequestError
+from vet.imbue_core.agents.llm_apis.errors import LanguageModelInvalidModelNameError
+from vet.imbue_core.agents.llm_apis.errors import MissingAPIKeyError
+from vet.imbue_core.agents.llm_apis.errors import PromptTooLongError
+from vet.imbue_core.agents.llm_apis.errors import TransientLanguageModelError
+from vet.imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
+from vet.imbue_core.agents.llm_apis.models import ModelInfo
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamStartEvent
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.frozen_utils import FrozenMapping
+from vet.imbue_core.itertools import only
+from vet.imbue_core.secrets_utils import get_secret
+
+# note: we require that these model versions are explicit, just like the rest of our dependencies
+# the reason is that these models are actually now mostly deterministic, and it is much easier to debug if we know what model was used
+# also, there's no need to troll yourself by wondering why results have improved (or gotten worse) when you dont realized that the version has shifted under you
+# if you want to use an upgraded model, just upgrade the model to the key displayed on the website
+# please do NOT set these back to the generic model names!
+
+
+# TODO: there are likely more models to add
+class GroqSupportedModelName(enum.StrEnum):
+ GROQ_GEMMA2_9B_IT = "groq/gemma2-9b-it"
+ GROQ_LLAMA3_70B_8192 = "groq/llama3-70b-8192"
+ GROQ_LLAMA3_8B_8192 = "groq/llama3-8b-8192"
+ GROQ_LLAMA_3_3_70B_SPECDEC = "groq/llama-3.3-70b-specdec"
+ GROQ_MIXTRAL_8X7B_32768 = "groq/mixtral-8x7b-32768"
+ GROQ_LLAMA_3_3_70B_VERSATILE = "groq/llama-3.3-70b-versatile"
+ GROQ_LLAMA_3_1_8B_INSTANT = "groq/llama-3.1-8b-instant"
+ GROQ_LLAMA_3_2_1B_PREVIEW = "groq/llama-3.2-1b-preview"
+ GROQ_LLAMA_3_2_3B_PREVIEW = "groq/llama-3.2-3b-preview"
+
+
+# Rate limits for Groq models based on custom rate limits for our organization.
+# See here https://console.groq.com/dashboard/limits (requires login, use your Google account)
+
+GROQ_MODEL_INFO_BY_NAME: FrozenMapping[GroqSupportedModelName, ModelInfo] = FrozenDict(
+ {
+ GroqSupportedModelName.GROQ_GEMMA2_9B_IT: ModelInfo(
+ model_name=str(GroqSupportedModelName.GROQ_GEMMA2_9B_IT),
+ cost_per_input_token=0.20 / 1_000_000,
+ cost_per_output_token=0.20 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
+ ),
+ GroqSupportedModelName.GROQ_LLAMA3_70B_8192: ModelInfo(
+ model_name=str(GroqSupportedModelName.GROQ_LLAMA3_70B_8192),
+ cost_per_input_token=0.59 / 1_000_000,
+ cost_per_output_token=0.79 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
+ ),
+ GroqSupportedModelName.GROQ_LLAMA3_8B_8192: ModelInfo(
+ model_name=str(GroqSupportedModelName.GROQ_LLAMA3_8B_8192),
+ cost_per_input_token=0.05 / 1_000_000,
+ cost_per_output_token=0.08 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
+ ),
+ GroqSupportedModelName.GROQ_LLAMA_3_3_70B_SPECDEC: ModelInfo(
+ model_name=str(GroqSupportedModelName.GROQ_LLAMA_3_3_70B_SPECDEC),
+ cost_per_input_token=0.59 / 1_000_000,
+ cost_per_output_token=0.99 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
+ ),
+ GroqSupportedModelName.GROQ_MIXTRAL_8X7B_32768: ModelInfo(
+ model_name=str(GroqSupportedModelName.GROQ_MIXTRAL_8X7B_32768),
+ cost_per_input_token=0.24 / 1_000_000,
+ cost_per_output_token=0.24 / 1_000_000,
+ max_input_tokens=32768,
+ max_output_tokens=None,
+ rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
+ ),
+ GroqSupportedModelName.GROQ_LLAMA_3_3_70B_VERSATILE: ModelInfo(
+ model_name=str(GroqSupportedModelName.GROQ_LLAMA_3_3_70B_VERSATILE),
+ cost_per_input_token=0.59 / 1_000_000,
+ cost_per_output_token=0.79 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=None,
+ rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
+ ),
+ GroqSupportedModelName.GROQ_LLAMA_3_1_8B_INSTANT: ModelInfo(
+ model_name=str(GroqSupportedModelName.GROQ_LLAMA_3_1_8B_INSTANT),
+ cost_per_input_token=0.05 / 1_000_000,
+ cost_per_output_token=0.08 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=None,
+ rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
+ ),
+ GroqSupportedModelName.GROQ_LLAMA_3_2_1B_PREVIEW: ModelInfo(
+ model_name=str(GroqSupportedModelName.GROQ_LLAMA_3_2_1B_PREVIEW),
+ cost_per_input_token=0.04 / 1_000_000,
+ cost_per_output_token=0.04 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=None,
+ rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
+ ),
+ GroqSupportedModelName.GROQ_LLAMA_3_2_3B_PREVIEW: ModelInfo(
+ model_name=str(GroqSupportedModelName.GROQ_LLAMA_3_2_3B_PREVIEW),
+ cost_per_input_token=0.06 / 1_000_000,
+ cost_per_output_token=0.06 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=None,
+ rate_limit_req=30 / 60, # 30 RPM = 0.50 RPS
+ ),
+ }
+)
+
+
+def get_model_info(model_name: GroqSupportedModelName) -> ModelInfo:
+ return GROQ_MODEL_INFO_BY_NAME[model_name]
+
+
+_CAPACITY_SEMAPHOR_BY_MODEL_NAME: Mapping[str, asyncio.Semaphore] = {
+ GroqSupportedModelName.GROQ_GEMMA2_9B_IT: asyncio.Semaphore(100),
+ GroqSupportedModelName.GROQ_LLAMA3_70B_8192: asyncio.Semaphore(100),
+ GroqSupportedModelName.GROQ_LLAMA3_8B_8192: asyncio.Semaphore(100),
+ GroqSupportedModelName.GROQ_LLAMA_3_3_70B_SPECDEC: asyncio.Semaphore(100),
+ GroqSupportedModelName.GROQ_MIXTRAL_8X7B_32768: asyncio.Semaphore(100),
+ GroqSupportedModelName.GROQ_LLAMA_3_3_70B_VERSATILE: asyncio.Semaphore(100),
+ GroqSupportedModelName.GROQ_LLAMA_3_1_8B_INSTANT: asyncio.Semaphore(100),
+ GroqSupportedModelName.GROQ_LLAMA_3_2_1B_PREVIEW: asyncio.Semaphore(100),
+ GroqSupportedModelName.GROQ_LLAMA_3_2_3B_PREVIEW: asyncio.Semaphore(100),
+}
+
+
+def _get_capacity_semaphor(model_name: str) -> asyncio.Semaphore:
+ return _CAPACITY_SEMAPHOR_BY_MODEL_NAME[model_name]
+
+
+# ref: https://github.com/groq/groq-python/blob/b74ce9e301115520c744e18425653a4c783cb6f5/src/groq/types/chat/chat_completion_chunk.py#L86
+_GROQ_STOP_REASON_TO_STOP_REASON: Final[FrozenMapping[str, ResponseStopReason]] = FrozenDict(
+ {
+ # Groq copies OpenAI and treats stop due to natural stop point and provided stop sequence the same
+ "stop": ResponseStopReason.END_TURN,
+ "length": ResponseStopReason.MAX_TOKENS,
+ "tool_calls": ResponseStopReason.TOOL_CALLS,
+ "function_call": ResponseStopReason.FUNCTION_CALL,
+ "content_filter": ResponseStopReason.CONTENT_FILTER,
+ }
+)
+
+
+@contextmanager
+def _groq_exception_manager() -> Iterator[None]:
+ """Simple context manager for parsing groq exceptions mostly based on how we parse OpenAI API exceptions."""
+ try:
+ yield
+ except BadRequestError as e:
+ logger.info("BadAPIRequestError {}", e)
+ raise BadAPIRequestError(str(e)) from e
+ except APIConnectionError as e:
+ logger.info("Rate limited? Received APIConnectionError {}", e)
+ raise TransientLanguageModelError("APIConnectionError") from e
+ except RateLimitError as e:
+ logger.info("Rate limited? {}", e)
+ raise TransientLanguageModelError("RateLimitError") from e
+ except httpx.RemoteProtocolError as e:
+ logger.info("{}", e)
+ raise TransientLanguageModelError("httpx.RemoteProtocolError") from e
+ except APIError as e:
+ if e.body["code"] == "context_length_exceeded": # type: ignore
+ # TODO: eventually fix elsewhere, since this doesn't actually give you any information in the body...
+ raise PromptTooLongError(prompt_len=1, max_prompt_len=1)
+ raise TransientLanguageModelError("APIError") from e
+
+
+class GroqChatAPI(LanguageModelAPI):
+ model_name: GroqSupportedModelName = GroqSupportedModelName.GROQ_LLAMA3_8B_8192
+ is_conversational: bool = True
+ presence_penalty: float = 0.0
+ # this shouldn't really ever even be used, but just in case
+ stop_token_log_probability: float = math.log(0.9999)
+
+ @field_validator("model_name") # pyre-ignore[56]: pyre doesn't understand pydantic
+ @classmethod
+ def validate_model_name(cls, v: str) -> str:
+ if v not in GROQ_MODEL_INFO_BY_NAME:
+ raise LanguageModelInvalidModelNameError(v, cls.__name__, list(GROQ_MODEL_INFO_BY_NAME))
+ return v
+
+ @property
+ def model_info(self) -> ModelInfo:
+ return GROQ_MODEL_INFO_BY_NAME[self.model_name]
+
+ @property
+ def external_model_name(self) -> str:
+ return self.model_name.replace("groq/", "")
+
+ def _get_client(self) -> AsyncGroq:
+ api_key = get_secret("GROQ_API_KEY")
+ if not api_key:
+ raise MissingAPIKeyError("GROQ_API_KEY environment variable is not set")
+ return AsyncGroq(api_key=api_key)
+
+ async def _call_api(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ network_failure_count: int = 0,
+ ) -> CostedLanguageModelResponse:
+ with _groq_exception_manager():
+ messages = convert_prompt_to_openai_messages(prompt)
+ client = self._get_client()
+ async with _get_capacity_semaphor(self.model_name):
+ # logger.info("Open requests: {}", semaphor._value)
+ api_result = await client.chat.completions.create(
+ model=self.external_model_name,
+ messages=messages, # type: ignore
+ max_tokens=params.max_tokens,
+ n=params.count,
+ temperature=params.temperature,
+ stop=params.stop,
+ logprobs=False,
+ seed=params.seed,
+ stream=False,
+ presence_penalty=self.presence_penalty,
+ )
+ assert isinstance(api_result, ChatCompletion)
+
+ results = []
+ for data in api_result.choices:
+ assert data.message.content is not None
+
+ assert data.logprobs is not None and data.logprobs.content is not None
+ text = data.message.content
+
+ stop_reason = _GROQ_STOP_REASON_TO_STOP_REASON[str(data.finish_reason)]
+
+ # Note, like OpenAI, Groq treats end turn and stop sequence the same
+ # Here we assume it is stop sequence if user has specified a stop sequence
+ if params.stop is not None and stop_reason == ResponseStopReason.END_TURN:
+ text += params.stop
+ result = LanguageModelResponse(
+ text=text,
+ token_count=0,
+ stop_reason=stop_reason,
+ network_failure_count=network_failure_count,
+ )
+ results.append(result)
+
+ logger.trace("text: " + results[0].text)
+ if api_result.usage is not None:
+ completion_tokens = api_result.usage.completion_tokens
+ prompt_tokens = api_result.usage.prompt_tokens
+ else:
+ completion_tokens = 0
+ prompt_tokens = 0
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
+ logger.trace("dollars used: {}", dollars_used)
+ return CostedLanguageModelResponse(
+ usage=LanguageModelResponseUsage(
+ prompt_tokens_used=prompt_tokens,
+ completion_tokens_used=completion_tokens,
+ dollars_used=dollars_used,
+ ),
+ responses=tuple(results),
+ )
+
+ async def _get_api_stream(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ with _groq_exception_manager():
+ messages = convert_prompt_to_openai_messages(prompt)
+ client = self._get_client()
+ async with _get_capacity_semaphor(self.model_name):
+ api_result = await client.chat.completions.create(
+ model=self.external_model_name,
+ messages=messages, # type: ignore
+ max_tokens=params.max_tokens,
+ n=1,
+ temperature=params.temperature,
+ stop=params.stop,
+ logprobs=False,
+ seed=params.seed,
+ stream=True,
+ # This field is currently unsupported by the groq API
+ # stream_options={"include_usage": True},
+ presence_penalty=self.presence_penalty,
+ )
+ assert isinstance(api_result, AsyncStream)
+ logger.info("API response status code: {}", api_result.response.status_code)
+
+ yield LanguageModelStreamStartEvent()
+
+ usage = None
+ finish_reason: str | None = None
+ async for chunk in api_result:
+ if chunk.choices:
+ assert len(chunk.choices) == 1, "Currently only count=1 supported for streaming API."
+ data = only(chunk.choices)
+ delta = data.delta.content
+ if delta is not None:
+ yield LanguageModelStreamDeltaEvent(delta=delta)
+ if data.finish_reason:
+ finish_reason = str(data.finish_reason)
+
+ stop_reason = _GROQ_STOP_REASON_TO_STOP_REASON[str(finish_reason)]
+ # Note, Open API treats end turn and stop sequence the same TODO: check if groq is the same
+ # Here we assume it is stop sequence if user has specified a stop sequence
+ if params.stop is not None and stop_reason == ResponseStopReason.END_TURN:
+ yield LanguageModelStreamDeltaEvent(delta=params.stop)
+
+ if usage is not None:
+ completion_tokens = usage.completion_tokens
+ prompt_tokens = usage.prompt_tokens
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
+ else:
+ completion_tokens = -1
+ prompt_tokens = -1
+ dollars_used = -1
+ logger.trace("dollars used: {}", dollars_used)
+
+ yield LanguageModelStreamEndEvent(
+ usage=LanguageModelResponseUsage(
+ prompt_tokens_used=prompt_tokens,
+ completion_tokens_used=completion_tokens,
+ dollars_used=dollars_used,
+ ),
+ stop_reason=stop_reason,
+ )
diff --git a/vet/imbue_core/agents/llm_apis/language_model_api.py b/vet/imbue_core/agents/llm_apis/language_model_api.py
@@ -0,0 +1,547 @@
+import abc
+import asyncio
+import contextvars
+import hashlib
+import inspect
+import os
+import random
+from pathlib import Path
+from types import FrameType
+from typing import AsyncGenerator
+from typing import Awaitable
+from typing import Callable
+from typing import TypeVar
+from typing import final
+from uuid import UUID
+from uuid import uuid4
+
+import anyio
+from loguru import logger
+
+from vet.imbue_core.agents.llm_apis.constants import approximate_token_count
+from vet.imbue_core.agents.llm_apis.data_types import CachedCostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import CachedCostedModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import CachingInfo
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import CountTokensResponse
+from vet.imbue_core.agents.llm_apis.data_types import InputsT
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelCompleteInputs
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelStreamInputs
+from vet.imbue_core.agents.llm_apis.data_types import ModelResponseT
+from vet.imbue_core.agents.llm_apis.errors import LanguageModelRetryLimitError
+from vet.imbue_core.agents.llm_apis.errors import PromptTooLongError
+from vet.imbue_core.agents.llm_apis.errors import TransientLanguageModelError
+from vet.imbue_core.agents.llm_apis.errors import UnsetCachePathError
+from vet.imbue_core.agents.llm_apis.models import ModelInfo
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamCallback
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
+from vet.imbue_core.agents.llm_apis.stream import PromptDebuggingCallback
+from vet.imbue_core.agents.llm_apis.stream import SettleSpendCallback
+from vet.imbue_core.agents.llm_apis.stream import StreamedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.stream import UpdateCacheCallback
+from vet.imbue_core.agents.llm_apis.stream import get_cached_response_stream
+from vet.imbue_core.agents.primitives.resource_limits import PaymentAuthorization
+from vet.imbue_core.agents.primitives.resource_limits import get_global_resource_limits
+from vet.imbue_core.async_utils import sync
+from vet.imbue_core.caching import AsyncCache
+from vet.imbue_core.cattrs_serialization import serialize_to_json
+from vet.imbue_core.pydantic_serialization import MutableModel
+
+# Context variable to disable caching.
+IS_LLM_CACHING_DISABLED_GLOBALLY = contextvars.ContextVar("is_llm_caching_disabled_globally", default=False)
+# Context variable for injecting a default seed which will become part of the LLM cache key.
+LLM_GLOBAL_DEFAULT_SEED = contextvars.ContextVar("llm_global_default_seed", default=0)
+
+# Maximum number of retries for network failures.
+MAX_RETRIES = 5
+
+
+EXCLUDED_CACHE_KEY_ARGS = ["self", "is_caching_enabled", "call_id"]
+
+
+def _create_base_cache_key_from_frame(frame: FrameType) -> str:
+ """Create a cache key from the args of a function by passing its frame."""
+ args, _, _, values = inspect.getargvalues(frame)
+ return "|".join(f"{arg}={values[arg]}" for arg in args if arg not in EXCLUDED_CACHE_KEY_ARGS)
+
+
+CostedResponseT = TypeVar("CostedResponseT", bound=CostedLanguageModelResponse | CountTokensResponse)
+FinalResponseT = TypeVar(
+ "FinalResponseT",
+ bound=CostedLanguageModelResponse | StreamedLanguageModelResponse | CountTokensResponse,
+)
+
+
+class LanguageModelAPI(abc.ABC, MutableModel):
+ model_name: str
+ cache_path: Path | None
+ is_caching_inputs: bool = False
+ is_running_offline: bool = False
+ is_conversational: bool = False
+ is_using_logprobs: bool = False
+
+ # retry/timeout values
+ retry_sleep_time: float = 2.0
+ retry_backoff_factor: float = 3.0
+ retry_jitter_factor: float = 0.5
+
+ # TODO: Consider storing the model_config here as well.
+
+ @property
+ @abc.abstractmethod
+ def model_info(self) -> ModelInfo: ...
+
+ def get_response_cache(self) -> AsyncCache[CachedCostedLanguageModelResponse]:
+ if self.cache_path is None:
+ raise UnsetCachePathError()
+ return AsyncCache(self.cache_path, CachedCostedLanguageModelResponse)
+
+ def _create_cache_key(self, base_key: str) -> str:
+ object_cache_attributes = self.model_dump(
+ exclude={
+ "cache_path",
+ "count_tokens_cache_path",
+ "base_url",
+ "api_key_env",
+ "context_window",
+ "max_output_tokens",
+ }
+ )
+ # have to reset the offline key to the same value so that that doesnt invalidate the cache
+ object_cache_attributes["is_running_offline"] = True
+ object_cache_attributes["__name__"] = self.__class__.__name__
+ base_key_md5 = hashlib.md5(base_key.encode()).hexdigest()
+ object_cache_attributes["__request_key_md5__"] = base_key_md5
+ return serialize_to_json(object_cache_attributes)
+
+ async def check_cache_core(
+ self,
+ cache_getter: Callable[[], AsyncCache[CachedCostedModelResponse[InputsT, ModelResponseT]]],
+ cache_key: str,
+ ) -> ModelResponseT | None:
+ async with cache_getter() as cache:
+ cached_result = await cache.get(cache_key)
+
+ if cached_result is not None:
+ if cached_result.error:
+ if cached_result.error.startswith(PromptTooLongError.__name__):
+ raise PromptTooLongError.from_string(cached_result.error)
+ raise Exception(f"Unknown cached result error type: {cached_result.error}")
+ assert cached_result.response is not None
+ return cached_result.response
+ return None
+
+ async def check_cache(self, cache_key: str) -> CostedLanguageModelResponse | None:
+ return await self.check_cache_core(self.get_response_cache, cache_key)
+
+ async def _get_auth(self, prompt: str, max_tokens: int | None) -> PaymentAuthorization | None:
+ global_resource_limits = get_global_resource_limits()
+ if global_resource_limits is not None:
+ prompt_tokens = self.count_tokens(prompt)
+ completion_tokens = max_tokens if max_tokens is not None else self.get_max_completion_size_in_tokens()
+ upper_bound_cost_estimate = self.estimate_cost(prompt_tokens, completion_tokens)
+ assert global_resource_limits is not None
+ auth: PaymentAuthorization = await global_resource_limits.authorize_spend(
+ upper_bound_cost_estimate,
+ debug_info={
+ "model_name": self.model_name,
+ "prompt_tokens": prompt_tokens,
+ "completion_tokens": completion_tokens,
+ },
+ )
+ return auth
+
+ if "PYTEST_CURRENT_TEST" not in os.environ:
+ logger.warning(
+ "You are trying to call a language model with no global resource limits set. That is a bad idea because the spend will not be restricted, and you may end up accidentally spending much more than you expected."
+ )
+ return None
+
+ async def _settle_spend(self, auth: PaymentAuthorization, dollars_used: float) -> None:
+ global_resource_limits = get_global_resource_limits()
+ assert global_resource_limits is not None
+ await global_resource_limits.settle_spend(auth, dollars_used)
+ return None
+
+ def assert_caching_enabled_if_offline(self, is_caching_enabled: bool) -> None:
+ if self.is_running_offline:
+ assert is_caching_enabled, "Caching must be enabled when running offline"
+
+ def assert_not_offline_if_cache_miss(self, prompt: str) -> None:
+ max_n_chars = 50
+ prompt_stub = prompt[:max_n_chars] + ("..." if len(prompt) > max_n_chars else "")
+ assert (
+ not self.is_running_offline
+ ), f"Running offline but did not have a cached response for this query! Prompt: {prompt_stub}"
+
+ async def complete(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ is_caching_enabled: bool = True,
+ ) -> tuple[LanguageModelResponse, ...]:
+ call_id = uuid4()
+ logger.trace(
+ "[{call_id}] Calling complete with params: {params} and is_caching_enabled={is_caching_enabled} and {prompt}",
+ call_id=call_id,
+ params=params,
+ is_caching_enabled=is_caching_enabled,
+ prompt=prompt[:40],
+ )
+ if _complete_concurrency_hook_fn is not None:
+ await _complete_concurrency_hook_fn(self)
+
+ is_caching_enabled_with_override = is_caching_enabled and not IS_LLM_CACHING_DISABLED_GLOBALLY.get()
+ if params.seed is None:
+ params = params.evolve(params.ref().seed, LLM_GLOBAL_DEFAULT_SEED.get())
+
+ return await self._complete(
+ prompt,
+ params,
+ is_caching_enabled=is_caching_enabled_with_override,
+ call_id=call_id,
+ )
+
+ complete_sync = sync(complete)
+
+ async def complete_with_usage(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ is_caching_enabled: bool = True,
+ ) -> CostedLanguageModelResponse:
+ call_id = uuid4()
+ if _complete_concurrency_hook_fn is not None:
+ await _complete_concurrency_hook_fn(self)
+
+ is_caching_enabled_with_override = is_caching_enabled and not IS_LLM_CACHING_DISABLED_GLOBALLY.get()
+ if params.seed is None:
+ params = params.evolve(params.ref().seed, LLM_GLOBAL_DEFAULT_SEED.get())
+
+ return await self._complete_with_usage(
+ prompt,
+ params,
+ is_caching_enabled=is_caching_enabled_with_override,
+ call_id=call_id,
+ )
+
+ complete_with_usage_sync = sync(complete_with_usage)
+
+ async def _complete_with_usage(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ is_caching_enabled: bool,
+ call_id: UUID,
+ ) -> CostedLanguageModelResponse:
+ self._warn_if_no_stop_condition_and_not_conversational(params)
+ self.assert_caching_enabled_if_offline(is_caching_enabled)
+
+ frame: FrameType | None = None
+ if is_caching_enabled:
+ frame = inspect.currentframe()
+
+ costed_response_to_output: Callable[[CostedLanguageModelResponse], CostedLanguageModelResponse] = lambda cr: cr
+
+ cache_key: str | None = None
+ if is_caching_enabled:
+ cache_key, cached_response = await self._get_from_cache(frame, costed_response_to_output)
+
+ if cached_response is not None:
+ return cached_response
+
+ self.assert_not_offline_if_cache_miss(prompt)
+
+ auth = await self._get_auth(prompt, params.max_tokens)
+
+ sleep_time = self.retry_sleep_time
+ last_error_msg: str | None = None
+ for network_failure_count in range(MAX_RETRIES):
+ try:
+ api_inputs = LanguageModelCompleteInputs(
+ prompt=prompt,
+ params=params,
+ network_failure_count=network_failure_count,
+ )
+ response = await self._call_api_one_arg(api_inputs)
+ if is_caching_enabled:
+ assert cache_key is not None
+ result = CachedCostedLanguageModelResponse(
+ response=response,
+ inputs=api_inputs if self.is_caching_inputs else None,
+ )
+ async with self.get_response_cache() as cache:
+ await cache.set(cache_key, result)
+
+ if auth is not None:
+ await self._settle_spend(auth, response.usage.dollars_used)
+
+ return response
+
+ except PromptTooLongError as e:
+ logger.trace(
+ "[{call_id}] Prompt too long error in model {model_name}",
+ call_id=call_id,
+ model_name=self.model_name,
+ )
+ if is_caching_enabled:
+ assert cache_key is not None
+ async with self.get_response_cache() as cache:
+ await cache.set(
+ cache_key,
+ CachedCostedLanguageModelResponse(error=e.to_string()),
+ )
+ raise
+ except TransientLanguageModelError as e:
+ last_error_msg = str(e)
+ if network_failure_count < MAX_RETRIES - 1:
+ if self.retry_jitter_factor > 0:
+ max_jitter = sleep_time * self.retry_jitter_factor
+ sleep_time += random.uniform(-max_jitter / 2, max_jitter / 2)
+ logger.debug(
+ f"Transient language model error ({str(e)}) in model {self.model_name}, retrying with sleep time {sleep_time} seconds..."
+ )
+ await asyncio.sleep(sleep_time)
+ sleep_time *= self.retry_backoff_factor
+ raise LanguageModelRetryLimitError(last_error_msg or "Unknown error (this should not happen)")
+
+ async def _complete(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ is_caching_enabled: bool,
+ call_id: UUID,
+ ) -> tuple[LanguageModelResponse, ...]:
+ # Delegate to _complete_with_usage and extract just the responses
+ # May have more than count responses cached, so just return first count responses
+ costed_response = await self._complete_with_usage(prompt, params, is_caching_enabled, call_id)
+ return costed_response.responses[: params.count]
+
+ @final
+ async def _call_api_one_arg(self, api_inputs: LanguageModelCompleteInputs) -> CostedLanguageModelResponse:
+ """Delegates to the abstract method _call_api, which must be implemented by subclasses."""
+ return await self._call_api(
+ prompt=api_inputs.prompt,
+ params=api_inputs.params,
+ network_failure_count=api_inputs.network_failure_count,
+ )
+
+ @abc.abstractmethod
+ async def _call_api(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ # this is used to track how many times we've retried due to network failures, since we want the return type to contain that information
+ network_failure_count: int = 0,
+ ) -> CostedLanguageModelResponse:
+ """If defined, the stop sequence should be part of the sequence (if it was actually generated)"""
+
+ async def stream(
+ self,
+ prompt: str,
+ is_caching_enabled: bool = True,
+ params: LanguageModelGenerationParams = LanguageModelGenerationParams(),
+ ) -> StreamedLanguageModelResponse:
+ if params.seed is None:
+ params = params.evolve(params.ref().seed, LLM_GLOBAL_DEFAULT_SEED.get())
+
+ is_caching_enabled_with_override = is_caching_enabled and not IS_LLM_CACHING_DISABLED_GLOBALLY.get()
+
+ return await self._stream(
+ prompt=prompt,
+ is_caching_enabled=is_caching_enabled_with_override,
+ params=params,
+ )
+
+ async def _stream(
+ self,
+ prompt: str,
+ is_caching_enabled: bool,
+ params: LanguageModelGenerationParams,
+ ) -> StreamedLanguageModelResponse:
+ assert params.count == 1, "Stream API currently only supports count=1 due to limitations of some APIs."
+
+ self._warn_if_no_stop_condition_and_not_conversational(params)
+ self.assert_caching_enabled_if_offline(is_caching_enabled)
+
+ frame: FrameType | None = None
+ if is_caching_enabled:
+ frame = inspect.currentframe()
+
+ # Note it's technically possible multiple responses cached for given prompt (e.g. from call to complete())
+ # for now we just return first one
+ costed_response_to_output = lambda cr: StreamedLanguageModelResponse(
+ get_cached_response_stream(cr),
+ network_failure_count=0,
+ completion_callbacks=(),
+ )
+ cache_key: str | None = None
+ if is_caching_enabled:
+ cache_key, cached_response = await self._get_from_cache(frame, costed_response_to_output)
+
+ if cached_response is not None:
+ return cached_response
+
+ self.assert_not_offline_if_cache_miss(prompt)
+
+ auth = await self._get_auth(prompt, params.max_tokens)
+
+ sleep_time = self.retry_sleep_time
+ last_error_msg: str | None = None
+ for network_failure_count in range(MAX_RETRIES):
+ # Loop until success or an exception is raised
+ try:
+ api_inputs = LanguageModelStreamInputs(prompt=prompt, params=params)
+ api_stream = await self._get_api_stream_one_arg(api_inputs)
+ callbacks: list[LanguageModelStreamCallback] = []
+ if is_caching_enabled:
+ assert cache_key is not None
+ cache = self.get_response_cache()
+ callbacks.append(
+ UpdateCacheCallback(
+ key=cache_key,
+ cache=cache,
+ api_inputs=api_inputs if self.is_caching_inputs else None,
+ )
+ )
+ llm_debug_output_folder = os.getenv("LLM_DEBUG_PATH", None)
+ if llm_debug_output_folder is not None:
+ output_path = anyio.Path(llm_debug_output_folder) / f"{uuid4()}.txt"
+ # write out the prompt (helps with debugging so we can see when things blow up)
+ await output_path.write_text(prompt)
+ # overwrite the file with the prompt and completion when done
+ callbacks.append(PromptDebuggingCallback(prompt=prompt, output_path=output_path))
+
+ if auth is not None:
+ callbacks.append(SettleSpendCallback(auth=auth))
+
+ return StreamedLanguageModelResponse(
+ api_stream,
+ network_failure_count=network_failure_count,
+ completion_callbacks=callbacks,
+ )
+
+ except PromptTooLongError as e:
+ if is_caching_enabled:
+ assert cache_key is not None
+ async with self.get_response_cache() as cache:
+ await cache.set(
+ cache_key,
+ CachedCostedLanguageModelResponse(error=e.to_string()),
+ )
+ raise
+ except TransientLanguageModelError as e:
+ last_error_msg = str(e)
+ if network_failure_count < MAX_RETRIES - 1:
+ if self.retry_jitter_factor > 0:
+ sleep_time += random.uniform(0, sleep_time * self.retry_jitter_factor)
+ logger.debug(
+ f"Transient language model error ({str(e)}) in model {self.model_name}, retrying with sleep time {sleep_time} seconds..."
+ )
+ await asyncio.sleep(sleep_time)
+ sleep_time *= self.retry_backoff_factor
+ raise LanguageModelRetryLimitError(last_error_msg or "Unknown error (this should not happen)")
+
+ @final
+ async def _get_api_stream_one_arg(
+ self, api_inputs: LanguageModelStreamInputs
+ ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ """Delegates to the abstract method _get_api_stream, which must be implemented by subclasses."""
+ return self._get_api_stream(prompt=api_inputs.prompt, params=api_inputs.params)
+
+ @abc.abstractmethod
+ def _get_api_stream(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ """If defined, the stop sequence should be part of the sequence (if it was actually generated)"""
+
+ def _warn_if_no_stop_condition_and_not_conversational(self, params: LanguageModelGenerationParams) -> None:
+ if (params.stop is None and params.max_tokens is None) and not self.is_conversational:
+ logger.debug(
+ "Did not specify either `max_tokens` or `stop`, and this is not a conversational model. The completion will go until the entire context window is filled. Preferably you don't do this, because it is fairly inefficient."
+ )
+
+ async def _get_from_cache_core(
+ self,
+ frame: FrameType | None,
+ costed_response_to_output: Callable[[CostedResponseT], FinalResponseT],
+ cache_checker: Callable[[str], Awaitable[CostedResponseT | None]],
+ ) -> tuple[str | None, FinalResponseT | None]:
+ cache_key: str | None
+
+ cache_key, costed_response = await self._get_costed_response_from_frame_core(cache_checker, frame)
+
+ if costed_response is not None:
+ return cache_key, costed_response_to_output(costed_response)
+ return cache_key, None
+
+ async def _get_from_cache(
+ self,
+ frame: FrameType | None,
+ costed_response_to_output: Callable[[CostedLanguageModelResponse], FinalResponseT],
+ ) -> tuple[str | None, FinalResponseT | None]:
+ return await self._get_from_cache_core(frame, costed_response_to_output, self.check_cache)
+
+ async def _get_costed_response_from_frame_core(
+ self,
+ cache_checker: Callable[[str], Awaitable[CostedResponseT | None]],
+ frame: FrameType | None,
+ ) -> tuple[str, CostedResponseT | None]:
+ assert frame is not None
+ cache_key = self._create_cache_key(_create_base_cache_key_from_frame(frame))
+ costed_response = await cache_checker(cache_key)
+ return cache_key, costed_response
+
+ def count_tokens(self, text: str) -> int:
+ # this is VERY approximate, but many of the child models have nothing, so...
+ return approximate_token_count(text)
+
+ def basic_calculate_cost(self, prompt_tokens: int, completion_tokens: int) -> float:
+ return (
+ prompt_tokens * self.model_info.cost_per_input_token
+ + completion_tokens * self.model_info.cost_per_output_token
+ )
+
+ def estimate_cost(self, prompt_tokens: int, completion_tokens: int) -> float:
+ """Estimate the cost of a request before it has been made. Doesn't use any caching info."""
+ return self.basic_calculate_cost(prompt_tokens, completion_tokens)
+
+ def calculate_cost(
+ self,
+ prompt_tokens: int,
+ completion_tokens: int,
+ caching_info: CachingInfo | None = None,
+ ) -> float:
+ """Overridden by subclasses which have more complex cost calculations, such as if caching is used."""
+ logger.info(
+ f"no calculate_cost implemented for {self.model_name}; using basic_calculate_cost",
+ model_name=self.model_name,
+ )
+ return self.basic_calculate_cost(prompt_tokens, completion_tokens)
+
+ def get_max_completion_size_in_tokens(self) -> int:
+ if self.model_info.max_output_tokens is not None:
+ return self.model_info.max_output_tokens
+ # assume max output is just the context window size
+ return self.model_info.max_input_tokens
+
+ def get_max_prompt_size_in_tokens(self) -> int:
+ return self.model_info.max_input_tokens
+
+ def get_context_window_size_in_tokens(self) -> int:
+ return self.get_max_completion_size_in_tokens() + self.get_max_prompt_size_in_tokens()
+
+
+COMPLETE_CONCURRENCY_HOOK_FN = Callable[[LanguageModelAPI], Awaitable[None]] | None
+_complete_concurrency_hook_fn: COMPLETE_CONCURRENCY_HOOK_FN = None
+
+
+def set_language_model_api_complete_concurrency_hook(
+ hook_fn: COMPLETE_CONCURRENCY_HOOK_FN,
+) -> None:
+ global _complete_concurrency_hook_fn
+ _complete_concurrency_hook_fn = hook_fn
diff --git a/vet/imbue_core/agents/llm_apis/mock_api.py b/vet/imbue_core/agents/llm_apis/mock_api.py
@@ -0,0 +1,193 @@
+import asyncio
+import enum
+from pathlib import Path
+from typing import AsyncGenerator
+
+import toml
+from loguru import logger
+from pydantic.fields import Field
+from pydantic.functional_validators import field_validator
+
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseWithLogits
+from vet.imbue_core.agents.llm_apis.data_types import ResponseStopReason
+from vet.imbue_core.agents.llm_apis.data_types import TokenProbability
+from vet.imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
+from vet.imbue_core.agents.llm_apis.models import ModelInfo
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
+from vet.imbue_core.itertools import only
+from vet.imbue_core.pydantic_serialization import MutableModel
+
+
+class MockModelName(enum.StrEnum):
+ MOCK_MODEL = "my-mock-model"
+
+
+MY_MOCK_MODEL_INFO = ModelInfo(
+ model_name=MockModelName.MOCK_MODEL,
+ cost_per_input_token=0.0 / 1_000_000,
+ cost_per_output_token=0.0 / 1_000_000,
+ max_input_tokens=32_768,
+ max_output_tokens=None,
+)
+
+
+class Stats(MutableModel):
+ complete_calls: int = 0
+
+
+class LanguageModelMock(LanguageModelAPI):
+ model_name: str = MY_MOCK_MODEL_INFO.model_name
+ cache_path: Path | None = None
+ # FIXME: can't have a mutable class inside a frozen pydantic model
+ stats: Stats = Field(default_factory=Stats)
+
+ @property
+ def model_info(self) -> ModelInfo:
+ return MY_MOCK_MODEL_INFO
+
+ async def complete(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ is_caching_enabled: bool = True,
+ ) -> tuple[LanguageModelResponse, ...]:
+ raise NotImplementedError()
+
+ def _get_token_probabilities(self, response_text: str) -> tuple[tuple[TokenProbability, ...], ...]:
+ return tuple(
+ (TokenProbability(token=pseudo_token, log_probability=0.0, is_stop=False),)
+ for pseudo_token in response_text.split()
+ )
+
+ async def _call_api(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ network_failure_count: int = 0,
+ ) -> CostedLanguageModelResponse:
+ raise NotImplementedError()
+
+ def _get_api_stream(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ # TODO Implement streaming support (?)
+ raise NotImplementedError()
+
+
+MOCK_STREAM_SLEEP_TIME = 5.0
+
+
+class FileBasedLanguageModelMock(LanguageModelMock):
+ """
+ A mock LLM API that reads responses from a toml file.
+ The response can either be identified using the toml key or a prompt which is part of the toml dictionary.
+ """
+
+ calls: int = 0
+
+ @field_validator("cache_path") # pyre-ignore[56]: pyre doesn't understand pydantic
+ @classmethod
+ def validate_cache_path(cls, v: Path | None) -> Path | None:
+ if v is None:
+ raise ValueError("Mock responses file path is not set.")
+ if not v.exists():
+ raise ValueError(f"Mock responses file {v} does not exist.")
+ if not v.suffix == ".toml":
+ raise ValueError(f"Mock responses file {v} is not a toml file.")
+ return v
+
+ def _get_user_message_from_prompt(self, prompt: str) -> str:
+ user_prompt = prompt.rsplit("[ROLE=USER]", 1)[-1].strip()
+ return user_prompt
+
+ def get_single_response(self, prompt: str) -> str:
+ return only(self.get_parts_of_response(prompt))
+
+ def get_parts_of_response(self, prompt: str) -> tuple[str, ...]:
+ """
+ Support both of the following possible identifiers:
+ [identifier]
+ prompt = "user message here"
+ [[identifier.responses]]
+ text = "response"
+ [[identifier.responses]]
+ text = "response2"
+
+ [identifier]
+ prompt = "user message here"
+ response = "response"
+ """
+ # this is checked during validation but i guess the type checker doesn't see it
+ assert self.cache_path is not None
+ # TODO: should we try something that is not toml? toml formatting is a little annoying
+ toml_dict = toml.load(self.cache_path)
+ # TODO: currently the identifier is the last user message, because the entire prompt is really long
+ # if we need to support the same user message with different responses, expand this, maybe chat history?
+ identifier = self._get_user_message_from_prompt(prompt)
+ logger.info("Getting response for identifier: {} from {}", identifier, toml_dict)
+ toml_item = toml_dict.get(identifier, None)
+ if toml_item is None:
+ for toml_key, response in toml_dict.items():
+ if "prompt" in response:
+ if response["prompt"] == identifier:
+ toml_item = response
+ break
+ if toml_item is None:
+ raise KeyError(f"No response found for the given identifier {identifier}")
+
+ if "responses" in toml_item:
+ responses = toml_item["responses"]
+ if isinstance(responses, list):
+ return tuple(r["text"] for r in responses if isinstance(r, dict) and "text" in r)
+ raise ValueError(f"Expected 'responses' to be a list of tables in section '{identifier}'")
+
+ if "response" in toml_item:
+ return (str(toml_item["response"]),)
+
+ raise ValueError(f"No valid response or responses found for identifier '{identifier}'")
+
+ async def complete(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ is_caching_enabled: bool = True,
+ ) -> tuple[LanguageModelResponse, ...]:
+ response = self.get_single_response(prompt)
+ self.stats.complete_calls += 1
+ token_probabilities = self._get_token_probabilities(response)
+ return (
+ LanguageModelResponseWithLogits(
+ text=response,
+ token_count=len(token_probabilities),
+ stop_reason=ResponseStopReason.NONE,
+ network_failure_count=0,
+ token_probabilities=token_probabilities,
+ ),
+ )
+
+ async def _get_api_stream(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ responses = self.get_parts_of_response(prompt)
+ self.stats.complete_calls += 1
+ if len(responses) == 1:
+ response = responses[0]
+ yield LanguageModelStreamDeltaEvent(delta=response)
+ else:
+ for response in responses:
+ yield LanguageModelStreamDeltaEvent(delta=response)
+ await asyncio.sleep(MOCK_STREAM_SLEEP_TIME)
+ yield LanguageModelStreamEndEvent(
+ usage=LanguageModelResponseUsage(prompt_tokens_used=0, completion_tokens_used=0, dollars_used=0),
+ stop_reason=ResponseStopReason.NONE,
+ )
diff --git a/vet/imbue_core/agents/llm_apis/models.py b/vet/imbue_core/agents/llm_apis/models.py
@@ -0,0 +1,17 @@
+from vet.imbue_core.agents.llm_apis.union_data_types import ProviderSpecificModelInfoUnion
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+
+class ModelInfo(SerializableModel):
+ model_name: str
+ cost_per_input_token: float
+ cost_per_output_token: float
+ max_input_tokens: int
+ max_output_tokens: int | None
+ # requests per second
+ rate_limit_req: float | None = None
+ # tokens per second
+ rate_limit_tok: float | None = None
+ rate_limit_output_tok: float | None = None
+ max_thinking_budget: int | None = None
+ provider_specific_info: ProviderSpecificModelInfoUnion | None = None
diff --git a/vet/imbue_core/agents/llm_apis/openai_api.py b/vet/imbue_core/agents/llm_apis/openai_api.py
@@ -0,0 +1,654 @@
+import asyncio
+import enum
+import re
+from collections import defaultdict
+from contextlib import contextmanager
+from functools import lru_cache
+from typing import AsyncGenerator
+from typing import Iterator
+from typing import Mapping
+
+import httpx
+import tiktoken
+from loguru import logger
+from openai import AsyncStream
+from openai import InternalServerError
+from openai import NOT_GIVEN
+from openai import NotGiven
+from openai._client import AsyncOpenAI
+from openai._exceptions import APIConnectionError
+from openai._exceptions import BadRequestError
+from openai._exceptions import RateLimitError
+from openai.types.chat import ChatCompletion
+from pydantic.functional_validators import field_validator
+
+from vet.imbue_core.agents.llm_apis.api_utils import convert_prompt_to_openai_messages
+from vet.imbue_core.agents.llm_apis.data_types import CachingInfo
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseWithLogits
+from vet.imbue_core.agents.llm_apis.data_types import ResponseStopReason
+from vet.imbue_core.agents.llm_apis.data_types import TokenProbability
+from vet.imbue_core.agents.llm_apis.errors import BadAPIRequestError
+from vet.imbue_core.agents.llm_apis.errors import LanguageModelInvalidModelNameError
+from vet.imbue_core.agents.llm_apis.errors import MissingAPIKeyError
+from vet.imbue_core.agents.llm_apis.errors import PromptTooLongError
+from vet.imbue_core.agents.llm_apis.errors import TransientLanguageModelError
+from vet.imbue_core.agents.llm_apis.models import ModelInfo
+from vet.imbue_core.agents.llm_apis.openai_compatible_api import OpenAICompatibleAPI
+from vet.imbue_core.agents.llm_apis.openai_compatible_api import (
+ _OPENAI_COMPATIBLE_STOP_REASON_TO_STOP_REASON,
+)
+from vet.imbue_core.agents.llm_apis.openai_data_types import OpenAICachingInfo
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamStartEvent
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.frozen_utils import FrozenMapping
+from vet.imbue_core.itertools import only
+from vet.imbue_core.secrets_utils import get_secret
+
+# note: we require that these model versions are explicit, just like the rest of our dependencies
+# the reason is that these models are actually now mostly deterministic, and it is much easier to debug if we know what model was used
+# also, there's no need to troll yourself by wondering why results have improved (or gotten worse) when you dont realized that the version has shifted under you
+# if you want to use an upgraded model, just upgrade the model to the key displayed here: https://platform.openai.com/docs/models/overview
+# please do NOT set these back to the generic model names!
