commit 3fc1fa6843dfe44231a12016cfc1b463f2425b1a
parent deb7c4949aec3fdd72511b6c31ec5ec65ebf4241
Author: andrewlaack-collab <andrew.laack@imbue.com>
Date: Thu, 29 Jan 2026 04:20:42 +0000
Setup Dependencies (#1)
* Imbue_verify works with minimal imports from imbue_core and tools. Still should import relevant tests from said dependencies, and do some other code pruning with vulture.
* Removing
* Bug fix
* Black reformat
* Fixing formatting
* Improve pyproject.toml
* Spyware begone
Diffstat:
157 files changed, 26868 insertions(+), 423 deletions(-)
diff --git a/imbue_core/README.md b/imbue_core/README.md
@@ -0,0 +1,3 @@
+# Imbue Core
+
+Utilities for `imbue-desktop`. This package is not meant to be installed on its own.
diff --git a/imbue_core/imbue_core/__init__.py b/imbue_core/imbue_core/__init__.py
diff --git a/imbue_core/imbue_core/agents/__init__.py b/imbue_core/imbue_core/agents/__init__.py
diff --git a/imbue_core/imbue_core/agents/agent_api/__init__.py b/imbue_core/imbue_core/agents/agent_api/__init__.py
diff --git a/imbue_core/imbue_core/agents/agent_api/api.py b/imbue_core/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 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
@@ -0,0 +1,36 @@
+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/__init__.py b/imbue_core/imbue_core/agents/agent_api/claude/__init__.py
diff --git a/imbue_core/imbue_core/agents/agent_api/claude/client.py b/imbue_core/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 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
@@ -0,0 +1,79 @@
+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
@@ -0,0 +1,117 @@
+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
@@ -0,0 +1,90 @@
+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.
+ # See https://gitlab.com/generally-intelligent/generally_intelligent/-/merge_requests/7323#note_2897340073
+ 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/imbue_core/imbue_core/agents/agent_api/codex/__init__.py
diff --git a/imbue_core/imbue_core/agents/agent_api/codex/client.py b/imbue_core/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 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
@@ -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 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
@@ -0,0 +1,218 @@
+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
@@ -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 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/errors.py b/imbue_core/imbue_core/agents/agent_api/errors.py
@@ -0,0 +1,62 @@
+"""Error types for Agent API."""
+
+from typing import Any
+
+
+class AgentAPIError(Exception):
+ """Base exception for all Agent API errors."""
+
+
+class AgentCLIConnectionError(AgentAPIError):
+ """Raised when unable to connect to Agent Code."""
+
+
+class AgentCLINotFoundError(AgentCLIConnectionError):
+ """Raised when Agent Code is not found or not installed."""
+
+ def __init__(self, message: str, cli_path: str | None = None) -> None:
+ if cli_path:
+ message = f"{message}: {cli_path}"
+ super().__init__(message)
+
+
+class AgentProcessError(AgentAPIError):
+ """Raised when the CLI process fails."""
+
+ def __init__(self, message: str, exit_code: int | None = None, stderr: str | None = None) -> None:
+ self.exit_code = exit_code
+ self.stderr = stderr
+
+ if exit_code is not None:
+ message = f"{message} (exit code: {exit_code})"
+ if stderr:
+ message = f"{message}\nError output: {stderr}"
+
+ super().__init__(message)
+
+
+class AgentCLIJSONDecodeError(AgentAPIError):
+ """Raised when unable to decode JSON from CLI output."""
+
+ def __init__(self, line: str, original_error: Exception) -> None:
+ self.line = line
+ self.original_error = original_error
+ super().__init__(f"Failed to decode JSON: {line[:100]}...")
+
+
+class AgentUnknownMessageTypeError(AgentAPIError):
+ """Raised when an unknown message type is encountered."""
+
+ def __init__(self, message_type: str, data: dict[str, Any]) -> None:
+ self.message_type = message_type
+ self.data = data
+ super().__init__(f"Unknown message type: {message_type}\nData: {data}")
+
+
+class AgentUnknownContentBlockTypeError(AgentAPIError):
+ """Raised when an unknown content block type is encountered."""
+
+ def __init__(self, block_type: str, data: dict[str, Any]) -> None:
+ self.block_type = block_type
+ self.data = data
+ super().__init__(f"Unknown content block type: {block_type}\nData: {data}")
diff --git a/imbue_core/imbue_core/agents/agent_api/interaction.py b/imbue_core/imbue_core/agents/agent_api/interaction.py
@@ -0,0 +1,96 @@
+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
@@ -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 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/agent_api/union_types.py b/imbue_core/imbue_core/agents/agent_api/union_types.py
@@ -0,0 +1,12 @@
+from typing import Annotated
+
+from pydantic import Tag
+
+from imbue_core.agents.agent_api.claude.data_types import ClaudeCodeOptions
+from imbue_core.agents.agent_api.codex.data_types import CodexOptions
+from imbue_core.pydantic_serialization import build_discriminator
+
+AgentOptionsUnion = Annotated[
+ Annotated[ClaudeCodeOptions, Tag("ClaudeCodeOptions")] | Annotated[CodexOptions, Tag("CodexOptions")],
+ build_discriminator(),
+]
diff --git a/imbue_core/imbue_core/agents/configs.py b/imbue_core/imbue_core/agents/configs.py
@@ -0,0 +1,123 @@
+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/data_types/__init__.py b/imbue_core/imbue_core/agents/data_types/__init__.py
diff --git a/imbue_core/imbue_core/agents/data_types/ids.py b/imbue_core/imbue_core/agents/data_types/ids.py
@@ -0,0 +1,64 @@
+from abc import ABC
+
+from pydantic import GetCoreSchemaHandler
+from pydantic_core import core_schema
+from typeid import TypeID
+from typeid import get_prefix_and_suffix
+
+
+class TypeIDPrefixMismatchError(Exception):
+ pass
+
+
+class ObjectID(TypeID, ABC):
+ """
+ A convenience class for string-based object IDs.
+
+ Use in place of strings for IDs. (We don't use raw UUIDs because they are not supported by SQLite.)
+
+ Use `tag` to prefix the ID with the ID type. (We don't use `prefix` because it's already taken by the ancestor class.)
+
+ """
+
+ # Override this in subclasses to specify the ID type.
+ tag: str = "oid"
+
+ def __init__(self, value: str | None = None) -> None:
+ if value is not None:
+ prefix, suffix = get_prefix_and_suffix(value)
+ # For convenience, don't require the caller to strip the prefix from existing IDs.
+ if prefix is not None:
+ if prefix != self.tag:
+ raise TypeIDPrefixMismatchError(f"Expected prefix '{self.tag}', got '{prefix}'")
+ value = suffix
+ super().__init__(self.tag, value)
+
+ @classmethod
+ def __get_pydantic_core_schema__(cls, source_type: type, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+ """
+ Support transparently deserializing strings into ObjectID instances and vice versa.
+ """
+ return core_schema.no_info_before_validator_function(
+ lambda raw_value: (cls(raw_value) if isinstance(raw_value, str) else raw_value),
+ core_schema.union_schema(
+ [
+ core_schema.is_instance_schema(cls),
+ core_schema.str_schema(),
+ ]
+ ),
+ serialization=core_schema.plain_serializer_function_ser_schema(
+ lambda instance: str(instance), return_schema=core_schema.str_schema()
+ ),
+ )
+
+
+class TaskID(ObjectID):
+ tag: str = "tsk"
+
+
+class ProjectID(ObjectID):
+ tag: str = "prj"
+
+
+class AgentMessageID(ObjectID):
+ tag: str = "agm"
diff --git a/imbue_core/imbue_core/agents/llm_apis/__init__.py b/imbue_core/imbue_core/agents/llm_apis/__init__.py
diff --git a/imbue_core/imbue_core/agents/llm_apis/anthropic_api.py b/imbue_core/imbue_core/agents/llm_apis/anthropic_api.py
@@ -0,0 +1,822 @@
+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")
+ # The standard environment variable for this is ANTHROPIC_AUTH_TOKEN,
+ # but we don't use it since it has some bad interactions with Claude Code.
+ auth_token = get_secret("IMBUE_ANTHROPIC_AUTH_TOKEN")
+ if not api_key and not auth_token:
+ raise MissingAPIKeyError(
+ "Neither of ANTHROPIC_API_KEY and IMBUE_ANTHROPIC_AUTH_TOKEN environment variables 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
@@ -0,0 +1,15 @@
+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
@@ -0,0 +1,110 @@
+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
@@ -0,0 +1,128 @@
+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
@@ -0,0 +1,73 @@
+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/constants.py b/imbue_core/imbue_core/agents/llm_apis/constants.py
@@ -0,0 +1,16 @@
+HUMAN_ROLE = "HUMAN"
+ASSISTANT_ROLE = "ASSISTANT"
+USER_ROLE = "USER"
+SYSTEM_ROLE = "SYSTEM"
+
+
+def approximate_token_count(text: str) -> int:
+ """Approximate token count using a fixed characters-per-token ratio.
+
+ This is used for custom/user-defined models where we don't have access to the actual tokenizer.
+ The ratio of 4.5 characters per token is a reasonable empirical estimate for most LLMs.
+
+ In the future, it might be useful to allow users to configure this ratio per-model in models.json,
+ but for now we use a single hardcoded value for simplicity.
+ """
+ return round(len(text) / 4.5)
diff --git a/imbue_core/imbue_core/agents/llm_apis/data_types.py b/imbue_core/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 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/errors.py b/imbue_core/imbue_core/agents/llm_apis/errors.py
@@ -0,0 +1,111 @@
+import abc
+from typing import Self
+
+
+class CachedException(Exception, abc.ABC):
+ """An exception that is stored in an LLM API cache.
+
+ Provides convenience methods for storing and loading str representation for
+ more efficient caching.
+ """
+
+ @classmethod
+ @abc.abstractmethod
+ def from_string(cls, data: str) -> Self: ...
+
+ @abc.abstractmethod
+ def to_string(self) -> str: ...
+
+
+SPLIT_TOKEN: str = "|"
+
+
+class PromptTooLongError(CachedException):
+ """Exception raised when prompt too long for model context window size.
+
+ We should cache these since no point trying same prompt again.
+ """
+
+ def __init__(self, prompt_len: int, max_prompt_len: int) -> None:
+ self.prompt_len = prompt_len
+ self.max_prompt_len = max_prompt_len
+
+ @classmethod
+ def from_string(cls, data: str) -> Self:
+ prompt_len, max_prompt_len = data.split(SPLIT_TOKEN)[1:3]
+ return cls(int(prompt_len), int(max_prompt_len))
+
+ def to_string(self) -> str:
+ string = SPLIT_TOKEN.join([self.__class__.__name__, str(self.prompt_len), str(self.max_prompt_len)])
+ return string
+
+ @property
+ def required_reduction_fraction(self) -> float:
+ return self.max_prompt_len / self.prompt_len
+
+
+class BadAPIRequestError(CachedException):
+ """Exception raised when request invalid (e.g. bad formatting, too long).
+
+ Basically all miscellaneous errors due to input. We should cache these since no point trying same prompt again.
+ """
+
+ def __init__(self, error_message: str) -> None:
+ self.error_message = error_message
+
+ @classmethod
+ def from_string(cls, data: str) -> Self:
+ error_message = data.split(SPLIT_TOKEN)[1]
+ return cls(error_message)
+
+ def to_string(self) -> str:
+ return SPLIT_TOKEN.join([self.__class__.__name__, self.error_message])
+
+
+class UnsetCachePathError(Exception):
+ def __init__(self) -> None:
+ super().__init__(
+ "Cache path must be specified in model config if you want to use model prompt-response caching. Caching can be disabled by setting `is_caching_enabled=False` when calling the LanguageModelAPI."
+ )
+
+
+class LanguageModelError(Exception):
+ pass
+
+
+class MissingAPIKeyError(LanguageModelError):
+ pass
+
+
+class RetriableLanguageModelError(LanguageModelError):
+ pass
+
+
+class TransientLanguageModelError(RetriableLanguageModelError):
+ pass
+
+
+class SafelyRetriableTransientLanguageModelError(TransientLanguageModelError):
+ pass
+
+
+class NewSeedRetriableLanguageModelError(RetriableLanguageModelError):
+ pass
+
+
+class LanguageModelRetryLimitError(Exception):
+ pass
+
+
+class LanguageModelInvalidModelNameError(ValueError):
+ """Exception raised when an invalid model name is provided to a language model API."""
+
+ def __init__(self, model_name: str, api_class_name: str, available_models: list[str]) -> None:
+ self.model_name = model_name
+ self.api_class_name = api_class_name
+ self.available_models = available_models
+
+ message = (
+ f"Model with name={model_name} not available for {api_class_name}. Available models: {available_models}."
+ )
+ super().__init__(message)
diff --git a/imbue_core/imbue_core/agents/llm_apis/gemini_api.py b/imbue_core/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 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
@@ -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 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! Josh will be very annoyed
+
+
+# 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
@@ -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 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 from outside of a hammer 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/llm_testing_utils.py b/imbue_core/imbue_core/agents/llm_apis/llm_testing_utils.py
@@ -0,0 +1,86 @@
+from google.genai.types import CountTokensResponse
+from syrupy.assertion import SnapshotAssertion
+from syrupy.extensions.single_file import SingleFileAmberSnapshotExtension
+from syrupy.extensions.single_file import SingleFileSnapshotExtension
+
+from imbue_core.agents.llm_apis.data_types import CachedCostedModelResponse
+from imbue_core.agents.llm_apis.data_types import CostedLanguageModelResponse
+from imbue_core.caching import AsyncCache
+from imbue_core.frozen_utils import FrozenMapping
+
+
+async def check_llm_responses_in_cache(snapshot: SnapshotAssertion, temp_cache: AsyncCache, suffix: str = "") -> None:
+ """Runs as the test fixture completes to check that the LLM inputs and outputs stay the same, in a human-readable format."""
+
+ async with temp_cache as cache:
+ all_keys: tuple[str, ...] = await cache.get_all_keys() # Contains both the streaming and non-streaming keys?
+ value_by_key: FrozenMapping[str, CachedCostedModelResponse | None] = await cache.get_all(all_keys)
+
+ cache_items: list[tuple[str, CachedCostedModelResponse]] = [
+ (k, v) for k, v in value_by_key.items() if v is not None
+ ]
+ cache_items.sort(key=lambda x: x[1].timestamp)
+ for cache_index, (cache_key, cached_response) in enumerate(cache_items):
+ prompt: bytes = b""
+ joined_responses: bytes = b""
+ metadata_lines: list[str] = []
+
+ metadata_lines.append(f"{cache_index=} (when cache is sorted by timestamp)")
+ metadata_lines.append(f"{cache_key=}") # Keys must be stable and not too big.
+ if cached_response.inputs is not None:
+ prompt = cached_response.inputs.prompt.encode("utf-8")
+ metadata_lines.append(f"request metdata ({type(cached_response.inputs)})")
+ for field, field_value in cached_response.inputs.__dict__.items():
+ if field != "prompt": # print the prompt separately below.
+ metadata_lines.append(f" {field}: {field_value}")
+
+ metadata_lines.append("cached_response metadata:")
+ for field, field_value in cached_response.__dict__.items():
+ if field not in ("inputs", "response"): # already printed above
+ metadata_lines.append(f" {field}: {field_value}")
+
+ if cached_response.response is not None:
+ match cached_response.response:
+ case CostedLanguageModelResponse():
+ joined_responses = "".join([r.text for r in cached_response.response.responses]).encode("utf-8")
+ for response_index, response in enumerate(cached_response.response.responses):
+ metadata_lines.append(f"response[{response_index}] metadata:")
+ for (
+ field,
+ field_value,
+ ) in cached_response.response.__dict__.items():
+ if field != "responses": # already printed the responses above
+ metadata_lines.append(f" {field}: {field_value}")
+ case CountTokensResponse():
+ metadata_lines.append("response metadata:")
+ for field, field_value in cached_response.response.__dict__.items():
+ metadata_lines.append(f" {field}: {field_value}")
+
+ snapshotted_prompt = snapshot(
+ extension_class=SingleFileSnapshotExtension,
+ name=f"{cache_index:03d}_inputs{suffix}",
+ )
+
+ # TODO nasty syrupy hacking
+ snapshot_contents, _ = snapshotted_prompt._recall_data(snapshotted_prompt.index)
+
+ assert (
+ snapshotted_prompt == prompt
+ ), f"Your prompt changed, did you mean for this to happen?\nExpected prompt: {snapshot_contents!r}\nPrompt: {prompt!r}"
+
+ snapshotted_response = snapshot(
+ extension_class=SingleFileSnapshotExtension,
+ name=f"{cache_index:03d}_response{suffix}",
+ )
+
+ assert (
+ snapshotted_response == joined_responses
+ ), "Your response changed; maybe you aren't actually hitting the cache?"
+
+ snapshotted_metadata = snapshot(
+ extension_class=SingleFileAmberSnapshotExtension,
+ name=f"{cache_index:03d}_metadata{suffix}",
+ )
+ assert snapshotted_metadata == "\n".join(
+ metadata_lines
+ ), "Metadata changed; maybe you aren't actually hitting the cache?"
diff --git a/imbue_core/imbue_core/agents/llm_apis/mock_api.py b/imbue_core/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 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
@@ -0,0 +1,17 @@
+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
@@ -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 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! Josh will be very annoyed
+
+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
@@ -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 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
@@ -0,0 +1,13 @@
+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
@@ -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 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
@@ -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 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
@@ -0,0 +1,20 @@
+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/errors.py b/imbue_core/imbue_core/agents/primitives/errors.py
@@ -0,0 +1,14 @@
+class SpecialError(BaseException):
+ pass
+
+
+class ResourceLimitExceeded(SpecialError):
+ pass
+
+
+class DollarLimitExceeded(ResourceLimitExceeded):
+ pass
+
+
+class MaximumSpendExceeded(ResourceLimitExceeded):
+ """This happens if you try to make a transation that is larger than your per-hour spend rate"""
diff --git a/imbue_core/imbue_core/agents/primitives/resource_limits.py b/imbue_core/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 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
@@ -0,0 +1,441 @@
+import asyncio
+import sys
+import traceback
+from asyncio import Future
+from types import TracebackType
+from typing import Any
+from typing import Sequence
+
+import sentry_sdk
+
+from imbue_core.constants import ExceptionPriority
+from imbue_core.error_utils import get_sentry_event_handler
+from imbue_core.error_utils import get_traceback_with_vars
+from imbue_core.errors import ExpectedError
+from imbue_core.s3_uploader import EXTRAS_UPLOADED_FILES_KEY
+from imbue_core.s3_uploader import get_s3_upload_key
+from imbue_core.s3_uploader import get_s3_upload_url
+from imbue_core.s3_uploader import upload_to_s3_with_key
+
+_IS_SHUTTING_DOWN = False
+
+
+# This is the name of the attribute we set on our exceptions to ensure they are logged (esp. to Sentry) at most once.
+EXCEPTION_LOGGED_FLAG = "_was_logged_by_log_exception"
+
+
+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 _upload_traceback(key: str, exception: BaseException) -> None:
+ tb_with_vars = get_traceback_with_vars(exception)
+ if tb_with_vars is not None:
+ upload_to_s3_with_key(key, tb_with_vars.encode())
+
+
+def pre_filter_exception(exc: BaseException, message: str | None = None) -> bool:
+ # deferred import, will have been imported anyway by this point
+ from loguru import logger
+
+ if getattr(exc, EXCEPTION_LOGGED_FLAG, False):
+ logger.info("Skipping duplicate log of exception {} with message {!r}", exc, message)
+ return True
+ try:
+ setattr(exc, EXCEPTION_LOGGED_FLAG, True)
+ except AttributeError:
+ logger.info("Unable to guarantee that {} will not be logged again", exc)
+ return False
+
+
+def inject_exception_and_log(
+ exc: BaseException,
+ message: str,
+ priority: ExceptionPriority | None = None,
+ *args: Any,
+ **kwargs: Any,
+) -> None:
+ # deferred import, will have been imported anyway by this point
+ from loguru import logger
+
+ # inject received exception stack trace into logger error message
+ options = (exc,) + logger._options[1:] # pyre-fixme[16]: pyre doesn't know that _options exists
+ if priority is not None:
+ level = priority.value
+ else:
+ level = "ERROR"
+ logger._log(level, False, options, message, args, kwargs) # pyre-fixme[16]: pyre doesn't know that _log exists
+
+
+def log_exception(
+ exc: BaseException,
+ message: str,
+ priority: ExceptionPriority | None = None,
+ *args: Any,
+ sentry_extra_uploads: dict[str, str] | None = None,
+ **kwargs: Any,
+) -> None:
+ """Josh doesn't like that `loguru.exception()` takes only a message, and grabs the current exception from sys.exc_info().
+
+ So this is a more explicit alternative that takes the exception as an argument.
+ """
+ should_skip = pre_filter_exception(exc, message)
+ if should_skip:
+ return None
+
+ # use a new scope to ensure these attachments don't bleed to other events that might have the same scope
+ # TODO: unify the uploading logic that we have in Sculptor with this, avoid coupling through two global objects (sentry event handler, s3_upload)
+ with sentry_sdk.new_scope() as scope:
+ sentry_event_handler = get_sentry_event_handler()
+ traceback_str = "".join(traceback.format_stack())
+ message = f"{message}\n\nlog_exception CALL SITE TRACEBACK:\n\n{traceback_str}\nORIGINAL EXCEPTION TRACEBACK FOLLOWS:\n"
+ if sentry_event_handler is not None:
+ s3_uploads = []
+ callbacks = []
+ traceback_str_s3_key = get_s3_upload_key("logsite_traceback", ".txt")
+
+ # attach traceback of log_exception callsite
+ logsite_url = get_s3_upload_url(traceback_str_s3_key)
+ s3_uploads.append(logsite_url)
+ callbacks.append(
+ lambda key=traceback_str_s3_key, data=traceback_str: upload_to_s3_with_key(key, data.encode())
+ )
+
+ # for original exception, get traceback with variables and attach
+ traceback_with_variables_s3_key = get_s3_upload_key("original_exc_traceback_with_vars", ".txt")
+ s3_uploads.append(get_s3_upload_url(traceback_with_variables_s3_key))
+ callbacks.append(
+ lambda key=traceback_with_variables_s3_key, exception=exc: _upload_traceback(key, exception)
+ )
+ # upload some extra data if provided
+ if sentry_extra_uploads is not None:
+ for key, value in sentry_extra_uploads.items():
+ key = get_s3_upload_key(key, ".txt")
+ s3_uploads.append(get_s3_upload_url(key))
+ callbacks.append(lambda key=key, data=value: upload_to_s3_with_key(key, data.encode()))
+
+ sentry_event_handler.schedule_callbacks(callbacks)
+
+ # watch out; this will stomp on existing "extras" in the event
+ s3_uploads = [upload for upload in s3_uploads if upload is not None]
+ if s3_uploads:
+ scope.set_extra(EXTRAS_UPLOADED_FILES_KEY, s3_uploads)
+
+ # inject received exception stack trace into logger error message
+ inject_exception_and_log(exc, message, priority, *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/imbue_core/imbue_core/async_monkey_patches_test.py
@@ -0,0 +1,186 @@
+from contextlib import contextmanager
+from typing import Any
+from typing import Callable
+from typing import Generator
+from typing import Iterator
+
+import pytest
+from loguru import logger
+
+from imbue_core.async_monkey_patches import log_exception
+from imbue_core.constants import ExceptionPriority
+
+
+class IncorrectErrorsLoggedDuringTesting(Exception):
+ pass
+
+
+@contextmanager
+def check_logged_errors(check_func: Callable[[list[str]], None]) -> Iterator[None]:
+ """Context manager that monkey patches logger._log to accumulate error messages instead of logging them.
+ Then it runs the check function on the accumulated errors."""
+ original_log_func = logger._log # pyre-fixme[16]: pyre doesn't know that _log exists
+ accumulated_errors: list[str] = []
+
+ error_level_names = (
+ "ERROR",
+ ExceptionPriority.LOW_PRIORITY.value,
+ ExceptionPriority.MEDIUM_PRIORITY.value,
+ ExceptionPriority.HIGH_PRIORITY.value,
+ )
+
+ logger._log = lambda level, flag, options, message, args, kwargs: (
+ (
+ accumulated_errors.append(message) is None
+ and original_log_func(
+ "INFO",
+ flag,
+ options,
+ "CAUGHT ERROR LOG: " + message.splitlines()[0][:100],
+ args,
+ kwargs,
+ )
+ )
+ if level in error_level_names
+ else original_log_func(level, flag, options, message, args, kwargs)
+ )
+ try:
+ yield
+ finally:
+ logger._log = original_log_func
+ check_func(accumulated_errors)
+
+
+def at_least_check_maker(expected_errors_set: set[str]) -> Callable[[list[str]], None]:
+ assert isinstance(expected_errors_set, set), "expected_errors must be a set"
+ expected_errors = list(expected_errors_set)
+
+ def check_func(accumulated_errors: list[str]) -> None:
+ if len(accumulated_errors) < len(expected_errors):
+ raise IncorrectErrorsLoggedDuringTesting(
+ f"{len(accumulated_errors)=} != {len(expected_errors)=}, {accumulated_errors=}"
+ )
+ for expected_error in expected_errors:
+ for accumulated_error in accumulated_errors:
+ if expected_error in accumulated_error:
+ break
+ else:
+ raise IncorrectErrorsLoggedDuringTesting(f"{expected_error=} is not in {accumulated_errors=}")
+
+ return check_func
+
+
+@contextmanager
+def expect_at_least_logged_errors(expected_errors: set[str]) -> Iterator[None]:
+ """Context manager that monkey patches logger._log to accumulate error messages instead of logging them.
+ Checks that all expected errors are in the accumulated errors, in no particular order.
+ """
+ check_func = at_least_check_maker(expected_errors)
+ with check_logged_errors(check_func):
+ yield
+
+
+def exact_check_maker(expected_errors: list[str]) -> Callable[[list[str]], None]:
+ assert isinstance(expected_errors, list), "expected_errors must be a list"
+
+ def check_func(accumulated_errors: list[str]) -> None:
+ if len(accumulated_errors) != len(expected_errors):
+ raise IncorrectErrorsLoggedDuringTesting(
+ f"{len(accumulated_errors)=} != {len(expected_errors)=}, {accumulated_errors=}"
+ )
+ for i, expected_error in enumerate(expected_errors):
+ if expected_error not in accumulated_errors[i]:
+ raise IncorrectErrorsLoggedDuringTesting(
+ f"At position {i=}, {expected_error=} is not in {accumulated_errors[i]=}"
+ )
+
+ return check_func
+
+
+@contextmanager
+def expect_exact_logged_errors(expected_errors: list[str]) -> Iterator[None]:
+ """Context manager that monkey patches logger._log to accumulate error messages instead of logging them.
+ Checks that all expected errors are in the accumulated errors, in the same order."""
+ check_func = exact_check_maker(expected_errors)
+ with check_logged_errors(check_func):
+ yield
+
+
+def test_log_exception() -> None:
+ with expect_exact_logged_errors(["Test log_exception"]):
+ try:
+ x = 1 / 0
+ except Exception as e:
+ log_exception(e, "Test log_exception")
+ assert True # If we reach here, the test passes
+ else:
+ assert False, "log_exception did not raise an exception"
+
+
+def test_log_exception_with_priority() -> None:
+ # ensure_core_log_levels_configured auto-used in conftest
+ with expect_exact_logged_errors(["Test log_exception"]):
+ try:
+ x = 1 / 0
+ except Exception as e:
+ log_exception(e, "Test log_exception", priority=ExceptionPriority.LOW_PRIORITY)
+ assert True # If we reach here, the test passes
+ else:
+ assert False, "log_exception did not raise an exception"
+
+
+@pytest.fixture
+def explode_on_error() -> Generator[None, None, None]:
+ """Fixture to explode on error."""
+ original_log_func = logger._log # pyre-fixme[16]: pyre doesn't know that _log exists
+ accumulated_errors: list[str] = []
+
+ def _log_wrapper(
+ level: str,
+ flag: int,
+ options: tuple[int, ...],
+ message: str,
+ args: tuple,
+ kwargs: dict,
+ ) -> Any:
+ if level == "ERROR":
+ accumulated_errors.append(message)
+ new_options = list(options)
+ new_options[1] = 1
+ return original_log_func(level, flag, tuple(new_options), message, args, kwargs)
+
+ logger._log = _log_wrapper
+
+ try:
+ yield
+ except BaseException:
+ raise
+ else:
+ if len(accumulated_errors) > 0:
+ raise IncorrectErrorsLoggedDuringTesting(f"Errors logged during testing: {accumulated_errors}")
+ finally:
+ logger._log = original_log_func
+
+
+def test_log_error(explode_on_error: Any) -> None:
+ with expect_exact_logged_errors(["Something bad happened"]):
+ logger.error("Something bad happened")
+
+ with pytest.raises(IncorrectErrorsLoggedDuringTesting):
+ with expect_exact_logged_errors(["Something bad happened"]):
+ pass
+
+ with pytest.raises(IncorrectErrorsLoggedDuringTesting):
+ with expect_exact_logged_errors(["Something bad happened"]):
+ logger.error("Something bad happened")
+ logger.error("Something else bad happened")
+
+
+def test_log_error_at_least(explode_on_error: Any) -> None:
+ with expect_at_least_logged_errors({"Something bad happened"}):
+ logger.error("Something bad happened")
+ logger.error("Something else bad happened")
+
+ with pytest.raises(IncorrectErrorsLoggedDuringTesting):
+ with expect_at_least_logged_errors({"Something bad happened", "Something else bad happened"}):
+ logger.error("Something bad happened")
diff --git a/imbue_core/imbue_core/async_utils.py b/imbue_core/imbue_core/async_utils.py
@@ -0,0 +1,526 @@
+import asyncio
+import functools
+import inspect
+import os
+import platform
+import sys
+import threading
+import traceback
+from contextlib import AbstractAsyncContextManager
+from contextlib import AbstractContextManager
+from contextlib import contextmanager
+from datetime import datetime
+from http.server import BaseHTTPRequestHandler
+from http.server import HTTPServer
+from pathlib import Path
+from types import FrameType
+from typing import Any
+from typing import AsyncGenerator
+from typing import Awaitable
+from typing import Callable
+from typing import Coroutine
+from typing import Generator
+from typing import Generic
+from typing import Iterable
+from typing import Iterator
+from typing import ParamSpec
+from typing import TypeVar
+from typing import cast
+from urllib.parse import parse_qs
+from urllib.parse import urlparse
+
+from loguru import logger
+from traceback_with_variables.core import _iter_lines
+
+from imbue_core.async_monkey_patches import log_exception
+from imbue_core.async_monkey_patches import safe_cancel
+
+P = ParamSpec("P")
+R = TypeVar("R")
+S = TypeVar("S")
+
+ALL_EVENT_LOOPS: list[asyncio.AbstractEventLoop] = []
+
+
+def sync(func: Callable[P, Awaitable[R]]) -> Callable[P, R]:
+ """Decorator that runs an async function synchronously by dispatching to
+ an event loop running in a separate thread.
+ """
+
+ @functools.wraps(func)
+ def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+ loop = _get_or_create_event_loop()
+ return asyncio.run_coroutine_threadsafe(func(*args, **kwargs), loop).result()
+
+ return wrapper
+
+
+def sync_generator(
+ func: Callable[P, AsyncGenerator[R, None]],
+) -> Callable[P, Generator[R, None, None]]:
+ """Decorator that runs an async generator synchronously by dispatching to
+ an event loop running in a separate thread.
+ """
+
+ @functools.wraps(func)
+ def wrapper(*args: P.args, **kwargs: P.kwargs) -> Generator[R, None, None]:
+ loop = _get_or_create_event_loop()
+ agen = func(*args, **kwargs)
+ while True:
+ try:
+ future = asyncio.run_coroutine_threadsafe(agen.__anext__(), loop)
+ yield future.result()
+ except StopAsyncIteration:
+ break
+
+ return wrapper
+
+
+@contextmanager
+# pyre-ignore[24]: pyre doesn't understand AbstractAsyncContextManager
+def sync_contextmanager(
+ async_context_manager: AbstractAsyncContextManager[S],
+) -> Generator[S, None, None]:
+ sync_aenter = sync(async_context_manager.__aenter__)
+ sync_aexit = sync(async_context_manager.__aexit__)
+
+ enter_result = sync_aenter()
+ try:
+ yield enter_result
+ except BaseException as e:
+ if not sync_aexit(e.__class__, e, e.__traceback__):
+ raise
+ else:
+ sync_aexit(None, None, None)
+
+
+# pyre doesn't understand AbstractAsyncContextManager
+def sync_contextmanager_func(
+ cm_func: Callable[P, AbstractAsyncContextManager[S]], # pyre-ignore[24]
+) -> Callable[P, AbstractContextManager[S]]: # pyre-ignore[24]
+ @functools.wraps(cm_func)
+ def wrapper(*args: P.args, **kwargs: P.kwargs) -> AbstractContextManager[S]: # pyre-ignore[24]
+ return sync_contextmanager(cm_func(*args, **kwargs))
+
+ return wrapper
+
+
+_LOOP: asyncio.AbstractEventLoop | None = None
+_LOOP_LOCK: threading.Lock = threading.Lock()
+
+
+def _get_or_create_event_loop() -> asyncio.AbstractEventLoop:
+ global _LOOP
+ if _LOOP is not None:
+ return _LOOP
+ with _LOOP_LOCK:
+ # Check again in case another thread created the loop while we were waiting for the lock.
+ if _LOOP is not None:
+ return _LOOP
+ _LOOP = asyncio.new_event_loop()
+ asyncio.set_event_loop(_LOOP)
+ # pyre-ignore[16]: we have _LOOP_LOCK, so _LOOP is still not None
+ threading.Thread(target=_LOOP.run_forever, daemon=True, name="async_loop").start()
+ return _LOOP # pyre-ignore[7]: we just made _LOOP, so it's not None unless it got destroyed just now
+
+
+def shorten_filename(filename: str) -> str:
+ path = Path(filename)
+ while path.parent:
+ path = path.parent
+ if not (path / "__init__.py").exists():
+ break
+
+ try:
+ shortened = str(Path(filename).relative_to(path))
+ except ValueError:
+ shortened = filename # in case the path cannot be made relative
+
+ return shortened
+
+
+# TODO: I'd really like to print these task groups in a hierarchical way instead of flat -- we know which groups
+# launched which other groups, so we could print them in a tree structure. That would be a lot more readable.
+# It might also be nice to print without any stacks at all. As long as the tasks had good names, that would make it
+# possible to very easily understand everything that was currently executing.
+# I could even imagine controls that allowed for printing just the task groups themselves, which would also be easier
+# to understand.
+def get_all_async_task_stacks(
+ num_skipped_frames: int = 0,
+ log_variables: bool = False,
+ loop: asyncio.AbstractEventLoop | None = None,
+) -> Iterator[str]:
+ """Yields the lines of a report for all stack frames of all async tasks including variables."""
+ tasks_by_task_group: dict[asyncio.TaskGroup | None, list[asyncio.Task]] = {}
+ owning_task_by_task_group: dict[asyncio.TaskGroup, asyncio.Task] = {}
+
+ for task in asyncio.all_tasks(loop=loop):
+ if task.done():
+ continue
+ task_group = cast(asyncio.TaskGroup | None, getattr(task, "task_group", None))
+ owned_task_group = cast(asyncio.TaskGroup | None, getattr(task, "owned_task_group", None))
+ if owned_task_group is not None:
+ owning_task_by_task_group[owned_task_group] = task
+ if owned_task_group not in tasks_by_task_group:
+ tasks_by_task_group[owned_task_group] = []
+ else:
+ tasks_by_task_group.setdefault(task_group, []).append(task)
+
+ all_owning_tasks = set(owning_task_by_task_group.values())
+
+ task_group_keys = list(tasks_by_task_group.keys())
+ for task_group in cast(list[asyncio.TaskGroup | None], [None]) + [x for x in task_group_keys if x is not None]:
+ if task_group not in tasks_by_task_group:
+ continue
+ tasks = tasks_by_task_group[task_group]
+ if task_group is None:
+ yield f"\n\n{'=' * 40}\nNo TaskGroup:\n"
+ else:
+ yield f"\n\n{'=' * 40}\nTaskGroup: {getattr(task_group, 'name', 'unknown')}\n"
+ owning_task = None
+ if task_group is not None:
+ owning_task = owning_task_by_task_group.get(task_group)
+ is_first_line_skipped = False
+ all_tasks = tasks
+ if owning_task is not None:
+ yield f"Owning Task: {owning_task.get_name()}\n"
+ is_first_line_skipped = True
+ all_tasks.insert(0, owning_task)
+ for task in all_tasks:
+ # skip these -- they'll be printed at the top of the group that they own
+ if task_group is None and task in all_owning_tasks:
+ continue
+ if is_first_line_skipped:
+ is_first_line_skipped = False
+ else:
+ yield f"{'-' * 40}\nTask {task.get_name()}:\n"
+ frames = extract_frames(task)
+ for frame in frames:
+ frame_infos = inspect.getouterframes(frame)[num_skipped_frames:]
+ # Use private method _iter_lines to traceback async tasks, which is not explicitly handled in the API
+ if log_variables:
+ for line in _iter_lines(
+ e=None,
+ frame_infos=frame_infos,
+ fmt=None,
+ for_file=None,
+ ):
+ yield line + "\n"
+ else:
+ frame_summaries = [
+ traceback.FrameSummary(
+ shorten_filename(info.filename),
+ lineno=info.lineno,
+ name=info.function,
+ line=(info.code_context[0].strip() if info.code_context else None),
+ )
+ for info in frame_infos
+ ]
+ yield from traceback.format_list(frame_summaries)
+
+
+def extract_frames(task: asyncio.Task) -> list[FrameType]:
+ """Extract the stack frames of an async task."""
+ coro = task.get_coro()
+ assert isinstance(coro, Coroutine)
+ frames = []
+ while coro is not None and coro.cr_frame is not None:
+ frames.append(coro.cr_frame)
+ coro = coro.cr_await # type: ignore
+ # this happens at the very bottom of the call stack, there it seems to often be a FutureIter, Event, etc
+ if type(coro).__name__ != "coroutine":
+ break
+ return frames
+
+
+def print_all_async_task_stacks(log_variables: bool = False) -> None:
+ """Prints the stack frames of all running tasks."""
+ for line in get_all_async_task_stacks(log_variables=log_variables):
+ print(line)
+
+
+def dump_all_async_task_stacks(log_path: str | Path, log_variables: bool = False) -> None:
+ """Dump the stack frames of all running tasks to file."""
+ with open(log_path, "w") as f:
+ for line in get_all_async_task_stacks(log_variables=log_variables):
+ if log_variables:
+ line += "\n"
+ f.write(line)
+
+
+async def periodically_log_async_stacks(log_dir: str | Path, interval: float, log_variables: bool = False) -> None:
+ """Periodically print the stack traces of all running tasks."""
+ Path(log_dir).mkdir(parents=True, exist_ok=True)
+ while True:
+ log_path = Path(log_dir) / f"{datetime.now().strftime('%Y%m%d_%H%M%S')}.log"
+ dump_all_async_task_stacks(log_path=log_path, log_variables=log_variables)
+ logger.debug("Dumped asyncio stack traces to {}", log_path)
+ await asyncio.sleep(interval)
+
+
+async def is_task_group_complete(
+ task_group: asyncio.TaskGroup, trace_task: asyncio.Task, buffer_time: float = 1.0
+) -> None:
+ """Continuously check if all tasks except the stack trace logger are done."""
+ while True:
+ if all(task.done() for task in task_group._tasks if task is not trace_task):
+ await asyncio.sleep(buffer_time) # Wait for buffer time in case new tasks are added
+ # Recheck to confirm
+ if all(task.done() for task in task_group._tasks if task is not trace_task):
+ break
+ await asyncio.sleep(1.0)
+
+
+async def inject_async_stack_trace_logger(
+ task_group: asyncio.TaskGroup,
+ log_dir: str | Path,
+ log_interval: float = 60.0,
+ log_variables: bool = False,
+) -> None:
+ """Inject a stack trace logger into the task group."""
+ trace_task = asyncio.create_task(
+ periodically_log_async_stacks(log_dir=log_dir, interval=log_interval, log_variables=log_variables),
+ name="periodically_log_async_stacks",
+ )
+ await is_task_group_complete(task_group, trace_task)
+ safe_cancel(trace_task)
+ try:
+ await trace_task
+ except asyncio.CancelledError:
+ pass
+
+
+class AsyncTaskStacksHandler(BaseHTTPRequestHandler):
+ def do_GET(self) -> None:
+ try:
+ parsed_url = urlparse(self.path)
+ query_params = parse_qs(parsed_url.query)
+ log_variables = query_params.get("locals", ["false"])[0].lower() in [
+ "true",
+ "1",
+ "yes",
+ ]
+
+ self.send_response(200)
+ self.send_header("Content-type", "text/plain")
+ self.end_headers()
+
+ pid = os.getpid()
+ command_line = " ".join(sys.argv)
+
+ # Path to the Python executable
+ python_executable = sys.executable
+
+ # Python version
+ python_version = platform.python_version()
+
+ # Print the collected information
+ self.wfile.write(f"Process {pid}: {python_executable} {command_line}\n".encode("utf-8"))
+ self.wfile.write(f"Python v{python_version} ({python_executable})\n\n".encode("utf-8"))
+
+ for loop in ALL_EVENT_LOOPS:
+ for line in get_all_async_task_stacks(log_variables=log_variables, loop=loop):
+ self.wfile.write(line.encode("utf-8"))
+ except BaseException as e:
+ log_exception(e, "exception in AsyncTaskStacksHandler")
+ raise
+
+
+def run_async_stackframe_server_thread(port_range_low: int, port_range_high: int) -> None:
+ try:
+ success = False
+ for port in range(port_range_low, port_range_high):
+ try:
+ server_address = ("localhost", port)
+ httpd = HTTPServer(server_address, AsyncTaskStacksHandler)
+ success = True
+ print(f"Starting async stack trace server on port {port}. Process pid: {os.getpid()}")
+ break
+ except OSError:
+ continue
+
+ if not success:
+ logger.info("Could not find an open port to start the async stack trace server, continuing without it.")
+ return
+
+ httpd.serve_forever()
+ except BaseException as e:
+ log_exception(e, "exception in run_async_stackframe_server_thread")
+ raise
+
+
+IS_STACKFRAME_SERVER_RUNNING = False
+STACKFRAME_SERVER_PORT_LOW = 60000
+STACKFRAME_SERVER_PORT_HIGH = 61000
+
+
+def run_async_stackframe_server_for_loop(event_loop: asyncio.AbstractEventLoop) -> None:
+ ALL_EVENT_LOOPS.append(event_loop)
+ run_async_stackframe_server()
+
+
+def run_async_stackframe_server() -> None:
+ global IS_STACKFRAME_SERVER_RUNNING
+ if not IS_STACKFRAME_SERVER_RUNNING:
+ IS_STACKFRAME_SERVER_RUNNING = True
+
+ port_str = os.environ.get("ASYNC_STACKFRAME_SERVER_PORT")
+ if port_str is not None:
+ try:
+ port = int(port_str)
+ port_range_low = port
+ port_range_high = port + 1
+ except ValueError:
+ logger.error("ASYNC_STACKFRAME_SERVER_PORT is not an integer: {}", port_str)
+ raise
+ else:
+ port_range_low = STACKFRAME_SERVER_PORT_LOW
+ port_range_high = STACKFRAME_SERVER_PORT_HIGH
+
+ thread = threading.Thread(
+ target=run_async_stackframe_server_thread,
+ daemon=True,
+ kwargs={
+ "port_range_low": port_range_low,
+ "port_range_high": port_range_high,
+ },
+ )
+ thread.start()
+
+
+def with_timeout(func: Callable[P, Awaitable[R]], timeout_secs: float) -> Callable[P, Awaitable[R]]:
+ """Decorator that adds a timeout to an async function."""
+
+ @functools.wraps(func)
+ async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+ return await asyncio.wait_for(func(*args, **kwargs), timeout_secs)
+
+ return wrapper
+
+
+T = TypeVar("T")
+
+
+async def gather_with_limited_concurrency(coros: Iterable[Awaitable[T]], n: int) -> list[T]:
+ """Like asyncio.gather() but will only run `n` in parallel at a time.
+
+ Note that a call like `asyncio.gather(*coros)` is now `gather_with_limited_concurrency(coros, n=10),
+ without the splat.
+ """
+ semaphore = asyncio.Semaphore(n)
+
+ async def sem_coro(coro: Awaitable[T]) -> T:
+ async with semaphore:
+ return await coro
+
+ return list(await asyncio.gather(*(sem_coro(c) for c in coros)))
+
+
+_NOT_FOUND = object()
+
+
+class AsyncCachedProperty(Generic[T]):
+ """A descriptor factory that behaves very similarly to `functools.cached_property`, but for
+ async methods!
+
+ The type annotations here are rough; it's not realistic to get them perfect without using a .pyi file.
+ """
+
+ def __init__(self, func: Callable[[Any], Coroutine[None, None, T]]) -> None:
+ self.func = func
+ self.attrname: str | None = None
+ self.__doc__ = func.__doc__
+
+ def __set_name__(self, owner: type, name: str) -> None:
+ if self.attrname is None:
+ self.attrname = name
+ elif name != self.attrname:
+ raise TypeError("Cannot assign the same AsyncCachedProperty to multiple names")
+
+ def _get_attrname(self) -> str:
+ if self.attrname is None:
+ raise TypeError("Cannot use AsyncCachedProperty instance without calling __set_name__")
+ return self.attrname
+
+ def _get_cache(self, instance: object) -> dict[str, Any]:
+ try:
+ return instance.__dict__
+ except AttributeError:
+ raise TypeError(
+ "Cannot use AsyncCachedProperty with instances that do not have a __dict__ attribute"
+ ) from None
+
+ def __get__(self, instance: object, owner: type | None = None) -> Awaitable[T]:
+ if instance is None:
+ return self # type: ignore
+ attrname = self._get_attrname()
+ cache = self._get_cache(instance)
+ val = cache.get(attrname, _NOT_FOUND)
+ if val is not _NOT_FOUND:
+ return cast(Awaitable[T], val)
+
+ task = asyncio.create_task(self.func(instance))
+ cache[attrname] = task
+ return task
+
+ def __delete__(self, instance: object) -> None:
+ if instance is None:
+ raise TypeError("Cannot delete AsyncCachedProperty on a class")
+ attrname = self._get_attrname()
+ cache = self._get_cache(instance)
+ try:
+ awaitable = cache.pop(attrname)
+ if not awaitable.done():
+ safe_cancel(awaitable)
+ except KeyError:
+ raise AttributeError(f"Cannot delete attribute {self.attrname!r}") from None
+
+ def __set__(self, instance: object, value: T) -> None:
+ if instance is None:
+ raise TypeError("Cannot set AsyncCachedProperty on a class")
+ attrname = self._get_attrname()
+ cache = self._get_cache(instance)
+ existing = cache.pop(attrname, None)
+ if existing is not None and not existing.done():
+ safe_cancel(existing)
+ fut: asyncio.Future[T] = asyncio.Future()
+ fut.set_result(value)
+ cache[attrname] = fut
+
+
+def wrapped_asyncio_run(
+ main: Awaitable[T],
+ *,
+ debug: bool | None = None,
+ loop_factory: Callable[..., asyncio.AbstractEventLoop] | None = None,
+) -> T:
+ """
+ This is a lightweight wrapper with a singular purpose -- it is here to enable apyspy
+
+ apyspy is our async equivalent to py-spy.
+
+ Without apyspy, it's really annoying to debug why some async task is stuck.
+ """
+
+ async def wrapper_main() -> T:
+ ALL_EVENT_LOOPS.append(asyncio.get_event_loop())
+ result = await main
+ ALL_EVENT_LOOPS.pop()
+ return result
+
+ run_async_stackframe_server()
+ with asyncio.Runner(debug=debug, loop_factory=loop_factory) as runner:
+ return runner.run(wrapper_main())
+
+
+def make_async(func: Callable[P, R]) -> Callable[P, Awaitable[R]]:
+ """
+ Turn the annotated function into an async function by running it in a thread.
+
+ This is useful for functions that perform blocking i/o.
+ """
+
+ async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+ return await asyncio.to_thread(func, *args, **kwargs)
+
+ return wrapper
diff --git a/imbue_core/imbue_core/caching.py b/imbue_core/imbue_core/caching.py
@@ -0,0 +1,235 @@
+from __future__ import annotations
+
+import asyncio
+import os
+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 delete(self, key: str, retry: bool = False) -> bool:
+ cache = self.cache
+ assert cache is not None
+ loop = asyncio.get_running_loop()
+ return await loop.run_in_executor(None, cache.delete, key, 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,
+ )
+
+
+def get_default_llm_response_cache() -> Path:
+ return Path(os.environ.get("RESPONSE_CACHE_PATH", os.path.expanduser("~/.llm_response_cache")))
+
+
+def get_default_count_tokens_cache() -> Path:
+ return Path(os.environ.get("COUNT_TOKENS_CACHE_PATH", os.path.expanduser("~/.count_tokens_cache")))
+
+
+def get_test_llm_response_cache() -> Path:
+ return Path(os.path.expanduser("~/.llm_test_response_cache"))
+
+
+class InMemoryCache(AsyncCacheInterface[ValueType], Generic[ValueType]):
+ def __init__(self, values: tuple[ValueType, ...]) -> None:
+ self.values = values
+
+ async def __aenter__(self) -> Self:
+ return self
+
+ async def __aexit__(
+ self,
+ exc_type: type[BaseException] | None,
+ exc_val: BaseException | None,
+ exc_tb: TracebackType | None,
+ ) -> None:
+ pass
+
+ 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:
+ return self.values[int(key)]
+
+ 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]:
+ return FrozenDict(zip(await self.get_all_keys(), self.values))
+
+ async def get_all_keys(self, reverse: bool = False) -> tuple[str, ...]:
+ return tuple(map(str, range(len(self.values))))
diff --git a/imbue_core/imbue_core/cattrs_serialization.py b/imbue_core/imbue_core/cattrs_serialization.py
@@ -0,0 +1,1013 @@
+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 method hammers.
+ # This lets us serialize ExecutionContexts for calls to class methods without serializing the class itself.
+ # The long-term solutions are 1) either get rid of all class method hammers,
+ # or 2) 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
+
+
+def deserialize_from_dict_with_type(data: dict[str, Any], obj_type: type[T]) -> T:
+ try:
+ converter = CONVERTER_FACTORY.get_converter(for_javascript=False, exclude_dont_serialize_fields=False)
+ result = converter.structure(data, obj_type)
+ assert isinstance(result, obj_type), f"Expected an object of type {obj_type}, but got {result}"
+ return result
+ except Exception as e:
+ raise SerializationError(str(e)) from e
+
+
+def deserialize_from_json_with_type(data: str | bytes | bytearray, obj_type: type[T]) -> T:
+ try:
+ converter = CONVERTER_FACTORY.get_converter(for_javascript=False, exclude_dont_serialize_fields=False)
+ return cast(
+ T,
+ _deserialize_serialized_object(json.loads(data), obj_type, converter=converter),
+ )
+ except Exception as e:
+ raise SerializationError(str(e)) from e
diff --git a/imbue_core/imbue_core/common.py b/imbue_core/imbue_core/common.py
@@ -0,0 +1,135 @@
+import functools
+import hashlib
+import inspect
+import os
+import platform
+import sys
+import uuid
+from pathlib import Path
+from types import FrameType
+
+import pathspec
+
+
+def is_on_osx() -> bool:
+ return platform.system().lower() == "darwin"
+
+
+def is_running_within_a_pytest_tree() -> bool:
+ """
+ This is true if this, or any parent process, is running under pytest.
+
+ This is different from `is_running_within_a_pytest_process` in that it is true if we are logically testing or not.
+
+ This is usually what you want to check
+ (eg, this will be true even if you are a separately launched integration server process)
+ """
+ return "PYTEST_CURRENT_TEST" in os.environ
+
+
+def is_running_within_a_pytest_process() -> bool:
+ """
+ This is true if the current process is literally running pytest.
+
+ This is different from `is_running_within_a_pytest_tree` in that it checks if the current process is pytest itself,
+ which is most useful for knowing whether we are running a bunch of unit tests in this process or not.
+ """
+ return "pytest" in sys.modules
+
+
+def is_live_debugging() -> bool:
+ """
+ Returns True if the current process is being debugged, for example by PyCharm or another IDE.
+ """
+ # this is unfortunately true when measuring coverage and in other cases, sigh
+ # return sys.gettrace() is not None
+ # but this is only true when debugging in pycharm, I think?
+ return sys.breakpointhook.__module__ != "sys"
+
+
+@functools.lru_cache(maxsize=1)
+def get_filesystem_root() -> str:
+ env_value = os.getenv("SCIENCE_FILESYSTEM_ROOT")
+ if not env_value:
+ if is_on_osx():
+ return "/tmp/science"
+ else:
+ # When on the physical cluster (and possibly other core clusters), this path is mounted to a unique per-container file path.
+ # Anything produced at runtime >10mb should likely go here, as well as anything you might want to dig up for later debugging.
+ # The hosts clean up the paths from dead containers periodically, but large data processing jobs should still clean up after themselves.
+ return "/mnt/private"
+ return env_value
+
+
+@functools.lru_cache(maxsize=1)
+def get_temp_dir() -> str:
+ temp_dir = os.path.join(get_filesystem_root(), "tmp")
+ os.makedirs(temp_dir, exist_ok=True)
+ return temp_dir
+
+
+def hash_string(string: str) -> str:
+ return hashlib.md5(string.encode("utf-8")).hexdigest()
+
+
+def get_current_function_name() -> str:
+ frame = inspect.currentframe()
+ if frame is None:
+ return "no_frame"
+ prev_frame = frame.f_back
+ if prev_frame is None or not isinstance(prev_frame, FrameType):
+ return "no_previous_frame"
+ return prev_frame.f_code.co_name
+
+
+def filter_excluded_files(files: list[Path], directory: Path, exclude_file_name: str = ".gitignore") -> list[Path]:
+ """Remove files from the list that are matched by a .gitignore or similarly-specified exclude file such as
+ .gitignore or ratchet_excluded.txt.
+ """
+
+ # Underneath the root directory, find all the excluders.
+ # They can occur in subfolders and if they do they apply only to that subfolder.
+ excluders = {path for path in directory.rglob(exclude_file_name) if not path.is_symlink()}
+
+ # Per excluder, make a pathspec.
+ for excluder in excluders:
+ with excluder.open("r") as exclude_file:
+ exclude_spec = pathspec.GitIgnoreSpec.from_lines(exclude_file)
+
+ # Now we have two cases - We keep the file if the excluder doesn't apply because it's in a different
+ # folder, or if it applies but doesn't match
+ prefix = os.path.dirname(excluder)
+ files = [
+ file
+ for file in files
+ if not (file.is_relative_to(prefix) and exclude_spec.match_file(file.relative_to(prefix)))
+ ]
+
+ return files
+
+
+def generate_id() -> str:
+ return uuid.uuid4().hex
+
+
+def generate_id_from_existing_id(existing_id: str, seed: int) -> str:
+ return hashlib.md5(f"{existing_id}-{seed}".encode()).hexdigest()
+
+
+def truncate_string(s: str, max_length: int) -> str:
+ if len(s) <= max_length:
+ return s
+ return s[: max_length - 3] + "..."
+
+
+def parse_bool_environment_variable(var_name: str) -> bool:
+ env_var = os.environ.get(var_name, "0").lower()
+
+ assert env_var in (
+ "0",
+ "1",
+ "true",
+ "false",
+ ), f"{var_name} environment variable must be '0', '1', 'true', or 'false'. Current value: '{env_var}'"
+
+ return env_var in ("1", "true")
diff --git a/imbue_core/imbue_core/computing_environment/__init__.py b/imbue_core/imbue_core/computing_environment/__init__.py
diff --git a/imbue_core/imbue_core/computing_environment/computing_environment.py b/imbue_core/imbue_core/computing_environment/computing_environment.py
@@ -0,0 +1,1080 @@
+from __future__ import annotations
+
+import asyncio
+import shlex
+import time
+from datetime import datetime
+from pathlib import Path
+from typing import Protocol
+from typing import Sequence
+from typing import TYPE_CHECKING
+from uuid import uuid4
+
+import anyio
+from loguru import logger
+from tenacity import TryAgain
+from tenacity import retry
+from tenacity import retry_all
+from tenacity import retry_if_exception_type
+from tenacity import stop_after_attempt
+from tenacity import wait_random_exponential
+
+from imbue_core.async_monkey_patches import log_exception
+from imbue_core.computing_environment.data_types import AnyPath
+from imbue_core.computing_environment.data_types import FailedToMakeCommitError
+from imbue_core.computing_environment.data_types import PatchApplicationError
+from imbue_core.computing_environment.data_types import RunCommandError
+from imbue_core.git_data_types import CommitTimestamp
+from imbue_core.retry_utils import log_before_sleep
+from imbue_core.section import Section
+from imbue_core.time_utils import get_current_time
+
+# Import the types needed for file modes
+if TYPE_CHECKING:
+ # for proper file mode typing
+ from _typeshed import OpenBinaryModeReading
+ from _typeshed import OpenBinaryModeWriting
+ from _typeshed import OpenTextModeReading
+ from _typeshed import OpenTextModeWriting
+
+
+class ComputingEnvironment(Protocol):
+ """Protocol defining the interface for a computing environment.
+
+ This protocol specifies the required methods for interacting with a computing
+ environment, including running commands and file operations.
+ """
+
+ async def run_command(
+ self,
+ command: Sequence[str],
+ check: bool = True,
+ secrets: dict[str, str] | None = None,
+ cwd: AnyPath | None = None,
+ is_error_logged: bool = True,
+ ) -> str: ...
+
+ async def run_git(
+ self,
+ command: Sequence[str],
+ check: bool = True,
+ cwd: AnyPath | None = None,
+ is_error_logged: bool = True,
+ is_stripped: bool = True,
+ retry_on_git_lock_error: bool = True,
+ ) -> str: ...
+
+ async def write_file(
+ self,
+ relative_path: AnyPath,
+ content: str | bytes | None,
+ cwd: AnyPath | None = None,
+ mode: OpenTextModeWriting | OpenBinaryModeWriting = "w",
+ mkdir_if_missing: bool = True,
+ ) -> None: ...
+
+ async def read_file(
+ self,
+ relative_path: AnyPath,
+ cwd: AnyPath | None = None,
+ mode: OpenTextModeReading | OpenBinaryModeReading = "r",
+ mkdir_if_missing: bool = True,
+ ) -> str | bytes: ...
+
+ async def delete_file(
+ self,
+ relative_path: AnyPath,
+ cwd: AnyPath | None = None,
+ ) -> None: ...
+
+
+def _get_temp_patch_file() -> anyio.Path:
+ # this is a bad idea because it triggers the file watcher
+ # patch_file = (self.base_path / str(uuid4())).with_suffix(".patch")
+ patch_file = (Path("/tmp") / uuid4().hex).with_suffix(".patch")
+ return anyio.Path(patch_file)
+
+
+async def run_command_with_retry_on_git_lock_error(
+ computing_environment: ComputingEnvironment,
+ command: Sequence[str],
+ check: bool = True,
+ is_error_logged: bool = True,
+ cwd: AnyPath | None = None,
+) -> str:
+ max_retries = 50
+ retry_count = 0
+ retry_delay = 0.1 # seconds
+ while True:
+ try:
+ return await computing_environment.run_command(
+ command,
+ check=check,
+ is_error_logged=is_error_logged and retry_count >= max_retries,
+ cwd=cwd,
+ )
+ except RunCommandError as e:
+ error_message = str(e)
+ if "fatal: Unable to create" in error_message and ".git/index.lock': File exists" in error_message:
+ if retry_count >= max_retries:
+ raise
+ await asyncio.sleep(retry_delay)
+ retry_count += 1
+ else:
+ raise
+
+
+@retry(
+ wait=wait_random_exponential(multiplier=0.1, max=2, exp_base=2),
+ reraise=True,
+ stop=stop_after_attempt(50),
+ before_sleep=log_before_sleep,
+)
+async def wait_for_git_index_lock_to_be_free(local_sync_repo_path: Path) -> None:
+ # Path to the git index lock file using anyio.Path to avoid blocking
+ lock_file_path = anyio.Path(local_sync_repo_path / ".git" / "index.lock")
+ if await lock_file_path.exists():
+ raise TryAgain
+
+
+async def apply_patch_without_git(computing_environment: ComputingEnvironment, diff: str) -> None:
+ if diff.strip() == "":
+ return
+ patch_file = _get_temp_patch_file()
+ try:
+ await computing_environment.write_file(patch_file, diff)
+ await computing_environment.run_command(("bash", "-c", f"patch -p1 < {patch_file}"))
+ except RunCommandError as e:
+ raise PatchApplicationError(f"Failed to apply patch: {e}") from e
+ finally:
+ await computing_environment.delete_file(patch_file)
+
+
+async def is_repo_dirty(computing_environment: ComputingEnvironment, is_untracked_considered: bool = True) -> bool:
+ """Check if the repo has any uncommitted changes."""
+ return bool(
+ await computing_environment.run_git(
+ (
+ "status",
+ "--porcelain",
+ *([] if is_untracked_considered else ["--untracked-files=no"]),
+ )
+ )
+ )
+
+
+async def are_all_commits_pushed(computing_environment: ComputingEnvironment) -> bool:
+ """Check if the repo has any unpushed commits."""
+ output = await computing_environment.run_git(("cherry",))
+ return output.strip() == ""
+
+
+async def are_all_remote_commits_pulled(
+ computing_environment: ComputingEnvironment,
+) -> bool:
+ # note this will fail if the branch hasn't been pushed to the remote
+ output = await computing_environment.run_command(
+ ("bash", "-c", "git fetch && git rev-list HEAD..@{upstream} --count")
+ )
+ return output.strip() == "0"
+
+
+async def assert_repo_is_clean(computing_environment: ComputingEnvironment) -> None:
+ """Assert that the repo has no uncommitted changes."""
+ assert not await is_repo_dirty(
+ computing_environment
+ ), "You have untracked files. Please address them before using this script (this is to prevent accidentally adding large files unintentionally)"
+
+
+async def get_branch_name(computing_environment: ComputingEnvironment, is_error_logged: bool = True) -> str:
+ """Get the name of the current branch."""
+ return await computing_environment.run_git(("symbolic-ref", "--short", "HEAD"), is_error_logged=is_error_logged)
+
+
+async def rename_branch(
+ computing_environment: ComputingEnvironment,
+ old_name: str,
+ new_name: str,
+ force_if_exists: bool = True,
+) -> None:
+ """Rename the given branch."""
+ if force_if_exists:
+ await computing_environment.run_git(("branch", "-M", old_name, new_name))
+ else:
+ await computing_environment.run_git(("branch", "-m", old_name, new_name))
+
+
+async def get_branch_description(computing_environment: ComputingEnvironment, branch_name: str) -> str:
+ """Get the description of the given branch."""
+ try:
+ return await computing_environment.run_git(
+ ("config", f"branch.{branch_name}.description"), is_error_logged=False
+ )
+ except RunCommandError as e:
+ if e.returncode == 1:
+ # no description set
+ return ""
+ raise
+
+
+async def is_branch_exists(computing_environment: ComputingEnvironment, branch_name: str) -> bool:
+ """Check if the given branch exists."""
+ result = await computing_environment.run_git(
+ ("rev-parse", "--verify", "--quiet", branch_name),
+ is_error_logged=False,
+ check=False,
+ )
+ return result.strip() != ""
+
+
+async def is_detached_head(computing_environment: ComputingEnvironment) -> bool:
+ """Check if the current HEAD is detached."""
+ result = await computing_environment.run_git(("rev-parse", "--abbrev-ref", "HEAD"), is_error_logged=False)
+ return result.strip() == "HEAD"
+
+
+async def set_branch_description(
+ computing_environment: ComputingEnvironment, branch_name: str, description: str
+) -> None:
+ """Set the description of the given branch."""
+ await computing_environment.run_git(("config", f"branch.{branch_name}.description", description))
+
+
+async def get_branch_commit(computing_environment: ComputingEnvironment, branch_name: str) -> str:
+ """Get the commit of the given branch."""
+ return await computing_environment.run_git(("rev-parse", branch_name))
+
+
+async def get_all_branch_names_pointing_to_commit(
+ computing_environment: ComputingEnvironment, commit_hash: str
+) -> tuple[str, ...]:
+ """Get all branch names that point to the given commit."""
+ result = await computing_environment.run_git(
+ (
+ "for-each-ref",
+ "refs/heads/",
+ "--format='%(refname:short)'",
+ "--points-at",
+ commit_hash,
+ )
+ )
+ branch_names = tuple(result.splitlines())
+ # strip the quotes
+ return tuple(branch_name.strip("'") for branch_name in branch_names)
+
+
+async def is_branch_child_of_branch(
+ computing_environment: ComputingEnvironment,
+ child_branch_name: str,
+ parent_branch_name: str,
+) -> bool:
+ """Check if the given branch is a child of the parent branch."""
+ try:
+ await computing_environment.run_git(
+ ("merge-base", "--is-ancestor", parent_branch_name, child_branch_name),
+ is_error_logged=False,
+ )
+ return True
+ except RunCommandError as e:
+ if e.stderr.strip() == "" and e.returncode == 1:
+ # we expect this command to give an empty stderr and a return code of 1
+ # if the child branch is not an ancestor of the parent branch
+ return False
+ raise
+
+
+async def is_commit_on_branch(
+ computing_environment: ComputingEnvironment,
+ commit_hash: str,
+ branch_name: str,
+ local_only: bool = True,
+) -> bool:
+ """Check if the given commit is on the given branch."""
+ if local_only:
+ result = await computing_environment.run_git(("branch", "--contains", commit_hash))
+ else:
+ result = await computing_environment.run_git(("branch", "-a", "--contains", commit_hash))
+ return any(branch_name == x.strip() for x in result.splitlines())
+
+
+async def fetch_and_get_first_entry_in_fetch_head(
+ computing_environment: ComputingEnvironment, remote: str, fetch_refs: Sequence[str]
+) -> str:
+ """Fetch the given refs from the remote and return the first entry in FETCH_HEAD."""
+ refs_str = " ".join(fetch_refs)
+ command = [
+ "bash",
+ "-c",
+ (
+ f"git fetch {remote} {refs_str} && "
+ # get first commit from FETCH_HEAD
+ "git rev-parse FETCH_HEAD"
+ ),
+ ]
+ result = await run_command_with_retry_on_git_lock_error(computing_environment, command)
+ return result.strip()
+
+
+async def fetch_branch(computing_environment: ComputingEnvironment, branch_name: str) -> None:
+ """Fetch the given branch from the remote."""
+ await computing_environment.run_git(("fetch", "origin", branch_name))
+
+
+async def is_branch_present(computing_environment: ComputingEnvironment, branch_name: str) -> bool:
+ """Check if branch with given name is present."""
+ result = await computing_environment.run_git(("branch",))
+ return branch_name in result.splitlines()
+
+
+async def create_reset_and_checkout_branch(computing_environment: ComputingEnvironment, branch_name: str) -> str:
+ """Create new branch with given name."""
+ return await computing_environment.run_git(("switch", "-C", branch_name))
+
+
+async def switch_branch(computing_environment: ComputingEnvironment, branch_name: str) -> str:
+ """Switch to branch with given name."""
+ return await computing_environment.run_git(("switch", branch_name))
+
+
+async def delete_branch(computing_environment: ComputingEnvironment, branch_name: str, delete_remote: bool) -> str:
+ """Delete branch with given name."""
+ result = await computing_environment.run_git(("branch", "-D", branch_name))
+ if delete_remote:
+ result = await computing_environment.run_git(("push", "origin", "--delete", branch_name))
+ return result
+
+
+async def update_branch_to_hash(computing_environment: ComputingEnvironment, branch_name: str, git_hash: str) -> None:
+ """Update the given branch to reference the given git hash."""
+ # here we do it without checking out the branch
+ await computing_environment.run_git(("branch", "-f", branch_name, git_hash))
+
+
+async def switch_and_create_branch_if_needed(computing_environment: ComputingEnvironment, branch_name: str) -> str:
+ """Switch to new branch, creating it if it doesn't already exist."""
+ if await is_branch_present(computing_environment, branch_name):
+ await switch_branch(computing_environment, branch_name)
+ else:
+ await create_reset_and_checkout_branch(computing_environment, branch_name)
+ return await get_branch_name(computing_environment)
+
+
+async def merge_branches(
+ computing_environment: ComputingEnvironment,
+ base_branch_name: str,
+ merge_branch_name: str,
+ is_moving_to_base_branch: bool = True,
+) -> str:
+ """Merge `merge_branch_name` into `base_branch_name`."""
+ await switch_branch(computing_environment, base_branch_name)
+ await computing_environment.run_git(("merge", merge_branch_name))
+ if not is_moving_to_base_branch:
+ await switch_branch(computing_environment, "-")
+ return await get_branch_name(computing_environment)
+
+
+async def get_merge_base(computing_environment: ComputingEnvironment, branch_name: str, target_branch: str) -> str:
+ """Get the merge base of the given branch and target branch.
+
+ The merge base is the most recent commit that is on both branches.
+ """
+ return await computing_environment.run_git(["merge-base", branch_name, target_branch], is_error_logged=False)
+
+
+async def checkout_hash(computing_environment: ComputingEnvironment, git_hash: str) -> str:
+ """Checkout given git hash."""
+ return await computing_environment.run_git(("checkout", git_hash))
+
+
+async def force_add(computing_environment: ComputingEnvironment, *paths: str) -> None:
+ """Force-add the specified paths to the git index."""
+ await computing_environment.run_git(("add", "-f", *paths))
+
+
+async def git_add(computing_environment: ComputingEnvironment, *paths: str) -> None:
+ """Add the specified paths to the git index."""
+ await computing_environment.run_git(("add", *paths))
+
+
+def convert_datetime_to_git_timestamp(dt: datetime) -> str:
+ return datetime.isoformat(dt)
+
+
+def convert_git_timestamp_to_datetime(timestamp: str) -> datetime:
+ return datetime.fromisoformat(timestamp)
+
+
+def get_commit_ts_for_current_time() -> CommitTimestamp:
+ """Get the commit timestamp for the current time."""
+ current_time = get_current_time()
+ return CommitTimestamp(
+ author_ts=convert_datetime_to_git_timestamp(current_time),
+ committer_ts=convert_datetime_to_git_timestamp(current_time),
+ )
+
+
+def _convert_time_to_commit_ts(
+ time: str | datetime | CommitTimestamp | None,
+) -> CommitTimestamp:
+ if time is None:
+ return get_commit_ts_for_current_time()
+ elif isinstance(time, datetime):
+ return CommitTimestamp(
+ author_ts=convert_datetime_to_git_timestamp(time),
+ committer_ts=convert_datetime_to_git_timestamp(time),
+ )
+ elif isinstance(time, CommitTimestamp):
+ return time
+ else:
+ # assume it's a git timestamp
+ return CommitTimestamp(author_ts=time, committer_ts=time)
+
+
+async def make_commit(
+ computing_environment: ComputingEnvironment,
+ commit_message: str,
+ allow_empty: bool = False,
+ amend: bool = False,
+ commit_time: str | datetime | CommitTimestamp | None = None,
+) -> str:
+ if commit_message.strip() == "":
+ commit_message = "No commit message provided"
+
+ commit_ts = _convert_time_to_commit_ts(commit_time)
+ time_args = f'GIT_AUTHOR_DATE="{commit_ts.author_ts}" GIT_COMMITTER_DATE="{commit_ts.committer_ts}" '
+
+ commit_message = shlex.quote(commit_message)
+ no_changes_message = "No changes to commit"
+ amend_args = "--amend " if amend else ""
+ if allow_empty or amend:
+ bash_command = f"""git add . && {time_args}git commit {amend_args}--allow-empty -m {commit_message} > /dev/null && git rev-parse HEAD"""
+ else:
+ bash_command = f"""git add . && ( git status | grep -q "nothing to commit" && echo "{no_changes_message}" ) || ( {time_args}git commit {amend_args}-m {commit_message} > /dev/null && git rev-parse HEAD )"""
+
+ with Section(f"committing changes with message: '{commit_message}'", log_level="DEBUG"):
+ stdout = await run_command_with_retry_on_git_lock_error(
+ computing_environment,
+ ["bash", "-c", bash_command],
+ )
+ stdout = stdout.strip()
+ if stdout == no_changes_message:
+ raise FailedToMakeCommitError(f"Failed to make commit with message: {commit_message}. {bash_command=}")
+ new_git_hash = stdout
+ return new_git_hash
+
+
+async def get_tree_hash_for_commit(computing_environment: ComputingEnvironment, commit: str) -> str:
+ """Get the tree hash for the given commit."""
+ return await computing_environment.run_git(["rev-parse", commit + "^{tree}"])
+
+
+async def get_commit_timestamp(computing_environment: ComputingEnvironment, commit: str) -> CommitTimestamp:
+ """Get the commit timestamp for the given commit."""
+ split_token = "<|>"
+ result = await computing_environment.run_git(["show", "-s", "--format=%aI<|>%cI", commit])
+ author_ts, committer_ts = result.split(split_token)
+ return CommitTimestamp(author_ts=author_ts.strip(), committer_ts=committer_ts.strip())
+
+
+async def tag_commit(computing_environment: ComputingEnvironment, tag: str, commit_hash: str) -> None:
+ """Tag the given commit with the given tag."""
+ # We use -f to force the tag to be created even if it already exists.
+ await computing_environment.run_git(("tag", "-f", tag, commit_hash))
+
+
+async def git_push(computing_environment: ComputingEnvironment, branch_name: str) -> str:
+ """Push changes to remote branch with given name."""
+ return await computing_environment.run_git(("push", "origin", branch_name))
+
+
+async def force_push(computing_environment: ComputingEnvironment, branch_name: str) -> str:
+ """Push changes to remote branch with given name."""
+ return await computing_environment.run_git(("push", "--force", "origin", branch_name))
+
+
+async def force_push_commit_with_retry(
+ computing_environment: ComputingEnvironment,
+ commit: str,
+ branch_name: str,
+ timeout: float = 30.0,
+) -> None:
+ start_time = time.monotonic()
+ sleep_time = 0.5
+ while True:
+ try:
+ await force_push_commit(computing_environment, commit, branch_name)
+ break
+ except Exception as exc:
+ if time.monotonic() - start_time > timeout:
+ raise TimeoutError(
+ f"Timeout reached: Could not force push {commit} to {branch_name} in {timeout} seconds."
+ ) from exc
+ logger.info("Force push of {} to {} failed; trying again...", commit, branch_name)
+ await asyncio.sleep(sleep_time)
+ sleep_time *= 2
+
+
+async def force_push_commit(computing_environment: ComputingEnvironment, commit: str, branch_name: str) -> None:
+ try:
+ await computing_environment.run_git(["push", "-f", "origin", f"{commit}:{branch_name}"], is_error_logged=False)
+ except RunCommandError as e:
+ if "fatal: bad object" in e.stderr:
+ # TODO (danielmewes): We're retrying failed fetches here. However, there is also a separate
+ # force_push_commit_with_retry method that retries the entire force_push_commit.
+ # We should probably try the fetch only once, and then rely on the outer
+ # force_push_commit_with_retry to retry the entire force_push_commit call when retrying is =
+ # desired?
+ NUM_TRIES = 3
+ for _ in range(NUM_TRIES):
+ try:
+ await computing_environment.run_git(["fetch", "origin", commit], is_error_logged=False)
+ except RunCommandError as fetch_e:
+ if "not our ref" in fetch_e.stderr:
+ # FIXME: actually, this has been getting worse... I suspect perhaps rate limiting or something? We are checking thing out much more than usual...
+ await asyncio.sleep(2)
+ else:
+ raise fetch_e
+ else:
+ start_time = time.monotonic()
+ while time.monotonic() - start_time < 10:
+ try:
+ await computing_environment.run_git(["push", "-f", "origin", f"{commit}:{branch_name}"])
+ except RunCommandError as repush_e:
+ if "not our ref" in repush_e.stderr:
+ # FIXME: actually, this has been getting worse... I suspect perhaps rate limiting or something? We are checking thing out much more than usual...
+ await asyncio.sleep(2)
+ else:
+ raise repush_e
+ else:
+ return
+ raise Exception(f"Could not force push commit {commit}")
+ raise Exception(f"Could not fetch commit {commit} to force push it")
+ else:
+ raise
+
+
+async def get_staged_files(
+ computing_environment: ComputingEnvironment,
+) -> tuple[str, ...]:
+ """Get list of all files in repo that are currently staged."""
+ result = await computing_environment.run_git(("diff", "--full-index", "--binary", "--name-only", "--cached"))
+ return tuple(result.splitlines())
+
+
+async def get_unstaged_files(
+ computing_environment: ComputingEnvironment,
+) -> tuple[str, ...]:
+ """Get list of all files in repo that are currently unstaged."""
+ result = await computing_environment.run_git(("diff", "--full-index", "--binary", "--name-only"))
+ return tuple(result.splitlines())
+
+
+async def restore_all_staged_files(computing_environment: ComputingEnvironment) -> None:
+ """Restore all staged files."""
+ await computing_environment.run_git(("restore", "--staged", "."))
+
+
+async def restore_all_unstaged_changes(
+ computing_environment: ComputingEnvironment,
+) -> None:
+ """Restore all unstaged changes."""
+ await computing_environment.run_git(("restore", "."))
+
+
+async def apply_patch_via_git_with_conflict_markers(
+ computing_environment: ComputingEnvironment,
+ git_diff: str,
+ is_error_logged: bool = True,
+) -> None:
+ """Apply a diff to repo with conflict markers."""
+ if git_diff.strip() == "":
+ return
+ if not git_diff.endswith("\n"):
+ # git requires a newline at the end of the patch
+ git_diff += "\n"
+ patch_file = _get_temp_patch_file()
+ try:
+ await computing_environment.write_file(patch_file, git_diff)
+ await computing_environment.run_command(
+ [
+ "bash",
+ "-c",
+ f"git add . && git apply --verbose {patch_file} || git apply -3 --verbose {patch_file}",
+ ],
+ is_error_logged=is_error_logged,
+ )
+ except RunCommandError as e:
+ raise PatchApplicationError(f"Failed to apply patch: {e}") from e
+ finally:
+ await computing_environment.delete_file(patch_file)
+
+
+async def is_repo_conflicted(computing_environment: ComputingEnvironment) -> bool:
+ output = await computing_environment.run_git(["status"], is_error_logged=False, check=False)
+ if "Unmerged paths:" in output:
+ return True
+ return False
+
+
+async def get_head_hash(computing_environment: ComputingEnvironment) -> str:
+ """Get the hash of the current HEAD commit."""
+ git_hash = await computing_environment.run_git(["rev-parse", "HEAD"])
+ assert len(git_hash) == 40, f"Expected 40-character git hash, got {git_hash}"
+ return git_hash
+
+
+async def get_parent_commit_hash(computing_environment: ComputingEnvironment, commit_hash: str) -> str:
+ """Get the parent commit hash of the given commit hash."""
+ git_hash = await computing_environment.run_git(["rev-parse", f"{commit_hash}^"])
+ assert len(git_hash) == 40, f"Expected 40-character git hash, got {git_hash}"
+ return git_hash
+
+
+async def get_most_recent_sibling_branch_of_branch(
+ computing_environment: ComputingEnvironment, target_branch_name: str
+) -> tuple[str, ...] | None:
+ """Get the most recent sibling branch of the given branch name.
+
+ This is the first branch that shares a common ancestor commit with the given branch name.
+ Note, that it is possible that there are multiple branches that share the most recent common ancestor.
+ In this case, we return all of them.
+
+ Also if there are no sibling branches (either at all or within the max lookback), we return None.
+ """
+ # FIXME: this is imprecise -- it really just needs to be head 2, but I didn't want to figure out the regex...
+ output = await computing_environment.run_command(
+ [
+ "bash",
+ "-c",
+ f'git log --decorate=full --oneline {target_branch_name} | grep "refs/heads/" | head -n 10',
+ ],
+ check=True,
+ )
+ for line in output.splitlines():
+ branch_list_string = line.split(" (", maxsplit=1)[-1].split(")", maxsplit=1)[0]
+ parsed_branch_names = []
+ for branch_name_string in branch_list_string.split(", "):
+ parsed_branch_name = branch_name_string.rsplit(" ")[-1]
+ if parsed_branch_name.startswith("refs/heads") and parsed_branch_name != f"refs/heads/{target_branch_name}":
+ parsed_branch_names.append(parsed_branch_name.replace("refs/heads/", "", 1))
+ # otherwise, just return the first one
+ if len(parsed_branch_names) > 0:
+ return tuple(parsed_branch_names)
+ return None
+
+
+async def get_nth_commit_ago(
+ computing_environment: ComputingEnvironment,
+ commit_hash: str,
+ n: int,
+ is_error_logged: bool = True,
+) -> str:
+ """Get the nth commit ago of the given commit hash."""
+ git_hash = await computing_environment.run_git(["rev-parse", f"{commit_hash}~{n}"], is_error_logged=is_error_logged)
+ assert len(git_hash) == 40, f"Expected 40-character git hash, got {git_hash}"
+ return git_hash
+
+
+async def get_initial_repo_commit_hash(computing_environment: ComputingEnvironment, commit_hash: str = "HEAD") -> str:
+ """Get the initial commit hash of the repo.
+
+ As written, if invoked on an empty repo with no commits (immediately after `git init`), this fails with:
+ `fatal: ambiguous argument 'HEAD': unknown revision or path not in the working tree.`
+ """
+ # --max-parents=0: only consider commits with no parents
+ # --date-order: sort by date (newest first)
+ output = await computing_environment.run_git(["rev-list", "--max-parents=0", commit_hash, "--date-order"])
+ # assume the oldest commit with no parents is the initial repo commit
+ all_root_commits = output.splitlines()
+ root_commit = all_root_commits[-1]
+ assert len(root_commit) == 40, f"Expected 40-character git hash, got {root_commit}"
+ return root_commit
+
+
+async def get_upto_nth_commit_ago(computing_environment: ComputingEnvironment, commit_hash: str, n: int) -> str:
+ """Get the commit hash of the upto nth commit ago of the given commit hash.
+
+ If the commit history is shorter than n, it will return the first commit.
+ """
+ try:
+ return await get_nth_commit_ago(computing_environment, commit_hash, n, is_error_logged=False)
+ except RunCommandError as e:
+ if "unknown revision or path not in the working tree" in e.stderr:
+ return await get_initial_repo_commit_hash(computing_environment, commit_hash)
+ raise
+
+
+async def get_commit_message(computing_environment: ComputingEnvironment, commit_hash: str) -> str:
+ """Get the commit message of the given commit hash."""
+ return await computing_environment.run_git(["log", "-1", "--pretty=%B", commit_hash])
+
+
+async def get_commit_count_between_hashes(
+ computing_environment: ComputingEnvironment, old_hash: str, new_hash: str
+) -> int:
+ """Get the number of commits between two hashes."""
+ output = await computing_environment.run_git(["rev-list", "--count", f"{old_hash}..{new_hash}"])
+ return int(output.strip())
+
+
+async def fetch_and_checkout_hash(
+ computing_environment: ComputingEnvironment,
+ git_hash: str,
+ is_error_logged: bool = True,
+) -> None:
+ await computing_environment.run_command(
+ ["bash", "-c", f"git fetch origin {git_hash} && git checkout {git_hash}"],
+ is_error_logged=is_error_logged,
+ )
+
+
+async def fetch_ref_and_checkout_hash(
+ computing_environment: ComputingEnvironment,
+ ref: str,
+ git_hash: str,
+ is_error_logged: bool = True,
+) -> None:
+ await computing_environment.run_command(
+ ["bash", "-c", f"git fetch origin {ref} && git checkout {git_hash}"],
+ is_error_logged=is_error_logged,
+ )
+
+
+@retry(
+ stop=stop_after_attempt(5),
+ wait=wait_random_exponential(min=0.5, max=30, exp_base=2),
+ reraise=True,
+ retry=retry_all(retry_if_exception_type(RunCommandError)),
+ before_sleep=log_before_sleep,
+)
+async def wait_for_git_hash_to_checkout(computing_environment: ComputingEnvironment, git_hash: str) -> None:
+ await fetch_and_checkout_hash(computing_environment, git_hash, is_error_logged=False)
+
+
+async def wait_for_git_hash_with_ref_to_checkout(
+ computing_environment: ComputingEnvironment,
+ git_hash: str,
+ ref: str,
+ timeout: float = 20.0,
+) -> None:
+ with Section(f"Checking out git hash {git_hash} with ref {ref}", log_level="DEBUG"):
+ start_time = time.monotonic()
+ while True:
+ try:
+ await fetch_ref_and_checkout_hash(computing_environment, ref, git_hash, is_error_logged=False)
+ break
+ except RunCommandError as exc:
+ if time.monotonic() - start_time > timeout:
+ log_exception(
+ exc,
+ "Timeout reached: Git hash {git_hash} is not available after {timeout} seconds.",
+ git_hash=git_hash,
+ timeout=timeout,
+ )
+ raise TimeoutError(f"Timeout reached: Git hash {git_hash} is not available.") from exc
+ logger.info("Waiting for git hash {} to be available...", git_hash)
+ await asyncio.sleep(0.5)
+
+
+async def force_checkout_git_hash_immediate_on_branch(
+ computing_environment: ComputingEnvironment, git_hash: str, branch_name: str
+) -> None:
+ # Here we clear any uncommited changes, then change branch, before checking out the new hash
+ # We need to do these three steps so we don't affect the currently checked out branch
+ command = f"git reset --hard && git checkout -B {branch_name} && git reset --hard {git_hash}"
+ logger.debug("Running command: {}", command)
+ await run_command_with_retry_on_git_lock_error(
+ computing_environment,
+ [
+ "bash",
+ "-c",
+ command,
+ ],
+ )
+
+
+async def get_git_folder_paths(
+ computing_environment: ComputingEnvironment,
+) -> tuple[str, ...]:
+ """Get the paths of all the git folders in the repo."""
+ result = await computing_environment.run_command(["ls", ".git"])
+ return tuple(result.splitlines())
+
+
+async def apply_patch_via_git(
+ computing_environment: ComputingEnvironment, git_diff: str, is_error_logged: bool
+) -> None:
+ """Apply a diff to repo."""
+ if git_diff.strip() == "":
+ return
+ patch_file = _get_temp_patch_file()
+ await computing_environment.write_file(patch_file, git_diff)
+ # NOTE: --allow-empty is necessary because the patch may be empty, or result in no changes
+ # update (2024-11-22) --allow-empty is not available in the git version in our devcontainers
+ # so we have to do a janky error check below
+ try:
+ await computing_environment.run_git(("apply", "--verbose", str(patch_file)), is_error_logged=is_error_logged)
+ except RunCommandError as e:
+ raise PatchApplicationError(f"Failed to apply patch: {e}") from e
+ finally:
+ await computing_environment.delete_file(patch_file)
+
+
+async def get_git_diff(
+ computing_environment: ComputingEnvironment,
+ commit_hash: str | None = None,
+ staged: bool = False,
+ is_error_logged: bool = True,
+ include_binary: bool = True,
+) -> str:
+ """Get the diff for the current repo state."""
+ # make sure `is_stripped=False` otherwise patch can be invalid
+ command = ["diff", "--full-index"]
+ if include_binary:
+ # Without --binary, diffs of binary files will just contain a summary statement such as "Binary files a/file.bin and b/file.bin differ".
+ # Such diffs cannot be applied, but are useful for inclusion in LLM prompts.
+ command.append("--binary")
+ if staged:
+ command.append("--staged")
+ if commit_hash:
+ command.append(commit_hash)
+ return await computing_environment.run_git(command, is_stripped=False, is_error_logged=is_error_logged)
+
+
+async def get_diff_between_hashes(computing_environment: ComputingEnvironment, old_hash: str, new_hash: str) -> str:
+ """Get the diff between two git hashes."""
+ # make sure `is_stripped=False` otherwise patch can be invalid
+ return await computing_environment.run_git(
+ ["diff", "--full-index", "--binary", old_hash, new_hash], is_stripped=False
+ )
+
+
+async def get_patch_for_commit(computing_environment: ComputingEnvironment, commit_hash: str) -> str:
+ """Get the patch for a given commit hash."""
+ return await computing_environment.run_git(["show", "--pretty=format:", "--patch", commit_hash], is_stripped=False)
+
+
+async def get_untracked_files(
+ computing_environment: ComputingEnvironment,
+) -> tuple[str, ...]:
+ """Get the untracked files in the repo."""
+ result = await computing_environment.run_git(["ls-files", "--others", "--exclude-standard"], is_error_logged=False)
+ return tuple([line.strip() for line in result.splitlines() if line.strip()])
+
+
+async def get_untracked_file_diff(
+ computing_environment: ComputingEnvironment,
+ file_path: str,
+ include_binary: bool = True,
+) -> str:
+ """Get the diff for a untracked file.
+
+ Note this function will raise a RunCommandError if the there is no diff for the untracked file or if there
+ is another error running the command. So it is best to use this function after checking that the file is untracked
+ using get_untracked_files function.
+ """
+ command = ["diff", "--no-index"]
+ if include_binary:
+ command.append("--binary")
+ untracked_diff = await computing_environment.run_git(
+ command + ["/dev/null", str(file_path)],
+ # Unfortunately, `--no-index` implies `--exit-code`, which will cause git diff to return an exit code of 1
+ # if the diff is not empty. So we can't use check=True here. We'll check for an empty output to detect failures.
+ check=False,
+ is_error_logged=True,
+ is_stripped=False,
+ )
+ if not untracked_diff:
+ raise RunCommandError(f"Unable to diff untracked file {file_path}")
+ return untracked_diff
+
+
+async def get_staged_unstaged_and_combined_diffs(
+ computing_environment: ComputingEnvironment, base_commit: str = "HEAD"
+) -> tuple[str, str, str]:
+ """Get the staged diff, the unstaged diff, and the combined diff"""
+ staged_diff = await get_git_diff(computing_environment, staged=True)
+ unstaged_diff = await get_git_diff(computing_environment, staged=False)
+ combined_diff = await get_git_diff(computing_environment, commit_hash=base_commit)
+ return staged_diff, unstaged_diff, combined_diff
+
+
+async def get_unmerged_blob_hashes(
+ computing_environment: ComputingEnvironment,
+) -> tuple[str, ...]:
+ """Get the blob hashes of all the unmerged files in the repo."""
+ result = await computing_environment.run_command(
+ ["bash", "-c", "git ls-files --unmerged | awk '{print $2}' | sort -u"],
+ check=False,
+ )
+ return tuple(line.strip() for line in result.splitlines() if line.strip() != "")
+
+
+async def get_staged_blob_hashes(
+ computing_environment: ComputingEnvironment,
+) -> tuple[str, ...]:
+ """Get the blob hashes of all the staged files in the repo."""
+ staged_blob_hashes = await computing_environment.run_command(
+ [
+ "bash",
+ "-c",
+ 'staged_blobs=$(git diff --full-index --binary --cached --name-only --diff-filter=ACMRT | while read file; do git ls-files --stage "$file" | awk \'{print $2}\'; done); echo "$staged_blobs"',
+ ]
+ )
+ return tuple(line.strip() for line in staged_blob_hashes.splitlines() if line.strip() != "")
+
+
+async def get_blob_content_by_hash(computing_environment: ComputingEnvironment, blob_hash: str) -> bytes:
+ """Get the content of a blob by its hash."""