+
+FINE_TUNED_GPT4O_MINI_2024_07_18_PREFIX = "ft:gpt-4o-mini-2024-07-18"
+FINE_TUNED_GPT4O_2024_08_06_PREFIX = "ft:gpt-4o-2024-08-06"
+
+
+class OpenAIModelName(enum.StrEnum):
+ GPT_3_5_TURBO = "gpt-3.5-turbo-0125"
+ GPT_4_0613 = "gpt-4-0613"
+ GPT_4_1106_PREVIEW = "gpt-4-1106-preview"
+ GPT_4_0125_PREVIEW = "gpt-4-0125-preview"
+ GPT_4_TURBO_2024_04_09 = "gpt-4-turbo-2024-04-09"
+ GPT_4O_2024_05_13 = "gpt-4o-2024-05-13"
+ GPT_4O_2024_08_06 = "gpt-4o-2024-08-06"
+ GPT_4O_MINI_2024_07_18 = "gpt-4o-mini-2024-07-18"
+ O1_2024_12_17 = "o1-2024-12-17"
+ GPT_4_1_2025_04_14 = "gpt-4.1-2025-04-14"
+ GPT_4_1_MINI_2025_04_14 = "gpt-4.1-mini-2025-04-14"
+ GPT_4_1_NANO_2025_04_14 = "gpt-4.1-nano-2025-04-14"
+ O3_2025_04_16 = "o3-2025-04-16"
+ O3_MINI_2025_01_31 = "o3-mini-2025-01-31"
+ O4_MINI_2025_04_16 = "o4-mini-2025-04-16"
+ GPT_5_2025_08_07 = "gpt-5-2025-08-07"
+ GPT_5_MINI_2025_08_07 = "gpt-5-mini-2025-08-07"
+ GPT_5_NANO_2025_08_07 = "gpt-5-nano-2025-08-07"
+ GPT_5_1_2025_11_13 = "gpt-5.1-2025-11-13"
+
+
+# Using Tier 5 rate limits
+# https://platform.openai.com/settings/organization/limits
+
+OPENAI_MODEL_INFO_BY_NAME: FrozenMapping[OpenAIModelName, ModelInfo] = FrozenDict(
+ {
+ OpenAIModelName.GPT_3_5_TURBO: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_3_5_TURBO),
+ cost_per_input_token=0.5 / 1_000_000,
+ cost_per_output_token=1.5 / 1_000_000,
+ max_input_tokens=16_385,
+ max_output_tokens=4096,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
+ ),
+ OpenAIModelName.GPT_4_0613: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_4_0613),
+ cost_per_input_token=30.0 / 1_000_000,
+ cost_per_output_token=60.0 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=8192,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
+ ),
+ OpenAIModelName.GPT_4_1106_PREVIEW: ModelInfo( # Cannot find this model
+ model_name=str(OpenAIModelName.GPT_4_1106_PREVIEW),
+ cost_per_input_token=10.0 / 1_000_000,
+ cost_per_output_token=30.0 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=4096,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
+ ),
+ OpenAIModelName.GPT_4_0125_PREVIEW: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_4_0125_PREVIEW),
+ cost_per_input_token=10.0 / 1_000_000,
+ cost_per_output_token=30.0 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=4096,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
+ ),
+ OpenAIModelName.GPT_4_TURBO_2024_04_09: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_4_TURBO_2024_04_09),
+ cost_per_input_token=10.0 / 1_000_000,
+ cost_per_output_token=30.0 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=4096,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
+ ),
+ OpenAIModelName.GPT_4O_2024_05_13: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_4O_2024_05_13),
+ cost_per_input_token=5.0 / 1_000_000,
+ cost_per_output_token=15.0 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=4096,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
+ ),
+ OpenAIModelName.GPT_4O_2024_08_06: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_4O_2024_08_06),
+ cost_per_input_token=2.5 / 1_000_000,
+ cost_per_output_token=10.0 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=16_384,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
+ ),
+ OpenAIModelName.GPT_4O_MINI_2024_07_18: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_4O_MINI_2024_07_18),
+ cost_per_input_token=0.15 / 1_000_000,
+ cost_per_output_token=0.60 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=16_384,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
+ ),
+ OpenAIModelName.O1_2024_12_17: ModelInfo(
+ model_name=str(OpenAIModelName.O1_2024_12_17),
+ cost_per_input_token=15 / 1_000_000,
+ cost_per_output_token=60 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=100_000,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
+ ),
+ OpenAIModelName.GPT_4_1_2025_04_14: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_4_1_2025_04_14),
+ cost_per_input_token=2 / 1_000_000,
+ cost_per_output_token=8 / 1_000_000,
+ max_input_tokens=1_047_576,
+ max_output_tokens=32_768,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
+ ),
+ OpenAIModelName.GPT_4_1_MINI_2025_04_14: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_4_1_MINI_2025_04_14),
+ cost_per_input_token=0.4 / 1_000_000,
+ cost_per_output_token=1.6 / 1_000_000,
+ max_input_tokens=1_047_576,
+ max_output_tokens=32_768,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
+ ),
+ OpenAIModelName.GPT_4_1_NANO_2025_04_14: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_4_1_NANO_2025_04_14),
+ cost_per_input_token=0.1 / 1_000_000,
+ cost_per_output_token=0.4 / 1_000_000,
+ max_input_tokens=1_047_576,
+ max_output_tokens=32_768,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
+ ),
+ OpenAIModelName.O4_MINI_2025_04_16: ModelInfo(
+ model_name=str(OpenAIModelName.O4_MINI_2025_04_16),
+ cost_per_input_token=1.1 / 1_000_000,
+ cost_per_output_token=4.4 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=100_000,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
+ ),
+ OpenAIModelName.O3_2025_04_16: ModelInfo(
+ model_name=str(OpenAIModelName.O3_2025_04_16),
+ cost_per_input_token=10 / 1_000_000,
+ cost_per_output_token=40 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=100_000,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS
+ ),
+ OpenAIModelName.O3_MINI_2025_01_31: ModelInfo(
+ model_name=str(OpenAIModelName.O3_MINI_2025_01_31),
+ cost_per_input_token=1.1 / 1_000_000,
+ cost_per_output_token=4.4 / 1_000_000,
+ max_input_tokens=200_000,
+ max_output_tokens=100_000,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
+ ),
+ OpenAIModelName.GPT_5_2025_08_07: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_5_2025_08_07),
+ cost_per_input_token=1.25 / 1_000_000,
+ cost_per_output_token=10 / 1_000_000,
+ max_input_tokens=400_000,
+ max_output_tokens=128_000,
+ rate_limit_req=15000 / 60, # 15000 RPM = 250 RPS
+ ),
+ OpenAIModelName.GPT_5_MINI_2025_08_07: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_5_MINI_2025_08_07),
+ cost_per_input_token=0.25 / 1_000_000,
+ cost_per_output_token=2.00 / 1_000_000,
+ max_input_tokens=400_000,
+ max_output_tokens=128_000,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
+ ),
+ OpenAIModelName.GPT_5_NANO_2025_08_07: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_5_NANO_2025_08_07),
+ cost_per_input_token=0.05 / 1_000_000,
+ cost_per_output_token=0.40 / 1_000_000,
+ max_input_tokens=400_000,
+ max_output_tokens=128_000,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS
+ ),
+ OpenAIModelName.GPT_5_1_2025_11_13: ModelInfo(
+ model_name=str(OpenAIModelName.GPT_5_1_2025_11_13),
+ cost_per_input_token=1.25 / 1_000_000,
+ cost_per_output_token=10 / 1_000_000,
+ max_input_tokens=400_000,
+ max_output_tokens=128_000,
+ rate_limit_req=15000 / 60, # 15000 RPM = 250 RPS
+ ),
+ }
+)
+
+
+# Pricing for fine-tuned models taken from here: https://platform.openai.com/docs/pricing
+def get_model_info(model_name: OpenAIModelName) -> ModelInfo:
+ # Check for the family of fine-tuned models.
+ if model_name.startswith(FINE_TUNED_GPT4O_MINI_2024_07_18_PREFIX):
+ return ModelInfo(
+ model_name=str(model_name),
+ cost_per_input_token=0.3 / 1_000_000,
+ cost_per_output_token=1.2 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=16_384,
+ rate_limit_req=30000 / 60, # 30000 RPM = 500 RPS (same as base model)
+ )
+ if model_name.startswith(FINE_TUNED_GPT4O_2024_08_06_PREFIX):
+ return ModelInfo(
+ model_name=str(model_name),
+ cost_per_input_token=3.75 / 1_000_000,
+ cost_per_output_token=15.0 / 1_000_000,
+ max_input_tokens=128_000,
+ max_output_tokens=16_384,
+ rate_limit_req=10000 / 60, # 10000 RPM = 166.67 RPS (same as base model)
+ )
+ # Otherwise, return the model info for the base model.
+ return OPENAI_MODEL_INFO_BY_NAME[model_name]
+
+
+_CAPACITY_SEMAPHOR_BY_MODEL_NAME: Mapping[OpenAIModelName, asyncio.Semaphore] = defaultdict(
+ lambda: asyncio.Semaphore(20),
+ {
+ OpenAIModelName.GPT_3_5_TURBO: asyncio.Semaphore(100),
+ OpenAIModelName.GPT_4_0613: asyncio.Semaphore(60),
+ OpenAIModelName.GPT_4_1_NANO_2025_04_14: asyncio.Semaphore(80),
+ },
+)
+
+
+def _get_capacity_semaphor(model_name: OpenAIModelName) -> asyncio.Semaphore:
+ # Fine-tuned models share rate limits with the base model.
+ if model_name.startswith(FINE_TUNED_GPT4O_MINI_2024_07_18_PREFIX):
+ model_name = OpenAIModelName.GPT_4O_MINI_2024_07_18
+ elif model_name.startswith(FINE_TUNED_GPT4O_2024_08_06_PREFIX):
+ model_name = OpenAIModelName.GPT_4O_2024_08_06
+ return _CAPACITY_SEMAPHOR_BY_MODEL_NAME[model_name]
+
+
+def is_openai_reasoning_model(model_name: str) -> bool:
+ return model_name in (
+ OpenAIModelName.O1_2024_12_17,
+ OpenAIModelName.O4_MINI_2025_04_16,
+ OpenAIModelName.O3_2025_04_16,
+ OpenAIModelName.O3_MINI_2025_01_31,
+ OpenAIModelName.GPT_5_2025_08_07,
+ OpenAIModelName.GPT_5_MINI_2025_08_07,
+ OpenAIModelName.GPT_5_NANO_2025_08_07,
+ )
+
+
+def is_fine_tuned_openai_model(model_name: OpenAIModelName) -> bool:
+ return model_name.value.startswith(FINE_TUNED_GPT4O_MINI_2024_07_18_PREFIX) or model_name.value.startswith(
+ FINE_TUNED_GPT4O_2024_08_06_PREFIX
+ )
+
+
+_OPENAI_COMPLETION_ERROR_PATTERN = re.compile(
+ r".*This model's maximum context length is (\d+) tokens, however you requested (\d+) tokens \((\d+) in your prompt; (\d+) for the completion\). Please reduce your prompt; or completion length.*"
+)
+
+_OPENAI_STOP_REASON_TO_STOP_REASON = _OPENAI_COMPATIBLE_STOP_REASON_TO_STOP_REASON
+
+
+@lru_cache(maxsize=1)
+def get_openai_tokenizer(model_name: str) -> tiktoken.Encoding:
+ """Get the appropriate tiktoken tokenizer for an OpenAI model.
+
+ Args:
+ model_name: The OpenAI model name (e.g., "gpt-4o-2024-08-06").
+
+ Returns:
+ The tiktoken Encoding for the model.
+ """
+ if model_name.startswith("gpt-4"):
+ fixed_model_name = "gpt-4"
+ elif model_name.startswith("gpt-3.5"):
+ fixed_model_name = "gpt-3.5"
+ else:
+ # Just default to `gpt-4o` for now, since this seems to be the most recent tokenizer
+ # and we are only using it for estimating token usage
+ fixed_model_name = "gpt-4o"
+ return tiktoken.encoding_for_model(fixed_model_name)
+
+
+def count_openai_tokens(text: str, model_name: str) -> int:
+ return len(get_openai_tokenizer(model_name).encode(text))
+
+
+@contextmanager
+def _openai_exception_manager() -> Iterator[None]:
+ """Simple context manager for parsing OpenAI API exceptions."""
+ try:
+ yield
+ except BadRequestError as e:
+ error_text_match = _OPENAI_COMPLETION_ERROR_PATTERN.search(str(e))
+ if error_text_match is not None:
+ max_prompt_len = int(error_text_match.group(1))
+ prompt_len = int(error_text_match.group(2))
+ logger.debug(
+ "PromptTooLongError max_prompt_len={max_prompt_len} prompt_len={prompt_len}",
+ max_prompt_len=max_prompt_len,
+ prompt_len=prompt_len,
+ )
+ raise PromptTooLongError(prompt_len, max_prompt_len) from e
+ logger.debug("BadAPIRequestError {e}", e=e)
+ raise BadAPIRequestError(str(e)) from e
+ except APIConnectionError as e:
+ logger.debug("Rate limited? Received APIConnectionError {e}", e=e)
+ raise TransientLanguageModelError("APIConnectionError") from e
+ except RateLimitError as e:
+ if e.code == "insufficient_quota":
+ raise
+ logger.debug("Rate limited? {e}", e=e)
+ raise TransientLanguageModelError("RateLimitError") from e
+ except httpx.RemoteProtocolError as e:
+ logger.debug("httpx.RemoteProtocolError {e}", e=e)
+ raise TransientLanguageModelError("httpx.RemoteProtocolError") from e
+ except InternalServerError as e:
+ logger.debug("InternalServerError {e}", e=e)
+ raise TransientLanguageModelError("InternalServerError") from e
+
+
+class OpenAIChatAPI(OpenAICompatibleAPI):
+ model_name: OpenAIModelName = OpenAIModelName.GPT_4O_MINI_2024_07_18
+
+ @field_validator("model_name") # pyre-ignore[56]: pyre doesn't understand pydantic
+ @classmethod
+ def validate_model_name(cls, v: str) -> str:
+ if v not in OPENAI_MODEL_INFO_BY_NAME:
+ raise LanguageModelInvalidModelNameError(v, cls.__name__, list(OPENAI_MODEL_INFO_BY_NAME))
+ return v
+
+ @property
+ def model_info(self) -> ModelInfo:
+ return get_model_info(self.model_name)
+
+ def _get_client(self) -> AsyncOpenAI:
+ api_key = get_secret("OPENAI_API_KEY")
+ if not api_key:
+ raise MissingAPIKeyError("OPENAI_API_KEY environment variable is not set")
+ return AsyncOpenAI( # pyre-ignore[16]: pyre doesn't understand the auto-generated openai._client
+ api_key=api_key
+ )
+
+ async def _call_api(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ network_failure_count: int = 0,
+ ) -> CostedLanguageModelResponse:
+ messages = convert_prompt_to_openai_messages(prompt)
+ with _openai_exception_manager():
+ client = self._get_client()
+
+ is_reasoning_model = is_openai_reasoning_model(self.model_name)
+
+ top_logprobs: NotGiven | int
+ if self.is_using_logprobs:
+ assert not is_reasoning_model, "Logprobs are not supported for reasoning models."
+ top_logprobs = 5
+ else:
+ top_logprobs = NOT_GIVEN
+
+ temperature: NotGiven | float = NOT_GIVEN if is_reasoning_model else params.temperature
+
+ async with _get_capacity_semaphor(self.model_name):
+ api_result = await client.chat.completions.create(
+ model=self.model_name,
+ messages=messages, # type: ignore
+ max_completion_tokens=params.max_tokens,
+ n=params.count,
+ temperature=temperature,
+ stream=False,
+ seed=params.seed,
+ stop=params.stop,
+ presence_penalty=self.presence_penalty,
+ logprobs=self.is_using_logprobs,
+ top_logprobs=top_logprobs,
+ )
+ assert isinstance(api_result, ChatCompletion)
+
+ usage = api_result.usage
+ if usage is not None:
+ completion_tokens = usage.completion_tokens
+ prompt_tokens = usage.prompt_tokens
+ cached_tokens = (
+ usage.prompt_tokens_details.cached_tokens if usage.prompt_tokens_details is not None else 0
+ ) or 0
+ caching_info = CachingInfo(
+ read_from_cache=cached_tokens,
+ provider_specific_data=OpenAICachingInfo(),
+ )
+ else:
+ completion_tokens = 0
+ prompt_tokens = self.count_tokens(prompt)
+ cached_tokens = None
+ caching_info = None
+
+ results: tuple[LanguageModelResponse | LanguageModelResponseWithLogits, ...]
+ if self.is_using_logprobs:
+ results = self._parse_response_with_logprobs(
+ api_result,
+ prompt_tokens=prompt_tokens,
+ stop=params.stop,
+ network_failure_count=network_failure_count,
+ )
+ else:
+ results = self._parse_response_without_logprobs(
+ api_result,
+ prompt_tokens=prompt_tokens,
+ stop=params.stop,
+ network_failure_count=network_failure_count,
+ )
+
+ logger.trace("text: {text}", text=results[0].text)
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
+ logger.trace("dollars used: {dollars_used}", dollars_used=dollars_used)
+ return CostedLanguageModelResponse(
+ usage=LanguageModelResponseUsage(
+ prompt_tokens_used=prompt_tokens,
+ completion_tokens_used=completion_tokens,
+ dollars_used=dollars_used,
+ caching_info=caching_info,
+ ),
+ responses=tuple(results),
+ )
+
+ async def _get_api_stream(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ messages = convert_prompt_to_openai_messages(prompt)
+ with _openai_exception_manager():
+ client = self._get_client()
+
+ is_reasoning_model = is_openai_reasoning_model(self.model_name)
+ temperature: NotGiven | float = NOT_GIVEN if is_reasoning_model else params.temperature
+
+ async with _get_capacity_semaphor(self.model_name):
+ api_result = await client.chat.completions.create(
+ model=self.model_name,
+ messages=messages, # type: ignore
+ max_completion_tokens=params.max_tokens,
+ n=1,
+ temperature=temperature,
+ stop=params.stop,
+ seed=params.seed,
+ stream=True,
+ stream_options={"include_usage": True},
+ presence_penalty=self.presence_penalty,
+ logprobs=False, # not used when streaming
+ top_logprobs=NOT_GIVEN, # only allowed when logprobs=True
+ )
+ assert isinstance(api_result, AsyncStream)
+
+ yield LanguageModelStreamStartEvent()
+
+ usage = None
+ finish_reason: str | None = None
+ async for chunk in api_result:
+ if hasattr(chunk, "usage") and chunk.usage is not None:
+ # final chunk containing usage info after all streaming is done
+ usage = chunk.usage
+ continue
+
+ if chunk.choices:
+ assert len(chunk.choices) == 1, "Currently only count=1 supported for streaming API."
+ data = only(chunk.choices)
+ delta = data.delta.content
+ if delta is not None:
+ yield LanguageModelStreamDeltaEvent(delta=delta)
+ if data.finish_reason:
+ finish_reason = str(data.finish_reason)
+
+ stop_reason = _OPENAI_STOP_REASON_TO_STOP_REASON[str(finish_reason)]
+ # Note, OpenAI API treats end turn and stop sequence the same
+ # Here we assume it is stop sequence if user has specified a stop sequence
+ if params.stop is not None and stop_reason == ResponseStopReason.END_TURN:
+ yield LanguageModelStreamDeltaEvent(delta=params.stop)
+
+ if usage is not None:
+ completion_tokens = usage.completion_tokens
+ prompt_tokens = usage.prompt_tokens
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
+ cached_tokens = usage.prompt_tokens_details.cached_tokens
+ logger.trace(
+ "Used this many cached read tokens: {cached_tokens}",
+ cached_tokens=cached_tokens,
+ )
+ caching_info = CachingInfo(
+ read_from_cache=cached_tokens,
+ provider_specific_data=OpenAICachingInfo(),
+ )
+ else:
+ completion_tokens = -1
+ prompt_tokens = -1
+ dollars_used = -1
+ caching_info = None
+ logger.trace("dollars used: {dollars_used}", dollars_used=dollars_used)
+
+ yield LanguageModelStreamEndEvent(
+ usage=LanguageModelResponseUsage(
+ prompt_tokens_used=prompt_tokens,
+ completion_tokens_used=completion_tokens,
+ dollars_used=dollars_used,
+ caching_info=caching_info,
+ ),
+ stop_reason=stop_reason,
+ )
+
+ def count_tokens(self, text: str) -> int:
+ return count_openai_tokens(text, self.model_name)
+
+ def _parse_response_without_logprobs(
+ self,
+ response: ChatCompletion,
+ prompt_tokens: int,
+ stop: str | None,
+ network_failure_count: int,
+ ) -> tuple[LanguageModelResponse, ...]:
+ results = []
+ for data in response.choices:
+ assert data.message.content is not None
+ text = data.message.content
+ token_count = self.count_tokens(text) + prompt_tokens
+ stop_reason = _OPENAI_STOP_REASON_TO_STOP_REASON[str(data.finish_reason)]
+ # Note, OpenAI API treats end turn and stop sequence the same
+ # Here we assume it is stop sequence if user has specified a stop sequence
+ if stop is not None and stop_reason == ResponseStopReason.END_TURN:
+ text += stop
+ result = LanguageModelResponse(
+ text=text,
+ token_count=token_count,
+ stop_reason=stop_reason,
+ network_failure_count=network_failure_count,
+ )
+ results.append(result)
+ return tuple(results)
+
+ def _parse_response_with_logprobs(
+ self,
+ response: ChatCompletion,
+ prompt_tokens: int,
+ stop: str | None,
+ network_failure_count: int,
+ ) -> tuple[LanguageModelResponseWithLogits, ...]:
+ results = []
+ for data in response.choices:
+ assert data.message.content is not None
+ logprobs = data.logprobs
+ assert logprobs is not None
+ logprobs_content = logprobs.content
+ assert logprobs_content is not None
+ text = data.message.content
+
+ token_probabilities = []
+ for logprob_token_entry in logprobs_content:
+ top_logprobs = logprob_token_entry.top_logprobs
+ top_entries = [
+ TokenProbability(
+ token=top_logprob_obj.token,
+ log_probability=top_logprob_obj.logprob,
+ is_stop=False,
+ )
+ for top_logprob_obj in top_logprobs
+ ]
+ selected_entry = TokenProbability(
+ token=logprob_token_entry.token,
+ log_probability=logprob_token_entry.logprob,
+ is_stop=False,
+ )
+ if selected_entry in top_entries:
+ top_entries.remove(selected_entry)
+ token_probabilities.append(tuple([selected_entry] + top_entries))
+
+ stop_reason = _OPENAI_STOP_REASON_TO_STOP_REASON[str(data.finish_reason)]
+
+ # Note, OpenAI API treats end turn and stop sequence the same
+ # Here we assume it is stop sequence if user has specified a stop sequence
+ if stop is not None and stop_reason == ResponseStopReason.END_TURN:
+ text += stop
+ token_probabilities.append(
+ tuple(
+ [
+ TokenProbability(
+ token=stop,
+ log_probability=self.stop_token_log_probability,
+ is_stop=True,
+ )
+ ]
+ )
+ )
+ result = LanguageModelResponseWithLogits(
+ text=text,
+ token_probabilities=tuple(token_probabilities),
+ token_count=len(logprobs_content) + prompt_tokens,
+ stop_reason=stop_reason,
+ network_failure_count=network_failure_count,
+ )
+ results.append(result)
+ return tuple(results)
diff --git a/vet/imbue_core/agents/llm_apis/openai_compatible_api.py b/vet/imbue_core/agents/llm_apis/openai_compatible_api.py
@@ -0,0 +1,286 @@
+import math
+from contextlib import contextmanager
+from typing import AsyncGenerator
+from typing import Iterator
+
+import httpx
+from loguru import logger
+from openai import AsyncOpenAI
+from openai import AsyncStream
+from openai import InternalServerError
+from openai import NotGiven
+from openai._exceptions import APIConnectionError
+from openai._exceptions import BadRequestError
+from openai._exceptions import RateLimitError
+from openai.types.chat import ChatCompletion
+
+from vet.imbue_core.agents.llm_apis.api_utils import convert_prompt_to_openai_messages
+from vet.imbue_core.agents.llm_apis.constants import approximate_token_count
+from vet.imbue_core.agents.llm_apis.data_types import CachingInfo
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
+from vet.imbue_core.agents.llm_apis.data_types import ResponseStopReason
+from vet.imbue_core.agents.llm_apis.errors import BadAPIRequestError
+from vet.imbue_core.agents.llm_apis.errors import PromptTooLongError
+from vet.imbue_core.agents.llm_apis.errors import TransientLanguageModelError
+from vet.imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
+from vet.imbue_core.agents.llm_apis.models import ModelInfo
+from vet.imbue_core.agents.llm_apis.openai_data_types import OpenAICachingInfo
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamStartEvent
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.frozen_utils import FrozenMapping
+from vet.imbue_core.itertools import only
+from vet.imbue_core.secrets_utils import get_secret
+
+_OPENAI_COMPATIBLE_STOP_REASON_TO_STOP_REASON: FrozenMapping[str, ResponseStopReason] = FrozenDict(
+ {
+ "stop": ResponseStopReason.END_TURN,
+ "length": ResponseStopReason.MAX_TOKENS,
+ "tool_calls": ResponseStopReason.TOOL_CALLS,
+ "function_call": ResponseStopReason.FUNCTION_CALL,
+ "content_filter": ResponseStopReason.CONTENT_FILTER,
+ "None": ResponseStopReason.NONE,
+ }
+)
+
+
+# TODO: Should the pre-defined OpenAI model class inherit from this?
+class OpenAICompatibleAPI(LanguageModelAPI):
+ model_name: str
+ base_url: str = "https://api.openai.com/v1"
+ api_key_env: str = "OPENAI_API_KEY"
+ context_window: int | None = None
+ max_output_tokens: int | None = None
+ is_conversational: bool = True
+ presence_penalty: float = 0.0
+ # this shouldn't really ever even be used, but just in case
+ stop_token_log_probability: float = math.log(0.9999)
+
+ @property
+ def model_info(self) -> ModelInfo:
+ if self.context_window is None or self.max_output_tokens is None:
+ raise ValueError("Must provide context_window and max_output_tokens, or subclass must override model_info")
+ return ModelInfo(
+ model_name=self.model_name,
+ cost_per_input_token=0.0,
+ cost_per_output_token=0.0,
+ max_input_tokens=self.context_window,
+ max_output_tokens=self.max_output_tokens,
+ rate_limit_req=None,
+ )
+
+ def _get_client(self) -> AsyncOpenAI:
+ api_key = get_secret(self.api_key_env) if self.api_key_env else ""
+ if not api_key:
+ api_key = "not-required"
+ logger.debug("API key not set, attempting to use API without key.")
+
+ return AsyncOpenAI(
+ api_key=api_key,
+ base_url=self.base_url,
+ )
+
+ @contextmanager
+ def _exception_handler(self, prompt: str) -> Iterator[None]:
+ try:
+ yield
+ except BadRequestError as e:
+ if e.code == "context_length_exceeded":
+ prompt_len = self.count_tokens(prompt)
+ max_prompt_len = self.model_info.max_input_tokens
+ logger.debug(
+ "PromptTooLongError max_prompt_len={max_prompt_len} prompt_len={prompt_len}",
+ max_prompt_len=max_prompt_len,
+ prompt_len=prompt_len,
+ )
+ raise PromptTooLongError(prompt_len, max_prompt_len) from e
+ logger.debug("BadAPIRequestError {e}", e=e)
+ raise BadAPIRequestError(str(e)) from e
+ except APIConnectionError as e:
+ logger.debug("API connection error: {e}", e=e)
+ raise TransientLanguageModelError("APIConnectionError") from e
+ except RateLimitError as e:
+ if e.code == "insufficient_quota":
+ raise
+ logger.debug("Rate limited: {e}", e=e)
+ raise TransientLanguageModelError("RateLimitError") from e
+ except httpx.RemoteProtocolError as e:
+ logger.debug("httpx.RemoteProtocolError {e}", e=e)
+ raise TransientLanguageModelError("httpx.RemoteProtocolError") from e
+ except InternalServerError as e:
+ logger.debug("InternalServerError {e}", e=e)
+ raise TransientLanguageModelError("InternalServerError") from e
+
+ async def _call_api(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ network_failure_count: int = 0,
+ ) -> CostedLanguageModelResponse:
+ messages = convert_prompt_to_openai_messages(prompt)
+
+ with self._exception_handler(prompt):
+ client = self._get_client()
+
+ temperature: NotGiven | float = params.temperature
+
+ api_result = await client.chat.completions.create(
+ model=self.model_name,
+ messages=messages,
+ max_completion_tokens=params.max_tokens,
+ n=params.count,
+ temperature=temperature,
+ stream=False,
+ seed=params.seed,
+ stop=params.stop,
+ presence_penalty=self.presence_penalty,
+ )
+ assert isinstance(api_result, ChatCompletion)
+
+ usage = api_result.usage
+ if usage is not None:
+ completion_tokens = usage.completion_tokens
+ prompt_tokens = usage.prompt_tokens
+ cached_tokens = (
+ usage.prompt_tokens_details.cached_tokens if usage.prompt_tokens_details is not None else 0
+ ) or 0
+ caching_info = CachingInfo(
+ read_from_cache=cached_tokens,
+ provider_specific_data=OpenAICachingInfo(),
+ )
+ else:
+ completion_tokens = 0
+ prompt_tokens = self.count_tokens(prompt)
+ cached_tokens = None
+ caching_info = None
+
+ results = self._parse_response(
+ api_result,
+ prompt_tokens=prompt_tokens,
+ stop=params.stop,
+ network_failure_count=network_failure_count,
+ )
+
+ logger.trace("text: {text}", text=results[0].text)
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
+ logger.trace("dollars used: {dollars_used}", dollars_used=dollars_used)
+
+ return CostedLanguageModelResponse(
+ usage=LanguageModelResponseUsage(
+ prompt_tokens_used=prompt_tokens,
+ completion_tokens_used=completion_tokens,
+ dollars_used=dollars_used,
+ caching_info=caching_info,
+ ),
+ responses=tuple(results),
+ )
+
+ async def _get_api_stream(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ messages = convert_prompt_to_openai_messages(prompt)
+
+ with self._exception_handler(prompt):
+ client = self._get_client()
+
+ temperature: NotGiven | float = params.temperature
+
+ api_result = await client.chat.completions.create(
+ model=self.model_name,
+ messages=messages,
+ max_completion_tokens=params.max_tokens,
+ n=1,
+ temperature=temperature,
+ stop=params.stop,
+ seed=params.seed,
+ stream=True,
+ stream_options={"include_usage": True},
+ presence_penalty=self.presence_penalty,
+ )
+ assert isinstance(api_result, AsyncStream)
+
+ yield LanguageModelStreamStartEvent()
+
+ usage = None
+ finish_reason: str | None = None
+ async for chunk in api_result:
+ if hasattr(chunk, "usage") and chunk.usage is not None:
+ usage = chunk.usage
+ continue
+
+ if chunk.choices:
+ assert len(chunk.choices) == 1, "Currently only count=1 supported for streaming API."
+ data = only(chunk.choices)
+ delta = data.delta.content
+ if delta is not None:
+ yield LanguageModelStreamDeltaEvent(delta=delta)
+ if data.finish_reason:
+ finish_reason = str(data.finish_reason)
+
+ stop_reason = _OPENAI_COMPATIBLE_STOP_REASON_TO_STOP_REASON.get(str(finish_reason), ResponseStopReason.NONE)
+ if params.stop is not None and stop_reason == ResponseStopReason.END_TURN:
+ yield LanguageModelStreamDeltaEvent(delta=params.stop)
+
+ if usage is not None:
+ completion_tokens = usage.completion_tokens
+ prompt_tokens = usage.prompt_tokens
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
+ cached_tokens = (
+ usage.prompt_tokens_details.cached_tokens if usage.prompt_tokens_details is not None else 0
+ ) or 0
+ caching_info = CachingInfo(
+ read_from_cache=cached_tokens,
+ provider_specific_data=OpenAICachingInfo(),
+ )
+ else:
+ completion_tokens = -1
+ prompt_tokens = -1
+ dollars_used = -1
+ caching_info = None
+ logger.trace("dollars used: {dollars_used}", dollars_used=dollars_used)
+
+ yield LanguageModelStreamEndEvent(
+ usage=LanguageModelResponseUsage(
+ prompt_tokens_used=prompt_tokens,
+ completion_tokens_used=completion_tokens,
+ dollars_used=dollars_used,
+ caching_info=caching_info,
+ ),
+ stop_reason=stop_reason,
+ )
+
+ def count_tokens(self, text: str) -> int:
+ return approximate_token_count(text)
+
+ def _parse_response(
+ self,
+ response: ChatCompletion,
+ prompt_tokens: int,
+ stop: str | None,
+ network_failure_count: int,
+ ) -> tuple[LanguageModelResponse, ...]:
+ results = []
+ for data in response.choices:
+ assert data.message.content is not None
+ text = data.message.content
+ token_count = self.count_tokens(text) + prompt_tokens
+ stop_reason = _OPENAI_COMPATIBLE_STOP_REASON_TO_STOP_REASON.get(
+ str(data.finish_reason), ResponseStopReason.NONE
+ )
+ if stop is not None and stop_reason == ResponseStopReason.END_TURN:
+ text += stop
+ result = LanguageModelResponse(
+ text=text,
+ token_count=token_count,
+ stop_reason=stop_reason,
+ network_failure_count=network_failure_count,
+ )
+ results.append(result)
+ return tuple(results)
diff --git a/vet/imbue_core/agents/llm_apis/openai_data_types.py b/vet/imbue_core/agents/llm_apis/openai_data_types.py
@@ -0,0 +1,13 @@
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+
+class OpenAIModelInfo(SerializableModel):
+ """Currently there isn't any model info specific to OpenAI"""
+
+ object_type: str = "OpenAIModelInfo"
+
+
+class OpenAICachingInfo(SerializableModel):
+ """Currently there isn't any caching info specific to OpenAI"""
+
+ object_type: str = "OpenAICachingInfo"
diff --git a/vet/imbue_core/agents/llm_apis/stream.py b/vet/imbue_core/agents/llm_apis/stream.py
@@ -0,0 +1,168 @@
+import abc
+import asyncio
+from collections.abc import AsyncIterator
+from contextlib import aclosing
+from typing import Any
+from typing import AsyncGenerator
+from typing import Sequence
+
+import anyio
+
+from vet.imbue_core.agents.llm_apis.data_types import CachedCostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelStreamInputs
+from vet.imbue_core.agents.llm_apis.data_types import ModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import ResponseStopReason
+from vet.imbue_core.agents.primitives.resource_limits import PaymentAuthorization
+from vet.imbue_core.agents.primitives.resource_limits import get_global_resource_limits
+from vet.imbue_core.caching import AsyncCache
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+
+class LanguageModelStreamStartEvent(SerializableModel):
+ pass
+
+
+class LanguageModelStreamDeltaEvent(SerializableModel):
+ delta: str
+ # TODO add per delta token count (if there is a demand)
+ # TODO add per delta logprobs (if there is a demand)
+
+
+class LanguageModelStreamEndEvent(SerializableModel):
+ usage: LanguageModelResponseUsage
+ stop_reason: ResponseStopReason
+
+
+LanguageModelStreamEvent = LanguageModelStreamStartEvent | LanguageModelStreamDeltaEvent | LanguageModelStreamEndEvent
+
+
+class LanguageModelStreamCallback(abc.ABC, SerializableModel):
+ @abc.abstractmethod
+ async def __call__(self, response: CostedLanguageModelResponse) -> None: ...
+
+
+class UpdateCacheCallback(LanguageModelStreamCallback):
+ key: str
+ cache: AsyncCache[CachedCostedLanguageModelResponse]
+ api_inputs: LanguageModelStreamInputs | None
+
+ async def __call__(self, response: CostedLanguageModelResponse) -> None:
+ async with self.cache:
+ await self.cache.set(
+ self.key,
+ CachedCostedLanguageModelResponse(response=response, inputs=self.api_inputs),
+ )
+
+
+class PromptDebuggingCallback(LanguageModelStreamCallback):
+ prompt: str
+ output_path: anyio.Path
+
+ async def __call__(self, response: CostedLanguageModelResponse) -> None:
+ await self.output_path.write_text(self.prompt + response.responses[0].text)
+
+
+class SettleSpendCallback(LanguageModelStreamCallback):
+ auth: PaymentAuthorization
+
+ async def __call__(self, response: CostedLanguageModelResponse) -> None:
+ dollars_used = response.usage.dollars_used
+ global_resource_limits = get_global_resource_limits()
+ assert global_resource_limits is not None
+ await global_resource_limits.settle_spend(self.auth, dollars_used)
+ return None
+
+
+async def consume_async_iterator(iterator: AsyncIterator[Any]) -> None:
+ async for _ in iterator:
+ ...
+
+
+async def get_cached_response_stream(
+ response: CostedLanguageModelResponse,
+) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ """Simple stream that return cached response in a single go.
+
+ Implemented here so user get's a consistent interface from stream().
+ """
+ yield LanguageModelStreamStartEvent()
+ yield LanguageModelStreamDeltaEvent(delta=response.responses[0].text)
+ yield LanguageModelStreamEndEvent(usage=response.usage, stop_reason=response.responses[0].stop_reason)
+
+
+class StreamedLanguageModelResponse(ModelResponse):
+ """A stream of LanguageModel API events."""
+
+ text_stream: AsyncIterator[str]
+
+ def __init__(
+ self,
+ # Note event_stream is AsyncGenerator as it supports aclose for better cleanup (unlike AsyncIterator)
+ event_stream: AsyncGenerator[LanguageModelStreamEvent, None],
+ network_failure_count: int,
+ completion_callbacks: Sequence[LanguageModelStreamCallback] = (),
+ ) -> None:
+ # the underlying stream coming from the API
+ self._event_stream = event_stream
+ self.text_stream = self._stream_text()
+ self._final_message_snapshot: LanguageModelResponse | None = None
+
+ self._completion_callbacks = completion_callbacks
+ # this is propogated to final message
+ self._network_failure_count = network_failure_count
+ self.stop_reason: ResponseStopReason | None = None
+
+ async def get_final_message(self) -> LanguageModelResponse:
+ # wait until final message
+ await consume_async_iterator(self._event_stream)
+ assert self._final_message_snapshot is not None
+ return self._final_message_snapshot
+
+ async def __aiter__(self) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ # iterator of events, with handling of shutdown
+ async with aclosing(self._event_stream) as event_stream:
+ deltas: list[str] = []
+ async for event in event_stream:
+ if isinstance(event, LanguageModelStreamStartEvent):
+ # Need nested if statement here for outer if-elif-else to correctly filter for unknown event types
+ if len(deltas) > 0 or self._final_message_snapshot is not None:
+ raise RuntimeError("Start event should be the first event in stream.")
+ elif isinstance(event, LanguageModelStreamDeltaEvent):
+ deltas.append(event.delta)
+ elif isinstance(event, LanguageModelStreamEndEvent):
+ self.stop_reason = event.stop_reason
+ self._final_message_snapshot = LanguageModelResponse(
+ text="".join(deltas),
+ token_count=(0 if event.usage is None else event.usage.completion_tokens_used),
+ stop_reason=event.stop_reason,
+ network_failure_count=self._network_failure_count,
+ )
+ if self._completion_callbacks is not None:
+ costed_response = CostedLanguageModelResponse(
+ usage=event.usage, responses=(self._final_message_snapshot,)
+ )
+ await asyncio.gather(*[callback(costed_response) for callback in self._completion_callbacks])
+ else:
+ raise ValueError(f"Unknown or Unexpected StreamEvent type {type(event)}.")
+
+ yield event
+
+ async def _stream_text(self) -> AsyncIterator[str]:
+ # iterator of text delta
+ async for event in self:
+ if isinstance(event, LanguageModelStreamDeltaEvent):
+ yield event.delta
+
+ async def __aenter__(self) -> "StreamedLanguageModelResponse":
+ return self
+
+ async def __aexit__(self, *exc: Any) -> None:
+ await self.aclose()
+
+ async def aclose(self) -> None:
+ """Close iterator."""
+ # clean up underlying stream (currently not sure how to do this/if we need to do this)
+ await self._event_stream.aclose()
diff --git a/vet/imbue_core/agents/llm_apis/together_api.py b/vet/imbue_core/agents/llm_apis/together_api.py
@@ -0,0 +1,611 @@
+import asyncio
+import enum
+import math
+from collections import defaultdict
+from contextlib import contextmanager
+from typing import AsyncGenerator
+from typing import Final
+from typing import Iterable
+from typing import Iterator
+from typing import Mapping
+from typing import cast
+
+from loguru import logger
+from pydantic.functional_validators import field_validator
+from together import AsyncTogether
+from together.abstract.api_requestor import APIRequestor
+from together.abstract.api_requestor import AioHTTPSession
+from together.error import APIConnectionError
+from together.error import APIError
+from together.error import AuthenticationError
+from together.error import InvalidRequestError
+from together.error import RateLimitError
+from together.error import ServiceUnavailableError
+from together.together_response import TogetherResponse
+from together.types import TogetherRequest
+from together.types.chat_completions import ChatCompletionResponse
+
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseWithLogits
+from vet.imbue_core.agents.llm_apis.data_types import ResponseStopReason
+from vet.imbue_core.agents.llm_apis.data_types import TokenProbability
+from vet.imbue_core.agents.llm_apis.errors import BadAPIRequestError
+from vet.imbue_core.agents.llm_apis.errors import LanguageModelInvalidModelNameError
+from vet.imbue_core.agents.llm_apis.errors import MissingAPIKeyError
+from vet.imbue_core.agents.llm_apis.errors import TransientLanguageModelError
+from vet.imbue_core.agents.llm_apis.language_model_api import LanguageModelAPI
+from vet.imbue_core.agents.llm_apis.models import ModelInfo
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamDeltaEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEndEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamEvent
+from vet.imbue_core.agents.llm_apis.stream import LanguageModelStreamStartEvent
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.frozen_utils import FrozenMapping
+from vet.imbue_core.itertools import only
+from vet.imbue_core.secrets_utils import get_secret
+
+
+# This function is monkeypatched as the original method does not catch BaseExceptions and asyncio.CancelledErrors are BaseExceptions
+async def arequest(
+ self: APIRequestor,
+ options: TogetherRequest,
+ stream: bool = False,
+ request_timeout: float | tuple[float, float] | None = None,
+) -> tuple[TogetherResponse | AsyncGenerator[TogetherResponse, None], bool, str | None]:
+ ctx = AioHTTPSession()
+ session = await ctx.__aenter__()
+ result = None
+ try:
+ result = await self.arequest_raw(
+ options,
+ session,
+ request_timeout=request_timeout,
+ )
+ resp, got_stream = await self._interpret_async_response(result, stream)
+ except BaseException:
+ # Close the request before exiting session context.
+ if result is not None:
+ result.release()
+ await ctx.__aexit__(None, None, None)
+ raise
+ if got_stream:
+
+ async def wrap_resp() -> AsyncGenerator[TogetherResponse, None]:
+ assert isinstance(resp, AsyncGenerator)
+ try:
+ async for r in resp:
+ yield r
+ finally:
+ # Close the request before exiting session context. Important to do it here
+ # as if stream is not fully exhausted, we need to close the request nevertheless.
+ result.release()
+ await ctx.__aexit__(None, None, None)
+
+ return wrap_resp(), got_stream, self.api_key
+ else:
+ # Close the request before exiting session context.
+ result.release()
+ await ctx.__aexit__(None, None, None)
+ return resp, got_stream, self.api_key
+
+
+APIRequestor.arequest = arequest # pyre-fixme[8]: pyre is confused about this
+
+
+class TogetherAIModelName(enum.StrEnum):
+ GOOGLE_GEMMA_2_27B_IT = "together/google/gemma-2-27b-it"
+ GOOGLE_GEMMA_2_9B_IT = "together/google/gemma-2-9b-it"
+ GOOGLE_GEMMA_2B_IT = "together/google/gemma-2b-it"
+ META_LLAMA_3_2_3B_INSTRUCT_TURBO = "together/meta-llama/Llama-3.2-3B-Instruct-Turbo"
+ META_LLAMA_3_3_70B_INSTRUCT_TURBO = "together/meta-llama/Llama-3.3-70B-Instruct-Turbo"
+ META_LLAMA_3_70B_CHAT_HF = "together/meta-llama/Llama-3-70b-chat-hf"
+ META_LLAMA_3_8B_CHAT_HF = "together/meta-llama/Llama-3-8b-chat-hf"
+ META_LLAMA_3_1_405B_INSTRUCT_TURBO = "together/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo"
+ META_LLAMA_3_1_70B_INSTRUCT_TURBO = "together/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo"
+ META_LLAMA_3_1_8B_INSTRUCT_TURBO = "together/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo"
+ META_LLAMA_3_70B_INSTRUCT_LITE = "together/meta-llama/Meta-Llama-3-70B-Instruct-Lite"
+ META_LLAMA_3_70B_INSTRUCT_TURBO = "together/meta-llama/Meta-Llama-3-70B-Instruct-Turbo"
+ META_LLAMA_3_8B_INSTRUCT_LITE = "together/meta-llama/Meta-Llama-3-8B-Instruct-Lite"
+ META_LLAMA_3_8B_INSTRUCT_TURBO = "together/meta-llama/Meta-Llama-3-8B-Instruct-Turbo"
+ MISTRALAI_MISTRAL_7B_INSTRUCT_V0_1 = "together/mistralai/Mistral-7B-Instruct-v0.1"
+ MISTRALAI_MISTRAL_7B_INSTRUCT_V0_2 = "together/mistralai/Mistral-7B-Instruct-v0.2"
+ MISTRALAI_MISTRAL_7B_INSTRUCT_V0_3 = "together/mistralai/Mistral-7B-Instruct-v0.3"
+ MISTRALAI_MIXTRAL_8X22B_INSTRUCT_V0_1 = "together/mistralai/Mixtral-8x22B-Instruct-v0.1"
+ MISTRALAI_MIXTRAL_8X7B_INSTRUCT_V0_1 = "together/mistralai/Mixtral-8x7B-Instruct-v0.1"
+ NOUSRESEARCH_NOUS_HERMES_2_MIXTRAL_8X7B_DPO = "together/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO"
+ DEEPSEEK_R1 = "together/deepseek-ai/DeepSeek-R1"
+ OPENAI_GPT_OSS_20B = "together/openai/gpt-oss-20b"
+ OPENAI_GPT_OSS_120B = "together/openai/gpt-oss-120b"
+ # TOGETHERCOMPUTER_LLAMA_3_8B_CHAT_HF_INT4 = "together/togethercomputer/Llama-3-8b-chat-hf-int4"
+ # TOGETHERCOMPUTER_LLAMA_3_8B_CHAT_HF_INT8 = "together/togethercomputer/Llama-3-8b-chat-hf-int8"
+
+
+# Rate limits for Together AI models based on published API documentation
+# Reference: https://docs.together.ai/docs/rate-limits
+# Using Tier 5 rate limits (6,000 RPM)
+
+TOGETHERAI_MODEL_INFO_BY_NAME: FrozenMapping[TogetherAIModelName, ModelInfo] = FrozenDict(
+ {
+ # ref https://docs.together.ai/docs/chat-models
+ # pricing ref https://www.together.ai/pricing
+ TogetherAIModelName.GOOGLE_GEMMA_2_27B_IT: ModelInfo(
+ model_name=str(TogetherAIModelName.GOOGLE_GEMMA_2_27B_IT),
+ cost_per_input_token=0.8 / 1_000_000,
+ cost_per_output_token=0.8 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.GOOGLE_GEMMA_2_9B_IT: ModelInfo(
+ model_name=str(TogetherAIModelName.GOOGLE_GEMMA_2_9B_IT),
+ cost_per_input_token=0.3 / 1_000_000,
+ cost_per_output_token=0.3 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.GOOGLE_GEMMA_2B_IT: ModelInfo(
+ model_name=str(TogetherAIModelName.GOOGLE_GEMMA_2B_IT),
+ cost_per_input_token=0.1 / 1_000_000,
+ cost_per_output_token=0.1 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_2_3B_INSTRUCT_TURBO: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_2_3B_INSTRUCT_TURBO),
+ cost_per_input_token=0.06 / 1_000_000,
+ cost_per_output_token=0.06 / 1_000_000,
+ max_input_tokens=131072,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_3_70B_INSTRUCT_TURBO: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_3_70B_INSTRUCT_TURBO),
+ cost_per_input_token=0.88 / 1_000_000,
+ cost_per_output_token=0.88 / 1_000_000,
+ max_input_tokens=131072,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_70B_CHAT_HF: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_70B_CHAT_HF),
+ cost_per_input_token=0.88 / 1_000_000,
+ cost_per_output_token=0.88 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_8B_CHAT_HF: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_8B_CHAT_HF),
+ cost_per_input_token=0.2 / 1_000_000,
+ cost_per_output_token=0.2 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_1_405B_INSTRUCT_TURBO: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_1_405B_INSTRUCT_TURBO),
+ cost_per_input_token=3.5 / 1_000_000,
+ cost_per_output_token=3.5 / 1_000_000,
+ max_input_tokens=130815,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_1_70B_INSTRUCT_TURBO: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_1_70B_INSTRUCT_TURBO),
+ cost_per_input_token=0.88 / 1_000_000,
+ cost_per_output_token=0.88 / 1_000_000,
+ max_input_tokens=131072,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_1_8B_INSTRUCT_TURBO: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_1_8B_INSTRUCT_TURBO),
+ cost_per_input_token=0.18 / 1_000_000,
+ cost_per_output_token=0.18 / 1_000_000,
+ max_input_tokens=131072,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_70B_INSTRUCT_LITE: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_70B_INSTRUCT_LITE),
+ cost_per_input_token=0.54 / 1_000_000,
+ cost_per_output_token=0.54 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_70B_INSTRUCT_TURBO: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_70B_INSTRUCT_TURBO),
+ cost_per_input_token=0.88 / 1_000_000,
+ cost_per_output_token=0.88 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_8B_INSTRUCT_LITE: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_8B_INSTRUCT_LITE),
+ cost_per_input_token=0.1 / 1_000_000,
+ cost_per_output_token=0.1 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.META_LLAMA_3_8B_INSTRUCT_TURBO: ModelInfo(
+ model_name=str(TogetherAIModelName.META_LLAMA_3_8B_INSTRUCT_TURBO),
+ cost_per_input_token=0.18 / 1_000_000,
+ cost_per_output_token=0.18 / 1_000_000,
+ max_input_tokens=8192,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_1: ModelInfo(
+ model_name=str(TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_1),
+ cost_per_input_token=0.2 / 1_000_000,
+ cost_per_output_token=0.2 / 1_000_000,
+ max_input_tokens=32768,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_2: ModelInfo(
+ model_name=str(TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_2),
+ cost_per_input_token=0.2 / 1_000_000,
+ cost_per_output_token=0.2 / 1_000_000,
+ max_input_tokens=32768,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_3: ModelInfo(
+ model_name=str(TogetherAIModelName.MISTRALAI_MISTRAL_7B_INSTRUCT_V0_3),
+ cost_per_input_token=0.2 / 1_000_000,
+ cost_per_output_token=0.2 / 1_000_000,
+ max_input_tokens=32768,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.MISTRALAI_MIXTRAL_8X22B_INSTRUCT_V0_1: ModelInfo(
+ model_name=str(TogetherAIModelName.MISTRALAI_MIXTRAL_8X22B_INSTRUCT_V0_1),
+ cost_per_input_token=1.2 / 1_000_000,
+ cost_per_output_token=1.2 / 1_000_000,
+ max_input_tokens=65536,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.MISTRALAI_MIXTRAL_8X7B_INSTRUCT_V0_1: ModelInfo(
+ model_name=str(TogetherAIModelName.MISTRALAI_MIXTRAL_8X7B_INSTRUCT_V0_1),
+ cost_per_input_token=0.6 / 1_000_000,
+ cost_per_output_token=0.6 / 1_000_000,
+ max_input_tokens=32768,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.NOUSRESEARCH_NOUS_HERMES_2_MIXTRAL_8X7B_DPO: ModelInfo(
+ model_name=str(TogetherAIModelName.NOUSRESEARCH_NOUS_HERMES_2_MIXTRAL_8X7B_DPO),
+ cost_per_input_token=0.6 / 1_000_000,
+ cost_per_output_token=0.6 / 1_000_000,
+ max_input_tokens=32768,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.DEEPSEEK_R1: ModelInfo(
+ model_name=str(TogetherAIModelName.DEEPSEEK_R1),
+ cost_per_input_token=3.0 / 1_000_000,
+ cost_per_output_token=7.0 / 1_000_000,
+ max_input_tokens=32768,
+ max_output_tokens=None,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.OPENAI_GPT_OSS_20B: ModelInfo(
+ model_name=str(TogetherAIModelName.OPENAI_GPT_OSS_20B),
+ cost_per_input_token=0.00 / 1_000_000,
+ cost_per_output_token=0.00 / 1_000_000,
+ max_input_tokens=131_072,
+ max_output_tokens=131_072,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ TogetherAIModelName.OPENAI_GPT_OSS_120B: ModelInfo(
+ model_name=str(TogetherAIModelName.OPENAI_GPT_OSS_120B),
+ cost_per_input_token=0.00 / 1_000_000,
+ cost_per_output_token=0.00 / 1_000_000,
+ max_input_tokens=131_072,
+ max_output_tokens=131_072,
+ rate_limit_req=6000 / 60, # 6000 RPM = 100.00 RPS
+ ),
+ }
+)
+
+
+def _default_capacity_semaphor() -> asyncio.Semaphore:
+ return asyncio.Semaphore(100)
+
+
+_CAPACITY_SEMAPHOR_BY_MODEL_NAME: Mapping[str, asyncio.Semaphore] = defaultdict(_default_capacity_semaphor)
+
+
+_ROLE_TO_TOGETHERAI_ROLE: Final[FrozenMapping] = FrozenDict(
+ {
+ "HUMAN": "user",
+ "ASSISTANT": "assistant",
+ "SYSTEM": "system",
+ "SYSTEM_CACHED": "system",
+ "USER": "user",
+ "USER_CACHED": "user",
+ }
+)
+
+# ref: https://github.com/togethercomputer/together-python/blob/main/src/together/types/common.py#L13
+_TOGETHERAI_STOP_REASON_TO_STOP_REASON: Final[FrozenMapping[str, ResponseStopReason]] = FrozenDict(
+ {
+ "length": ResponseStopReason.MAX_TOKENS,
+ # This is a little sketchy, we treat them the same since we don't know which models emit stop sequence reasons
+ # Since we don't want to break downstream applications that may require ending in the stop sequence
+ # This is similar to how openai models are treated
+ "stop": ResponseStopReason.END_TURN,
+ "eos": ResponseStopReason.END_TURN,
+ "tool_calls": ResponseStopReason.TOOL_CALLS,
+ "error": ResponseStopReason.ERROR,
+ "None": ResponseStopReason.NONE,
+ }
+)
+
+
+def convert_prompt_to_together_messages(prompt: str) -> list[dict[str, str]]:
+ prompt = prompt.lstrip()
+ assert prompt.endswith("\n[ROLE=ASSISTANT]\n"), "prompt must end with [ROLE=ASSISTANT], prompt=\n" + prompt
+ prompt = "".join(prompt.rsplit("\n[ROLE=ASSISTANT]\n", 1))
+ assert prompt.startswith("[ROLE=")
+ prompt = prompt.replace("[ROLE=", "", 1)
+ chunks = prompt.split("\n[ROLE=")
+ messages: list[dict[str, str]] = []
+ for chunk in chunks:
+ lines = chunk.split("\n")
+ role = lines[0].strip().rstrip("]")
+ assert role in (
+ "HUMAN",
+ "ASSISTANT",
+ "USER",
+ "SYSTEM",
+ "SYSTEM_CACHED",
+ "USER_CACHED",
+ ), f"Unknown role {role} in prompt {prompt}"
+ lines.pop(0)
+ if len(messages) > 0:
+ messages[-1]["content"] = messages[-1]["content"] + "\n"
+ messages.append({"role": _ROLE_TO_TOGETHERAI_ROLE[role], "content": "\n".join(lines)})
+ return messages
+
+
+@contextmanager
+def _together_exception_manager() -> Iterator[None]:
+ """Simple context manager for parsing TogetherAI API exceptions."""
+ # ref https://github.com/togethercomputer/together-python/blob/main/src/together/abstract/api_requestor.py#L332
+ try:
+ yield
+ except RateLimitError as e:
+ logger.info("Rate limited? {}", e)
+ raise TransientLanguageModelError("RateLimitError") from e
+ except InvalidRequestError as e:
+ logger.info("BadAPIRequestError {}", e)
+ raise BadAPIRequestError(str(e)) from e
+ except AuthenticationError as e:
+ logger.info("API Authentication error {}", e)
+ raise
+ except APIError as e:
+ logger.info("Received APIError {}", e)
+ raise
+ except ServiceUnavailableError as e:
+ logger.info("Received ServiceUnavailableError {}", e)
+ raise TransientLanguageModelError("ServiceUnavailableError") from e
+ except APIConnectionError as e:
+ logger.info("Received APIConnectionError {}", e)
+ raise TransientLanguageModelError("APIConnectionError") from e
+ # Note, the together python SDK uses aiohttp under the hood
+ # but takes care of parsing the main aiohttp exceptions into together API exceptions
+ # ref https://github.com/togethercomputer/together-python/blob/main/src/together/abstract/api_requestor.py#L554
+
+
+class TogetherAPI(LanguageModelAPI):
+ model_name: TogetherAIModelName = TogetherAIModelName.META_LLAMA_3_1_8B_INSTRUCT_TURBO
+ is_conversational: bool = True
+ presence_penalty: float = 0.0
+ # this shouldn't really ever even be used, but just in case
+ stop_token_log_probability: float = math.log(0.9999)
+
+ @field_validator("model_name") # pyre-ignore[56]: pyre doesn't understand pydantic
+ @classmethod
+ def validate_model_name(cls, v: str) -> str:
+ if v not in TOGETHERAI_MODEL_INFO_BY_NAME:
+ raise LanguageModelInvalidModelNameError(v, cls.__name__, list(TOGETHERAI_MODEL_INFO_BY_NAME))
+ return v
+
+ @property
+ def model_info(self) -> ModelInfo:
+ return TOGETHERAI_MODEL_INFO_BY_NAME[self.model_name]
+
+ @property
+ def external_model_name(self) -> str:
+ return self.model_name.replace("together/", "")
+
+ def _get_client(self) -> AsyncTogether:
+ api_key = get_secret("TOGETHER_API_KEY")
+ if not api_key:
+ raise MissingAPIKeyError("TOGETHER_API_KEY environment variable is not set")
+ return AsyncTogether(api_key=api_key)
+
+ async def _call_api(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ network_failure_count: int = 0,
+ ) -> CostedLanguageModelResponse:
+ if params.max_tokens is None:
+ logger.debug(
+ "Togetherai API breaks if `max_tokens` not specified. Defaulting to `max_tokens=512`, make sure to specify this if you want something different."
+ )
+ params.max_tokens = 512
+
+ with _together_exception_manager():
+ messages = convert_prompt_to_together_messages(prompt)
+ client = self._get_client()
+ async with _CAPACITY_SEMAPHOR_BY_MODEL_NAME[self.model_name]:
+ # ref: https://github.com/togethercomputer/together-python/blob/main/src/together/resources/chat/completions.py#L153
+ api_result = await client.chat.completions.create(
+ model=self.external_model_name,
+ messages=messages,
+ max_tokens=params.max_tokens,
+ stop=[params.stop] if params.stop else None,
+ temperature=params.temperature,
+ top_k=5,
+ presence_penalty=self.presence_penalty,
+ stream=False,
+ logprobs=True,
+ n=params.count,
+ # currently don't specify this since tokenizer may change between models
+ logit_bias=None,
+ )
+ assert isinstance(api_result, ChatCompletionResponse)
+
+ results = []
+ choices = api_result.choices
+ assert choices is not None
+ for data in choices:
+ message = data.message
+ assert message is not None
+ text = message.content
+ assert text is not None
+ assert isinstance(text, str) # TODO: this is suspicious
+
+ # TogetherAI only provides the logprob for the selected token
+ logprobs = data.logprobs
+ assert logprobs is not None
+ tokens = logprobs.tokens
+ token_logprobs = logprobs.token_logprobs
+ assert tokens is not None and token_logprobs is not None
+ assert all(token is not None for token in tokens)
+ assert all(logprob is not None for logprob in token_logprobs)
+ tokens = cast(Iterable[str], tokens)
+ token_logprobs = cast(Iterable[float], token_logprobs)
+ token_probabilities = [
+ (TokenProbability(token=token, log_probability=logprob, is_stop=False),)
+ for token, logprob in zip(tokens, token_logprobs)
+ ]
+
+ if data.finish_reason:
+ stop_reason = _TOGETHERAI_STOP_REASON_TO_STOP_REASON[data.finish_reason.value]
+ else:
+ stop_reason = ResponseStopReason.NONE
+ stop = params.stop
+ if stop is not None and stop_reason == ResponseStopReason.END_TURN:
+ text += stop
+ token_probabilities.append(
+ (
+ TokenProbability(
+ token=stop,
+ log_probability=self.stop_token_log_probability,
+ is_stop=True,
+ ),
+ )
+ )
+ result = LanguageModelResponseWithLogits(
+ text=text,
+ token_probabilities=tuple(token_probabilities),
+ token_count=len(token_probabilities),
+ stop_reason=stop_reason,
+ network_failure_count=network_failure_count,
+ )
+ results.append(result)
+
+ logger.trace("text: " + results[0].text)
+ if api_result.usage is not None:
+ completion_tokens = api_result.usage.completion_tokens
+ prompt_tokens = api_result.usage.prompt_tokens
+ else:
+ completion_tokens = 0
+ prompt_tokens = 0
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
+ logger.trace("dollars used: {}", dollars_used)
+ return CostedLanguageModelResponse(
+ usage=LanguageModelResponseUsage(
+ prompt_tokens_used=prompt_tokens,
+ completion_tokens_used=completion_tokens,
+ dollars_used=dollars_used,
+ ),
+ responses=tuple(results),
+ )
+
+ async def _get_api_stream(
+ self,
+ prompt: str,
+ params: LanguageModelGenerationParams,
+ ) -> AsyncGenerator[LanguageModelStreamEvent, None]:
+ if params.max_tokens is None:
+ logger.debug(
+ "Togetherai API breaks if `max_tokens` not specified. Defaulting to `max_tokens=512`, make sure to specify this if you want something different."
+ )
+ params.max_tokens = 512
+
+ with _together_exception_manager():
+ messages = convert_prompt_to_together_messages(prompt)
+ client = self._get_client()
+ async with _CAPACITY_SEMAPHOR_BY_MODEL_NAME[self.model_name]:
+ # ref: https://github.com/togethercomputer/together-python/blob/main/src/together/resources/chat/completions.py#L153
+ api_result = await client.chat.completions.create(
+ model=self.external_model_name,
+ messages=messages,
+ max_tokens=params.max_tokens,
+ stop=[params.stop] if params.stop else None,
+ temperature=params.temperature,
+ top_k=5,
+ presence_penalty=self.presence_penalty,
+ stream=True,
+ # currently we don't support logprobs with streaming
+ logprobs=False,
+ n=1,
+ # currently don't specify this since tokenizer may change between models
+ logit_bias=None,
+ )
+ assert isinstance(api_result, AsyncGenerator)
+
+ yield LanguageModelStreamStartEvent()
+
+ usage = None
+ finish_reason: str | None = None
+ async for chunk in api_result:
+ if chunk.usage:
+ usage = chunk.usage
+
+ if chunk.finish_reason:
+ finish_reason = chunk.finish_reason.value
+
+ chunk_choices = chunk.choices
+ if chunk_choices:
+ assert len(chunk_choices) == 1, "Currently only count=1 supported for streaming API."
+ delta = only(chunk_choices).delta
+ if delta and delta.content:
+ yield LanguageModelStreamDeltaEvent(delta=delta.content)
+
+ stop_reason = _TOGETHERAI_STOP_REASON_TO_STOP_REASON[str(finish_reason)]
+
+ if params.stop is not None and stop_reason == ResponseStopReason.END_TURN:
+ yield LanguageModelStreamDeltaEvent(delta=params.stop)
+
+ if usage is not None:
+ completion_tokens = usage.completion_tokens
+ prompt_tokens = usage.prompt_tokens
+ else:
+ completion_tokens = 0
+ prompt_tokens = 0
+ dollars_used = self.calculate_cost(prompt_tokens, completion_tokens)
+ logger.trace("dollars used: {}", dollars_used)
+
+ yield LanguageModelStreamEndEvent(
+ usage=LanguageModelResponseUsage(
+ prompt_tokens_used=prompt_tokens,
+ completion_tokens_used=completion_tokens,
+ dollars_used=dollars_used,
+ ),
+ stop_reason=stop_reason,
+ )
diff --git a/vet/imbue_core/agents/llm_apis/union_data_types.py b/vet/imbue_core/agents/llm_apis/union_data_types.py
@@ -0,0 +1,20 @@
+from typing import Annotated
+
+from pydantic import Tag
+
+from vet.imbue_core.agents.llm_apis.anthropic_data_types import AnthropicCachingInfo
+from vet.imbue_core.agents.llm_apis.anthropic_data_types import AnthropicModelInfo
+from vet.imbue_core.agents.llm_apis.openai_data_types import OpenAICachingInfo
+from vet.imbue_core.agents.llm_apis.openai_data_types import OpenAIModelInfo
+from vet.imbue_core.pydantic_serialization import build_discriminator
+
+ProviderSpecificModelInfoUnion = Annotated[
+ Annotated[AnthropicModelInfo, Tag("AnthropicModelInfo")] | Annotated[OpenAIModelInfo, Tag("OpenAIModelInfo")],
+ build_discriminator(),
+]
+
+ProviderSpecificCachingInfoUnion = Annotated[
+ Annotated[AnthropicCachingInfo, Tag("AnthropicCachingInfo")]
+ | Annotated[OpenAICachingInfo, Tag("OpenAICachingInfo")],
+ build_discriminator(),
+]
diff --git a/imbue_core/imbue_core/agents/primitives/errors.py b/vet/imbue_core/agents/primitives/errors.py
diff --git a/vet/imbue_core/agents/primitives/resource_limits.py b/vet/imbue_core/agents/primitives/resource_limits.py
@@ -0,0 +1,485 @@
+import asyncio
+import datetime
+import os
+from asyncio import CancelledError
+from asyncio import Task
+from asyncio import TaskGroup
+from typing import Any
+from typing import Callable
+from typing import Coroutine
+from typing import Optional
+from uuid import uuid4
+
+import attr
+from loguru import logger
+
+from vet.imbue_core.agents.primitives.errors import DollarLimitExceeded
+from vet.imbue_core.agents.primitives.errors import MaximumSpendExceeded
+from vet.imbue_core.async_monkey_patches import safe_cancel
+from vet.imbue_core.itertools import first
+from vet.imbue_core.serialization_types import Serializable
+from vet.imbue_core.time_utils import get_current_time
+
+# TODO: someday in the future we can be smarter about this...
+_AUTH_PAYMENT_TIMEOUT_SECONDS = 60 * 60 * 24
+_ONE_HOUR_IN_SECONDS = 60 * 60
+
+
+class InvalidResourceLimitsError(Exception):
+ pass
+
+
+class AuthorizationInvalidated(Exception):
+ pass
+
+
+@attr.s(auto_attribs=True, frozen=True)
+class PaymentAuthorization:
+ dollars: float
+ authorization_id: str
+ authorized_at: datetime.datetime
+
+
+@attr.s(auto_attribs=True, frozen=True)
+class ResourceLimitState(Serializable):
+ hard_cap_dollars: float
+ hard_cap_seconds: float
+ warn_cap_dollars: float
+ warn_cap_seconds: float
+ dollars_per_hour: float | None
+
+ @classmethod
+ def build_for_increase(
+ cls,
+ hard_cap_dollars: float = 0.001,
+ hard_cap_seconds: float = 0.001,
+ warn_cap_dollars: float = 0.001,
+ warn_cap_seconds: float = 0.001,
+ dollars_per_hour: float | None = None,
+ ) -> "ResourceLimitState":
+ return cls(
+ hard_cap_dollars=hard_cap_dollars,
+ hard_cap_seconds=hard_cap_seconds,
+ warn_cap_dollars=warn_cap_dollars,
+ warn_cap_seconds=warn_cap_seconds,
+ dollars_per_hour=dollars_per_hour,
+ )
+
+
+def _float_or_none(value: str | None) -> float | None:
+ if value is None:
+ return None
+ return float(value)
+
+
+@attr.s(auto_attribs=True)
+class ResourceLimits:
+ hard_cap_dollars: float
+ hard_cap_seconds: float
+ warn_cap_dollars: float
+ warn_cap_seconds: float
+ # note that setting this effectively caps the size of any given authorization request to this quantity as well
+ # (since it is impossible to spend less than $X per hour if you are spending $X+1 in total)
+ # in such a case, MaximumSpendExceeded will be raised
+ dollars_per_hour: float | None = None
+ parent_limits: Optional["ResourceLimits"] = None
+ dollars_spent: float = 0.0
+ save_spend_callback: Callable[[float], Coroutine[Any, Any, None]] | None = None
+ open_authorizations: dict[str, PaymentAuthorization] = attr.ib(factory=dict)
+ recent_spend_events: list[PaymentAuthorization] = attr.ib(factory=list)
+ # ensure that only a single spend is being authorized at once
+ spend_lock: asyncio.Lock = attr.ib(factory=asyncio.Lock)
+ # prevent us from mutating our state from multiple coroutines at once
+ state_lock: asyncio.Lock = attr.ib(factory=asyncio.Lock)
+ # triggered when the limits are updated
+ limits_updated_event: asyncio.Event = attr.ib(factory=asyncio.Event)
+ # triggered when a settlement happens
+ next_settlement_event: asyncio.Event = attr.ib(factory=asyncio.Event)
+
+ @classmethod
+ def build(
+ cls,
+ *,
+ max_dollars: float | None = None,
+ max_seconds: float | None = None,
+ warn_fraction: float | None = None,
+ dollars_per_hour: float | None = None,
+ ) -> "ResourceLimits":
+ if max_dollars is None and "DEFAULT_MAX_HAMMER_DOLLARS" in os.environ:
+ max_dollars = _float_or_none(os.getenv("DEFAULT_MAX_HAMMER_DOLLARS"))
+ assert max_dollars != float("inf"), "max_dollars must be finite"
+ if max_dollars is None:
+ max_dollars = 0.0
+ assert max_dollars >= 0, "max_dollars must be non-negative"
+ if max_seconds is None and "DEFAULT_MAX_HAMMER_SECONDS" in os.environ:
+ max_seconds = _float_or_none(os.getenv("DEFAULT_MAX_HAMMER_SECONDS"))
+ if max_seconds is None:
+ max_seconds = float("inf")
+ assert max_seconds >= 0, "max_seconds must be non-negative"
+ if warn_fraction is None:
+ # TODO: is it DEFAULT_WARN_FRACTION or DEFAULT_HAMMER_WARN_FRACTION?
+ if "DEFAULT_WARN_FRACTION" in os.environ:
+ warn_fraction = _float_or_none(os.getenv("DEFAULT_HAMMER_WARN_FRACTION"))
+ assert warn_fraction is not None
+ else:
+ warn_fraction = 0.25
+ if dollars_per_hour is None and "DEFAULT_DOLLARS_PER_HOUR" in os.environ:
+ dollars_per_hour = _float_or_none(os.getenv("DEFAULT_DOLLARS_PER_HOUR"))
+ result = cls(
+ hard_cap_dollars=max_dollars,
+ hard_cap_seconds=max_seconds,
+ warn_cap_dollars=max_dollars * warn_fraction,
+ warn_cap_seconds=max_seconds * warn_fraction,
+ dollars_per_hour=dollars_per_hour,
+ )
+ # check that we're not currently in a hammer, otherwise should be calling create_restricted_limits instead
+ try:
+ if get_global_resource_limits() is not None:
+ # If you encounter this, it probably means you're trying to mix hammer and non-hammer resource limiting.
+ # For simplicity and correctness, try to avoid that.
+ raise InvalidResourceLimitsError(
+ "Should not create a new ResourceLimits while global limits are in place. Instead, call create_restricted_limits"
+ )
+ except RuntimeError:
+ pass
+ return result
+
+ def create_restricted_limits(
+ self,
+ *,
+ max_dollars: float | None = None,
+ max_seconds: float | None = None,
+ warn_fraction: float | None = None,
+ hard_cap_dollars: float | None = None,
+ hard_cap_seconds: float | None = None,
+ warn_cap_dollars: float | None = None,
+ warn_cap_seconds: float | None = None,
+ dollars_per_hour: float | None = None,
+ ) -> "ResourceLimits":
+ if max_dollars is not None:
+ assert hard_cap_dollars is None, "Cannot specify both max_dollars and hard_cap_dollars"
+ hard_cap_dollars = max_dollars
+ if warn_fraction is not None:
+ assert warn_cap_dollars is None, "Cannot specify both warn_fraction and warn_cap_dollars"
+ warn_cap_dollars = max_dollars * warn_fraction
+
+ if max_seconds is not None:
+ assert hard_cap_seconds is None, "Cannot specify both max_seconds and hard_cap_seconds"
+ hard_cap_seconds = max_seconds
+ if warn_fraction is not None:
+ assert warn_cap_seconds is None, "Cannot specify both warn_fraction and warn_cap_seconds"
+ warn_cap_seconds = max_seconds * warn_fraction
+
+ if hard_cap_dollars is not None:
+ hard_cap_dollars = min(hard_cap_dollars, self.hard_cap_dollars)
+ if hard_cap_seconds is not None:
+ hard_cap_seconds = min(hard_cap_seconds, self.hard_cap_seconds)
+ if warn_cap_dollars is not None:
+ warn_cap_dollars = min(warn_cap_dollars, self.warn_cap_dollars)
+ if warn_cap_seconds is not None:
+ warn_cap_seconds = min(warn_cap_seconds, self.warn_cap_seconds)
+ if dollars_per_hour is not None and self.dollars_per_hour is not None:
+ dollars_per_hour = min(dollars_per_hour, self.dollars_per_hour)
+ return ResourceLimits(
+ hard_cap_dollars=hard_cap_dollars or self.hard_cap_dollars,
+ hard_cap_seconds=hard_cap_seconds or self.hard_cap_seconds,
+ warn_cap_dollars=warn_cap_dollars or self.warn_cap_dollars,
+ warn_cap_seconds=warn_cap_seconds or self.warn_cap_seconds,
+ dollars_per_hour=dollars_per_hour or self.dollars_per_hour,
+ parent_limits=self,
+ )
+
+ def _get_excessive_spend_message(self, dollars: float, debug_info: Any = None) -> str:
+ msg = f"Tried to spend {dollars} but only {self.hard_cap_dollars - self.dollars_spent} left (of {self.hard_cap_dollars})."
+ if self.hard_cap_dollars == 0:
+ msg += " You might want to set DEFAULT_MAX_HAMMER_DOLLARS to something non-zero"
+ if debug_info is not None:
+ msg += f"\nDebug info: {debug_info}"
+ return msg
+
+ async def authorize_spend(self, dollars: float, debug_info: Any | None = None) -> PaymentAuthorization:
+ # note that we purposefully lock here, even though we are potentially waiting inside the loop below.
+ # the reason for this is that otherwise a large transaction could be starved by a series of smaller transactions
+ # this is annoying to reason about, so this makes it FIFO instead (though potentially at the cost of having to
+ # wait for a while if you are near the limit and there are smaller transactions that could have made it through)
+ async with self.spend_lock:
+ await self._clear_old_authorizations()
+
+ if self.dollars_spent + dollars > self.hard_cap_dollars:
+ raise DollarLimitExceeded(self._get_excessive_spend_message(dollars, debug_info))
+
+ # if we have outstanding authorizations that mean that this transaction would put us over the limit, wait
+ while (await self.get_dollars_authorized_and_spent()) + dollars > self.hard_cap_dollars and (
+ await self.get_dollars_currently_authorized()
+ ) > 0:
+ await self.next_settlement_event.wait()
+
+ # now that some authorizations have settled and we're done waiting, have to check again if this would put us over
+ if self.dollars_spent + dollars > self.hard_cap_dollars:
+ raise DollarLimitExceeded(self._get_excessive_spend_message(dollars))
+
+ dollars_per_hour = self.dollars_per_hour
+ if dollars_per_hour is not None:
+ if dollars > dollars_per_hour:
+ raise MaximumSpendExceeded(
+ f"Tried to spend ${dollars} but only ${dollars_per_hour} / hr allowed, which caps the total spend"
+ )
+
+ # wait until the spend rate is low enough
+ while (await self.get_dollars_authorized_and_spent_in_the_last_hour()) + dollars > dollars_per_hour:
+ oldest_event = first(
+ sorted(
+ [x for x in self.recent_spend_events],
+ key=lambda x: x.authorized_at,
+ )
+ )
+ if oldest_event is None:
+ break
+ time_since_oldest_event = (get_current_time() - oldest_event.authorized_at).total_seconds()
+ time_until_next_event_expires = _ONE_HOUR_IN_SECONDS - time_since_oldest_event
+ logger.debug(
+ f"Waiting until spend rate has subsided (currently at {(await self.get_dollars_authorized_and_spent_in_the_last_hour())} / hr)"
+ )
+ waiting_task = asyncio.create_task(self._wait_until_updated())
+ try:
+ await asyncio.wait_for(waiting_task, timeout=time_until_next_event_expires + 0.01)
+ except TimeoutError:
+ pass
+ await self._clear_old_authorizations()
+
+ assert (await self.get_dollars_authorized_and_spent_in_the_last_hour()) + dollars <= dollars_per_hour
+
+ async with self.state_lock:
+ if self.parent_limits is None:
+ auth = PaymentAuthorization(
+ dollars=dollars,
+ authorization_id=uuid4().hex,
+ authorized_at=get_current_time(),
+ )
+ else:
+ auth = await self.parent_limits.authorize_spend(dollars)
+ self.open_authorizations[auth.authorization_id] = auth
+ return auth
+
+ async def settle_spend(self, authorization: PaymentAuthorization, dollars: float) -> None:
+ async with self.state_lock:
+ await self._clear_old_authorizations(_is_already_locked=True)
+
+ if self.parent_limits is not None:
+ await self.parent_limits.settle_spend(authorization, dollars)
+
+ is_threshold_exceeded_by_this_transaction = (
+ self.dollars_spent < self.warn_cap_dollars <= self.dollars_spent + dollars
+ )
+
+ if authorization.authorization_id not in self.open_authorizations:
+ raise AuthorizationInvalidated(
+ f"Authorization {authorization.authorization_id} has timed out or already been settled"
+ )
+ del self.open_authorizations[authorization.authorization_id]
+ self.recent_spend_events.append(authorization)
+ self.dollars_spent += dollars
+ assert self.save_spend_callback is not None, "Should have been initialized by now"
+ await self.save_spend_callback(self.dollars_spent)
+
+ # notify anything waiting on the next settlement
+ self.next_settlement_event.set()
+ self.next_settlement_event.clear()
+
+ logger.trace(
+ "Settled spend of {}, remaining: {}",
+ dollars,
+ self.hard_cap_dollars - self.dollars_spent,
+ )
+
+ if is_threshold_exceeded_by_this_transaction:
+ await self._warn(f"Spent ${self.dollars_spent} already (will be stopped at ${self.hard_cap_dollars})")
+
+ # TODO: make a more configurable warning system, right now just logs
+ async def _warn(self, message: str) -> None:
+ logger.warning(message)
+
+ async def _clear_old_authorizations(self, _is_already_locked: bool = False) -> None:
+ if not _is_already_locked:
+ await self.state_lock.acquire()
+ try:
+ now = get_current_time()
+ self.open_authorizations = {
+ k: v
+ for k, v in self.open_authorizations.items()
+ if (now - v.authorized_at).total_seconds() < _AUTH_PAYMENT_TIMEOUT_SECONDS
+ }
+ self.recent_spend_events = [
+ x for x in self.recent_spend_events if (now - x.authorized_at).total_seconds() < _ONE_HOUR_IN_SECONDS
+ ]
+ finally:
+ if not _is_already_locked:
+ self.state_lock.release()
+
+ async def get_dollars_currently_authorized(self) -> float:
+ async with self.state_lock:
+ return sum(x.dollars for x in self.open_authorizations.values())
+
+ async def get_dollars_authorized_and_spent(self) -> float:
+ async with self.state_lock:
+ dollars_currently_authorized = float(sum(x.dollars for x in self.open_authorizations.values()))
+ return dollars_currently_authorized + self.dollars_spent
+
+ async def get_dollars_authorized_and_spent_in_the_last_hour(self) -> float:
+ async with self.state_lock:
+ dollars_currently_authorized = float(sum(x.dollars for x in self.open_authorizations.values()))
+ return dollars_currently_authorized + sum(x.dollars for x in self.recent_spend_events)
+
+ async def bump_limits(self, limits: ResourceLimitState) -> ResourceLimitState:
+ """
+ Can only raise limits. This makes it easier for hammers to reason about how much they will be able to spend.
+
+ If we were to allow reducing limits, we'd need to be quite careful to update existing timers, and to check
+ conditions at the end of the wait loop in authorize_spend as well.
+ """
+ if self.parent_limits is None:
+ assert limits.hard_cap_dollars < float("inf"), "Cannot unlimit spend for the top-level hammer"
+
+ async with self.state_lock:
+ self.hard_cap_dollars = max(self.hard_cap_dollars, limits.hard_cap_dollars)
+ self.hard_cap_seconds = max(self.hard_cap_seconds, limits.hard_cap_seconds)
+ self.warn_cap_dollars = max(self.warn_cap_dollars, limits.warn_cap_dollars)
+ self.warn_cap_seconds = max(self.warn_cap_seconds, limits.warn_cap_seconds)
+ if self.dollars_per_hour is None:
+ self.dollars_per_hour = limits.dollars_per_hour
+ else:
+ if limits.dollars_per_hour is not None and limits.dollars_per_hour > self.dollars_per_hour:
+ self.dollars_per_hour = limits.dollars_per_hour
+
+ # notify anything waiting in case we just bumped what they were waiting on
+ if self.dollars_per_hour and limits.dollars_per_hour is not None:
+ self.limits_updated_event.set()
+ self.limits_updated_event.clear()
+
+ return ResourceLimitState(
+ hard_cap_dollars=self.hard_cap_dollars,
+ hard_cap_seconds=self.hard_cap_seconds,
+ warn_cap_dollars=self.warn_cap_dollars,
+ warn_cap_seconds=self.warn_cap_seconds,
+ dollars_per_hour=self.dollars_per_hour,
+ )
+
+ def resume(self, dollars: float, limits: ResourceLimitState) -> None:
+ self.hard_cap_dollars = limits.hard_cap_dollars
+ self.hard_cap_seconds = limits.hard_cap_seconds
+ self.warn_cap_dollars = limits.warn_cap_dollars
+ self.warn_cap_seconds = limits.warn_cap_seconds
+ self.dollars_per_hour = limits.dollars_per_hour
+ self.dollars_spent = dollars
+
+ if self.dollars_spent > self.hard_cap_dollars:
+ raise DollarLimitExceeded(
+ f"Have already spent {self.dollars_spent} dollars (more than the hard cap of {self.hard_cap_dollars})"
+ )
+
+ async def _wait_until_updated(self) -> None:
+ await self.limits_updated_event.wait()
+
+
+@attr.s(auto_attribs=True)
+class HammerTimer:
+ limits: ResourceLimits
+ timer_started_at: datetime.datetime
+ timer_task: Task | None = None
+ is_timeout_warning_issued: bool = False
+
+ # TODO: need to save whether or not we warned about the time so that we dont warn again when resuming
+ async def on_hammer_started(self, task_group: TaskGroup, callback: Callable[[], Coroutine[Any, Any, None]]) -> None:
+ seconds_ago = (get_current_time() - self.timer_started_at).total_seconds()
+ possible_wait_times = [x - seconds_ago for x in [self.limits.hard_cap_seconds, self.limits.warn_cap_seconds]]
+ positive_wait_times = [x for x in possible_wait_times if x > 0]
+ if len(positive_wait_times) == 0:
+ await callback()
+ return
+ time_until_limit_check = min(positive_wait_times)
+ self.timer_task = task_group.create_task(self._timeout_after(time_until_limit_check, callback))
+
+ async def on_hammer_stopped(self) -> None:
+ # cancel the timer (if still running
+ timer_task = self.timer_task
+ if timer_task:
+ safe_cancel(timer_task)
+ try:
+ await timer_task
+ except CancelledError:
+ pass
+ self.timer_task = None
+
+ async def _timeout_after(self, seconds: float, callback: Callable[[], Coroutine[Any, Any, None]]) -> None:
+ while True:
+ await asyncio.sleep(seconds)
+
+ # re-schedule ourselves for the next check if the limits have been updated, or we were just here for a warning
+ time_since_started = (get_current_time() - self.timer_started_at).total_seconds()
+ if time_since_started < self.limits.hard_cap_seconds:
+ # emit a warning if necessary
+ if time_since_started > self.limits.warn_cap_seconds and not self.is_timeout_warning_issued:
+ self.is_timeout_warning_issued = True
+ await self.limits._warn(
+ f"Taking longer than expected ({seconds} sec so far, will be killed at {self.limits.hard_cap_seconds}"
+ )
+ # figure out how long to sleep for
+ seconds = min(
+ [
+ self.limits.hard_cap_seconds - time_since_started,
+ self.limits.warn_cap_seconds - time_since_started,
+ ]
+ )
+ continue
+
+ # if we're still here, we've timed out
+ await callback()
+ return
+
+
+# Even if you don't use hammers, you can still benefit from having global resource limits.
+# (When in hammer-less context, this global variable is checked by LanguageModelAPI. Only the financial limits are enforced.)
+
+_GLOBAL_RESOURCE_LIMITS: ResourceLimits | None = None
+
+
+def ensure_global_resource_limits(
+ *,
+ max_dollars: float | None = None,
+ max_seconds: float | None = None,
+ warn_fraction: float | None = None,
+ dollars_per_hour: float | None = None,
+ reset_if_already_set: bool = False,
+) -> None:
+ global _GLOBAL_RESOURCE_LIMITS
+ if _GLOBAL_RESOURCE_LIMITS is None or reset_if_already_set:
+ _GLOBAL_RESOURCE_LIMITS = ResourceLimits.build(
+ max_dollars=max_dollars,
+ max_seconds=max_seconds,
+ warn_fraction=warn_fraction,
+ dollars_per_hour=dollars_per_hour,
+ )
+ _GLOBAL_RESOURCE_LIMITS.save_spend_callback = _dummy_save_spend_callback
+
+
+def get_global_resource_limits() -> ResourceLimits | None:
+ return _GLOBAL_RESOURCE_LIMITS
+
+
+async def _dummy_save_spend_callback(dollars: float) -> None:
+ pass
+
+
+async def get_global_resource_limits_summary() -> str:
+ if _GLOBAL_RESOURCE_LIMITS is None:
+ return "No global resource limits"
+ output = [
+ "Global resource limits summary:",
+ ]
+ max_dollars = _GLOBAL_RESOURCE_LIMITS.hard_cap_dollars
+ amount_spent = await _GLOBAL_RESOURCE_LIMITS.get_dollars_authorized_and_spent()
+ amount_remaining = max_dollars - amount_spent
+ output.append(f"- Max dollars: ${max_dollars:.4f}")
+ output.append(f"- Amount spent: ${amount_spent:.4f}")
+ output.append(f"- Amount remaining: ${amount_remaining:.4f}")
+ return "\n".join(output)
diff --git a/vet/imbue_core/async_monkey_patches.py b/vet/imbue_core/async_monkey_patches.py
@@ -0,0 +1,343 @@
+import asyncio
+import sys
+import traceback
+from asyncio import Future
+from types import TracebackType
+from typing import Any
+from typing import Sequence
+
+from loguru import logger
+
+from vet.imbue_core.errors import ExpectedError
+
+_IS_SHUTTING_DOWN = False
+
+
+def notify_task_groups_of_shutdown() -> None:
+ global _IS_SHUTTING_DOWN
+ _IS_SHUTTING_DOWN = True
+
+
+class PropagatingTaskGroup(asyncio.TaskGroup):
+ """Improves over TaskGroup by ensuring that cancelation messages are actually propagated"""
+
+ def __init__(self) -> None:
+ # deferring the import in case this doesn't get used
+ pass
+
+ python_version: sys._version_info = sys.version_info
+ if python_version[:2] != (3, 11) and python_version[:2] != (3, 12):
+ raise RuntimeError(
+ f"Python version 3.11 or 3.12 is required. You are using {python_version.major}.{python_version.minor}"
+ )
+ super().__init__()
+ self._entered = False
+ self._exiting = False
+ self._aborting = False
+ self._loop = None
+ self._parent_task: asyncio.Task | None = None
+ self._parent_cancel_requested = False
+ self._tasks: set[asyncio.Task] = set()
+ self._errors: list[BaseException] = []
+ self._base_error: BaseException | None = None
+ self._on_completed_fut: Future[Any] | None = None
+ self._original_message: str | None = None
+
+ async def __aexit__(
+ self,
+ et: type[BaseException] | None,
+ exc: BaseException | None,
+ tb: TracebackType | None,
+ ) -> None:
+ try:
+ return await self._aexit(et, exc)
+ finally:
+ # Exceptions are heavy objects that can have object
+ # cycles (bad for GC); let's not keep a reference to
+ # a bunch of them. It would be nicer to use a try/finally
+ # in __aexit__ directly but that introduced some diff noise
+ self._parent_task = None
+ self._errors = [] # Clear the list instead of setting to None
+ self._base_error = None
+ exc = None
+
+ async def _aexit(self, et: type[BaseException] | None, exc: BaseException | None) -> None:
+ self._exiting = True
+
+ if exc is not None and self._is_base_error(exc) and self._base_error is None: # type: ignore
+ self._base_error = exc
+
+ propagate_cancellation_error = exc if et is asyncio.exceptions.CancelledError else None
+ if self._parent_cancel_requested:
+ # If this flag is set we *must* call uncancel().
+ assert self._parent_task
+ if self._parent_task.uncancel() == 0:
+ # If there are no pending cancellations left,
+ # don't propagate CancelledError.