+ result = await computing_environment.run_git(["cat-file", "-p", blob_hash], is_stripped=False)
+ return result.encode("utf-8")
+
+
+async def get_unmerged_and_staged_blob_contents_by_hash(
+ computing_environment: ComputingEnvironment,
+) -> dict[str, bytes]:
+ """Get the contents of all the unmerged and staged blobs in the repo."""
+ unmerged_blob_hashes = await get_unmerged_blob_hashes(computing_environment)
+ staged_blob_hashes = await get_staged_blob_hashes(computing_environment)
+ all_relevant_blob_hashes = unmerged_blob_hashes + staged_blob_hashes
+ blob_content_tasks_by_hash = {
+ blob_hash: get_blob_content_by_hash(computing_environment, blob_hash) for blob_hash in all_relevant_blob_hashes
+ }
+ blob_contents = await asyncio.gather(*blob_content_tasks_by_hash.values())
+ return {
+ blob_hash: blob_content for blob_hash, blob_content in zip(blob_content_tasks_by_hash.keys(), blob_contents)
+ }
+
+
+async def write_blob_content(computing_environment: ComputingEnvironment, blob_hash: str, blob_content: bytes) -> None:
+ """Write the content of a blob to the repo."""
+ # write the blob content to a temp file
+ temp_file = anyio.Path(f"/tmp/{blob_hash}")
+ try:
+ await computing_environment.write_file(temp_file, blob_content, mode="wb")
+ # write the blob to the repo
+ result = await computing_environment.run_git(["hash-object", "-w", str(temp_file)])
+ assert result.strip() == blob_hash, f"Expected blob hash {blob_hash}, got {result.strip()}"
+ finally:
+ await computing_environment.delete_file(temp_file)
+
+
+async def write_blob_content_by_hash(
+ computing_environment: ComputingEnvironment, blob_content_by_hash: dict[str, bytes]
+) -> None:
+ """Write the content of all the blobs to the repo."""
+ tasks = []
+ for blob_hash, blob_content in blob_content_by_hash.items():
+ tasks.append(write_blob_content(computing_environment, blob_hash, blob_content))
+ await asyncio.gather(*tasks)
+
+
+async def get_modified_files_with_conflicts(
+ computing_environment: ComputingEnvironment,
+) -> tuple[str, ...]:
+ """Get the modified files with conflicts."""
+ commands = [
+ "diff --check --staged --full-index --binary",
+ "diff --check --full-index --binary",
+ ]
+ conflicted_files = set()
+ for command in commands:
+ result = await computing_environment.run_git(command.split(), check=False, is_error_logged=False)
+ # output is of the form:
+ # test.txt:2: leftover conflict marker
+
+ for line in result.splitlines():
+ parts = line.split(":", maxsplit=1)
+ if len(parts) == 2:
+ file_path, message = parts
+ if "leftover conflict marker" in message.strip():
+ conflicted_files.add(file_path)
+ return tuple(conflicted_files)
+
+
+async def get_conflicted_pathnames(
+ computing_environment: ComputingEnvironment,
+) -> tuple[str, ...]:
+ """Get the pathnames of all the conflicted files in the repo."""
+ result = await computing_environment.run_git(["diff", "--full-index", "--binary", "--name-only", "--diff-filter=U"])
+ return tuple(result.splitlines())
+
+
+async def get_conflicted_contents_by_path(
+ computing_environment: ComputingEnvironment,
+) -> dict[str, bytes]:
+ """Get the contents of all the conflicted files in the repo."""
+ conflicted_files = await get_conflicted_pathnames(computing_environment)
+ conflicted_contents_by_path: dict[str, bytes] = {}
+ for file_path in conflicted_files:
+ content = await computing_environment.read_file(file_path, mode="rb")
+ assert isinstance(content, bytes), f"Expected bytes, got {type(content)}"
+ conflicted_contents_by_path[file_path] = content
+ return conflicted_contents_by_path
+
+
+async def get_modified_pathnames(
+ computing_environment: ComputingEnvironment,
+) -> tuple[str, ...]:
+ """Get the pathnames of all the modified files in the repo."""
+ result = await computing_environment.run_command(["bash", "-c", "git status --porcelain | awk '{print $2}'"])
+ return tuple(result.splitlines())
+
+
+async def get_modified_file_contents_by_path(
+ computing_environment: ComputingEnvironment,
+) -> dict[str, bytes]:
+ """Get the contents of all the modified files in the repo."""
+ modified_pathnames = await get_modified_pathnames(computing_environment)
+ modified_file_contents_by_path: dict[str, bytes] = {}
+ for pathname in modified_pathnames:
+ content = await computing_environment.read_file(pathname, mode="rb")
+ assert isinstance(content, bytes), f"Expected bytes, got {type(content)}"
+ modified_file_contents_by_path[pathname] = content
+ return modified_file_contents_by_path
+
+
+async def get_repo_url(computing_environment: ComputingEnvironment) -> str:
+ repo_url = await computing_environment.run_git(["remote", "get-url", "origin"])
+ if repo_url.startswith("git@"):
+ # convert ssh url to https
+ repo_url = repo_url.replace(":", "/")
+ repo_url = f"https://{repo_url[4:]}"
+ if "https://oauth2:" in repo_url:
+ # remove the oauth2 prefix
+ # repo_url is something like https://oauth2:{token}@gitlab.com/.../.git
+ # change it to https://gitlab.com/.../.git
+ suffix = repo_url.split("@")[-1]
+ repo_url = "https://" + suffix
+ return repo_url
+
+
+async def get_main_branch_name_for_repo(
+ computing_environment: ComputingEnvironment, default_branch: str | None = None
+) -> str:
+ """Get the name of the main branch for the repo.
+
+ Attempts to detect whether the repository uses 'main', 'master', or another name
+ as its primary branch by checking for common branch names in order of preference.
+ """
+ possible_main_branches = ["main", "master", "trunk", "development"]
+
+ if default_branch is not None and default_branch not in possible_main_branches:
+ possible_main_branches.insert(0, default_branch)
+
+ # First check if any of the common main branch names exist
+ # and return the first one that does
+ for branch in possible_main_branches:
+ if await is_branch_exists(computing_environment, branch):
+ return branch
+
+ # If we couldn't find a common main branch, try to determine the default branch
+ # This gets the branch that HEAD points to in a newly cloned repo
+ default_remote_branch = await computing_environment.run_git(
+ ["symbolic-ref", "refs/remotes/origin/HEAD"], is_error_logged=False
+ )
+ if default_remote_branch:
+ # Format is typically refs/remotes/origin/main, so extract the last part
+ default_remote_branch = default_remote_branch.strip().split("/")[-1]
+ return default_remote_branch
+ raise ValueError("Could not detect main branch for repo.")
diff --git a/imbue_core/imbue_core/computing_environment/data_types.py b/imbue_core/imbue_core/computing_environment/data_types.py
@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+import subprocess
+from pathlib import Path
+from typing import Any
+
+import anyio
+
+# Use AnyPath type to match Sanctum
+AnyPath = Path | str | anyio.Path
+
+
+class RunCommandError(subprocess.CalledProcessError):
+ """Custom exception for errors encountered during Git commands."""
+
+ def __init__(self, *args: Any, **kwargs: Any) -> None:
+ self.cwd = kwargs.get("cwd", None)
+ if "cwd" in kwargs:
+ del kwargs["cwd"]
+ super().__init__(*args, **kwargs)
+
+ def __str__(self) -> str:
+ return f"Command `{self.cmd}` returned non-zero exit status {self.returncode}.\nOutput: {self.stdout}\nError: {self.stderr}\nCWD: {self.cwd}"
+
+
+class PatchApplicationError(Exception):
+ """Custom exception for errors encountered during patch application."""
+
+
+class FailedToMakeCommitError(Exception):
+ """Custom exception for errors encountered during commit creation."""
diff --git a/imbue_core/imbue_core/conftest.py b/imbue_core/imbue_core/conftest.py
@@ -0,0 +1,41 @@
+import contextlib
+from pathlib import Path
+from tempfile import NamedTemporaryFile
+from typing import Generator
+from typing import Iterator
+
+import pytest
+
+from imbue_core.log_utils import ensure_core_log_levels_configured
+from imbue_core.test_repo_utils import make_test_data_mock_repo
+
+
+@pytest.fixture(scope="session", autouse=True)
+def setup_logging_and_secrets() -> None:
+ ensure_core_log_levels_configured()
+
+
+@contextlib.contextmanager
+def create_temp_file(contents: str, suffix: str, root_dir: Path) -> Generator[Path, None, None]:
+ with NamedTemporaryFile(mode="w", suffix=suffix, dir=root_dir, delete=False) as temp_file:
+ temp_file.write(contents)
+ temp_file.flush()
+ yield Path(temp_file.name)
+ temp_file.close()
+ Path(temp_file.name).unlink()
+
+
+mock_repo = pytest.fixture(make_test_data_mock_repo)
+
+
+@contextlib.contextmanager
+def dummy_exception_manager() -> Iterator[None]:
+ """
+ Use with patch to disable exception managing for LLM calls.
+ Useful when you want a test to fail fast.
+
+ Example:
+ with patch("imbue_core.agents.llm_apis.anthropic_api._anthropic_exception_manager", dummy_exception_manager):
+ # your test code here
+ """
+ yield
diff --git a/imbue_core/imbue_core/constants.py b/imbue_core/imbue_core/constants.py
@@ -0,0 +1,21 @@
+from enum import StrEnum
+from pathlib import Path
+
+IMBUE_TOML_PATH = Path("~/.imbue/config.toml").expanduser()
+
+DEFAULT_PROJECT_SECRET_PATH = Path(".env")
+
+LOW_PRIORITY_LEVEL = 37
+MEDIUM_PRIORITY_LEVEL = 38
+HIGH_PRIORITY_LEVEL = 39
+
+DISCORD_URL = "https://discord.gg/sBAVvHPUTE"
+
+
+class ExceptionPriority(StrEnum):
+ # for issues that will result in the app crashing
+ HIGH_PRIORITY = "HIGH_PRIORITY"
+ # for issues that will cause major functionality to stop working
+ MEDIUM_PRIORITY = "MEDIUM_PRIORITY"
+ # everything else -- e.g. exception sites that are typically retriable, but may catch something unrecoverable
+ LOW_PRIORITY = "LOW_PRIORITY"
diff --git a/imbue_core/imbue_core/data_types.py b/imbue_core/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 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/error_utils.py b/imbue_core/imbue_core/error_utils.py
@@ -0,0 +1,570 @@
+import functools
+import os
+import re
+import sys
+import threading
+import time
+import traceback
+from collections.abc import Callable
+from collections.abc import Collection
+from collections.abc import Hashable
+from enum import StrEnum
+from typing import Any
+from typing import Iterable
+from typing import Mapping
+from typing import MutableMapping
+from typing import assert_never
+
+import sentry_sdk
+import sentry_sdk.utils
+import traceback_with_variables
+from loguru import logger
+from pydantic import Field
+from pydantic import PrivateAttr
+from sentry_sdk import HttpTransport
+from sentry_sdk.attachments import Attachment
+from sentry_sdk.consts import EndpointType
+from sentry_sdk.envelope import Envelope
+from sentry_sdk.integrations.stdlib import StdlibIntegration
+from sentry_sdk.types import Event
+from sentry_sdk.types import Hint
+from traceback_with_variables import Format
+
+from imbue_core.common import truncate_string
+from imbue_core.pydantic_serialization import FrozenModel
+from imbue_core.pydantic_serialization import MutableModel
+from imbue_core.s3_uploader import upload_to_s3
+from imbue_core.sentry_loguru_handler import SENTRY_LOG_FORMAT
+from imbue_core.sentry_loguru_handler import SentryBreadcrumbHandler
+from imbue_core.sentry_loguru_handler import SentryEventHandler
+from imbue_core.sentry_loguru_handler import SentryLoguruLoggingLevels
+from imbue_core.sentry_loguru_handler import log_error_inside_sentry
+
+try:
+ import brotli # type: ignore
+except ImportError:
+ brotli = None
+
+
+# sentry's size limits are annoyingly hard to evaluate before sending the event. we'll just try to be conservative.
+# https://docs.sentry.io/concepts/data-management/size-limits/
+# https://develop.sentry.dev/sdk/data-model/envelopes/#size-limits
+MAX_SENTRY_ATTACHMENT_SIZE = 10 * 1024 * 1024
+
+
+class SentryEventRejected(Exception):
+ pass
+
+
+class ExceptionKey(FrozenModel):
+ exception_type: type[BaseException] | None
+ exception_args: tuple[Hashable, ...]
+
+ @classmethod
+ def build_from_exception_or_fingerprint(
+ cls, exception: BaseException | None, log_fingerprint: str | None
+ ) -> "ExceptionKey":
+ if exception is None:
+ return cls(
+ exception_type=None,
+ exception_args=(log_fingerprint,),
+ )
+ else:
+ return cls(
+ exception_type=type(exception),
+ # FIXME: we may grab things with references here unnecessarily. Let's store only the hash here and stringified representation.
+ exception_args=tuple(arg for arg in exception.args if isinstance(arg, Hashable)),
+ )
+
+
+class ExceptionHistory(MutableModel):
+ total_sent: int = 0
+ total_throttled: int = 0
+
+ last_reported_at: float | None = None # monotonic clock value
+ throttled_since_last_report: int = 0
+
+ @property
+ def since_last_report(self) -> float:
+ last_reported_at = self.last_reported_at
+ if last_reported_at is None:
+ return float("inf")
+ return time.monotonic() - last_reported_at
+
+ def log_throttled(self):
+ self.throttled_since_last_report += 1
+ self.total_throttled += 1
+
+ def log_reported(self):
+ self.last_reported_at = time.monotonic()
+ self.throttled_since_last_report = 0
+ self.total_sent += 1
+
+
+def _first_line_of_log_message(event: Event) -> str | None:
+ """Extracts the first line of the log message from the event, if any."""
+ message = event.get("logentry", {}).get("message")
+ if message and isinstance(message, str):
+ message_lines = message.strip().splitlines()
+ if message_lines:
+ return message_lines[0]
+ return None
+
+
+def _get_full_location_from_event(event: Event) -> str | None:
+ """Extracts the `full_location` field that we are supposed to generate in our log handlers."""
+ extra = event.get("extra", {}).get("extra")
+ if extra and isinstance(extra, dict):
+ full_location = extra.get("full_location")
+ if full_location and isinstance(full_location, str):
+ return full_location.strip() or None
+ return None
+
+
+class _ReasonToAllowSendingEvent(StrEnum):
+ PASS_THRU = "pass_thru"
+ NO_RATE_LIMIT_INFO = "no_rate_limit_info"
+ TOO_MANY_TRACKED_EXCEPTIONS = "too_many_tracked_exceptions"
+ INITIAL = "initial"
+ INITIAL_GRACE_PERIOD = "initial_grace_period"
+ TIMEOUT_ELAPSED = "timeout_elapsed"
+
+
+class _SentryEventRateLimiter(MutableModel):
+ """Prevent logging the same specific exceptions multiple times to sentry.
+
+ Each allowed exception is assumed to be sent.
+ """
+
+ # these exception will never be rate limited
+ pass_thru_exception_types: Collection[type[BaseException]] = Field(default_factory=set)
+ # the number of initial reports to allow before starting to apply rate limiting
+ initial_reports_without_rate_limiting: int = 2
+ # the time (in seconds) that must pass since the last report of a given exception before allowing
+ # another report it is multiplied by the number of times the exception has been passed-thru since
+ # the app start after the first throttling event
+ timeout_factor: float = 60.0
+ # maximum number of different exceptions to track for rate limiting
+ # once this number is exceeded, all events will be passed through unfiltered
+ max_tracked_rate_limited_exceptions: int = 10_000
+
+ # we should not be called in parallel, but better safe than sorry
+ # this lock protects access to _exception_history, its contents, and the total counters
+ _lock: threading.Lock = PrivateAttr(default_factory=threading.Lock)
+ _exception_history: MutableMapping[ExceptionKey, ExceptionHistory] = PrivateAttr(default_factory=dict)
+ _total_throttled: int = PrivateAttr(default=0)
+ _total_sent: int = PrivateAttr(default=0)
+
+ def _annotate_event(
+ self,
+ event: Event,
+ reason_to_allow: _ReasonToAllowSendingEvent,
+ past_history: ExceptionHistory | None = None,
+ ) -> Event:
+ logger.trace("Annotating event with rate limiter: {}", reason_to_allow)
+
+ annotation: dict[str, Any] = {
+ "reason_to_allow": reason_to_allow.value,
+ "application": {
+ "total_throttled": self._total_throttled,
+ "total_sent": self._total_sent,
+ "total_tracked": len(
+ self._exception_history
+ ), # thread-safe to read without lock since we don't care about consistency
+ },
+ }
+ if past_history is not None:
+ annotation["instance"] = {
+ "since_last_report": past_history.since_last_report,
+ "throttled_since_last_report": past_history.throttled_since_last_report,
+ "total_throttled": past_history.total_throttled,
+ "total_sent": past_history.total_sent,
+ }
+
+ event.setdefault("extra", {})
+ event["extra"]["rate_limiter"] = annotation
+
+ event.setdefault("tags", {})
+ event["tags"]["rate_limiter_reason_to_allow"] = reason_to_allow
+ return event
+
+ def before_send(self, event: Event, hint: Hint) -> Event | None:
+ annotated_event = self._before_send(event, hint)
+ with self._lock:
+ if annotated_event is None:
+ self._total_throttled += 1
+ else:
+ self._total_sent += 1
+
+ return annotated_event
+
+ def _before_send(self, event: Event, hint: Hint) -> Event | None:
+ exception = None
+ exception_type = None
+ # see sentry_sdk._types.ExcInfo which sadly we can't import
+ if "exc_info" in hint:
+ exception_type, exception, _ = hint["exc_info"]
+
+ if (exception_type is not None) and (exception_type in self.pass_thru_exception_types):
+ return self._annotate_event(event, _ReasonToAllowSendingEvent.PASS_THRU)
+
+ first_line = _first_line_of_log_message(event)
+ full_location = _get_full_location_from_event(event)
+ if first_line and full_location:
+ log_fingerprint = "\n".join([first_line, full_location])
+ else:
+ log_fingerprint = None
+
+ if not (log_fingerprint or exception):
+ # nothing to rate limit on
+ return self._annotate_event(event, _ReasonToAllowSendingEvent.NO_RATE_LIMIT_INFO)
+
+ key = ExceptionKey.build_from_exception_or_fingerprint(exception, log_fingerprint)
+ with self._lock:
+ if key not in self._exception_history:
+ # we could LRU but if we got to this point, there's something else to figure out, like bad keying
+ if len(self._exception_history) >= self.max_tracked_rate_limited_exceptions:
+ return self._annotate_event(event, _ReasonToAllowSendingEvent.TOO_MANY_TRACKED_EXCEPTIONS)
+ history = ExceptionHistory(last_reported_at=time.monotonic(), total_sent=1)
+ self._exception_history[key] = history
+ return self._annotate_event(event, _ReasonToAllowSendingEvent.INITIAL)
+
+ history = self._exception_history[key]
+ reason_to_allow: _ReasonToAllowSendingEvent | None = None
+ if history.total_sent < self.initial_reports_without_rate_limiting:
+ reason_to_allow = _ReasonToAllowSendingEvent.INITIAL_GRACE_PERIOD
+ else:
+ current_timeout = self.timeout_factor * max(
+ 1,
+ history.total_sent - self.initial_reports_without_rate_limiting + 1,
+ )
+ if history.since_last_report >= current_timeout:
+ logger.trace("Timeout elapsed for event: {}, {}", key, current_timeout)
+ reason_to_allow = _ReasonToAllowSendingEvent.TIMEOUT_ELAPSED
+
+ if reason_to_allow:
+ event = self._annotate_event(event, reason_to_allow=reason_to_allow, past_history=history)
+ history.log_reported()
+ return event
+ history.log_throttled()
+
+ logger.trace("Rate limiting event: {}", key)
+ return None
+
+
+class ImbueSentryHttpTransport(HttpTransport):
+ """The sentry python sdk has pretty lame behavior if the event is too large.
+ It'll just drop it, and record stats indicating that an event was dropped.
+ You can see these at `https://generally-intelligent-e3.sentry.io/stats`, category "invalid".
+ But there's no way to recover any information about the dropped event.
+
+ We could try to just ensure the events don't violate the size limit, which we try to do,
+ but their size limits are a bit complicated and thus hard to pre-verify. So we also want to know if anything slips through.
+
+ The actual sentry web API does return a status code (413) if the event was rejected,
+ so we need to handle this at the level of the sentry HttpTransport and do something with it.
+ """
+
+ def _send_request(
+ self,
+ body: bytes,
+ headers: dict[str, str],
+ endpoint_type: EndpointType = EndpointType.ENVELOPE,
+ envelope: Envelope | None = None,
+ ) -> None:
+ """This is a copy of the original `_send_request` method from the HttpTransport class,
+ with a hook to call `on_too_large_event` added.
+ """
+
+ def record_loss(reason: str) -> None:
+ if envelope is None:
+ self.record_lost_event(reason, data_category="error")
+ else:
+ envelope_items = envelope.items
+ assert envelope_items is not None
+ for item in envelope_items:
+ self.record_lost_event(reason, item=item)
+
+ headers.update(
+ {
+ "User-Agent": str(self._auth.client),
+ "X-Sentry-Auth": str(self._auth.to_header()),
+ }
+ )
+ try:
+ response = self._request(
+ "POST",
+ endpoint_type,
+ body,
+ headers,
+ )
+ except Exception:
+ self.on_dropped_event("network")
+ record_loss("network_error")
+ raise
+
+ try:
+ self._update_rate_limits(response)
+
+ if response.status == 429:
+ # if we hit a 429. Something was rate limited but we already
+ # acted on this in `self._update_rate_limits`. Note that we
+ # do not want to record event loss here as we will have recorded
+ # an outcome in relay already.
+ self.on_dropped_event("status_429")
+
+ elif response.status >= 300 or response.status < 200:
+ sentry_sdk.utils.logger.error(
+ "Unexpected status code: %s (body: %s)",
+ response.status,
+ getattr(response, "data", getattr(response, "content", None)),
+ )
+ self.on_dropped_event("status_{}".format(response.status))
+ record_loss("network_error")
+
+ if response.status == 413:
+ assert envelope is not None
+ self.on_too_large_event(body, envelope)
+ finally:
+ response.close()
+
+ def on_too_large_event(self, body: bytes, envelope: Envelope) -> None:
+ """we want to log _something_ to sentry, because otherwise we have no idea what happened,
+ but we also need to be super careful that this fallback doesn't itself fail.
+
+ exceptions raised here will simply get eaten and result in nothing getting logged to sentry,
+ both due to sentry's usage of `capture_internal_exceptions`
+ and that we're running in a worker thread and i don't think they make an effort to re-surface exceptions from threads.
+ """
+ msg = "request was too large to send to sentry"
+ try:
+ raise SentryEventRejected(msg)
+ except SentryEventRejected as e:
+ stripped_envelope = Envelope(headers=envelope.headers)
+ attachment_sizes = {}
+ envelope_items = envelope.items
+ assert envelope_items is not None
+ for item in envelope_items:
+ if item.data_category == "attachment":
+ payload = item.payload
+ payload_bytes_len = len(payload.get_bytes() if not isinstance(payload, (bytes, str)) else payload)
+ item_headers = item.headers
+ assert item_headers is not None
+ attachment_sizes[item_headers["filename"]] = payload_bytes_len
+ continue
+ stripped_envelope.add_item(item)
+ # this is uncompressed (so we can inspect it)
+ serialized_stripped_envelope = stripped_envelope.serialize()
+
+ extra: dict[str, str | int] = {
+ "uncompressed_attachment_sizes": str(attachment_sizes),
+ "original_compressed_request_body_size": len(body),
+ "uncompressed_stripped_envelope_size": len(serialized_stripped_envelope),
+ }
+
+ # send stripped envelope to S3 -- is preceding code now overkill?
+ upload_name = upload_to_s3("stripped_envelope", ".txt", serialized_stripped_envelope)
+
+ log_error_inside_sentry(
+ e,
+ msg,
+ extra=extra,
+ additional_s3_uploads=(upload_name,) if upload_name else None,
+ )
+
+
+def get_traceback_with_vars(exception: BaseException | None = None) -> str:
+ # be careful of potential performance regressions with increasing these limits
+ tb_format = Format(max_value_str_len=100_000, max_exc_str_len=2_000_000)
+ if exception is None:
+ # no exception passed in; get the current exception. this will still be None if not in an exception handler
+ exception = sys.exception()
+ try:
+ if exception is not None:
+ # we are in an exception handler, use that for the traceback
+ # for some reason this breaks when casting to an `Exception`, so just using type: ignore
+ return traceback_with_variables.format_exc(exception, fmt=tb_format) # type: ignore
+ else:
+ # not in an exception handler, just get the current stack
+ return traceback_with_variables.format_cur_tb(fmt=tb_format)
+ except Exception as e:
+ return (
+ f"got exception while formatting traceback with `traceback_with_variables`: {traceback.format_exception(e)}"
+ )
+
+
+def _default_sentry_add_extra_info_hook(event: Event, hint: Hint) -> tuple[Event, Hint, tuple[Callable, ...]]:
+ """Add traceback with variables to the event as an attachment."""
+ # TODO: We just use sentry attachments here; we could also upload to S3, but figure this hook is itself a fallback, so leaving it for now?
+ expected_attachments = []
+ tb_with_vars = truncate_string(get_traceback_with_vars(), MAX_SENTRY_ATTACHMENT_SIZE)
+ hint["attachments"].append(Attachment(tb_with_vars.encode(), filename="traceback_with_variables.txt"))
+ expected_attachments.append("traceback_with_variables.txt")
+ # record the names of the expected attachments just in case there's any weirdness about attachments not showing up
+ event.setdefault("extra", {})["expected_attachments"] = str(expected_attachments)
+ return event, hint, ()
+
+
+# We define BeforeSendType here to be one or more callables that match the signature of sentry's before_send hook.
+# The event will be passed through each one in our wrapping code.
+BaseBeforeSendType = Callable[[Event, Hint], Event | None]
+BeforeSendType = BaseBeforeSendType | list[BaseBeforeSendType]
+
+
+# NOTE: if the actual event (without attachments) being too large is a problem, then it will be handled
+# in our custom logic in ImbueSentryHttpTransport above.
+def _before_send_wrapper(
+ event: Event,
+ hint: Hint,
+ before_send_list: Iterable[BaseBeforeSendType],
+) -> Event | None:
+ try:
+ for before_send in before_send_list:
+ # pyre-fixme[9]: the result of before_send can be None which is not compatible with event annotation.
+ event = before_send(event, hint)
+ if event is None:
+ return None
+
+ return event
+ except Exception as e:
+ # It is critical that we catch errors here and print them, because this is called from sentry
+ # Failing to do so means that we will see NOTHING about the failure!
+ # See this PR for more: https://gitlab.com/generally-intelligent/generally_intelligent/-/merge_requests/5789
+ #
+ # Questions to the above:
+ # - why are we not relying on the Sentry's logger for this?
+ # - won't the call to `logger.exception` itself try to send something to Sentry causing recursion?
+ # - the following message will likely hit an error inside Loguru handler because it is not allowed
+ # to call emit from inside emit (that's what we're in here).
+ logger.exception("Failure when processing event in before_send hook: {}", e)
+ # NOTE: this re-raise will get suppressed by Sentry and treated as if `before_send` returned `None`
+ raise
+
+
+def _fixup_release_id(release_id: str) -> str:
+ """
+ For pre-release release candidate versions, Sentry requires the release ID to be in the semver format.
+
+ E.g. "0.1.0rc1" should be converted to "0.1.0-rc.1".
+
+ """
+ return re.sub(r"(\d+\.\d+\.\d+)rc(\d+)", r"\1-rc.\2", release_id)
+
+
+def setup_sentry(
+ dsn: str,
+ release_id: str,
+ global_user_context: Mapping[str, str] | None = None,
+ integrations: tuple[Any, ...] = (),
+ before_send: BeforeSendType | None = None,
+ add_extra_info_hook: Callable[[Event, Hint], tuple[Event, Hint, tuple[Callable, ...]]] | None = None,
+ environment: str | None = None,
+) -> None:
+ """Sets up the main Sentry instance for this process.
+
+ This should be done *after* setting up normal loguru loggers, to ensure that sentry handling happens after normal logging.
+ In case the sentry stuff hangs or something odd, we want to make sure to at least get regular log output.
+
+ Args:
+ ...
+ add_extra_info_hook: If provided, this function will be called with the event at Handle time.
+ before_send: If provided, this function (or list of functions) will be called in order to handle and mutate the event before sending to Sentry.
+ """
+ assert (
+ "SENTRY_DSN" not in os.environ
+ ), "Please `unset SENTRY_DSN` in your environment. Set the DSN via the server settings FRONTEND_SENTRY_DSN and BACKEND_SENTRY_DSN instead."
+
+ before_send_unrolled = []
+
+ if isinstance(before_send, list):
+ before_send_unrolled = list(before_send)
+ elif callable(before_send):
+ before_send_unrolled = [before_send]
+ elif before_send is None:
+ pass
+ else:
+ assert_never(before_send)
+
+ # NOTE: the rate limiter object's lifetime is maintained by being captured in the
+ # closure of the before_send function
+ rate_limiter = _SentryEventRateLimiter()
+ before_send_unrolled.append(rate_limiter.before_send)
+
+ before_send = functools.partial(
+ _before_send_wrapper,
+ before_send_list=before_send_unrolled,
+ )
+
+ sentry_sdk.init(
+ sample_rate=1.0,
+ environment=environment,
+ traces_sample_rate=1.0,
+ # required for `logger.error` calls to include stacktraces
+ attach_stacktrace=True,
+ # note this will capture unhandled exceptions even if not explicitly logged, among other things
+ # https://docs.sentry.io/platforms/python/integrations/default-integrations/
+ default_integrations=True,
+ # this doesn't affect the default integrations, but prevents any other ones from being added automatically
+ auto_enabling_integrations=False,
+ integrations=[
+ *integrations,
+ ],
+ disabled_integrations=[
+ # this only adds hooks to subprocess and httplib, which imo just adds noisy breadcrumbs.
+ StdlibIntegration()
+ ],
+ dsn=dsn,
+ # may want to get more restrictive about this in the future
+ # see https://docs.sentry.io/platforms/python/data-management/data-collected/
+ send_default_pii=True,
+ # sentry has a max payload size of 1MB, so we can't make this infinite
+ max_value_length=10_000,
+ add_full_stack=True,
+ before_send=before_send,
+ release=_fixup_release_id(release_id),
+ # default is 100; can't make it too large because total event size must be <1MB
+ max_breadcrumbs=100,
+ # if the locals is very large, sentry gets to be quite slow to log errors if this is enabled.
+ # we log our own traceback_with_variables anyways.
+ include_local_variables=False,
+ transport=ImbueSentryHttpTransport,
+ )
+ logger.info("Sentry initialized")
+
+ if global_user_context is not None:
+ sentry_sdk.set_user(dict(global_user_context))
+
+ # capture loguru errors/exceptions with a custom handler
+ min_sentry_level: int = SentryLoguruLoggingLevels.LOW_PRIORITY.value
+ handler = SentryEventHandler(
+ level=min_sentry_level,
+ add_extra_info_hook=add_extra_info_hook or _default_sentry_add_extra_info_hook,
+ )
+ register_sentry_event_handler(handler)
+ logger.add(
+ handler,
+ level=min_sentry_level,
+ diagnose=False,
+ format=SENTRY_LOG_FORMAT,
+ )
+ # capture lower level loguru messages to add as breadcrumbs on events
+ # the extra info is not helpful here and makes the breadcrumbs larger; they're still available in the log file attachment
+ breadcrumb_level: int = SentryLoguruLoggingLevels.INFO.value
+ logger.add(
+ SentryBreadcrumbHandler(level=breadcrumb_level, strip_extra=True),
+ level=breadcrumb_level,
+ diagnose=False,
+ format=SENTRY_LOG_FORMAT,
+ )
+
+
+_SENTRY_EVENT_HANDLER: SentryEventHandler | None = None
+
+
+def register_sentry_event_handler(handler: SentryEventHandler) -> None:
+ global _SENTRY_EVENT_HANDLER
+ _SENTRY_EVENT_HANDLER = handler
+
+
+def get_sentry_event_handler() -> SentryEventHandler | None:
+ return _SENTRY_EVENT_HANDLER
diff --git a/imbue_core/imbue_core/errors.py b/imbue_core/imbue_core/errors.py
@@ -0,0 +1,36 @@
+"""This module contains the base errors for the Imbue applications.
+
+Please subclass from one of the errors in this module for errors that you expect other Imbumans to handle.
+"""
+
+from typing import Any
+
+
+class ImbueRuntimeException(BaseException):
+ """Base class for all things that could go wrong within Imbue code.
+
+ An ImbueRuntimeException may or may not be recoverable. Most library code should not be throwing
+ ImbueRuntimeExceptions.
+ """
+
+ _was_logged_by_log_exception: bool
+
+ def __init__(self, *args: Any) -> None:
+ super().__init__(*args)
+ # New instances start out marked as not-yet-logged.
+ self._was_logged_by_log_exception = False
+
+
+class ImbueError(ImbueRuntimeException, Exception):
+ """Base class for all errors that could possibly be handled.
+
+ When you are building the external API of your subcomponent, the errors you throw should subclass from this.
+ """
+
+
+class ExpectedError(ImbueError):
+ """Base class for all Imbue errors that we expect to be handled.
+
+ Use this subclass of ImbueError to represent an exception that -must- be handled by a caller. The usual use-case is
+ when caller and callee are part of the same subsystem.
+ """
diff --git a/imbue_core/imbue_core/fixed_traceback.py b/imbue_core/imbue_core/fixed_traceback.py
@@ -0,0 +1,35 @@
+from types import TracebackType
+from typing import Any
+from typing import Self
+from typing import cast
+
+from tblib import Traceback
+
+
+class FixedTraceback(Traceback):
+ """
+ This class exists mostly to fix a bug in tblib where tb_lasti is not properly initialized.
+ We don't care about that value, so we just set it to -1.
+
+ While I was at it, I also fixed the types for the methods we use.
+ """
+
+ def __init__(self, tb: TracebackType) -> None:
+ super().__init__(tb)
+ tb_next = self
+ while tb_next:
+ setattr(tb_next, "tb_lasti", -1)
+ tb_next = tb_next.tb_next
+
+ def as_traceback(self) -> TracebackType | None:
+ return cast(TracebackType | None, super().as_traceback())
+
+ @classmethod
+ def from_tb(cls, tb: TracebackType) -> Self:
+ result = cls(tb)
+ return result
+
+ @classmethod
+ def from_dict(cls, dct: dict[str, Any]) -> Self:
+ # pyre-fixme[11]: pyre seems to have some trouble with Self in some specific cases, including cast
+ return cast(cls, super().from_dict(dct))
diff --git a/imbue_core/imbue_core/frozen_utils.py b/imbue_core/imbue_core/frozen_utils.py
@@ -0,0 +1,141 @@
+from abc import ABC
+from abc import abstractmethod
+from copy import deepcopy
+from functools import cached_property
+from typing import Any
+from typing import Iterable
+from typing import Mapping
+from typing import NoReturn
+from typing import Protocol
+from typing import Sequence
+from typing import TYPE_CHECKING
+from typing import TypeAlias
+from typing import TypeVar
+from typing import cast
+
+if TYPE_CHECKING:
+ from _typeshed import SupportsKeysAndGetItem
+
+
+class _SupportsLessThan(Protocol):
+ def __lt__(self, __other: Any) -> bool: ...
+
+
+T = TypeVar("T")
+TV = TypeVar("TV")
+TK = TypeVar("TK", bound=_SupportsLessThan)
+
+
+class FrozenMapping(Mapping[T, TV], ABC):
+ @abstractmethod
+ def __hash__(self) -> int: ...
+
+
+class FrozenDict(dict[T, TV], FrozenMapping[T, TV]):
+ def _key(self) -> frozenset[tuple[T, TV]]:
+ return frozenset(self.items())
+
+ @cached_property
+ def _hash(self) -> int:
+ # bawr said it should be fine
+ return hash(self._key())
+
+ def __hash__(self) -> int: # type: ignore
+ return self._hash
+
+ def _mutation_error(self, method: str) -> RuntimeError:
+ return RuntimeError(f"Cannot call mutation method {method} on _FrozenDict {self}")
+
+ def __setitem__(self, __name: T, __value: TV) -> NoReturn:
+ raise self._mutation_error("__setitem__")
+
+ def __delitem__(self, __name: T) -> NoReturn:
+ raise self._mutation_error("__delitem__")
+
+ # pyre-fixme[14]: pyre thinks this is an inconsistent override and i don't feel like fixing it because the function ignores its arguments
+ def update(
+ self,
+ __m: "SupportsKeysAndGetItem[T, TV] | Iterable[tuple[T, TV]]" = (),
+ **kwargs: TV,
+ ) -> NoReturn:
+ raise self._mutation_error("update")
+
+ # pyre-fixme[14]: pyre thinks this is an inconsistent override and i don't feel like fixing it because the function ignores its arguments
+ def setdefault(self, *args: Any, **kwargs: Any) -> NoReturn:
+ raise self._mutation_error("setdefault")
+
+ # pyre-fixme[14]: pyre thinks this is an inconsistent override and i don't feel like fixing it because the function ignores its arguments
+ def pop(self, *args: Any, **kwargs: Any) -> NoReturn:
+ raise self._mutation_error("pop")
+
+ # pyre-ignore[15]: pyre doesn't like overriding the return type with NoReturn
+ def popitem(self) -> NoReturn:
+ raise self._mutation_error("popitem")
+
+ # pyre-ignore[15]: pyre doesn't like overriding the return type with NoReturn
+ def clear(self) -> NoReturn:
+ raise self._mutation_error("clear")
+
+ def __repr__(self) -> str:
+ return f"_FrozenDict({super().__repr__()})"
+
+ def __copy__(self) -> "FrozenDict":
+ return type(self)(self)
+
+ def __deepcopy__(self, memo: dict[int, Any]) -> "FrozenDict":
+ memo[id(self)] = self
+ copied_items = ((deepcopy(key, memo), deepcopy(value, memo)) for key, value in self.items())
+ return type(self)(copied_items)
+
+ def __reduce__(self) -> tuple[Any, ...]:
+ return (FrozenDict, (dict(self),))
+
+
+def empty_mapping() -> FrozenDict[Any, Any]:
+ return FrozenDict()
+
+
+def deep_freeze_mapping(mapping: Mapping[T, TV]) -> FrozenDict[T, Any]:
+ return FrozenDict({key: cast(TV, _deep_freeze_any(value)) for key, value in mapping.items()})
+
+
+def _freeze_iterable_values(iterable: Iterable[T]) -> Iterable[Any]:
+ return (cast(T, _deep_freeze_any(value)) for value in iterable)
+
+
+def deep_freeze_set(input_set: set[T] | frozenset[T]) -> frozenset[Any]:
+ return frozenset(_freeze_iterable_values(input_set))
+
+
+def _deep_freeze_any(input_object: object) -> object:
+ if isinstance(input_object, Mapping):
+ return deep_freeze_mapping(input_object)
+
+ if isinstance(input_object, (set, frozenset)):
+ return deep_freeze_set(input_object)
+
+ if isinstance(input_object, Iterable) and not isinstance(input_object, str) and not isinstance(input_object, bytes):
+ return tuple(_freeze_iterable_values(input_object))
+
+ return input_object
+
+
+def deep_freeze_sequence(sequence: Sequence[T]) -> tuple[Any, ...]:
+ return tuple(_freeze_iterable_values(sequence))
+
+
+# Recursive type alias that captures the possible types of JSON objects (e.g. from json.loads).
+JSON: TypeAlias = "str | int | bool | float | None | dict[str, JSON] | list[JSON]"
+
+
+# Immutable version of JSON.
+FrozenJSON: TypeAlias = "str | int | bool | float | None | FrozenDict[str, FrozenJSON] | tuple[FrozenJSON, ...]"
+
+
+def deep_freeze_json(json: JSON) -> FrozenJSON:
+ if isinstance(json, dict):
+ return FrozenDict({k: deep_freeze_json(v) for k, v in json.items()})
+ elif isinstance(json, list):
+ return tuple(deep_freeze_json(v) for v in json)
+ else:
+ return json
diff --git a/imbue_core/imbue_core/git.py b/imbue_core/imbue_core/git.py
@@ -0,0 +1,587 @@
+"""Utility abstractions for interacting with git repositories."""
+
+from __future__ import annotations
+
+import asyncio
+import contextlib
+import shlex
+import shutil
+import subprocess
+import sys
+from asyncio.subprocess import PIPE
+from asyncio.subprocess import STDOUT
+from contextlib import asynccontextmanager
+from io import StringIO
+from pathlib import Path
+from types import TracebackType
+from typing import Any
+from typing import AsyncGenerator
+from typing import AsyncIterator
+from typing import Self
+from typing import Sequence
+from typing import TYPE_CHECKING
+from typing import TextIO
+
+import anyio
+import attr
+from loguru import logger
+
+from imbue_core.async_monkey_patches import log_exception
+from imbue_core.async_utils import sync
+from imbue_core.async_utils import sync_contextmanager_func
+from imbue_core.computing_environment.computing_environment import assert_repo_is_clean
+from imbue_core.computing_environment.computing_environment import get_head_hash
+from imbue_core.computing_environment.computing_environment import git_add
+from imbue_core.computing_environment.computing_environment import is_repo_dirty
+from imbue_core.computing_environment.computing_environment import make_commit
+from imbue_core.computing_environment.computing_environment import (
+ restore_all_staged_files,
+)
+from imbue_core.computing_environment.computing_environment import (
+ restore_all_unstaged_changes,
+)
+from imbue_core.computing_environment.computing_environment import (
+ run_command_with_retry_on_git_lock_error,
+)
+from imbue_core.computing_environment.data_types import AnyPath
+from imbue_core.computing_environment.data_types import RunCommandError
+
+if TYPE_CHECKING:
+ # for proper file mode typing
+ from _typeshed import OpenBinaryMode
+ from _typeshed import OpenBinaryModeReading
+ from _typeshed import OpenBinaryModeWriting
+ from _typeshed import OpenTextMode
+ from _typeshed import OpenTextModeReading
+ from _typeshed import OpenTextModeWriting
+
+PYTHON_EXTENSION = ".py"
+
+
+def is_path_in_git_repo(path: Path) -> bool:
+ """Check if a path is in a git repository."""
+ if path.is_file():
+ path = path.parent
+ completed_process = subprocess.run(
+ ["git", "-C", path, "rev-parse", "--is-inside-work-tree"],
+ stdin=subprocess.DEVNULL,
+ stdout=subprocess.PIPE,
+ stderr=subprocess.PIPE,
+ check=False,
+ )
+ if completed_process.returncode != 0:
+ return False
+ result = completed_process.stdout.decode().strip()
+ assert result in ("true", "false"), result
+ return result == "true"
+
+
+def get_git_repo_root() -> Path:
+ """Gets a Path to the current git repo root, assuming that our cwd is somewhere inside the repo."""
+ completed_process = subprocess.run(
+ ("git", "rev-parse", "--show-toplevel"),
+ stdin=subprocess.DEVNULL,
+ stdout=subprocess.PIPE,
+ stderr=subprocess.PIPE,
+ check=True,
+ )
+ root_dir = Path(completed_process.stdout.decode().strip())
+ assert root_dir.is_dir(), f"{root_dir} must be a directory"
+ return root_dir
+
+
+def get_git_repo_root_from_path(path: Path) -> Path:
+ """Gets a Path to the git repo root for the given path."""
+ if path.is_file():
+ path = path.parent
+ completed_process = subprocess.run(
+ ["git", "-C", path, "rev-parse", "--show-toplevel"],
+ stdin=subprocess.DEVNULL,
+ stdout=subprocess.PIPE,
+ stderr=subprocess.PIPE,
+ check=True,
+ )
+ root_dir = Path(completed_process.stdout.decode().strip())
+ assert root_dir.is_dir(), f"{root_dir} must be a directory"
+ return root_dir
+
+
+@attr.s(auto_attribs=True, frozen=True)
+class LocalGitRepo:
+ """
+ DEPRECATED: Unless you either need asyncio or the ability to run git commands remotely on non-local compute environments, consider using SyncLocalGitRepo
+ from simple_git.py instead. SimpleGitRepo provides a subset of the functions available through computing_environment.py + LocalGitRepo, but
+ in a single class made for synchronous use.
+ """
+
+ base_path: Path
+
+ @classmethod
+ def build_from_cwd(cls) -> Self:
+ """Create a `LocalGitRepo` instance from the current working directory."""
+ return cls(get_git_repo_root())
+
+ async def run_git(
+ self,
+ command: Sequence[str],
+ check: bool = True,
+ cwd: AnyPath | None = None,
+ is_error_logged: bool = True,
+ is_stripped: bool = True,
+ retry_on_git_lock_error: bool = True,
+ ) -> str:
+ """Run a git command in the repo.
+
+ Example:
+ ```
+ git_repo.run_git("status")
+ ```
+ """
+ # TODO: check for whether hooks should actually be run when we call this function
+ # Note: this used to be within an asyncio lock to prevent the program from concurrently running git commands.
+ # This lock was removed since it was within global state, a dangerous pattern, and wasn't preventing other users from interacting with the git repo.
+ command = ["git"] + list(command)
+ if not retry_on_git_lock_error:
+ result = await self.run_command(command, check=check, is_error_logged=is_error_logged, cwd=cwd)
+ else:
+ result = await run_command_with_retry_on_git_lock_error(
+ self, command, check=check, is_error_logged=is_error_logged, cwd=cwd
+ )
+ if is_stripped:
+ return result.strip()
+ return result
+
+ sync_run_git = sync(run_git)
+
+ async def run_command(
+ self,
+ command: Sequence[str],
+ check: bool = True,
+ secrets: dict[str, str] | None = None,
+ cwd: AnyPath | None = None,
+ is_error_logged: bool = True,
+ ) -> str:
+ """Run a command in the repo.