+ propagate_cancellation_error = None
+
+ if et is not None:
+ if not self._aborting:
+ # Our parent task is being cancelled:
+ #
+ # async with TaskGroup() as g:
+ # g.create_task(...)
+ # await ... # <- CancelledError
+ #
+ # or there's an exception in "async with":
+ #
+ # async with TaskGroup() as g:
+ # g.create_task(...)
+ # 1 / 0
+ assert exc is not None
+ self._abort_and_propagate(exc)
+
+ # We use while-loop here because "self._on_completed_fut"
+ # can be cancelled multiple times if our parent task
+ # is being cancelled repeatedly (or even once, when
+ # our own cancellation is already in progress)
+ while self._tasks:
+ if self._on_completed_fut is None:
+ assert self._loop
+ self._on_completed_fut = self._loop.create_future()
+
+ try:
+ await self._on_completed_fut
+ except asyncio.exceptions.CancelledError as ex:
+ if not self._aborting:
+ # Our parent task is being cancelled:
+ #
+ # async def wrapper():
+ # async with TaskGroup() as g:
+ # g.create_task(foo)
+ #
+ # "wrapper" is being cancelled while "foo" is
+ # still running.
+ propagate_cancellation_error = ex
+ self._abort_and_propagate(ex)
+
+ self._on_completed_fut = None
+
+ assert not self._tasks
+
+ if self._base_error is not None:
+ try:
+ raise self._base_error
+ finally:
+ exc = None
+
+ # Propagate CancelledError if there is one, except if there
+ # are other errors -- those have priority.
+ try:
+ if propagate_cancellation_error and not self._errors:
+ try:
+ raise propagate_cancellation_error
+ finally:
+ exc = None
+ finally:
+ propagate_cancellation_error = None
+
+ if et is not None and et is not asyncio.exceptions.CancelledError:
+ assert exc is not None
+ self._errors.append(exc)
+
+ if self._errors:
+ try:
+ raise BaseExceptionGroup(
+ "unhandled errors in a TaskGroup: see earlier in logs for causal error!",
+ self._errors,
+ ) from None
+ finally:
+ exc = None
+
+ def _abort(self) -> None:
+ raise Exception("Please call _abort_and_propagate instead")
+
+ def _abort_and_propagate(self, exc: BaseException) -> None:
+ global _IS_SHUTTING_DOWN
+ self._aborting = True
+
+ if self._original_message is None:
+ if isinstance(exc, asyncio.exceptions.CancelledError) and len(exc.args) > 0:
+ self._original_message = "TaskGroup canceled because:\n" + exc.args[0]
+ else:
+ if not isinstance(exc, asyncio.exceptions.CancelledError):
+ if not _IS_SHUTTING_DOWN and not isinstance(exc, ExpectedError):
+ log_exception(
+ exc,
+ "Emergency print of error that caused task group to die:",
+ )
+ self._original_message = f"TaskGroup died because: {type(exc).__name__}: {exc}\n" + "".join(
+ traceback.extract_tb(exc.__traceback__).format()
+ )
+
+ for t in self._tasks:
+ if not t.done():
+ t.cancel(self._original_message)
+
+ def _on_task_done(self, task: asyncio.Task) -> None:
+ self._tasks.discard(task)
+
+ on_completed_fut = self._on_completed_fut
+ if on_completed_fut is not None and not self._tasks:
+ if not on_completed_fut.done():
+ on_completed_fut.set_result(True)
+
+ if task.cancelled():
+ return
+
+ exc = task.exception()
+ if exc is None:
+ return
+
+ self._errors.append(exc)
+ if self._is_base_error(exc) and self._base_error is None: # type: ignore
+ self._base_error = exc
+
+ parent_task = self._parent_task
+ assert parent_task
+ if parent_task.done():
+ # Not sure if this case is possible, but we want to handle
+ # it anyways.
+ assert self._loop
+ self._loop.call_exception_handler(
+ {
+ "message": f"Task {task!r} has errored out but its parent task {parent_task} is already completed",
+ "exception": exc,
+ "task": task,
+ }
+ )
+ return
+
+ if not self._aborting and not self._parent_cancel_requested:
+ # If parent task *is not* being cancelled, it means that we want
+ # to manually cancel it to abort whatever is being run right now
+ # in the TaskGroup. But we want to mark parent task as
+ # "not cancelled" later in __aexit__. Example situation that
+ # we need to handle:
+ #
+ # async def foo():
+ # try:
+ # async with TaskGroup() as g:
+ # g.create_task(crash_soon())
+ # await something # <- this needs to be canceled
+ # # by the TaskGroup, e.g.
+ # # foo() needs to be cancelled
+ # except Exception:
+ # # Ignore any exceptions raised in the TaskGroup
+ # pass
+ # await something_else # this line has to be called
+ # # after TaskGroup is finished.
+ self._abort_and_propagate(exc)
+ self._parent_cancel_requested = True
+ parent_task.cancel(self._original_message)
+
+
+def safe_cancel(task: asyncio.Task, msg: str | None = None) -> None:
+ """
+ NOTE: this is probably not what you want! See safe_cancel_and_wait_for_cleanup below for the more common use case.
+
+ Cancels a task in a way that preserves information about who canceled it.
+
+ Without using this, it is super obnoxious to figure out why your function is being canceled --
+ you just get a CancelledError with no traceback.
+
+ We try to ensure that *all* of our tasks are canceled in this way, which makes debugging much easier.
+
+ Even safe_cancel_and_wait_for_cleanup will cancel in this way. The only difference is that that function
+ also waits for the task to actually be canceled. Otherwise, cancellation just enqueues a cancellation.
+
+ Note also that cancellation is never guaranteed -- all it does is raise a CancelledError in the task.
+ This is why it is so important to never swallow those errors!
+ """
+ task.is_being_canceled_by_us = True # type: ignore
+ message = f"Task canceled by: \n {''.join(traceback.format_stack()[:-1])}"
+ if msg:
+ message += f"\nOriginal message: {msg}"
+
+ task.cancel(message)
+
+
+async def safe_cancel_and_wait_for_cleanup(
+ task: asyncio.Task,
+ msg: str | None = None,
+ exception_types_to_ignore: Sequence[type[BaseException]] = (),
+) -> None:
+ """
+ Convenience function for calling safe_cancel_multiple_and_wait_for_cleanup with a single task.
+
+ See safe_cancel_multiple_and_wait_for_cleanup for docs.
+ """
+ await safe_cancel_multiple_and_wait_for_cleanup([task], msg, exception_types_to_ignore)
+
+
+async def safe_cancel_multiple_and_wait_for_cleanup(
+ tasks: Sequence[asyncio.Task],
+ msg: str | None = None,
+ exception_types_to_ignore: Sequence[type[BaseException]] = (),
+) -> None:
+ """
+ Calls safe_cancel (see docs above) on each task in tasks, then waits for them to be done.
+
+ Note that you can pass in a list of exception types to ignore.
+ This is important for suppressing exceptions from third party libraries.
+ You should probably make a constant in your project that lists these exceptions.
+
+ We cannot simply suppress all BaseExceptions here because you really don't want to do that for things like signals and OutOfMemoryError
+ """
+ for task in tasks:
+ safe_cancel(task, msg)
+ # if you really want something to be canceled, you need to wait for it to be done
+ # https://docs.python.org/3/library/asyncio-task.html#asyncio.Task.cancel
+ results = await asyncio.gather(*tasks, return_exceptions=True)
+ exceptions = []
+ for result in results:
+ if isinstance(result, BaseException):
+ if isinstance(result, asyncio.CancelledError):
+ pass
+ elif isinstance(result, ExceptionGroup):
+ exceptions.extend(_filter_exception_group(result))
+ else:
+ exceptions.append(result) # type: ignore
+ # cannot do this because the task may have just finished
+ # assert (
+ # False
+ # ), f"While cancelling async task and waiting for cleanup, expected None or CancelledError, got `{type(x)}: {x}`"
+
+ filtered_exceptions = []
+ for exception in exceptions:
+ if not any(isinstance(exception, exception_type) for exception_type in exception_types_to_ignore):
+ filtered_exceptions.append(exception)
+
+ if len(filtered_exceptions) == 1:
+ raise filtered_exceptions[0]
+ elif len(filtered_exceptions) > 1:
+ raise ExceptionGroup("Multiple exceptions in task group while canceling", filtered_exceptions)
+
+
+def _filter_exception_group(exc_group: ExceptionGroup) -> list[Exception]:
+ """Recursively extract exceptions from ExceptionGroups (ignoring canceled errors)."""
+ result = []
+ for exc in exc_group.exceptions:
+ if isinstance(exc, asyncio.CancelledError):
+ continue
+ elif isinstance(exc, ExceptionGroup):
+ result.extend(_filter_exception_group(exc))
+ else:
+ result.append(exc)
+ return result
+
+
+def log_exception(
+ exc: BaseException,
+ message: str,
+ *args: Any,
+ **kwargs: Any,
+) -> None:
+ """Log an exception with its traceback to stderr via loguru."""
+ logger.opt(exception=exc).error(message, *args, **kwargs)
+
+
+def apply() -> None:
+ asyncio.TaskGroup = PropagatingTaskGroup # type: ignore
+ asyncio.taskgroups.TaskGroup = PropagatingTaskGroup # type: ignore
diff --git a/imbue_core/imbue_core/async_monkey_patches_test.py b/vet/imbue_core/async_monkey_patches_test.py
diff --git a/imbue_core/imbue_core/async_utils.py b/vet/imbue_core/async_utils.py
diff --git a/vet/imbue_core/caching.py b/vet/imbue_core/caching.py
@@ -0,0 +1,183 @@
+from __future__ import annotations
+
+import asyncio
+from functools import lru_cache
+from pathlib import Path
+from types import TracebackType
+from typing import Generic
+from typing import Self
+from typing import Sequence
+from typing import TypeVar
+
+from diskcache import Cache
+from diskcache import JSONDisk
+
+from vet.imbue_core.cattrs_serialization import deserialize_from_json
+from vet.imbue_core.cattrs_serialization import serialize_to_json
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.frozen_utils import FrozenMapping
+
+ValueType = TypeVar("ValueType", covariant=True)
+
+
+class AsyncCacheInterface(Generic[ValueType]):
+ async def __aenter__(self) -> Self:
+ raise NotImplementedError()
+
+ async def __aexit__(
+ self,
+ exc_type: type[BaseException] | None,
+ exc_val: BaseException | None,
+ exc_tb: TracebackType | None,
+ ) -> None:
+ raise NotImplementedError()
+
+ async def set(
+ self,
+ key: str,
+ # pyre-fixme[46]: ValueType is covariant
+ value: ValueType,
+ expire: int | None = None,
+ read: bool = False,
+ tag: str | None = None,
+ retry: bool = False,
+ ) -> bool:
+ raise NotImplementedError()
+
+ async def get(
+ self,
+ key: str,
+ default: ValueType | None = None,
+ read: bool = False,
+ expire_time: bool = False,
+ tag: bool = False,
+ retry: bool = False,
+ ) -> ValueType | None:
+ raise NotImplementedError()
+
+ async def get_all(
+ self,
+ keys: Sequence[str],
+ default: ValueType | None = None,
+ read: bool = False,
+ expire_time: bool = False,
+ tag: bool = False,
+ retry: bool = False,
+ ) -> FrozenMapping[str, ValueType | None]:
+ raise NotImplementedError()
+
+ async def get_all_keys(self, reverse: bool = False) -> tuple[str, ...]:
+ raise NotImplementedError()
+
+
+class AsyncCache(AsyncCacheInterface[ValueType], Generic[ValueType]):
+ def __init__(self, path: Path, value_cls: type[ValueType]) -> None:
+ self.path = path
+ self.value_cls = value_cls
+ self.cache: Cache | None = None
+
+ async def _build_cache(self) -> Cache:
+ loop = asyncio.get_running_loop()
+ # pyre-ignore[6]: pyre doesn't like the lru cache here
+ return await loop.run_in_executor(None, get_cache, self.path)
+
+ async def __aenter__(self) -> Self:
+ loop = asyncio.get_running_loop()
+ cache = await self._build_cache()
+ self.cache = cache
+ await loop.run_in_executor(None, cache.__enter__)
+ return self
+
+ async def __aexit__(
+ self,
+ exc_type: type[BaseException] | None,
+ exc_val: BaseException | None,
+ exc_tb: TracebackType | None,
+ ) -> None:
+ loop = asyncio.get_running_loop()
+ cache = self.cache
+ assert cache is not None
+ result = await loop.run_in_executor(
+ None, cache.__exit__, exc_type, exc_val, exc_tb
+ )
+ self.cache = None
+ return result
+
+ async def set(
+ self,
+ key: str,
+ # pyre-fixme[46]: ValueType is covariant
+ value: ValueType,
+ expire: int | None = None,
+ read: bool = False,
+ tag: str | None = None,
+ retry: bool = False,
+ ) -> bool:
+ cache = self.cache
+ assert cache is not None
+ loop = asyncio.get_running_loop()
+ assert isinstance(value, self.value_cls), (
+ f"Expected {self.value_cls}, got {type(value)}"
+ )
+ serialized_value = serialize_to_json(value)
+ return await loop.run_in_executor(
+ None, cache.set, key, serialized_value, expire, read, tag, retry
+ )
+
+ async def get(
+ self,
+ key: str,
+ default: ValueType | None = None,
+ read: bool = False,
+ expire_time: bool = False,
+ tag: bool = False,
+ retry: bool = False,
+ ) -> ValueType | None:
+ cache = self.cache
+ assert cache is not None
+ loop = asyncio.get_running_loop()
+ value = await loop.run_in_executor(
+ None, cache.get, key, None, read, expire_time, tag, retry
+ )
+ if value is None:
+ return default
+ deserialized_value = deserialize_from_json(value)
+ assert isinstance(deserialized_value, self.value_cls), (
+ f"Expected {self.value_cls}, got {type(deserialized_value)}"
+ )
+ return deserialized_value
+
+ # TODO: this is not smart implementation, but at least it will be possible to optimize later without refactoring
+ async def get_all(
+ self,
+ keys: Sequence[str],
+ default: ValueType | None = None,
+ read: bool = False,
+ expire_time: bool = False,
+ tag: bool = False,
+ retry: bool = False,
+ ) -> FrozenMapping[str, ValueType | None]:
+ tasks = {}
+ for key in keys:
+ tasks[key] = self.get(key, default, read, expire_time, tag, retry)
+ results = await asyncio.gather(*tasks.values())
+ return FrozenDict(zip(tasks.keys(), results))
+
+ # TODO: might be nice to get iterkeys back someday, but whatever for now, too annoying to get the sync/async right
+ async def get_all_keys(self, reverse: bool = False) -> tuple[str, ...]:
+ cache = self.cache
+ assert cache is not None
+ loop = asyncio.get_running_loop()
+ return tuple(await loop.run_in_executor(None, cache.iterkeys, reverse))
+
+
+@lru_cache
+def get_cache(data_path: Path) -> Cache:
+ # not sure if the size limit applies when eviction is none, but ~64GB should be enough for now
+ return Cache(
+ str(data_path),
+ disk=JSONDisk,
+ disk_compress_level=0,
+ eviction_policy="none",
+ size_limit=2**36,
+ )
diff --git a/vet/imbue_core/cattrs_serialization.py b/vet/imbue_core/cattrs_serialization.py
@@ -0,0 +1,991 @@
+import abc
+import asyncio
+import base64
+import builtins
+import datetime
+import functools
+import importlib
+import inspect
+import json
+from decimal import Decimal
+from enum import Enum
+from functools import cached_property
+from functools import lru_cache
+from functools import partial
+from pathlib import Path
+from pathlib import PosixPath
+from types import NoneType
+from types import UnionType
+from typing import Any
+from typing import Callable
+from typing import ForwardRef
+from typing import Hashable
+from typing import Mapping
+from typing import TypeVar
+from typing import Union
+from typing import cast
+from typing import get_origin
+from uuid import UUID
+
+import anyio
+import attr
+from cachetools import LRUCache
+from cattrs import Converter
+from cattrs._compat import is_generic
+from cattrs.gen import make_dict_unstructure_fn
+from cattrs.gen import override
+from httpx import URL
+from humps import camelize # pyre-ignore[21]: pyre doesn't understand this import
+from pydantic import BaseModel
+from pydantic_core import PydanticUndefined
+
+from vet.imbue_core.errors import ImbueError
+from vet.imbue_core.fixed_traceback import FixedTraceback
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.frozen_utils import FrozenMapping
+from vet.imbue_core.serialization import SerializedException
+from vet.imbue_core.serialization_types import Serializable
+
+T = TypeVar("T")
+TYPE_KEY = "__type"
+EXCEPTION_KEY = "__exception"
+
+# LABELS for marking attributes with special handling
+DONT_SERIALIZE_METADATA_KEY = "_imbue_dont_serialize"
+DONT_SERIALIZE = {DONT_SERIALIZE_METADATA_KEY: True}
+SERIALIZE_WITH_DEFAULT_KEY = "_imbue_serialize_with_default"
+SERIALIZE_WITH_DEFAULT = {SERIALIZE_WITH_DEFAULT_KEY: True}
+
+SERIALIZABLE_PROPERTY_KEY = "_imbue_is_serializable_property"
+CACHED_SERIALIZABLE_PROPERTY_KEY = "_imbue_is_cached_serializable_property"
+
+
+##########################################################################################
+# UTILITY FUNCTIONS
+##########################################################################################
+
+
+def _safe_issubclass(t1: type, t2: type) -> bool:
+ return inspect.isclass(t1) and issubclass(t1, t2)
+
+
+def _is_frozen_mapping_type(t: type) -> bool:
+ return _safe_issubclass(get_origin(t) or t, FrozenMapping)
+
+
+def _is_mapping_type(t: type) -> bool:
+ return _safe_issubclass(get_origin(t) or t, Mapping)
+
+
+_ALLOWED_SPECIAL_MAPPING_TYPES = (LRUCache,)
+
+
+def _is_special_mapping_type(t: type) -> bool:
+ return t in _ALLOWED_SPECIAL_MAPPING_TYPES
+
+
+def _is_str_type_special_mapping_type(t: str) -> bool:
+ return t in [_type_to_string(t, fully_qualified=True) for t in _ALLOWED_SPECIAL_MAPPING_TYPES]
+
+
+def _is_obj_supported_primitive(obj: Any) -> bool:
+ return type(obj) in {bool, int, float, str, NoneType}
+
+
+def _type_to_string(t: type, fully_qualified: bool) -> str:
+ name = t.__name__
+ if fully_qualified:
+ return f"{t.__module__}.{name}"
+ else:
+ return name
+
+
+def _type_from_string(type_str: str) -> Any:
+ if "[" in type_str:
+ class_details, _ = type_str.split("[", 1)
+ else:
+ class_details = type_str
+ if "." in class_details:
+ module_path, class_name = class_details.rsplit(".", 1)
+ module = importlib.import_module(module_path)
+ else:
+ class_name = class_details
+ module = builtins
+ result = getattr(module, class_name)
+ return result
+
+
+def get_serializable_properties(obj: Any) -> dict[str, Any]:
+ members = inspect.getmembers(type(obj))
+ marked_members = {}
+ for name, member in members:
+ if is_serializable_property(member):
+ marked_members[name] = getattr(obj, name)
+ return marked_members
+
+
+def is_serializable_property(func: Callable) -> bool:
+ return getattr(func, CACHED_SERIALIZABLE_PROPERTY_KEY, False) or (
+ isinstance(func, property) and getattr(func.fget, SERIALIZABLE_PROPERTY_KEY, False)
+ )
+
+
+def cached_serializable_property(func: Callable[..., T]) -> cached_property[T]:
+ property_to_return = cached_property(func)
+ setattr(property_to_return, CACHED_SERIALIZABLE_PROPERTY_KEY, True)
+ return property_to_return
+
+
+def serializable_property(func: Callable[..., T]) -> property:
+ property_to_return = func
+ # NOTE: this will be stored in the fget attribute of the property, which is also the function
+ # we are decorating, so we must check in `func.fget` to see if the property is serializable.
+ # We need to do it this way because we cannot set the attribute on the property object/wrapper
+ # itself, because of the way the inbuilt `property` decorator works.
+ setattr(property_to_return, SERIALIZABLE_PROPERTY_KEY, True)
+ return property(property_to_return)
+
+
+def get_dont_serialize_member_names_of_type(obj_type: type) -> list[str]:
+ if not attr.has(obj_type):
+ return []
+ return [field.name for field in attr.fields(obj_type) if field.metadata.get(DONT_SERIALIZE_METADATA_KEY, False)]
+
+
+def get_serialize_with_default_member_names_of_type(
+ obj_type: type,
+) -> Mapping[str, Any]:
+ if _safe_issubclass(obj_type, BaseModel):
+ model_fields = getattr(obj_type, "model_fields", {})
+ return {
+ name: None if field.default == PydanticUndefined else field.default for name, field in model_fields.items()
+ }
+ if not attr.has(obj_type):
+ return {}
+ return {
+ field.name: None if field.default == attr.NOTHING else field.default
+ for field in attr.fields(obj_type)
+ if field.metadata.get(SERIALIZE_WITH_DEFAULT_KEY, False)
+ }
+
+
+def get_dont_serialize_member_names(obj: Any) -> list[str]:
+ if not attr.has(obj):
+ return []
+ members = inspect.getmembers(obj)
+ marked_members = []
+ for name, _ in members:
+ if is_dont_serialize_member(obj, name):
+ marked_members.append(name)
+ return marked_members
+
+
+def is_dont_serialize_member(obj: Any, member_name: str) -> bool:
+ if not attr.has(obj):
+ return False
+ for field in attr.fields(obj.__class__): # type: ignore
+ if field.name == member_name:
+ return bool(field.metadata.get(DONT_SERIALIZE_METADATA_KEY, False))
+ return False
+
+
+class SerializationError(ImbueError):
+ """Raised when we encounter problems related to Serialization or Deserialization."""
+
+
+def _to_json_dumpable_object_without_type_keys(data: Any) -> Any:
+ if isinstance(data, dict):
+ if data.get(TYPE_KEY, "") in {
+ _type_to_string(PosixPath, fully_qualified=True),
+ _type_to_string(Path, fully_qualified=True),
+ _type_to_string(UUID, fully_qualified=True),
+ }:
+ return data["value"]
+ else:
+ return {
+ key: _to_json_dumpable_object_without_type_keys(value) for key, value in data.items() if key != TYPE_KEY
+ }
+ elif isinstance(data, list):
+ return [_to_json_dumpable_object_without_type_keys(item) for item in data]
+ elif _is_obj_supported_primitive(data):
+ return data
+ else:
+ return str(data)
+
+
+def _camelize_keys_which_represent_python_names(data: Any) -> Any:
+ """Converts JSON-style objects to use camel case keys.
+
+ Takes a JSON-style object produced by CONVERTER.structure and returns the same object with certain
+ keys converted to camel case. Camel cases keys which are derived from names of Python attributes and properties.
+ Does not camel-case keys which were keys of dictionaries before serialization.
+
+ See cattrs_serialization_test.test_camel_casing for an example.
+ """
+ if isinstance(data, dict):
+ if TYPE_KEY not in data or issubclass(_type_from_string(data[TYPE_KEY]), Mapping):
+ return {key: _camelize_keys_which_represent_python_names(value) for key, value in data.items()}
+ else:
+ # pyre-ignore[16]: pyre doesn't understand the import of camelize
+ return {camelize(key): _camelize_keys_which_represent_python_names(value) for key, value in data.items()}
+ elif isinstance(data, list):
+ return [_camelize_keys_which_represent_python_names(item) for item in data]
+ else:
+ return data
+
+
+##########################################################################################
+# CLASS-SPECIFIC HOOKS
+##########################################################################################
+
+
+class _ShouldDeserialize:
+ pass
+
+
+# FIXME: Types such as LRUCache will always serialize without errors since they inherit from Mapping but will not deserialize correctly.
+# We should either document this behavior or change it so that the serialization fails if the type is not supported.
+def _serialize_mapping_to_json_dict(data: Mapping, converter: Converter) -> Any:
+ assert _is_mapping_type(type(data)), f"Attempted to serialize object of type {type(data)} as a mapping."
+ return {str(converter.unstructure(k)): converter.unstructure(v) for k, v in data.items()}
+
+
+def _serialize_mapping(data: Mapping, converter: Converter) -> Any:
+ assert _is_mapping_type(type(data)), f"Attempted to serialize object of type {type(data)} as a mapping."
+ entries = [(converter.unstructure(k), converter.unstructure(v)) for k, v in data.items()]
+ return {
+ TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
+ "__entries": entries,
+ }
+
+
+def _deserialize_special_mapping_types(data: dict, type_key: str) -> Mapping:
+ if type_key == _type_to_string(LRUCache, fully_qualified=True):
+ # FIXME: We're not serializing the object correctly and so the deserialization is hacky
+ obj: LRUCache = LRUCache(maxsize=10000)
+ return obj
+ else:
+ raise ValueError(f"Unsupported type {type_key}")
+
+
+def _deserialize_mapping(data: dict, mapping_type: type, converter: Converter) -> Mapping:
+ if TYPE_KEY in data and _is_str_type_special_mapping_type(data[TYPE_KEY]):
+ return _deserialize_special_mapping_types(data, data[TYPE_KEY])
+
+ out = {}
+ if "__entries" in data:
+ entries = data["__entries"]
+ else:
+ # We keep this branch for backwards compatibility with mappings serialized as dictionaries.
+ # We do not support Yasoo's DictWithSerializedKeys -- those will need to be migrated to the new format.
+ if TYPE_KEY in data:
+ del data[TYPE_KEY]
+ entries = data.items()
+
+ for k, v in entries:
+ out[converter.structure(k, _ShouldDeserialize)] = converter.structure(v, _ShouldDeserialize)
+
+ if _is_frozen_mapping_type(mapping_type):
+ return FrozenDict(out)
+ return out
+
+
+def _serialize_frozen_set(data: frozenset, converter: Converter) -> dict:
+ assert type(data) is frozenset, f"Attempted to serialize object of type {type(data)} as a frozenset."
+ value = converter.unstructure(data, unstructure_as=list)
+ return {"value": value, TYPE_KEY: _type_to_string(type(data), fully_qualified=True)}
+
+
+def _deserialize_frozen_set(data: dict, _: type, converter: Converter) -> frozenset:
+ return frozenset(converter.structure(data["value"], list))
+
+
+def _serialize_uuid(data: UUID) -> dict:
+ if type(data) is UUID:
+ return {
+ "value": data.hex,
+ TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
+ }
+ elif type(data) is str:
+ return {"value": data, TYPE_KEY: _type_to_string(UUID, fully_qualified=True)}
+ else:
+ raise TypeError("Tried to serialize " + str(data) + ", which is neither a string nor a UUID, as a UUID.")
+
+
+def _deserialize_uuid(data: dict[str, str] | str, _: type) -> UUID:
+ if isinstance(data, dict):
+ return UUID(data["value"])
+ elif isinstance(data, str):
+ return UUID(data)
+ else:
+ raise TypeError("Tried to deserialize something which is neither a string nor a dictionary, as a UUID.")
+
+
+def _serialize_tuple(data: tuple, converter: Converter) -> dict:
+ assert type(data) is tuple, f"Attempted to serialize object of type {type(data)} as a tuple."
+ return {
+ "value": [converter.unstructure(x) for x in data],
+ TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
+ }
+
+
+def _deserialize_tuple(data: dict, _: type, converter: Converter) -> tuple:
+ return tuple(converter.structure(x, _ShouldDeserialize) for x in data["value"])
+
+
+def _serialize_url(data: URL) -> dict:
+ assert type(data) is URL, f"Tried to serialize {data} which is not a URL."
+ return {
+ "value": str(data),
+ TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
+ }
+
+
+def _deserialize_url(data: dict, _: type) -> URL:
+ return URL(data["value"])
+
+
+def _serialize_decimal(data: Decimal) -> dict:
+ assert type(data) is Decimal, f"Attempted to serialize object of type {type(data)} as a Decimal."
+ return {
+ "value": str(data),
+ TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
+ }
+
+
+def _deserialize_decimal(data: dict, _: type) -> Decimal:
+ return Decimal(data["value"])
+
+
+def _serialize_traceback(data: FixedTraceback) -> dict:
+ assert _safe_issubclass(
+ type(data), FixedTraceback
+ ), f"Attempted to serialize object of type {type(data)} as a traceback."
+ return {
+ "value": data.to_dict(),
+ TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
+ }
+
+
+def _deserialize_traceback(data: dict, _: type) -> FixedTraceback:
+ return FixedTraceback.from_dict(data["value"])
+
+
+def _serialize_path(data: Path) -> dict:
+ assert _safe_issubclass(type(data), Path), f"Attempted to serialize an object of type {type(data)} as a Path."
+ return {
+ "value": str(data),
+ TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
+ }
+
+
+def _deserialize_path(data: Any, _: type) -> Path:
+ if type(data) is dict:
+ return Path(data["value"])
+ return Path(data)
+
+
+def _serialize_anyio_path(data: anyio.Path) -> dict:
+ assert _safe_issubclass(type(data), anyio.Path), f"Attempted to serialize an object of type {type(data)} as a Path."
+ return {
+ "value": str(data),
+ TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
+ }
+
+
+def _deserialize_anyio_path(data: Any, _: type) -> anyio.Path:
+ if type(data) is dict:
+ return anyio.Path(data["value"])
+ return anyio.Path(data)
+
+
+def _serialize_datetime(data: datetime.datetime) -> dict:
+ assert _safe_issubclass(
+ type(data), datetime.datetime
+ ), f"Attempted to serialize object of type {type(data)} as a datetime."
+ return {
+ TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
+ "time": data.astimezone(datetime.timezone.utc).timestamp(),
+ "tzaware": data.tzinfo is not None,
+ }
+
+
+def _deserialize_datetime(data: dict, _: type) -> datetime.datetime:
+ return datetime.datetime.fromtimestamp(data["time"], datetime.timezone.utc if data.get("tzaware", None) else None)
+
+
+def _serialize_bytes(data: bytes) -> dict:
+ assert type(data) is bytes, f"Attempted to serialize object of type {type(data)} as bytes."
+ return {
+ TYPE_KEY: _type_to_string(type(data), fully_qualified=True),
+ # use ascii since base64 guarantees ascii characters only
+ "value": base64.b64encode(data).decode("ascii"),
+ }
+
+
+def _deserialize_bytes(data: dict, _: type) -> bytes:
+ return base64.b64decode(data["value"])
+
+
+def _is_forward_ref(t: type) -> bool:
+ return isinstance(t, ForwardRef)
+
+
+def _serialize_forward_ref(data: Any, converter: Converter) -> Any:
+ return converter.unstructure(data, unstructure_as=type(data))
+
+
+def _deserialize_forward_ref(data: Any, _: type, converter: Converter) -> Any:
+ # TODO: think of a way to evaluate the ForwardRef _, to improve type safety.
+ # Once we do that, we can swap out the evaluated type for ShouldDeserialize
+ # and enforce that we're getting an object of the correct type.
+ return _deserialize_serialized_object(data, _ShouldDeserialize, converter)
+
+
+def _is_union_type(t: type) -> bool:
+ origin = get_origin(t)
+ return origin is Union or origin is UnionType
+
+
+def _deserialize_union_type(data: Any, type_of_data: type, converter: Converter) -> Any:
+ return converter.structure(data, _ShouldDeserialize)
+
+
+def _serialize_enum(data: Enum, converter: Converter) -> Any:
+ assert inspect.isclass(type(data)) and issubclass(
+ type(data), Enum
+ ), f"Attempted to serialize object of type {type(data)} as an Enum."
+ return converter._unstructure_enum(data)
+
+
+def _deserialize_enum(data: dict[str, str] | str, t: type) -> Any:
+ # We include this complicated logic to preserve backwards compatibility with old JSON that was
+ # serialized by Yasoo. Yasoo serialized enums by converting them into the form
+ # {"__type": "...", "value": "..."}. Strangely, Yasoo converted this dictionary into a string
+ # whenever an enum value occurred as a dictionary key, but did not convert it into a string
+ # when it occurred anywhere else. Hence we need to handle enums that are represented by
+ # dictionaries, stringified dictionaries, and strings.
+
+ assert _safe_issubclass(t, Enum)
+
+ if isinstance(data, str):
+ try:
+ # This is the case where data is an enum value, serialized by Cattrs.
+ return t(data)
+ except ValueError:
+ # This is the case where data is a stringified dictionary, serialized by Yasoo.
+ data_as_dict = json.loads(data)
+ return t[data_as_dict["value"]] # type: ignore
+ else:
+ # This is the case where data is a dictionary, serialized by Yasoo.
+ return t[data["value"]] # type: ignore
+
+
+##########################################################################################
+# TYPE KEY LOGIC
+##########################################################################################
+
+
+class _AvoidTypeKeyLogic:
+ pass
+
+
+@lru_cache
+def flag_to_ignore_type_key_hooks(t: type) -> type:
+ class GivenTypeFlaggedToAvoidTypeKeyLogic(t, _AvoidTypeKeyLogic):
+ pass
+
+ GivenTypeFlaggedToAvoidTypeKeyLogic.__name__ = t.__name__
+ GivenTypeFlaggedToAvoidTypeKeyLogic.__qualname__ = t.__qualname__
+
+ # pyre-fixme[16]: pyre doesn't understand dynamically created classes
+ return GivenTypeFlaggedToAvoidTypeKeyLogic
+
+
+def get_pydantic_model_attributes(model: BaseModel) -> dict[str, Any]:
+ # This is a hack to dump only the top level but also avoid dumping any properties
+ attributes = getattr(type(model), "model_fields", {})
+ return {a: getattr(model, a) for a in attributes}
+
+
+# These two factory functions produce the functions for serializing attr classes.
+# Only one of them should be registered at a time, depending on whether we are including
+# do-not-serialize fields in the serialization.
+def _serialize_attr_class_factory(cls: type, converter: Converter) -> Callable[[Any], Any]:
+ return make_dict_unstructure_fn(cls, converter)
+
+
+def _serialize_attr_class_without_dont_serialize_fields(
+ cls: type, converter: Converter, is_camel_case: bool
+) -> Callable[[Any], Any]:
+ members_to_omit = get_dont_serialize_member_names_of_type(cls)
+ omit_kwargs = {name: override(omit=True) for name in members_to_omit}
+ return make_dict_unstructure_fn(cls, converter, **omit_kwargs) # type: ignore
+
+
+def _serialize_with_type_key(data: Any, converter: Converter, for_javascript: bool = False) -> Any:
+ type_of_data = type(data)
+
+ if _is_obj_supported_primitive(data) or isinstance(data, list) or isinstance(data, tuple):
+ # This means that data was annotated as a Serializable, but it is a primitive or a tuple.
+ return converter.unstructure(data, unstructure_as=type_of_data)
+
+ type_of_data_with_typekey_already_added = flag_to_ignore_type_key_hooks(type_of_data) # type: ignore
+
+ # This is a hack which is necessary because cattrs does not work well with Protocols.
+ # Protocols are generic classes, but they don't have __orig_bases__, which cattrs
+ # assumes them to have.
+ if is_generic(type_of_data_with_typekey_already_added):
+ old_orig_bases = getattr(type_of_data_with_typekey_already_added, "__orig_bases__", ())
+ setattr(type_of_data_with_typekey_already_added, "__orig_bases__", old_orig_bases)
+
+ if isinstance(data, BaseModel):
+ # This is a shortcut: when you encounter a Pydantic model, just use Pydantic serialization.
+ # NOTE: currently we don't support `DONT_SERIALIZE` fields in pydantic models.
+ # so we just serialize all fields.
+ unstructured = data.model_dump(by_alias=for_javascript, mode="json")
+ else:
+ unstructured = converter.unstructure(data, unstructure_as=type_of_data_with_typekey_already_added)
+
+ assert isinstance(unstructured, dict)
+
+ if for_javascript:
+ unstructured.update({k: converter.unstructure(v) for k, v in get_serializable_properties(data).items()})
+
+ return {
+ TYPE_KEY: _type_to_string(type_of_data, fully_qualified=True),
+ **unstructured,
+ }
+
+
+# This is the predicate used in the factory functions above, so they trigger for serializable and attr classes
+# that have had their type key logic handled.
+def _should_serialize_without_type_key(t: type) -> bool:
+ is_serializable_class = _safe_issubclass(t, Serializable) or attr.has(t) or _safe_issubclass(t, BaseModel)
+ return is_serializable_class and _safe_issubclass(t, _AvoidTypeKeyLogic)
+
+
+def _should_add_type_key(t: type) -> bool:
+ is_serializable_class = _safe_issubclass(t, Serializable) or attr.has(t) or _safe_issubclass(t, BaseModel)
+ return is_serializable_class and not _safe_issubclass(t, _AvoidTypeKeyLogic)
+
+
+def _deserialize_serialized_object(data: Any, type_of_data: type, converter: Converter) -> Any:
+ if isinstance(data, list):
+ # Data is a list of objects.
+ return converter.structure(data, list[_ShouldDeserialize])
+ elif not isinstance(data, Mapping):
+ # Data is a primitive, like an integer or a string.
+ return converter.structure(data, type(data))
+ else:
+ # Data is a dictionary with a type key, representing an attrs object, a Pydantic model, or a Mapping
+ return _deserialize_using_type_marker(data, type_of_data, converter)
+
+
+def _should_deserialize_with_type_key_logic(t: type) -> bool:
+ is_type_that_should_be_deserialized = (
+ attr.has(t)
+ or _safe_issubclass(t, Serializable)
+ or _safe_issubclass(t, _ShouldDeserialize)
+ or t is Hashable
+ or _is_mapping_type(t)
+ or _safe_issubclass(t, BaseModel)
+ )
+ should_avoid_type_key_logic = _safe_issubclass(t, _AvoidTypeKeyLogic) or _safe_issubclass(
+ get_origin(t) or NoneType, _AvoidTypeKeyLogic
+ )
+ return is_type_that_should_be_deserialized and not should_avoid_type_key_logic
+
+
+def deserialized_object_violates_target_type(obj: Any, target_type: type) -> bool:
+ if target_type is _ShouldDeserialize or target_type is Serializable:
+ return False
+ if type(target_type) is TypeVar:
+ # We're not really able to check if the object is an instance of a type that's behind a TypeVar.
+ return False
+ return not isinstance(obj, get_origin(target_type) or target_type)
+
+
+# Note that expected_type_based_on_annotations may be much more vague than the actual type of the object.
+# For example: it may be Serializable, when the object is supposed to be
+# deserialized as a HammerResult. We get the real type from the "__type" key.
+def _deserialize_using_type_marker(
+ obj: Mapping[Any, Any],
+ expected_type_based_on_annotations: type[T],
+ converter: Converter,
+) -> T:
+ if TYPE_KEY in obj:
+ type_of_obj = _type_from_string(obj[TYPE_KEY])
+ else:
+ type_of_obj = expected_type_based_on_annotations
+
+ if _is_special_mapping_type(type_of_obj):
+ pass
+ elif _is_frozen_mapping_type(type_of_obj):
+ obj.pop(TYPE_KEY, None) # type: ignore
+ type_of_obj = FrozenMapping[_ShouldDeserialize, _ShouldDeserialize]
+ elif _is_mapping_type(type_of_obj):
+ obj.pop(TYPE_KEY, None) # type: ignore
+ type_of_obj = dict[_ShouldDeserialize, _ShouldDeserialize]
+ elif _safe_issubclass(type_of_obj, BaseModel):
+ assert isinstance(obj, dict)
+ obj.pop(TYPE_KEY, None)
+ return cast(T, type_of_obj.model_validate(obj))
+ elif not attr.has(type_of_obj):
+ # This happens when there is a primitive object which is annotated as Serializable.
+ return converter.structure(obj, type_of_obj) # type: ignore
+
+ # By mixing in the "avoid type key logic" class, force cattrs to do its normal behavior.
+ ret: T = converter.structure(obj, flag_to_ignore_type_key_hooks(type_of_obj))
+
+ if inspect.isclass(type_of_obj):
+ # Upcast the result so that it has the correct type again, without the mixin.
+ object.__setattr__(ret, "__class__", type_of_obj)
+
+ if deserialized_object_violates_target_type(ret, expected_type_based_on_annotations):
+ raise TypeError(
+ f"Tried to deserialize into type {expected_type_based_on_annotations}, but got object of type {type(ret)}"
+ )
+
+ return ret
+
+
+def _resolve_default(default: Any) -> Any:
+ if isinstance(default, attr.Factory): # type: ignore
+ return default.factory()
+ return default
+
+
+def _serialize_with_defaults(cls: type, converter: Converter) -> Callable[[Any], Any]:
+ # Handle a pydantic model
+ if _safe_issubclass(cls, BaseModel):
+ return lambda x: {k: converter.unstructure(v) for k, v in get_pydantic_model_attributes(x).items()}
+
+ members_with_defaults = get_serialize_with_default_member_names_of_type(cls)
+ overriden_kwargs = {
+ name: override(unstruct_hook=(lambda _, value=_resolve_default(default): value)) # type: ignore
+ for name, default in members_with_defaults.items()
+ }
+ return make_dict_unstructure_fn(cls, converter, **overriden_kwargs) # type: ignore
+
+
+def _should_serialize_as_serialized_exception(t: type) -> bool:
+ return (
+ _safe_issubclass(get_origin(t) or t, BaseException) and not attr.has(t) and not _safe_issubclass(t, BaseModel)
+ )
+
+
+##########################################################################################
+# CONVERTER FACTORY
+##########################################################################################
+
+
+class _ConverterFactory:
+ """Factory for creating converters with different configurations.
+
+ e.g. for serializing to javascript, or python, or to include do-not-serialize fields.
+ """
+
+ def build_base_converter(self) -> Converter:
+ # Builds of new base converter object, which registers all the hooks that are common to all converters.
+ # The idea being that all new converters start from this base and then override hooks they need to change
+ # NOTE: we need to generate a new converter object for each independent concrete converter (as opposed to
+ # using converter.copy()) since we use partial functions/closures and this way we ensure the function is
+ # being called with the correct converter object.
+ converter = Converter()
+
+ converter.register_structure_hook_func(_is_mapping_type, partial(_deserialize_mapping, converter=converter))
+ # serialization of mapping types depends on the specific converter so is done in the get_converter factory method
+
+ converter.register_unstructure_hook(frozenset, partial(_serialize_frozen_set, converter=converter))
+ converter.register_structure_hook(frozenset, partial(_deserialize_frozen_set, converter=converter))
+
+ converter.register_unstructure_hook(UUID, _serialize_uuid)
+ converter.register_structure_hook(UUID, _deserialize_uuid)
+
+ converter.register_unstructure_hook(URL, _serialize_url)
+ converter.register_structure_hook(URL, _deserialize_url)
+
+ converter.register_unstructure_hook(Decimal, _serialize_decimal)
+ converter.register_structure_hook(Decimal, _deserialize_decimal)
+
+ converter.register_unstructure_hook(FixedTraceback, _serialize_traceback)
+ converter.register_structure_hook(FixedTraceback, _deserialize_traceback)
+
+ converter.register_unstructure_hook(Path, _serialize_path)
+ converter.register_structure_hook(Path, _deserialize_path)
+
+ converter.register_unstructure_hook(anyio.Path, _serialize_anyio_path)
+ converter.register_structure_hook(anyio.Path, _deserialize_anyio_path)
+
+ converter.register_unstructure_hook(datetime.datetime, _serialize_datetime)
+ converter.register_structure_hook(datetime.datetime, _deserialize_datetime)
+
+ converter.register_unstructure_hook(bytes, _serialize_bytes)
+ converter.register_structure_hook(bytes, _deserialize_bytes)
+
+ converter.register_unstructure_hook(PosixPath, _serialize_path)
+ converter.register_structure_hook(PosixPath, _deserialize_path)
+
+ converter.register_unstructure_hook_func(_is_forward_ref, partial(_serialize_forward_ref, converter=converter))
+ converter.register_structure_hook_func(_is_forward_ref, partial(_deserialize_forward_ref, converter=converter))
+
+ converter.register_structure_hook_func(_is_union_type, partial(_deserialize_union_type, converter=converter))
+
+ converter.register_structure_hook(NoneType, lambda data, _: None)
+
+ converter.register_unstructure_hook(Enum, partial(_serialize_enum, converter=converter))
+ converter.register_structure_hook(Enum, _deserialize_enum)
+
+ converter.register_unstructure_hook_func(
+ _should_serialize_as_serialized_exception,
+ lambda e: serialize_to_dict(
+ SerializedException.build(e),
+ use_defaults_for_unserializable_fields=True,
+ ),
+ )
+
+ converter.register_structure_hook_func(
+ _should_deserialize_with_type_key_logic,
+ partial(_deserialize_serialized_object, converter=converter),
+ )
+
+ converter.register_structure_hook_func(
+ lambda t: isinstance(t, TypeVar),
+ partial(_deserialize_serialized_object, converter=converter),
+ )
+
+ return converter
+
+ def get_converter_with_defaults(self, converter: Converter) -> Converter:
+ converter.register_unstructure_hook(asyncio.Lock, lambda _: None)
+ converter.register_structure_hook(asyncio.Lock, lambda data, _: asyncio.Lock())
+
+ converter.register_unstructure_hook(asyncio.Task, lambda _: None)
+ converter.register_structure_hook(asyncio.Task, lambda data, _: None)
+
+ converter.register_unstructure_hook(asyncio.Queue, lambda _: None)
+ converter.register_structure_hook(asyncio.Queue, lambda data, _: None)
+
+ converter.register_unstructure_hook(asyncio.Event, lambda _: None)
+ converter.register_structure_hook(asyncio.Event, lambda data, _: None)
+
+ converter.register_unstructure_hook(asyncio.Semaphore, lambda _: None)
+ converter.register_structure_hook(asyncio.Semaphore, lambda data, _: None)
+
+ converter.register_unstructure_hook(abc.ABCMeta, lambda _: None)
+ converter.register_structure_hook(abc.ABCMeta, lambda data, _: None)
+ converter.register_unstructure_hook_factory(
+ _should_serialize_without_type_key,
+ partial(_serialize_with_defaults, converter=converter),
+ )
+
+ return converter
+
+ @functools.cache
+ def get_converter(
+ self,
+ for_javascript: bool = False,
+ exclude_dont_serialize_fields: bool = False,
+ use_defaults_for_unserializable_fields: bool = False,
+ ) -> Converter:
+ """Returns a converter with the given configuration.