+
+ Note, this can be used to run any command, not just git.
+ """
+ command_string = shlex.join(command)
+ logger.trace(
+ f"Running command: {command_string=} from cwd={cwd or self.base_path} with {secrets=} {check=} {is_error_logged=}"
+ )
+ proc = await asyncio.create_subprocess_exec(
+ *command,
+ cwd=cwd or self.base_path,
+ stdin=subprocess.DEVNULL,
+ stdout=subprocess.PIPE,
+ stderr=subprocess.PIPE,
+ env=secrets,
+ )
+ # note, need to be carefull not to strip() lines since whitespace may be important (e.g. for diffs)
+ # return joined lines since mostly we only use the output for logging, and this way we arn't
+ # passing around lots of lists. Also it's easy to parse by lines if needed
+ stdout_bytes, stderr_bytes = await proc.communicate()
+ try:
+ stdout = stdout_bytes.decode("UTF-8")
+ except UnicodeDecodeError as e:
+ # If we don't encounter this, it likely means something was fixed upstream and we can safely delete
+ log_exception(
+ e,
+ "Command {command_string} failed to decode stdout, replacing any invalid bytes which could lead to problems later",
+ command_string=command_string,
+ )
+ stdout = stdout_bytes.decode("UTF-8", errors="replace")
+ stderr = stderr_bytes.decode("UTF-8")
+ if check and proc.returncode != 0:
+ error_message = f"command run from cwd={self.base_path} failed with exit code {proc.returncode} and stdout:\n{stdout}\nstderr:\n{stderr}"
+ if is_error_logged:
+ logger.error(
+ f"command attempted: '{command_string}' from cwd={self.base_path}\nerror message: {error_message}"
+ )
+ # this should not be None, but do this to satisfy type checker, int or None we throw the same error
+ returncode = proc.returncode or -1
+ raise RunCommandError(
+ cmd=command_string,
+ stderr=stderr,
+ returncode=returncode,
+ cwd=cwd or self.base_path,
+ )
+ return stdout
+
+ @contextlib.asynccontextmanager
+ async def _open_file(
+ self,
+ relative_path: AnyPath,
+ cwd: AnyPath | None = None,
+ mode: OpenTextMode | OpenBinaryMode = "r",
+ mkdir_if_missing: bool = True,
+ ) -> AsyncGenerator[anyio.AsyncFile[Any], None]:
+ logger.trace("opening file {} in cwd {} with mode {}", relative_path, cwd, mode)
+ if cwd is not None:
+ sb_file_path = str(Path(cwd) / relative_path)
+ else:
+ sb_file_path = str(self.base_path / relative_path)
+
+ if mkdir_if_missing:
+ parent_dir = anyio.Path(sb_file_path).parent
+ await parent_dir.mkdir(parents=True, exist_ok=True)
+
+ f: anyio.AsyncFile[Any] | None = None
+ try:
+ f = await anyio.Path(sb_file_path).open(mode=mode) # type: ignore
+ yield f
+ finally:
+ if f is not None:
+ await f.aclose()
+
+ async def write_file(
+ self,
+ relative_path: AnyPath,
+ content: str | bytes | None,
+ cwd: AnyPath | None = None,
+ mode: OpenTextModeWriting | OpenBinaryModeWriting = "w",
+ mkdir_if_missing: bool = True,
+ ) -> None:
+ if content is None:
+ await self.delete_file(relative_path, cwd=cwd)
+ return
+
+ async with self._open_file(relative_path, cwd=cwd, mode=mode, mkdir_if_missing=mkdir_if_missing) as f:
+ logger.trace("writing to file {} in cwd {} with mode {}", relative_path, cwd, mode)
+ # pyre-fixme[6]: content can be bytes
+ await f.write(content)
+
+ async def delete_file(self, relative_path: AnyPath, cwd: AnyPath | None = None) -> None:
+ logger.trace("deleting the file {} in cwd {}", relative_path, cwd)
+ if cwd is not None:
+ sb_file_path = str(Path(cwd) / relative_path)
+ else:
+ sb_file_path = str(self.base_path / relative_path)
+ await anyio.Path(sb_file_path).unlink()
+
+ async def read_file(
+ self,
+ relative_path: AnyPath,
+ cwd: AnyPath | None = None,
+ mode: OpenTextModeReading | OpenBinaryModeReading = "r",
+ mkdir_if_missing: bool = True,
+ ) -> str | bytes:
+ async with self._open_file(relative_path, cwd=cwd, mode=mode, mkdir_if_missing=mkdir_if_missing) as f:
+ logger.trace("reading file {} in cwd {} with mode {}", relative_path, cwd, mode)
+ content = await f.read()
+ assert isinstance(content, str) or isinstance(content, bytes)
+ return content
+
+ async def head_hash(self) -> str:
+ """Get the hash of the current HEAD commit."""
+ return await get_head_hash(self)
+
+ async def is_git_repo(self) -> bool:
+ """Check that repo is valid git repo."""
+ return await anyio.Path(self.base_path / ".git").exists()
+
+ sync_is_git_repo = sync(is_git_repo)
+
+ async def assert_clean(self) -> None:
+ await assert_repo_is_clean(self)
+
+ sync_assert_clean = sync(assert_clean)
+
+ async def configure_git(
+ self,
+ git_user_name: str | None = None,
+ git_user_email: str | None = None,
+ initial_commit_message: str = "initial commit",
+ is_recreating: bool = False,
+ ) -> None:
+ """Configure git repo with user name and email."""
+ if is_recreating:
+ if await self.is_git_repo():
+ await asyncio.to_thread(shutil.rmtree, self.base_path / ".git")
+
+ # order here is important
+ # ref https://stackoverflow.com/questions/11656761/git-please-tell-me-who-you-are-error?noredirect=1
+ await self.run_git(("init",))
+ if git_user_name:
+ await self.run_git(("config", "user.name", f"'{git_user_name}'"))
+ if git_user_email:
+ await self.run_git(("config", "user.email", f"'{git_user_email}'"))
+ await self.run_git(("add", "."))
+ await self.run_git(("commit", "-m", f"'{initial_commit_message}'"))
+ branch_name = await self.run_git(("symbolic-ref", "HEAD"))
+ if not branch_name == "refs/heads/main":
+ # rename master to main for consistency
+ await self.run_git(("branch", "-m", "master", "main"))
+
+ sync_configure_git = sync(configure_git)
+
+ @asynccontextmanager
+ async def temporary_commit(
+ self,
+ tag_prefix: str,
+ commit_message: str,
+ raise_on_head_hash_change: bool = False,
+ ) -> AsyncIterator[str]:
+ """Context manager to make a temporary commit and tag in the repo."""
+ await self.run_git(("commit", "-am", commit_message, "--allow-empty", "--no-verify"))
+ head_hash = await self.head_hash()
+ tag = f"{tag_prefix}/{head_hash}"
+ await self.run_git(("tag", "--force", tag))
+ await self.run_git(("push", "origin", tag, "--no-verify"))
+ try:
+ yield head_hash
+ finally:
+ # This is susceptible to a race condition (if the user makes a commit between the time we check the head hash and the time we reset the state).
+ # So it's important to keep any block that uses this context manager short - make the commit, copy it to the controller, and work there. Don't hold the repo hostage.
+ current_head_hash = await self.head_hash()
+ if current_head_hash != head_hash and raise_on_head_hash_change:
+ raise AssertionError(
+ f"Head hash has changed from {head_hash} to {current_head_hash} since the temporary commit was made. Giving up on resetting git state, please address this manually."
+ )
+ else:
+ await self.run_git(("reset", "HEAD~"))
+
+ sync_temporary_commit = sync_contextmanager_func(temporary_commit)
+
+ async def copy_repo(self, new_repo_path: Path, exists_ok: bool = True) -> "LocalGitRepo":
+ """Make a full copy of this repo in a new directory.
+
+ Note, this will copy all the files in the repo into a new local directory, but will not handle
+ configuring the new directory as a git repo.
+ """
+ if await anyio.Path(new_repo_path).exists():
+ if not exists_ok:
+ raise FileExistsError(
+ f"New repo path '{new_repo_path} already exists. Set `exists_ok=True` if you are happy overwriting it, otherwise select new path."
+ )
+ await asyncio.to_thread(shutil.rmtree, new_repo_path)
+ await asyncio.to_thread(
+ shutil.copytree,
+ self.base_path,
+ new_repo_path,
+ dirs_exist_ok=True,
+ ignore=shutil.ignore_patterns(".git", ".gitsecret"),
+ )
+ return LocalGitRepo(new_repo_path)
+
+ sync_copy_repo = sync(copy_repo)
+
+ async def is_path_in_repo(self, file_path: str | Path | anyio.Path) -> bool:
+ """Check whether a given file path is within this repo.
+
+ FIXME: It doesn't seem entirely necessary to enumerate all of the files with a particular extension
+ just to check if a single file (whose path we know) is in the repo.
+ """
+ if isinstance(file_path, (str, Path)):
+ file_path = anyio.Path(file_path)
+ extension = file_path.suffix
+ return file_path in await self.get_all_files_by_extension(extension=extension)
+
+ async def _get_file_path(self, file_path: str | Path) -> anyio.Path:
+ path = anyio.Path(file_path)
+ if not path.is_absolute():
+ path = anyio.Path(self.base_path / path)
+ assert await path.exists(), f"File {path} does not exist."
+ return path
+
+ async def safely_read_file_from_repo(self, file_path: str | Path) -> str:
+ """Safely read file from repo."""
+ path = await self._get_file_path(file_path)
+ assert await self.is_path_in_repo(path), f"File {path} is not in repo."
+ return await path.read_text()
+
+ sync_safely_read_file_from_repo = sync(safely_read_file_from_repo)
+
+ async def get_all_files_by_extension(self, extension: str = PYTHON_EXTENSION) -> tuple[Path, ...]:
+ """Get absolute path of all files in the repo with given extension."""
+ paths: list[Path] = []
+ async for path in anyio.Path(self.base_path).rglob(f"*{extension}"):
+ paths.append(Path(path))
+ return tuple(paths)
+
+
+@attr.s(auto_attribs=True, frozen=True, kw_only=True)
+class WritableLocalGitRepo(LocalGitRepo):
+ """A Local Git Repo with support for modifying files and reseting to an initial state.
+
+ Note, this does not handle creating a copy of an existing repo, or anything. Rather it adds some additional
+ functionality for actually writing to files in the repo. For the creation of a separate copy of an existing
+ repo which supports making changes without affecting the main repo see the `temp_writable_local_git_repo` context
+ manager.
+
+ It is also recommended the `build_from_repo` function when creating a WritableLocalGitRepo, as this will
+ make sure that any untracked and uncommited changes are managed correctly.
+ """
+
+ initial_git_hash: str
+ stash_git_hash: str | None
+
+ @classmethod
+ async def build_from_repo(cls, repo: LocalGitRepo) -> "WritableLocalGitRepo":
+ """Create a writable repo from an local repo."""
+ init_hash = await repo.head_hash()
+ if await is_repo_dirty(repo):
+ await repo.run_git(("add", "."))
+ stash_hash = await make_commit(repo, "stashing uncommited and untracked changes")
+ else:
+ stash_hash = None
+
+ return cls(
+ base_path=repo.base_path,
+ initial_git_hash=init_hash,
+ stash_git_hash=stash_hash,
+ )
+
+ async def _setup(self) -> None:
+ init_hash = await self.head_hash()
+ expected_hash = self.stash_git_hash or self.initial_git_hash
+ assert init_hash == expected_hash, "git repo is not currently at expected commit"
+ assert await self.is_git_repo(), f"{self.base_path} is not a git repo"
+ await self.assert_clean()
+
+ async def reset(self) -> None:
+ """Reset the repo to the state it was in when this class was created."""
+ await restore_all_staged_files(self)
+ await restore_all_unstaged_changes(self)
+ if self.stash_git_hash:
+ # hard to reset to commit with stashed untracked and uncommited changes
+ await self.run_git(("reset", "--hard", self.stash_git_hash))
+ # soft reset to return to initial commit but keep the untracked and uncommited changes
+ await self.run_git(("reset", "--soft", self.initial_git_hash))
+ # unstage untracked and uncommited changes
+ await restore_all_staged_files(self)
+ else:
+ await self.run_git(("reset", "--hard", self.initial_git_hash))
+ await self.run_git(("clean", "-f"))
+ await self.run_git(("checkout", self.initial_git_hash))
+
+ current_git_hash = await self.head_hash()
+ assert (
+ current_git_hash == self.initial_git_hash
+ ), f"base branch changed, current git hash ({current_git_hash}) != initial git hash ({self.initial_git_hash})"
+ await self.assert_clean()
+
+ async def apply_change_to_file(self, file_path: str | Path, new_contents: str) -> None:
+ """Apply change to a single file."""
+ path = await self._get_file_path(file_path)
+ assert await self.is_path_in_repo(path), f"File {path} is not in repo."
+ await path.write_text(new_contents)
+ await git_add(self, str(path))
+
+ async def __aenter__(self) -> "WritableLocalGitRepo":
+ await self._setup()
+ return self
+
+ async def __aexit__(
+ self,
+ exc_type: type[BaseException] | None,
+ exc_val: BaseException | None,
+ exc_tb: TracebackType | None,
+ ) -> None:
+ await self.reset()
+
+
+async def copy_files_from_one_repo_to_another(
+ src_repo_path: Path, dst_repo_path: Path, relative_file_paths: Sequence[str | Path]
+) -> None:
+ """Copies files from src to dst repo using the relative file paths."""
+ for relative_path in relative_file_paths:
+ src_file_path = src_repo_path / relative_path
+ dst_file_path = anyio.Path(dst_repo_path / relative_path)
+ # make sure necessary directories exist in destination
+ await dst_file_path.parent.mkdir(parents=True, exist_ok=True)
+ await asyncio.to_thread(shutil.copy2, src_file_path, dst_file_path)
+
+
+def get_repo_url_from_folder(repo_path: Path) -> str:
+ try:
+ repo_url = subprocess.check_output(
+ ["git", "remote", "get-url", "origin"],
+ cwd=repo_path,
+ universal_newlines=True,
+ ).strip()
+ except subprocess.CalledProcessError:
+ raise
+ else:
+ if repo_url.startswith("git@"):
+ # convert ssh url to https
+ repo_url = repo_url.replace(":", "/")
+ repo_url = f"https://{repo_url[4:]}"
+ if "https://oauth2:" in repo_url:
+ # remove the oauth2 prefix
+ # repo_url is something like https://oauth2:{token}@gitlab.com/.../.git
+ # change it to https://gitlab.com/.../.git
+ # This will happen if repo was originallycloned using oauth2
+ suffix = repo_url.split("@")[-1]
+ repo_url = "https://" + suffix
+ return repo_url
+
+
+def get_repo_base_path() -> Path:
+ working_directory = Path(__file__).parent
+ try:
+ return Path(
+ _run_command_and_capture_output(["git", "rev-parse", "--show-toplevel"], cwd=working_directory).strip()
+ )
+ except subprocess.CalledProcessError as e:
+ try:
+ return working_directory.parents[1]
+ except IndexError:
+ raise UnableToFindRepoBase() from e
+
+
+def _run_command_and_capture_output(args: Sequence[str], cwd: Path | None = None) -> str:
+ arg_str = " ".join(shlex.quote(arg) for arg in args)
+ print(f"Running command: {arg_str}", file=sys.stderr)
+ with subprocess.Popen(args, text=True, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) as proc:
+ with StringIO() as output:
+ _handle_output(proc, output, sys.stderr)
+ if proc.wait() != 0:
+ raise subprocess.CalledProcessError(proc.returncode, cmd=args, output=output.getvalue())
+ return output.getvalue()
+
+
+class UnableToFindRepoBase(Exception):
+ """Raised when the base of the repository cannot be found."""
+
+
+def _handle_output(process: subprocess.Popen[str], *files: TextIO) -> None:
+ process_stdout = process.stdout
+ assert process_stdout is not None
+ while True:
+ output = process_stdout.read(1)
+ if output:
+ for f in files:
+ f.write(output)
+ elif process.poll() is not None:
+ break
+
+
+def get_diff_without_index(diff: str) -> str:
+ new_lines = []
+ for line in diff.splitlines():
+ if line.startswith("index "):
+ # We replace index lines with "index 0000000..0000000 100644" because:
+ # - `0000000..0000000` ensures no real object hashes are referenced, making the diff neutral.
+ # - `100644` is the standard file mode for non-executable files in git diffs, ensuring compatibility.
+ # - This keeps the diff format valid while removing specific index information.
+ new_lines.append("index 0000000..0000000 100644")
+ else:
+ new_lines.append(line)
+ return "\n".join(new_lines).strip()
+
+
+def is_diffs_without_index_equal(diff_1: str, diff_2: str) -> bool:
+ return get_diff_without_index(diff_1) == get_diff_without_index(diff_2)
+
+
+# Copy-pasted from imbue to avoid moving the whole hammers machinery over to imbue-core.
+async def get_lines_from_process(shell_command: str, is_exit_code_validated: bool = True, **kwargs: Any) -> list[str]:
+ p = await asyncio.create_subprocess_shell(shell_command, stdin=PIPE, stdout=PIPE, stderr=STDOUT, **kwargs)
+ lines = [x.decode("UTF-8") for x in (await p.communicate())[0].splitlines()]
+ if is_exit_code_validated:
+ joined_lines = "\n".join(lines)
+ assert (
+ p.returncode == 0
+ ), f"command failed: {shell_command}\nwith output:\n{joined_lines} with exit code {p.returncode}"
+ return lines
diff --git a/imbue_core/imbue_core/git_data_types.py b/imbue_core/imbue_core/git_data_types.py
@@ -0,0 +1,35 @@
+from datetime import datetime
+from typing import Annotated
+
+from pydantic.functional_validators import PlainValidator
+
+from imbue_core.pydantic_serialization import FrozenModel
+from imbue_core.pydantic_serialization import SerializableModel
+
+
+def _validate_git_timestamp(value: str) -> str:
+ try:
+ datetime.fromisoformat(value)
+ return value
+ except ValueError:
+ raise ValueError(f"Invalid git timestamp: {value}")
+
+
+class CommitTimestamp(SerializableModel):
+ author_ts: Annotated[str, PlainValidator(_validate_git_timestamp)]
+ committer_ts: Annotated[str, PlainValidator(_validate_git_timestamp)]
+
+
+class CommitMetadata(FrozenModel):
+ commit: str
+ tree_hash: str
+ message: str
+ commit_time: CommitTimestamp
+
+ @property
+ def body(self) -> str:
+ return self.message.split("\n", 1)[-1]
+
+ @property
+ def subject(self) -> str:
+ return self.message.split("\n", 1)[0]
diff --git a/imbue_core/imbue_core/ids.py b/imbue_core/imbue_core/ids.py
@@ -0,0 +1,44 @@
+from typing import Any
+from typing import Self
+
+from pydantic import GetCoreSchemaHandler
+from pydantic_core import core_schema
+
+
+class NonEmptyStr(str):
+ # pyre-fixme[11]: pyre seems to have some trouble with Self in some specific cases, including type[Self]
+ def __new__(cls: type[Self], *args: Any, **kwargs: Any) -> Self:
+ value = str.__new__(cls, *args, **kwargs)
+ if len(value) == 0:
+ raise ValueError("NonEmptyStr cannot be empty")
+ return value
+
+ @classmethod
+ def __get_pydantic_core_schema__(cls, source_type: type, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
+ """
+ Support transparently deserializing strings into ObjectID instances and vice versa.
+ """
+ return core_schema.no_info_before_validator_function(
+ lambda raw_value: (cls(raw_value) if isinstance(raw_value, str) else raw_value),
+ core_schema.union_schema(
+ [
+ core_schema.is_instance_schema(cls),
+ core_schema.str_schema(),
+ ]
+ ),
+ serialization=core_schema.plain_serializer_function_ser_schema(
+ lambda instance: str(instance), return_schema=core_schema.str_schema()
+ ),
+ )
+
+
+class ExternalID(NonEmptyStr):
+ pass
+
+
+class AssistantMessageID(ExternalID):
+ pass
+
+
+class ToolUseID(ExternalID):
+ pass
diff --git a/imbue_core/imbue_core/imbue_cli/__init__.py b/imbue_core/imbue_core/imbue_cli/__init__.py
diff --git a/imbue_core/imbue_core/imbue_cli/action.py b/imbue_core/imbue_core/imbue_cli/action.py
@@ -0,0 +1,71 @@
+from typing import Annotated
+from typing import Literal
+
+from pydantic import Field
+from pydantic import Tag
+
+from imbue_core.imbue_cli.scout_message_types import ScoutMessageUnion
+from imbue_core.issues import CheckFailedIssue
+from imbue_core.issues import IdentifiedIssue
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.pydantic_serialization import build_discriminator
+from imbue_core.suggestions import CheckOutputID
+from imbue_core.suggestions import Suggestion
+
+
+class UserDisplayOutput(SerializableModel):
+ object_type: str
+
+
+class ErroredOutput(UserDisplayOutput):
+ object_type: Literal["ErroredOutput"] = "ErroredOutput"
+ error_message: str
+
+
+class CommandTextOutput(UserDisplayOutput):
+ object_type: Literal["CommandTextOutput"] = "CommandTextOutput"
+ output: str
+
+
+class CommandHTMLOutput(UserDisplayOutput):
+ object_type: Literal["CommandHTMLOutput"] = "CommandHTMLOutput"
+ output: str
+
+
+UserDisplayOutputUnion = Annotated[
+ Annotated[ErroredOutput, Tag("ErroredOutput")]
+ | Annotated[CommandTextOutput, Tag("CommandTextOutput")]
+ | Annotated[CommandHTMLOutput, Tag("CommandHTMLOutput")],
+ build_discriminator(),
+]
+
+
+class RetrieveOutput(SerializableModel):
+ object_type: str = "RetrieveOutput"
+ files: tuple[str, ...]
+
+
+class ScoutOutput(SerializableModel):
+ object_type: str = "ScoutOutput"
+ id: CheckOutputID
+ data: ScoutMessageUnion
+
+
+ActionOutputUnion = Annotated[
+ (
+ Annotated[ScoutOutput, Tag("ScoutOutput")]
+ | Annotated[Suggestion, Tag("Suggestion")]
+ | Annotated[CheckFailedIssue, Tag("CheckFailedIssue")]
+ | Annotated[IdentifiedIssue, Tag("IdentifiedIssue")]
+ | Annotated[RetrieveOutput, Tag("RetrieveOutput")]
+ ),
+ build_discriminator(),
+]
+
+
+class ActionOutput(SerializableModel):
+ command: str = Field(description="The command that was executed to produce the output.")
+ outputs: tuple[ActionOutputUnion, ...] = Field(description="The structured output data from the action.")
+ user_display: UserDisplayOutputUnion = Field(
+ description="The user display output from the action. This can be used by consumers to display a user-friendly version of the action output."
+ )
diff --git a/imbue_core/imbue_core/imbue_cli/scout_data_types.py b/imbue_core/imbue_core/imbue_cli/scout_data_types.py
@@ -0,0 +1,40 @@
+from typing import Literal
+
+from imbue_core.pydantic_serialization import SerializableModel
+
+# TODO refactor evidence example to use different classes for different output types
+
+
+class ScoutEvidenceExample(SerializableModel):
+ """A single example of evidence for the report."""
+
+ description: str
+ type: Literal["positive", "negative"]
+ command: str | None = None
+ output: str | None = None
+ code: str | None = None
+ image_path: str | None = None
+ image_data: bytes | None = None
+ image_format: str | None = None
+ image_caption: str | None = None
+
+
+class ScoutEvidence(SerializableModel):
+ """A piece of evidence for the report."""
+
+ question: str
+ action: str
+ result: str
+ score: Literal["Good", "Moderate", "Bad"]
+ confidence: Literal["High", "Medium", "Low"]
+ reference: str | None = None
+ examples: list[ScoutEvidenceExample] | None = None
+
+
+class ScoutReport(SerializableModel):
+ """A report of the scout analysis."""
+
+ goal: str
+ evidence: list[ScoutEvidence]
+ total_cost: float
+ total_time_taken: float
diff --git a/imbue_core/imbue_core/imbue_cli/scout_message_types.py b/imbue_core/imbue_core/imbue_cli/scout_message_types.py
@@ -0,0 +1,96 @@
+"""Message types for scout output."""
+
+import time
+from typing import Annotated
+from typing import Literal
+
+from pydantic import Field
+from pydantic import Tag
+from pydantic import TypeAdapter
+
+from imbue_core.imbue_cli.scout_data_types import ScoutEvidenceExample
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.pydantic_serialization import build_discriminator
+
+
+class ScoutMessage(SerializableModel):
+ """Base class for all scout output messages."""
+
+ object_type: str = Field(description="Discriminator field for message type")
+ timestamp: float = Field(default_factory=time.time, description="Unix timestamp when message was created")
+
+
+class EvidenceMessage(ScoutMessage):
+ """Message containing a piece of evidence."""
+
+ object_type: Literal["EvidenceMessage"] = "EvidenceMessage"
+
+ # Evidence fields (same as current ScoutEvidence)
+ question: str
+ action: str
+ result: str
+ score: Literal["Good", "Moderate", "Bad"]
+ confidence: Literal["High", "Medium", "Low"]
+ reference: str | None = None
+ examples: list[ScoutEvidenceExample] | None = None
+
+
+class ScoreMessage(ScoutMessage):
+ """Message containing an overall score assessment."""
+
+ object_type: Literal["ScoreMessage"] = "ScoreMessage"
+
+ overall_score: float # 0.0 to 1.0
+ evidence_count: int # Number of evidence pieces contributing to this score
+ score_breakdown: dict[str, int] # Distribution of Good/Moderate/Bad evidence counts
+ confidence_breakdown: dict[str, int] # Distribution of High/Medium/Low confidence counts
+ time_elapsed: float # Time elapsed since start in seconds
+
+
+class MetadataMessage(ScoutMessage):
+ """Message containing metadata about the scout run."""
+
+ object_type: Literal["MetadataMessage"] = "MetadataMessage"
+
+ goal: str
+ repo_path: str
+ model: str
+ started_at: float
+
+
+class CostMessage(ScoutMessage):
+ """Message containing cost information."""
+
+ object_type: Literal["CostMessage"] = "CostMessage"
+
+ total_cost_usd: float
+ tokens_used: int | None = None
+
+
+class StatusMessage(ScoutMessage):
+ """Message containing status updates."""
+
+ object_type: Literal["StatusMessage"] = "StatusMessage"
+
+ status: Literal["started", "running", "completed", "failed"]
+ message: str | None = None
+
+
+# Union type for all messages
+ScoutMessageUnion = Annotated[
+ (
+ Annotated[EvidenceMessage, Tag("EvidenceMessage")]
+ | Annotated[ScoreMessage, Tag("ScoreMessage")]
+ | Annotated[MetadataMessage, Tag("MetadataMessage")]
+ | Annotated[CostMessage, Tag("CostMessage")]
+ | Annotated[StatusMessage, Tag("StatusMessage")]
+ ),
+ build_discriminator(),
+]
+
+_scout_message_type_adapter = TypeAdapter(ScoutMessageUnion)
+
+
+def deserialize_scout_message_json(data: str) -> ScoutMessageUnion:
+ print(f"Parsing scout message json: {data}")
+ return _scout_message_type_adapter.validate_json(data)
diff --git a/imbue_core/imbue_core/issues.py b/imbue_core/imbue_core/issues.py
@@ -0,0 +1,67 @@
+from enum import StrEnum
+from typing import Annotated
+
+from pydantic import Tag
+
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.pydantic_serialization import build_discriminator
+
+
+class IssueSeverityLevel(StrEnum):
+ CRITICAL = "CRITICAL"
+ ERROR = "ERROR"
+ WARNING = "WARNING"
+ NIT = "NIT"
+
+
+class IssueKey(SerializableModel):
+ # issues from different commands should not collide
+ command: str
+ # this should NOT contain line numbers, as we want it to be stable across changes as much as possible
+ # NOTE: we do some initial formatting to avoid issues around message containing code
+ identifier: str
+
+
+class Issue(SerializableModel):
+ key: IssueKey
+ severity: IssueSeverityLevel
+
+ def is_equal(self, other: "Issue") -> bool:
+ return self.key == other.key
+
+ def summary_line(self) -> str:
+ return f"{self.severity}: {self.key}"
+
+
+class CheckFailedIssue(Issue):
+ object_type: str = "CheckFailedIssue"
+ error_message: str
+
+ raw: str | None = None
+
+
+class IdentifiedIssue(Issue):
+ object_type: str = "IdentifiedIssue"
+ issue_location: str | None = None
+
+ # The oneliner message of the problem.
+ # PYTEST: AssertionError
+ # MYPY: error: Function is missing a type annotation
+ message: str
+
+ # The full description of the issue (can be many lines)
+ # Examples:
+ # PYTEST: the longrepr, which is "def test_pearson_correlation_basic_lists():\n x = [1, 2, 3, 4, 5]\n y = [2, 4, 6, 8, 10]\n expected_correlation = 0.9819805060619657\n> ..." # noqa
+ # MYPY: "def calculate_pearson_correlation(x, y):"
+ description: str
+
+ def summary_line(self) -> str:
+ return f"{super().summary_line()} message={self.message!r}" + (
+ f" ({self.issue_location})" if self.issue_location else ""
+ )
+
+
+IssueUnion = Annotated[
+ (Annotated[CheckFailedIssue, Tag("CheckFailedIssue")] | Annotated[IdentifiedIssue, Tag("IdentifiedIssue")]),
+ build_discriminator(),
+]
diff --git a/imbue_core/imbue_core/itertools.py b/imbue_core/imbue_core/itertools.py
@@ -0,0 +1,80 @@
+import contextlib
+import itertools
+from typing import AsyncGenerator
+from typing import Callable
+from typing import Generator
+from typing import Iterable
+from typing import Sequence
+from typing import TypeVar
+from typing import cast
+
+from imbue_core.errors import ImbueError
+
+T = TypeVar("T")
+TV = TypeVar("TV")
+
+
+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)
+
+
+async def iterable_to_async(iterable: Iterable[T]) -> AsyncGenerator[T, None]:
+ for item in iterable:
+ yield item
+
+
+# TODO delete/migrate out computronium/computronium/universal/utils.py
+def generate_unique(
+ source: Iterable[T],
+ key: Callable[[T], TV] = cast(Callable[[T], TV], lambda item: item),
+) -> Generator[T, None, None]:
+ unique = set()
+ for item in source:
+ value = key(item)
+ if value in unique:
+ continue
+ yield item
+ unique.add(value)
+
+
+def generate_flattened(iterable: Iterable[Iterable[T]]) -> Generator[T, None, None]:
+ for item in iterable:
+ yield from item
+
+
+# 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/imbue_core/imbue_core/language_model_mode.py
@@ -0,0 +1,8 @@
+from enum import StrEnum
+
+
+class LanguageModelMode(StrEnum):
+ LIVE = "LIVE"
+ UPDATE_SNAPSHOT = "UPDATE_SNAPSHOT"
+ OFFLINE = "OFFLINE"
+ MOCKED = "MOCKED"
diff --git a/imbue_core/imbue_core/llm_testing_utils.py b/imbue_core/imbue_core/llm_testing_utils.py
@@ -0,0 +1,57 @@
+from pathlib import Path
+
+from loguru import logger
+from syrupy.assertion import SnapshotAssertion
+
+from imbue_core.caching import AsyncCache
+from imbue_core.cattrs_serialization import deserialize_from_json
+from imbue_core.cattrs_serialization import serialize_to_json
+
+
+async def preload_llm_cache(persistent_cache_path: Path, temp_cache: AsyncCache) -> None:
+ logger.info(
+ "Loading existing cache from {persistent_cache_path}",
+ persistent_cache_path=persistent_cache_path,
+ )
+ assert persistent_cache_path.exists(), f"Cache file {persistent_cache_path} does not exist."
+ existing_data = deserialize_from_json(persistent_cache_path.read_text())
+ async with temp_cache as cache:
+ for key, value in existing_data.items():
+ await cache.set(key, value)
+
+
+async def record_llm_responses_in_cache(temp_cache: AsyncCache, persistent_cache_path: Path) -> None:
+ logger.info(
+ "Updating cache (!!!) at {persistent_cache_path}",
+ persistent_cache_path=persistent_cache_path,
+ )
+ async with temp_cache as cache:
+ all_keys = await cache.get_all_keys()
+ data = await cache.get_all(all_keys)
+ if data:
+ persistent_cache_path.parent.mkdir(parents=True, exist_ok=True)
+ persistent_cache_path.write_text(serialize_to_json(data), encoding="utf-8")
+
+
+def _sanitize_snapshot_name(snapshot_name: str) -> str:
+ return snapshot_name.replace("/", "").replace("\\", "")
+
+
+def get_cache_file_from_snapshot_core(snapshot: SnapshotAssertion, suffix: str) -> Path:
+ # To prevent syrupy from cleaning the cache up immediately after written, we use this suffix and register it with pytest.
+ # Make sure to add a line like `--snapshot-ignore-file-extensions={suffix}` to the project's pytest.ini
+
+ # Goal here is a cache file per test, not per test-file.
+ test_file = Path(snapshot.test_location.filepath)
+ snapshot_dir = test_file.parent / "__snapshots__" / test_file.stem
+ snapshot_dir.mkdir(parents=True, exist_ok=True)
+ cache_file = snapshot_dir / f"{_sanitize_snapshot_name(snapshot.test_location.testname)}.{suffix}"
+ return cache_file
+
+
+def get_cache_file_from_snapshot(snapshot: SnapshotAssertion) -> Path:
+ return get_cache_file_from_snapshot_core(snapshot, "llm_cache_json")
+
+
+def get_count_tokens_cache_file_from_snapshot(snapshot: SnapshotAssertion) -> Path:
+ return get_cache_file_from_snapshot_core(snapshot, "count_tokens_cache_json")
diff --git a/imbue_core/imbue_core/log_utils.py b/imbue_core/imbue_core/log_utils.py
@@ -0,0 +1,146 @@
+from __future__ import annotations
+
+import asyncio
+from pathlib import Path
+from typing import Any
+from typing import Callable
+from typing import Mapping
+from typing import TYPE_CHECKING
+
+import loguru
+
+from imbue_core.constants import ExceptionPriority
+from imbue_core.constants import HIGH_PRIORITY_LEVEL
+from imbue_core.constants import LOW_PRIORITY_LEVEL
+from imbue_core.constants import MEDIUM_PRIORITY_LEVEL
+
+if TYPE_CHECKING:
+ loguru_record = loguru.Record
+else:
+ loguru_record = dict[str, Any]
+FilterDict = dict[str | None, str | int | bool]
+FilterFunction = Callable[[loguru_record], bool]
+LOCATION_WIDTH = 60
+TRACE = "TRACE"
+
+# between DEBUG and INFO: https://loguru.readthedocs.io/en/stable/api/logger.html
+DETAIL = "DETAIL"
+DETAIL_LEVEL = 15
+
+# the first 4 chars are used for "tsk_" and the next ~7 bytes are used for the timestamp
+# thus we need at least a few extra characters to make sure we don't troll ourselves when two tasks are created
+# very close in time
+TASK_ID_MESSAGE_WIDTH = 16
+
+LOG_LEVEL_NO_COLOR_TUPLES = [
+ (DETAIL, DETAIL_LEVEL, "<fg 128,128,128>"),
+ (ExceptionPriority.LOW_PRIORITY.value, LOW_PRIORITY_LEVEL, "<yellow>"),
+ (ExceptionPriority.MEDIUM_PRIORITY.value, MEDIUM_PRIORITY_LEVEL, "<fg 255,127,0>"),
+ (ExceptionPriority.HIGH_PRIORITY.value, HIGH_PRIORITY_LEVEL, "<red>"),
+]
+
+
+def fix_full_location(record: "loguru.Record") -> str:
+ """
+ One goal of this function is to format the location in an IDE-friendly way,
+ so that control-clicking on the logged location opens the correct file
+ and puts the cursor at the correct line.
+
+ `record` looks like this:
+ ```
+ {
+ "elapsed": datetime.timedelta(seconds=5, microseconds=152312),
+ "exception": None,
+ "extra": {
+ "machine": "32de5bcafaa8",
+ "user": "user",
+ "agent_type": None,
+ "agent_id": None,
+ "parent_id": None,
+ "async_task_id": None,
+ "formatted_task_id": "",
+ "formatted_agent_id": "",
+ "sandbox_id": None,
+ "formatted_sandbox_id": "",
+ },
+ "file": (
+ name="error_dump_utils.py",
+ path="/thad/dropbox/Thad Hughes/src/generally_intelligent/computronium/computronium/common/error_dump_utils.py",
+ ),
+ "function": "write_exception",
+ "level": (name="INFO", no=20, icon="ℹ️"),
+ "line": 214,
+ "message": "Full traceback for is available at http://node-004.snake-blues.ts.net:7777/exceptions/thad__notebook_2025_01_28_legolas/1000__2025_01_28_17_47_19_170102__P000_W0000/C0000__user_100.110.58.95_5045/2025_03_06_09_59_10_188008_KeyboardInterrupt_traceback.txt",
+ "module": "error_dump_utils",
+ "name": "computronium.common.error_dump_utils",
+ "process": (id=3099015, name="MainProcess"),
+ "thread": (id=140434013706048, name="MainThread"),
+ "time": datetime(
+ 2025, 3, 6, 9, 59, 10, 920101, tzinfo=datetime.timezone(datetime.timedelta(days=-1, seconds=57600), "PST")
+ ),
+ }
+ ```
+ """
+ log_path = Path(record["file"].path)
+ try:
+ cwd = Path.cwd()
+ except FileNotFoundError:
+ cwd = Path("/")
+
+ if log_path.is_relative_to(cwd):
+ log_path = log_path.relative_to(cwd)
+ location: str = record["extra"].get("full_location", f"{str(log_path)}:{record['line']}:{record['function']}")
+ while len(location) > LOCATION_WIDTH and "/" in location:
+ location = location[location.find("/") + 1 :]
+ return location[-LOCATION_WIDTH:].rjust(LOCATION_WIDTH)
+
+
+def format_task_id(async_task_id: str) -> str:
+ return async_task_id[:TASK_ID_MESSAGE_WIDTH].rjust(TASK_ID_MESSAGE_WIDTH)
+
+
+def patch_log_context_in_place(record: "loguru.Record", format_task_id: Callable[[str], str] = format_task_id) -> None:
+ record["extra"]["full_location"] = fix_full_location(record)
+
+ async_task_id = None
+ try:
+ # get the task id
+ current_task = asyncio.current_task()
+ if current_task is not None:
+ async_task_id = current_task.get_name()
+ except RuntimeError:
+ # we're not in an asyncio event loop
+ pass
+
+ if async_task_id:
+ formatted_task_id = format_task_id(async_task_id)
+ record["extra"]["formatted_task_id"] = f" [{formatted_task_id}]"
+ record["extra"]["async_task_id"] = async_task_id
+
+
+# TODO: Consider moving all levels from computronium log_utils.py _ensure_levels_configured here
+def ensure_core_log_levels_configured(
+ additional_log_levels: Mapping[str, int] | None = None,
+) -> None:
+ from loguru import logger
+
+ logger.trace("configuring detail and ExceptionPriority log levels")
+ for level, no, color in LOG_LEVEL_NO_COLOR_TUPLES:
+ try:
+ logger.level(level, no=no, color=color)
+ except (TypeError, ValueError) as e:
+ is_level_already_set_thus_ok = "already exists, you can't update its severity no" in str(e)
+ if not is_level_already_set_thus_ok:
+ raise
+
+ if additional_log_levels is None:
+ return
+
+ logger.trace("configuring additional log levels {}", additional_log_levels)
+ for level_name, level_no in additional_log_levels.items():
+ try:
+ logger.level(level_name, no=level_no)
+ except (TypeError, ValueError) as e:
+ is_level_already_set_thus_ok = "already exists, you can't update its severity no" in str(e)
+ if not is_level_already_set_thus_ok:
+ raise
diff --git a/imbue_core/imbue_core/nested_evolver.py b/imbue_core/imbue_core/nested_evolver.py
@@ -0,0 +1,251 @@
+"""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.
+
+See: https://imbue-ai.slack.com/archives/C05D0SM2RT5/p1726185313480779?thread_ts=1722865932.537289&cid=C05D0SM2RT5
+
+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 TypeGuard
+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")
+ObjectType = TypeVar("ObjectType")
+
+
+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()
+
+
+_threading_local = threading.local()
+
+
+# TODO: since mutate and thaw are stateful, if you call one without the other, you run into problems.
+def thaw(obj: _T) -> _T:
+ global _threading_local
+ if hasattr(_threading_local, "evolved_obj"):
+ raise ValueError("Thaw does not support nested thawing.")
+ # pyre-ignore[16]: we're deliberately setting evolved_obj for the first time here
+ _threading_local.evolved_obj = evolver(obj)
+ return _threading_local.evolved_obj
+
+
+# TODO: mypy complains because the input isn't anything related to ObjectType, but the output is.
+# This also means the type checking doesn't quite work since it can't infer the return type of this function correctly
+def mutate(dest: _T, src: Callable[[], _T]) -> ObjectType: # type: ignore
+ assign(dest, src)
+ try:
+ # pyre-ignore[34]: we don't have generic functions yet, so pyre complains that ObjectType isn't in the input
+ evolved_obj: ObjectType = _threading_local.evolved_obj # pyre-ignore[16]: pyre doesn't know about evolved_obj
+ return chill(evolved_obj)
+ except AttributeError as e:
+ raise ValueError("You must call mutate on a thawed object") from e
+ finally:
+ delattr(_threading_local, "evolved_obj")
+
+
+def mutate_from_dict(dest: ObjectType, src: dict[str, Any]) -> ObjectType:
+ # Warning: using this function doesn't provide mypy type checking at the call site, but it allows a single interface for attrs and pydantic classes
+ # In most cases the above function should be used instead
+ evolved_obj = evolver(dest)
+ for key, value in src.items():
+ assign(getattr(evolved_obj, key), lambda: value)
+ return chill(evolved_obj)
+
+
+def evolver_isinstance(evolver: Any, cls: type[_T]) -> TypeGuard[_T]:
+ assert isinstance(evolver, _Evolver) # Tricked you, type system!
+ return evolver.isinstance(cls)
+
+
+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)=}.")
+
+ def isinstance(self, cls: type[_T]) -> bool:
+ """Check if the object being evolved is an instance of the given class."""
+ if isinstance(self._value, _AttrValue):
+ return isinstance(self._value.attr_value, cls)
+ elif isinstance(self._value, _PydanticModelValue):
+ return isinstance(self._value.pydantic_model_value, cls)
+ elif isinstance(self._value, _FrozenDictValue):
+ return cls == FrozenDict
+ return False
diff --git a/imbue_core/imbue_core/py.typed b/imbue_core/imbue_core/py.typed
diff --git a/imbue_core/imbue_core/pydantic_serialization.py b/imbue_core/imbue_core/pydantic_serialization.py
@@ -0,0 +1,175 @@
+import threading
+from typing import Any
+from typing import TypeVar
+from typing import cast
+from typing import get_args
+
+from pydantic import BaseModel
+from pydantic import ConfigDict
+from pydantic import Discriminator
+from pydantic import GetCoreSchemaHandler
+from pydantic import Json
+from pydantic.alias_generators import to_camel
+from pydantic_core import core_schema as pyd_core_schema
+
+from imbue_core.frozen_utils import FrozenDict
+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 FrozenModel(EvolvableModel, BaseModel):
+ """
+ The base class for most internal data (that does not need to be serialized).
+
+ We generally prefer to keep data immutable in order to avoid side effects, race conditions, etc
+ """
+
+ model_config = ConfigDict(
+ frozen=True,
+ extra="forbid",
+ arbitrary_types_allowed=False,
+ )
+
+
+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()
+
+
+def model_dump(obj: BaseModel, is_camel_case: bool = False) -> dict:
+ return obj.model_dump(by_alias=is_camel_case)
+
+
+def model_load(model_type: type[T], data: dict) -> T:
+ return model_type.model_validate(data)
+
+
+def model_dump_json(obj: BaseModel | Json, is_camel_case: bool = False) -> str:
+ # pyre-fixme[16]: pyre complains that obj can be pydantic.types.AnyType, which has no model_dump_json
+ return obj.model_dump_json(by_alias=is_camel_case)
+
+
+def model_load_json(model_type: type[T], data: str) -> T:
+ return model_type.model_validate_json(data)
+
+
+# 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)
+
+
+class PydanticFrozenDictAnnotation:
+ @classmethod
+ def __get_pydantic_core_schema__(
+ cls, source_type: Any, handler: GetCoreSchemaHandler
+ ) -> pyd_core_schema.CoreSchema:
+ def validate_from_dict(d: dict | FrozenDict) -> FrozenDict:
+ return FrozenDict(d)
+
+ frozendict_schema = pyd_core_schema.chain_schema(
+ [
+ # pyre-ignore[16]: pyre is confused by using dict as a type like this
+ handler.generate_schema(dict[*get_args(source_type)]),
+ pyd_core_schema.no_info_plain_validator_function(validate_from_dict),
+ pyd_core_schema.is_instance_schema(FrozenDict),
+ ]
+ )
+ return pyd_core_schema.json_or_python_schema(
+ json_schema=frozendict_schema,
+ python_schema=frozendict_schema,
+ serialization=pyd_core_schema.plain_serializer_function_ser_schema(dict),
+ )
diff --git a/imbue_core/imbue_core/pydantic_utils.py b/imbue_core/imbue_core/pydantic_utils.py
@@ -0,0 +1,39 @@
+from typing import Any
+from typing import TypeVar
+
+from pydantic import BaseModel
+
+T = TypeVar("T", bound=BaseModel)
+
+
+def model_update(model: T, update: dict[str, Any]) -> T:
+ """
+ Update a Pydantic model with a dictionary of updates.