+
+ The result of this method is cached, so subsequent calls with the same arguments will return the same converter.
+ """
+ assert not (
+ exclude_dont_serialize_fields and use_defaults_for_unserializable_fields
+ ), f"Expected exactly one flag to be set, got {exclude_dont_serialize_fields=}, {use_defaults_for_unserializable_fields=}"
+
+ converter = self.build_base_converter()
+ if for_javascript:
+ converter.register_unstructure_hook_func(
+ _is_mapping_type,
+ partial(_serialize_mapping_to_json_dict, converter=converter),
+ )
+ else:
+ converter.register_unstructure_hook_func(_is_mapping_type, partial(_serialize_mapping, converter=converter))
+ converter.register_unstructure_hook(tuple, partial(_serialize_tuple, converter=converter))
+ converter.register_structure_hook(tuple, partial(_deserialize_tuple, converter=converter))
+
+ if exclude_dont_serialize_fields:
+ converter.register_unstructure_hook_factory(
+ _should_serialize_without_type_key,
+ partial(
+ _serialize_attr_class_without_dont_serialize_fields,
+ converter=converter,
+ is_camel_case=for_javascript,
+ ),
+ )
+ else:
+ converter.register_unstructure_hook_factory(
+ _should_serialize_without_type_key,
+ partial(_serialize_attr_class_factory, converter=converter),
+ )
+ if use_defaults_for_unserializable_fields:
+ converter = self.get_converter_with_defaults(converter)
+
+ converter.register_unstructure_hook_func(
+ _should_add_type_key,
+ partial(
+ _serialize_with_type_key,
+ converter=converter,
+ for_javascript=for_javascript,
+ ),
+ )
+
+ return converter
+
+
+CONVERTER_FACTORY = _ConverterFactory()
+
+
+##########################################################################################
+# ENTRY POINTS
+##########################################################################################
+
+
+def _serialize_to_json_dumpable_object(
+ obj: Any,
+ is_reversible: bool = True,
+ for_javascript: bool = False,
+ exclude_dont_serialize_fields: bool = False,
+ use_defaults_for_unserializable_fields: bool = False,
+) -> Any:
+ if exclude_dont_serialize_fields:
+ # Check and raise error to make it clear to the caller that the object cannot be deserialized.
+ # This is a sanity check, to make it easier to debug when using do-not-serialize fields.
+ # NOTE: this will only catch cases where non-serializable fields are in obj, but not cases where
+ # the non-serializable fields are in nested objects, checking for the nested case is a little complicated
+ # so we don't do it basically.
+ assert (
+ not is_reversible
+ ), "Cannot deserialize object when excluding do-not-serialize fields (i.e. when `exclude_dont_serialize_fields=True`). If you want to serialize an object and exclude do-not-serialize fields, make sure to set `is_reversible=False`."
+
+ if use_defaults_for_unserializable_fields:
+ # The point of the use_defaults_for_unserializable_fields flag is to make it possible to serialize objects
+ # and then recreate them later even if certain fields are not fully saved. We never want to use this flag
+ # with `is_reversible=False` since we won't know the type to be able to recreate the object.
+ assert is_reversible, "Cannot restructure inputs if is_reversible=False"
+
+ # TODO: this is a hack to make it possible to serialize ExecutionContexts for class methods.
+ # This lets us serialize ExecutionContexts for calls to class methods without serializing the class itself.
+ # The long-term solution is to write a custom hook that can serialize type objects.
+ if type(obj) is dict and "__class__" in obj:
+ del obj["__class__"]
+
+ converter = CONVERTER_FACTORY.get_converter(
+ for_javascript=for_javascript,
+ exclude_dont_serialize_fields=exclude_dont_serialize_fields,
+ use_defaults_for_unserializable_fields=use_defaults_for_unserializable_fields,
+ )
+
+ dict_result = converter.unstructure(obj)
+ if for_javascript:
+ dict_result = _camelize_keys_which_represent_python_names(dict_result)
+
+ if not is_reversible:
+ return _to_json_dumpable_object_without_type_keys(dict_result)
+
+ return dict_result
+
+
+def serialize_to_dict(
+ obj: Any,
+ is_reversible: bool = True,
+ for_javascript: bool = False,
+ exclude_dont_serialize_fields: bool = False,
+ use_defaults_for_unserializable_fields: bool = False,
+) -> dict[str, Any]:
+ """Serialize to a python dict."""
+ return cast(
+ dict[str, Any],
+ _serialize_to_json_dumpable_object(
+ obj,
+ is_reversible=is_reversible,
+ for_javascript=for_javascript,
+ exclude_dont_serialize_fields=exclude_dont_serialize_fields,
+ use_defaults_for_unserializable_fields=use_defaults_for_unserializable_fields,
+ ),
+ )
+
+
+def serialize_to_json(
+ obj: Any,
+ indent: int | None = None,
+ sort_keys: bool = False,
+ is_reversible: bool = True,
+ for_javascript: bool = False,
+ exclude_dont_serialize_fields: bool = False,
+ use_defaults_for_unserializable_fields: bool = False,
+) -> str:
+ """Serialize an object to a JSON string.
+
+ This is the main serialization entrypoint.
+
+ `is_reversible` controls whether we enforce that the result can be deserialized. In some cases we don't care about
+ reversibility, e.g. when serializing data for a frontend we often don't care whether we can deserialize.
+
+ `for_javascript` controls whether we use camelCase for keys that originally were Python identifiers.
+
+ `exclude_dont_serialize_fields` controls whether we include do-not-serialize fields in the serialization.
+ If this is `False` then any attr class fields marked with as don't serialize, e.g. with `attr.ib(metadata=DONT_SERIALIZE)`,
+ will still be included in the serialization. If this is `True` then they will be excluded, however this also means that
+ the result will not be reversible (and thus the caller will have to set `is_reversible=False`).
+
+ `use_defaults_for_unserializable_fields` controls whether we fill fields that cannot be serialized with their default values.
+ IMPORTANT: If you use this flag, data may be discarded during deserialization.
+ The goal is to be able to deserialize fields to the original type without caring about the data contained.
+ Default value choices (guided by crafty serialization requirements):
+ - Fields that are marked with attr.ib(metadata=SERIALIZE_WITH_DEFAULT) have the following default values:
+ - Fields that are marked with `attr.ib(default=...)` or `attr.ib(factory=...)` use their default values.
+ - Fields that do not have a default value are filled with None.
+ - Asyncio objects are filled with None.
+ - Exceptions are replaced with a string representation
+ """
+ try:
+ unstructured = _serialize_to_json_dumpable_object(
+ obj,
+ is_reversible=is_reversible,
+ for_javascript=for_javascript,
+ exclude_dont_serialize_fields=exclude_dont_serialize_fields,
+ use_defaults_for_unserializable_fields=use_defaults_for_unserializable_fields,
+ )
+ return json.dumps(unstructured, indent=indent, sort_keys=sort_keys)
+ except Exception as e:
+ raise SerializationError(str(e)) from e
+
+
+def deserialize_from_json(
+ data: str,
+ for_javascript: bool = False,
+ exclude_dont_serialize_fields: bool = False,
+ use_defaults_for_unserializable_fields: bool = False,
+) -> Any:
+ try:
+ converter = CONVERTER_FACTORY.get_converter(
+ for_javascript=for_javascript,
+ exclude_dont_serialize_fields=exclude_dont_serialize_fields,
+ use_defaults_for_unserializable_fields=use_defaults_for_unserializable_fields,
+ )
+ return _deserialize_serialized_object(json.loads(data), _ShouldDeserialize, converter=converter)
+ except Exception as e:
+ raise SerializationError(str(e)) from e
+
+
+def deserialize_from_dict(
+ data: dict[str, Any],
+ as_type: type = _ShouldDeserialize,
+ for_javascript: bool = False,
+ exclude_dont_serialize_fields: bool = False,
+ use_defaults_for_unserializable_fields: bool = False,
+) -> Any:
+ try:
+ converter = CONVERTER_FACTORY.get_converter(
+ for_javascript=for_javascript,
+ exclude_dont_serialize_fields=exclude_dont_serialize_fields,
+ use_defaults_for_unserializable_fields=use_defaults_for_unserializable_fields,
+ )
+ return _deserialize_using_type_marker(data, as_type, converter=converter)
+ except Exception as e:
+ raise SerializationError(str(e)) from e
diff --git a/imbue_core/imbue_core/common.py b/vet/imbue_core/common.py
diff --git a/imbue_core/imbue_core/conftest.py b/vet/imbue_core/conftest.py
diff --git a/vet/imbue_core/data_types.py b/vet/imbue_core/data_types.py
@@ -0,0 +1,317 @@
+"""
+Interfaces and data types for the issue identification system.
+
+We foresee two basic kinds of issue identifiers:
+
+ 1. Heavily specialized ones.
+ - Those would typically only check for a single well-defined issue.
+ - The user only wants to know if a specific thing is wrong.
+ 2. General ones.
+ - They would still have a certain focus but they would typically check for a broader range of issues.
+ - We wouldn't know the whole range of possible issues in advance.
+ - Here, the user asks for up to N most problematic issues of the given kind in a given scope.
+
+Issue identifiers can either be created:
+ - by implementing the IssueIdentifier protocol in an arbitrary way
+ - or by leaning on a common LLM-based zero-shot classifier
+ - This has the advantage of being more efficient.
+ - We can ask for a (set of) score(s) on various metrics / error types for a list of scopes.
+ - These computations can basically all be batched together into a single call to the LLM.
+ - NOTE: as of writing this, this hasn't been implemented yet.
+
+What follows is a list of possible issues we may eventually want to identify:
+
+
+- docstrings / documentation / tests / constraints / validation
+ - missing
+ - outdated
+ - ambiguous
+ - poorly written / duplicated
+ - conflicting
+ - insufficient
+- assumptions
+ - unstated
+ - violated
+ - conflicting
+- possible race condition
+- overly complex code
+- code in need of refactoring
+- project layout in need of refactoring
+- duplicated code
+- brittle logic
+- use of state (at all, where unnecessary, where needless)
+- gross inefficiency
+- caching (the presence of, at all)
+- bad/confusing/unclear naming
+- forbidden stylistic patterns (that cannot be caught by ratchets)
+- poorly handled edge cases
+- just plain ol bugs, overall correctness, etc
+- missing test coverage (not line based, but more meaning based, esp around integration tests)
+- disagreeing ensembles
+- missing features / implementations / etc
+- refactoring elements that were missed
+- invocation outputs that seem suspect
+- overly broad types
+- misunderstanding the users mental model
+- architectural flaws
+- general critiques
+- better alternatives
+- reinvented wheels / places where some library or external service should have been used
+- mutated globals / global state / imported globals
+- any unnecessary complexity
+
+There are also things we explicitly don't want to catch with this system:
+
+- runtime errors (when running a main script or tests)
+- most ratchet errors
+- anything caught by an existing tool (typing, pylint, tests, etc)
+- errors during the deployment process itself
+- errors when building the image
+- errors about packaging, installation, dependencies, etc
+- errors collected from production
+
+"""
+
+from enum import StrEnum
+from typing import Literal
+
+from pydantic import Field
+
+from vet.imbue_core.common import generate_id
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+# Define semantics for the normalized confidence and severity scores.
+CONFIDENCE_CERTAINLY_FINE = (0.0, 0.2)
+CONFIDENCE_RATHER_FINE = (0.2, 0.4)
+CONFIDENCE_NOT_SURE = (0.4, 0.6)
+CONFIDENCE_RATHER_PROBLEMATIC = (0.6, 0.8)
+CONFIDENCE_CERTAINLY_PROBLEMATIC = (0.8, 1.0)
+
+
+class ConfidenceScore(SerializableModel):
+ """
+ A score for the confidence in the issue / error detection.
+
+ - The raw score is the score as output by the underlying model.
+ - The normalized score is rescaled in such a way that the interval between 0 and 1 maps to the defined confidence levels.
+
+ """
+
+ raw: float
+ normalized: float
+
+
+class SeverityScore(SerializableModel):
+ """
+ A score for the severity of the issue / error.
+
+ - The raw score is the score as potentially output by the underlying model.
+ - The normalized score is rescaled in such a way that the interval between 0 and 1 maps to the defined severity levels.
+
+ """
+
+ raw: float
+ normalized: float
+
+
+class LineRange(SerializableModel):
+ start: int
+ end: int
+
+ def __lt__(self, other: "LineRange") -> bool:
+ if self.start != other.start:
+ return self.start < other.start
+ return self.end < other.end
+
+ @classmethod
+ def build_from_substring(cls, file_contents: str, substring: str) -> tuple["LineRange", ...]:
+ """
+ Convert a substring in a file to a tuple of LineRange instances.
+
+ Each LineRange instance corresponds to a single occurrence of the substring in the file.
+ (Except when multiple occurences are on the same line, in which case only one LineRange is
+ created to represent them).
+
+ LineRanges are returned in the order they appear in the file.
+
+ In case the substring can't be found, an empty tuple is returned.
+
+ """
+
+ line_ranges = set()
+ offset_chars = 0
+ offset_lines = 0
+ while True:
+ cut_contents = file_contents[offset_chars:]
+ start_index = cut_contents.find(substring)
+ if start_index == -1:
+ break
+ end_index = start_index + len(substring)
+ line_start = offset_lines + cut_contents.count("\n", 0, start_index)
+ line_end = offset_lines + cut_contents.count("\n", 0, end_index)
+ offset_chars += end_index
+ offset_lines = line_end
+ line_ranges.add(LineRange(start=line_start, end=line_end))
+ return tuple(sorted(line_ranges))
+
+
+class AgenticPhase(StrEnum):
+ """Phases of agentic analysis."""
+
+ ISSUE_IDENTIFICATION = "issue_identification"
+ COLLATION = "collation"
+ FILTRATION = "filtration"
+ DEDUPLICATION = "deduplication"
+
+
+class IssueIdentifierType(StrEnum):
+ BATCHED_COMMIT_CHECK = "batched_commit_check"
+ CORRECTNESS_COMMIT_CLASSIFIER = "correctness_commit_classifier"
+ AGENTIC_ISSUE_IDENTIFIER = "agentic_issue_identifier"
+ CONVERSATION_HISTORY_IDENTIFIER = "conversation_history_issue_identifier"
+
+
+class IssueCode(StrEnum):
+ """
+ A code for the type of issue / error detected.
+
+ The code can either correspond something very specific (e.g. "ambiguous_docstring")
+ or to something more general (e.g. "function_implementation").
+
+ The latter case would be used as an "umbrella" code in cases we don't know what exactly comes out of an issue verifier.
+
+ """
+
+ # Verifier-based.
+ INCORRECT_FUNCTION_IMPLEMENTATION = "incorrect_function_implementation"
+
+ # Batched file checks
+ INEFFICIENT_CODE = "inefficient_code"
+ BAD_NAMING = "bad_naming"
+ POOR_DOCSTRING = "poor_docstring"
+ RACE_CONDITION = "race_condition"
+ HARDCODED_SECRET = "hardcoded_secret"
+ DUPLICATE_CODE = "duplicate_code"
+ UNUSED_CODE = "unused_code"
+ COMMIT_MESSAGE_MISMATCH = "commit_message_mismatch"
+
+ # Batched commit checks
+ INCOMPLETE_INTEGRATION_WITH_EXISTING_CODE = "incomplete_integration_with_existing_code"
+ DOCUMENTATION_IMPLEMENTATION_MISMATCH = "documentation_implementation_mismatch"
+ USER_REQUEST_ARTIFACTS_LEFT_IN_CODE = "user_request_artifacts_left_in_code"
+ POOR_NAMING = "poor_naming"
+ REPETITIVE_OR_DUPLICATE_CODE = "repetitive_or_duplicate_code"
+ REFACTORING_NEEDED = "refactoring_needed"
+ TEST_COVERAGE = "test_coverage"
+ RESOURCE_LEAKAGE = "resource_leakage"
+ DEPENDENCY_MANAGEMENT = "dependency_management"
+ INSECURE_CODE = "insecure_code"
+ CORRECTNESS_SYNTAX_ISSUES = "correctness_syntax_issues"
+ FAILS_SILENTLY = "fails_silently"
+ INSTRUCTION_FILE_DISOBEYED = "instruction_file_disobeyed"
+ ABSTRACTION_VIOLATION = "abstraction_violation"
+
+ # Correctness commit classifier
+ LOGIC_ERROR = "logic_error"
+ RUNTIME_ERROR_RISK = "runtime_error_risk"
+ INCORRECT_ALGORITHM = "incorrect_algorithm"
+ ERROR_HANDLING_MISSING = "error_handling_missing"
+ ASYNC_CORRECTNESS = "async_correctness"
+ TYPE_SAFETY_VIOLATION = "type_safety_violation"
+
+ # Conversation history identifier
+ MISLEADING_BEHAVIOR = "misleading_behavior"
+ INSTRUCTION_TO_SAVE = "instruction_to_save"
+
+ # Github dataset, not yet implemented in commit checks
+ MISMATCHED_CODE_PATTERNS = "mismatched_code_patterns"
+
+ # Issue code for flagging suggested improvements or new features, as opposed to actual issues
+ SUGGESTED_IMPROVEMENT = "suggested_improvement"
+
+ # Catchall
+ MISCELLANEOUS = "miscellaneous"
+ ALL_CODE_ISSUES = "all_code_issues"
+
+ # Deprecated
+ _DEPRECATED_LLM_ARTIFACTS_LEFT_IN_CODE = "llm_artifacts_left_in_code"
+
+
+class IssueLocation(SerializableModel):
+ """A location in a file."""
+
+ line_start: int
+ line_end: int
+ filename: str | None = None
+ # The scope of the issue. Usually the qualified name of the function that the issue is located in.
+ # If the issue is part of a class definition (and not limited to a particular method),
+ # the name of the class. If the issue is at the global file level, None.
+ scope: str | None = None
+
+
+IssueID = str
+
+
+class IdentifiedVerifyIssue(SerializableModel):
+ """An identified code issue / error."""
+
+ issue_id: IssueID | None = Field(default_factory=generate_id)
+ code: IssueCode
+ description: str
+ severity_score: SeverityScore
+ location: tuple[IssueLocation, ...] = Field(default_factory=tuple)
+ confidence_score: ConfidenceScore | None = None
+ fix: str | None = None
+ violating_instruction: str | None = None
+ violating_instruction_location: IssueLocation | None = None
+ # TODO: remove these fields
+
+ # An issue is fundamentally fixable if we can change the implementation to make the issue go away.
+ # (An example of a non-fixable issue is a nonsensical commit message - changing the implementation doesn't help here.)
+ # - iffv something is not fixable why would we want to report it?
+ is_fixable: bool = True
+
+
+class InvocationInfo(SerializableModel):
+ """Information about an LLM invocation including token usage, timing, and cost. Populate whichever fields are available."""
+
+ input_tokens: int | None = None
+ cache_creation_input_tokens: int | None = None
+ cache_read_input_tokens: int | None = None
+ total_input_tokens: int | None = None
+ output_tokens: int | None = None
+ duration_ms: float | None = None
+ cost: float | None = None
+ num_turns: int | None = None
+
+
+class IssueIdentificationLLMResponseMetadata(SerializableModel):
+ """Configuration metadata for LLM responses."""
+
+ type: Literal["IssueIdentificationLLMResponseMetadata", "IssueIdentificationLLMResponseConfig"] = (
+ "IssueIdentificationLLMResponseMetadata"
+ )
+ agentic_phase: AgenticPhase | None = None
+ issue_type: IssueCode | None = None
+ identifier_name: str | None = None
+ issue_ids: tuple[IssueID] | None = None
+
+
+class LLMResponse(SerializableModel):
+ metadata: IssueIdentificationLLMResponseMetadata # Make this a union if there are other types of LLM responses
+ raw_response: tuple[str, ...]
+ invocation_info: InvocationInfo | None = None
+
+ # Deprecated fields
+ config: IssueIdentificationLLMResponseMetadata | None = Field(default=None, deprecated=True)
+
+
+class IssueIdentificationDebugInfo(SerializableModel):
+ llm_responses: tuple[LLMResponse, ...]
+
+
+class IssueIdentifierResult(SerializableModel):
+ """Container for an identified issue along with the LLM responses that generated it."""
+
+ issue: IdentifiedVerifyIssue
+ passes_filtration: bool = True
diff --git a/imbue_core/imbue_core/errors.py b/vet/imbue_core/errors.py
diff --git a/imbue_core/imbue_core/fixed_traceback.py b/vet/imbue_core/fixed_traceback.py
diff --git a/imbue_core/imbue_core/frozen_utils.py b/vet/imbue_core/frozen_utils.py
diff --git a/vet/imbue_core/itertools.py b/vet/imbue_core/itertools.py
@@ -0,0 +1,52 @@
+import contextlib
+import itertools
+from typing import Generator
+from typing import Iterable
+from typing import Sequence
+from typing import TypeVar
+
+from vet.imbue_core.errors import ImbueError
+
+T = TypeVar("T")
+
+
+class ImbueItertoolsValueError(ImbueError, ValueError):
+ """This value error is thrown when the assumptions of the itertools module are violated."""
+
+
+def flatten(iterable: Iterable[Iterable[T]]) -> list[T]:
+ return list(itertools.chain.from_iterable(iterable))
+
+
+def remove_none(data: Iterable[T | None]) -> list[T]:
+ return [x for x in data if x is not None]
+
+
+def only(x: Iterable[T]) -> T:
+ try:
+ (value,) = x
+ except ValueError as e:
+ message = "Expected exactly one value"
+ if isinstance(x, Sequence):
+ with contextlib.suppress():
+ message += f" but got {len(x)} {x[:3]=}"
+ raise ImbueItertoolsValueError(message) from e
+
+ return value
+
+
+def first(iterable: Iterable[T]) -> T | None:
+ return next(iter(iterable), None)
+
+
+# TODO replace with itertools.batched when we can require Python 3.12+
+def generate_chunks(iterable: Iterable[T], chunk_size: int) -> Generator[tuple[T, ...], None, None]:
+ """Yield successive n-sized chunks from any iterable"""
+ chunk = []
+ for item in iterable:
+ chunk.append(item)
+ if len(chunk) == chunk_size:
+ yield tuple(chunk)
+ chunk = []
+ if len(chunk) > 0:
+ yield tuple(chunk)
diff --git a/imbue_core/imbue_core/language_model_mode.py b/vet/imbue_core/language_model_mode.py
diff --git a/vet/imbue_core/nested_evolver.py b/vet/imbue_core/nested_evolver.py
@@ -0,0 +1,220 @@
+"""Evolver uses duck-typing to give the appearance of editing a frozen, nested structure of attrs classes and tuples, recording changes in a way that they can be applied to generate a newly frozen instance.
+
+One of the design goals is that mypy, autocomplete, and automatic refactoring work for the assignments made into these nested structures.
+
+If you make changes here and then the tests fail with:
+```
+E RecursionError: maximum recursion depth exceeded
+!!! Recursion detected (same locals & position)
+```
+It's possible that you're accidentally invoking `Evolver.something_undefined` and that's causing the infinite recursion.
+Mypy cannot catch this when it thinks the type is an `Evolver` because the `Evolver` class has a `__getattr__` method that makes it look like any attribute access could be valid.
+"""
+
+import threading
+from typing import Any
+from typing import Callable
+from typing import Generic
+from typing import TypeVar
+from typing import cast
+
+import attr
+from pydantic import BaseModel
+
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.pydantic_utils import model_update
+
+_T = TypeVar("_T")
+
+_threading_local = threading.local()
+
+
+def evolver(obj: _T) -> _T:
+ """Creates a wrapper around an immutable attrs object, tuple, or FrozenDict that records potentially nested attribute assignments.
+
+ The return type is our first white lie to the type system.
+ """
+ result = _Evolver[_T](obj)
+ # The cast is a little white lie to the type system to make type-checking, autocomplete, and refactoring work.
+ return cast(_T, result)
+
+
+def assign(dest: _T, src: Callable[[], _T]) -> None:
+ """Since mypy would complain about assignments to frozen attrs fields, use this function to make assignments.
+
+ The only reason src is a `Callable[[], _T]` instead of just `_T` is that it makes type checking signal attempts to assign
+ the wrong type to the field. Surprisingly, just using `(dest: _T, src: _T)` doesn't cause mypy to complain about type mismatch.
+ """
+ assert isinstance(dest, _Evolver) # Tricked you, type system!
+ dest_evolver: _Evolver[_T] = cast(_Evolver[_T], dest)
+ dest_evolver.assign(src())
+
+
+def chill(evolver: _T) -> _T:
+ """Produces a new frozen instance with the recorded changes applied.
+
+ The name `chill` is a play on the fact that original input was frozen, and we are now re-freezing it.
+ """
+ assert isinstance(evolver, _Evolver) # Tricked you, type system!
+ cast_evolver = cast(_Evolver[_T], evolver)
+ return cast_evolver.chill()
+
+
+class _RegularValue:
+ regular_value: Any
+
+ def __init__(self, value: Any) -> None:
+ self.regular_value = value
+
+
+class _AttrValue:
+ attr_value: Any
+ child_evolver_by_name: dict[str, "_Evolver[Any]"]
+
+ def __init__(self, value: Any) -> None:
+ self.attr_value = value
+ self.child_evolver_by_name = {}
+
+
+class _PydanticModelValue:
+ pydantic_model_value: Any
+ child_evolver_by_name: dict[str, "_Evolver[Any]"]
+
+ def __init__(self, value: Any) -> None:
+ self.pydantic_model_value = value
+ self.child_evolver_by_name = {}
+
+
+class _TupleValue:
+ tuple_evolvers: list["_Evolver[Any]"]
+
+ def __init__(self, value: tuple[Any, ...]) -> None:
+ # It may be premature to create evolvers for all the elements of the tuple, but it's easier.
+ self.tuple_evolvers = [evolver(item) for item in value]
+
+
+class _FrozenDictValue:
+ frozen_dict_evolvers: dict[Any, "_Evolver[Any]"]
+
+ def __init__(self, value: dict[Any, Any]) -> None:
+ # It may be premature to create evolvers for all the elements of dict, but it's easier.
+ self.frozen_dict_evolvers = {k: evolver(v) for k, v in value.items()}
+
+
+class _Evolver(Generic[_T]):
+ # pyre-ignore[13]: pyre is confused by the trickery here
+ _value: (
+ _RegularValue
+ | _AttrValue
+ | _TupleValue
+ | _FrozenDictValue
+ | _PydanticModelValue
+ )
+
+ def __init__(self, initial_value: _T) -> None:
+ super().__init__()
+ self.assign(initial_value)
+
+ def assign(self, new_value: _T) -> None:
+ """Assign a new value to this Evolver, recording a change to the frozen structure to be later applied during `chill()`."""
+
+ if attr.has(type(new_value)):
+ self._value = _AttrValue(new_value)
+ elif isinstance(new_value, BaseModel):
+ self._value = _PydanticModelValue(new_value)
+ elif isinstance(new_value, tuple):
+ self._value = _TupleValue(new_value)
+ elif isinstance(new_value, FrozenDict):
+ self._value = _FrozenDictValue(new_value)
+ else:
+ self._value = _RegularValue(new_value)
+
+ def __getattr__(self, item: str) -> "_Evolver[Any]":
+ """Access Evolvers for nested members of a frozen attrs object."""
+ try:
+ value = self._value
+ if isinstance(value, _AttrValue):
+ if item not in value.child_evolver_by_name:
+ child_obj = getattr(value.attr_value, item)
+ result = evolver(child_obj)
+ assert isinstance(result, _Evolver), (
+ "Expose a lie to the type system."
+ )
+ value.child_evolver_by_name[item] = result
+ return value.child_evolver_by_name[item]
+ elif isinstance(value, _PydanticModelValue):
+ if item not in value.child_evolver_by_name:
+ child_obj = getattr(value.pydantic_model_value, item)
+ result = evolver(child_obj)
+ assert isinstance(result, _Evolver), (
+ "Expose a lie to the type system."
+ )
+ value.child_evolver_by_name[item] = result
+ return value.child_evolver_by_name[item]
+ raise TypeError(
+ f"You're trying to access field {item=} on an object of {type(value)=} that doesn't have that field (should have been a mypy error)."
+ )
+ except BaseException as e:
+ if hasattr(_threading_local, "evolved_obj"):
+ # pyre-ignore[16]: pyre is suspicious of the trickery here
+ if getattr(_threading_local, "evolved_obj") == self:
+ delattr(_threading_local, "evolved_obj")
+ raise e
+
+ # TODO: It wouldn't be terribly difficult to support "appending" to the tuple as well, by appending to this list.
+ def __getitem__(self, key: Any) -> "_Evolver[Any]":
+ """Access Evolvers for the elements of a tuple or dict."""
+ value = self._value
+ if isinstance(value, _TupleValue):
+ assert isinstance(key, int)
+ return value.tuple_evolvers[key]
+ elif isinstance(value, _FrozenDictValue):
+ if key not in value.frozen_dict_evolvers:
+ # Presumably we're going to evolver_assign to this very soon.
+ cast(_FrozenDictValue, self._value).frozen_dict_evolvers[key] = (
+ _Evolver(_RegularValue(None))
+ )
+ return cast(_FrozenDictValue, self._value).frozen_dict_evolvers[key]
+ raise TypeError(
+ f"You're using [square_brackets] access {key=} on an object of {type(self._value)=} that doesn't support this (should have been a mypy error)."
+ )
+
+ def chill(self) -> _T:
+ """Recursively apply the recorded changes to the original object and return a new frozen instance."""
+ if isinstance(self._value, _AttrValue):
+ new_children: dict[str, Any] = {
+ name: chill(child)
+ for name, child in self._value.child_evolver_by_name.items()
+ }
+ assert attr.has(self._value.attr_value.__class__)
+ return cast(
+ _T,
+ attr.evolve(
+ cast(Any, cast(_AttrValue, self._value).attr_value), **new_children
+ ),
+ )
+ elif isinstance(self._value, _PydanticModelValue):
+ return cast(
+ _T,
+ model_update(
+ self._value.pydantic_model_value,
+ update={
+ name: chill(child)
+ for name, child in self._value.child_evolver_by_name.items()
+ },
+ ),
+ )
+ elif isinstance(self._value, _TupleValue):
+ return cast(
+ _T, tuple(evolver.chill() for evolver in self._value.tuple_evolvers)
+ )
+ elif isinstance(self._value, _RegularValue):
+ return cast(_T, self._value.regular_value)
+ elif isinstance(self._value, _FrozenDictValue):
+ return cast(
+ _T,
+ FrozenDict(
+ {k: v.chill() for k, v in self._value.frozen_dict_evolvers.items()}
+ ),
+ )
+ raise ValueError(f"This Evolver has no value to evolve, {type(self._value)=}.")
diff --git a/imbue_core/imbue_core/py.typed b/vet/imbue_core/py.typed
diff --git a/vet/imbue_core/pydantic_serialization.py b/vet/imbue_core/pydantic_serialization.py
@@ -0,0 +1,118 @@
+import threading
+from typing import Any
+from typing import TypeVar
+from typing import cast
+
+from pydantic import BaseModel
+from pydantic import ConfigDict
+from pydantic import Discriminator
+from pydantic.alias_generators import to_camel
+
+from vet.imbue_core.nested_evolver import _Evolver
+from vet.imbue_core.nested_evolver import chill
+from vet.imbue_core.nested_evolver import evolver
+from vet.imbue_core.serialization_types import Serializable
+
+T = TypeVar("T", bound=BaseModel)
+V = TypeVar("V")
+
+_threading_local = threading.local()
+
+
+class EvolvableModel:
+ # pyre-ignore[47]: pyre is not so easily tricked
+ def evolve(self: T, attribute: V, new_value: V) -> T:
+ # pyre-ignore[16]: pyre doesn't know about evolved_obj
+ assert _threading_local.evolved_obj is not None, (
+ ".ref() must be called before evolve"
+ )
+
+ assert isinstance(attribute, _Evolver) # Tricked you, type system!
+ dest_evolver: _Evolver[T] = cast(_Evolver[T], attribute)
+ dest_evolver.assign(new_value)
+
+ result = chill(_threading_local.evolved_obj)
+ _threading_local.evolved_obj = None
+ return result
+
+ # pyre-ignore[47]: pyre is not so easily tricked
+ def ref(self: T) -> T:
+ # pyre-ignore[16]: pyre doesn't know about evolved_obj
+ _threading_local.evolved_obj = evolver(self)
+ return _threading_local.evolved_obj
+
+
+class MutableModel(BaseModel):
+ """
+ The base class for any internal data that strictly must be mutable. Should be used sparingly.
+ """
+
+ model_config = ConfigDict(
+ frozen=False,
+ extra="forbid",
+ # FIXME: go back to preventing arbitrary types once we're done converting
+ # arbitrary_types_allowed=False,
+ arbitrary_types_allowed=True,
+ )
+
+
+class SerializableModel(EvolvableModel, BaseModel, Serializable):
+ """
+ The base class for all data that can be serialized to/from JSON.
+ """
+
+ model_config = ConfigDict(
+ frozen=True,
+ ser_json_bytes="base64",
+ val_json_bytes="base64",
+ alias_generator=to_camel,
+ validate_by_alias=True,
+ validate_by_name=True,
+ # any extra values will end up in the __pydantic_extra__ field
+ # this is effectively required for backwards compatibility
+ # IMPORTANT: note that, by default, we clear this below! These types are ONLY for backwards compatibility
+ extra="allow",
+ # this is also effectively required for backwards compatibility
+ arbitrary_types_allowed=True,
+ )
+
+ # this is a place where we might way to do any backwards compatibility related logic
+ def model_post_init(self, __context: Any) -> None:
+ pydantic_extra = self.__pydantic_extra__
+ assert pydantic_extra is not None
+ pydantic_extra.clear()
+
+
+# this is mostly here for the default cases.
+# When you want to upgrade a model (and keep it backwards compatible), you can make a custom discriminator
+# (eg, that looks for the old type name or converts the old class names)
+def build_discriminator(
+ field_name: str = "object_type",
+ additional_types_and_string_representations: tuple[tuple[type, str], ...] = (),
+) -> Discriminator:
+ """
+ Build a discriminator function for a Pydantic model.
+
+ Args:
+ field_name (str): The name of the field to use as the discriminator.
+ additional_types_and_string_representations (Tuple[Tuple[Type, str], ...]): Register additional types to the discriminator.
+
+ Returns:
+ Callable[[T | dict], str]: A function that takes an instance of T or a dictionary and returns the value of the
+ specified field.
+ """
+
+ def discriminator(obj: T | dict) -> str:
+ for (
+ model_type,
+ string_representation,
+ ) in additional_types_and_string_representations:
+ if isinstance(obj, model_type):
+ return string_representation
+ if isinstance(obj, dict):
+ if field_name not in obj:
+ return obj[to_camel(field_name)]
+ return obj[field_name]
+ return getattr(obj, field_name)
+
+ return Discriminator(discriminator=discriminator)
diff --git a/imbue_core/imbue_core/pydantic_utils.py b/vet/imbue_core/pydantic_utils.py
diff --git a/imbue_core/imbue_core/secrets_utils.py b/vet/imbue_core/secrets_utils.py
diff --git a/vet/imbue_core/serialization.py b/vet/imbue_core/serialization.py
@@ -0,0 +1,424 @@
+import builtins
+import datetime
+import json
+from enum import Enum
+from functools import cached_property
+from importlib import import_module
+from importlib.metadata import version
+from pathlib import PosixPath
+from traceback import format_tb
+from types import TracebackType
+from typing import Any
+from typing import Hashable
+from typing import Iterable
+from typing import Mapping
+from typing import TypeVar
+from typing import cast
+from uuid import UUID
+
+from loguru import logger
+from typing_extensions import TypeAliasType
+from yasoo import Deserializer
+from yasoo import Serializer
+from yasoo.constants import ENUM_VALUE_KEY
+from yasoo.objects import DictWithSerializedKeys
+from yasoo.serialization import _convert_to_json_serializable
+from yasoo.utils import get_fields
+from yasoo.utils import is_obj_supported_primitive
+from yasoo.utils import normalize_type
+from yasoo.utils import resolve_types
+
+from vet.imbue_core.fixed_traceback import FixedTraceback
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_core.serialization_types import Serializable
+
+assert version("yasoo") == "0.12.6", (
+ "This code was written for yasoo 0.12.6 and requires inheriting / monkeypatching the deserializer, so you probably don't want to use any other version without fixing TupleDeserializer"
+)
+
+T = TypeVar("T", bound=Hashable)
+
+
+class TupleDeserializer(Deserializer):
+ def _deserialize(
+ self,
+ data: bool | int | float | str | list[Any] | dict[str, Any] | None,
+ obj_type: type[T] | None,
+ type_key: str | None,
+ allow_extra_fields: bool,
+ external_globals: dict[str, Any],
+ ignore_custom_deserializer: bool = False,
+ ) -> object:
+ all_globals = dict(globals())
+ all_globals.update(external_globals)
+ if is_obj_supported_primitive(data):
+ return data
+ if isinstance(data, list):
+ list_types = self._get_list_types(obj_type, data)
+ return tuple(
+ [
+ self._deserialize(d, t, type_key, allow_extra_fields, all_globals)
+ for t, d in list_types
+ ]
+ )
+
+ assert isinstance(data, dict), f"Expected a dict, but got {type(data)}"
+
+ # load wrapped primitives
+ if type_key is not None:
+ type_data = data.get(type_key, None)
+
+ if (
+ type_data is not None
+ and type_data.startswith("builtins.")
+ and type_data != "builtins.dict"
+ ):
+ return data["value"]
+
+ # TODO: we need to potentially handle `builtins.dict`
+ # if type_key is not None:
+ # type_data = data.get(type_key, None)
+ #
+ # # TODO: serialization currently breaks with builtin.dicts and dicts with non-string keys
+ # if type_data == "builtins.dict":
+ # raise NotImplementedError(
+ # "Only `FrozenMapping` is supported for dict serialization/deserialization, call `freeze_mapping` on your dict before serializing"
+ # )
+ # if type_data is not None and type_data.startswith("builtins.") and type_data != "builtins.dict":
+ # return data["value"]
+
+ # TODO: remove this hack. Many of our sqlite files (search s3_sqlite_path) have FrozenDicts
+ if (
+ isinstance(type_key, str)
+ and data.get(type_key, None) == "flax.core.frozen_dict.FrozenDict"
+ ):
+ data[type_key] = "imbue_core.frozen_utils.FrozenMapping"
+ # we deliberately pass in a `None` type_key sometimes, which results in just returning obj_type
+ obj_type = self._get_object_type(
+ obj_type, data, type_key, all_globals
+ ) # pyre-ignore[6]
+ if type_key in data:
+ data.pop(type_key)
+ real_type, generic_args = normalize_type(obj_type, all_globals)
+ if external_globals and isinstance(real_type, type):
+ bases = {real_type}
+ while bases:
+ all_globals.update((b.__name__, b) for b in bases)
+ bases = {ancestor for b in bases for ancestor in b.__bases__}
+
+ if not ignore_custom_deserializer:
+ deserialization_method = self._custom_deserializers.get(
+ obj_type, self._custom_deserializers.get(real_type)
+ )
+ if deserialization_method:
+ return deserialization_method(data)
+ for base_class, method in self._inheritance_deserializers.items():
+ if issubclass(real_type, base_class):
+ return method(data, real_type)
+
+ key_type = None
+ try:
+ # pyre-fixme[6]: obj_type needs to be Hashable, but pyre isn't sure that it is
+ fields = {f.name: f for f in get_fields(obj_type)}
+ except TypeError:
+ if obj_type is FixedTraceback:
+ return FixedTraceback.from_dict(data["value"])
+ if issubclass(real_type, Enum):
+ value = data[ENUM_VALUE_KEY]
+ if isinstance(value, str):
+ try:
+ return real_type[value]
+ except KeyError:
+ for e in real_type:
+ if e.name.lower() == value.lower():
+ return e
+ return real_type(value)
+ # TODO: serialization currently breaks with builtin.dicts and dicts with non-string keys
+ # if you have weird keys in your dict this branch won't be hit and your object won't be properly deserialized
+ elif issubclass(real_type, Mapping):
+ key_type = generic_args[0] if generic_args else None
+ if self._is_mapping_dict_with_serialized_keys(key_type, data):
+ # pyre-fixme[9]: obj_type needs to be Hashable, but pyre doesn't realize that type[DictWithSerializedKeys] is ok
+ obj_type = DictWithSerializedKeys
+ # pyre-fixme[6]: arg of get_fields needs to be Hashable, but pyre doesn't realize that type[DictWithSerializedKeys] is ok
+ fields = {f.name: f for f in get_fields(DictWithSerializedKeys)}
+ value_type = generic_args[1] if generic_args else Any
+ fields["data"].field_type = dict[str, value_type] # type: ignore
+ else:
+ return self._load_mapping(
+ data,
+ real_type,
+ generic_args,
+ type_key,
+ allow_extra_fields,
+ all_globals,
+ )
+ elif issubclass(real_type, Iterable):
+ # If we got here it means data is not a list, so obj_type came from the data itself and is safe to use
+ return self._load_iterable(
+ data, obj_type, type_key, allow_extra_fields, all_globals
+ )
+ elif real_type != obj_type:
+ return self._deserialize(
+ data, real_type, type_key, allow_extra_fields, external_globals
+ )
+ else:
+ raise
+
+ self._check_for_missing_fields(data, fields, obj_type)
+ self._check_for_extraneous_fields(data, fields, obj_type, allow_extra_fields)
+ self._load_inner_fields(data, fields, type_key, allow_extra_fields, all_globals)
+ if obj_type is DictWithSerializedKeys:
+ return self._load_dict_with_serialized_keys(
+ obj_type(**data), key_type, type_key, allow_extra_fields, all_globals
+ )
+ kwargs = {k: v for k, v in data.items() if fields[k].init}
+ assert obj_type is not None
+ result = obj_type(**kwargs)
+ for k, v in data.items():
+ if k not in kwargs:
+ setattr(result, k, v)
+ return result
+
+
+# TODO: probably a good idea to ensure that all dicts are frozen as well...
+class FrozenSerializer(Serializer):
+ def __init__(
+ self, force_serialization: bool, allow_unsafe_list_serialization: bool = False
+ ) -> None:
+ super().__init__()
+ self._force_serialization = force_serialization
+ self._allow_unsafe_list_serialization = allow_unsafe_list_serialization
+
+ def _serialize_iterable(
+ self,
+ obj: Iterable[object],
+ type_key: Any,
+ fully_qualified_types: Any,
+ preserve_iterable_types: Any,
+ stringify_dict_keys: Any,
+ ) -> list[object]:
+ if isinstance(obj, list):
+ if self._allow_unsafe_list_serialization:
+ logger.info("Converting list to tuple for serialization: {}", obj)
+ obj = tuple(obj)
+ else:
+ raise Exception(
+ f"Lists are not allowed for serialization. Use tuples instead. Current iterable: {obj}"
+ )
+ assert isinstance(obj, (tuple, frozenset, bytes)), (
+ f"All iterables should be tuples or frozenset. Received {obj}"
+ )
+ return cast(
+ list[object],
+ tuple(
+ self._serialize(
+ item,
+ type_key,
+ fully_qualified_types,
+ preserve_iterable_types,
+ stringify_dict_keys,
+ )
+ for item in obj
+ ),
+ )
+
+ # overriding this method just to get some better error messages out--previously it would just "type error" and
+ # moan about things like int64 not being serializable, which is fine, but it is nicer if the key is included
+ def serialize(
+ self,
+ obj: Any,
+ type_key: str | None = "__type",
+ fully_qualified_types: bool = True,
+ preserve_iterable_types: bool = False,
+ stringify_dict_keys: bool = True,
+ globals: dict[str, Any] | None = None,
+ ) -> bool | int | float | str | list | dict[str, Any] | None:
+ try:
+ if is_obj_supported_primitive(obj):
+ return obj # type: ignore
+
+ if globals:
+ self._custom_serializers = resolve_types(
+ self._custom_serializers, globals
+ ) # type: ignore
+
+ result = self._serialize(
+ obj,
+ type_key,
+ fully_qualified_types,
+ preserve_iterable_types,
+ stringify_dict_keys,
+ inner=False,
+ )
+ try:
+ result = _convert_to_json_serializable(result)
+ except TypeError:
+ _convert_to_json_serializable_with_better_errors(result)
+ assert False, "previous method should have raised..."