+ Validation is performed to ensure items in the update dictionary are valid fields in the model.
+ Use the Evolver class (imbue_core/imbue_core/nested_evolver.py) for type checking
+
+ Args:
+ model (BaseModel): The original Pydantic model.
+ update (Dict[str, Any]): A dictionary of updates to apply to the model.
+
+ Returns:
+ BaseModel: A new instance of the model with the updates applied.
+
+ """
+ update_dict_fields = update.keys()
+ model_fields = set(model.__class__.model_fields)
+ extra_fields = update_dict_fields - model_fields
+ if extra_fields:
+ raise ValueError(f"Invalid fields: {extra_fields}")
+ return fields_only_model_copy(model, update=update)
+
+
+def fields_only_model_copy(model: T, update: dict[str, Any] = {}) -> T:
+ """
+ Create a copy of a Pydantic model with only the fields defined in the model.
+
+ (Specifically, do not copy cached properties.)
+
+ """
+ fields = {name: update.get(name, getattr(model, name)) for name in model.__class__.model_fields}
+ return model.__class__(**fields)
diff --git a/imbue_core/imbue_core/repo_state.py b/imbue_core/imbue_core/repo_state.py
@@ -0,0 +1,109 @@
+from enum import StrEnum
+from functools import cached_property
+from typing import Annotated
+from typing import Any
+
+from pydantic import Field
+from pydantic import Tag
+from pydantic import computed_field
+
+from imbue_core.frozen_utils import FrozenDict
+from imbue_core.pydantic_serialization import PydanticFrozenDictAnnotation
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.pydantic_serialization import build_discriminator
+
+ResourceURL = str
+
+
+class ConflictType(StrEnum):
+ MERGE = "MERGE"
+ REBASE = "REBASE"
+ CHERRY_PICK = "CHERRY_PICK"
+ APPLY = "APPLY"
+ REVERT = "REVERT"
+ BISECT = "BISECT"
+
+
+class RepoOperation(SerializableModel):
+ pass
+
+ @computed_field
+ @cached_property
+ def is_empty(self) -> bool:
+ """Whether this repo operation leaves the repo unchanged.
+
+ Defaults to False. But should be overridden by subclasses as appropriate.
+ """
+ return False
+
+
+class ConflictedRepoOperation(RepoOperation):
+ object_type: str = "ConflictedRepoOperation"
+
+ blob_content_by_hash: Annotated[FrozenDict[str, bytes], PydanticFrozenDictAnnotation]
+ index_content: bytes
+ modified_file_contents_by_path: Annotated[FrozenDict[str, bytes], PydanticFrozenDictAnnotation]
+ conflict_type: ConflictType
+ special_git_file_contents_by_path: Annotated[FrozenDict[str, bytes], PydanticFrozenDictAnnotation]
+
+
+class CleanRepoOperation(RepoOperation):
+ """
+ A clean repo operation is a repo operation that has no conflicts.
+
+ It is a contains the staged diff, the unstaged diff, and the combination of the previous two.
+ """
+
+ object_type: str = "CleanRepoOperation"
+ combined_diff: str
+ staged_diff: str = ""
+ unstaged_diff: str = ""
+
+ # FIXME: this is now doing validation -- should be converted to the pydantic way of doing this!
+ def model_post_init(self, __context: Any) -> None:
+ super().model_post_init(__context)
+ if self.combined_diff.strip() != "":
+ assert (
+ self.staged_diff.strip() != "" or self.unstaged_diff.strip() != ""
+ ), "combined diff is not empty, so staged and unstaged diffs must be non-empty"
+
+ @computed_field
+ @cached_property
+ def is_empty(self) -> bool:
+ return self.combined_diff.strip() == ""
+
+
+class RepoState(SerializableModel):
+ git_hash: str
+ repo_operation: (
+ Annotated[CleanRepoOperation, Tag("CleanRepoOperation")]
+ | Annotated[ConflictedRepoOperation, Tag("ConflictedRepoOperation")]
+ ) = Field(discriminator=build_discriminator())
+
+ @computed_field
+ @cached_property
+ def is_conflicted(self) -> bool:
+ return isinstance(self.repo_operation, ConflictedRepoOperation)
+
+ @computed_field
+ @cached_property
+ def has_operations(self) -> bool:
+ repo_operation = self.repo_operation
+ return isinstance(repo_operation, ConflictedRepoOperation) or repo_operation.combined_diff.strip() != ""
+
+ @computed_field
+ @cached_property
+ def type_name(self) -> str:
+ return self.__class__.__name__
+
+ def build_with_new_commit(self, git_hash: str) -> "RepoState":
+ return RepoState(git_hash=git_hash, repo_operation=self.repo_operation)
+
+
+GIT_FILE_PATH_NAMES_BY_CONFLICT_TYPE: dict[ConflictType, tuple[str, ...]] = {
+ ConflictType.MERGE: ("MERGE_HEAD", "AUTO_MERGE", "MERGE_MSG", "MERGE_MODE"),
+ ConflictType.REBASE: ("REBASE_HEAD",),
+ ConflictType.CHERRY_PICK: ("CHERRY_PICK_HEAD",),
+ ConflictType.APPLY: (),
+ ConflictType.REVERT: ("REVERT_HEAD",),
+}
diff --git a/imbue_core/imbue_core/retry_utils.py b/imbue_core/imbue_core/retry_utils.py
@@ -0,0 +1,30 @@
+from typing import Callable
+
+from loguru import logger
+from tenacity import RetryCallState
+
+
+def _log_before_sleep(retry_state: RetryCallState, log_fn: Callable[[str], None]) -> None:
+ fn_name = getattr(retry_state.fn, "__name__", "unknown")
+ sleep_time = retry_state.next_action.sleep if retry_state.next_action is not None else 0
+ outcome = retry_state.outcome
+ if outcome is not None:
+ exception = outcome.exception()
+ error_message = type(exception).__name__ + ": " + str(exception)
+ else:
+ error_message = "unknown"
+ log_fn(
+ f"Retrying {fn_name} in {sleep_time:.2f} seconds, attempt {retry_state.attempt_number} after error: {error_message}"
+ )
+
+
+def log_before_sleep(retry_state: RetryCallState) -> None:
+ _log_before_sleep(retry_state, logger.debug)
+
+
+def log_error_before_sleep(retry_state: RetryCallState) -> None:
+ _log_before_sleep(retry_state, logger.error)
+
+
+def log_trace_before_sleep(retry_state: RetryCallState) -> None:
+ _log_before_sleep(retry_state, logger.trace)
diff --git a/imbue_core/imbue_core/s3_uploader.py b/imbue_core/imbue_core/s3_uploader.py
@@ -0,0 +1,213 @@
+import threading
+import time
+import typing
+import uuid
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime
+from datetime import timezone
+
+import boto3
+from botocore import UNSIGNED
+from botocore.config import Config
+from loguru import logger
+
+if typing.TYPE_CHECKING:
+ # type: ignore[import-not-found]: pyre on modal does't believe in mypy_boto3_s3
+ from mypy_boto3_s3 import Client
+else:
+ Client = object
+
+from pydantic import PrivateAttr
+
+from imbue_core.pydantic_serialization import FrozenModel
+
+EXTRAS_UPLOADED_FILES_KEY = "uploaded_files"
+
+PRODUCTION_UPLOADS_BUCKET = "traceback-uploads-production"
+STAGING_UPLOADS_BUCKET = "traceback-uploads-staging"
+
+DEFAULT_REGION = "us-west-2"
+MAXIMUM_QUEUED_S3_UPLOADS = (
+ 50 # rather arbitrary but better to err on the side of caution when going from the current unbounded
+)
+
+
+class _S3Uploader(FrozenModel):
+ bucket: str
+ region: str
+ maximum_concurrency: int = MAXIMUM_QUEUED_S3_UPLOADS
+
+ _s3_client: Client = PrivateAttr() # type: ignore[valid-type]
+
+ # protects access to the thread collections
+ _thread_pool: ThreadPoolExecutor = PrivateAttr()
+ _thread_limiter: threading.Semaphore = PrivateAttr()
+
+ def model_post_init(self, context) -> None:
+ # NOTE: we use an unsigned client to avoid the need to provide AWS credentials.
+ self._s3_client = boto3.client("s3", region_name=self.region, config=Config(signature_version=UNSIGNED))
+ self._thread_pool = ThreadPoolExecutor(max_workers=None, thread_name_prefix=f"s3_upload")
+ # Unfortunately, there's no safe access to the queue size of the thread pool so calculating that number precisely
+ # using a semaphore. Each queued up uploads acquires a single value and returns it only after its thread is done
+ # interacting with S3. The value of the semaphore at any given time is the number of available work slots, and
+ # it cannot go negative. The semaphore is bounded purely to track that there are no more releases than acquisitions.
+ self._thread_limiter = threading.BoundedSemaphore(self.maximum_concurrency)
+
+ def _upload_thread(self, key: str, contents: bytes) -> None:
+ try:
+ logger.debug("Uploading to s3://{}/{}", self.bucket, key)
+ # NOTE: we use put_object instead of upload_file because we don't want multipart uploads
+ # multipart uploads are not allowed for unsigned clients
+ self._s3_client.put_object(
+ Bucket=self.bucket,
+ Key=key,
+ Body=contents,
+ )
+ logger.debug("Done uploading to s3://{}/{}", self.bucket, key) # XXX remove before merge
+ except Exception as e:
+ logger.info("Failed to upload {} to S3: {}", key, e)
+ # if re-raised, who would even catch this exception?
+ finally:
+ self._thread_limiter.release()
+
+ def s3_uri_from_key(self, key: str) -> str:
+ return f"s3://{self.bucket}/{key}"
+
+ def upload_if_possible(self, key: str, contents: bytes) -> str | None:
+ """Returns the S3 URL of the upload or None if the upload is not possible"""
+ if not self._thread_limiter.acquire(timeout=0):
+ logger.debug(
+ "Skipping upload to {key}, maximum concurrent uploads in progress already (limit={limit})",
+ key=key,
+ limit=self.maximum_concurrency,
+ )
+ return None
+
+ try:
+ self._thread_pool.submit(self._upload_thread, key, contents)
+ except Exception as e:
+ # we have to release the semaphore since the thread didn't start
+ # this shouldn't ever happen but the docs for `.submit` don't promise
+ # anything
+ self._thread_limiter.release()
+ logger.debug("Failed to queue a thread for an upload to {key}: {e}", key=key, e=e)
+ return None
+
+ return self.s3_uri_from_key(key)
+
+ def wait_for_all_uploads(self, timeout: float | None, is_shutting_down: bool) -> bool:
+ """Waits for all the uploads that may still be in progress or queued.
+
+ When is_shutting_down is True, the function will block until all uploads are completed and will disable any
+ future use of the uploader.
+
+ The is_shutting_down parameter is meant to be overridden only in tests as a way to checkpoint before
+ proceeding to schedule more work.
+ """
+ deadline = None
+ if timeout is not None:
+ deadline = time.monotonic() + timeout
+
+ # tracks the number of work slots from semaphore that this function already acquired
+ # if all self.maximum_concurrency values are collected then we guarantee that no other
+ # work is queue or in progress
+ n = 0
+
+ # a fast pass to collect available tickets before we start waiting and log messages to the user.
+ while self._thread_limiter.acquire(timeout=0):
+ n += 1
+
+ while (deadline is None or time.monotonic() < deadline) and n < self.maximum_concurrency:
+ if is_shutting_down:
+ logger.info(
+ "Please stand by: waiting for remaining uploads to finish! Still uploading: {} reports",
+ self.maximum_concurrency - n,
+ )
+ timeout = None if deadline is None else deadline - time.monotonic()
+ if self._thread_limiter.acquire(timeout=timeout):
+ n += 1
+
+ all_done = n == self.maximum_concurrency
+ if is_shutting_down:
+ # block more work from getting scheduled in case we didn't gobble up all the slots
+ # this may also make the semaphore out of sync, as cancelled queued features will not
+ # release theirs
+ self._thread_pool.shutdown(wait=False, cancel_futures=True)
+ if not all_done:
+ logger.info(
+ "Failed to upload the S3 reports after timeout reached (timeout={}), {} reports still uploading",
+ timeout,
+ self.maximum_concurrency - n,
+ )
+ elif n > 0:
+ # allow further work
+ logger.debug("Letting go of {n} slots", n=n)
+ self._thread_limiter.release(n)
+
+ return all_done
+
+
+# FIXME: move the methods below to error-handling specific module and get rid of this global variable if possible
+_S3_UPLOADER: _S3Uploader | None = None
+
+
+def setup_s3_uploads(is_production: bool = False) -> None:
+ """Set up S3 upload settings."""
+ global _S3_UPLOADER
+ if _S3_UPLOADER is not None:
+ logger.debug("S3 upload settings already initialized, skipping setup")
+ return
+ if is_production:
+ bucket_name = PRODUCTION_UPLOADS_BUCKET
+ else:
+ bucket_name = STAGING_UPLOADS_BUCKET
+ _S3_UPLOADER = _S3Uploader(bucket=bucket_name, region=DEFAULT_REGION)
+
+
+def get_s3_upload_key(key_prefix: str, key_suffix: str) -> str:
+ """Get a URL for an S3 upload."""
+ key = (
+ "_".join(
+ [
+ key_prefix,
+ datetime.now(timezone.utc).strftime("%Y-%m-%dT%H-%M-%S"),
+ uuid.uuid4().hex,
+ ]
+ )
+ + key_suffix
+ )
+ return key
+
+
+def get_s3_upload_url(key: str) -> str | None:
+ """Get a URL for an S3 upload."""
+ if _S3_UPLOADER is None:
+ logger.info("S3 upload settings not initialized. Skipping upload.")
+ return None
+ return _S3_UPLOADER.s3_uri_from_key(key)
+
+
+def upload_to_s3_with_key(key: str, contents: bytes) -> str | None:
+ """Upload a file to S3 and return the S3 URL. Returns None if upload is not possible."""
+ if _S3_UPLOADER is None:
+ logger.info("S3 upload settings not initialized. Skipping upload.")
+ return None
+ return _S3_UPLOADER.upload_if_possible(key, contents)
+
+
+def upload_to_s3(key_prefix: str, key_suffix: str, contents: bytes) -> str | None:
+ """Upload a file to S3 in the background."""
+ if _S3_UPLOADER is None:
+ logger.info("S3 upload settings not initialized. Skipping upload.")
+ return None
+
+ key = get_s3_upload_key(key_prefix, key_suffix)
+ return upload_to_s3_with_key(key, contents)
+
+
+def wait_for_s3_uploads(timeout: float | None, is_shutting_down: bool) -> bool | None:
+ logger.info("Checking whether S3 uploads are still in progress!")
+ if _S3_UPLOADER is None:
+ return None
+
+ return _S3_UPLOADER.wait_for_all_uploads(timeout=timeout, is_shutting_down=is_shutting_down)
diff --git a/imbue_core/imbue_core/sculptor/__init__.py b/imbue_core/imbue_core/sculptor/__init__.py
@@ -0,0 +1,4 @@
+"""
+This package defines Sculptor types and utilities that need to be shared with other projects,
+currently imbue_cli and imbue_verify.
+"""
diff --git a/imbue_core/imbue_core/sculptor/state/chat_state.py b/imbue_core/imbue_core/sculptor/state/chat_state.py
@@ -0,0 +1,188 @@
+from enum import StrEnum
+from typing import Annotated
+from typing import Any
+from typing import Literal
+
+from pydantic import Field
+from pydantic import Tag
+
+from imbue_core.agents.data_types.ids import AgentMessageID
+from imbue_core.agents.data_types.ids import TaskID
+from imbue_core.ids import ToolUseID
+from imbue_core.imbue_cli.action import ActionOutput as ImbueCLIActionOutput
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.pydantic_serialization import build_discriminator
+
+# ========================
+# 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")
+
+
+class ImbueCLIToolContent(ToolResultContent):
+ """Content for Imbue CLI MCP results that may be composed of multiple actions"""
+
+ content_type: Literal["imbue_cli"] = "imbue_cli"
+
+ action_outputs: list[ImbueCLIActionOutput] = Field(
+ ..., description="List of action outputs from the Imbue CLI tool"
+ )
+
+
+ToolResultContentType = GenericToolContent | DiffToolContent | ImbueCLIToolContent
+
+
+class ToolResultBlockSimple(ContentBlock):
+ object_type: str = "ToolResultBlockSimple"
+ type: Literal["tool_result_simple"] = "tool_result_simple"
+ 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: SimpleToolContent | ImbueCLIToolContent = Field(..., description="Result content from the tool execution")
+ is_error: bool = Field(default=False, description="Whether the tool execution resulted in an error")
+
+
+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(),
+]
+
+
+class ChatMessageRole(StrEnum):
+ USER = "USER"
+ ASSISTANT = "ASSISTANT"
+
+
+class ChatMessage(SerializableModel):
+ """Chat message with content blocks. A ChatMessage corresponds to a single turn in the conversation."""
+
+ role: ChatMessageRole
+ id: AgentMessageID
+ content: tuple[ContentBlockTypes, ...]
+ snapshot_id: str | None = None
+ did_snapshot_fail: bool = False
diff --git a/imbue_core/imbue_core/sculptor/state/messages.py b/imbue_core/imbue_core/sculptor/state/messages.py
@@ -0,0 +1,141 @@
+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.agents.data_types.ids import AgentMessageID
+from imbue_core.ids import AssistantMessageID
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.pydantic_serialization import build_discriminator
+from imbue_core.sculptor.state.chat_state import ContentBlockTypes
+from imbue_core.sculptor.telemetry import PosthogEventPayload
+from imbue_core.sculptor.telemetry_constants import ConsentLevel
+from imbue_core.sculptor.telemetry_utils import with_consent
+from imbue_core.sculptor.telemetry_utils import without_consent
+from imbue_core.time_utils import get_current_time
+
+
+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 the AGENT (in-container LLM), USER (chat messages & direct interactions), SCULPTOR_SYSTEM (multifaceted sculptor app and service code) and RUNNER (the process controlling a task on the server.)
+ """
+
+ # Messages coming directly from the agent from inside the environment.
+ AGENT = "AGENT"
+
+ # Messages coming directly from a user interacting with the interface, ie chat
+ USER = "USER"
+
+ # Messages coming from sculptor-mediated actions and automations, like local sync updates or manual sync operations.
+ # If there is ambiguity, (ie, "the user _did_ click a button but we did a lot of magic in the resolution") prefer SCULPTOR_SYSTEM.
+ SCULPTOR_SYSTEM = "SCULPTOR_SYSTEM"
+
+ # Messages coming from the task runner wrapper, such as environment shutdown.
+ # conceptually a subset of SCULPTOR_SYSTEM
+ 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.
+ # FIXME: get rid of the explicit passing of message_id
+ 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.
+ # note that this is approximate due to clock skew -- these messages are created on different machines.
+ # you should *not* sort by this field -- instead, rely on the order in which the messages are received.
+ approximate_creation_time: datetime.datetime = Field(default_factory=get_current_time)
+
+ # if the message is ephemeral, it will be logged but not saved to the database
+ # if it is persistent, it will be logged AND saved to the database
+ @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, PosthogEventPayload):
+ """
+ One of two base classes for messages sent from the user.
+ Persistent user messages are saved to the database.
+ Persistent user messages are queued in the task runner before they are sent to the agent.
+ """
+
+ # Override inherited fields with consent annotations
+ # TODO (moishe): if other classes that derive from Message also start getting logged,
+ # change the base Message class to derive from PosthogEventPayload. For now, doing
+ # that is overkill and requires lots of annotations of irrelevant classes.
+ #
+ # TODO (mjr): We should really have `PersistentHoggableMessage` and `EphemeralHoggableMessage` or something
+ object_type: str = without_consent(description="Type discriminator for user messages")
+ message_id: AgentMessageID = without_consent(
+ default_factory=AgentMessageID,
+ description="Unique identifier for the user message",
+ )
+ source: AgentMessageSource = without_consent(default=AgentMessageSource.USER)
+ approximate_creation_time: datetime.datetime = without_consent(
+ default_factory=get_current_time,
+ description="Approximate UTC timestamp when user message was created",
+ )
+
+
+class ChatInputUserMessage(PersistentUserMessage):
+ object_type: str = without_consent(default="ChatInputUserMessage")
+ text: str = with_consent(ConsentLevel.LLM_LOGS, description="User input text content")
+ model_name: LLMModel = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=None,
+ description="Selected LLM model for the chat request",
+ )
+ files: list[str] = with_consent(
+ ConsentLevel.LLM_LOGS,
+ default_factory=list,
+ description="List of file paths (images, PDFs, etc., stored in Electron app folder) 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/imbue_core/imbue_core/sculptor/telemetry.py b/imbue_core/imbue_core/sculptor/telemetry.py
@@ -0,0 +1,809 @@
+"""This module exposes an interface for instrumenting telemetry in Sculptor. It's implemented in Imbue Core so that it
+may be re-used between both Sculptor and Imbue CLI.
+
+To use this module well, you MUST:
+
+* call either init_posthog or init_anonymous_posthog on application or lifetime startup.
+* Make sure to call shutdown_posthog() on application close.
+
+* emit_posthog_event() is a low-level library function to send an event to PostHog. If you are a product developer on
+ Sculptor, you should probably use fire_posthog_event instead.
+
+Similary you can call
+
+* init_sentry()
+* and MUST call flush_sentry_and_exit_program()
+
+For some reason, flush_sentry_and_exit_program() also shuts down posthog. We will figure this out.
+"""
+
+import os
+import traceback
+from collections import defaultdict
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Any
+from typing import Callable
+from typing import Generator
+from typing import Generic
+from typing import Mapping
+from typing import Optional
+from typing import Protocol
+from typing import TypeVar
+from typing import cast
+from typing import runtime_checkable
+
+import sentry_sdk
+from loguru import logger
+from posthog import Posthog
+from posthog.scopes import identify_context
+from posthog.scopes import new_context
+from pydantic import BaseModel
+from pydantic import ConfigDict
+from pydantic import Field
+from pydantic import ValidationError
+from pydantic import create_model
+from pydantic.fields import FieldInfo
+from sentry_sdk.types import Event
+from sentry_sdk.types import Hint
+
+from imbue_core.agents.data_types.ids import TaskID
+from imbue_core.async_monkey_patches import inject_exception_and_log
+from imbue_core.async_monkey_patches import log_exception
+from imbue_core.async_monkey_patches import pre_filter_exception
+from imbue_core.common import is_running_within_a_pytest_tree
+from imbue_core.constants import ExceptionPriority
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.pydantic_utils import model_update
+from imbue_core.sculptor.telemetry_constants import ConsentLevel
+from imbue_core.sculptor.telemetry_constants import ProductComponent
+from imbue_core.sculptor.telemetry_constants import SculptorPosthogEvent
+from imbue_core.sculptor.telemetry_constants import UserAction
+from imbue_core.sculptor.telemetry_utils import with_consent
+from imbue_core.sculptor.telemetry_utils import without_consent
+from imbue_core.sculptor.user_config import PrivacySettings
+from imbue_core.sculptor.user_config import UserConfig
+
+# This file is written into the state directory by the Sculptor server inside the task container
+# to provide it with the telemetry info for the task.
+TELEMETRY_TASK_INFO_JSON_STATE_FILE = "telemetry_task_info.json"
+
+
+class TelemetryInfo(SerializableModel):
+ """Information needed for setting up telemetry.
+
+ This data structure is generated once in the Sculptor server,
+ and gets propagated elsewhere (such as to Imbue CLI).
+ """
+
+ # Putting the User Config into this object is a smell. The UserConfig can and will change idependently of this
+ # model, and that can lead to all sorts of issues. Consider refactoring this code.
+ user_config: UserConfig
+ sculptor_version: str
+ sculptor_git_sha: str
+ sculptor_execution_instance_id: str
+ posthog_token: str
+ posthog_api_host: str
+ sentry_dsn: str
+
+
+class TelemetryProjectInfo(SerializableModel):
+ """Used to communicate project-level information tasks inside containers."""
+
+ telemetry_info: TelemetryInfo
+ project_id: str
+
+ # Does not contain a token -- that should come through the environment.
+ gitlab_mirror_repo_url: str | None
+ original_git_repo_url: str | None
+
+
+class TelemetryTaskInfo(SerializableModel):
+ """Used to communicate task-level information tasks inside containers."""
+
+ telemetry_project_info: TelemetryProjectInfo
+ task_id: TaskID
+
+
+class PosthogEventPayload(SerializableModel):
+ """A base model for PostHog events that validates the presence of
+ 'consent_level' metadata on each field.
+ """
+
+ def __init_subclass__(cls, **kwargs: Any) -> None:
+ super().__init_subclass__(**kwargs) # pyre-fixme[6]: pyre can't type check this untyped dict
+
+ # Run validation after subclass is defined
+ cls._validate_class()
+
+ @classmethod
+ def _validate_class(cls) -> None:
+ """
+ Checks that every field has a 'consent_level' in its JSON schema metadata.
+ Issues a UserWarning if the metadata is missing.
+ """
+ for field_name in cls.__annotations__.keys():
+ field_info = cls.__dict__[field_name]
+ # Check that we're using pydantic.Field
+ assert isinstance(field_info, FieldInfo), "Field {} does not extend pydantic.Field".format(field_name)
+ # Get the extra schema info, defaulting to an empty dict if it's None
+ extra_schema: dict[str, Any] = {}
+ match field_info.json_schema_extra:
+ # If it's a callable we can call it to get the FieldInfo
+ case None:
+ pass
+ case dict() as d:
+ extra_schema = d
+ case func if callable(func):
+ maybe_extra_schema = func({})
+ # TODO: func is supposed to be -> None, so the following should in theory never happen...
+ if maybe_extra_schema is not None:
+ extra_schema = cast(dict[str, Any], maybe_extra_schema)
+ case _:
+ pass
+
+ assert (
+ "consent_level" in extra_schema
+ ), """Field '{}' in '{}' is missing the
+'consent_level' metadata. Please use the decorator
+with_consent or without_consent to populate the field annotation:
+`json_schema_extra={{'consent_level': ...}}`""".format(
+ field_name, cls.__name__
+ )
+
+
+# All data models sent to PostHog MUST define consent annotations and subclass PosthogEventPayload.
+T = TypeVar("T", bound=PosthogEventPayload)
+
+
+# Potentially we could have a ratchet test to remind folks to use
+# consent decorators.
+class PosthogEventModel(SerializableModel, Generic[T]):
+ """
+ Represents a PostHog event, with each field tagged
+ with the minimum consent level required for logging.
+ """
+
+ # Always defined fields
+ name: SculptorPosthogEvent = without_consent(description="Name of event, give it meaning!")
+ component: ProductComponent = without_consent(description="App component")
+
+ # User Activity field
+ action: UserAction | None = with_consent(ConsentLevel.PRODUCT_ANALYTICS)
+
+ # Task ID - should be set if this event is associated with a task.
+ task_id: str | None = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ description="The task id if this event is task-specific",
+ )
+
+ # Payload field with consent level
+ payload: T | None = without_consent(description="PostHog Event payload Model")
+
+
+def _create_posthog_event_payload_event_data_class(
+ additional_field_definitions: Mapping[str, Any] | None = None,
+) -> type[PosthogEventPayload]:
+ """Generates a subclass of PosthogEventPayload with the type annotations and consent declarations set up.
+
+ The PosthogEventPayload type can very based on the Event, so we are going to provide a mechanism to call this for
+ any Posthog Event that needs it.
+ """
+ field_definitions = {}
+
+ additional_field_definitions = additional_field_definitions or {}
+
+ for field_name, field_info in UserConfig.model_fields.items():
+ field_type = field_info.annotation | None if field_info.annotation else None
+ field_definitions[field_name] = (field_type, field_info)
+
+ for field_name, field_tuple in additional_field_definitions.items():
+ field_definitions[field_name] = field_tuple
+
+ field_definitions["sculptor_version"] = (Optional[str], without_consent())
+
+ return create_model(
+ "TelemetryInfoEventData",
+ __base__=PosthogEventPayload,
+ **field_definitions,
+ )
+
+
+# When we don't know what kind of Payload to use, we use this, which is the bare minimum TelemetryInfo data.
+TelemetryInfoEventData: type[PosthogEventPayload] = _create_posthog_event_payload_event_data_class()
+
+
+# For every Event, we define additional fields that it might have.
+# NOTE: This will need to be refactored into a proper covariant pattern, but this works for now.
+SCULPTOR_POSTHOG_EVENT_TO_PAYLOAD_TYPE = defaultdict(
+ lambda: TelemetryInfoEventData,
+ {
+ SculptorPosthogEvent.ONBOARDING_EMAIL_CONFIRMATION: _create_posthog_event_payload_event_data_class(
+ {"did_opt_in_to_marketing": (Optional[bool], without_consent())}
+ )
+ },
+)
+
+
+def make_telemetry_event_data(telemetry_info: TelemetryInfo) -> PosthogEventPayload:
+ user_config_data = telemetry_info.user_config.model_dump()
+ return TelemetryInfoEventData(**user_config_data)
+
+
+@runtime_checkable
+class PosthogProtocol(Protocol):
+ """
+ A protocol satisfied by the Posthog client class and a stub implementation.
+ """
+
+ host: str
+
+ def capture(
+ self,
+ distinct_id=None,
+ event=None,
+ properties=None,
+ timestamp=None,
+ uuid=None,
+ groups=None,
+ send_feature_flags=False,
+ disable_geoip=None,
+ ) -> None:
+ """
+ Capture a telemetry event.
+ """
+
+ def identify(self, identifier, properties=None) -> None:
+ """
+ Identifies a user.
+ """
+
+ def alias(
+ self,
+ previous_id=None,
+ distinct_id=None,
+ context=None,
+ timestamp=None,
+ uuid=None,
+ disable_geoip=None,
+ ) -> None:
+ """
+ Links two users together, distinct_id -(maps)-> previous_id
+ """
+
+ def shutdown(self) -> None:
+ """
+ Flush all messages and cleanly shut down the client.
+ """
+
+ def capture_exception(self, exception: BaseException, distinct_id=None, properties=None) -> None:
+ """Capture an exception event."""
+
+
+# TODO: Should this inherit from PosthogProtocol?
+class StubPosthog:
+ host: str = "stub"
+
+ def capture(
+ self,
+ distinct_id=None,
+ event=None,
+ properties=None,
+ timestamp=None,
+ uuid=None,
+ groups=None,
+ send_feature_flags=False,
+ disable_geoip=None,
+ ) -> None:
+ # Do nothing for now.
+ #
+ # If we want to test calls to posthog.capture later,
+ # we can augment this method to save the arguments internally.
+ pass
+
+ def identify(self, identifier, properties=None) -> None:
+ pass
+
+ def alias(
+ self,
+ previous_id=None,
+ distinct_id=None,
+ context=None,
+ timestamp=None,
+ uuid=None,
+ disable_geoip=None,
+ ) -> None:
+ pass
+
+ def shutdown(self) -> None:
+ pass
+
+ def capture_exception(self, exception: BaseException, distinct_id=None, properties=None) -> None:
+ pass
+
+
+class PosthogUserInstance(BaseModel):
+ model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True)
+ posthog_instance: PosthogProtocol
+ is_anonymous: bool
+
+ # Every PosthogUserInstance needs to access the UserConfig, this is a function which returns that.
+ user_config_accessor: Callable[[], UserConfig]
+
+ def access_user_config(self) -> UserConfig:
+ return self.user_config_accessor()
+
+
+class AnonymousPosthogUserInstance(PosthogUserInstance):
+ """This specific case is used to store a user when they happen to be initted."""
+
+ initial_user_id: str
+
+
+# This private in-memory cached value is set or updated whenever we
+# * begin with an anonymous user (init_posthog)
+# * begin with an identified user (init_anonymous_posthog)
+# * convert an identified user into an anonymous user (identify_user)
+_POSTHOG_USER_INSTANCE: PosthogUserInstance | AnonymousPosthogUserInstance | None = None
+
+
+def is_posthog_identified() -> bool:
+ return _POSTHOG_USER_INSTANCE is not None and not _POSTHOG_USER_INSTANCE.is_anonymous
+
+
+# This used to be cached, but we wanted the user to be able to change telemetry preferences within a container.
+def _get_telemetry_task_info_if_inside_container() -> TelemetryTaskInfo | None:
+ """Mock this for testing.
+
+ It is arranged thus because `get_telemetry_task_info_if_inside_container` is imported in many places,
+ and to mock, monkeypatch would need to replace definitions at those locations.
+
+ With this, monkeypatch only needs to replace this function."""
+ telemetry_info_path = Path("/imbue_addons/state") / TELEMETRY_TASK_INFO_JSON_STATE_FILE
+ if telemetry_info_path.exists():
+ try:
+ telemetry_task_info = TelemetryTaskInfo.model_validate_json(telemetry_info_path.read_text())
+ return telemetry_task_info
+ except ValidationError as e:
+ log_exception(
+ e,
+ "Telemetry info file {telemetry_info_path} invalid, not initializing Posthog.",
+ telemetry_info_path=telemetry_info_path,
+ )
+ return None
+
+
+def get_telemetry_task_info_if_inside_container() -> TelemetryTaskInfo | None:
+ """Loads the telemetry task info from the expected location in the container.
+ This is a no-op if the file doesn't exist."""
+ return _get_telemetry_task_info_if_inside_container()
+
+
+# TODO (CAP-636): Remove upstream git repo logic once GitLab mirroring is completed.
+def get_original_git_repo_url_if_inside_container() -> str | None:
+ telemetry_task_info = get_telemetry_task_info_if_inside_container()
+ if telemetry_task_info and telemetry_task_info.telemetry_project_info.original_git_repo_url:
+ return telemetry_task_info.telemetry_project_info.original_git_repo_url
+ return None
+
+
+def init_posthog(
+ info: TelemetryInfo,
+ source: str,
+ user_config_accessor: Callable[[], UserConfig] | None = None,
+ is_anonymous: bool = False,
+) -> None:
+ """Initialize Posthog for a _known_ user.
+
+ After this function is called,
+ get_user_posthog_instance and posthog_context can be used.
+
+ This function lives here so that the Sculptor backend and Imbue CLI can initialize PostHog in the same way.
+
+ Args:
+ user_config_accessor: Primarily exists to provide the PosthogUser instance a way to get the _latest_ user config at any time.
+ """
+ global _POSTHOG_USER_INSTANCE
+ if _POSTHOG_USER_INSTANCE is not None:
+ raise RuntimeError("Posthog endpoint already initialized.")
+
+ posthog: Posthog | StubPosthog
+
+ # TODO: Try to remove this test-specific code if possible.
+ if is_running_within_a_pytest_tree():
+ posthog = StubPosthog()
+ else:
+ posthog = Posthog(
+ info.posthog_token,
+ host=info.posthog_api_host,
+ super_properties={
+ "source": source,
+ "sculptor_version": info.sculptor_version,
+ "session": {
+ # In theory we should go through the accessor here,
+ # but at init time, when the user start the first time,
+ # they should be the same.
+ "instance_id": info.user_config.instance_id,
+ "execution_instance_id": info.sculptor_execution_instance_id,
+ },
+ },
+ )
+ if not is_anonymous:
+ posthog.identify(
+ info.user_config.user_id,
+ {"email": info.user_config.user_email},
+ )
+
+ if is_anonymous:
+ _POSTHOG_USER_INSTANCE = AnonymousPosthogUserInstance(
+ posthog_instance=posthog, # pyre-fixme[6]: pyre seems confused by PosthogProtocol
+ is_anonymous=True,
+ initial_user_id=info.user_config.user_id,
+ user_config_accessor=user_config_accessor or (lambda: info.user_config),
+ )
+ else:
+ _POSTHOG_USER_INSTANCE = PosthogUserInstance(
+ posthog_instance=posthog, # pyre-fixme[6]: pyre seems confused by PosthogProtocol
+ is_anonymous=False,
+ user_config_accessor=user_config_accessor or (lambda: info.user_config),
+ )
+
+
+def identify_posthog_user(user_config_accessor: Callable[[], UserConfig]) -> None:
+ """Update the initialized PostHog instance with user identity.
+
+ At this point, the previous posthog instance should be an anonymous PostHog instance.
+ This means logs emitted prior to this call will create a new person entry and not
+ populate `person.properties.email` field.
+
+ We will use PostHog client's alias function to associate two unique ids together to
+ the same person entry.
+ """
+ global _POSTHOG_USER_INSTANCE
+ if _POSTHOG_USER_INSTANCE is None:
+ logger.error("Posthog endpoint not initialized")
+ return
+ if not _POSTHOG_USER_INSTANCE.is_anonymous:
+ logger.error("Posthog endpoint already identified with user")
+ return
+
+ if not isinstance(_POSTHOG_USER_INSTANCE, AnonymousPosthogUserInstance):
+ raise RuntimeError("Anonymous PosthogUser instance expected")
+
+ # At this point, the previous DataModel holds the anonymous instance id generated.
+ initial_user_id = _POSTHOG_USER_INSTANCE.initial_user_id
+
+ # We can preserve the previously running posthog instance.
+ posthog = _POSTHOG_USER_INSTANCE.posthog_instance
+
+ _POSTHOG_USER_INSTANCE = PosthogUserInstance(
+ posthog_instance=posthog,
+ is_anonymous=False,
+ user_config_accessor=user_config_accessor,
+ )
+
+ latest_user_config = _POSTHOG_USER_INSTANCE.access_user_config()
+
+ # Identify should only be called once per instance lifetime.
+ posthog.identify(
+ initial_user_id,
+ {"email": latest_user_config.user_email},
+ )
+ posthog.alias(previous_id=latest_user_config.user_id, distinct_id=initial_user_id)
+
+ logger.info(
+ "Associating identified user {} with current instance initial user {}",
+ latest_user_config.user_id,
+ initial_user_id,
+ )
+
+
+def get_user_posthog_instance() -> PosthogUserInstance | None:
+ """Returns the global PostHog client or None if it has not been configured"""
+ return _POSTHOG_USER_INSTANCE
+
+
+@contextmanager
+def posthog_context(
+ posthog_user_instance: PosthogUserInstance | None = None,
+) -> Generator[PosthogProtocol, None, None]:
+ """A context manager that creates a PostHog context with the appropriate user ID.
+
+ Must be called after init_posthog or with a passed in posthog_user_instance.
+
+ TODO: Can we delete this in favor of emit_posthog_event? If not, explain here when to use this.
+ TODO: Do we actually need to yield the PosthogProtocol?
+ """
+ posthog_user_instance = posthog_user_instance or _POSTHOG_USER_INSTANCE
+ assert posthog_user_instance is not None
+ with new_context():
+ # Not having a distinct ID is troublesome...
+ current_user_id = posthog_user_instance.access_user_config().user_id
+ identify_context(current_user_id)
+ try:
+ yield posthog_user_instance.posthog_instance
+ except Exception as e:
+ log_exception(e, "Error in logging to posthog")
+
+
+def is_consent_allowable(required_consent: ConsentLevel | None, privacy_settings: PrivacySettings) -> bool:
+ """Check the appropriate value of user consent fields to establish allowable consent."""
+ if required_consent is None:
+ return True
+ elif required_consent == ConsentLevel.NONE:
+ return True
+ elif required_consent == ConsentLevel.ERROR_REPORTING:
+ return privacy_settings.is_error_reporting_enabled
+ elif required_consent == ConsentLevel.PRODUCT_ANALYTICS:
+ return privacy_settings.is_product_analytics_enabled
+ elif required_consent == ConsentLevel.LLM_LOGS:
+ return privacy_settings.is_llm_logs_enabled
+ elif required_consent == ConsentLevel.SESSION_RECORDING:
+ return privacy_settings.is_session_recording_enabled
+ elif required_consent == ConsentLevel.NEVER_PERSIST:
+ return False
+ else:
+ logger.info("Unexpected consent level: {}", required_consent)
+ return False
+
+
+def filter_model_by_consent(model: SerializableModel, privacy_settings: PrivacySettings) -> SerializableModel:
+ """Recursively filter a SerializableModel based on consent toggles.
+
+ Args:
+ model: The model to filter
+ user_config: The user's configuration with consent toggles
+
+ Returns:
+ SerializableModel with None for fields that don't meet the consent requirements.
+ Tries to handle ValidationError gracefully by creating compatible models.
+ """
+ updates: dict[str, SerializableModel | list[SerializableModel] | None] = {}
+
+ for field_name, field_info in model.__class__.model_fields.items():
+ field_value = getattr(model, field_name)
+
+ # Retrieve the metadata we attached using the decorator
+ metadata = field_info.json_schema_extra or {}
+ required_level = metadata.get("consent_level")
+
+ # A field without a consent level is considered public OR
+ # Include the field if the user's consent level is sufficient
+ if is_consent_allowable(required_level, privacy_settings):
+ # If the field value is also a SerializableModel, recursively filter it
+ if isinstance(field_value, SerializableModel):
+ updates[field_name] = filter_model_by_consent(field_value, privacy_settings)
+ elif isinstance(field_value, list):
+ filtered_list = []
+ for item in field_value:
+ if isinstance(item, SerializableModel):
+ filtered_list.append(filter_model_by_consent(item, privacy_settings))
+ else:
+ filtered_list.append(item)
+ updates[field_name] = filtered_list
+ else:
+ updates[field_name] = field_value
+ else:
+ # Set field to None if consent is not allowable
+ updates[field_name] = None
+
+ try:
+ # Try the standard approach first
+ return model_update(model, updates)
+ except ValidationError:
+ # If validation fails due to non-optional fields being set to None,
+ # create a new model where those fields are Optional
+
+ field_definitions: dict[str, tuple[type[Any] | None, FieldInfo | None]] = {}
+ for field_name, field_info in model.__class__.model_fields.items():
+ if field_name in updates and updates[field_name] is None:
+ # Make this field Optional since we're setting it to None, preserving metadata
+ field_definitions[field_name] = (
+ field_info.annotation | None,
+ Field(default=None, json_schema_extra=field_info.json_schema_extra),
+ )
+ else:
+ # Keep original field definition
+ field_definitions[field_name] = (field_info.annotation, field_info)
+
+ # Create a new model class with filtered fields made Optional
+ base_class = (
+ model.__class__
+ if hasattr(model.__class__, "__bases__") and model.__class__.__bases__
+ else SerializableModel
+ )
+ filtered_model_class = create_model(
+ f"{model.__class__.__name__}Filtered",
+ __base__=base_class,
+ **field_definitions, # pyre-ignore[6]: pyre can't check this since it's an untyped dict
+ )
+ return filtered_model_class(**updates) # pyre-ignore[6]: pyre can't check this since it's an untyped dict
+
+
+def emit_posthog_event(posthog_event: PosthogEventModel[Any]) -> None:
+ """Filters properties from a Pydantic model instance based on the user's given consent level.
+
+ This can be called both from inside the imblue-cli task container, or the Sculptor backend.
+ If invoked from inside the imbue-cli task container, task_id is added to the event_data by calling
+ get_telemetry_task_info_when_inside_container().
+
+ If you are in the Sculptor backend, you should probably use fire_posthog_event instead, as that ensures you're using
+ a known event, and attaches correct context.
+ """
+ posthog_user_instance = get_user_posthog_instance()
+
+ if posthog_user_instance:
+ user_config_instance = posthog_user_instance.access_user_config()
+ privacy_settings = user_config_instance.privacy_settings
+
+ with posthog_context(posthog_user_instance):
+ if not is_consent_allowable(ConsentLevel.NONE, privacy_settings):
+ # User did not opt-into any data collection.
+ # We should not log user-identifiable PostHog events.
+ return
+ elif posthog_event.action is not None and not is_consent_allowable(
+ ConsentLevel.PRODUCT_ANALYTICS, privacy_settings
+ ):
+ # If this is a user_activity event but user has not consented
+ # to product analytics logging level, do not emit event.
+ return
+ event_name = posthog_event.name.value
+
+ # Use the recursive filtering function
+ try:
+ filtered_model = filter_model_by_consent(posthog_event, privacy_settings)
+ properties = filtered_model.model_dump()
+ except Exception as e:
+ logger.info("Failed to filter posthog event: {}", e)
+ # We could also choose to drop the entire event, or replace payload with an error message.
+ properties = posthog_event.model_dump()
+
+ # some events don't have a payload, but they should still be logged.
+ if properties.get("payload"):
+ payload = properties["payload"]
+ if not any(value is not None for value in payload.values()):
+ logger.debug("No payload data to log for event of type {}", event_name)
+ return
+
+ # Check for task-specific telemetry info and add it to the properties
+ telemetry_task_info = get_telemetry_task_info_if_inside_container()
+ if telemetry_task_info:
+ # I think we will further change where we put this.
+ properties["task_id"] = str(telemetry_task_info.task_id)
+
+ posthog_user_instance.posthog_instance.capture(event=event_name, properties=properties)
+
+
+def shutdown_posthog() -> None:
+ """Flush all messages and cleanly shut down the client."""
+ posthog_instance = get_user_posthog_instance()
+ if posthog_instance is not None:
+ posthog_instance.posthog_instance.shutdown()
+
+
+class PosthogExceptionPayload(PosthogEventPayload):
+ exception_name: str = with_consent(ConsentLevel.ERROR_REPORTING, description="The name of the raised exception.")
+ exception_value: str = with_consent(ConsentLevel.ERROR_REPORTING, description="The value of the raised exception.")