+ return result # type: ignore
+ except Exception:
+ if self._force_serialization:
+ return repr(obj)
+ else:
+ raise
+
+
+JsonTypeAlias = TypeAliasType(
+ "JsonTypeAlias",
+ "dict[str, JsonTypeAlias] | list[JsonTypeAlias] | str | int | float | bool | None",
+)
+
+
+class SerializedException(SerializableModel):
+ """A serializable dataclass that represents an exception"""
+
+ exception: str
+ args: "tuple[SerializedException | JsonTypeAlias, ...]" # pyre-ignore[11]: pyre doesn't like TypeAliasType
+ traceback_dict: JsonTypeAlias
+
+ @classmethod
+ def build(
+ cls, exception: BaseException, traceback: TracebackType | None = None
+ ) -> "SerializedException":
+ if traceback is None:
+ traceback = exception.__traceback__
+ assert traceback is not None, " ".join(
+ (
+ "No traceback deriveable or as a concrete argument!",
+ f"You probably want to convert_to_serialized_exception in your except clause: {exception=}",
+ )
+ )
+ return SerializedException( # pyre-fixme[28]: pyre doesn't understand pydantic
+ exception=get_fully_qualified_name_for_error(exception),
+ args=tuple(
+ _convert_serialized_exception_args(x, traceback) for x in exception.args
+ ),
+ traceback_dict=FixedTraceback.from_tb(traceback).as_dict(),
+ )
+
+
+def _convert_serialized_exception_args(
+ error: Serializable, traceback: TracebackType | None = None
+) -> JsonTypeAlias:
+ if isinstance(error, BaseException):
+ return SerializedException.build(error, traceback=traceback)
+ elif isinstance(error, (list, tuple)):
+ return tuple(_convert_serialized_exception_args(x, traceback) for x in error)
+ return error
+
+
+def get_fully_qualified_name_for_error(e: BaseException) -> str:
+ if e.__class__.__module__ == "builtins":
+ return e.__class__.__name__
+ return f"{e.__class__.__module__}.{e.__class__.__name__}"
+
+
+def _convert_to_json_serializable_with_better_errors(
+ obj: Any, path: str = ""
+) -> int | float | str | list | dict | None:
+ if is_obj_supported_primitive(obj):
+ return obj # type: ignore
+ if isinstance(obj, Mapping):
+ return {
+ key: _convert_to_json_serializable_with_better_errors(
+ value, f"{path}.{key}"
+ )
+ for key, value in obj.items()
+ }
+ if isinstance(obj, Iterable):
+ return [
+ _convert_to_json_serializable_with_better_errors(item, f"{path}[{i}]")
+ for i, item in enumerate(obj)
+ ]
+ raise TypeError(
+ f'Found object of type "{type(obj).__name__}" at {path} which cannot be serialized'
+ )
+
+
+SERIALIZER = FrozenSerializer(
+ force_serialization=False, allow_unsafe_list_serialization=False
+)
+DESERIALIZER = TupleDeserializer()
+
+# note: you cannot change this without changing other calls to yasoo, this is its default
+TYPE_KEY = "__type"
+
+
+class SerializationError(Exception):
+ pass
+
+
+@SERIALIZER.register()
+def serialize_frozen_set(data: frozenset) -> dict:
+ value = SERIALIZER.serialize(tuple(data))
+ return {"value": value}
+
+
+@DESERIALIZER.register()
+def deserialize_frozen_set(data: dict) -> frozenset:
+ return frozenset(DESERIALIZER.deserialize(data["value"], tuple))
+
+
+@SERIALIZER.register()
+def serialize_uuid(data: UUID) -> dict:
+ return {"value": data.hex}
+
+
+@DESERIALIZER.register()
+def deserialize_uuid(data: dict) -> UUID:
+ return UUID(data["value"])
+
+
+@SERIALIZER.register()
+def serialize_traceback(data: FixedTraceback) -> dict:
+ return {"value": data.to_dict()}
+
+
+@DESERIALIZER.register()
+def deserialize_traceback(data: dict) -> FixedTraceback:
+ return FixedTraceback.from_dict(data["value"])
+
+
+@SERIALIZER.register()
+def serialize_posix_path(data: PosixPath) -> dict:
+ return {"value": str(data)}
+
+
+@DESERIALIZER.register()
+def deserialize_posix_path(data: dict) -> PosixPath:
+ return PosixPath(data["value"])
+
+
+@SERIALIZER.register()
+def serialize_datetime(data: datetime.datetime) -> dict:
+ return {
+ "time": data.astimezone(datetime.timezone.utc).timestamp(),
+ "tzaware": data.tzinfo is not None,
+ "__type": "datetime.datetime",
+ }
+
+
+@DESERIALIZER.register()
+def deserialize_datetime(data: dict) -> datetime.datetime:
+ return datetime.datetime.fromtimestamp(
+ data["time"], datetime.timezone.utc if data.get("tzaware", None) else None
+ )
+
+
+def serialize_to_json(
+ obj: Any, indent: int | None = None, sort_keys: bool = False
+) -> str:
+ try:
+ return json.dumps(SERIALIZER.serialize(obj), indent=indent, sort_keys=sort_keys)
+ except Exception as e:
+ raise SerializationError(str(e)) from e
+
+
+def deserialize_from_json(data: str) -> Any:
+ try:
+ return DESERIALIZER.deserialize(
+ json.loads(data)
+ ) # pyre-ignore[20]: pyre doesn't understand deserialize
+ except Exception as e:
+ raise SerializationError(str(e)) from e
diff --git a/imbue_core/imbue_core/serialization_types.py b/vet/imbue_core/serialization_types.py
diff --git a/imbue_core/imbue_core/test_repo_utils.py b/vet/imbue_core/test_repo_utils.py
diff --git a/imbue_core/imbue_core/time_utils.py b/vet/imbue_core/time_utils.py
diff --git a/imbue_tools/imbue_tools/__init__.py b/vet/imbue_tools/__init__.py
diff --git a/vet/imbue_tools/get_conversation_history/get_conversation_history.py b/vet/imbue_tools/get_conversation_history/get_conversation_history.py
@@ -0,0 +1,70 @@
+import json
+from typing import assert_never
+
+from loguru import logger
+from pydantic import TypeAdapter
+from pydantic import ValidationError
+
+from vet.vet_types.chat_state import ContentBlockTypes
+from vet.vet_types.messages import ChatInputUserMessage
+from vet.vet_types.messages import ConversationMessageUnion
+from vet.vet_types.messages import ResponseBlockAgentMessage
+
+
+class ConversationLoadingError(Exception):
+ pass
+
+
+# === formatting for prompt ===
+
+
+def delete_unnecessary_content_block_fields(block: ContentBlockTypes) -> str:
+ """Returns the content as a json-serialized string without the fields that we don't want to include in the prompt"""
+ fields_to_remove = {"id"}
+ return block.model_dump_json(exclude=fields_to_remove)
+
+
+def delete_unnecessary_conversation_message_fields(
+ message: ConversationMessageUnion,
+) -> str:
+ """Returns the message as a json-serialized string without the fields that we don't want to include in the prompt"""
+ general_fields_to_remove = {"message_id", "source", "approximate_creation_time"}
+ match message:
+ case ChatInputUserMessage():
+ # remove the 'files' field if it's empty
+ fields_to_remove = general_fields_to_remove | {"model_name"} | {"files"} if not message.files else set()
+ return message.model_dump_json(exclude=fields_to_remove)
+ case ResponseBlockAgentMessage():
+ fields_to_remove = general_fields_to_remove | {"assistant_message_id"}
+ return json.dumps(
+ message.model_dump(mode="json", exclude=fields_to_remove)
+ | {"content": [delete_unnecessary_content_block_fields(block) for block in message.content]}
+ )
+ case _ as unreachable:
+ assert_never(unreachable)
+
+
+def format_conversation_history_for_prompt(
+ conversation_history: tuple[ConversationMessageUnion, ...],
+) -> str:
+ formatted_messages = [delete_unnecessary_conversation_message_fields(message) for message in conversation_history]
+ return "\n".join(message for message in formatted_messages if message is not None)
+
+
+# === loading from file ===
+
+
+def parse_conversation_history(
+ conversation_str: str,
+) -> tuple[ConversationMessageUnion, ...]:
+ """Load a jsonl string into a list of conversation messages"""
+ messages = []
+ for line in conversation_str.strip().splitlines():
+ try:
+ # deserialize the message with pydantic
+ message: ConversationMessageUnion = TypeAdapter(ConversationMessageUnion).validate_json(line)
+ except ValidationError:
+ logger.info("Skipping malformed history line {}", line)
+ continue
+ messages.append(message)
+ return tuple(messages)
diff --git a/vet/imbue_tools/get_conversation_history/input_data_types.py b/vet/imbue_tools/get_conversation_history/input_data_types.py
@@ -0,0 +1,72 @@
+from typing import Self
+from typing import TypeVar
+
+from pydantic import model_validator
+
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.vet_types.messages import ConversationMessageUnion
+
+
+class IdentifierInputsMissingError(Exception):
+ pass
+
+
+class IdentifierInputs(SerializableModel):
+ # goal (for now, commit message) and diff to check
+ maybe_goal: str | None = None
+ maybe_diff: str | None = None
+
+ # whole files to check
+ maybe_files: tuple[str, ...] | None = None
+
+ # conversation history to check
+ maybe_conversation_history: tuple[ConversationMessageUnion, ...] | None = None
+
+
+class CommitInputs(IdentifierInputs):
+ # pyre-ignore[56]: pyre's stubs don't match pydantic v2 decorator signatures
+ @model_validator(mode="after")
+ def validate_goal_not_none(self) -> Self:
+ if self.maybe_goal is None:
+ raise IdentifierInputsMissingError("goal cannot be None for CommitInputs")
+ return self
+
+ # pyre-ignore[56]: pyre's stubs don't match pydantic v2 decorator signatures
+ @model_validator(mode="after")
+ def validate_diff_not_none(self) -> Self:
+ if self.maybe_diff is None:
+ raise IdentifierInputsMissingError("goal cannot be None for CommitInputs")
+ return self
+
+ @property
+ def goal(self) -> str:
+ assert self.maybe_goal is not None
+ return self.maybe_goal
+
+ @property
+ def diff(self) -> str:
+ assert self.maybe_diff is not None
+ return self.maybe_diff
+
+
+class ConversationInputs(IdentifierInputs):
+ # pyre-ignore[56]: pyre's stubs don't match pydantic v2 decorator signatures
+ @model_validator(mode="after")
+ def validate_conversation_history_not_none(self) -> Self:
+ if self.maybe_conversation_history is None:
+ raise IdentifierInputsMissingError("conversation_history is required for conversation inputs")
+ return self
+
+ @property
+ def conversation_history(self) -> tuple[ConversationMessageUnion, ...]:
+ assert self.maybe_conversation_history is not None
+ return self.maybe_conversation_history
+
+
+SpecificIdentifierInputsType = TypeVar("SpecificIdentifierInputsType", bound=IdentifierInputs)
+
+
+def to_specific_inputs_type(
+ identifier_inputs: IdentifierInputs, to_type: type[SpecificIdentifierInputsType]
+) -> SpecificIdentifierInputsType:
+ return to_type(**identifier_inputs.model_dump())
diff --git a/vet/imbue_tools/llm_output_parsing/parse_model_json_response.py b/vet/imbue_tools/llm_output_parsing/parse_model_json_response.py
@@ -0,0 +1,51 @@
+import json
+import re
+from typing import TypeVar
+
+from pydantic import ValidationError
+
+from vet.imbue_core.async_monkey_patches import log_exception
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+
+def parse_json_block_from_response_text(response_text: str) -> str:
+ """Clean markdown formatting and extra content from LLM response."""
+ response_text = response_text.strip()
+ # Parse content between first ```json and ``` block
+ json_start_line = re.search(r"^.*?```json\s*", response_text, flags=re.MULTILINE)
+ if json_start_line:
+ response_text = response_text[json_start_line.end() :]
+ json_end_line = re.search(r"```\s*$", response_text, flags=re.MULTILINE)
+ if json_end_line:
+ response_text = response_text[: json_end_line.start()]
+ return response_text.strip()
+
+
+ResponseSchema = TypeVar("ResponseSchema", bound=SerializableModel)
+
+
+class ResponseParsingError(Exception):
+ pass
+
+
+def parse_model_json_response(response_text: str, result_type: type[ResponseSchema]) -> ResponseSchema:
+ """Parse a JSON response from the LLM into a Pydantic model."""
+ cleaned_response = parse_json_block_from_response_text(response_text)
+ try:
+ return result_type.model_validate_json(cleaned_response)
+ except json.JSONDecodeError as e:
+ log_exception(
+ e,
+ "Response is not valid JSON.\nraw_response: {response_text}\ncleaned_response: {cleaned_response}",
+ response_text=response_text,
+ cleaned_response=cleaned_response,
+ )
+ raise ResponseParsingError(str(e)) from e
+ except ValidationError as e:
+ log_exception(
+ e,
+ "Response does not match the expected schema.\nraw_response: {response_text}\ncleaned_response: {cleaned_response}",
+ response_text=response_text,
+ cleaned_response=cleaned_response,
+ )
+ raise ResponseParsingError(str(e)) from e
diff --git a/imbue_tools/imbue_tools/py.typed b/vet/imbue_tools/py.typed
diff --git a/imbue_tools/imbue_tools/repo_utils/__init__.py b/vet/imbue_tools/repo_utils/__init__.py
diff --git a/vet/imbue_tools/repo_utils/context_prefix.py b/vet/imbue_tools/repo_utils/context_prefix.py
@@ -0,0 +1,613 @@
+import functools
+from enum import StrEnum
+from pathlib import Path
+from typing import Any
+from typing import Iterable
+from typing import Mapping
+from typing import assert_never
+
+from loguru import logger
+from pydantic import BaseModel
+from pydantic import ConfigDict
+
+from vet.imbue_core.agents.configs import LanguageModelGenerationConfig
+from vet.imbue_core.async_monkey_patches import log_exception
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_tools.repo_utils.context_utils import escape_prompt_markers
+from vet.imbue_tools.repo_utils.context_utils import maybe_get_file_path_from_qualified_name
+from vet.imbue_tools.repo_utils.data_types import FileContextUnion
+from vet.imbue_tools.repo_utils.errors import ContextLengthExceededError
+from vet.imbue_tools.repo_utils.file_system import InMemoryFileSystem
+from vet.imbue_tools.repo_utils.python_imports import QualifiedName
+from vet.imbue_tools.repo_utils.python_imports import STANDARD_LIBRARIES
+from vet.imbue_tools.repo_utils.python_imports import get_global_imports
+from vet.imbue_tools.repo_utils.subrepo_formatting import BaseFilenamePattern
+from vet.imbue_tools.repo_utils.subrepo_formatting import ContextFormatStyle
+from vet.imbue_tools.repo_utils.subrepo_formatting import ExactFilenamePattern
+from vet.imbue_tools.repo_utils.subrepo_formatting import FilenamePattern
+from vet.imbue_tools.repo_utils.subrepo_formatting import IntersectionFilenamePattern
+from vet.imbue_tools.repo_utils.subrepo_formatting import NegatedFilenamePattern
+from vet.imbue_tools.repo_utils.subrepo_formatting import REPO_CONTEXT_TEMPLATE
+from vet.imbue_tools.repo_utils.subrepo_formatting import SubrepoContextMatchers
+from vet.imbue_tools.repo_utils.subrepo_formatting import UnionFilenamePattern
+from vet.imbue_tools.repo_utils.subrepo_formatting import compute_file_context_format_styles
+from vet.imbue_tools.repo_utils.subrepo_formatting import format_subrepo_context
+from vet.imbue_tools.repo_utils.subrepo_formatting import (
+ parse_subrepo_context_matchers_from_toml,
+)
+
+
+class SubrepoContext(SerializableModel):
+ repo_context_files: tuple[FileContextUnion, ...]
+ subrepo_context_strategy_label: str
+
+
+class SubrepoContextWithFormattedContext(SubrepoContext):
+ formatted_repo_context: str
+
+
+def is_qualified_name_from_stdlib(qualified_name: QualifiedName) -> bool:
+ return qualified_name.top_level_name.value in STANDARD_LIBRARIES
+
+
+def get_immediate_first_party_import_paths_for_python_file(
+ current_file_path: str, full_repo_contents_map: InMemoryFileSystem
+) -> set[str] | None:
+ file_contents = full_repo_contents_map.get_text(current_file_path)
+ if not file_contents or not current_file_path.endswith(".py"):
+ return None
+
+ try:
+ global_imports = get_global_imports(file_contents)
+ except SyntaxError as e:
+ log_exception(
+ e,
+ "Failed to parse imports for {current_file_path}",
+ current_file_path=current_file_path,
+ )
+ return None
+
+ parent_names: set[QualifiedName] = set()
+ for import_ in global_imports:
+ parent_name = import_.qualified_name.parent_name
+ parent_names.add(parent_name)
+
+ imported_file_paths = set()
+ all_file_paths = [Path(x) for x in full_repo_contents_map.text_files.keys()]
+ for parent_name in parent_names:
+ other_file_path = maybe_get_file_path_from_qualified_name(parent_name, all_file_paths)
+ # if this doesn't exist it's likely not a first party import so we can ignore it
+ if not other_file_path or is_qualified_name_from_stdlib(parent_name):
+ continue
+ imported_file_paths.add(str(other_file_path))
+
+ return imported_file_paths
+
+
+FULL_REPO_PATHSPEC = BaseFilenamePattern.from_lines(["/**"])
+DOC_FILE_EXTENSIONS = [".md", ".txt"]
+DOC_PATHSPEC = BaseFilenamePattern.from_lines([f"**/*{ext}" for ext in DOC_FILE_EXTENSIONS])
+
+# Common files that we want to exclude since they can be large and are of low signal for issue identification.
+EXCLUSIONS_PATHSPEC = BaseFilenamePattern.from_lines(["uv.lock", "**/__snapshots__/**"])
+
+
+def escape_gitignore_pattern(path: str) -> str:
+ """
+ Escape a path into a GitIgnore pattern that matches exactly the path.
+
+ GitWildMatchPattern assigns special meaning to the following characters, which need to be escaped:
+ `*`, `?`, `[`, `]` and `\\`.
+ At the beginning of a line, we additionally need to escape leading `#` and `!` characters,
+ and at the end of a line we need to escape trailing ` ` (space) characters.
+ For simplicity, we simply escape these characters everywhere, which should still work correctly.
+ """
+ return (
+ path.replace("\\", "\\\\")
+ .replace("*", "\\*")
+ .replace("?", "\\?")
+ .replace("[", "\\[")
+ .replace("]", "\\]")
+ .replace("#", "\\#")
+ .replace("!", "\\!")
+ .replace(" ", "\\ ")
+ )
+
+
+def first_level_files_along_paths(file_paths: Iterable[str]) -> FilenamePattern:
+ """
+ Create a pathspec that matches all files along the given paths, but doesn't match adjacent directories.
+ """
+ # for each level in the path, we create an IntersectionFilenamePattern
+ # which has one branch that matches everything starting with that path,
+ # and another branch which matches everything except subdirectories starting with that path
+ # then we OR these all together as a UnionFilenamePattern
+ sorted_file_paths = sorted(file_paths)
+ file_patterns = []
+ for file_path in sorted_file_paths:
+ for parent in Path(file_path).parents:
+ escaped_parent = Path(escape_gitignore_pattern(str(parent)))
+ match_all = BaseFilenamePattern.from_lines([str("/" / escaped_parent / "*")])
+ match_except_subdirectories = NegatedFilenamePattern.build_from_positive_pattern(
+ BaseFilenamePattern.from_lines([str("/" / escaped_parent / "*/*")])
+ )
+ file_patterns.append(IntersectionFilenamePattern(specs=(match_all, match_except_subdirectories)))
+ return UnionFilenamePattern(specs=tuple(file_patterns))
+
+
+# cache this since it's reused across strategies
+@functools.lru_cache(maxsize=5)
+def make_docs_pathspec_along_paths(file_paths: frozenset[str]) -> FilenamePattern:
+ """
+ Create a pathspec that matches documentation files (.md, .txt) along each parent folder of the given file paths.
+ """
+ return IntersectionFilenamePattern(specs=(DOC_PATHSPEC, first_level_files_along_paths(file_paths=file_paths)))
+
+
+INSTRUCTIONS_PATHSPEC = BaseFilenamePattern.from_lines(["**/.claude.md", "**/CLAUDE.md", "**/AGENTS.md"])
+
+
+@functools.lru_cache(maxsize=5)
+def make_relevant_files_pathspec(file_paths: frozenset[str]) -> FilenamePattern:
+ """
+ Create a pathspec that matches the given file paths.
+ """
+ return ExactFilenamePattern(filenames=tuple(sorted(file_paths)))
+
+
+# cache this since it's reused across strategies
+@functools.lru_cache(maxsize=5)
+def make_instructions_pathspec_along_paths(
+ file_paths: frozenset[str],
+) -> FilenamePattern:
+ """
+ Create a pathspec that matches instruction files (e.g. .claude.md, CLAUDE.md, AGENTS.md) along each parent folder of the given file paths.
+
+ Should match a strict subset of make_docs_pathspec_along_paths.
+ """
+ return IntersectionFilenamePattern(
+ specs=(
+ INSTRUCTIONS_PATHSPEC,
+ first_level_files_along_paths(file_paths=file_paths),
+ )
+ )
+
+
+# cache this since it's reused across strategies
+@functools.lru_cache(maxsize=5)
+def make_imports_pathspec_for_paths(
+ file_paths: frozenset[str], full_repo_contents: InMemoryFileSystem
+) -> FilenamePattern:
+ """
+ Create a pathspec that matches Python files that are imported by the given file paths.
+ """
+ full_repo_python_file_contents_map = InMemoryFileSystem.build(
+ {k: v for k, v in full_repo_contents.files.items() if k.endswith(".py")}
+ )
+
+ imported_file_paths = set()
+ for file_path in file_paths:
+ if file_path.endswith(".py"):
+ # Include first party imports
+ imported_paths = get_immediate_first_party_import_paths_for_python_file(
+ file_path, full_repo_python_file_contents_map
+ )
+ if imported_paths:
+ imported_file_paths.update(imported_paths)
+
+ return ExactFilenamePattern(filenames=tuple(sorted(imported_file_paths)))
+
+
+class SubrepoContextStrategy(BaseModel):
+ model_config = ConfigDict(frozen=True, arbitrary_types_allowed=True)
+ label: str
+ matchers: SubrepoContextMatchers
+
+
+class SubrepoContextStrategyType(StrEnum):
+ # defaults if we have relevant files
+ FULL_REPO_CONTENTS = "full repo contents"
+ RELEVANT_WHOLE_FILES_IMPORTS_DOCS_AND_ELSEWHERE_FILENAME = (
+ "relevant files + immediate imports + docs along relevant paths + filenames elsewhere"
+ )
+ RELEVANT_WHOLE_FILES_IMPORTS_DOCS = "relevant files + immediate imports + docs along relevant paths"
+ RELEVANT_WHOLE_FILES_AND_RELEVANT_STUBBIFIED_IMPORTS_DOCS = (
+ "relevant files + stubbified imports + docs along relevant paths"
+ )
+ RELEVANT_WHOLE_FILES_DOCS = "relevant files + docs along relevant paths"
+ RELEVANT_WHOLE_FILES_INSTRUCTIONS = "relevant files + agent instructions along relevant paths"
+ RELEVANT_WHOLE_FILES = "relevant files"
+ RELEVANT_STUBBIFIED_FILES = "relevant stubbified files"
+ NOTHING = "nothing"
+
+ # defaults if we don't have relevant files (missing FULL_REPO_CONTENTS and NOTHING because they're already listed for if we do have relevant files)
+ WHOLE_DOCS_AND_OTHERWISE_FILENAMES = "docs + filenames elsewhere"
+ WHOLE_INSTRUCTIONS_AND_OTHERWISE_FILENAMES = "agent instructions + filenames elsewhere"
+ WHOLE_INSTRUCTIONS = "agent instructions"
+
+ # defaults for providing instruction files if we have relevant files
+ WHOLE_DOCS = "docs"
+ WHOLE_INSTRUCTIONS_AND_RELEVANT_DOCS = "agent instructions + relevant docs"
+ RELEVANT_DOCS = "relevant docs"
+ RELEVANT_INSTRUCTIONS = "relevant agent instructions"
+
+ # custom
+ CUSTOM = "custom"
+
+
+class StrategyMode(StrEnum):
+ REGULAR = "regular"
+ DOCS = "docs"
+
+
+class AvailableInfoMode(StrEnum):
+ YES_FILES = "yes_files"
+ NO_FILES = "no_files"
+
+
+DEFAULT_STRATEGY_TYPES: dict[tuple[StrategyMode, AvailableInfoMode], tuple[SubrepoContextStrategyType, ...]] = {
+ (StrategyMode.REGULAR, AvailableInfoMode.YES_FILES): (
+ SubrepoContextStrategyType.FULL_REPO_CONTENTS,
+ SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_IMPORTS_DOCS_AND_ELSEWHERE_FILENAME,
+ SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_IMPORTS_DOCS,
+ SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_AND_RELEVANT_STUBBIFIED_IMPORTS_DOCS,
+ SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_DOCS,
+ SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_INSTRUCTIONS,
+ SubrepoContextStrategyType.RELEVANT_WHOLE_FILES,
+ SubrepoContextStrategyType.RELEVANT_STUBBIFIED_FILES,
+ SubrepoContextStrategyType.NOTHING,
+ ),
+ (StrategyMode.REGULAR, AvailableInfoMode.NO_FILES): (
+ SubrepoContextStrategyType.FULL_REPO_CONTENTS,
+ SubrepoContextStrategyType.WHOLE_DOCS_AND_OTHERWISE_FILENAMES,
+ SubrepoContextStrategyType.WHOLE_INSTRUCTIONS_AND_OTHERWISE_FILENAMES,
+ SubrepoContextStrategyType.WHOLE_INSTRUCTIONS,
+ SubrepoContextStrategyType.NOTHING,
+ ),
+ (StrategyMode.DOCS, AvailableInfoMode.YES_FILES): (
+ SubrepoContextStrategyType.WHOLE_DOCS,
+ SubrepoContextStrategyType.WHOLE_INSTRUCTIONS_AND_RELEVANT_DOCS,
+ SubrepoContextStrategyType.RELEVANT_DOCS,
+ SubrepoContextStrategyType.RELEVANT_INSTRUCTIONS,
+ # these don't have `nothing` strategies because having some user instructions is crucial for
+ # the issue identifier which uses this, whereas the others can do ok with just a diff
+ ),
+ (StrategyMode.DOCS, AvailableInfoMode.NO_FILES): (
+ SubrepoContextStrategyType.WHOLE_DOCS,
+ SubrepoContextStrategyType.WHOLE_INSTRUCTIONS,
+ ),
+}
+
+
+def build_strategy(
+ strategy_type: SubrepoContextStrategyType,
+ full_repo_contents: InMemoryFileSystem,
+ relevant_file_paths: frozenset[str] | None,
+) -> SubrepoContextStrategy:
+ match strategy_type:
+ case SubrepoContextStrategyType.FULL_REPO_CONTENTS as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=((ContextFormatStyle.FULL_FILE, FULL_REPO_PATHSPEC),),
+ )
+ case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_IMPORTS_DOCS_AND_ELSEWHERE_FILENAME as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_relevant_files_pathspec(relevant_file_paths),
+ ),
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_imports_pathspec_for_paths(relevant_file_paths, full_repo_contents),
+ ),
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_docs_pathspec_along_paths(relevant_file_paths),
+ ),
+ (ContextFormatStyle.FILENAME_ONLY, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_IMPORTS_DOCS as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_relevant_files_pathspec(relevant_file_paths),
+ ),
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_imports_pathspec_for_paths(relevant_file_paths, full_repo_contents),
+ ),
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_docs_pathspec_along_paths(relevant_file_paths),
+ ),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_AND_RELEVANT_STUBBIFIED_IMPORTS_DOCS as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_relevant_files_pathspec(relevant_file_paths),
+ ),
+ (
+ ContextFormatStyle.STUB,
+ make_imports_pathspec_for_paths(relevant_file_paths, full_repo_contents),
+ ),
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_docs_pathspec_along_paths(relevant_file_paths),
+ ),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_DOCS as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_relevant_files_pathspec(relevant_file_paths),
+ ),
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_docs_pathspec_along_paths(relevant_file_paths),
+ ),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES_INSTRUCTIONS as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_relevant_files_pathspec(relevant_file_paths),
+ ),
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_instructions_pathspec_along_paths(relevant_file_paths),
+ ),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.RELEVANT_WHOLE_FILES as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_relevant_files_pathspec(relevant_file_paths),
+ ),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.RELEVANT_STUBBIFIED_FILES as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (
+ ContextFormatStyle.STUB,
+ make_relevant_files_pathspec(relevant_file_paths),
+ ),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.NOTHING as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=((ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),),
+ )
+
+ case SubrepoContextStrategyType.WHOLE_DOCS_AND_OTHERWISE_FILENAMES as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (ContextFormatStyle.FULL_FILE, DOC_PATHSPEC),
+ (ContextFormatStyle.FILENAME_ONLY, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.WHOLE_INSTRUCTIONS_AND_OTHERWISE_FILENAMES as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (ContextFormatStyle.FULL_FILE, INSTRUCTIONS_PATHSPEC),
+ (ContextFormatStyle.FILENAME_ONLY, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.WHOLE_INSTRUCTIONS as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (ContextFormatStyle.FULL_FILE, INSTRUCTIONS_PATHSPEC),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+
+ case SubrepoContextStrategyType.WHOLE_DOCS as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (ContextFormatStyle.FULL_FILE, DOC_PATHSPEC),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.WHOLE_INSTRUCTIONS_AND_RELEVANT_DOCS as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (ContextFormatStyle.FULL_FILE, INSTRUCTIONS_PATHSPEC),
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_docs_pathspec_along_paths(relevant_file_paths),
+ ),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.RELEVANT_DOCS as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_docs_pathspec_along_paths(relevant_file_paths),
+ ),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case SubrepoContextStrategyType.RELEVANT_INSTRUCTIONS as s:
+ return SubrepoContextStrategy(
+ label=s,
+ matchers=(
+ (
+ ContextFormatStyle.FULL_FILE,
+ make_instructions_pathspec_along_paths(relevant_file_paths),
+ ),
+ (ContextFormatStyle.HIDDEN, FULL_REPO_PATHSPEC),
+ ),
+ )
+ case _ as unreachable:
+ assert_never(unreachable) # pyre-ignore[6]: pyre doesn't understand enums
+
+
+def generate_subrepo_strategies(
+ mode: StrategyMode,
+ full_repo_contents: InMemoryFileSystem,
+ relevant_file_paths: frozenset[str] | None = None,
+) -> list[SubrepoContextStrategy]:
+ available_info = AvailableInfoMode.YES_FILES if relevant_file_paths else AvailableInfoMode.NO_FILES
+ return [
+ build_strategy(strategy_type, full_repo_contents, relevant_file_paths)
+ for strategy_type in DEFAULT_STRATEGY_TYPES[(mode, available_info)]
+ ]
+
+
+def select_desired_subrepo_strategies(
+ full_repo_contents: InMemoryFileSystem,
+ relevant_file_paths: frozenset[str] | None = None,
+ subrepo_context_config: str | None = None,
+ strategy_types_to_try: tuple[SubrepoContextStrategyType] | None = None,
+ strategy_mode: StrategyMode | None = None, # if no config option is set, defaults to StrategyMode.REGULAR
+) -> list[SubrepoContextStrategy]:
+ num_ways_config_was_set = sum(
+ 1 for v in [subrepo_context_config, strategy_types_to_try, strategy_mode] if v is not None
+ )
+ if num_ways_config_was_set > 1:
+ assert False, "Can only specify one of subrepo_context_config, strategy_types_to_try, and strategy_mode"
+
+ if subrepo_context_config is not None:
+ # An explicit subrepo context config was provided. Use it exclusively.
+ subrepo_context_matchers = parse_subrepo_context_matchers_from_toml(subrepo_context_config)
+ return [
+ SubrepoContextStrategy(
+ label=SubrepoContextStrategyType.CUSTOM,
+ matchers=subrepo_context_matchers,
+ )
+ ]
+ elif strategy_types_to_try is not None:
+ return [
+ build_strategy(strategy_type, full_repo_contents, relevant_file_paths)
+ for strategy_type in strategy_types_to_try
+ ]
+ else:
+ strategy_mode_to_use = strategy_mode if strategy_mode is not None else StrategyMode.REGULAR
+ return generate_subrepo_strategies(
+ strategy_mode_to_use,
+ full_repo_contents=full_repo_contents,
+ relevant_file_paths=relevant_file_paths,
+ )
+
+
+# Caching results because this function is quite expensive. We compose multiple repo_context prefixes, and
+# also have to tokenize them to check their respective lengths. Both of these operations are expensive,
+@functools.lru_cache(maxsize=10)
+def get_repo_context(
+ model_config: LanguageModelGenerationConfig,
+ full_repo_contents: InMemoryFileSystem,
+ # how many tokens to reserve for additional prompt messages and output
+ tokens_to_reserve: int,
+ relevant_file_paths: frozenset[str] | None = None,
+ subrepo_context_config: str | None = None,
+ strategy_types_to_try: tuple[SubrepoContextStrategyType] | None = None,
+ strategy_mode: StrategyMode | None = None, # if no config option is set, defaults to StrategyMode.REGULAR
+ template: str = REPO_CONTEXT_TEMPLATE,
+) -> SubrepoContextWithFormattedContext:
+ """
+ Make sure to try pass the same `full_repo_contents` when making multiple similar calls.
+ Ordering of the dict is relevant for caching.
+ """
+ subrepo_context_strategies_to_try = select_desired_subrepo_strategies(
+ full_repo_contents,
+ relevant_file_paths,
+ subrepo_context_config,
+ strategy_types_to_try,
+ strategy_mode,
+ )
+
+ last_context_length_exceeded_error: ContextLengthExceededError | None = None
+ for subrepo_context_strategy in subrepo_context_strategies_to_try:
+ try:
+ path_to_format_style = compute_file_context_format_styles(
+ file_paths=full_repo_contents.text_files.keys(),
+ subrepo_context_matchers=subrepo_context_strategy.matchers,
+ exclusions=EXCLUSIONS_PATHSPEC,
+ )
+ repo_context_str, repo_context_files = format_subrepo_context(
+ full_repo_contents=full_repo_contents.text_files,
+ model_config=model_config,
+ path_to_format_style=path_to_format_style,
+ tokens_to_reserve=tokens_to_reserve,
+ template=template,
+ )
+ logger.info("Selected subrepo context strategy: {}", subrepo_context_strategy.label)
+
+ if subrepo_context_strategy.label == SubrepoContextStrategyType.NOTHING:
+ # log an error if we have to use the NOTHING strategy, but still proceed with the call
+ logger.error("Selected NOTHING subrepo context strategy; hopefully this doesn't happen too often!")
+
+ return SubrepoContextWithFormattedContext(
+ formatted_repo_context=repo_context_str,
+ repo_context_files=repo_context_files,
+ subrepo_context_strategy_label=subrepo_context_strategy.label,
+ )
+ except ContextLengthExceededError as e:
+ last_context_length_exceeded_error = e
+
+ # We have exhausted all subrepo context strategies, and none of them worked.
+ assert last_context_length_exceeded_error is not None
+ raise last_context_length_exceeded_error from last_context_length_exceeded_error
+
+
+# TODO: why not just render this here?
+def create_context_prompt_prefix(repo_context: str) -> tuple[str, Mapping[str, Any]]:
+ """Create a message that provides context about the repo contents."""
+ cached_prefix_template = """[ROLE=SYSTEM_CACHED]
+You are a detail-oriented, expert software developer.
+
+Your goal is to help the user develop a particular commit to make a change to their program.
+
+{{repo_context}}
+
+{% if recent_git_history -%}
+
+As additional context, here are some of the most recent changes made to the codebase (the output of `git log` and diffs for each of those commits):
+
+```
+{{recent_git_history}}
+```
+{% endif -%}
+"""
+
+ return (
+ cached_prefix_template,
+ dict(
+ repo_context=escape_prompt_markers(repo_context),
+ recent_git_history=None,
+ ),
+ )
diff --git a/vet/imbue_tools/repo_utils/context_retrieval.py b/vet/imbue_tools/repo_utils/context_retrieval.py
@@ -0,0 +1,121 @@
+import threading
+import time
+from pathlib import Path
+from typing import Generator
+
+import pygit2
+from loguru import logger
+from pygit2.enums import ObjectType
+from pygit2.repository import Repository
+
+from vet.imbue_core.async_utils import make_async
+from vet.imbue_tools.repo_utils.diff_utils import apply_diffs_to_files
+from vet.imbue_tools.repo_utils.file_system import FileContents
+from vet.imbue_tools.repo_utils.file_system import InMemoryFileSystem
+from vet.imbue_tools.repo_utils.file_system import SymlinkContents
+
+
+class RepoContextManagerError(Exception):
+ pass
+
+
+class RepoContextManager:
+ """A manager for handling retrieval of files, etc from the repo."""
+
+ def __init__(self, repo_path: Path, project_name: str) -> None:
+ self.project_name = project_name
+ self.repo_path = repo_path
+ self._repo = Repository(path=str(repo_path))
+
+ # We need the sync lock due to pygit2 being synchronous.
+ # It is mostly used for the blob data cache, but also for the repo contents by git hash cache.
+ self._lock = threading.Lock()
+
+ @classmethod
+ def build(cls, repo_path: Path) -> "RepoContextManager":
+ try:
+ # make sure we are in a git repo
+ Repository(path=str(repo_path))
+ except pygit2.GitError as e:
+ raise RepoContextManagerError(f"Failed to initialize git repo at {repo_path}") from e
+
+ repo_context_manager = cls(repo_path=repo_path, project_name=repo_path.name)
+ return repo_context_manager
+
+ async def get_full_repo_contents_at_repo_state(self, git_hash: str, diff: str) -> InMemoryFileSystem:
+ final_contents = await self.get_full_repo_contents_at_commit(git_hash)
+ final_contents = await apply_diffs_to_files(final_contents, (diff,))
+ return final_contents
+
+ def get_full_repo_contents_at_commit_sync(self, git_hash: str) -> InMemoryFileSystem:
+ # NOTE: most of the time we want to get the contents at a repo state, not a git hash.
+ # Call get_full_repo_contents_at_repo_state instead in that case.
+ with self._lock:
+ start_time = time.perf_counter()
+
+ # Assert against use of HEAD specifically because there could be some existing code
+ # that uses it, and we want to catch that. It would fail below as well with a KeyError,
+ # but this assert makes the exception message more explicit.
+ assert git_hash != "HEAD", "Only proper commit hashes are supported, not HEAD"
+ commit = self._repo[git_hash]
+ assert isinstance(commit, pygit2.Commit), f"Expected a pygit2.Commit, got {type(commit)}"
+
+ full_repo_contents = self._read_blobs_from_commit(commit)
+
+ end_time = time.perf_counter()
+ logger.debug(
+ "Loaded full repo contents for git hash {git_hash} in {duration:.2f} seconds",
+ git_hash=git_hash,
+ duration=end_time - start_time,
+ )
+ return full_repo_contents
+
+ @make_async
+ def get_full_repo_contents_at_commit(self, git_hash: str) -> InMemoryFileSystem:
+ return self.get_full_repo_contents_at_commit_sync(git_hash)
+
+ def _read_blobs_from_commit(self, commit: pygit2.Commit) -> InMemoryFileSystem:
+ """Read all blobs in a given commit."""
+ file_system_dict: dict[str, FileContents] = {}
+
+ for path, blob in self._list_blobs_from_tree(commit.tree, skip_binary=False, skip_symlinks=False):
+ if blob.filemode == 0o120000:
+ # Blob is a symbolic link. Its contents in git represent the target path.
+ file_system_dict[path] = SymlinkContents(target_path=blob.data.decode("utf-8"))
+ else:
+ file_system_dict[path] = blob.data
+ return InMemoryFileSystem.build(file_system_dict)
+
+ def _list_blobs_from_tree(
+ self, tree: pygit2.Tree, skip_binary: bool, skip_symlinks: bool
+ ) -> Generator[tuple[str, pygit2.Blob], None, None]:
+ """Recursively list all blobs in a tree, including its subtrees."""
+ assert self._lock.locked()
+ for entry in tree:
+ if entry.type == ObjectType.BLOB:
+ assert isinstance(entry, pygit2.Blob)
+ if skip_binary and entry.is_binary:
+ continue
+ if skip_symlinks and entry.filemode == 0o120000:
+ continue
+
+ blob_path = entry.name
+ assert blob_path is not None
+ yield blob_path, entry
+
+ elif entry.type == ObjectType.TREE:
+ assert isinstance(entry, pygit2.Tree)
+ # Recurse into a subtree (folder)
+ sub_tree = self._repo[entry.id]
+ assert isinstance(sub_tree, pygit2.Tree)
+ for sub_path, sub_blob in self._list_blobs_from_tree(
+ sub_tree, skip_binary=skip_binary, skip_symlinks=skip_symlinks
+ ):
+ yield f"{entry.name}/{sub_path}", sub_blob
+
+ elif entry.type == ObjectType.COMMIT:
+ # A COMMIT object indicates a submodule, which we do not traverse for the time being.
+ logger.info("Skipping submodule in repo context: {}", entry.name)
+
+ else:
+ raise ValueError(f"Unexpected entry type in git tree: {entry.type}")
diff --git a/vet/imbue_tools/repo_utils/context_utils.py b/vet/imbue_tools/repo_utils/context_utils.py
@@ -0,0 +1,52 @@
+from pathlib import Path
+from typing import Iterable
+
+from vet.imbue_tools.repo_utils.python_imports import QualifiedName
+
+
+def escape_prompt_markers(text: str) -> str:
+ markers = [
+ "[ROLE=ASSISTANT]",
+ "[ROLE=USER]",
+ "[ROLE=USER_CACHED]",
+ "[ROLE=SYSTEM]",
+ "[ROLE=SYSTEM_CACHED]",
+ "[ROLE=HUMAN]",
+ ]
+ for marker in markers:
+ text = text.replace(marker, f"[{marker}]")
+ return text
+
+
+def escape_all_jinja_variables(text: str) -> str:
+ return "{% raw %}" + text + "{% endraw %}"
+
+
+def does_relative_path_match_target_path_suffix(target_path: Path, relative_file_path: Path) -> bool:
+ """
+ Checks if the parts of a relative path match the suffix of a target path.
+ """
+ possible_parts = relative_file_path.parts
+ target_parts = target_path.parts
+
+ if len(possible_parts) > len(target_parts):
+ return False
+
+ for i in range(1, len(possible_parts) + 1):
+ if possible_parts[-i] != target_parts[-i]:
+ return False
+ return True
+
+
+def maybe_get_file_path_from_qualified_name(
+ qualified_name: QualifiedName, all_file_paths: Iterable[Path]
+) -> Path | None:
+ """
+ Tries to find the file path that corresponds to qualified name. This requires the qualified name to be a file in the repo.
+ """
+ possible_relative_file_path = qualified_name.to_path()
+ # NOTE: it's possible to make this faster by doing some upfront computation
+ for target_file_path in all_file_paths:
+ if does_relative_path_match_target_path_suffix(target_file_path, possible_relative_file_path):
+ return target_file_path
+ return None
diff --git a/vet/imbue_tools/repo_utils/data_types.py b/vet/imbue_tools/repo_utils/data_types.py
@@ -0,0 +1,51 @@
+from abc import ABC
+from abc import abstractmethod
+from typing import Annotated
+
+from pydantic import Tag
+
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_core.pydantic_serialization import build_discriminator
+
+
+class FileContext(ABC, SerializableModel):
+ object_type: str
+ path: str
+
+ @abstractmethod
+ def format_for_agent(self) -> str:
+ pass
+
+
+class FullFileContext(FileContext):
+ object_type: str = "FullFileContext"
+ path: str
+ contents: str = "RAW FILE CONTENTS"
+
+ def format_for_agent(self) -> str:
+ return f"<FILE>\n<PATH>\n{self.path}\n</PATH>\n<CONTENTS>\n{self.contents}\n</CONTENTS>\n</FILE>\n\n"
+
+
+class FilenameContext(FileContext):
+ object_type: str = "FilenameContext"
+ path: str
+
+ def format_for_agent(self) -> str:
+ return f"<FILE>\n<PATH>\n{self.path}\n</PATH>\n</FILE>\n\n"
+
+
+class StubFileContext(FileContext):
+ object_type: str = "StubFileContext"
+ path: str
+ stub: str
+
+ def format_for_agent(self) -> str:
+ return f"<FILE>\n<PATH>\n{self.path}\n</PATH>\n<STUBIFIED_CONTENTS>\n{self.stub}\n</STUBIFIED_CONTENTS>\n</FILE>\n\n"
+
+
+FileContextUnion = Annotated[
+ Annotated[FullFileContext, Tag("FullFileContext")]
+ | Annotated[FilenameContext, Tag("FilenameContext")]
+ | Annotated[StubFileContext, Tag("StubFileContext")],
+ build_discriminator(),
+]
diff --git a/vet/imbue_tools/repo_utils/diff_utils.py b/vet/imbue_tools/repo_utils/diff_utils.py
@@ -0,0 +1,96 @@
+import re
+import subprocess
+import tempfile
+from pathlib import Path
+
+import pygit2
+from async_lru import alru_cache # type: ignore[undefined-attribute]: pyre on modal has an issue with this
+from loguru import logger
+
+from vet.imbue_tools.repo_utils.errors import DiffApplicationError
+from vet.imbue_tools.repo_utils.file_system import FileContents
+from vet.imbue_tools.repo_utils.file_system import InMemoryFileSystem
+from vet.imbue_tools.repo_utils.file_system import SymlinkContents
+from vet.imbue_tools.repo_utils.file_system_utils import (
+ create_initial_placeholder_commit_for_dir,
+)
+from vet.imbue_tools.repo_utils.file_system_utils import (
+ temporary_local_dir_from_in_memory_file_system,
+)
+
+
+@alru_cache
+async def apply_diffs_to_files(file_contents: InMemoryFileSystem, diff_strings: tuple[str, ...]) -> InMemoryFileSystem:
+ # Have to do this wrapping and unwrapping into dicts to allow @alru_cache to work
+ files_with_diffs = file_contents
+ for diff_string in diff_strings:
+ files_with_diffs = await _apply_diff_to_files(file_contents=files_with_diffs, diff_string=diff_string)
+ return files_with_diffs
+
+
+async def _apply_diff_to_files(file_contents: InMemoryFileSystem, diff_string: str) -> InMemoryFileSystem:
+ if diff_string.strip() == "":
+ return file_contents
+
+ file_pattern = re.compile(r"^diff --git a/(.+?) b/(.+)$", re.MULTILINE)
+ matches = file_pattern.findall(diff_string)
+
+ relevant_file_contents_dict = {}
+ for match in matches:
+ assert len(match) == 2
+ for file_path in match:
+ contents = file_contents.get(file_path, None)
+ if contents is not None:
+ relevant_file_contents_dict[file_path] = contents
+
+ async with temporary_local_dir_from_in_memory_file_system(
+ InMemoryFileSystem.build(relevant_file_contents_dict)
+ ) as temp_repo_dir:
+ repo = pygit2.init_repository(temp_repo_dir, bare=False)
+ create_initial_placeholder_commit_for_dir(repo)
+
+ with tempfile.NamedTemporaryFile(delete=False) as temp_patch_file:
+ temp_patch_file.write(diff_string.encode("utf-8"))
+ temp_patch_file.flush()
+ patch_file_path = temp_patch_file.name
+
+ try:
+ result = subprocess.run(
+ ("git", "apply", "--verbose", patch_file_path),
+ cwd=temp_repo_dir,
+ capture_output=True,
+ text=True,
+ timeout=10.0,
+ check=True,
+ )
+ except Exception as e:
+ logger.trace("Unable to apply patch: {error}", error=e)
+ raise DiffApplicationError from e
+
+ try:
+ updated_file_contents = _read_file_contents_from_dir_without_git(temp_repo_dir)
+ except Exception as e:
+ raise DiffApplicationError from e
+
+ combined_file_contents_dict = dict(updated_file_contents.files)
+ for file_path, contents in file_contents.files.items():
+ if file_path not in relevant_file_contents_dict:
+ combined_file_contents_dict[file_path] = contents
+
+ return InMemoryFileSystem.build(combined_file_contents_dict)
+
+
+def _read_file_contents_from_dir_without_git(dir_path_str: str) -> InMemoryFileSystem:
+ file_system_dict: dict[str, FileContents] = {}
+ for file_path in Path(dir_path_str).rglob("*"):
+ if ".git" in file_path.parts:
+ continue
+ if file_path.is_symlink():
+ relative_path = str(file_path.relative_to(dir_path_str))
+ target_path = str(file_path.readlink())
+ file_system_dict[relative_path] = SymlinkContents(target_path=target_path)
+ elif file_path.is_file():
+ relative_path = str(file_path.relative_to(dir_path_str))
+ with open(file_path, "rb") as file:
+ file_system_dict[relative_path] = file.read()
+ return InMemoryFileSystem.build(file_system_dict)
diff --git a/imbue_tools/imbue_tools/repo_utils/errors.py b/vet/imbue_tools/repo_utils/errors.py
diff --git a/vet/imbue_tools/repo_utils/file_system.py b/vet/imbue_tools/repo_utils/file_system.py
@@ -0,0 +1,71 @@
+from typing import Generator
+from typing import Mapping
+
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.frozen_utils import deep_freeze_mapping
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+
+class SymlinkContents(SerializableModel):
+ """A special type to represent a symbolic link in a file system."""
+
+ target_path: str
+
+ # Need to make SymlinkContents non-Iterable, or else deep_freeze_mapping will convert this to a tuple in InMemoryFileSystem.build.
+ __iter__ = None # type: ignore
+
+
+DecodedTextFileContents = str
+FileContents = bytes | SymlinkContents
+
+
+class InMemoryFileSystem(SerializableModel):
+ """
+ A simple representation of in-memory file system. Can contain both text and binary files.
+ """
+
+ # Mapping from file path to contents
+ files: FrozenDict[str, FileContents]
+ # Only text files, decoded as UTF-8. Excludes symlinks and binary files.
+ text_files: FrozenDict[str, DecodedTextFileContents]
+
+ @classmethod
+ def build(cls, files: Mapping[str, FileContents]) -> "InMemoryFileSystem":
+ sorted_files = {k: v for k, v in sorted(files.items())}
+ sorted_decoded_files: dict[str, DecodedTextFileContents | None] = {
+ k: _try_decode_file_contents(c) for k, c in sorted_files.items()
+ }
+ sorted_text_files: dict[str, DecodedTextFileContents] = {
+ k: c for k, c in sorted_decoded_files.items() if c is not None
+ }
+ return cls(
+ files=deep_freeze_mapping(sorted_files),
+ text_files=deep_freeze_mapping(sorted_text_files),
+ )
+
+ def get(self, file_path: str, default: FileContents | None = None) -> FileContents | None:
+ if file_path in self.files:
+ return self.files[file_path]
+ return default
+
+ def get_text(
+ self, file_path: str, default: DecodedTextFileContents | None = None
+ ) -> DecodedTextFileContents | None:
+ """Get a the contents of a text file as a string. Returns `default` if the file does not exist, is a symlink, or is a binary file."""
+ if file_path in self.text_files:
+ return self.text_files[file_path]
+ return default
+
+ def __iter__(self) -> Generator[tuple[str, FileContents], None, None]:
+ return (file for file in self.files.items())
+
+
+def _try_decode_file_contents(contents: FileContents) -> DecodedTextFileContents | None:
+ if isinstance(contents, SymlinkContents):
+ return None
+ else:
+ assert isinstance(contents, bytes)
+ try:
+ return contents.decode("utf-8")
+ except UnicodeDecodeError:
+ return None
diff --git a/vet/imbue_tools/repo_utils/file_system_utils.py b/vet/imbue_tools/repo_utils/file_system_utils.py
@@ -0,0 +1,70 @@
+import asyncio
+import tempfile
+from contextlib import asynccontextmanager
+from pathlib import Path
+from typing import AsyncGenerator
+from typing import cast
+
+import anyio
+import pygit2
+from loguru import logger
+
+from vet.imbue_tools.repo_utils.file_system import FileContents
+from vet.imbue_tools.repo_utils.file_system import InMemoryFileSystem
+from vet.imbue_tools.repo_utils.file_system import SymlinkContents
+
+
+async def write_file_contents_to_dir(file_contents: InMemoryFileSystem, dir_path_str: str) -> None:
+ dir_path = Path(dir_path_str)
+ tasks = [
+ asyncio.create_task(_write_single_file_to_dir(dir_path / file_path, content))
+ for file_path, content in file_contents.files.items()
+ ]
+ await asyncio.gather(*tasks)
+
+
+async def _write_single_file_to_dir(full_path: Path, content: FileContents) -> None:
+ await anyio.to_thread.run_sync(_write_file_sync, full_path, content)
+
+
+def _write_file_sync(full_path: Path, content: FileContents) -> None:
+ full_path.parent.mkdir(parents=True, exist_ok=True)
+ if isinstance(content, bytes):
+ full_path.write_bytes(content)
+ elif isinstance(content, SymlinkContents):
+ full_path.symlink_to(content.target_path)
+ else:
+ logger.error(
+ "Tried to write contents that were neither bytes nor SymlinkContents: {content}",
+ content=content,
+ )
+
+
+@asynccontextmanager
+async def temporary_local_dir_from_in_memory_file_system(
+ file_contents: InMemoryFileSystem,
+) -> AsyncGenerator[str, None]:
+ with tempfile.TemporaryDirectory() as temp_dir:
+ await write_file_contents_to_dir(file_contents, temp_dir)
+ yield temp_dir
+
+
+def create_initial_placeholder_commit_for_dir(repo: pygit2.Repository) -> pygit2.Commit:
+ # pyre-ignore[16]: pyre doesn't understand the inheritance of Repository from BaseRepository
+ repo_index = repo.index
+ repo_index.add_all()
+ repo_index.write()
+ tree = repo_index.write_tree()
+ signature = pygit2.Signature("placeholder", "placeholder@example.com")
+
+ commit_oid = repo.create_commit(
+ "refs/heads/master",
+ signature,
+ signature,
+ "placeholder commit for diff utils",
+ tree,
+ [],
+ )
+ # pyre-ignore[16]: pyre doesn't understand the inheritance of Repository from BaseRepository
+ commit = repo.get(commit_oid)
+ return cast(pygit2.Commit, commit)
diff --git a/vet/imbue_tools/repo_utils/project_context.py b/vet/imbue_tools/repo_utils/project_context.py
@@ -0,0 +1,234 @@
+from functools import cached_property
+from functools import lru_cache
+from pathlib import Path
+from typing import Annotated
+from typing import Self
+
+import jinja2
+from pydantic import Tag
+from pydantic import computed_field
+
+from vet.imbue_core.agents.configs import LanguageModelGenerationConfig
+from vet.imbue_core.agents.configs import OpenAICompatibleModelConfig
+from vet.imbue_core.async_utils import sync
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_core.pydantic_serialization import build_discriminator
+from vet.imbue_tools.repo_utils.context_prefix import StrategyMode
+from vet.imbue_tools.repo_utils.context_prefix import SubrepoContext
+from vet.imbue_tools.repo_utils.context_prefix import SubrepoContextWithFormattedContext
+from vet.imbue_tools.repo_utils.context_prefix import create_context_prompt_prefix
+from vet.imbue_tools.repo_utils.context_prefix import get_repo_context
+from vet.imbue_tools.repo_utils.context_retrieval import RepoContextManager
+from vet.imbue_tools.repo_utils.file_system import InMemoryFileSystem
+from vet.imbue_tools.repo_utils.subrepo_formatting import (
+ REPO_CONTEXT_TEMPLATE_WITH_NO_MENTION_OF_DIFF,
+)
+
+
+@lru_cache
+def _get_repo_context_manager_for_repo_path(repo_path: Path) -> RepoContextManager:
+ """
+ Wrapper around RepoContextManager.build() to cache the resulting repo context manager.
+
+ Internally, the RepoContextManager object will itself cache the repo contents.
+ """
+ return RepoContextManager.build(repo_path)
+
+
+class BaseProjectContext(SerializableModel):
+ """
+ Holds the context of the checked project including all its files.
+
+ For LLM-based issue identifiers, no matter the scope, we want to use a fixed prompt prefix to leverage caching.
+ We use the stable cached_prompt_prefix for that purpose.
+
+ """
+
+ object_type: str = "BaseProjectContext"
+
+ file_contents_by_path: FrozenDict[str, str]
+ cached_prompt_prefix: str
+ # 0 - n most recent commits, with the most recent one being the first.
+ # The state of the project (file_contents_by_path) is the state after the most recent commit.
+
+ subrepo_context: SubrepoContext | None = None
+ instruction_context: SubrepoContext | None = None
+ repo_path: Path | None = None
+
+ def get_file_contents(self, file_path: str) -> str | None:
+ return self.file_contents_by_path.get(file_path)
+
+ def get_computed_contexts(
+ self,
+ ) -> tuple[SubrepoContext | None, SubrepoContext | None]:
+ """To match usage for LazyProjectContext; all fields are always computed because this isn't lazy"""
+ return self.subrepo_context, self.instruction_context
+
+
+class LazyProjectContext(SerializableModel):
+ object_type: str = "LazyProjectContext"
+
+ base_commit: str
+ diff: str
+ language_model_name: str
+ repo_path: Path
+ tokens_to_reserve: int
+
+ # Optional context window override. If not provided, the model's default context window
+ # will be looked up from the model registry (which fails for unknown models).
+ context_window: int | None = None
+
+ # If True, this is a custom/user-defined model (uses approximate token counting).
+ is_custom_model: bool = False
+
+ def get_file_contents(self, file_path: str) -> str | None:
+ return self.file_contents_by_path.get(file_path)
+
+ @classmethod
+ def build(
+ cls,
+ base_commit: str,
+ diff: str,
+ language_model_name: str,
+ repo_path: Path,
+ # How many tokens to keep for the vet specific prompt and any output tokens.
+ tokens_to_reserve: int,
+ context_window: int | None = None,
+ is_custom_model: bool = False,
+ ) -> Self:
+ return cls(
+ base_commit=base_commit,
+ diff=diff,
+ language_model_name=language_model_name,
+ repo_path=repo_path,
+ tokens_to_reserve=tokens_to_reserve,
+ context_window=context_window,
+ is_custom_model=is_custom_model,
+ )
+
+ # The fields are computed and cached because they are quite expensive to compute.
+ # We compose multiple repo_context prefixes, and also have to tokenize them to check their respective lengths.
+ # Both of these operations are expensive
+
+ @computed_field
+ @cached_property
+ def repo_context_manager(self) -> RepoContextManager:
+ return _get_repo_context_manager_for_repo_path(self.repo_path)
+
+ @computed_field
+ @cached_property
+ def original_content_by_path(self) -> InMemoryFileSystem:
+ original_content_by_path = sync(self.repo_context_manager.get_full_repo_contents_at_commit)(self.base_commit)
+ return original_content_by_path
+
+ @computed_field
+ @cached_property
+ def content_by_path(self) -> InMemoryFileSystem:
+ if self.diff:
+ return sync(self.repo_context_manager.get_full_repo_contents_at_repo_state)(self.base_commit, self.diff)
+ else:
+ return self.original_content_by_path
+
+ @computed_field
+ @property
+ def file_contents_by_path(self) -> FrozenDict[str, str]:
+ return self.content_by_path.text_files
+
+ @computed_field
+ @cached_property
+ def cached_prompt_prefix(self) -> str:
+ prompt_prefix_template, prompt_prefix_params = create_context_prompt_prefix(
+ repo_context=self.subrepo_context.formatted_repo_context,
+ )
+ env = jinja2.Environment(undefined=jinja2.StrictUndefined)
+ jinja_template = env.from_string(prompt_prefix_template)
+ cached_prompt_prefix = jinja_template.render(**prompt_prefix_params)
+ return cached_prompt_prefix
+
+ @computed_field
+ @cached_property
+ def modified_file_paths(self) -> frozenset[str]:
+ modified_file_paths = []
+ for file_path in self.content_by_path.files.keys():
+ if self.content_by_path.get(file_path) != self.original_content_by_path.get(file_path):
+ modified_file_paths.append(file_path)
+ return frozenset(modified_file_paths)
+
+ def _create_model_config(self) -> LanguageModelGenerationConfig:
+ """Create the appropriate model config for context building.
+
+ For custom models (is_custom_model=True), creates an OpenAICompatibleModelConfig
+ that uses approximate token counting and the specified context window.
+
+ For known models, creates a standard LanguageModelGenerationConfig that uses
+ the model registry for token counting and context window lookup.
+ """
+ if self.is_custom_model:
+ if self.context_window is None:
+ raise ValueError(
+ "context_window must be provided when is_custom_model=True "
+ + "(custom models don't have a known context window)"
+ )
+ return OpenAICompatibleModelConfig(
+ model_name=self.language_model_name,
+ custom_base_url="", # Not used for context building
+ custom_api_key_env="", # Not used for context building
+ custom_context_window=self.context_window,
+ custom_max_output_tokens=0, # Not used for context building
+ )
+ else:
+ return LanguageModelGenerationConfig(model_name=self.language_model_name)
+
+ @computed_field
+ @cached_property
+ def subrepo_context(self) -> SubrepoContextWithFormattedContext:
+ model_config = self._create_model_config()
+
+ subrepo_context = get_repo_context(
+ full_repo_contents=self.content_by_path,
+ model_config=model_config,
+ relevant_file_paths=self.modified_file_paths,
+ tokens_to_reserve=self.tokens_to_reserve,
+ )
+ return subrepo_context
+
+ def to_base_project_context(self) -> BaseProjectContext:
+ return BaseProjectContext(
+ file_contents_by_path=self.file_contents_by_path,
+ cached_prompt_prefix=self.cached_prompt_prefix,
+ subrepo_context=self.subrepo_context,
+ instruction_context=self.instruction_context,
+ repo_path=self.repo_path,
+ )
+
+ @computed_field
+ @cached_property
+ def instruction_context(self) -> SubrepoContextWithFormattedContext:
+ model_config = self._create_model_config()
+ return get_repo_context(
+ model_config=model_config,
+ full_repo_contents=self.content_by_path,
+ relevant_file_paths=None,
+ tokens_to_reserve=self.tokens_to_reserve,
+ strategy_mode=StrategyMode.DOCS,
+ template=REPO_CONTEXT_TEMPLATE_WITH_NO_MENTION_OF_DIFF,
+ )
+
+ def get_computed_contexts(
+ self,
+ ) -> tuple[
+ SubrepoContextWithFormattedContext | None,
+ SubrepoContextWithFormattedContext | None,
+ ]:
+ """Returns subrepo context and instruction context, but only if they have already been computed; those that haven't been computed are None"""
+ # checking for presence in __dict__ does not trigger computation
+ subrepo_context = self.subrepo_context if "subrepo_context" in self.__dict__ else None
+ instruction_context = self.instruction_context if "instruction_context" in self.__dict__ else None
+ return subrepo_context, instruction_context
+
+
+ProjectContext = Annotated[
+ Annotated[BaseProjectContext, Tag("BaseProjectContext")] | LazyProjectContext,
+ build_discriminator(),
+]
diff --git a/vet/imbue_tools/repo_utils/python_imports.py b/vet/imbue_tools/repo_utils/python_imports.py
@@ -0,0 +1,130 @@
+import ast
+import sys
+from pathlib import Path
+
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+STANDARD_LIBRARIES: frozenset[str] = sys.stdlib_module_names | frozenset(sys.builtin_module_names)
+
+
+class QualifiedName(SerializableModel):
+ """A qualified name like 'foo.bar.baz'."""
+
+ value: str
+
+ @property
+ def top_level_name(self) -> "QualifiedName":
+ """Return the top-level module name (e.g., 'foo' from 'foo.bar.baz')."""
+ return QualifiedName(value=self.value.split(".", maxsplit=1)[0])
+
+ @property
+ def parent_name(self) -> "QualifiedName":
+ """Return the parent module name (e.g., 'foo.bar' from 'foo.bar.baz')."""
+ return QualifiedName(value=self.value.rsplit(".", maxsplit=1)[0])
+
+ def to_path(self) -> Path:
+ """Convert qualified name to a file path (e.g., 'foo.bar' -> 'foo/bar.py')."""
+ return Path(self.value.replace(".", "/") + ".py")
+
+
+class Import(SerializableModel):
+ """Represents a single import statement."""
+
+ source: str
+ alias: str | None
+ qualified_name: QualifiedName
+
+
+def _collect_global_imports(node: ast.AST, imports: list[Import]) -> None:
+ """
+ Recursively collect import statements at global scope.
+
+ Stops recursing into function and class definitions since imports
+ inside those are not at global scope.
+
+ Args:
+ node: The AST node to process
+ imports: List to accumulate found imports
+ """
+ if isinstance(node, ast.Import):
+ # Handle: import foo, bar
+ # Handle: import foo as bar
+ for alias in node.names:
+ if alias.asname:
+ source = f"import {alias.name} as {alias.asname}"
+ alias_name = alias.asname
+ else:
+ source = f"import {alias.name}"
+ alias_name = None
+ imports.append(
+ Import(
+ source=source,
+ alias=alias_name,
+ qualified_name=QualifiedName(value=alias.name),
+ )
+ )
+ elif isinstance(node, ast.ImportFrom):
+ # Handle: from foo import bar, baz
+ module = node.module or ""
+ if node.names[0].name == "*":
+ # from foo import *
+ source = f"from {module} import *"
+ imports.append(
+ Import(
+ source=source,
+ alias=None,
+ qualified_name=QualifiedName(value=f"{module}.*"),
+ )
+ )
+ else:
+ for alias in node.names:
+ if module:
+ full_name = f"{module}.{alias.name}"
+ else:
+ # relative import like: from . import foo
+ full_name = alias.name
+
+ if alias.asname:
+ source = f"from {module} import {alias.name} as {alias.asname}"
+ alias_name = alias.asname
+ else:
+ source = f"from {module} import {alias.name}"
+ alias_name = None
+
+ imports.append(
+ Import(
+ source=source,
+ alias=alias_name,
+ qualified_name=QualifiedName(value=full_name),
+ )
+ )
+
+ # Don't recurse into function or class definitions - imports there are not global
+ if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef, ast.ClassDef, ast.Lambda)):
+ return
+
+ # Recurse into child nodes
+ for child in ast.iter_child_nodes(node):
+ _collect_global_imports(child, imports)
+
+
+def get_global_imports(source_code: str) -> tuple[Import, ...]:
+ """
+ Extract all global imports from Python source code.
+
+ This includes imports at module level, as well as imports inside conditionals
+ or other control flow structures at the top level (not inside functions or classes).
+
+ Args:
+ source_code: Python source code as a string
+
+ Returns:
+ Tuple of Import objects representing all imports in the file
+
+ Raises:
+ SyntaxError: If the source code cannot be parsed
+ """
+ tree = ast.parse(source_code)
+ imports: list[Import] = []
+ _collect_global_imports(tree, imports)
+ return tuple(imports)
diff --git a/imbue_tools/imbue_tools/repo_utils/stubify_file.py b/vet/imbue_tools/repo_utils/stubify_file.py
diff --git a/vet/imbue_tools/repo_utils/subrepo_formatting.py b/vet/imbue_tools/repo_utils/subrepo_formatting.py
@@ -0,0 +1,345 @@
+import functools
+from enum import Enum
+from typing import Annotated
+from typing import Iterable
+from typing import Mapping
+from typing import Self
+from typing import assert_never
+
+import jinja2
+from pathspec import GitIgnoreSpec
+from pydantic import Tag
+
+from vet.imbue_core.agents.configs import LanguageModelGenerationConfig
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_core.pydantic_serialization import build_discriminator
+from vet.imbue_tools.repo_utils.context_utils import escape_all_jinja_variables
+from vet.imbue_tools.repo_utils.context_utils import escape_prompt_markers
+from vet.imbue_tools.repo_utils.data_types import FileContext
+from vet.imbue_tools.repo_utils.data_types import FileContextUnion
+from vet.imbue_tools.repo_utils.data_types import FilenameContext
+from vet.imbue_tools.repo_utils.data_types import FullFileContext
+from vet.imbue_tools.repo_utils.data_types import StubFileContext
+from vet.imbue_tools.repo_utils.errors import ContextLengthExceededError
+from vet.imbue_tools.repo_utils.stubify_file import stubify_code_file
+
+
+class ContextFormatStyle(Enum):
+ FULL_FILE = "FULL_FILE"
+ STUB = "STUB"
+ FILENAME_ONLY = "FILENAME_ONLY"
+ HIDDEN = "HIDDEN"
+
+
+class BaseFilenamePattern(SerializableModel):
+ """
+ Extends the functionality of `GitIgnoreSpec` to be serializable.
+ """
+
+ object_type: str = "BaseFilenamePattern"
+ lines: tuple[str, ...]
+
+ @functools.cached_property
+ def git_ignore_spec(self) -> GitIgnoreSpec:
+ return GitIgnoreSpec.from_lines(self.lines)
+
+ @classmethod
+ def from_lines(cls, lines: Iterable[str]) -> Self:
+ return cls(lines=tuple(sorted(lines)))
+
+ def match_file(self, file: str) -> bool:
+ return self.git_ignore_spec.match_file(file)
+
+
+class NegatedFilenamePattern(BaseFilenamePattern):
+ """Matches everything except the files matched by the base pattern."""
+
+ object_type: str = "NegatedFilenamePattern"
+
+ def match_file(self, file: str) -> bool:
+ return not self.git_ignore_spec.match_file(file)
+
+ @classmethod
+ def build_from_positive_pattern(cls, positive_pattern: BaseFilenamePattern) -> Self:
+ return cls(lines=positive_pattern.lines)
+
+
+class IntersectionFilenamePattern(SerializableModel):
+ object_type: str = "IntersectionFilenamePattern"
+ specs: tuple["FilenamePattern", ...]
+
+ def match_file(self, file: str) -> bool:
+ return all(spec.match_file(file) for spec in self.specs)
+
+
+class UnionFilenamePattern(SerializableModel):
+ object_type: str = "UnionFilenamePattern"
+ specs: tuple["FilenamePattern", ...]
+
+ def match_file(self, file: str) -> bool:
+ return any(spec.match_file(file) for spec in self.specs)
+
+
+class ExactFilenamePattern(SerializableModel):
+ """
+ Similar to a BaseFilenamePattern, but more efficient thanks to the use of a hash set.
+ However, it only supports exact filename matches and no patterns.
+
+ O(1) matching over n filenames, instead of O(n) with BaseFilenamePattern.
+ """
+
+ object_type: str = "ExactFilenamePattern"
+ # Will match these exact filenames.
+ # We store this as a tuple instead of frozenset to have deterministic ordering. Helps with snapshot tests.
+ filenames: tuple[str, ...]
+
+ def match_file(self, file: str) -> bool:
+ return file in self.filenames_set
+
+ @functools.cached_property
+ def filenames_set(self) -> set[str]:
+ return set(self.filenames)
+
+
+FilenamePattern = Annotated[
+ Annotated[BaseFilenamePattern, Tag("BaseFilenamePattern")]
+ | Annotated[NegatedFilenamePattern, Tag("NegatedFilenamePattern")]
+ | Annotated[IntersectionFilenamePattern, Tag("IntersectionFilenamePattern")]
+ | Annotated[UnionFilenamePattern, Tag("UnionFilenamePattern")]
+ | Annotated[ExactFilenamePattern, Tag("ExactFilenamePattern")],
+ build_discriminator(),
+]
+
+
+SubrepoContextMatchers = tuple[tuple[ContextFormatStyle, FilenamePattern], ...]
+
+
+@functools.lru_cache(maxsize=100)
+def stubify_file_contents_cached(path: str, contents: str) -> str:
+ # TODO: there's various flags here we could try
+ # TODO: we may want an option to suppress comments, which end up being a large percent of the lines
+ if path.endswith(".py"):
+ return stubify_code_file(path, contents, keep_indent=True)
+ else:
+ # For non-Python files, maintain the full contents for now.
+ return contents
+
+
+def stubify_and_format_for_agent_context(path: str, contents: str | None) -> StubFileContext:
+ contents_to_use = contents if contents is not None else "RAW FILE CONTENTS"
+ stub = stubify_file_contents_cached(path=path, contents=contents_to_use)
+ return StubFileContext(path=path, stub=stub)
+
+
+def format_filename_only_for_agent_context(path: str) -> FilenameContext:
+ return FilenameContext(path=path)
+
+
+def format_full_file_for_agent_context(path: str, contents: str) -> FullFileContext:
+ return FullFileContext(path=path, contents=contents)
+
+
+BASE_REPO_CONTEXT_TEMPLATE = """
+<REPO_CONTEXT>
+{{repo_context}}
+</REPO_CONTEXT>
+"""
+
+REPO_CONTEXT_TEMPLATE_WITH_NO_MENTION_OF_DIFF = (
+ """
+{% if not is_shortened %}For context, here are the contents of all files currently in the project:
+{% else %}The project's repository is too large to show in full, so we have chosen a useful subset for your context.
+Files in the repository may be shown in either full, stubified, or filename-only form{% if has_hidden_files %}, or they may be hidden entirely{% endif %}. Here are the contents of some of the files in the repository:{% endif %}
+"""
+ + BASE_REPO_CONTEXT_TEMPLATE
+)
+
+REPO_CONTEXT_TEMPLATE = (
+ REPO_CONTEXT_TEMPLATE_WITH_NO_MENTION_OF_DIFF
+ + """
+
+If any files have been changed, the changes will be described next. Otherwise, you can assume this is the current state of the project.
+"""
+)
+
+
+def format_file_for_agent_context(
+ path: str, contents: str, format_style: ContextFormatStyle
+) -> FileContextUnion | None:
+ if format_style == ContextFormatStyle.FULL_FILE:
+ return format_full_file_for_agent_context(path, contents)
+ elif format_style == ContextFormatStyle.STUB:
+ return stubify_and_format_for_agent_context(path, contents)
+ elif format_style == ContextFormatStyle.FILENAME_ONLY:
+ return format_filename_only_for_agent_context(path)
+ elif format_style == ContextFormatStyle.HIDDEN:
+ return None
+ else:
+ assert_never(format_style) # pyre-ignore[6]: pyre doesn't understand enums
+
+
+@functools.lru_cache(maxsize=20)
+def parse_subrepo_context_matchers_from_toml(
+ subrepo_context_config_toml: str,
+) -> SubrepoContextMatchers:
+ current_mode: ContextFormatStyle
+ matchers = []
+ for line in subrepo_context_config_toml.splitlines():
+ line = line.strip()
+ if not line:
+ continue
+ if line.startswith("["):
+ current_mode = ContextFormatStyle[line.replace("[", "").replace("]", "").upper()] # type: ignore
+ continue
+
+ exclude_spec = BaseFilenamePattern.from_lines([line])
+ matchers.append((current_mode, exclude_spec))
+ return tuple(matchers)
+
+
+def compute_file_format_style(
+ file_path: str,
+ subrepo_context_matchers: SubrepoContextMatchers,
+ exclusions: FilenamePattern | None = None,
+) -> ContextFormatStyle:
+ for matcher_format, matcher in subrepo_context_matchers:
+ if matcher.match_file(file_path):
+ if exclusions and matcher_format != ContextFormatStyle.HIDDEN and exclusions.match_file(file_path):
+ # Exclusions override any non-hidden format and downgrade it to FILENAME_ONLY.
+ return ContextFormatStyle.FILENAME_ONLY
+ # Return the first match
+ return matcher_format
+ return ContextFormatStyle.HIDDEN
+
+
+def compute_file_context_format_styles(
+ file_paths: Iterable[str],
+ subrepo_context_matchers: SubrepoContextMatchers,
+ exclusions: FilenamePattern | None = None,
+) -> Mapping[str, ContextFormatStyle]:
+ return {
+ file_path: compute_file_format_style(file_path, subrepo_context_matchers, exclusions)
+ for file_path in file_paths
+ }
+
+
+def get_estimated_lower_bound_token_count_for_text_and_model(text: str, model_name: str) -> int:
+ # A factor of 1/4.5 appears to be a reasonable empirical estimate for current models.
+ # We use a slighly smaller factor (1/5) to give more of a lower bound estimate.
+ return round(len(text) / 5)
+
+
+def format_all_for_agent(repo_contents: tuple[FileContext, ...]) -> dict[str, str]:
+ return {contents.path: contents.format_for_agent() for contents in repo_contents}
+
+
+def format_subrepo(formatted_repo_contents: Mapping[str, str]) -> str:
+ repo_context_str = "".join([contents for contents in formatted_repo_contents.values() if contents is not None])
+ return escape_all_jinja_variables(escape_prompt_markers(repo_context_str))
+
+
+def formatted_subrepo_to_prompt(
+ repo_context_str: str, is_shortened: bool, has_hidden_files: bool, template: str
+) -> str:
+ env = jinja2.Environment(undefined=jinja2.StrictUndefined)
+ jinja_template = env.from_string(template)
+ repo_context_prompt = jinja_template.render(
+ repo_context=repo_context_str,
+ is_shortened=is_shortened,
+ has_hidden_files=has_hidden_files,
+ )
+ return repo_context_prompt
+
+
+def format_subrepo_context_into_filecontexts(
+ full_repo_contents: Mapping[str, str],
+ path_to_format_style: Mapping[str, ContextFormatStyle],
+) -> tuple[FileContextUnion, ...]:
+ repo_contents = tuple(
+ format_file_for_agent_context(path, contents, path_to_format_style[path])
+ for path, contents in full_repo_contents.items()
+ )
+ repo_contents_with_hidden_removed = tuple(contents for contents in repo_contents if contents is not None)
+ return repo_contents_with_hidden_removed
+
+
+def build_context_from_filecontexts(
+ repo_contents_with_hidden_removed: tuple[FileContextUnion, ...],
+ model_config: LanguageModelGenerationConfig,
+ # how many tokens to reserve for additional prompt messages and output
+ tokens_to_reserve: int,
+ template: str = REPO_CONTEXT_TEMPLATE,
+ path_to_format_style: Mapping[str, ContextFormatStyle] | None = None,
+) -> str:
+ """
+ Returns the repo contents formatted according to the format styles as a string.
+ Includes (at least if the default template is used) an explanation at the beginning
+ saying that these are the contents of the repo, potentially truncated or hidden.
+
+ If there are no repo contents, returns an empty string.
+ """
+
+ if not repo_contents_with_hidden_removed:
+ return ""
+
+ max_context_length = model_config.get_max_context_length()
+ available_tokens = max_context_length - tokens_to_reserve
+
+ formatted_repo_contents_with_hidden_removed = format_all_for_agent(repo_contents_with_hidden_removed)
+
+ repo_context_str = format_subrepo(formatted_repo_contents_with_hidden_removed)
+
+ if model_config.is_custom_model():
+ # For custom models, approximate_token_count is already fast, so skip the estimation step.
+ full_repo_context_token_count = model_config.count_tokens(repo_context_str)
+ else:
+ # First use an estimation of the token count to see if we are likely below the maximum length. Then
+ # double-check with the exact token count.
+ # We do this because getting the exact token count is quite slow.
+ estimated_full_repo_context_token_count = get_estimated_lower_bound_token_count_for_text_and_model(
+ repo_context_str, model_config.model_name
+ )
+ if estimated_full_repo_context_token_count > available_tokens:
+ raise ContextLengthExceededError(
+ f"Estimated context has size {estimated_full_repo_context_token_count}; available tokens {available_tokens}"
+ )
+ full_repo_context_token_count = model_config.count_tokens(repo_context_str)
+ if full_repo_context_token_count > available_tokens:
+ raise ContextLengthExceededError(
+ f"Context has size {full_repo_context_token_count}; available tokens {available_tokens}"
+ )
+
+ if path_to_format_style is None:
+ path_to_format_style = {
+ contents.path: ContextFormatStyle.FULL_FILE for contents in repo_contents_with_hidden_removed
+ }
+
+ is_shortened = any([style != ContextFormatStyle.FULL_FILE for style in path_to_format_style.values()])
+ has_hidden_files = any([style == ContextFormatStyle.HIDDEN for style in path_to_format_style.values()])
+
+ repo_context_prompt = formatted_subrepo_to_prompt(
+ repo_context_str=repo_context_str,
+ is_shortened=is_shortened,
+ has_hidden_files=has_hidden_files,
+ template=template,
+ )
+ return repo_context_prompt
+
+
+def format_subrepo_context(
+ full_repo_contents: Mapping[str, str],
+ path_to_format_style: Mapping[str, ContextFormatStyle],
+ model_config: LanguageModelGenerationConfig,
+ # how many tokens to reserve for additional prompt messages and output
+ tokens_to_reserve: int,
+ template: str = REPO_CONTEXT_TEMPLATE,
+) -> tuple[str, tuple[FileContextUnion, ...]]:
+ repo_contents_tuple = format_subrepo_context_into_filecontexts(full_repo_contents, path_to_format_style)
+ repo_context_str = build_context_from_filecontexts(
+ repo_contents_tuple,
+ model_config,
+ tokens_to_reserve,
+ template,
+ path_to_format_style,
+ )
+ return repo_context_str, repo_contents_tuple
diff --git a/vet/imbue_tools/types/vet_config.py b/vet/imbue_tools/types/vet_config.py
@@ -0,0 +1,100 @@
+from pathlib import Path
+
+from vet.imbue_core.agents.configs import LanguageModelGenerationConfig
+from vet.imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.pydantic_serialization import SerializableModel
+
+DEFAULT_CONFIDENCE_THRESHOLD = 0.8
+
+
+class VetConfig(SerializableModel):
+ """Configuration for the vet system."""
+
+ # If none, all registered identifiers are used.
+ # Otherwise, only the identifiers in this tuple are used.
+ enabled_identifiers: tuple[str, ...] | None = None
+
+ # Issue identifiers that are disabled are never used.
+ disabled_identifiers: tuple[str, ...] | None = None
+
+ # Similar to the above, but for reporting specific types of issues.
+ # (Use the values from the vet.data_types.IssueCode enum.)
+ enabled_issue_codes: tuple[IssueCode, ...] | None = None
+ disabled_issue_codes: tuple[IssueCode, ...] | None = ()
+
+ # Todo: Different models for different issue identifiers
+ language_model_generation_config: LanguageModelGenerationConfig = LanguageModelGenerationConfig(
+ model_name=AnthropicModelName.CLAUDE_4_5_HAIKU_2025_10_01
+ )
+ max_identifier_spend_dollars: float = 5.0
+ max_output_tokens: int = 20000
+ enable_parallel_agentic_issue_identification: bool = False
+ max_identify_workers: int | None = None
+ temperature: float = 0.5
+
+ # If True, apply an additional LLM-based filtering stage, where each identified issue is evaluated
+ # according to a number of quality criteria. Only issues that pass the evaluation are returned.
+ filter_issues: bool = True
+ filter_issues_through_llm_evaluator: bool = True
+ filter_issues_below_confidence: float | None = DEFAULT_CONFIDENCE_THRESHOLD
+
+ enable_deduplication: bool = True
+ enable_collation: bool = True
+
+ # If True, we attempt to cache the full prompts including specific inputs with the LLM provider.
+ # There can be an additional cost for such a cache write, but it can help save cost in evaluation
+ # contexts (such as black_box_evals) where the same inputs are being evaluated multiple times.
+ cache_full_prompt: bool = False
+
+ extra_context: str | None = None
+
+ @classmethod
+ def build(
+ cls,
+ language_model_name: str | None = None,
+ language_model_cache_path: Path | None = None,
+ enabled_identifiers: tuple[str, ...] | None = None,
+ enable_parallel_agentic_issue_identification: bool = False,
+ max_identify_workers: int | None = None,
+ filter_issues: bool = True,
+ filter_issues_below_confidence: float | None = DEFAULT_CONFIDENCE_THRESHOLD,
+ enable_deduplication: bool = True,
+ enable_collation: bool = True,
+ enabled_issue_codes: tuple[IssueCode, ...] | None = None,
+ temperature: float = 0.5,
+ retry_jitter_factor: float = 0.0,
+ cache_full_prompt: bool = False,
+ ) -> "VetConfig":
+ if not language_model_name:
+ language_model_name = AnthropicModelName.CLAUDE_4_5_HAIKU_2025_10_01
+ language_model_generation_config = LanguageModelGenerationConfig(
+ model_name=language_model_name,
+ cache_path=language_model_cache_path,
+ retry_jitter_factor=retry_jitter_factor,
+ )
+ return cls(
+ language_model_generation_config=language_model_generation_config,
+ enabled_identifiers=enabled_identifiers,
+ enable_parallel_agentic_issue_identification=enable_parallel_agentic_issue_identification,
+ max_identify_workers=max_identify_workers,
+ filter_issues=filter_issues,
+ filter_issues_below_confidence=filter_issues_below_confidence,
+ enable_deduplication=enable_deduplication,
+ enable_collation=enable_collation,
+ enabled_issue_codes=enabled_issue_codes,
+ temperature=temperature,
+ cache_full_prompt=cache_full_prompt,
+ )
+
+
+def get_enabled_issue_codes(config: VetConfig) -> set[IssueCode]:
+ all_issue_code_values = {item.value for item in IssueCode}
+ explicitly_enabled = config.enabled_issue_codes or tuple()
+ explicitly_disabled = config.disabled_issue_codes or tuple()
+ for code in explicitly_enabled + explicitly_disabled:
+ if code not in all_issue_code_values:
+ raise ValueError(f"Bad config: unknown issue code: {code}")
+ possibly_enabled_values = set(explicitly_enabled) if len(explicitly_enabled) > 0 else set(v for v in IssueCode)
+ disabled_values = set(explicitly_disabled)
+ return possibly_enabled_values - disabled_values
diff --git a/imbue_tools/imbue_tools/util_prompts/conversation_prefix.py b/vet/imbue_tools/util_prompts/conversation_prefix.py
diff --git a/vet/imbue_tools/util_prompts/goal_from_conversation.py b/vet/imbue_tools/util_prompts/goal_from_conversation.py
@@ -0,0 +1,64 @@
+import jinja2
+
+from vet.imbue_core.agents.configs import LanguageModelGenerationConfig
+from vet.imbue_core.agents.llm_apis.build_apis import build_language_model_from_config
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.itertools import only
+from vet.vet_types.messages import ConversationMessageUnion
+from vet.imbue_tools.get_conversation_history.get_conversation_history import (
+ format_conversation_history_for_prompt,
+)
+from vet.imbue_tools.util_prompts.conversation_prefix import CONVERSATION_PREFIX_TEMPLATE
+
+# TODO: see how this does on actual examples where the agent did something other than what the user asked for
+PROMPT_TEMPLATE = (
+ CONVERSATION_PREFIX_TEMPLATE
+ + """
+[ROLE=USER]
+What is the user's goal based on the preceding conversation?
+Pay attention only to what the user asks for, not what the agent does.
+Respond with a brief description of the goal--a few sentences at most.
+The goal should be listed as an imperative; for example "Implement XYZ" rather than "The user's goal is to implement XYZ".
+Do not include any reasoning or other text in your response.
+"""
+)
+
+# should be totally sufficient for a goal that's only supposed to be a few sentences
+MAX_OUTPUT_TOKENS = 500
+
+GOAL_GENERATION_DEFAULT_PARAMS = LanguageModelGenerationParams(temperature=0.0, max_tokens=MAX_OUTPUT_TOKENS)
+
+
+def prompt_for_getting_goal_from_conversation(
+ conversation_history: tuple[ConversationMessageUnion, ...],
+) -> str:
+ env = jinja2.Environment(undefined=jinja2.StrictUndefined)
+ jinja_template = env.from_string(PROMPT_TEMPLATE)
+ return jinja_template.render(conversation_history=format_conversation_history_for_prompt(conversation_history))
+
+
+def get_goal_from_conversation_with_usage(
+ conversation_history: tuple[ConversationMessageUnion, ...],
+ language_model_generation_config: LanguageModelGenerationConfig,
+) -> CostedLanguageModelResponse:
+ """Query an LLM with the conversation history to get the user's goal, and include usage info in the response."""
+ language_model = build_language_model_from_config(language_model_generation_config)
+ prompt = prompt_for_getting_goal_from_conversation(conversation_history)
+ costed_response = language_model.complete_with_usage_sync(
+ prompt,
+ params=GOAL_GENERATION_DEFAULT_PARAMS,
+ is_caching_enabled=language_model.cache_path is not None,
+ )
+ return costed_response
+
+
+def get_goal_from_conversation(
+ conversation_history: tuple[ConversationMessageUnion, ...],
+ language_model_generation_config: LanguageModelGenerationConfig,
+) -> str:
+ """Query an LLM with the conversation history to get the user's goal."""