+ exception_traceback: str | None = with_consent(
+ ConsentLevel.ERROR_REPORTING,
+ description="Formatted traceback of the raised exception.",
+ )
+ message: str | None = with_consent(
+ ConsentLevel.ERROR_REPORTING,
+ description="The message that accompanies the raised exception.",
+ )
+
+
+def get_exception_payload(
+ exception: BaseException,
+ message: str | None = None,
+ include_traceback: bool = False,
+) -> PosthogExceptionPayload:
+ formatted_traceback = "".join(traceback.format_exception(type(exception), exception, exception.__traceback__))
+ return PosthogExceptionPayload(
+ exception_name=type(exception).__name__,
+ exception_value=str(exception),
+ exception_traceback=formatted_traceback if include_traceback else None,
+ message=message,
+ )
+
+
+def send_exception_to_posthog(
+ error_source: SculptorPosthogEvent,
+ exception: BaseException,
+ message: str | None = None,
+ include_traceback: bool = False,
+ component: ProductComponent = ProductComponent.CROSS_COMPONENT,
+ task_id: TaskID | None = None,
+) -> None:
+ """Sends error details to PostHog for telemetry purposes.
+
+ The idea is that for some exceptions, we don't want to send them to Sentry because we're not able to act on them anyway.
+ But we should still keep an eye on how often they happen so we send them to PostHog instead.
+ """
+
+ # TODO: do we want to include this filtering even if we're sending it to posthog rather than sentry?
+ should_skip = pre_filter_exception(exception, message)
+ if should_skip:
+ return
+
+ emit_posthog_event(
+ PosthogEventModel(
+ name=error_source,
+ component=component,
+ payload=get_exception_payload(exception, message, include_traceback),
+ task_id=str(task_id) if task_id else None,
+ )
+ )
+
+ inject_exception_and_log(exception, message or "", priority=ExceptionPriority.LOW_PRIORITY)
+
+
+def flush_sentry_and_exit_program(exit_code: int, final_message: str) -> None:
+ """Flush Sentry events and then immediately exit the program with a final message.
+
+ We enforce the final message so that the last line that the user sees is relevant to the shutdown.
+ """
+ sentry_sdk.flush()
+ shutdown_posthog()
+ logger.info(final_message)
+ os._exit(exit_code)
+
+
+def mirror_exception_to_posthog(event: Event, hint: Hint) -> Event:
+ """Helper/utility function to mirror an exception from Sentry to PostHog.
+
+ When this is wired up to the before_send hook in Sentry, it will send a correctly-shaped event to PostHog, and annotate the Sentry event with the PostHog user id.
+ """
+ # Only mirror error events
+ if event.get("level") in ("warning", "error", "fatal") and hint and hint.get("exc_info"):
+ logger.info("We are going to mirror this exception to posthog")
+ _, exc_value, _ = hint["exc_info"]
+ # Attach useful Sentry context as PostHog properties
+ props = {
+ "$exception_level": event.get("level"),
+ "sentry_event_id": event.get("event_id"),
+ "sentry_issue_id": event.get("contexts", {}).get("trace", {}).get("trace_id"),
+ "tags": event.get("tags"),
+ "release": event.get("release"),
+ "environment": event.get("environment"),
+ "log_message": event.get("logentry", {}).get("message"),
+ }
+
+ user_posthog_instance = get_user_posthog_instance()
+ if user_posthog_instance:
+ user_config = user_posthog_instance.access_user_config()
+ try:
+ user_posthog_instance.posthog_instance.capture_exception(
+ exc_value,
+ # We're relying on the fact that we are in a single-user context to always have a distinct_id.
+ distinct_id=user_config.user_id,
+ properties=props,
+ )
+
+ event.setdefault("tags", {})
+ assert "tags" in event, "Only to shut up typechecker below"
+
+ event["tags"]["posthog_exception_mirrored"] = "true"
+
+ event.setdefault("extra", {})
+ assert "extra" in event, "Only to shut up typechecker below"
+
+ event["extra"]["posthog_user_id"] = (user_config.user_id,)
+
+ posthog_app_domain = user_posthog_instance.posthog_instance.host.replace(
+ ".i.posthog.com", ".posthog.com"
+ )
+
+ event["extra"]["posthog_user_link"] = f"{posthog_app_domain}/persons/{user_config.user_id}"
+
+ except Exception as e:
+ # We don't want to trigger an infinite loop of exceptions if PostHog is down. We're sending a message to
+ # Sentry after all.
+ logger.debug("Failed to mirror exception to PostHog: {}", e)
+ finally:
+ # We must return the event in all code paths to ensure sentry continues.
+ return event
+
+ # We must return the event in all code paths to ensure sentry continues.
+ return event
diff --git a/imbue_core/imbue_core/sculptor/telemetry_constants.py b/imbue_core/imbue_core/sculptor/telemetry_constants.py
@@ -0,0 +1,216 @@
+from enum import Enum
+
+
+class ConsentLevel(Enum):
+ """Defines the hierarchy of user consent levels."""
+
+ NONE = 0
+ ERROR_REPORTING = 1 # PostHog and Sentry’s error reporting
+ PRODUCT_ANALYTICS = 2 # PostHog’s pageview and autocapture events
+ LLM_LOGS = 3 # Capability logging
+ SESSION_RECORDING = 4 # PostHog and Sentry’s session recording
+
+ NEVER_PERSIST = "never_persist"
+
+
+class ProductComponent(Enum):
+ AGENT_TASK = "agent_task"
+ CHECKS = "checks"
+ TASK = "task"
+ ONBOARDING = "onboarding"
+ STARTUP = "startup"
+ ENVIRONMENT_SETUP = "environment_setup"
+ FIX = "fix"
+ CLAUDE_CODE = "claude_code"
+ IMBUE_VERIFY = "imbue_verify"
+ IMBUE_CLI = "imbue_cli"
+ AUTH = "auth"
+ DATABASE = "database"
+ LOCAL_SYNC = "local_sync"
+ MANUAL_SYNC = "manual_sync"
+ # CROSS_COMPONENT is for logging concerns that are not local to a specific component.
+ CROSS_COMPONENT = "cross_component"
+ CONFIGURATION = "configuration"
+
+
+class UserAction(Enum):
+ CLICKED = "clicked"
+ CALLED = "called"
+ # more to be defined later
+
+
+# Adding a new event? Please see _get_posthog_token_and_api_host for information about
+# using the developer posthog instance as you build/test your event.
+class SculptorPosthogEvent(Enum):
+ """
+ DO NOT MUTATE the string values!
+
+ Mark as deprecated enums when no longer used.
+ """
+
+ # TESTING
+ TEST_EVENT = "test_event"
+
+ # ONBOARDING
+ ONBOARDING_INITIALIZATION = "onboarding_initialization"
+ ONBOARDING_CONFIGURATION_WIZARD = "onboarding_configuration_wizard"
+ ONBOARDING_EMAIL_CONFIRMATION = "onboarding_email_confirmation"
+ ONBOARDING_TELEMETRY_CONSENT = "onboarding_telemetry_consent"
+ ONBOARDING_STARTUP_CHECKS = "onboarding_startup_checks"
+ ONBOARDING_USER_CONFIG_SETTINGS = "onboarding_user_config_settings" # Deprecated, use the following one:
+ ONBOARDING_USER_CONFIG_SETTINGS_LOADED = "onboarding_user_config_settings_loaded"
+ ONBOARDING_COMPLETED = "onboarding_completed"
+
+ ONBOARDING_ANTHROPIC_API_KEY_SET = "onboarding_anthropic_api_key_set"
+ ONBOARDING_ANTHROPIC_CREDENTIALS_EXIST = (
+ "onboarding_anthropic_credentials_exist" # This only means that oauth completed.
+ )
+ ONBOARDING_ANTHROPIC_OAUTH_STARTED = "onboarding_anthropic_oauth_started"
+ ONBOARDING_ANTHROPIC_OAUTH_CANCELLED = "onboarding_anthropic_oauth_cancelled"
+ ONBOARDING_ANTHROPIC_AUTHORIZED = (
+ "onboarding_anthropic_authorized" # We've successfully authorized, whether via Oauth or API key
+ )
+ ONBOARDING_OPENAI_AUTHORIZED = "onboarding_openai_authorized"
+ ONBOARDING_DOCKER_INSTALLED = "onboarding_docker_installed"
+ ONBOARDING_DOCKER_STARTED = "onboarding_docker_started"
+ ONBOARDING_GIT_INSTALLED = "onboarding_git_installed"
+
+ # STARTUP
+ STARTUP_REMOTE_URL = "startup_remote_url"
+ DESKTOP_BACKEND_STARTED = "desktop_backend_started"
+
+ # Settings, configuration and preferences
+ USER_CONFIG_SETTINGS_EDITED = "user_config_settings_edited"
+
+ # TASK
+ TASK_PREDICT_BRANCH_NAME = "task_predict_branch_name"
+ TASK_START_MESSAGE = "task_start_message"
+ TASK_START_REQUESTED = "task_start_requested"
+ TASK_FORK_REQUESTED = "task_fork_requested"
+ TASK_RUN_TASK_STARTED = "task_run_task_started"
+ TASK_USER_MESSAGE = "task_user_message"
+ TASK_USER_COMMAND = "task_user_command"
+ TASK_USER_FEEDBACK = "task_user_feedback"
+
+ # ENVIRONMENT SETUP
+ ENVIRONMENT_SETUP_REUSED_EXISTING_ENVIRONMENT = "environment_setup_reused_existing_environment"
+ ENVIRONMENT_SETUP_FAILED_TO_REUSE_EXISTING_ENVIRONMENT = "environment_setup_failed_to_reuse_existing_environment"
+ ENVIRONMENT_SETUP_IMAGE_CREATION_STARTED = "environment_setup_image_creation_started"
+ ENVIRONMENT_SETUP_USING_EXISTING_IMAGE = "environment_setup_using_existing_image"
+ ENVIRONMENT_SETUP_IMAGE_CREATION_FINISHED = "environment_setup_image_creation_finished"
+ ENVIRONMENT_SETUP_IMAGE_ENSURED = "environment_setup_image_ensured"
+ ENVIRONMENT_SETUP_HARD_OVERWROTE_WORKSPACE = "environment_setup_hard_overwrote_workspace"
+ ENVIRONMENT_SETUP_DOCKER_CONTROL_PLANE_ALREADY_DOWNLOADED = (
+ "environment_setup_docker_control_plane_already_downloaded"
+ )
+ ENVIRONMENT_SETUP_DOCKER_CONTROL_PLANE_DOWNLOAD_FINISHED = (
+ "environment_setup_docker_control_plane_download_finished"
+ )
+ ENVIRONMENT_SETUP_WAITING_FOR_CONTROL_PLANE_SETUP = "environment_setup_waiting_for_control_plane_setup"
+ ENVIRONMENT_SETUP_DOCKER_STARTED_EXISTING_CONTAINER = "environment_setup_docker_started_existing_container"
+ ENVIRONMENT_SETUP_DOCKER_CONTAINER_CREATED = "environment_setup_docker_container_created"
+ ENVIRONMENT_SETUP_DOCKER_CONTAINER_FINISHED_SETUP = "environment_setup_docker_container_finished_setup"
+ ENVIRONMENT_SETUP_REPO_ARCHIVE_CREATED = "environment_setup_repo_archive_created"
+ ENVIRONMENT_SETUP_IMAGE_CREATED = "environment_setup_image_created"
+ ENVIRONMENT_SETUP_LOCAL_DOCKERFILE_BUILT = "environment_setup_local_dockerfile_built"
+ ENVIRONMENT_SETUP_FELL_BACK_TO_DEFAULT_DEVCONTAINER = "environment_setup_fell_back_to_default_devcontainer"
+ ENVIRONMENT_SETUP_WRAPPER_DOCKERFILE_BUILT = "environment_setup_wrapper_dockerfile_built"
+
+ # TOOL READINESS
+ TOOL_READINESS_EVENT_COMPLETED = "tool_readiness_event_completed"
+
+ # AGENT_TASK
+ AGENT_TASK_ENVIRONMENT_SETUP_FINISHED = "agent_task_environment_setup_finished"
+ AGENT_TASK_GIT_SETUP_FINALIZED = "agent_task_git_setup_finalized"
+ AGENT_TASK_RUNNING_IN_ENVIRONMENT = "agent_task_running_in_environment"
+ AGENT_TASK_RECEIVED_FIRST_TOKEN_FROM_AGENT = "agent_task_received_first_token_from_agent"
+
+ # FIX
+ FIX_ISSUE_SELECT = "fix_issue_select"
+
+ # AGENT RESPONSES
+ AGENT_INIT = "agent_init"
+ AGENT_ASSISTANT_MESSAGE = "agent_assistant_message"
+ AGENT_TOOL_RESULT = "agent_tool_result"
+ AGENT_SESSION_END = "agent_session_end"
+
+ # USER MESSAGES
+ USER_CHAT_INPUT = "user_chat_input"
+ USER_COMMAND_INPUT = "user_command_input"
+ USER_WRITE_FILE = "user_write_file"
+ USER_STOP_AGENT = "user_stop_agent"
+ USER_INTERRUPT_PROCESS = "user_interrupt_process"
+ USER_FORK_AGENT = "user_fork_agent"
+ USER_REMOVE_QUEUED_MESSAGE = "user_remove_queued_message"
+ USER_GIT_COMMIT_AND_PUSH = "user_git_commit_and_push"
+ USER_GIT_PULL = "user_git_pull"
+ USER_COMPACT_TASK_MESSAGE = "user_compact_task_message"
+ USER_CONFIGURATION_DATA = "user_configuration_data"
+ PROJECT_CONFIGURATION_DATA = "project_configuration_data"
+ COMPACTION_SUCCESS = "compaction_success"
+
+ # CHECKS
+ CHECK_STARTED = "check_started"
+ USER_STOP_CHECK_MESSAGE = "user_stop_check_message"
+ USER_RESTART_CHECK_MESSAGE = "user_restart_check_message"
+
+ # SYSTEM MESSAGES (eg Local * Manual Sync)
+ LOCAL_SYNC_SETUP_STARTED = "local_sync_setup_started"
+ LOCAL_SYNC_SETUP_AND_ENABLED = "local_sync_setup_and_enabled"
+ LOCAL_SYNC_UPDATE_PENDING = "local_sync_update_pending"
+ LOCAL_SYNC_UPDATE_COMPLETED = "local_sync_update_completed"
+ LOCAL_SYNC_UPDATE_PAUSED = "local_sync_update_paused"
+ LOCAL_SYNC_DISABLED = "local_sync_disabled"
+ MANUAL_SYNC_MERGE_INTO_USER_ATTEMPTED = "manual_sync_merge_into_user_attempted"
+ MANUAL_SYNC_MERGE_INTO_AGENT_ATTEMPTED = "manual_sync_merge_into_agent_attempted"
+ RUNNER_RESUME_USER_MESSAGE = "runner_resume_user_message"
+ WARNING_AGENT_MESSAGE = "warning_agent_message"
+
+ # This is poorly named; it refers to starting a claude -p command in the environment
+ # CLAUDE MESSAGES
+ CLAUDE_COMMAND = "claude_command"
+
+ # This is poorly named; it refers to starting a codex exec command in the environment
+ # CODEX MESSAGES
+ CODEX_COMMAND = "codex_command"
+
+ # IMBUE VERIFY
+ IMBUE_VERIFY_CALLED = "imbue_verify_called"
+ TRIMMED_IMBUE_VERIFY_CALLED = "trimmed_imbue_verify_called"
+ IMBUE_VERIFY_FAILED = "imbue_verify_failed"
+
+ # IMBUE CLI
+ IMBUE_CLI_START = "imbue_cli_start"
+ IMBUE_CLI_CHECK_INITIATED = "imbue_cli_check_initiated"
+
+ # LOGIN
+ LOGIN_INITIATED = "login_initiated"
+ LOGIN_SUCCEEDED = "login_succeeded"
+
+ # DATABASE
+ DB_WRITE = "db_write"
+
+ # RUNTIME TRACKING
+ RUNTIME_MEASUREMENT = "runtime_measurement"
+
+ # SPACE USAGE TRACKING
+ SNAPSHOT_SIZE_MEASUREMENT = "snapshot_size_measurement"
+ IMAGE_INFORMATION = "image_information"
+
+ # EXCEPTIONS
+ # NOTE: if you're adding a new call to log_error_to_posthog, you should most likely add a new value here!
+ # this is the only way that we determine where the error originated (unless you set include_traceback=True),
+ # so we don't want to reuse these without a good reason
+ IRRECOVERABLE_EXCEPTION = (
+ "irrecoverable_exception" # only use this if we have no other information on the error's source
+ )
+ SENTRY_EXCEPTION_DATA_COLLECTION_TOO_SLOW = "sentry_exception_data_collection_too_slow"
+ CLAUDE_TRANSIENT_ERROR = "claude_transient_error"
+ DATABASE_LOCK_ACQUISITION_TIMEOUT = "database_lock_acquisition_timeout"
+ INCOMPATIBLE_DATABASE_LIKELY_FROM_DOWNGRADE = "incompatible_database_likely_from_downgrade"
+ FAILED_TO_PARSE_LLM_RESPONSE_WHEN_GENERATING_ISSUES = "failed_to_parse_llm_response_when_generating_issues"
+ INVALID_FILE_PATH_FROM_LLM_IN_ISSUE_LOCATION = "invalid_file_path_from_llm_in_issue_location"
+ TASK_FAILED_WITH_EXPECTED_ERROR = "task_failed_with_expected_error"
+ AGENT_RUNNER_FAILED_BECAUSE_DOCKER_IS_DOWN = "agent_runner_failed_because_docker_is_down"
+ FAILED_TO_SNAPSHOT_IMAGE_DURING_SHUTDOWN = "failed_to_snapshot_image_during_shutdown"
+ THREAD_IRRECOVERABLE_EXCEPTION = "thread_irrecoverable_exception"
diff --git a/imbue_core/imbue_core/sculptor/telemetry_utils.py b/imbue_core/imbue_core/sculptor/telemetry_utils.py
@@ -0,0 +1,56 @@
+from typing import Any
+
+from pydantic import Field
+
+from imbue_core.sculptor.telemetry_constants import ConsentLevel
+
+
+def _with_consent_level(
+ level: ConsentLevel,
+ default_factory: Any | None = None,
+ default: Any | None = None,
+ **kwargs: Any,
+) -> Any:
+ """A Pydantic Field factory to annotate a field with a consent level.
+ It attaches the level as metadata within the field's JSON schema extras.
+ """
+ if default_factory is not None:
+ assert default is None, "Cannot specify both default and default_factory"
+ # pyre-fixme[6]: pyre is confused by the dict literal here, especially since level is not a JsonValue
+ return Field(
+ default_factory=default_factory,
+ json_schema_extra={"consent_level": level},
+ **kwargs,
+ )
+
+ # pyre-fixme[6]: pyre is confused by the dict literal here, especially since level is not a JsonValue
+ return Field(default, json_schema_extra={"consent_level": level}, **kwargs)
+
+
+def with_consent(
+ level: ConsentLevel,
+ default_factory: Any | None = None,
+ default: Any | None = None,
+ **kwargs: Any,
+) -> Any:
+ """A Pydantic Field factory to annotate a field with a consent level.
+ It attaches the level as metadata within the field's JSON schema extras.
+ """
+ return _with_consent_level(level, default_factory=default_factory, default=default, **kwargs)
+
+
+def without_consent(default: Any | None = None, default_factory: Any | None = None, **kwargs: Any) -> Any:
+ """A Pydantic Field factory to annotate a field without a consent level."""
+ return _with_consent_level(ConsentLevel.NONE, default_factory=default_factory, default=default, **kwargs)
+
+
+def never_log(default: Any | None = None, default_factory: Any | None = None, **kwargs: Any) -> Any:
+ """A Pydantic Field factory to annotate a field that should never be logged.
+ This is used for in-memory or temporary data that should not be stored long-term.
+ """
+ return _with_consent_level(
+ ConsentLevel.NEVER_PERSIST,
+ default_factory=default_factory,
+ default=default,
+ **kwargs,
+ )
diff --git a/imbue_core/imbue_core/sculptor/user_config.py b/imbue_core/imbue_core/sculptor/user_config.py
@@ -0,0 +1,252 @@
+import sys
+from enum import StrEnum
+from typing import Any
+
+from pydantic import Field
+from pydantic.alias_generators import to_camel
+
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.sculptor.telemetry_constants import ConsentLevel
+from imbue_core.sculptor.telemetry_utils import never_log
+from imbue_core.sculptor.telemetry_utils import with_consent
+from imbue_core.sculptor.telemetry_utils import without_consent
+
+_DEFAULT_MODIFIER_KEY = "Cmd" if sys.platform == "darwin" else "Ctrl"
+
+
+class UpdateChannel(StrEnum):
+ """Update channel for receiving Sculptor updates."""
+
+ STABLE = "STABLE"
+ ALPHA = "ALPHA"
+
+
+class PrivacySettings(SerializableModel):
+ """This model contains a subset of the the privacy fields that we support."""
+
+ is_error_reporting_enabled: bool = Field(False, description="Whether to enable error reporting, i.e. Sentry")
+ is_product_analytics_enabled: bool = Field(
+ False, description="Whether to enable product analytics, e.g. through PostHog"
+ )
+ is_llm_logs_enabled: bool = Field(False, description="Whether to enable LLM logs spooling to our systems")
+ is_session_recording_enabled: bool = Field(False, description="Whether to enable session recording")
+ is_repo_backup_enabled: bool = Field(False, description="Whether to enable repo backup")
+ is_full_contribution: bool = Field(
+ False,
+ description="Synthetic field to let us know if the user has selected full contribution. This includes 'full LLM logs, including code' to train our agent.",
+ )
+ telemetry_consent_level: str = Field("", description="Telemetry level description")
+
+
+class UserConfig(SerializableModel):
+ """Most configuration for user and for Sculptor app behavior should go here.
+
+ All required fields must be provided or validation will fail.
+
+ When you add a new field, you should add it as a field with a default value so that it is backwards compatible.
+ """
+
+ user_email: str = without_consent(..., description="User email address")
+ user_full_name: str | None = without_consent(None, description="Full name of the user")
+ user_git_username: str = without_consent(..., description="Git User name")
+ user_id: str = without_consent(..., description="User ID")
+ anonymous_access_token: str = never_log(
+ ..., description="Unique and local anonymous access token for imbue_gateway"
+ )
+ organization_id: str = without_consent(..., description="Organization ID")
+ instance_id: str = without_consent(..., description="Instance ID")
+ is_error_reporting_enabled: bool = without_consent(False, description="Whether to enable error reporting")
+ is_product_analytics_enabled: bool = without_consent(False, description="Whether to enable product analytics")
+ is_llm_logs_enabled: bool = without_consent(False, description="Whether to enable LLM logs")
+ is_session_recording_enabled: bool = without_consent(False, description="Whether to enable session recording")
+ is_repo_backup_enabled: bool = without_consent(False, description="Whether to enable repo backup")
+ is_full_contribution: bool = without_consent(
+ False,
+ description="Synthetic field to let us know if the user has selected full contribution. This includes 'full LLM logs, including code' to train our agent.",
+ )
+ telemetry_consent_level: str = without_consent("", description="Telemetry level description")
+ # For now, we give users the option to opt-out of syncing their Claude settings with Sculptor.
+ is_claude_configuration_synchronized: bool = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=True,
+ description="Whether user's local Claude Code configuration is synchronized with Sculptor.",
+ )
+ anthropic_api_key: str | None = never_log(None, description="Anthropic API key")
+ openai_api_key: str | None = never_log(None, description="OpenAI API key")
+ gemini_api_key: str | None = never_log(None, description="Gemini API key")
+ is_privacy_policy_consented: bool = without_consent(
+ False, description="Whether the user consented to our privacy policy"
+ )
+ is_telemetry_level_set: bool = without_consent(
+ False, description="Whether the user consented to our telemetry level"
+ )
+ # App configuration:
+ app_theme: str = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default="system",
+ description="App theme: light, dark, or system",
+ )
+ does_send_message_shortcut_include_modifier: bool = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=True,
+ description="True if the send message shortcut includes the modifier key. Eg. Cmd+Enter instead of Enter alone.)",
+ )
+ new_agent_shortcut: str = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=f"{_DEFAULT_MODIFIER_KEY}+N",
+ description="Shortcut for creating a new agent",
+ )
+ search_agents_shortcut: str = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=f"{_DEFAULT_MODIFIER_KEY}+K",
+ description="Shortcut for searching agents",
+ )
+ toggle_sidebar_shortcut: str = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=f"{_DEFAULT_MODIFIER_KEY}+S",
+ description="Shortcut for toggling the sidebar",
+ )
+ global_hotkey: str = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default="",
+ description="Global hotkey to open Sculptor",
+ )
+ default_llm: str | None = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=None,
+ description="Default LLM model for new agents. If None, then most recently used LLM will be used.",
+ )
+ has_seen_pairing_mode_modal: bool = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=False,
+ description="Whether the user has seen the pairing mode modal",
+ )
+ are_suggestions_enabled: bool = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=True,
+ description="Whether to enable the suggestions feature",
+ )
+ imbue_verify_run_frequency: str = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default="auto",
+ description="Frequency for running Imbue Verify: auto or manual",
+ )
+ imbue_verify_token_usage_requirement: str = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default="low",
+ description="Token threshold for running Imbue Verify: none, low, medium, or high",
+ )
+ is_forking_beta_feature_on: bool = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=False,
+ description="Whether to enable the forking beta feature",
+ )
+ is_pairing_mode_stashing_beta_feature_on: bool = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=False,
+ description="Whether to enable the pairing mode stashing beta feature",
+ )
+ is_pairing_mode_warning_before_stash_enabled: bool = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=True,
+ description="Whether to show a warning dialog before stashing changes when starting pairing mode",
+ )
+ are_dev_suggestions_on: bool = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=False,
+ description="Whether to enable the dev suggestions pane",
+ )
+ is_scout_beta_feature_on: bool = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=False,
+ description="Whether to enable the scout beta feature",
+ )
+
+ # NOTE: The electron frontend might read this value directly in configFallback.ts. Please remember to keep them in sync.
+ update_channel: UpdateChannel = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=UpdateChannel.STABLE,
+ description="Update channel for receiving Sculptor updates (stable or alpha)",
+ )
+ max_snapshot_size_bytes: int = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=50 * 1024 * 1024,
+ description="Maximum snapshot size in bytes.",
+ )
+ min_free_disk_gb: float = with_consent(
+ ConsentLevel.PRODUCT_ANALYTICS,
+ default=2.0,
+ description="The minimum free disk space before Sculptor will stop allowing new tasks and messages",
+ )
+
+ @property
+ def is_imbue_user(self) -> bool:
+ return self.user_email.endswith("@imbue.com")
+
+ @property
+ def free_disk_gb_warn_limit(self) -> float:
+ return self.min_free_disk_gb * 3.0
+
+ @property
+ def privacy_settings(self) -> PrivacySettings:
+ """Retrieves the subset of fields associated with Privacy Settings"""
+ return PrivacySettings(
+ is_error_reporting_enabled=self.is_error_reporting_enabled,
+ is_product_analytics_enabled=self.is_product_analytics_enabled,
+ is_llm_logs_enabled=self.is_llm_logs_enabled,
+ is_session_recording_enabled=self.is_session_recording_enabled,
+ is_repo_backup_enabled=self.is_repo_backup_enabled,
+ is_full_contribution=self.is_full_contribution,
+ telemetry_consent_level=self.telemetry_consent_level,
+ )
+
+ @property
+ def sentry_user_context(self) -> dict[str, str]:
+ """Returns a dictionary of user context information for Sentry error reporting."""
+ return {
+ "id": self.user_id, # this is conveniently the same id as used by posthog client
+ "email": self.user_email,
+ "username": self.user_email, # traditionally what we have been setting as username
+ }
+
+
+# At Runtime, ensure that all fields in PrivacySettings are also in UserConfig
+for field in PrivacySettings.model_fields:
+ assert field in UserConfig.model_fields, f"PrivacySettings field {field} is missing from UserConfig"
+
+
+def _generate_user_config_field_enum() -> type[StrEnum]:
+ """Generate UserConfigField enum from UserConfig model fields"""
+ fields = {}
+ for field_name in UserConfig.model_fields.keys():
+ # Convert field name to SCREAMING_SNAKE_CASE for enum constant
+ enum_name = field_name.upper()
+ fields[enum_name] = to_camel(field_name)
+ # pyre thinks this is an instance of a StrEnum because it doesn't understand enums
+ return StrEnum("UserConfigField", fields) # pyre-ignore[7, 19]
+
+
+UserConfigField: type[StrEnum] = _generate_user_config_field_enum()
+
+
+def calculate_user_config_prior_values(
+ old_config: UserConfig, new_config: UserConfig, privacy_settings: PrivacySettings
+) -> dict[str, Any]:
+ from imbue_core.sculptor.telemetry import is_consent_allowable
+
+ old_dict = old_config.model_dump()
+ new_dict = new_config.model_dump()
+ prior_values: dict[str, Any] = {}
+
+ for field_name in old_dict:
+ if old_dict[field_name] != new_dict[field_name]:
+ field_info = UserConfig.model_fields.get(field_name)
+ if field_info:
+ metadata = field_info.json_schema_extra or {}
+ required_level = metadata.get("consent_level")
+ if is_consent_allowable(required_level, privacy_settings):
+ prior_values[field_name] = old_dict[field_name]
+ else:
+ prior_values[field_name] = None
+
+ return prior_values
diff --git a/imbue_core/imbue_core/secrets_utils.py b/imbue_core/imbue_core/secrets_utils.py
@@ -0,0 +1,81 @@
+import os
+import pathlib
+
+from pydantic import SecretStr
+
+
+class Secret(SecretStr):
+ """Pydantic-aware secret wrapper that hides values in logs."""
+
+ def __str__(self) -> str:
+ return "[redacted]"
+
+ __repr__ = __str__
+
+ def unwrap(self) -> str:
+ return self.get_secret_value()
+
+
+class YouAreBeingTooFancyInYourSettingsFile(Exception):
+ pass
+
+
+def parse_secrets_file(filepath: str | pathlib.Path) -> dict[str, str]:
+ """Parse bashenv_secrets.sh-style file into a dict.
+ We should REALLY NOT BE DOING THIS EVER but unfortunately that's not the case so at least let's only do it once here
+
+ Not a great parser; will break in probably many scenarios but end-of-line comments are one that comes to mind
+ """
+ out: dict[str, str] = {}
+ with open(filepath) as f:
+ for line in f:
+ if "$" in line:
+ raise YouAreBeingTooFancyInYourSettingsFile(
+ "Yeah, don't do that. This .sh file is meant to be simple definitions, it should not use any features of bash or sh, including string interpolation via $"
+ )
+ if "#" in line:
+ if not line.startswith("#"):
+ raise YouAreBeingTooFancyInYourSettingsFile("Put comments at the start of the line")
+ continue
+ if "\\" in line:
+ raise YouAreBeingTooFancyInYourSettingsFile("No line continuations or other character escapes allowed")
+ if line.startswith("export "):
+ k, v = line.strip("export ").strip().split("=", maxsplit=1)
+ k = k.strip()
+ if k != k.upper():
+ raise YouAreBeingTooFancyInYourSettingsFile(f"Key {k} must be uppercase")
+ v = v.strip()
+ if v.startswith('"'):
+ if not v.endswith('"'):
+ raise YouAreBeingTooFancyInYourSettingsFile(f"Value {v} must end with a double quote")
+ v = v[1:-1]
+ if v.startswith("'"):
+ if not v.endswith("'"):
+ raise YouAreBeingTooFancyInYourSettingsFile(f"Value {v} must end with a single quote")
+ v = v[1:-1]
+ out[k] = v
+ elif line.strip():
+ raise YouAreBeingTooFancyInYourSettingsFile(
+ f"All lines must start with 'export ', but this line did not: {line}"
+ )
+ return out
+
+
+# TODO: this is gross and bad--we should make better handling for secrets.
+# Right now we read the necessary secrets out of the bashenv files
+def get_secret(secret_name: str) -> str | None:
+ value = os.environ.get(secret_name)
+ if value is not None:
+ return value
+ secrets_files = (
+ "science/secrets/environment_vars/bashenv.sh",
+ "science/secrets/environment_vars/bashenv_secrets.sh",
+ "science/secrets/environment_vars/common_vars.sh",
+ )
+ for file in secrets_files:
+ if os.path.exists(file):
+ secrets = parse_secrets_file(file)
+ value = secrets.get(secret_name, None)
+ if value is not None:
+ return value
+ return None
diff --git a/imbue_core/imbue_core/section.py b/imbue_core/imbue_core/section.py
@@ -0,0 +1,131 @@
+"""
+Provides a context manager for logging a potentially time-consuming process, or a "section".
+
+- Prints logs at start and end of a section.
+
+- Prints a Markdown-like heading for nested sections: "#" for top-level sections, "##" for one level down, and so on.
+
+- Emits structured logs for easier query.
+
+The SectionWrapper context manager can be used to time the __enter__ and __exit__ methods of an existing context manager.
+"""
+
+import contextlib
+import threading
+import time
+from asyncio import CancelledError
+from types import TracebackType
+from typing import TypeVar
+
+from loguru import logger
+
+_monotonic_base = time.monotonic()
+
+
+def _monotonic_time() -> float:
+ """A wrapper around time.monotonic() to make the return values a bit smaller and easier to read by a human."""
+ return time.monotonic() - _monotonic_base
+
+
+class _ThreadLocal(threading.local):
+ def __init__(self) -> None:
+ self.next_section_level: int = 0
+
+
+_thread_local = _ThreadLocal()
+
+
+class Section(contextlib.ContextDecorator):
+ def __init__(self, message: str, log_level: int | str = "INFO") -> None:
+ # TODO: loguru doesn't properly display integer log levels like e.g. logging.INFO
+ self.message = message
+ self.log_level = log_level
+
+ def __enter__(self) -> "Section":
+ level = _thread_local.next_section_level
+ _thread_local.next_section_level += 1
+ self.header = "#" * (level + 1) # pyre-ignore[16]
+ self.start_monotonic_time = _monotonic_time() # pyre-ignore[16]
+ start_clock_time = time.time()
+ self.section = { # pyre-ignore[16]
+ "name": self.message,
+ "level": level,
+ "start_monotonic_time": self.start_monotonic_time,
+ "start_clock_time": start_clock_time,
+ }
+ logger.log(
+ self.log_level,
+ f"{self.header} Start: {self.message}",
+ section=self.section,
+ )
+ return self
+
+ def __exit__(
+ self,
+ exc_type: type[BaseException] | None,
+ exc_val: BaseException | None,
+ exc_tb: TracebackType | None,
+ ) -> None:
+ _thread_local.next_section_level -= 1
+ finish_monotonic_time = _monotonic_time()
+ finish_clock_time = time.time()
+ # pyre-ignore[16]: we set this on __enter__
+ duration_seconds = finish_monotonic_time - self.start_monotonic_time
+ section = self.section | { # pyre-ignore[16]: we set this on __enter__
+ "finish_monotonic_time": finish_monotonic_time,
+ "finish_clock_time": finish_clock_time,
+ "duration_seconds": duration_seconds,
+ }
+ self.elapsed = duration_seconds # pyre-ignore[16]
+
+ header = self.header # pyre-ignore[16]: we set this on __enter__
+
+ if exc_val is None:
+ logger.log(
+ self.log_level,
+ f"{header} Done: {self.message} (took {duration_seconds:.2f} seconds)",
+ section=section | {"result": "success"},
+ )
+ else:
+ if isinstance(exc_val, CancelledError):
+ logger.log(
+ self.log_level,
+ f"{header} Cancelled: {self.message} (took {duration_seconds:.2f} seconds)",
+ section=section | {"result": "cancelled"},
+ )
+ else:
+ logger.log(
+ self.log_level,
+ f"{header} Failed: {self.message} (within {duration_seconds:.2f} seconds)",
+ section=section | {"result": "failed"},
+ )
+
+
+T = TypeVar("T")
+
+
+# pyre-ignore[24]: pyre doesn't understand AbstractContextManager
+class SectionWrapper(contextlib.AbstractContextManager[T]):
+ # pyre-ignore[24]
+ def __init__(
+ self,
+ cm: contextlib.AbstractContextManager[T],
+ enter_message: str,
+ exit_message: str,
+ ) -> None:
+ self._cm = cm
+ self._enter_message = enter_message
+ self._exit_message = exit_message
+
+ def __enter__(self) -> T:
+ with Section(self._enter_message):
+ return self._cm.__enter__()
+
+ def __exit__(
+ self,
+ exc_type: type[BaseException] | None,
+ exc_val: BaseException | None,
+ exc_tb: TracebackType | None,
+ ) -> None:
+ with Section(self._exit_message):
+ self._cm.__exit__(exc_type, exc_val, exc_tb)
diff --git a/imbue_core/imbue_core/sentry_loguru_handler.py b/imbue_core/imbue_core/sentry_loguru_handler.py
@@ -0,0 +1,367 @@
+"""
+inlines sentry_sdk.integrations.loguru and sentry_sdk.integrations.logging, so we can make some changes.
+i'm intentionally keeping most of the old logic so this still behaves roughly as expected/documented.
+
+we probably could/should go through and fully streamline this though to do just what we need.
+
+The changes so far (could be out of date):
+- adds `strip_extra` to the breadcrumb handler
+- adds `add_extra_info_hook` to the event handler, with a watchdog to make sure it doesn't slow things down
+"""
+
+import asyncio
+import enum
+import logging
+from concurrent.futures import Future
+from concurrent.futures import ThreadPoolExecutor
+from concurrent.futures import wait
+from datetime import datetime
+from datetime import timezone
+from fnmatch import fnmatch
+from typing import Any
+from typing import Callable
+from typing import Iterable
+from typing import Sequence
+
+import sentry_sdk
+from loguru import logger
+from sentry_sdk import new_scope
+
+# "This disables recording (both in breadcrumbs and as events) calls to a logger of a specific name. Among other uses, many of our integrations
+# use this to prevent their actions being recorded as breadcrumbs. Exposed to users as a way to quiet spammy loggers."
+# We have to import it so that existing setters work properly
+from sentry_sdk.integrations.logging import _IGNORED_LOGGERS
+from sentry_sdk.types import Event
+from sentry_sdk.types import Hint
+from sentry_sdk.utils import current_stacktrace
+from sentry_sdk.utils import event_from_exception
+from sentry_sdk.utils import to_string
+
+from imbue_core.constants import HIGH_PRIORITY_LEVEL
+from imbue_core.constants import LOW_PRIORITY_LEVEL
+from imbue_core.constants import MEDIUM_PRIORITY_LEVEL
+from imbue_core.s3_uploader import EXTRAS_UPLOADED_FILES_KEY
+
+# for formatting the log message. we don't want the timestamp/level because sentry already tracks that,
+# and it messes up event grouping since this string becomes the event title.
+SENTRY_LOG_FORMAT = "{name}:{function}:{line} - {message}"
+
+
+class SentryLoguruLoggingLevels(enum.IntEnum):
+ TRACE = 5
+ DEBUG = 10
+ INFO = 20
+ SUCCESS = 25
+ WARNING = 30
+ # Additional loguru levels for sentry hot-wiring that we also present with custom colors in the console.
+ # The mapping to sentry levels for both breadcrumbs and reporting is done in map_to_sentry_name()
+ LOW_PRIORITY = LOW_PRIORITY_LEVEL # pyre-ignore[8]: pyre doesn't understand enums
+ MEDIUM_PRIORITY = MEDIUM_PRIORITY_LEVEL # pyre-ignore[8]: pyre doesn't understand enums
+ HIGH_PRIORITY = HIGH_PRIORITY_LEVEL # pyre-ignore[8]: pyre doesn't understand enums
+ ERROR = 40
+ CRITICAL = 50
+
+ def map_to_sentry_name(self) -> str:
+ # Sentry only understands and respects "debug", "info", "warning", "error", "critical", "fatal"
+ match self:
+ case SentryLoguruLoggingLevels.TRACE | SentryLoguruLoggingLevels.DEBUG:
+ return "debug"
+ case SentryLoguruLoggingLevels.INFO | SentryLoguruLoggingLevels.SUCCESS:
+ return "info"
+ case SentryLoguruLoggingLevels.LOW_PRIORITY:
+ return "info"
+ case SentryLoguruLoggingLevels.MEDIUM_PRIORITY | SentryLoguruLoggingLevels.WARNING:
+ return "warning"
+ case SentryLoguruLoggingLevels.HIGH_PRIORITY | SentryLoguruLoggingLevels.ERROR:
+ return "error"
+ case SentryLoguruLoggingLevels.CRITICAL:
+ return "critical"
+ case _:
+ return ""
+
+
+class _BaseHandler(logging.Handler):
+ COMMON_RECORD_ATTRS = frozenset(
+ (
+ "args",
+ "created",
+ "exc_info",
+ "exc_text",
+ "filename",
+ "funcName",
+ "levelname",
+ "levelno",
+ "linenno",
+ "lineno",
+ "message",
+ "module",
+ "msecs",
+ "msg",
+ "name",
+ "pathname",
+ "process",
+ "processName",
+ "relativeCreated",
+ "stack",
+ "tags",
+ "taskName",
+ "thread",
+ "threadName",
+ "stack_info",
+ )
+ )
+
+ def _can_record(self, record: logging.LogRecord) -> bool:
+ """Prevents ignored loggers from recording"""
+ for logger in _IGNORED_LOGGERS:
+ if fnmatch(record.name, logger):
+ return False
+ return True
+
+ def _extra_from_record(self, record: logging.LogRecord) -> dict[str, object]:
+ return {
+ k: v
+ for k, v in vars(record).items()
+ if k not in self.COMMON_RECORD_ATTRS and (not isinstance(k, str) or not k.startswith("_"))
+ }
+
+ def _logging_to_event_level(self, record: logging.LogRecord) -> str:
+ try:
+ return SentryLoguruLoggingLevels(record.levelno).map_to_sentry_name()
+ except ValueError:
+ return record.levelname.lower() if record.levelname else ""
+
+
+def _wrap_callback(callback: Callable) -> None:
+ try:
+ callback()
+ except Exception as e:
+ log_error_inside_sentry(e, "Sentry callback raised")
+
+
+class SentryEventHandler(_BaseHandler):
+ """A logging handler that emits Sentry events for each log record."""
+
+ def __init__(
+ self,
+ level: int = logging.NOTSET,
+ add_extra_info_hook: Callable[[Event, Hint], tuple[Event, Hint, tuple[Callable, ...]]] | None = None,
+ ) -> None:
+ super().__init__(level=level)
+ self.add_extra_info_hook = add_extra_info_hook
+ self.add_extra_info_previously_timed_out = False
+ self._executor: ThreadPoolExecutor | None = ThreadPoolExecutor()
+ self._futures: list[Future] = []
+
+ def emit(self, record: logging.LogRecord) -> Any:
+ self.format(record)
+ return self._emit(record)
+
+ def schedule_callbacks(self, callbacks: Sequence[Callable]) -> None:
+ executor = self._executor
+ if executor is not None:
+ logger.info(f"Sentry event handler registered {len(callbacks)} callbacks with an executor")
+ for callback in callbacks:
+ future = executor.submit(lambda c=callback: _wrap_callback(c))
+ self._futures.append(future)
+ else:
+ logger.debug(
+ f"Sentry event handler failed to register {len(callbacks)} callbacks because no executor was found"
+ )
+
+ def close(self) -> None:
+ executor = self._executor
+ if executor is not None:
+ executor.shutdown(wait=False)
+ wait(self._futures, timeout=5.0)
+ self._executor = None
+ super().close()
+
+ def _emit(self, record: logging.LogRecord) -> None:
+ if not self._can_record(record):
+ return
+
+ # Filter out KeyboardInterrupt and CancelledError exceptions from being logged to Sentry
+ if record.exc_info and record.exc_info[0] is not None:
+ exc_type = record.exc_info[0]
+ if exc_type is KeyboardInterrupt or exc_type is asyncio.CancelledError:
+ return
+
+ client = sentry_sdk.get_client()
+ if not client.is_active():
+ return
+
+ client_options = client.options
+
+ # exc_info might be None or (None, None, None)
+ #
+ # exc_info may also be any falsy value due to Python stdlib being
+ # liberal with what it receives and Celery's billiard being "liberal"
+ # with what it sends. See
+ # https://github.com/getsentry/sentry-python/issues/904
+ if record.exc_info and record.exc_info[0] is not None:
+ event, hint = event_from_exception(
+ record.exc_info,
+ client_options=client_options,
+ mechanism={"type": "logging", "handled": True},
+ )
+ elif (record.exc_info and record.exc_info[0] is None) or record.stack_info:
+ event: Event = {}
+ hint: Hint = {}
+ event["threads"] = {
+ "values": [
+ {
+ "stacktrace": current_stacktrace(
+ include_local_variables=client_options["include_local_variables"],
+ max_value_length=client_options["max_value_length"],
+ ),
+ "crashed": False,
+ "current": True,
+ }
+ ]
+ }
+ else:
+ event: Event = {}
+ hint: Hint = {}
+
+ hint["log_record"] = record
+
+ level = self._logging_to_event_level(record)
+ if level in {"debug", "info", "warning", "error", "critical", "fatal"}:
+ # standard levels that sentry understands, it ignores any other types
+ event["level"] = level # type: ignore[typeddict-item]
+
+ event["logger"] = record.name
+
+ # Log records from `warnings` module as separate issues
+ record_captured_from_warnings_module = record.name == "py.warnings" and record.msg == "%s"
+ if record_captured_from_warnings_module:
+ # use the actual message and not "%s" as the message
+ # this prevents grouping all warnings under one "%s" issue
+ msg = record.args[0] # type: ignore
+
+ event["logentry"] = {
+ "message": msg,
+ "params": (),
+ }
+
+ else:
+ event["logentry"] = {
+ # TODO: a bit lame, but we don't have access to the unformatted message, so we just reverse our current format...
+ "message": to_string(record.msg).split(" - ")[-1],
+ "params": record.args,
+ }
+
+ event["extra"] = self._extra_from_record(record)
+
+ if self.add_extra_info_hook:
+ event, hint, callbacks = self.add_extra_with_watchdog(event, hint, timeout=1)
+ self.schedule_callbacks(callbacks)
+
+ sentry_sdk.capture_event(event, hint)
+
+ def add_extra_with_watchdog(
+ self, event: Event, hint: Hint, timeout: float
+ ) -> tuple[Event, Hint, tuple[Callable, ...]]:
+ """Call the add_extra_info_hook with a watchdog so we can skip it if it's slow, and get another sentry error about that."""
+ if self.add_extra_info_previously_timed_out:
+ event.setdefault("extra", {})["add_extra_info_previously_timed_out"] = True
+ return event, hint, tuple()
+ if "attachments" not in hint:
+ hint["attachments"] = []
+ executor = ThreadPoolExecutor()
+ add_extra_info_hook = self.add_extra_info_hook
+ assert add_extra_info_hook is not None
+ future = executor.submit(add_extra_info_hook, event, hint)
+ try:
+ event, hint, callbacks = future.result(timeout=timeout)
+ executor.shutdown()
+ return event, hint, callbacks
+ except TimeoutError as e:
+ from imbue_core.sculptor.telemetry import ProductComponent
+ from imbue_core.sculptor.telemetry import SculptorPosthogEvent
+ from imbue_core.sculptor.telemetry import send_exception_to_posthog
+
+ send_exception_to_posthog(
+ SculptorPosthogEvent.SENTRY_EXCEPTION_DATA_COLLECTION_TOO_SLOW,
+ e,
+ component=ProductComponent.CROSS_COMPONENT,
+ )
+ # this will leave the thread still running; there's no real way to cancel it.
+ # we'll at least set this flag so future errors don't try to run the (bugged?) hook again.
+ executor.shutdown(wait=False)
+ self.add_extra_info_previously_timed_out = True
+
+ # continue with the main event without the extra info
+ return event, hint, tuple()
+
+
+class SentryBreadcrumbHandler(_BaseHandler):
+ """
+ A logging handler that records breadcrumbs for each log record.
+
+ Note that you do not have to use this class if the logging integration is enabled, which it is by default.
+ """
+
+ def __init__(self, level: int = logging.NOTSET, strip_extra: bool = False) -> None:
+ super().__init__(level=level)
+ self.strip_extra = strip_extra
+
+ def emit(self, record: logging.LogRecord) -> Any:
+ # is this needed? keeping in case there are side effects that we want to trigger here
+ self.format(record)
+ return self._emit(record)
+
+ def _emit(self, record: logging.LogRecord) -> None:
+ if not self._can_record(record):
+ return
+
+ sentry_sdk.add_breadcrumb(self._breadcrumb_from_record(record), hint={"log_record": record})
+
+ def _breadcrumb_from_record(self, record: logging.LogRecord) -> dict[str, Any]:
+ return {
+ "type": "log",
+ "level": self._logging_to_event_level(record),
+ "category": record.name,
+ "message": record.message,
+ "timestamp": datetime.fromtimestamp(record.created, timezone.utc),
+ "data": self._extra_from_record(record) if not self.strip_extra else {},
+ }
+
+
+def log_error_inside_sentry(
+ exception: Exception,
+ message: str,
+ extra: dict[str, str | int] | None = None,
+ additional_s3_uploads: Iterable[str] | None = None,
+) -> None:
+ """Log an error to sentry that happens during processing of a sentry event.
+
+ This needs to be done very carefully to ensure it won't fail - we don't want to have to have a fallback-fallback handler.
+ The caller should ensure everything passed into this is small so there's no chance of size issues.
+ """
+ client = sentry_sdk.get_client()
+ # we want to get rid of any breadcrumbs, attachments, and other stuff that might have caused the original request to fail.
+ # this will obviously make it harder to debug; we may want to selectively add some of this back.
+ with new_scope() as scope:
+ scope.clear()
+ event, hint = event_from_exception(
+ exception,
+ client_options=client.options,
+ mechanism={"type": "watchdog", "handled": True},
+ )
+ event["message"] = message
+ if extra is not None:
+ if "extra" not in event:
+ event["extra"] = {}
+ for k, v in extra.items():
+ event["extra"][k] = v
+
+ # record any other files uploaded to s3
+ if additional_s3_uploads is not None:
+ event["extra"][EXTRAS_UPLOADED_FILES_KEY + "_erred"] = str(list(additional_s3_uploads))
+
+ # Note that new_scope() gives a new "current scope" but doesn't affect the global or isolation scope,
+ # which is where most info is actually stored. Typically all 3 scopes are merged before logging the event.
+ # So we'll make sure to call capture_event in such a way that this merging doesn't happen.
+ client.capture_event(event=event, hint=hint, scope=scope)
diff --git a/imbue_core/imbue_core/serialization.py b/imbue_core/imbue_core/serialization.py
@@ -0,0 +1,471 @@
+import builtins
+import datetime
+import inspect
+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 Callable
+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.async_monkey_patches import EXCEPTION_LOGGED_FLAG
+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 (49780118-61e5-446b-b44b-cabb3ffc0ba2): 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
+ was_logged_by_log_exception: bool = False
+
+ @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(),
+ was_logged_by_log_exception=getattr(exception, EXCEPTION_LOGGED_FLAG, False),
+ )
+
+ @cached_property
+ def traceback(self) -> FixedTraceback | None:
+ traceback_dict = self.traceback_dict
+ if traceback_dict is None:
+ return None
+ return FixedTraceback.from_dict(traceback_dict)
+
+ @cached_property
+ def exception_module(self) -> str:
+ if "." in self.exception:
+ return self.exception.rsplit(".", maxsplit=1)[0]
+ return ""
+
+ @cached_property
+ def exception_type(self) -> str:
+ return self.exception.rsplit(".", maxsplit=1)[-1]
+
+ @cached_property
+ def exception_class(self) -> type[BaseException]:
+ if self.exception_module:
+ return cast(
+ type[BaseException],
+ getattr(import_module(self.exception_module), self.exception_type, None),
+ )
+ else:
+ return cast(type[BaseException], getattr(builtins, self.exception_type, None))
+
+ def construct_instance(self) -> BaseException:
+ try:
+ exception = self.exception_class(*cast(tuple[Serializable, ...], self.args))
+ except TypeError as e:
+ message_with_arg_info = (
+ f"Failed to construct exception {self.exception_class} with args {self.args}.",
+ "Ensure that the exception class is serializable and can be constructed with the provided args.",
+ )
+ raise TypeError(" ".join(message_with_arg_info)) from e
+
+ try:
+ setattr(exception, EXCEPTION_LOGGED_FLAG, True)
+ except AttributeError:
+ # We could not set the flag correctly
+ pass
+
+ return exception
+
+ def as_formatted_traceback(self) -> str:
+ if self.traceback is None:
+ traceback_str = ""
+ else:
+ # pyre-ignore[6]: pyre doesn't know that FixedTraceback is a traceback (since it's not a TracebackType)
+ traceback_str = "".join(format_tb(self.traceback))
+ return f"Traceback (most recent call last):\n{traceback_str}\n{self.exception}: {self.args}"
+
+
+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_dict(obj: Any) -> dict[str, Any]:
+ return cast(dict[str, Any], SERIALIZER.serialize(obj))
+
+
+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
+
+
+def deserialize_from_dict(data: dict[str, Any]) -> Any:
+ try:
+ return DESERIALIZER.deserialize(data) # pyre-ignore[20]: pyre doesn't understand deserialize
+ except Exception as e:
+ raise SerializationError(str(e)) from e
+
+
+def deserialize_from_dict_with_type(data: dict[str, Any], obj_type: type[T]) -> T:
+ try:
+ result = DESERIALIZER.deserialize(data, obj_type=obj_type)
+ assert isinstance(result, obj_type), f"Expected an object of type {obj_type}, but got {result}"
+ return result
+ except Exception as e:
+ raise SerializationError(str(e)) from e
+
+
+def deserialize_from_json_with_type(data: str | bytes | bytearray, obj_type: type[T]) -> T:
+ try:
+ return deserialize_from_dict_with_type(json.loads(data), obj_type=obj_type)
+ except Exception as e:
+ raise SerializationError(str(e)) from e
+
+
+def get_serializable_properties(obj: Any) -> dict[str, Any]:
+ members = inspect.getmembers(type(obj))
+ marked_members = [name for name, member in members if is_serializable_property(member)]
+ return {name: getattr(obj, name) for name in marked_members}
+
+
+def is_serializable_property(func: Callable) -> bool:
+ return getattr(func, "_imbue_is_serializable_property", False)
diff --git a/imbue_core/imbue_core/serialization_types.py b/imbue_core/imbue_core/serialization_types.py
@@ -0,0 +1,3 @@
+# Related comment from Sam: "One of the reasons I don't really like using subclasses for things like this is that __init_subclass__ gets called before dataclass decorators are applied (so there's not really a great way to make sure that a class that inherits from Serializable is actually serializable). I think it's fine just as a marker for now, but might be worth thinking about at some point."
+class Serializable:
+ pass
diff --git a/imbue_core/imbue_core/simple_git.py b/imbue_core/imbue_core/simple_git.py
@@ -0,0 +1,215 @@
+"""This file implements a subset of the functionality found in git.py and compute_environment.py, but everything is synchronous."""
+
+import shlex
+import subprocess
+import time
+from pathlib import Path
+from typing import Sequence
+
+from loguru import logger
+
+from imbue_core.async_monkey_patches import log_exception
+from imbue_core.computing_environment.data_types import AnyPath
+from imbue_core.computing_environment.data_types import RunCommandError
+
+
+class SyncLocalGitRepo:
+ """
+ Provides different operations that you can perform over a git repository.
+
+ Implements a subset of our async LocalGitRepo, but also pulls in some of the functions from computing_environment.py
+ that normally would operate on a LocalGitRepo.
+
+ Over time, we can probably replace LocalGitRepo and computing_environment more or less fully, as we're migrating
+ code away from asyncio.
+
+ Feel free to move additional functions from computing_environment.py into this class as needed. Usually, you just need
+ to remove async/await keywords, and replace calls to compute_environment. member functions with self. calls.
+ """
+
+ _base_path: Path
+
+ def __init__(self, base_path: Path) -> None:
+ self._base_path = base_path
+
+ @property
+ def base_path(self) -> Path:
+ """The base path of the git repo."""
+ return self._base_path
+
+ def run_git(
+ self,
+ command: Sequence[str],
+ check: bool = True,
+ cwd: AnyPath | None = None,
+ is_error_logged: bool = True,
+ is_stripped: bool = True,
+ retry_on_git_lock_error: bool = True,
+ ) -> str:
+ """Run a git command in the repo.
+
+ Example:
+ ```
+ git_repo.run_git("status")
+ ```
+ """
+ command = ["git"] + list(command)
+ if not retry_on_git_lock_error:
+ result = self.run_command(command, check=check, is_error_logged=is_error_logged, cwd=cwd)
+ else:
+ result = self._run_command_with_retry_on_git_lock_error(
+ command, check=check, is_error_logged=is_error_logged, cwd=cwd
+ )
+ if is_stripped:
+ return result.strip()
+ return result
+
+ def run_command(
+ self,
+ command: Sequence[str],
+ check: bool = True,
+ secrets: dict[str, str] | None = None,
+ cwd: AnyPath | None = None,
+ is_error_logged: bool = True,
+ ) -> str:
+ """Run a command in the repo.
+
+ Note, this can be used to run any command, not just git.
+ """
+ command_string = shlex.join(command)
+ logger.trace(
+ f"Running command: {command_string=} from cwd={cwd or self.base_path} with {secrets=} {check=} {is_error_logged=}"
+ )
+ completed_proc = subprocess.run(
+ command,
+ cwd=cwd or self._base_path,
+ stdin=subprocess.DEVNULL,
+ stdout=subprocess.PIPE,
+ stderr=subprocess.PIPE,
+ env=secrets,
+ )
+ # note, need to be carefull not to strip() lines since whitespace may be important (e.g. for diffs)
+ # return joined lines since mostly we only use the output for logging, and this way we arn't
+ # passing around lots of lists. Also it's easy to parse by lines if needed
+ try:
+ stdout = completed_proc.stdout.decode("UTF-8")
+ except UnicodeDecodeError as e:
+ # If we don't encounter this, it likely means something was fixed upstream and we can safely delete
+ log_exception(
+ e,
+ "Command {command_string} failed to decode stdout, replacing any invalid bytes which could lead to problems later",
+ command_string=command_string,
+ )
+ stdout = completed_proc.stdout.decode("UTF-8", errors="replace")
+ stderr = completed_proc.stderr.decode("UTF-8")
+ if check and completed_proc.returncode != 0:
+ error_message = f"command run from cwd={self.base_path} failed with exit code {completed_proc.returncode} and stdout:\n{stdout}\nstderr:\n{stderr}"
+ if is_error_logged:
+ logger.error(
+ f"command attempted: '{command_string}' from cwd={self.base_path}\nerror message: {error_message}"
+ )
+ # this should not be None, but do this to satisfy type checker, int or None we throw the same error
+ returncode = completed_proc.returncode or -1
+ raise RunCommandError(
+ cmd=command_string,
+ stderr=stderr,
+ returncode=returncode,
+ cwd=cwd or self.base_path,
+ )
+ return stdout
+
+ def get_git_diff(
+ self,
+ commit_hash: str | None = None,
+ staged: bool = False,
+ is_error_logged: bool = True,
+ include_binary: bool = True,
+ ) -> str:
+ """Get the diff for the current repo state."""
+ # make sure `is_stripped=False` otherwise patch can be invalid
+ command = ["diff", "--full-index"]
+ if include_binary:
+ # Without --binary, diffs of binary files will just contain a summary statement such as "Binary files a/file.bin and b/file.bin differ".
+ # Such diffs cannot be applied, but are useful for inclusion in LLM prompts.
+ command.append("--binary")
+ if staged:
+ command.append("--staged")
+ if commit_hash:
+ command.append(commit_hash)
+ return self.run_git(command, is_stripped=False, is_error_logged=is_error_logged)
+
+ def get_untracked_files(self) -> tuple[str, ...]:
+ """Get the untracked files in the repo."""
+ result = self.run_git(["ls-files", "--others", "--exclude-standard"], is_error_logged=False)
+ return tuple([line.strip() for line in result.splitlines() if line.strip()])
+
+ def get_untracked_file_diff(self, file_path: str, include_binary: bool = True) -> str:
+ """Get the diff for a untracked file.
+
+ Note this function will raise a RunCommandError if the there is no diff for the untracked file or if there
+ is another error running the command. So it is best to use this function after checking that the file is untracked
+ using get_untracked_files function.
+ """
+ command = ["diff", "--no-index"]
+ if include_binary:
+ command.append("--binary")
+ untracked_diff = self.run_git(
+ command + ["/dev/null", str(file_path)],
+ # Unfortunately, `--no-index` implies `--exit-code`, which will cause git diff to return an exit code of 1
+ # if the diff is not empty. So we can't use check=True here. We'll check for an empty output to detect failures.
+ check=False,
+ is_error_logged=True,
+ is_stripped=False,
+ )
+ if not untracked_diff:
+ raise RunCommandError(f"Unable to diff untracked file {file_path}")
+ return untracked_diff
+
+ def is_commit_a_branch(self, commit_hash: str) -> bool:
+ """Check if the given git ref is a branch."""
+ try:
+ self.run_git(
+ ("show-ref", "--verify", "-q", f"refs/heads/{commit_hash}"),
+ is_error_logged=False,
+ check=True,
+ )
+ return True
+ except RunCommandError as e:
+ if e.returncode == 1:
+ return False
+ raise
+
+ def get_merge_base(self, branch_name: str, target_branch: str) -> str:
+ """Get the merge base of the given branch and target branch.
+
+ The merge base is the most recent commit that is on both branches.
+ """
+ return self.run_git(["merge-base", branch_name, target_branch], is_error_logged=False)
+
+ def _run_command_with_retry_on_git_lock_error(
+ self,
+ command: Sequence[str],
+ check: bool = True,
+ is_error_logged: bool = True,
+ cwd: AnyPath | None = None,
+ ) -> str:
+ max_retries = 50
+ retry_count = 0
+ retry_delay = 0.1 # seconds
+ while True:
+ try:
+ return self.run_command(
+ command,
+ check=check,
+ is_error_logged=is_error_logged and retry_count >= max_retries,
+ cwd=cwd,
+ )
+ except RunCommandError as e:
+ error_message = str(e)
+ if "fatal: Unable to create" in error_message and ".git/index.lock': File exists" in error_message:
+ if retry_count >= max_retries:
+ raise
+ time.sleep(retry_delay)
+ retry_count += 1
+ else:
+ raise
diff --git a/imbue_core/imbue_core/subprocess_utils.py b/imbue_core/imbue_core/subprocess_utils.py
@@ -0,0 +1,798 @@
+from __future__ import annotations
+
+import os
+import shlex
+import subprocess
+import time
+from functools import cached_property
+from io import BytesIO
+from pathlib import Path
+from threading import Event
+from typing import Callable
+from typing import Final
+from typing import IO
+from typing import Mapping
+from typing import Protocol
+from typing import Sequence
+
+import attr
+from loguru import logger
+from typing_extensions import Self
+
+from imbue_core.async_monkey_patches import log_exception
+from imbue_core.constants import ExceptionPriority
+from imbue_core.context_managers import call_on_exit
+from imbue_core.errors import ExpectedError
+from imbue_core.event_utils import CompoundEvent
+from imbue_core.event_utils import MutableEvent
+from imbue_core.event_utils import ReadOnlyEvent
+from imbue_core.log_utils import DETAIL
+from imbue_core.log_utils import TRACE
+from imbue_core.pydantic_serialization import FrozenModel
+
+# Received a shutdown signal
+SUBPROCESS_STOPPED_BY_REQUEST_EXIT_CODE = -9999
+
+SSH_ERROR_RETURN_CODE = 255
+
+
+class HasStdoutAndStderr(Protocol):
+ stdout: bytes
+ stderr: bytes
+
+
+class HasSshLogs(HasStdoutAndStderr, Protocol):
+ ssh_logs: str | None
+
+
+# Useful for streaming logs from a subprocess which may themselves be generated from log lines.
+def log_subprocess_output_line_for_sculptor(
+ output_line: str,
+ relog_loguru_lines: bool = False,
+ is_logging_without_loguru_formatting: bool = False,
+) -> None:
+ return log_subprocess_output_line(
+ output_line=output_line,
+ relog_loguru_lines=relog_loguru_lines,
+ is_logging_without_loguru_formatting=is_logging_without_loguru_formatting,
+ is_logging_traced=True,
+ )
+
+
+def log_subprocess_output_line(
+ output_line: str,
+ relog_loguru_lines: bool = False,
+ is_logging_without_loguru_formatting: bool = False,
+ # TODO: remove this -- Sculptor code should not be calling this function. Will be fixed in a followup PR.
+ is_logging_traced: bool = False,
+) -> None:
+ log_level = TRACE if is_logging_traced else DETAIL
+ output_line = output_line.rstrip("\n")
+ # very brittle parsing of log format for recursive logging: ef460144-072f-4b74-a712-0f728fdd3f50
+ if len(output_line) >= 36 and output_line[4] == "-" and output_line[7] == "-" and output_line[34:36] == "Tuple ":
+ # these lines have already been logged in the child; only relog them if we really want to.
+ if relog_loguru_lines:
+ logger.opt(raw=True).log(log_level, output_line.rstrip("\n") + "\n")
+ else:
+ if is_logging_without_loguru_formatting:
+ logger.opt(raw=True).log(log_level, output_line.rstrip("\n") + "\n")
+ else:
+ logger.log(log_level, "> " + output_line)
+
+
+def maybe_truncate_middle(output: str, size: int) -> str:
+ assert size > 1000, "This doesn't handle small sizes nicely"
+ if len(output) < size:
+ return output
+ # Note: not doing any sort of escaping or special formatting because this should only be for human consumption
+ truncate_message = f"\n... OUTPUT TRUNCATED DUE TO BEING OVER {size:_} CHARACTERS ...\n"
+ truncate_size = (size - len(truncate_message)) // 2 - 1
+ return output[:truncate_size] + truncate_message + output[-truncate_size:]
+
+
+def _stdout_str(has_stdout: "HasStdoutAndStderr") -> str:
+ return has_stdout.stdout.decode("utf-8", errors="replace")
+
+
+def _stderr_str(has_stderr: "HasStdoutAndStderr") -> str:
+ return has_stderr.stderr.decode("utf-8", errors="replace")
+
+
+def _create_output_from_stdout_and_stderr(
+ has_stdout_and_stderr: "HasStdoutAndStderr",
+) -> str:
+ return _stdout_str(has_stdout_and_stderr) + _stderr_str(has_stdout_and_stderr)
+
+
+def _create_output_from_stdout_and_stderr_and_ssh_logs(has_ssh_logs: HasSshLogs) -> str:
+ without_ssh_logs = _create_output_from_stdout_and_stderr(has_ssh_logs)
+ if has_ssh_logs.ssh_logs is None:
+ return without_ssh_logs
+ return has_ssh_logs.ssh_logs + without_ssh_logs
+
+
+@attr.s(auto_exc=True, auto_attribs=True)
+class CommandError(Exception):
+ returncode: int
+ stdout: bytes
+ stderr: bytes
+ command: str
+ is_output_already_logged: bool
+
+ stdout_str = property(_stdout_str)
+ stderr_str = property(_stderr_str)
+ output = cached_property(_create_output_from_stdout_and_stderr)
+
+ def __str__(self) -> str:
+ s = f"Command failed with return code {self.returncode}. command=`{self.command}`"
+ if not self.is_output_already_logged:
+ # TODO: Consider making truncation configurable
+ maybe_truncated_output = maybe_truncate_middle(self.output, 8_000)
+ s += f"\noutput:\n{maybe_truncated_output}"
+ return s
+
+
+@attr.s(auto_exc=True, auto_attribs=True)
+class RemoteCommandError(CommandError):
+ """Remotely executed command failure not due to ssh, meaning `returncode not in (0, 255)`."""
+
+ # TODO: include a machine on this so that we can print out the actual hostname!
+
+ # We make these errors slightly differently from CommandErrors -
+ # where those have the full command line as command, in this we set that to be just the remote command and this var will hold the ssh command.
+ ssh_command: str
+ ssh_logs: str | None
+
+ output = cached_property(_create_output_from_stdout_and_stderr_and_ssh_logs)
+
+ def full_command(self) -> str:
+ return self.ssh_command + " " + self.command
+
+ def __str__(self) -> str:
+ s = f"Remote command failed with returncode {self.returncode}. ssh_command= `{self.ssh_command}`, command=`{self.command}`"
+ if not self.is_output_already_logged:
+ maybe_truncated_output = maybe_truncate_middle(self.output, 8_000)
+ s += f"\noutput:\n{maybe_truncated_output}"
+ if self.ssh_logs is not None:
+ maybe_truncated_ssh_logs = maybe_truncate_middle(self.ssh_logs, 8_000)
+ s += f"\nssh_logs:\n{maybe_truncated_ssh_logs}"
+ return s
+
+
+@attr.s(auto_exc=True, auto_attribs=True)
+class SSHConnectionError(CommandError):
+ """Error for ssh connections that return 255.
+
+ No other shell commands should exit 255, as it will be conflated with ssh's error code.
+ """
+
+ # TODO: include a machine on this so that we can print out the actual hostname!
+
+ returncode = SSH_ERROR_RETURN_CODE
+ # We make these errors slightly differently from CommandErrors -
+ # where those have the full command line as command, in this we set that to be just the remote command and this var will hold the ssh command.
+ ssh_command: str
+
+ ssh_logs: str | None
+
+ output = cached_property(_create_output_from_stdout_and_stderr_and_ssh_logs)
+
+ _CONFLATION_WARNING = "WARNING: the remote command may have returned 255 rather than SSH -- Don't do that!"
+
+ def __str__(self) -> str:
+ return f"SSH failed to connect with returncode {self.returncode}. ssh_command= `{self.ssh_command}`, command=`{self.command}`, ssh_logs=`{self.ssh_logs}`\n{self._CONFLATION_WARNING}"
+
+
+@attr.s(auto_attribs=True, kw_only=True)
+class CompletedProcess:
+ """
+ Mostly a reimplementation of subprocess.CompletedProcess but allows us to deal with some GI-specific concerns.
+ A class to make process results easier to work with for us. We have a couple concerns that are different from typical:
+ We run commands over SSH a lot and care about making sure that those errors clearly show both the command being run and the host being run on.
+ """
+
+ returncode: int
+ stdout: bytes
+ stderr: bytes
+ command: str
+ is_output_already_logged: bool
+
+ stdout_str = property(_stdout_str)
+ stderr_str = property(_stderr_str)
+ output = cached_property(_create_output_from_stdout_and_stderr)
+
+ def check(self) -> Self:
+ if self.returncode != 0:
+ error = CommandError(
+ command=self.command,
+ returncode=self.returncode,
+ stdout=self.stdout,
+ stderr=self.stderr,
+ is_output_already_logged=self.is_output_already_logged,
+ )
+ if "output" in self.__dict__:
+ # We've already calculated the output, so we can just set it here.
+ error.output = self.output
+ raise error
+ # So that this can be chained. For example,
+ # hostname = run_local_command("hostname").check().stdout
+ return self
+
+
+class ProcessError(ExpectedError):
+ def __init__(
+ self,
+ command: tuple[str, ...],
+ stdout: str,
+ stderr: str,
+ returncode: int | None = None,
+ is_output_already_logged: bool | None = False,
+ message: str | None = "Command failed with non-zero exit code",
+ ) -> None:
+ self.returncode = returncode
+ self.stdout = stdout
+ self.stderr = stderr
+ self.command = command
+ self.is_output_already_logged = is_output_already_logged
+ self.message = message
+
+ def describe(self, is_output_included: bool, output_truncation: int | None = 8_000) -> str:
+ command = " ".join(shlex.quote(arg) for arg in self.command)
+ s = f"{self.message} {self.returncode}. command=`{command}`"
+ if is_output_included:
+ # TODO: Consider making truncation configurable
+ output = self.stdout + "\n" + self.stderr
+ maybe_truncated_output = maybe_truncate_middle(output, output_truncation) if output_truncation else output
+ s += f"\noutput:\n{maybe_truncated_output}"
+ return s
+
+ def __str__(self) -> str:
+ return self.describe(is_output_included=True)
+
+
+class ProcessTimeoutError(ProcessError):
+ def __init__(
+ self,
+ command: tuple[str, ...],
+ stdout: str,
+ stderr: str,
+ is_output_already_logged: bool = False,
+ ) -> None:
+ super().__init__(
+ command,
+ stdout,
+ stderr,
+ None,
+ is_output_already_logged=is_output_already_logged,
+ message="Command timed out",
+ )
+
+
+class ProcessSetupError(ProcessError):
+ def __init__(
+ self,
+ command: tuple[str, ...],
+ stdout: str,
+ stderr: str,
+ is_output_already_logged: bool = False,
+ ) -> None:
+ super().__init__(
+ command,
+ stdout,
+ stderr,
+ None,
+ is_output_already_logged=is_output_already_logged,
+ message="Command failed to start",
+ )
+
+
+class FinishedProcess(FrozenModel):
+ """
+ Mostly a reimplementation of subprocess.CompletedProcess but allows us to deal with some GI-specific concerns.
+ A class to make process results easier to work with for us. We have a couple concerns that are different from typical:
+ We run commands over SSH a lot and care about making sure that those errors clearly show both the command being run and the host being run on.
+ """
+
+ returncode: int | None = None
+ stdout: str
+ stderr: str
+ command: tuple[str, ...]
+ is_timed_out: bool = False
+ is_output_already_logged: bool
+
+ def check(self) -> Self:
+ if self.is_timed_out:
+ raise ProcessTimeoutError(
+ command=self.command,
+ stdout=self.stdout,
+ stderr=self.stderr,
+ is_output_already_logged=self.is_output_already_logged,
+ )
+ if self.returncode != 0:
+ raise ProcessError(
+ command=self.command,
+ returncode=self.returncode,
+ stdout=self.stdout,
+ stderr=self.stderr,
+ is_output_already_logged=self.is_output_already_logged,
+ )
+ # So that this can be chained. For example,
+ # hostname = run_local_command("hostname").check().stdout
+ return self
+
+
+@attr.s(auto_attribs=True, kw_only=True)
+class RemoteCompletedProcess(CompletedProcess):
+ """
+ A remote completed process. Beyond CompletedProcess, includes ssh information.
+ """
+
+ ssh_command: str
+
+ # The ssh logs if they were split out.
+ ssh_logs: str | None = None
+
+ output = cached_property(_create_output_from_stdout_and_stderr_and_ssh_logs)
+
+ def check(self) -> Self:
+ if self.returncode == SSH_ERROR_RETURN_CODE:
+ ssh_connection_error = SSHConnectionError(
+ command=self.command,
+ returncode=SSH_ERROR_RETURN_CODE,
+ stdout=self.stdout,
+ stderr=self.stderr,
+ ssh_command=self.ssh_command,
+ is_output_already_logged=self.is_output_already_logged,
+ ssh_logs=self.ssh_logs,
+ )
+ if "output" in self.__dict__:
+ # We've already calculated the output, so we can just set it here.
+ ssh_connection_error.output = self.output
+ raise ssh_connection_error
+ if self.returncode != 0:
+ remote_command_error = RemoteCommandError(
+ command=self.command,
+ ssh_command=self.ssh_command,
+ returncode=self.returncode,
+ stdout=self.stdout,
+ stderr=self.stderr,
+ is_output_already_logged=self.is_output_already_logged,
+ ssh_logs=self.ssh_logs,
+ )
+ if "output" in self.__dict__:
+ # We've already calculated the output, so we can just set it here.
+ remote_command_error.output = self.output
+ raise remote_command_error
+ return self
+
+
+_READ_SIZE: Final[int] = 2**20
+
+
+@attr.s(auto_attribs=True)
+class PartialOutputContainer:
+ """A helper class to make reconstructing log lines returned by pipe.read() easier."""
+
+ buffer: BytesIO = attr.ib(factory=BytesIO)
+ # Note: This in-memory line could become huge if no newlines are output
+ in_progress_line: bytearray = attr.ib(factory=bytearray)
+ on_complete_line: Callable[[str], None] | None = None
+
+ def write(self, output: bytes) -> None:
+ """`output` is the output of pipe.read(), ie a string that may contain newlines."""
+ self.buffer.write(output)
+ on_complete_line = self.on_complete_line
+ if on_complete_line is None:
+ # If we don't have a callback, we don't need to do anything else.
+ return
+
+ lines = output.splitlines(keepends=True)
+ for line in lines:
+ self.in_progress_line.extend(line)
+ if line.endswith((b"\n", b"\r")):
+ on_complete_line(self.in_progress_line.decode("utf-8", errors="replace"))
+ self.in_progress_line.clear()
+
+ def get_complete_output(self) -> bytes:
+ return self.buffer.getvalue()
+
+
+@attr.s(auto_attribs=True)
+class OutputGatherer:
+ stdout: IO[bytes]
+ stderr: IO[bytes]
+ stdout_container: PartialOutputContainer
+ stderr_container: PartialOutputContainer
+ shutdown_event: ReadOnlyEvent
+
+ @classmethod
+ def build_from_popen(
+ cls,
+ popen: subprocess.Popen[bytes],
+ on_complete_line_from_stdout: Callable[[str], None] | None,
+ on_complete_line_from_stderr: Callable[[str], None] | None,
+ shutdown_event: ReadOnlyEvent,
+ ) -> Self:
+ stdout = popen.stdout
+ stderr = popen.stderr
+ assert stdout is not None
+ assert stderr is not None
+ # this makes reads on process.stdout nonblocking
+ os.set_blocking(stdout.fileno(), False)
+ os.set_blocking(stderr.fileno(), False)
+
+ return cls(
+ stdout=stdout,
+ stderr=stderr,
+ stdout_container=PartialOutputContainer(on_complete_line=on_complete_line_from_stdout),
+ stderr_container=PartialOutputContainer(on_complete_line=on_complete_line_from_stderr),
+ shutdown_event=shutdown_event,
+ )
+
+ def gather_output(self) -> None:
+ is_more_from_stdout = True
+ is_more_from_stderr = True
+ # We may drop some output if the shutdown event is set, but that's okay.
+ while not self.shutdown_event.is_set() and (is_more_from_stdout or is_more_from_stderr):
+ # We always attempt to read from both streams to avoid starvation.
+ partial_stdout = self.stdout.read(_READ_SIZE)
+ if partial_stdout is not None:
+ self.stdout_container.write(partial_stdout)
+ is_more_from_stdout = len(partial_stdout) == _READ_SIZE
+ else:
+ is_more_from_stdout = False
+ partial_stderr = self.stderr.read(_READ_SIZE)
+ if partial_stderr is not None:
+ self.stderr_container.write(partial_stderr)
+ is_more_from_stderr = len(partial_stderr) == _READ_SIZE
+ else:
+ is_more_from_stderr = False
+
+ def get_output(self) -> tuple[bytes, bytes]:
+ return (
+ self.stdout_container.get_complete_output(),
+ self.stderr_container.get_complete_output(),
+ )
+
+ def get_incomplete_lines(self) -> tuple[str, str]:
+ return self.stdout_container.in_progress_line.decode(
+ "utf-8", errors="replace"
+ ), self.stderr_container.in_progress_line.decode("utf-8", errors="replace")
+
+
+def _shutdown_popen(process: subprocess.Popen[bytes], command: str, shutdown_timeout_sec: float) -> int | None:
+ logger.debug(
+ f"run_local_command: aborting command (via sigterm to {process.pid}) due to signal...\n",
+ command=truncate_command(command, 500),
+ )
+ # this sends SIGTERM, which is "the normal way to politely ask a program to terminate"
+ process.terminate()
+ try:
+ process.wait(timeout=shutdown_timeout_sec)
+ return process.returncode
+ except subprocess.TimeoutExpired as e:
+ extra = {"command": command, "shutdown_timeout_sec": str(shutdown_timeout_sec)}
+ log_exception(
+ e,
+ "process didn't die within shutdown_timeout_sec of SIGTERM",
+ extra=extra,
+ priority=ExceptionPriority.LOW_PRIORITY,
+ )
+ # this sends SIGKILL which immediately kills the process
+ process.kill()
+ try:
+ process.wait(timeout=2)
+ return process.returncode
+ except subprocess.TimeoutExpired as e:
+ log_exception(
+ e,
+ "process didn't die after kill()",
+ extra=extra,
+ priority=ExceptionPriority.LOW_PRIORITY,
+ )
+ return None
+
+
+def _log_input_command(command: str) -> None:
+ input_lines = command.splitlines()
+ truncation_context = 2
+ is_worth_truncating = len(input_lines) > 3 * truncation_context
+ if is_worth_truncating:
+ input_lines = (
+ input_lines[:truncation_context]
+ + [" (...content truncated...)"]
+ + input_lines[-truncation_context:]
+ )
+ for line in input_lines:
+ logger.trace("< " + line)
+
+
+def _is_timeout(timeout_time: float | None = None) -> bool:
+ if timeout_time is None:
+ return False
+ else:
+ return time.time() > timeout_time
+
+
+def run_local_command(
+ command: str,
+ is_checked: bool = True,
+ timeout: float | None = None,
+ trace_output: bool = True,
+ cwd: Path | None = None,
+ trace_on_complete_line_callback: Callable[[str], None] | None = log_subprocess_output_line,
+ trace_log_context: Mapping[str, object] | None = None,
+ shutdown_event: Event | CompoundEvent | None = None,
+ shutdown_timeout_sec: float = 30.0,
+) -> CompletedProcess:
+ """
+ implementation notes:
+ - this function is really tricky to implement well! check with josh, bawr, or zack before making nontrivial changes
+ - the reason it's tricky is that we need to both monitor the shutdown event, while also reading the subprocess
+ output in realtime to allow realtime log tracing of the output.
+ - we previously had an implementation that used a helper thread to read the output, but never seemed to shutdown
+ cleanly and left a mess of warnings in the logs, even though it seemed to be implemented properly.
+ - the current implementation aims to use just the main thread to avoid this. but then you need to be very careful
+ to avoid anything blocking.
+ - thus we set the pipe to nonblocking mode so that reads are nonblocking, and we also don't use readline()
+ as that could potentially block/deadlock if the process prints long lines with no newlines.
+ - don't redirect the process output to a file, as then the command may detect an interactive terminal and use
+ line buffering.
+ - (bawr) DO NOT CHANGE STDIN TO ANYTHING BESIDES DEV NULL, that'll cause race conditions.
+ - potentially there's a cleaner implementation using asyncio, but better the devil you know, yknow?
+ """
+ trace_log_context = trace_log_context if trace_log_context is not None else {}
+ shutdown_event = shutdown_event or Event()
+
+ if shutdown_event.is_set():
+ result = CompletedProcess(
+ returncode=SUBPROCESS_STOPPED_BY_REQUEST_EXIT_CODE,
+ stdout=b"",
+ stderr=b"",
+ command=command,
+ is_output_already_logged=trace_output,
+ )
+ if is_checked:
+ result.check()
+ return result
+
+ if trace_output:
+ _log_input_command(command)
+
+ # with bufsize 0 and not setting text, encoding, or errors, the pipe objects will be RawIOBase.
+ # use read(2**30) or similar if using this for a nonblocking read.
+ # with nonzero bufsize, they will be BufferedIOBase. use read1() if using this for a nonblocking read.
+ # with text, encoding, or errors, they will be TextIOBase.
+ # this doesn't seem to play nice with nonblocking mode and read().
+ process = subprocess.Popen(
+ command,
+ cwd=cwd,
+ shell=True,
+ executable="/bin/bash",
+ bufsize=0,
+ stdin=subprocess.DEVNULL,
+ stdout=subprocess.PIPE,
+ stderr=subprocess.PIPE,
+ env={**os.environ, "TERM": "dumb"},
+ )
+
+ gatherer = OutputGatherer.build_from_popen(
+ process,
+ on_complete_line_from_stdout=(trace_on_complete_line_callback if trace_output else None),
+ on_complete_line_from_stderr=(trace_on_complete_line_callback if trace_output else None),
+ shutdown_event=shutdown_event,
+ )
+
+ timeout_time = time.time() + timeout if timeout is not None else None
+
+ with logger.contextualize(**trace_log_context):
+ while not shutdown_event.wait(0.001) and not _is_timeout(timeout_time):
+ maybe_exit_code = process.poll()
+ gatherer.gather_output()
+ if maybe_exit_code is not None:
+ exit_code = maybe_exit_code
+ break
+ else:
+ # The shutdown event was set or a timeout limit has been reached,
+ # so we should shutdown the process.
+ _shutdown_popen(process, command, shutdown_timeout_sec)
+ exit_code = SUBPROCESS_STOPPED_BY_REQUEST_EXIT_CODE
+
+ stdout, stderr = gatherer.get_output()
+ result = CompletedProcess(
+ returncode=exit_code,
+ stdout=stdout,
+ stderr=stderr,
+ command=command,
+ is_output_already_logged=trace_output,
+ )
+ if is_checked:
+ result.check()
+
+ return result
+
+
+# NOTE: this function is largely duplicated with the above, but subtle changes to the types
+# most of the logic should be the same though, with the exception that we assume stdout and stderr are strings
+def run_local_command_modern_version(
+ command: Sequence[str],
+ is_checked: bool = True,
+ timeout: float | None = None,
+ trace_output: bool = False,
+ cwd: Path | None = None,
+ # this is called for each line, including the last line even if it doesn't end with a newline
+ # if there is no output, it is never called
+ trace_on_line_callback: Callable[[str, bool], None] | None = None,
+ trace_log_context: Mapping[str, object] | None = None,
+ shutdown_event: MutableEvent | None = None,
+ shutdown_timeout_sec: float = 30.0,
+ poll_time: float = 0.01,
+ env: Mapping[str, str] | None = None,
+ # This callback gets called once either the process is running or it failed to start.
+ # The argument is None on success, or the Exception on failure.
+ on_initialization_complete: Callable[[BaseException | None], None] = lambda success: None,
+) -> FinishedProcess:
+ """
+ implementation notes:
+ - this function is really tricky to implement well! check with josh, bawr, or zack before making nontrivial changes
+ - the reason it's tricky is that we need to both monitor the shutdown event, while also reading the subprocess
+ output in realtime to allow realtime log tracing of the output.
+ - we previously had an implementation that used a helper thread to read the output, but never seemed to shutdown
+ cleanly and left a mess of warnings in the logs, even though it seemed to be implemented properly.
+ - the current implementation aims to use just the main thread to avoid this. but then you need to be very careful
+ to avoid anything blocking.
+ - thus we set the pipe to nonblocking mode so that reads are nonblocking, and we also don't use readline()
+ as that could potentially block/deadlock if the process prints long lines with no newlines.
+ - don't redirect the process output to a file, as then the command may detect an interactive terminal and use
+ line buffering.
+ - (bawr) DO NOT CHANGE STDIN TO ANYTHING BESIDES DEV NULL, that'll cause race conditions.
+ - potentially there's a cleaner implementation using asyncio, but better the devil you know, yknow?
+ - if `env` is set, will overwrite contents passed into subprocess.Popen
+
+ raises ProcessError
+ """
+ with call_on_exit(on_initialization_complete):
+ trace_log_context = trace_log_context if trace_log_context is not None else {}
+ shutdown_event = shutdown_event or Event()
+ command_as_string = " ".join(shlex.quote(arg) for arg in command)
+ # NOTE: We create the process even when shutdown_event is already set.
+ # It will be terminated almost immediately after starting but the benefit is that the behavior stays consistent.
+
+ if trace_output:
+ _log_input_command(command_as_string)
+
+ # with bufsize 0 and not setting text, encoding, or errors, the pipe objects will be RawIOBase.
+ # use read(2**30) or similar if using this for a nonblocking read.
+ # with nonzero bufsize, they will be BufferedIOBase. use read1() if using this for a nonblocking read.
+ # with text, encoding, or errors, they will be TextIOBase.
+ # this doesn't seem to play nice with nonblocking mode and read().
+ try:
+ process = subprocess.Popen(
+ command,
+ cwd=cwd,
+ bufsize=0,
+ stdin=subprocess.DEVNULL,
+ stdout=subprocess.PIPE,
+ stderr=subprocess.PIPE,
+ env=env,
+ )
+ except (OSError, ValueError) as e:
+ # Raise setup error if process fails to start.
+ # OSError: subprocess.Popen fails to start the requested command
+ # ValueError: subprocess.Popen malformed arguments
+ raise ProcessSetupError(
+ command=tuple(command),
+ stdout="",
+ stderr="",
+ is_output_already_logged=trace_output,
+ ) from e
+
+ if trace_output:
+ # NOTE: We could probably condense the truth value of trace_output to
+ # just be bool(trace_on_line_callback). However, there's a lot of scary code
+ # that depends on these values that I didn't want to audit just now.
+ assert trace_on_line_callback, "Must pass trace_on_line_callback"
+ on_complete_line_from_stdout = lambda line: trace_on_line_callback(line, True)
+ on_complete_line_from_stderr = lambda line: trace_on_line_callback(line, False)
+ else:
+ on_complete_line_from_stdout = None
+ on_complete_line_from_stderr = None
+
+ gatherer = OutputGatherer.build_from_popen(
+ process,
+ on_complete_line_from_stdout=on_complete_line_from_stdout,
+ on_complete_line_from_stderr=on_complete_line_from_stderr,
+ shutdown_event=shutdown_event,
+ )
+
+ timeout_time = time.time() + timeout if timeout is not None else None
+
+ with logger.contextualize(**trace_log_context):
+ while not shutdown_event.wait(poll_time) and not _is_timeout(timeout_time):
+ maybe_exit_code = process.poll()
+ gatherer.gather_output()
+ if maybe_exit_code is not None:
+ exit_code = maybe_exit_code
+ break
+ else:
+ # The shutdown event was set or a timeout limit has been reached,
+ # so we should shutdown the process.
+ exit_code = _shutdown_popen(process, command_as_string, shutdown_timeout_sec)
+
+ stdout, stderr = gatherer.get_output()
+
+ # send the final incomplete lines as well
+ incomplete_stdout_line, incomplete_stderr_line = gatherer.get_incomplete_lines()
+ if incomplete_stdout_line:
+ if trace_on_line_callback:
+ trace_on_line_callback(incomplete_stdout_line, True)
+ if incomplete_stderr_line:
+ if trace_on_line_callback:
+ trace_on_line_callback(incomplete_stderr_line, False)
+
+ result = FinishedProcess(
+ returncode=exit_code,
+ stdout=stdout.decode("utf-8", errors="replace"),
+ stderr=stderr.decode("utf-8", errors="replace"),
+ command=tuple(command),
+ is_timed_out=_is_timeout(timeout_time),
+ is_output_already_logged=trace_output,
+ )
+ if is_checked:
+ result.check()
+
+ return result
+
+
+def run_remote_command(
+ machine_ssh_command: str,
+ remote_command: str,
+ is_checked: bool = True,
+ trace_output: bool = True,
+ trace_on_complete_line_callback: Callable[[str], None] = log_subprocess_output_line,
+ trace_log_context: Mapping[str, object] | None = None,
+ shutdown_event: Event | CompoundEvent | None = None,
+ shutdown_timeout_sec: float = 30.0,
+) -> RemoteCompletedProcess:
+ """
+ :raises SSHConnectionError: if `is_checked and returncode == 255` (the ssh reserved error code)
+ :raises RemoteCommandError: if `is_checked and returncode not in (0, 255)`
+ """
+ # Please please please use proper quoting tools
+ escaped_remote_command = shlex.quote(remote_command)
+ result = run_local_command(
+ f"{machine_ssh_command} {escaped_remote_command}",
+ is_checked=False,
+ trace_output=trace_output,
+ trace_on_complete_line_callback=trace_on_complete_line_callback,
+ trace_log_context=trace_log_context,
+ shutdown_event=shutdown_event,
+ shutdown_timeout_sec=shutdown_timeout_sec,
+ )
+
+ remote_result = RemoteCompletedProcess(
+ returncode=result.returncode,
+ stdout=result.stdout,
+ stderr=result.stderr,
+ ssh_command=machine_ssh_command,
+ command=remote_command,
+ is_output_already_logged=result.is_output_already_logged,
+ )
+ if is_checked:
+ remote_result.check()
+ return remote_result
+
+
+def truncate_command(command: str, num_chars: int = 2000) -> str:
+ """Truncates a command to include just the first `num_chars` of the first line."""