+ response = only(
+ get_goal_from_conversation_with_usage(conversation_history, language_model_generation_config).responses
+ )
+ return response.text
diff --git a/vet/issue_identifiers/agentic_issue_collation.py b/vet/issue_identifiers/agentic_issue_collation.py
@@ -4,19 +4,19 @@ from typing import Iterable
import jinja2
-from imbue_core.data_types import AgenticPhase
-from imbue_core.data_types import IssueCode
-from imbue_core.data_types import IssueIdentificationDebugInfo
-from imbue_core.data_types import IssueIdentificationLLMResponseMetadata
-from imbue_core.data_types import LLMResponse
-from imbue_tools.get_conversation_history.input_data_types import CommitInputs
-from imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
-from imbue_tools.get_conversation_history.input_data_types import (
+from vet.imbue_core.data_types import AgenticPhase
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.data_types import IssueIdentificationDebugInfo
+from vet.imbue_core.data_types import IssueIdentificationLLMResponseMetadata
+from vet.imbue_core.data_types import LLMResponse
+from vet.imbue_tools.get_conversation_history.input_data_types import CommitInputs
+from vet.imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
+from vet.imbue_tools.get_conversation_history.input_data_types import (
to_specific_inputs_type,
)
-from imbue_tools.repo_utils.context_utils import escape_prompt_markers
-from imbue_tools.repo_utils.project_context import ProjectContext
-from imbue_tools.types.vet_config import VetConfig
+from vet.imbue_tools.repo_utils.context_utils import escape_prompt_markers
+from vet.imbue_tools.repo_utils.project_context import ProjectContext
+from vet.imbue_tools.types.vet_config import VetConfig
from vet.issue_identifiers.common import GeneratedIssueSchema
from vet.issue_identifiers.common import GeneratedResponseSchema
from vet.issue_identifiers.common import extract_invocation_info_from_messages
diff --git a/vet/issue_identifiers/base.py b/vet/issue_identifiers/base.py
@@ -3,15 +3,15 @@ from typing import Generator
from typing import Generic
from typing import TypeVar
-from imbue_core.data_types import IssueCode
-from imbue_core.data_types import IssueIdentificationDebugInfo
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
-from imbue_tools.get_conversation_history.input_data_types import (
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.data_types import IssueIdentificationDebugInfo
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
+from vet.imbue_tools.get_conversation_history.input_data_types import (
to_specific_inputs_type,
)
-from imbue_tools.repo_utils.project_context import ProjectContext
-from imbue_tools.types.vet_config import VetConfig
+from vet.imbue_tools.repo_utils.project_context import ProjectContext
+from vet.imbue_tools.types.vet_config import VetConfig
from vet.issue_identifiers.common import GeneratedIssueSchema
T = TypeVar("T", bound=IdentifierInputs)
diff --git a/vet/issue_identifiers/common.py b/vet/issue_identifiers/common.py
@@ -11,34 +11,34 @@ from loguru import logger
from pydantic import Field
from pydantic import PrivateAttr
-from imbue_core.agents.agent_api.api import get_agent_client
-from imbue_core.agents.agent_api.claude.data_types import ClaudeCodeOptions
-from imbue_core.agents.agent_api.data_types import AgentAssistantMessage
-from imbue_core.agents.agent_api.data_types import AgentMessage
-from imbue_core.agents.agent_api.data_types import AgentResultMessage
-from imbue_core.agents.agent_api.data_types import AgentTextBlock
-from imbue_core.agents.agent_api.data_types import AgentToolName
-from imbue_core.agents.agent_api.data_types import READ_ONLY_TOOLS
-from imbue_core.agents.llm_apis.anthropic_data_types import AnthropicCachingInfo
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.async_monkey_patches import log_exception
-from imbue_core.data_types import ConfidenceScore
-from imbue_core.data_types import IdentifiedVerifyIssue
-from imbue_core.data_types import InvocationInfo
-from imbue_core.data_types import IssueCode
-from imbue_core.data_types import IssueIdentificationDebugInfo
-from imbue_core.data_types import IssueIdentifierResult
-from imbue_core.data_types import IssueLocation
-from imbue_core.data_types import LineRange
-from imbue_core.data_types import SeverityScore
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_tools.llm_output_parsing.parse_model_json_response import (
+from vet.imbue_core.agents.agent_api.api import get_agent_client
+from vet.imbue_core.agents.agent_api.claude.data_types import ClaudeCodeOptions
+from vet.imbue_core.agents.agent_api.data_types import AgentAssistantMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentResultMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentTextBlock
+from vet.imbue_core.agents.agent_api.data_types import AgentToolName
+from vet.imbue_core.agents.agent_api.data_types import READ_ONLY_TOOLS
+from vet.imbue_core.agents.llm_apis.anthropic_data_types import AnthropicCachingInfo
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.async_monkey_patches import log_exception
+from vet.imbue_core.data_types import ConfidenceScore
+from vet.imbue_core.data_types import IdentifiedVerifyIssue
+from vet.imbue_core.data_types import InvocationInfo
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.data_types import IssueIdentificationDebugInfo
+from vet.imbue_core.data_types import IssueIdentifierResult
+from vet.imbue_core.data_types import IssueLocation
+from vet.imbue_core.data_types import LineRange
+from vet.imbue_core.data_types import SeverityScore
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_tools.llm_output_parsing.parse_model_json_response import (
ResponseParsingError,
)
-from imbue_tools.llm_output_parsing.parse_model_json_response import (
+from vet.imbue_tools.llm_output_parsing.parse_model_json_response import (
parse_model_json_response,
)
-from imbue_tools.repo_utils.project_context import ProjectContext
+from vet.imbue_tools.repo_utils.project_context import ProjectContext
from vet.issue_identifiers.identification_guides import (
IssueIdentificationGuide,
)
diff --git a/vet/issue_identifiers/common_test.py b/vet/issue_identifiers/common_test.py
@@ -2,19 +2,19 @@ import json
from pathlib import Path
from typing import Iterable
-from imbue_core.async_monkey_patches_test import expect_exact_logged_errors
-from imbue_core.data_types import IdentifiedVerifyIssue
-from imbue_core.data_types import IssueCode
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.itertools import only
-from imbue_tools.llm_output_parsing.parse_model_json_response import (
+from vet.imbue_core.async_monkey_patches_test import expect_exact_logged_errors
+from vet.imbue_core.data_types import IdentifiedVerifyIssue
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.itertools import only
+from vet.imbue_tools.llm_output_parsing.parse_model_json_response import (
ResponseParsingError,
)
-from imbue_tools.llm_output_parsing.parse_model_json_response import (
+from vet.imbue_tools.llm_output_parsing.parse_model_json_response import (
parse_model_json_response,
)
-from imbue_tools.repo_utils.project_context import BaseProjectContext
-from imbue_tools.repo_utils.project_context import ProjectContext
+from vet.imbue_tools.repo_utils.project_context import BaseProjectContext
+from vet.imbue_tools.repo_utils.project_context import ProjectContext
from vet.issue_identifiers.common import GeneratedResponseSchema
from vet.issue_identifiers.common import (
convert_generated_issue_to_identified_issue,
diff --git a/vet/issue_identifiers/harnesses/agentic.py b/vet/issue_identifiers/harnesses/agentic.py
@@ -12,21 +12,21 @@ from typing import Generator
import jinja2
from loguru import logger
-from imbue_core.agents.agent_api.claude.data_types import ClaudeCodeOptions
-from imbue_core.agents.agent_api.data_types import AgentMessage
-from imbue_core.agents.agent_api.data_types import AgentToolName
-from imbue_core.agents.agent_api.data_types import READ_ONLY_TOOLS
-from imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
-from imbue_core.async_monkey_patches import log_exception
-from imbue_core.data_types import AgenticPhase
-from imbue_core.data_types import IssueCode
-from imbue_core.data_types import IssueIdentificationDebugInfo
-from imbue_core.data_types import IssueIdentificationLLMResponseMetadata
-from imbue_core.data_types import LLMResponse
-from imbue_tools.get_conversation_history.input_data_types import CommitInputs
-from imbue_tools.repo_utils.context_utils import escape_prompt_markers
-from imbue_tools.repo_utils.project_context import ProjectContext
-from imbue_tools.types.vet_config import VetConfig
+from vet.imbue_core.agents.agent_api.claude.data_types import ClaudeCodeOptions
+from vet.imbue_core.agents.agent_api.data_types import AgentMessage
+from vet.imbue_core.agents.agent_api.data_types import AgentToolName
+from vet.imbue_core.agents.agent_api.data_types import READ_ONLY_TOOLS
+from vet.imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
+from vet.imbue_core.async_monkey_patches import log_exception
+from vet.imbue_core.data_types import AgenticPhase
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.data_types import IssueIdentificationDebugInfo
+from vet.imbue_core.data_types import IssueIdentificationLLMResponseMetadata
+from vet.imbue_core.data_types import LLMResponse
+from vet.imbue_tools.get_conversation_history.input_data_types import CommitInputs
+from vet.imbue_tools.repo_utils.context_utils import escape_prompt_markers
+from vet.imbue_tools.repo_utils.project_context import ProjectContext
+from vet.imbue_tools.types.vet_config import VetConfig
from vet.issue_identifiers.base import IssueIdentifier
from vet.issue_identifiers.common import GeneratedIssueSchema
from vet.issue_identifiers.common import GeneratedResponseSchema
diff --git a/vet/issue_identifiers/harnesses/base.py b/vet/issue_identifiers/harnesses/base.py
@@ -2,7 +2,7 @@ import abc
from typing import Generic
from typing import TypeVar
-from imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
+from vet.imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
from vet.issue_identifiers.base import IssueIdentifier
from vet.issue_identifiers.identification_guides import (
IssueIdentificationGuide,
diff --git a/vet/issue_identifiers/harnesses/conversation_single_prompt.py b/vet/issue_identifiers/harnesses/conversation_single_prompt.py
@@ -10,21 +10,21 @@ from typing import Generator
import jinja2
-from imbue_core.agents.llm_apis.build_apis import build_language_model_from_config
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.data_types import AgenticPhase
-from imbue_core.data_types import IssueCode
-from imbue_core.data_types import IssueIdentificationDebugInfo
-from imbue_core.data_types import IssueIdentificationLLMResponseMetadata
-from imbue_core.data_types import LLMResponse
-from imbue_core.itertools import only
-from imbue_tools.get_conversation_history.get_conversation_history import (
+from vet.imbue_core.agents.llm_apis.build_apis import build_language_model_from_config
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.data_types import AgenticPhase
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.data_types import IssueIdentificationDebugInfo
+from vet.imbue_core.data_types import IssueIdentificationLLMResponseMetadata
+from vet.imbue_core.data_types import LLMResponse
+from vet.imbue_core.itertools import only
+from vet.imbue_tools.get_conversation_history.get_conversation_history import (
format_conversation_history_for_prompt,
)
-from imbue_tools.get_conversation_history.input_data_types import ConversationInputs
-from imbue_tools.repo_utils.project_context import ProjectContext
-from imbue_tools.types.vet_config import VetConfig
-from imbue_tools.util_prompts.conversation_prefix import CONVERSATION_PREFIX_TEMPLATE
+from vet.imbue_tools.get_conversation_history.input_data_types import ConversationInputs
+from vet.imbue_tools.repo_utils.project_context import ProjectContext
+from vet.imbue_tools.types.vet_config import VetConfig
+from vet.imbue_tools.util_prompts.conversation_prefix import CONVERSATION_PREFIX_TEMPLATE
from vet.issue_identifiers.base import IssueIdentifier
from vet.issue_identifiers.common import GeneratedIssueSchema
from vet.issue_identifiers.common import GeneratedResponseSchema
diff --git a/vet/issue_identifiers/harnesses/conversation_single_prompt_test.py b/vet/issue_identifiers/harnesses/conversation_single_prompt_test.py
@@ -1,15 +1,15 @@
import pytest
-from imbue_core.data_types import IssueCode
-from vet_types.chat_state import TextBlock
-from vet_types.ids import AssistantMessageID
-from vet_types.messages import AgentMessageSource
-from vet_types.messages import ChatInputUserMessage
-from vet_types.messages import LLMModel
-from vet_types.messages import ResponseBlockAgentMessage
-from imbue_tools.get_conversation_history.input_data_types import ConversationInputs
-from imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
-from imbue_tools.get_conversation_history.input_data_types import (
+from vet.imbue_core.data_types import IssueCode
+from vet.vet_types.chat_state import TextBlock
+from vet.vet_types.ids import AssistantMessageID
+from vet.vet_types.messages import AgentMessageSource
+from vet.vet_types.messages import ChatInputUserMessage
+from vet.vet_types.messages import LLMModel
+from vet.vet_types.messages import ResponseBlockAgentMessage
+from vet.imbue_tools.get_conversation_history.input_data_types import ConversationInputs
+from vet.imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
+from vet.imbue_tools.get_conversation_history.input_data_types import (
IdentifierInputsMissingError,
)
from vet.issue_identifiers.harnesses.conversation_single_prompt import (
diff --git a/vet/issue_identifiers/harnesses/single_prompt.py b/vet/issue_identifiers/harnesses/single_prompt.py
@@ -8,18 +8,18 @@ from typing import Generator
import jinja2
-from imbue_core.agents.llm_apis.build_apis import build_language_model_from_config
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.data_types import AgenticPhase
-from imbue_core.data_types import IssueCode
-from imbue_core.data_types import IssueIdentificationDebugInfo
-from imbue_core.data_types import IssueIdentificationLLMResponseMetadata
-from imbue_core.data_types import LLMResponse
-from imbue_core.itertools import only
-from imbue_tools.get_conversation_history.input_data_types import CommitInputs
-from imbue_tools.repo_utils.context_utils import escape_prompt_markers
-from imbue_tools.repo_utils.project_context import ProjectContext
-from imbue_tools.types.vet_config import VetConfig
+from vet.imbue_core.agents.llm_apis.build_apis import build_language_model_from_config
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.data_types import AgenticPhase
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.data_types import IssueIdentificationDebugInfo
+from vet.imbue_core.data_types import IssueIdentificationLLMResponseMetadata
+from vet.imbue_core.data_types import LLMResponse
+from vet.imbue_core.itertools import only
+from vet.imbue_tools.get_conversation_history.input_data_types import CommitInputs
+from vet.imbue_tools.repo_utils.context_utils import escape_prompt_markers
+from vet.imbue_tools.repo_utils.project_context import ProjectContext
+from vet.imbue_tools.types.vet_config import VetConfig
from vet.issue_identifiers.base import IssueIdentifier
from vet.issue_identifiers.common import GeneratedIssueSchema
from vet.issue_identifiers.common import GeneratedResponseSchema
diff --git a/vet/issue_identifiers/harnesses/single_prompt_test.py b/vet/issue_identifiers/harnesses/single_prompt_test.py
@@ -7,21 +7,21 @@ from unittest import mock
import pytest
-from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
-from imbue_core.agents.llm_apis.data_types import LanguageModelResponseWithLogits
-from imbue_core.agents.llm_apis.data_types import ResponseStopReason
-from imbue_core.agents.llm_apis.mock_api import LanguageModelMock
-from imbue_core.data_types import IssueCode
-from imbue_core.frozen_utils import FrozenDict
-from imbue_tools.get_conversation_history.input_data_types import CommitInputs
-from imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
-from imbue_tools.get_conversation_history.input_data_types import (
+from vet.imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseUsage
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelResponseWithLogits
+from vet.imbue_core.agents.llm_apis.data_types import ResponseStopReason
+from vet.imbue_core.agents.llm_apis.mock_api import LanguageModelMock
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_tools.get_conversation_history.input_data_types import CommitInputs
+from vet.imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
+from vet.imbue_tools.get_conversation_history.input_data_types import (
IdentifierInputsMissingError,
)
-from imbue_tools.repo_utils.project_context import BaseProjectContext
-from imbue_tools.types.vet_config import VetConfig
+from vet.imbue_tools.repo_utils.project_context import BaseProjectContext
+from vet.imbue_tools.types.vet_config import VetConfig
from vet.issue_identifiers.base import IssueIdentifier
from vet.issue_identifiers.harnesses.single_prompt import SinglePromptHarness
from vet.issue_identifiers.identification_guides import (
diff --git a/vet/issue_identifiers/identification_guides.py b/vet/issue_identifiers/identification_guides.py
@@ -1,5 +1,5 @@
-from imbue_core.data_types import IssueCode
-from imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.pydantic_serialization import SerializableModel
class IssueIdentificationGuide(SerializableModel):
diff --git a/vet/issue_identifiers/issue_deduplication.py b/vet/issue_identifiers/issue_deduplication.py
@@ -4,16 +4,16 @@ from typing import Iterable
import jinja2
-from imbue_core.agents.llm_apis.build_apis import build_language_model_from_config
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.data_types import AgenticPhase
-from imbue_core.data_types import IssueCode
-from imbue_core.data_types import IssueIdentificationDebugInfo
-from imbue_core.data_types import IssueIdentificationLLMResponseMetadata
-from imbue_core.data_types import LLMResponse
-from imbue_core.itertools import only
-from imbue_tools.repo_utils.context_utils import escape_prompt_markers
-from imbue_tools.types.vet_config import VetConfig
+from vet.imbue_core.agents.llm_apis.build_apis import build_language_model_from_config
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.data_types import AgenticPhase
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.data_types import IssueIdentificationDebugInfo
+from vet.imbue_core.data_types import IssueIdentificationLLMResponseMetadata
+from vet.imbue_core.data_types import LLMResponse
+from vet.imbue_core.itertools import only
+from vet.imbue_tools.repo_utils.context_utils import escape_prompt_markers
+from vet.imbue_tools.types.vet_config import VetConfig
from vet.issue_identifiers.common import GeneratedIssueSchema
from vet.issue_identifiers.common import GeneratedResponseSchema
from vet.issue_identifiers.common import (
diff --git a/vet/issue_identifiers/issue_evaluation.py b/vet/issue_identifiers/issue_evaluation.py
@@ -2,30 +2,30 @@ from typing import Generator
import jinja2
-from imbue_core.agents.llm_apis.build_apis import build_language_model_from_config
-from imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
-from imbue_core.data_types import AgenticPhase
-from imbue_core.data_types import IssueCode
-from imbue_core.data_types import IssueIdentificationDebugInfo
-from imbue_core.data_types import IssueIdentificationLLMResponseMetadata
-from imbue_core.data_types import LLMResponse
-from imbue_core.itertools import only
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_tools.get_conversation_history.get_conversation_history import (
+from vet.imbue_core.agents.llm_apis.build_apis import build_language_model_from_config
+from vet.imbue_core.agents.llm_apis.data_types import LanguageModelGenerationParams
+from vet.imbue_core.data_types import AgenticPhase
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.data_types import IssueIdentificationDebugInfo
+from vet.imbue_core.data_types import IssueIdentificationLLMResponseMetadata
+from vet.imbue_core.data_types import LLMResponse
+from vet.imbue_core.itertools import only
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_tools.get_conversation_history.get_conversation_history import (
format_conversation_history_for_prompt,
)
-from imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
-from imbue_tools.llm_output_parsing.parse_model_json_response import (
+from vet.imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
+from vet.imbue_tools.llm_output_parsing.parse_model_json_response import (
ResponseParsingError,
)
-from imbue_tools.llm_output_parsing.parse_model_json_response import (
+from vet.imbue_tools.llm_output_parsing.parse_model_json_response import (
parse_model_json_response,
)
-from imbue_tools.repo_utils.context_utils import escape_prompt_markers
-from imbue_tools.repo_utils.project_context import ProjectContext
-from imbue_tools.types.vet_config import DEFAULT_CONFIDENCE_THRESHOLD
-from imbue_tools.types.vet_config import VetConfig
-from imbue_tools.util_prompts.conversation_prefix import CONVERSATION_PREFIX_TEMPLATE
+from vet.imbue_tools.repo_utils.context_utils import escape_prompt_markers
+from vet.imbue_tools.repo_utils.project_context import ProjectContext
+from vet.imbue_tools.types.vet_config import DEFAULT_CONFIDENCE_THRESHOLD
+from vet.imbue_tools.types.vet_config import VetConfig
+from vet.imbue_tools.util_prompts.conversation_prefix import CONVERSATION_PREFIX_TEMPLATE
from vet.issue_identifiers.common import GeneratedIssueSchema
from vet.issue_identifiers.common import (
extract_invocation_info_from_costed_response,
diff --git a/vet/issue_identifiers/registry.py b/vet/issue_identifiers/registry.py
@@ -11,19 +11,19 @@ from typing import TypeVar
from loguru import logger
-from imbue_core.agents.primitives.resource_limits import ensure_global_resource_limits
-from imbue_core.data_types import IssueCode
-from imbue_core.data_types import IssueIdentificationDebugInfo
-from imbue_core.data_types import IssueIdentificationLLMResponseMetadata
-from imbue_core.data_types import IssueIdentifierResult
-from imbue_core.data_types import IssueIdentifierType
-from imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
-from imbue_tools.get_conversation_history.input_data_types import (
+from vet.imbue_core.agents.primitives.resource_limits import ensure_global_resource_limits
+from vet.imbue_core.data_types import IssueCode
+from vet.imbue_core.data_types import IssueIdentificationDebugInfo
+from vet.imbue_core.data_types import IssueIdentificationLLMResponseMetadata
+from vet.imbue_core.data_types import IssueIdentifierResult
+from vet.imbue_core.data_types import IssueIdentifierType
+from vet.imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
+from vet.imbue_tools.get_conversation_history.input_data_types import (
IdentifierInputsMissingError,
)
-from imbue_tools.repo_utils.project_context import ProjectContext
-from imbue_tools.types.vet_config import VetConfig
-from imbue_tools.types.vet_config import get_enabled_issue_codes
+from vet.imbue_tools.repo_utils.project_context import ProjectContext
+from vet.imbue_tools.types.vet_config import VetConfig
+from vet.imbue_tools.types.vet_config import get_enabled_issue_codes
from vet.issue_identifiers.agentic_issue_collation import (
collate_issues_with_agent,
)
diff --git a/vet/issue_identifiers/test_prompt_lengths.py b/vet/issue_identifiers/test_prompt_lengths.py
@@ -1,9 +1,9 @@
-from imbue_core.data_types import IssueIdentifierType
-from imbue_core.frozen_utils import FrozenDict
-from imbue_core.itertools import first
-from imbue_tools.get_conversation_history.input_data_types import CommitInputs
-from imbue_tools.repo_utils.project_context import BaseProjectContext
-from imbue_tools.types.vet_config import VetConfig
+from vet.imbue_core.data_types import IssueIdentifierType
+from vet.imbue_core.frozen_utils import FrozenDict
+from vet.imbue_core.itertools import first
+from vet.imbue_tools.get_conversation_history.input_data_types import CommitInputs
+from vet.imbue_tools.repo_utils.project_context import BaseProjectContext
+from vet.imbue_tools.types.vet_config import VetConfig
from vet.issue_identifiers import registry
from vet.issue_identifiers.identification_guides import (
ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE,
diff --git a/vet/repo_utils.py b/vet/repo_utils.py
@@ -1,6 +1,6 @@
from pathlib import Path
-from imbue_core.async_monkey_patches import log_exception
+from vet.imbue_core.async_monkey_patches import log_exception
from vet.errors import GitException
from vet.errors import RunCommandError
from vet.git import SyncLocalGitRepo
diff --git a/vet/repo_utils_test.py b/vet/repo_utils_test.py
@@ -3,11 +3,11 @@ from pathlib import Path
from syrupy.assertion import SnapshotAssertion
-from imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
-from imbue_core.nested_evolver import assign
-from imbue_core.nested_evolver import chill
-from imbue_core.nested_evolver import evolver
-from imbue_tools.repo_utils.project_context import LazyProjectContext
+from vet.imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
+from vet.imbue_core.nested_evolver import assign
+from vet.imbue_core.nested_evolver import chill
+from vet.imbue_core.nested_evolver import evolver
+from vet.imbue_tools.repo_utils.project_context import LazyProjectContext
from vet.repo_utils import get_code_to_check
diff --git a/vet/vet_types/__init__.py b/vet/vet_types/__init__.py
@@ -0,0 +1,33 @@
+"""Shared type definitions for Vet."""
+
+from vet.vet_types.chat_state import ContentBlock
+from vet.vet_types.chat_state import ContentBlockTypes
+from vet.vet_types.chat_state import TextBlock
+from vet.vet_types.chat_state import ToolResultBlock
+from vet.vet_types.chat_state import ToolUseBlock
+from vet.vet_types.ids import AgentMessageID
+from vet.vet_types.ids import AssistantMessageID
+from vet.vet_types.ids import TaskID
+from vet.vet_types.ids import ToolUseID
+from vet.vet_types.messages import AgentMessageSource
+from vet.vet_types.messages import ChatInputUserMessage
+from vet.vet_types.messages import ConversationMessageUnion
+from vet.vet_types.messages import LLMModel
+from vet.vet_types.messages import ResponseBlockAgentMessage
+
+__all__ = [
+ "AgentMessageID",
+ "AgentMessageSource",
+ "AssistantMessageID",
+ "ChatInputUserMessage",
+ "ContentBlock",
+ "ContentBlockTypes",
+ "ConversationMessageUnion",
+ "LLMModel",
+ "ResponseBlockAgentMessage",
+ "TaskID",
+ "TextBlock",
+ "ToolResultBlock",
+ "ToolUseBlock",
+ "ToolUseID",
+]
diff --git a/vet/vet_types/chat_state.py b/vet/vet_types/chat_state.py
@@ -0,0 +1,153 @@
+"""Chat state types for Vet."""
+
+from typing import Annotated
+from typing import Any
+from typing import Literal
+
+from pydantic import Field
+from pydantic import Tag
+
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_core.pydantic_serialization import build_discriminator
+from vet.vet_types.ids import TaskID
+from vet.vet_types.ids import ToolUseID
+
+
+# ========================
+# Chat Type Definitions
+# ========================
+
+
+class ContentBlock(SerializableModel):
+ object_type: str = Field(..., description="Type discriminator for content blocks")
+ type: str = Field(..., description="Type discriminator for content blocks")
+
+
+class TextBlock(ContentBlock):
+ object_type: str = "TextBlock"
+ type: Literal["text"] = "text"
+ text: str
+
+
+class ContextSummaryBlock(ContentBlock):
+ object_type: str = "ContextSummaryBlock"
+ type: Literal["context_summary"] = "context_summary"
+ text: str
+
+
+class ResumeResponseBlock(ContentBlock):
+ object_type: str = "ResumeResponseBlock"
+ type: Literal["resume_response"] = "resume_response"
+
+
+class ForkedToBlock(ContentBlock):
+ object_type: str = "ForkedToBlock"
+ type: Literal["forked_to"] = "forked_to"
+ forked_to_task_id: TaskID
+
+
+class ForkedFromBlock(ContentBlock):
+ object_type: str = "ForkedFromBlock"
+ type: Literal["forked_from"] = "forked_from"
+ forked_from_task_id: TaskID
+
+
+class CommandBlock(ContentBlock):
+ object_type: str = "CommandBlock"
+ type: Literal["command"] = "command"
+ command: str
+ is_automated: bool = Field(default=False, description="Whether the command is automated")
+
+
+ToolInput = dict[str, Any]
+
+
+class ToolUseBlock(ContentBlock):
+ object_type: str = "ToolUseBlock"
+ type: Literal["tool_use"] = "tool_use"
+ id: ToolUseID = Field(..., description="Unique identifier for this tool use")
+ name: str = Field(..., description="Name of the tool being used")
+ input: ToolInput = Field(default_factory=ToolInput, description="Input parameters for the tool")
+
+
+class ToolResultContent(SerializableModel):
+ """Base class for tool result content with type discriminator"""
+
+ content_type: str = Field(..., description="Type discriminator for tool result content")
+
+
+class SimpleToolContent(ToolResultContent):
+ """Generic tool content, or information to reconstruct diff tool content"""
+
+ content_type: Literal["simple"] = "simple"
+ text: str = Field(..., description="The tool output as text")
+ tool_input: ToolInput
+ tool_content: Any
+
+
+class GenericToolContent(ToolResultContent):
+ """Generic content for most tools - just a string"""
+
+ content_type: Literal["generic"] = "generic"
+ text: str = Field(..., description="The tool output as text")
+
+
+class DiffToolContent(ToolResultContent):
+ """Content for diff-producing tools (Write, Edit, MultiEdit)"""
+
+ content_type: Literal["diff"] = "diff"
+ diff: str = Field(..., description="The git diff string")
+ file_path: str = Field(..., description="The file that was modified")
+
+
+ToolResultContentType = GenericToolContent | DiffToolContent
+
+
+class ToolResultBlock(ContentBlock):
+ object_type: str = "ToolResultBlock"
+ type: Literal["tool_result"] = "tool_result"
+ tool_use_id: ToolUseID = Field(..., description="ID of the corresponding tool use")
+ tool_name: str = Field(..., description="Name of the tool that was used")
+ invocation_string: str = Field(..., description="String representation of how the tool was invoked")
+ content: ToolResultContentType = Field(..., description="Result content from the tool execution")
+ is_error: bool = Field(default=False, description="Whether the tool execution resulted in an error")
+
+
+class WarningBlock(ContentBlock):
+ object_type: str = "WarningBlock"
+ type: Literal["warning"] = "warning"
+ message: str = Field(..., description="Warning message")
+ traceback: str | None = Field(..., description="Warning traceback")
+ warning_type: str | None = Field(..., description="Type of warning, i.e. name of the exception that was raised")
+
+
+class ErrorBlock(ContentBlock):
+ object_type: str = "ErrorBlock"
+ type: Literal["error"] = "error"
+ message: str = Field(..., description="Error message")
+ traceback: str = Field(..., description="Error traceback")
+ error_type: str = Field(..., description="Type of error, i.e. name of the exception that was raised")
+
+
+class FileBlock(ContentBlock):
+ object_type: str = "FileBlock"
+ type: Literal["file"] = "file"
+ source: str = Field(..., description="A file path on the users local machine.")
+
+
+ContentBlockTypes = Annotated[
+ (
+ Annotated[TextBlock, Tag("TextBlock")]
+ | Annotated[CommandBlock, Tag("CommandBlock")]
+ | Annotated[ToolUseBlock, Tag("ToolUseBlock")]
+ | Annotated[ToolResultBlock, Tag("ToolResultBlock")]
+ | Annotated[ErrorBlock, Tag("ErrorBlock")]
+ | Annotated[WarningBlock, Tag("WarningBlock")]
+ | Annotated[ContextSummaryBlock, Tag("ContextSummaryBlock")]
+ | Annotated[ResumeResponseBlock, Tag("ResumeResponseBlock")]
+ | Annotated[FileBlock, Tag("FileBlock")]
+ | Annotated[ForkedToBlock, Tag("ForkedToBlock")]
+ | Annotated[ForkedFromBlock, Tag("ForkedFromBlock")]
+ ),
+ build_discriminator(),
+]
diff --git a/vet_types/vet_types/ids.py b/vet/vet_types/ids.py
diff --git a/vet/vet_types/messages.py b/vet/vet_types/messages.py
@@ -0,0 +1,130 @@
+"""Message types for Vet conversation history.
+
+These are simplified versions that avoid dependencies on external telemetry libraries.
+"""
+
+import datetime
+from enum import StrEnum
+from typing import Annotated
+from typing import Literal
+
+from pydantic import Field
+from pydantic import Tag
+
+from vet.imbue_core.pydantic_serialization import SerializableModel
+from vet.imbue_core.pydantic_serialization import build_discriminator
+from vet.imbue_core.time_utils import get_current_time
+from vet.vet_types.chat_state import ContentBlockTypes
+from vet.vet_types.ids import AgentMessageID
+from vet.vet_types.ids import AssistantMessageID
+
+
+class LLMModel(StrEnum):
+ CLAUDE_4_OPUS = "CLAUDE-4-OPUS"
+ CLAUDE_4_SONNET = "CLAUDE-4-SONNET"
+ CLAUDE_4_HAIKU = "CLAUDE-4-HAIKU"
+ GPT_5_1_CODEX = "GPT-5.1-CODEX"
+ GPT_5_1_CODEX_MINI = "GPT-5.1-CODEX-MINI"
+ GPT_5_1 = "GPT-5.1"
+ GPT_5_2 = "GPT-5.2"
+
+
+# ==================================
+# Backend Message Type Definitions
+# ==================================
+
+
+class AgentMessageSource(StrEnum):
+ """
+ Messages can come from the AGENT (LLM), USER (chat messages & direct interactions),
+ SYSTEM (app and service code) and RUNNER (the process controlling a task).
+ """
+
+ # Messages coming directly from the agent.
+ AGENT = "AGENT"
+
+ # Messages coming directly from a user interacting with the interface, ie chat.
+ USER = "USER"
+
+ # Messages coming from system-mediated actions and automations.
+ SYSTEM = "SYSTEM"
+
+ # Messages coming from the task runner wrapper, such as environment shutdown.
+ RUNNER = "RUNNER"
+
+
+class Message(SerializableModel):
+ """Base class for all messages sent to or from the agent and user."""
+
+ # used to dispatch and discover the type of message
+ object_type: str
+ # the unique ID of the message, used to track it across the system and prevent duplicates.
+ message_id: AgentMessageID = Field(default_factory=AgentMessageID)
+ # the source of the message, which can be either the agent, user, or runner.
+ source: AgentMessageSource
+ # roughly when the message was created, in UTC.
+ approximate_creation_time: datetime.datetime = Field(default_factory=get_current_time)
+
+ @property
+ def is_ephemeral(self) -> bool:
+ raise NotImplementedError("All messages must be subclassed off of PersistentMessage or EphemeralMessage")
+
+
+class PersistentMessage(Message):
+ @property
+ def is_ephemeral(self) -> bool:
+ return False
+
+
+class PersistentUserMessage(PersistentMessage):
+ """
+ One of two base classes for messages sent from the user.
+ Persistent user messages are saved to the database.
+ """
+
+ object_type: str = Field(
+ default="PersistentUserMessage",
+ description="Type discriminator for user messages",
+ )
+ message_id: AgentMessageID = Field(
+ default_factory=AgentMessageID,
+ description="Unique identifier for the user message",
+ )
+ source: AgentMessageSource = Field(default=AgentMessageSource.USER)
+ approximate_creation_time: datetime.datetime = Field(
+ default_factory=get_current_time,
+ description="Approximate UTC timestamp when user message was created",
+ )
+
+
+class ChatInputUserMessage(PersistentUserMessage):
+ object_type: str = Field(default="ChatInputUserMessage")
+ text: str = Field(..., description="User input text content")
+ model_name: LLMModel | None = Field(
+ default=None,
+ description="Selected LLM model for the chat request",
+ )
+ files: list[str] = Field(
+ default_factory=list,
+ description="List of file paths attached to this message",
+ )
+
+
+class PersistentAgentMessage(PersistentMessage):
+ """Base class for messages sent from the agent."""
+
+ source: AgentMessageSource = AgentMessageSource.AGENT
+
+
+class ResponseBlockAgentMessage(PersistentAgentMessage):
+ object_type: str = "ResponseBlockAgentMessage"
+ role: Literal["user", "assistant", "system"]
+ assistant_message_id: AssistantMessageID
+ content: tuple[ContentBlockTypes, ...]
+
+
+ConversationMessageUnion = Annotated[
+ Annotated[ResponseBlockAgentMessage, Tag("ResponseBlockAgentMessage")]
+ | Annotated[ChatInputUserMessage, Tag("ChatInputUserMessage")],
+ build_discriminator(),
+]
diff --git a/vet_types/pyproject.toml b/vet_types/pyproject.toml
@@ -1,20 +0,0 @@
-[build-system]
-requires = ["setuptools", "wheel"]
-build-backend = "setuptools.build_meta"
-
-[project]
-name = "vet_types"
-version = "0.1.0"
-description = "Type definitions for Vet without telemetry dependencies"
-dependencies = [
- "pydantic",
- "imbue_core",
- "typeid-python",
-]
-requires-python = ">=3.11"
-
-[tool.setuptools]
-package-data.vet_types = ["py.typed"]
-
-[tool.setuptools.packages.find]
-include = ["vet_types*"]
diff --git a/vet_types/vet_types/__init__.py b/vet_types/vet_types/__init__.py
@@ -1,33 +0,0 @@
-"""Shared type definitions for Vet."""
-
-from vet_types.chat_state import ContentBlock
-from vet_types.chat_state import ContentBlockTypes
-from vet_types.chat_state import TextBlock
-from vet_types.chat_state import ToolResultBlock
-from vet_types.chat_state import ToolUseBlock
-from vet_types.ids import AgentMessageID
-from vet_types.ids import AssistantMessageID
-from vet_types.ids import TaskID
-from vet_types.ids import ToolUseID
-from vet_types.messages import AgentMessageSource
-from vet_types.messages import ChatInputUserMessage
-from vet_types.messages import ConversationMessageUnion
-from vet_types.messages import LLMModel
-from vet_types.messages import ResponseBlockAgentMessage
-
-__all__ = [
- "AgentMessageID",
- "AgentMessageSource",
- "AssistantMessageID",
- "ChatInputUserMessage",
- "ContentBlock",
- "ContentBlockTypes",
- "ConversationMessageUnion",
- "LLMModel",
- "ResponseBlockAgentMessage",
- "TaskID",
- "TextBlock",
- "ToolResultBlock",
- "ToolUseBlock",
- "ToolUseID",
-]
diff --git a/vet_types/vet_types/chat_state.py b/vet_types/vet_types/chat_state.py
@@ -1,153 +0,0 @@
-"""Chat state types for Vet."""
-
-from typing import Annotated
-from typing import Any
-from typing import Literal
-
-from pydantic import Field
-from pydantic import Tag
-
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_core.pydantic_serialization import build_discriminator
-from vet_types.ids import TaskID
-from vet_types.ids import ToolUseID
-
-
-# ========================
-# Chat Type Definitions
-# ========================
-
-
-class ContentBlock(SerializableModel):
- object_type: str = Field(..., description="Type discriminator for content blocks")
- type: str = Field(..., description="Type discriminator for content blocks")
-
-
-class TextBlock(ContentBlock):
- object_type: str = "TextBlock"
- type: Literal["text"] = "text"
- text: str
-
-
-class ContextSummaryBlock(ContentBlock):
- object_type: str = "ContextSummaryBlock"
- type: Literal["context_summary"] = "context_summary"
- text: str
-
-
-class ResumeResponseBlock(ContentBlock):
- object_type: str = "ResumeResponseBlock"
- type: Literal["resume_response"] = "resume_response"
-
-
-class ForkedToBlock(ContentBlock):
- object_type: str = "ForkedToBlock"
- type: Literal["forked_to"] = "forked_to"
- forked_to_task_id: TaskID
-
-
-class ForkedFromBlock(ContentBlock):
- object_type: str = "ForkedFromBlock"
- type: Literal["forked_from"] = "forked_from"
- forked_from_task_id: TaskID
-
-
-class CommandBlock(ContentBlock):
- object_type: str = "CommandBlock"
- type: Literal["command"] = "command"
- command: str
- is_automated: bool = Field(default=False, description="Whether the command is automated")
-
-
-ToolInput = dict[str, Any]
-
-
-class ToolUseBlock(ContentBlock):
- object_type: str = "ToolUseBlock"
- type: Literal["tool_use"] = "tool_use"
- id: ToolUseID = Field(..., description="Unique identifier for this tool use")
- name: str = Field(..., description="Name of the tool being used")
- input: ToolInput = Field(default_factory=ToolInput, description="Input parameters for the tool")
-
-
-class ToolResultContent(SerializableModel):
- """Base class for tool result content with type discriminator"""
-
- content_type: str = Field(..., description="Type discriminator for tool result content")
-
-
-class SimpleToolContent(ToolResultContent):
- """Generic tool content, or information to reconstruct diff tool content"""
-
- content_type: Literal["simple"] = "simple"
- text: str = Field(..., description="The tool output as text")
- tool_input: ToolInput
- tool_content: Any
-
-
-class GenericToolContent(ToolResultContent):
- """Generic content for most tools - just a string"""
-
- content_type: Literal["generic"] = "generic"
- text: str = Field(..., description="The tool output as text")
-
-
-class DiffToolContent(ToolResultContent):
- """Content for diff-producing tools (Write, Edit, MultiEdit)"""
-
- content_type: Literal["diff"] = "diff"
- diff: str = Field(..., description="The git diff string")
- file_path: str = Field(..., description="The file that was modified")
-
-
-ToolResultContentType = GenericToolContent | DiffToolContent
-
-
-class ToolResultBlock(ContentBlock):
- object_type: str = "ToolResultBlock"
- type: Literal["tool_result"] = "tool_result"
- tool_use_id: ToolUseID = Field(..., description="ID of the corresponding tool use")
- tool_name: str = Field(..., description="Name of the tool that was used")
- invocation_string: str = Field(..., description="String representation of how the tool was invoked")
- content: ToolResultContentType = Field(..., description="Result content from the tool execution")
- is_error: bool = Field(default=False, description="Whether the tool execution resulted in an error")
-
-
-class WarningBlock(ContentBlock):
- object_type: str = "WarningBlock"
- type: Literal["warning"] = "warning"
- message: str = Field(..., description="Warning message")
- traceback: str | None = Field(..., description="Warning traceback")
- warning_type: str | None = Field(..., description="Type of warning, i.e. name of the exception that was raised")
-
-
-class ErrorBlock(ContentBlock):
- object_type: str = "ErrorBlock"
- type: Literal["error"] = "error"
- message: str = Field(..., description="Error message")
- traceback: str = Field(..., description="Error traceback")
- error_type: str = Field(..., description="Type of error, i.e. name of the exception that was raised")
-
-
-class FileBlock(ContentBlock):
- object_type: str = "FileBlock"
- type: Literal["file"] = "file"
- source: str = Field(..., description="A file path on the users local machine.")
-
-
-ContentBlockTypes = Annotated[
- (
- Annotated[TextBlock, Tag("TextBlock")]
- | Annotated[CommandBlock, Tag("CommandBlock")]
- | Annotated[ToolUseBlock, Tag("ToolUseBlock")]
- | Annotated[ToolResultBlock, Tag("ToolResultBlock")]
- | Annotated[ErrorBlock, Tag("ErrorBlock")]
- | Annotated[WarningBlock, Tag("WarningBlock")]
- | Annotated[ContextSummaryBlock, Tag("ContextSummaryBlock")]
- | Annotated[ResumeResponseBlock, Tag("ResumeResponseBlock")]
- | Annotated[FileBlock, Tag("FileBlock")]
- | Annotated[ForkedToBlock, Tag("ForkedToBlock")]
- | Annotated[ForkedFromBlock, Tag("ForkedFromBlock")]
- ),
- build_discriminator(),
-]
diff --git a/vet_types/vet_types/messages.py b/vet_types/vet_types/messages.py
@@ -1,130 +0,0 @@
-"""Message types for Vet conversation history.
-
-These are simplified versions that avoid dependencies on external telemetry libraries.
-"""
-
-import datetime
-from enum import StrEnum
-from typing import Annotated
-from typing import Literal
-
-from pydantic import Field
-from pydantic import Tag
-
-from imbue_core.pydantic_serialization import SerializableModel
-from imbue_core.pydantic_serialization import build_discriminator
-from imbue_core.time_utils import get_current_time
-from vet_types.chat_state import ContentBlockTypes
-from vet_types.ids import AgentMessageID
-from vet_types.ids import AssistantMessageID
-
-
-class LLMModel(StrEnum):
- CLAUDE_4_OPUS = "CLAUDE-4-OPUS"
- CLAUDE_4_SONNET = "CLAUDE-4-SONNET"
- CLAUDE_4_HAIKU = "CLAUDE-4-HAIKU"
- GPT_5_1_CODEX = "GPT-5.1-CODEX"
- GPT_5_1_CODEX_MINI = "GPT-5.1-CODEX-MINI"
- GPT_5_1 = "GPT-5.1"
- GPT_5_2 = "GPT-5.2"
-
-
-# ==================================
-# Backend Message Type Definitions
-# ==================================
-
-
-class AgentMessageSource(StrEnum):
- """
- Messages can come from the AGENT (LLM), USER (chat messages & direct interactions),
- SYSTEM (app and service code) and RUNNER (the process controlling a task).
- """
-
- # Messages coming directly from the agent.
- AGENT = "AGENT"
-
- # Messages coming directly from a user interacting with the interface, ie chat.
- USER = "USER"
-
- # Messages coming from system-mediated actions and automations.
- SYSTEM = "SYSTEM"
-
- # Messages coming from the task runner wrapper, such as environment shutdown.
- RUNNER = "RUNNER"
-
-
-class Message(SerializableModel):
- """Base class for all messages sent to or from the agent and user."""
-
- # used to dispatch and discover the type of message
- object_type: str
- # the unique ID of the message, used to track it across the system and prevent duplicates.
- message_id: AgentMessageID = Field(default_factory=AgentMessageID)
- # the source of the message, which can be either the agent, user, or runner.
- source: AgentMessageSource
- # roughly when the message was created, in UTC.
- approximate_creation_time: datetime.datetime = Field(default_factory=get_current_time)
-
- @property
- def is_ephemeral(self) -> bool:
- raise NotImplementedError("All messages must be subclassed off of PersistentMessage or EphemeralMessage")
-
-
-class PersistentMessage(Message):
- @property
- def is_ephemeral(self) -> bool:
- return False
-
-
-class PersistentUserMessage(PersistentMessage):
- """
- One of two base classes for messages sent from the user.
- Persistent user messages are saved to the database.
- """
-
- object_type: str = Field(
- default="PersistentUserMessage",
- description="Type discriminator for user messages",
- )
- message_id: AgentMessageID = Field(
- default_factory=AgentMessageID,
- description="Unique identifier for the user message",
- )
- source: AgentMessageSource = Field(default=AgentMessageSource.USER)
- approximate_creation_time: datetime.datetime = Field(
- default_factory=get_current_time,
- description="Approximate UTC timestamp when user message was created",
- )
-
-
-class ChatInputUserMessage(PersistentUserMessage):
- object_type: str = Field(default="ChatInputUserMessage")
- text: str = Field(..., description="User input text content")
- model_name: LLMModel | None = Field(
- default=None,
- description="Selected LLM model for the chat request",
- )
- files: list[str] = Field(
- default_factory=list,
- description="List of file paths attached to this message",
- )
-
-
-class PersistentAgentMessage(PersistentMessage):
- """Base class for messages sent from the agent."""
-
- source: AgentMessageSource = AgentMessageSource.AGENT
-
-
-class ResponseBlockAgentMessage(PersistentAgentMessage):
- object_type: str = "ResponseBlockAgentMessage"
- role: Literal["user", "assistant", "system"]
- assistant_message_id: AssistantMessageID
- content: tuple[ContentBlockTypes, ...]
-
-
-ConversationMessageUnion = Annotated[
- Annotated[ResponseBlockAgentMessage, Tag("ResponseBlockAgentMessage")]
- | Annotated[ChatInputUserMessage, Tag("ChatInputUserMessage")],
- build_discriminator(),
-]