+ truncated = False
+ split_command = command.split("\n")
+ if len(split_command) > 1:
+ truncated = True
+ command = split_command[0]
+ if len(command) > num_chars:
+ truncated = True
+ command = command[:num_chars]
+ if truncated:
+ command += "... (truncated)"
+ return command
diff --git a/imbue_core/imbue_core/suggestions.py b/imbue_core/imbue_core/suggestions.py
@@ -0,0 +1,85 @@
+from typing import Annotated
+from typing import Any
+
+from pydantic import AnyUrl
+from pydantic import Field
+from pydantic import Tag
+
+from imbue_core.agents.data_types.ids import ObjectID
+from imbue_core.data_types import IdentifiedVerifyIssue
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.pydantic_serialization import build_discriminator
+
+
+class SuggestionAction(SerializableModel):
+ object_type: str
+ # more important -> lower number. Think about it as "my first priority is to..."
+ # different actions *may* share the same priority rank, in which case the ties will be broken by a canonical ordering of importance
+ priority_rank: int = 0
+
+
+class UseSuggestionAction(SuggestionAction):
+ object_type: str = "UseSuggestionAction"
+ content: str
+
+
+class VisitLinkSuggestionAction(SuggestionAction):
+ object_type: str = "VisitLinkSuggestionAction"
+ url: AnyUrl
+ link_text: str
+
+
+SuggestionActionTypes = Annotated[
+ Annotated[UseSuggestionAction, Tag("UseSuggestionAction")]
+ | Annotated[VisitLinkSuggestionAction, Tag("VisitLinkSuggestionAction")],
+ build_discriminator(),
+]
+
+
+# FIXME(johnny): move to imbue_core.imbue_cli.action
+class CheckOutputID(ObjectID):
+ tag: str = "chko"
+
+
+class Suggestion(SerializableModel):
+ # TODO: this is here because we're treating this like an issue, but we may not want to do that
+ object_type: str = "Suggestion"
+ # TODO: remove the default factory once we have properly migrated to the new check output protocol
+ # Also see sculptor/sculptor/tasks/handlers/run_agent/checks/check_process.py::275
+ id: CheckOutputID = Field(default_factory=CheckOutputID)
+ title: str = Field(min_length=1)
+ description: str = ""
+ # (if sculptor speaks true) pyre doesn't like float values for gt/ge/le because... it's looking for def __gt__(self: T, __other: T) -> bool and float has def __gt__(self, value: float, /) -> bool
+
+ # will probably be technically implemented by asking an LLM to come up with a number between 1 and 10,
+ # so probably really will range between 0.1 and 1.0
+ severity_score: float = Field(
+ ge=0.0, # pyre-ignore[6]
+ le=1.0, # pyre-ignore[6]
+ description="A score between 0.0 and 1.0 indicating how severe the issue is that this suggestion addresses.",
+ )
+ # unlike the severity, this is about how sure we are that this is a good suggestion,
+ # for example, you can be confident that there is a real problem (high confidence_score)
+ # but it might be about some edge case that doesn't matter (low severity_score)
+ confidence_score: float = Field(
+ ge=0.0, # pyre-ignore[6]
+ le=1.0, # pyre-ignore[6]s
+ description="A score between 0.0 and 1.0 indicating how confident we are that this suggestion addresses a real issue.",
+ )
+ # these are the possible actions that the user can take with this suggestion
+ # for right now, the only ones implemented are "USE" and "COPY"
+ actions: tuple[SuggestionActionTypes, ...]
+ original_issues: tuple[IdentifiedVerifyIssue, ...]
+
+
+# FIXME(johnny): move these to the right location, use the right types, etc -- these are just placeholders to demonstrate how this works
+# FIXME(johnny): migrate to just using the ActionOutputUnion from imbue_core.imbue_cli.action
+CheckOutputTypes = Annotated[
+ Annotated[Suggestion, Tag("Suggestion")],
+ build_discriminator(),
+]
+
+
+# FIXME(johnny): remove this once we move to using the ActionOutputUnion from imbue_core.imbue_cli.action
+def is_check_output(output: Any) -> bool:
+ return isinstance(output, Suggestion)
diff --git a/imbue_core/imbue_core/test_repo_utils.py b/imbue_core/imbue_core/test_repo_utils.py
@@ -0,0 +1,57 @@
+"""Utilities for tests involving a git repository."""
+
+import subprocess
+import tempfile
+from pathlib import Path
+from typing import Generator
+
+from imbue_core.common import get_temp_dir
+from imbue_core.git import LocalGitRepo
+from imbue_core.git import get_git_repo_root
+from imbue_core.test_utils import create_temp_dir
+
+
+def make_simple_test_git_repo() -> Generator[Path, None, None]:
+ """Create a temporary git repository for testing.
+
+ Creates a simple repository with two commits and two files.
+ - Initial commit with file1.txt
+ - Initial commit file2 with file2.txt
+ """
+ with tempfile.TemporaryDirectory() as temp_dir:
+ repo_path = Path(temp_dir)
+
+ # Initialize git repo
+ subprocess.run(["git", "init"], cwd=repo_path, check=True, capture_output=True)
+ subprocess.run(
+ ["git", "config", "user.email", "test@example.com"],
+ cwd=repo_path,
+ check=True,
+ )
+ subprocess.run(["git", "config", "user.name", "Test User"], cwd=repo_path, check=True)
+
+ # Create initial commit
+ (repo_path / "file1.txt").write_text("initial content")
+ subprocess.run(["git", "add", "file1.txt"], cwd=repo_path, check=True)
+ subprocess.run(["git", "commit", "-m", "Initial commit"], cwd=repo_path, check=True)
+ (repo_path / "file2.txt").write_text("initial content file 2")
+ subprocess.run(["git", "add", "file2.txt"], cwd=repo_path, check=True)
+ subprocess.run(["git", "commit", "-m", "Initial commit file2"], cwd=repo_path, check=True)
+
+ yield repo_path
+
+
+def make_mock_repo(path: Path, is_recreating: bool = False) -> Generator[LocalGitRepo, None, None]:
+ mock_repo = LocalGitRepo(base_path=path)
+ with create_temp_dir(root_dir=Path(get_temp_dir())) as temp_dir:
+ temp_repo = mock_repo.sync_copy_repo(temp_dir)
+ temp_repo.sync_configure_git(
+ git_user_name="AGI (Automated Software Inspector)",
+ git_user_email="the_true_AGI@running.pytest.com",
+ is_recreating=is_recreating,
+ )
+ yield temp_repo
+
+
+def make_test_data_mock_repo() -> Generator[LocalGitRepo, None, None]:
+ yield from make_mock_repo(get_git_repo_root() / "imbue/imbue/test_data/mock_repo", is_recreating=False)
diff --git a/imbue_core/imbue_core/test_utils.py b/imbue_core/imbue_core/test_utils.py
@@ -0,0 +1,88 @@
+import contextlib
+import shutil
+import tempfile
+import time
+from pathlib import Path
+from typing import AsyncGenerator
+from typing import Callable
+from typing import Generator
+
+from loguru import logger
+from syrupy.assertion import SnapshotAssertion
+
+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 CachedCountTokensResponse
+from imbue_core.agents.llm_apis.llm_testing_utils import check_llm_responses_in_cache
+from imbue_core.caching import AsyncCache
+from imbue_core.llm_testing_utils import get_cache_file_from_snapshot
+from imbue_core.llm_testing_utils import get_count_tokens_cache_file_from_snapshot
+from imbue_core.llm_testing_utils import preload_llm_cache
+from imbue_core.llm_testing_utils import record_llm_responses_in_cache
+
+
+def info_if_not_quiet(quiet: bool, message: str) -> None:
+ if not quiet:
+ logger.info(message)
+
+
+@contextlib.contextmanager
+def create_temp_dir(root_dir: Path) -> Generator[Path, None, None]:
+ with tempfile.TemporaryDirectory(dir=root_dir) as temp_dir:
+ yield Path(temp_dir)
+ shutil.rmtree(temp_dir)
+
+
+def wait_until(condition: Callable[[], bool], timeout: float = 5.0, interval: float = 0.5) -> None:
+ start_time = time.monotonic()
+ while True:
+ if condition():
+ return
+ if time.monotonic() - start_time > timeout:
+ raise TimeoutError("Condition not met within timeout period")
+ time.sleep(interval)
+
+
+async def make_llm_cache_with_snapshot_core(
+ snapshot: SnapshotAssertion,
+ json_cache_file: Path,
+ value_cls: type[CachedCostedModelResponse],
+ quiet: bool = True,
+ suffix: str = "",
+) -> AsyncGenerator[Path, None]:
+ info_if_not_quiet(quiet, f"Using llm_cache_pathfixture: {json_cache_file=}")
+
+ with tempfile.TemporaryDirectory() as cache_path_string:
+ cache_path = Path(cache_path_string)
+ cache_context = AsyncCache(cache_path, value_cls)
+
+ if not snapshot.session.update_snapshots:
+ if json_cache_file.exists():
+ await preload_llm_cache(json_cache_file, cache_context)
+
+ yield cache_path
+ info_if_not_quiet(quiet, "Finished with llm_cache_pathfixture, updating cache if needed.")
+
+ if snapshot.session.update_snapshots:
+ await record_llm_responses_in_cache(cache_context, json_cache_file)
+
+ await check_llm_responses_in_cache(snapshot, cache_context, suffix)
+ info_if_not_quiet(quiet, "Finished with llm_cache_pathfixture, checking cache.")
+
+
+async def make_llm_cache_with_snapshot(snapshot: SnapshotAssertion, quiet: bool = True) -> AsyncGenerator[Path, None]:
+ json_cache_file = get_cache_file_from_snapshot(snapshot)
+ async for path in make_llm_cache_with_snapshot_core(
+ snapshot, json_cache_file, CachedCostedLanguageModelResponse, quiet
+ ):
+ yield path
+
+
+async def make_count_tokens_cache_with_snapshot(
+ snapshot: SnapshotAssertion, quiet: bool = True
+) -> AsyncGenerator[Path, None]:
+ json_cache_file = get_count_tokens_cache_file_from_snapshot(snapshot)
+ async for path in make_llm_cache_with_snapshot_core(
+ snapshot, json_cache_file, CachedCountTokensResponse, quiet, "_count_tokens"
+ ):
+ yield path
diff --git a/imbue_core/imbue_core/testing_utils.py b/imbue_core/imbue_core/testing_utils.py
@@ -0,0 +1,189 @@
+import asyncio
+import inspect
+import os
+import shutil
+from contextlib import asynccontextmanager
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Any
+from typing import AsyncGenerator
+from typing import Callable
+from typing import ContextManager
+from typing import Coroutine
+from typing import Generator
+from typing import Iterable
+from typing import Protocol
+from typing import Sequence
+from typing import TYPE_CHECKING
+from typing import TypeVar
+from uuid import uuid4
+
+import anyio
+import pytest
+import pytest_asyncio
+from _pytest.fixtures import Config
+from _pytest.python import Function
+
+from imbue_core.common import get_temp_dir
+
+if TYPE_CHECKING:
+ # noinspection PyProtectedMember
+ from _pytest.fixtures import _ScopeName
+
+T = TypeVar("T")
+
+_TestFunc = Callable[..., None] | Callable[..., Coroutine[Any, Any, None]]
+
+
+def fixture(
+ fixture_function: Any | None = None,
+ *,
+ scope: "_ScopeName | Callable[[str, Config], _ScopeName]" = "function",
+ params: Iterable[object] | None = None,
+ autouse: bool = False,
+ ids: Sequence[object | None] | Callable[[Any], object | None] | None = None,
+) -> Any:
+ def decorator(function: Any) -> Any:
+ true_name = function.__name__[:-1]
+ if inspect.iscoroutinefunction(function):
+ return pytest_asyncio.fixture(
+ function,
+ name=true_name,
+ scope=scope,
+ params=params,
+ autouse=autouse,
+ ids=ids, # type: ignore
+ )
+ else:
+ return pytest.fixture(
+ function,
+ name=true_name,
+ scope=scope,
+ params=params,
+ autouse=autouse,
+ ids=ids,
+ )
+
+ if fixture_function is not None and callable(fixture_function):
+ return decorator(fixture_function)
+
+ return decorator
+
+
+def placeholder_param_for_mark(
+ marks: pytest.MarkDecorator | list[pytest.MarkDecorator],
+) -> object:
+ """Returns a param for annotating a fixture with marks.
+
+ It can be useful to add marks to a fixture that propagate to all functions that use it.
+ However, decorating a fixture function with "@pytest.mark.foo_bar" has no effect.
+
+ On the other hand, parameterized fixtures can have marks attached to each parameter;
+ thus a workaround is to parameterize the fixture with a single placeholder param.
+ Use this function like this:
+
+ @pytest.fixture(params=placeholder_param_for_mark(pytest.mark.foo_bar))
+
+ One slightly annoying side effect is that the param will show up in the test name.
+ We can get rid of it with pytest_collection_modifyitems,
+ but it's probably simpler to live with it.
+
+ You don't need this function if the fixture is already parameterized;
+ simply add the desired mark to the params:
+
+ @pytest.fixture(params=[pytest.param(0, marks=pytest.mark.foo_bar),
+ pytest.param(1, marks=pytest.mark.foo_bar)])
+
+ See https://github.com/pytest-dev/pytest/issues/1368 for more context.
+ """
+ return pytest.param("placeholder_param", marks=marks)
+
+
+def use(*args: Callable[..., Any]) -> Any:
+ true_names = [x.__name__[:-1] for x in args]
+ return pytest.mark.usefixtures(*true_names)
+
+
+def integration_test(function: _TestFunc) -> Any:
+ return pytest.mark.integration_test(function)
+
+
+def slow_integration_test(function: _TestFunc) -> Any:
+ return pytest.mark.slow_integration_test(function)
+
+
+class RequestFixture(Protocol[T]):
+ """Yes, there is a class called FixtureRequest, but the types are quite bad for it"""
+
+ node: Function
+ param: T
+
+
+@fixture
+def temp_file_path_() -> Generator[Path, None, None]:
+ with create_temp_file_path() as output:
+ yield output
+
+
+@fixture
+def temp_path_() -> Generator[Path, None, None]:
+ with temp_dir(get_temp_dir()) as output:
+ yield output
+
+
+def create_temp_file_path(cleanup: bool = True) -> ContextManager[Path]:
+ @contextmanager
+ def context() -> Generator[Path, None, None]:
+ random_id = uuid4()
+ output_path = os.path.join(get_temp_dir(), str(random_id))
+ try:
+ yield Path(output_path)
+ finally:
+ if cleanup and os.path.exists(output_path):
+ if os.path.isfile(output_path):
+ os.remove(output_path)
+ else:
+ shutil.rmtree(output_path)
+
+ # noinspection PyTypeChecker
+ return context()
+
+
+def temp_dir(base_dir: str, is_uuid_concatenated: bool = False) -> ContextManager[Path]:
+ @contextmanager
+ def context() -> Generator[Path, None, None]:
+ random_id = uuid4()
+ if is_uuid_concatenated:
+ output_path = Path(base_dir.rstrip("/") + "_" + str(random_id))
+ else:
+ output_path = Path(base_dir) / str(random_id)
+ output_path.mkdir(parents=True, exist_ok=True)
+ try:
+ yield output_path
+ finally:
+ if output_path.exists():
+ try:
+ shutil.rmtree(str(output_path))
+ except OSError:
+ os.unlink(str(output_path))
+
+ # noinspection PyTypeChecker
+ return context()
+
+
+@asynccontextmanager
+async def async_temp_dir(base_dir: str, is_uuid_concatenated: bool = False) -> AsyncGenerator[Path, None]:
+ random_id = uuid4()
+ if is_uuid_concatenated:
+ output_path = anyio.Path(base_dir.rstrip("/") + "_" + str(random_id))
+ else:
+ output_path = anyio.Path(base_dir) / str(random_id)
+ await output_path.mkdir(parents=True, exist_ok=True)
+ try:
+ yield Path(output_path)
+ finally:
+ if await output_path.exists():
+ try:
+ await asyncio.to_thread(shutil.rmtree, str(output_path))
+ except OSError:
+ await output_path.unlink()
diff --git a/imbue_core/imbue_core/time_utils.py b/imbue_core/imbue_core/time_utils.py
@@ -0,0 +1,5 @@
+import datetime
+
+
+def get_current_time() -> datetime.datetime:
+ return datetime.datetime.now(datetime.UTC)
diff --git a/imbue_core/pyproject.toml b/imbue_core/pyproject.toml
@@ -0,0 +1,67 @@
+[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"
+# NOTE: This list is replicated in sculptor/pyproject.toml, and the copy there must be kept in sync.
+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 = [
+ "moto>=4.1.12",
+]
diff --git a/imbue_core/uv.lock b/imbue_core/uv.lock
@@ -0,0 +1,1981 @@
+version = 1
+revision = 3
+requires-python = ">=3.11, <3.13"
+resolution-markers = [
+ "python_full_version >= '3.12'",
+ "python_full_version < '3.12'",
+]
+
+[[package]]
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diff --git a/imbue_tools/README.md b/imbue_tools/README.md
@@ -0,0 +1,9 @@
+# Purpose
+Shared functionality for imbue-cli tools like imbue-verify, imbue-retrieve, etc.
+
+# 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/__init__.py b/imbue_tools/imbue_tools/__init__.py
diff --git a/imbue_tools/imbue_tools/capabilities_data_logging/common.py b/imbue_tools/imbue_tools/capabilities_data_logging/common.py
@@ -0,0 +1,83 @@
+import abc
+import datetime
+import subprocess
+from enum import StrEnum
+from pathlib import Path
+from typing import Any
+from typing import Iterable
+
+from loguru import logger
+from psycopg.sql import SQL
+
+from imbue_core.pydantic_serialization import SerializableModel
+
+IMBUE_AUTOMATIC_TESTING_ORGANIZATION_ID = "imbue-automatic-testing"
+NEON_PROJECT_ID = (
+ "holy-butterfly-05886102" # This is the ID of the crafty project in neon.tech. TODO: reuse this for now
+)
+
+
+# TODO: this should be shared with `sculptor` but there's not a great shared module right now.
+# Make sure if this is updated the value in `sculptor` also gets updated
+# Path where imbue verify will log data and logged data will be expected to be found
+CAPABILITIES_DATA_LOGGING_PATH = Path("/tmp/sculptor/capabilities_logging")
+
+
+class ProductDataBaseEventRecord(SerializableModel, abc.ABC):
+ """
+ This is a base class for all product data events.
+ It contains the fields that are common to all product data events.
+ """
+
+ id: str
+ user_id: str
+ organization_id: str
+ created_at: datetime.datetime
+
+
+class SculptorDataTables(StrEnum):
+ PRODUCT_TOOL_DATA = "PRODUCT_TOOL_DATA"
+
+
+def build_product_feature_data_query_args(
+ user_id: str | None = None,
+ creation_bounds: tuple[datetime.datetime | None, datetime.datetime | None] = (
+ None,
+ None,
+ ),
+ ids: Iterable[str] | None = None,
+ filter_test_users: bool = False,
+) -> tuple[Any, tuple[Any, ...]]:
+ where_clause: Any = SQL("1 = 1")
+ where_args: tuple[Any, ...] = ()
+ start, end = creation_bounds
+ if start is not None:
+ where_clause = SQL("{} AND {}").format(where_clause, SQL("created_at >= %s"))
+ where_args += (start,)
+ if end is not None:
+ where_clause = SQL("{} AND {}").format(where_clause, SQL("created_at <= %s"))
+ where_args += (end,)
+ if user_id is not None:
+ where_clause = SQL("{} AND {}").format(where_clause, SQL("user_id = %s"))
+ where_args += (user_id,)
+ if ids is not None:
+ where_clause = SQL("{} AND {}").format(where_clause, SQL("id = ANY(%s)"))
+ where_args += (ids,)
+ if filter_test_users:
+ where_clause = SQL("{} AND NOT {}").format(where_clause, SQL("organization_id = %s"))
+ where_args += (IMBUE_AUTOMATIC_TESTING_ORGANIZATION_ID,)
+ return where_clause, where_args
+
+
+UNKNOWN_USER_NAME = "unknown"
+
+
+def get_current_user_name() -> str:
+ try:
+ possible_username = subprocess.check_output(["git", "config", "user.name"], universal_newlines=True).strip()
+ assert possible_username != ""
+ return possible_username
+ except subprocess.CalledProcessError:
+ pass
+ logger.info("Using UNKNOWN_USER_NAME")
+ return UNKNOWN_USER_NAME
diff --git a/imbue_tools/imbue_tools/capabilities_data_logging/data_types.py b/imbue_tools/imbue_tools/capabilities_data_logging/data_types.py
@@ -0,0 +1,250 @@
+"""
+Any fields in this file that are optional are not strictly required to be present in the database.
+Any fields that are non-optional are required and should not be changed without talking to capabilities eval group.
+Much of this code is taken from an unmerged branch `pranali/backwards_compatibility`.
+The write_code event is also supported in the minimal format in that branch.
+"""
+
+import uuid
+from datetime import datetime
+from datetime import timezone
+from enum import StrEnum
+from typing import Annotated
+from typing import Any
+from typing import Callable
+from typing import Self
+from typing import TypeVar
+
+from pydantic import ConfigDict
+from pydantic import Field
+from pydantic import PlainValidator
+from pydantic import ValidationError
+from pydantic import field_validator
+from pydantic import model_validator
+
+from imbue_core.agents.configs import LanguageModelGenerationConfig
+from imbue_core.common import generate_id
+from imbue_core.data_types import IdentifiedVerifyIssue
+from imbue_core.data_types import LLMResponse
+from imbue_core.frozen_utils import FrozenDict
+from imbue_core.frozen_utils import empty_mapping
+from imbue_core.nested_evolver import assign
+from imbue_core.nested_evolver import chill
+from imbue_core.nested_evolver import evolver
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.repo_state import RepoState
+from imbue_core.sculptor.state.messages import ConversationMessageUnion
+from imbue_tools.repo_utils.context_prefix import SubrepoContext
+from imbue_tools.types.imbue_verify_config import ImbueVerifyConfig
+
+TypeInput = TypeVar("TypeInput")
+TypeOutput = TypeVar("TypeOutput")
+
+
+class UnknownFeatureType(Exception):
+ """Exception raised when an unknown feature type is encountered."""
+
+
+class LoggedFeatureType(StrEnum):
+ VERIFY_EXCEPTION = "VERIFY_EXCEPTION"
+ COMMAND_RUN = "COMMAND_RUN"
+ ISSUE_FEEDBACK = "ISSUE_FEEDBACK"
+ UNKNOWN = "UNKNOWN"
+
+
+class CommandType(StrEnum):
+ IMBUE_VERIFY = "IMBUE_VERIFY"
+
+
+# TODO this is WEIRD
+EVENT_BY_LOGGED_FEATURE_TYPE: dict[LoggedFeatureType, Callable[[], type["CapabilitiesLoggedEvent"]]] = {
+ LoggedFeatureType.VERIFY_EXCEPTION: lambda: ImbueVerifyEvent,
+ LoggedFeatureType.COMMAND_RUN: lambda: ImbueVerifyEvent,
+ LoggedFeatureType.ISSUE_FEEDBACK: lambda: IssueFeedbackReport,
+}
+
+# TODO: had to pull in code from crafty in here to make this work, revisit what needs to stay, what should become shared, what we don't need
+
+
+def make_string_safe_for_formatting(s: str) -> str:
+ """
+ Make a string safe for things like str.format()
+ """
+ # Replace each '{' with '{{' and each '}' with '}}'
+ return s.replace("{", "{{").replace("}", "}}")
+
+
+class IssueKey(SerializableModel):
+ # TODO: this should likely be shared with the product code in v1
+ issue_type: CommandType
+ # this should NOT contain line numbers, as we want it to be stable across changes as much as possible
+ # NOTE: we do some initial formatting to avoid issues around message containing code
+ message: Annotated[str, PlainValidator(make_string_safe_for_formatting)]
+
+ # NOTE: this is the error code for pyre, ruff, and imbue_verify, and the test name for pytest
+ error_type: str | None = None
+
+ def commit_message(self) -> str:
+ return f"Fix {self.issue_type} issue: {self.message[:20]}"
+
+
+class CapabilitiesLoggedEvent(SerializableModel):
+ # TODO: Maybe convert empty optional strings and tuples and other datatypes to be Nones so there is only one notion of emptiness
+ # Though in some cases there actually is a difference: eg current_issues not existing or there being 0
+ # Null values are generally going to correspond to (possibly intentionally) 'missing' data
+ """
+ Most types in this class are optional since they may not be present for every type of event.
+ In the future additional fields may be added to this class to support new events, but they should be optional.
+ """
+
+ model_config = ConfigDict(frozen=True)
+ created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
+ id: str = Field(default_factory=lambda: uuid.uuid4().hex)
+ user_id: str
+ organization_id: str
+ git_url: str | None = None
+ git_hash: str | None = None
+ subrepo_context: SubrepoContext | None = None
+ instruction_context: SubrepoContext | None = None
+ conversation_history: tuple[ConversationMessageUnion, ...] | None = None
+
+ server_version: FrozenDict[str, Any] = Field(default_factory=empty_mapping)
+
+ feature_name: LoggedFeatureType | None = None
+ repo_state: RepoState | None = None
+
+ # TODO: Add configuration settings about how sculptor was started
+
+ # Command run specific fields
+ diff: str | None = None
+ command_type: CommandType | None = None
+ task_description: str | None = None
+ # For feedback
+ issue_key: IssueKey | None = None
+
+ # Output fields
+ has_output: bool = False
+ output_completion_time: datetime | None = None
+ llm_response: str | None = Field(default=None, deprecated=True)
+ llm_responses: tuple[LLMResponse, ...] | None = None
+ # Command run output fields
+ issues: tuple[IdentifiedVerifyIssue, ...] | None = None
+
+ # Feedback fields
+ feedback_rating: str | None = None
+ feedback_text: str | None = None
+
+ # Imbue verify specific fields
+ generation_config: LanguageModelGenerationConfig | None = None
+ imbue_verify_config: ImbueVerifyConfig | None = None
+
+ # pyre-ignore[56]: pyre's stubs don't match pydantic v2 decorator signatures
+ @field_validator("server_version", mode="before")
+ @classmethod
+ def validate_server_version(cls, v: Any) -> Any:
+ if isinstance(v, dict):
+ return FrozenDict(v)
+ return v
+
+ @classmethod
+ def build_from_json(cls, json_data: str) -> "CapabilitiesLoggedEvent":
+ # mypy is unhappy if this is -> Self because of event = event_type.build_from_json(json_data) below
+ event = cls.model_validate_json(json_data)
+ if event.feature_name:
+ feature_name = event.feature_name
+ if feature_name in EVENT_BY_LOGGED_FEATURE_TYPE:
+ event_type = EVENT_BY_LOGGED_FEATURE_TYPE[feature_name]()
+ try:
+ event = event_type.build_from_json(json_data)
+ except ValidationError as e:
+ print(e)
+ return event
+
+ def build_new_event_with_outputs(self, outputs: Any) -> Self:
+ raise NotImplementedError("Should be implemented by subclasses")
+
+
+class ImbueVerifyUsage(SerializableModel):
+ model_config = ConfigDict(frozen=True)
+
+ num_llm_calls: int
+ total_cost: float
+ total_input_tokens: int
+ total_output_tokens: int
+ total_cache_creation_tokens: int
+ total_cache_read_tokens: int
+
+
+class CostedImbueVerifyEvent(SerializableModel):
+ model_config = ConfigDict(frozen=True)
+ event: "ImbueVerifyEvent"
+ usage: ImbueVerifyUsage
+
+
+class ImbueVerifyEvent(CapabilitiesLoggedEvent):
+ """
+ Events for `imbue_verify`.
+ """
+
+ diff: str
+ command_type: CommandType
+ feature_name: LoggedFeatureType = LoggedFeatureType.COMMAND_RUN
+
+ task_description: str
+ generation_config: LanguageModelGenerationConfig
+ imbue_verify_config: ImbueVerifyConfig = Field(default_factory=ImbueVerifyConfig)
+ server_version: FrozenDict[str, Any] = Field(default_factory=empty_mapping)
+ # TODO: Get the direct llm response for command run events as well
+
+ exception_name: str | None = None
+
+ # pyre-ignore[56]: pyre's stubs don't match pydantic v2 decorator signatures
+ @model_validator(mode="after")
+ def ensure_reasonable_output(self) -> Self:
+ if self.has_output:
+ if self.issues is None:
+ raise ValueError("If has_output is True, there must be some issues")
+ return self
+
+ # TODO the only sites at which this is constructed have this info available.
+ # should simplify by providing it at construction time rather than using evolver
+ def build_new_event_with_outputs(
+ self,
+ outputs: tuple[tuple[IdentifiedVerifyIssue, ...], tuple[LLMResponse, ...], str | None],
+ ) -> Self:
+ issues, llm_responses, git_url = outputs
+ event_evolver = evolver(self)
+ assign(event_evolver.issues, lambda: issues)
+ assign(event_evolver.llm_responses, lambda: llm_responses)
+ assign(event_evolver.has_output, lambda: True)
+ assign(event_evolver.id, lambda: generate_id())
+ assign(event_evolver.output_completion_time, lambda: datetime.now(timezone.utc))
+ assign(event_evolver.git_url, lambda: git_url)
+ event_with_outputs = chill(event_evolver)
+ return event_with_outputs
+
+ @classmethod
+ def build_from_json(cls, json_data: str) -> Self:
+ return cls.model_validate_json(json_data)
+
+
+class IssueFeedbackReport(CapabilitiesLoggedEvent):
+ # TODO: this is copied and not updated from crafty
+ issue_key: IssueKey
+ device_id: str | None = None
+ session_id: str | None = None
+ browser_id: str | None = None
+ tab_id: str | None = None
+ feature_name: LoggedFeatureType = LoggedFeatureType.ISSUE_FEEDBACK
+
+ # pyre-ignore[56]: pyre's stubs don't match pydantic v2 decorator signatures
+ @model_validator(mode="after")
+ def has_some_feedback(self) -> Self:
+ if self.feedback_rating is None and self.feedback_text is None:
+ raise ValueError("At least one of feedback_rating or feedback_text must be set")
+ return self
+
+ @classmethod
+ def build_from_json(cls, json_data: str) -> "IssueFeedbackReport":
+ event = IssueFeedbackReport.model_validate_json(json_data)
+ return event
diff --git a/imbue_tools/imbue_tools/conftest.py b/imbue_tools/imbue_tools/conftest.py
@@ -0,0 +1,44 @@
+from pathlib import Path
+from typing import Callable
+from typing import Generator
+
+import pytest
+from pytest_asyncio import fixture as async_fixture
+from syrupy.assertion import SnapshotAssertion
+
+from imbue_core.agents.configs import LanguageModelGenerationConfig
+from imbue_core.agents.llm_apis.anthropic_api import AnthropicModelName
+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 imbue_core.test_utils import make_llm_cache_with_snapshot
+
+llm_cache_path = async_fixture(make_llm_cache_with_snapshot)
+
+
+# this is copied from sculptor/conftest.py
+# (it must be copied rather than imported because of the autouse)
+@pytest.fixture(autouse=True)
+def always_explode_on_error(
+ explode_on_error: Callable[[], Generator[None, None, None]],
+) -> Generator[None, None, None]:
+ """
+ Ensures that we do not log errors or exceptions during testing.
+
+ If your test is checking error handling behavior (and you expect to see a log_exception call),
+ use the `expect_exact_logged_errors` decorator to suppress the logging of those errors.
+ """
+ yield
+
+
+def llm_config_for_test(
+ llm_cache_path: Path, snapshot: SnapshotAssertion, is_caching_inputs: bool = False
+) -> LanguageModelGenerationConfig:
+ return LanguageModelGenerationConfig(
+ model_name=AnthropicModelName.CLAUDE_4_SONNET_2025_05_14,
+ cache_path=llm_cache_path,
+ is_running_offline=not snapshot.session.update_snapshots,
+ is_caching_inputs=is_caching_inputs,
+ )
+
+
+simple_test_git_repo = pytest.fixture(make_simple_test_git_repo)
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
@@ -0,0 +1,82 @@
+import json
+from pathlib import Path
+from typing import assert_never
+
+from loguru import logger
+from pydantic import TypeAdapter
+from pydantic import ValidationError
+
+from imbue_core.sculptor.state.chat_state import ContentBlockTypes
+from imbue_core.sculptor.state.messages import ChatInputUserMessage
+from imbue_core.sculptor.state.messages import ConversationMessageUnion
+from imbue_core.sculptor.state.messages import ResponseBlockAgentMessage
+
+CONVERSATION_FILE_ENV_VAR = "CONVERSATION_FILE"
+TASK_SOURCE_BRANCH_ENV_VAR = "TASK_SOURCE_BRANCH"
+
+
+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 load_conversation_history(
+ conversation_file_path: Path,
+) -> tuple[ConversationMessageUnion, ...]:
+ """Load a jsonl file into a list of conversation messages"""
+ file_contents = conversation_file_path.read_text()
+ return parse_conversation_history(file_contents)
+
+
+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
@@ -0,0 +1,72 @@
+from typing import Self
+from typing import TypeVar
+
+from pydantic import model_validator
+
+from imbue_core.pydantic_serialization import SerializableModel
+from imbue_core.sculptor.state.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
@@ -0,0 +1,51 @@
+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/py.typed b/imbue_tools/imbue_tools/py.typed
diff --git a/imbue_tools/imbue_tools/repo_utils/__init__.py b/imbue_tools/imbue_tools/repo_utils/__init__.py
diff --git a/imbue_tools/imbue_tools/repo_utils/context_prefix.py b/imbue_tools/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 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
@@ -0,0 +1,136 @@
+import asyncio
+import threading
+import time
+from contextlib import asynccontextmanager
+from pathlib import Path
+from typing import AsyncGenerator
+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_core.git import LocalGitRepo
+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()
+ # We need the async lock for tests TODO: we can probably remove this
+ self._local_repo_async_lock: asyncio.Lock = asyncio.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}")
+
+ @asynccontextmanager
+ async def tmp_repo_context(self) -> AsyncGenerator[LocalGitRepo, None]:
+ """
+ This function is only used in tests
+ TODO: we can probably remove it
+ """
+ async with self._local_repo_async_lock:
+ yield LocalGitRepo(self.repo_path)
diff --git a/imbue_tools/imbue_tools/repo_utils/context_utils.py b/imbue_tools/imbue_tools/repo_utils/context_utils.py
@@ -0,0 +1,52 @@
+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
@@ -0,0 +1,51 @@
+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
@@ -0,0 +1,156 @@
+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.errors import DiffCalculationError
+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,
+)
+from imbue_tools.repo_utils.file_system_utils import write_file_contents_to_dir
+
+
+class NonZeroReturncodeError(Exception):
+ pass
+
+
+async def get_diff_between_files(old_file_contents: InMemoryFileSystem, new_file_contents: InMemoryFileSystem) -> str:
+ with (
+ tempfile.TemporaryDirectory() as old_repo_dir,
+ tempfile.TemporaryDirectory() as new_repo_dir,
+ ):
+ # Get all changed file contents to prevent writing more than necessary
+ changed_old_file_contents_dict = {}
+ changed_new_file_contents_dict = {}
+ old_file_contents_dict = old_file_contents.files
+ new_file_contents_dict = new_file_contents.files
+ for file_path in old_file_contents_dict.keys() | new_file_contents_dict.keys():
+ if file_path not in old_file_contents_dict:
+ changed_new_file_contents_dict[file_path] = new_file_contents_dict[file_path]
+ elif file_path not in new_file_contents_dict:
+ changed_old_file_contents_dict[file_path] = old_file_contents_dict[file_path]
+ elif old_file_contents_dict[file_path] != new_file_contents_dict[file_path]:
+ changed_old_file_contents_dict[file_path] = old_file_contents_dict[file_path]
+ changed_new_file_contents_dict[file_path] = new_file_contents_dict[file_path]
+
+ changed_old_file_contents = InMemoryFileSystem.build(changed_old_file_contents_dict)
+ changed_new_file_contents = InMemoryFileSystem.build(changed_new_file_contents_dict)
+
+ await write_file_contents_to_dir(changed_old_file_contents, old_repo_dir)
+ await write_file_contents_to_dir(changed_new_file_contents, new_repo_dir)
+
+ try:
+ result = subprocess.run(
+ (
+ "git",
+ "diff",
+ "--no-index",
+ "--relative",
+ "--full-index",
+ "--binary",
+ old_repo_dir,
+ new_repo_dir,
+ ),
+ capture_output=True,
+ text=True,
+ timeout=10.0,
+ )
+ if result.returncode == 0 or result.returncode == 1:
+ diff = result.stdout
+ else:
+ raise NonZeroReturncodeError(f"git diff process returned with non-zero returncode {result.returncode}")
+ except Exception as e:
+ raise DiffCalculationError from e
+
+ diff = diff.replace(old_repo_dir, "")
+ diff = diff.replace(new_repo_dir, "")
+
+ return diff
+
+
+@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/imbue_tools/imbue_tools/repo_utils/errors.py
@@ -0,0 +1,25 @@
+from imbue_core.errors import ExpectedError
+
+
+class PromptAssemblyError(Exception):
+ """Raised when there is an error assembling the prompt."""
+
+
+class ContextLengthExceededError(PromptAssemblyError):
+ """Raised when the context length exceeds the maximum allowed length."""
+
+
+class InvalidVersionedConfigError(ExpectedError):
+ pass
+
+
+class MissingVersionedConfigError(ExpectedError):
+ pass
+
+
+class DiffApplicationError(Exception):
+ pass
+
+
+class DiffCalculationError(Exception):
+ pass
diff --git a/imbue_tools/imbue_tools/repo_utils/file_system.py b/imbue_tools/imbue_tools/repo_utils/file_system.py
@@ -0,0 +1,102 @@
+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
+from imbue_tools.repo_utils.subrepo_formatting import BaseFilenamePattern
+
+
+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
+
+
+def filter_files_patterns(
+ file_system: InMemoryFileSystem,
+ include_patterns: tuple[str, ...] | None = None,
+ exclude_patterns: tuple[str, ...] | None = None,
+) -> InMemoryFileSystem:
+ """Filter all files based on include/exclude patterns.
+ If an include pattern is provided, only files that match the include pattern will be included.
+ If an exclude pattern is provided, files that match the exclude pattern will be excluded. If no include or exclude patterns are provided, all files will be included.
+
+ Args:
+ file_system: The file system to filter
+ include_patterns: Glob patterns for files to include
+ exclude_patterns: Glob patterns for files to exclude
+
+ Returns:
+ Filtered InMemoryFileSystem
+ """
+ include_spec = BaseFilenamePattern.from_lines(include_patterns or ())
+ exclude_spec = BaseFilenamePattern.from_lines(exclude_patterns or ())
+ filtered_files = {
+ file_path: content
+ for file_path, content in file_system.files.items()
+ if (
+ (not include_patterns or include_spec.match_file(file_path))
+ and (not exclude_patterns or not exclude_spec.match_file(file_path))
+ )
+ }
+ return InMemoryFileSystem.build(filtered_files)
diff --git a/imbue_tools/imbue_tools/repo_utils/file_system_utils.py b/imbue_tools/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 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/find_relative_to.py b/imbue_tools/imbue_tools/repo_utils/find_relative_to.py
@@ -0,0 +1,30 @@
+from pathlib import Path
+
+from imbue_core.simple_git import SyncLocalGitRepo
+
+
+def find_relative_to_commit_hash(relative_to: str, repo_path: Path) -> str:
+ """
+ Find the commit hash to use as the source to compare against.
+ - If relative_to is "HEAD", it will return the current commit hash.
+ - If relative_to is a branch name, it will find the last common ancestor between that branch and the current state.
+ - If relative_to is a commit hash or tag, it will return that commit hash.
+ """
+ repo = SyncLocalGitRepo(repo_path)
+ if relative_to.startswith("HEAD"):
+ # The current commit hash or relative to it (e.g. "HEAD~1")
+ base_commit = repo.run_git(["rev-parse", relative_to], check=True)
+ else:
+ # Check if relative_to is the name of a branch.
+ is_branch = repo.is_commit_a_branch(relative_to)
+ if is_branch:
+ # Yes, it's a branch.
+ # Since we're comparing to a branch, this command will find the last common ancestor
+ # between that branch and the current state. This is typically what we want for branches.
+ # (Think of this as getting the diff that would be applied if this branch was to be merged into relative_to.)
+ base_commit = repo.get_merge_base(relative_to, "HEAD")
+ else:
+ # Not a branch. relative_to might be a commit hash or tag.
+ base_commit = relative_to
+
+ return base_commit
diff --git a/imbue_tools/imbue_tools/repo_utils/project_context.py b/imbue_tools/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 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 imbue_verify 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
@@ -0,0 +1,130 @@
+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/stubify_file.py b/imbue_tools/imbue_tools/repo_utils/stubify_file.py
@@ -0,0 +1,167 @@
+"""From https://github.com/OpenAutoCoder/Agentless/blob/main/agentless/util/compress_file.py"""
+
+import re
+from typing import Any
+from typing import Callable
+
+import libcst as cst
+import libcst.matchers as m
+from loguru import logger
+
+
+def check_on_body(stmt: cst.CSTNode, check: Callable[[cst.CSTNode], bool]) -> bool:
+ if not m.matches(stmt, m.SimpleStatementLine()):
+ return False
+ # pyre-ignore[16]: m.SimpleStatementLine has a body attribute which is a Sequence
+ first_body_item = stmt.body[0]
+ return check(first_body_item)
+
+
+def is_assign(stmt: cst.CSTNode) -> bool:
+ if not m.matches(stmt, m.SimpleStatementLine()):
+ return False
+ # pyre-ignore[16]: m.SimpleStatementLine has a body attribute which is a Sequence
+ first_body_item = stmt.body[0]
+ if m.matches(first_body_item, m.Assign()):
+ return True
+ return False
+
+
+class CompressTransformer(cst.CSTTransformer):
+ DESCRIPTION = str = "Replaces function body with ..."
+ replacement_string = '"__FUNC_BODY_REPLACEMENT_STRING__"'
+
+ def __init__(self, keep_constant: bool = True, keep_indent: bool = False) -> None:
+ self.keep_constant = keep_constant
+ self.keep_indent = keep_indent
+
+ def leave_Module(self, original_node: cst.Module, updated_node: cst.Module) -> cst.Module:
+ new_body = [
+ stmt
+ for stmt in updated_node.body
+ if m.matches(stmt, m.ClassDef())
+ or m.matches(stmt, m.FunctionDef())
+ or (
+ self.keep_constant
+ and check_on_body(stmt, lambda first_body_item: m.matches(first_body_item, m.Assign()))
+ )
+ ]
+ return updated_node.with_changes(body=new_body)
+
+ def leave_ClassDef(self, original_node: cst.ClassDef, updated_node: cst.ClassDef) -> cst.ClassDef:
+ # Remove docstring in the class body
+ new_body = [
+ stmt
+ for stmt in updated_node.body.body
+ if not check_on_body(
+ stmt,
+ lambda first_body_item: m.matches(first_body_item, m.Expr())
+ or (hasattr(first_body_item, "value") and m.matches(first_body_item.value, m.SimpleString())),
+ )
+ ]
+ # pyre-fixme[6]: cst.IndentedBlock has a body attribute which is a Sequence[BaseStatement], not a Sequence[BaseSmallStatement] like new_body
+ return updated_node.with_changes(body=cst.IndentedBlock(body=new_body))
+
+ def leave_FunctionDef(self, original_node: cst.FunctionDef, updated_node: cst.FunctionDef) -> cst.BaseStatement:
+ if not self.keep_indent:
+ # replace with unindented statement
+ new_expr = cst.Expr(value=cst.SimpleString(value=self.replacement_string))
+ # pyre-fixme[6]: cst.Expr is a BaseSmallStatement, not a BaseStatement like new_expr
+ new_body = cst.IndentedBlock((new_expr,))
+ return updated_node.with_changes(body=new_body)
+ else:
+ # replace with indented statement
+ # new_expr = [cst.Pass()]
+ new_expr = [
+ cst.Expr(value=cst.SimpleString(value=self.replacement_string)),
+ ]
+ return updated_node.with_changes(body=cst.IndentedBlock(body=[cst.SimpleStatementLine(body=new_expr)]))
+
+
+class GlobalVariableVisitor(cst.CSTVisitor):
+ METADATA_DEPENDENCIES = (cst.metadata.PositionProvider,)
+
+ def __init__(self) -> None:
+ self.assigns: list[list[Any]] = []
+
+ def leave_Assign(self, original_node: cst.Assign) -> None:
+ stmt = original_node
+ start_pos = self.get_metadata(cst.metadata.PositionProvider, stmt).start
+ end_pos = self.get_metadata(cst.metadata.PositionProvider, stmt).end
+ self.assigns.append([stmt, start_pos, end_pos])
+
+
+def remove_lines(raw_code: str, remove_line_intervals: list[tuple[int, int]]) -> str:
+ # TODO: speed up this function
+ # remove_line_intervals.sort()
+
+ # Remove lines
+ new_code = ""
+ for i, line in enumerate(raw_code.splitlines()):
+ # intervals are one-based
+ if not any(start <= i + 1 <= end for start, end in remove_line_intervals):
+ new_code += line + "\n"
+ if any(start == i + 1 for start, _ in remove_line_intervals):
+ new_code += "...\n"
+ return new_code
+
+
+def compress_assign_stmts(raw_code: str, total_lines: int = 30, prefix_lines: int = 10, suffix_lines: int = 10) -> str:
+ try:
+ tree = cst.parse_module(raw_code)
+ except cst.ParserSyntaxError as e:
+ logger.debug(
+ "Failed to compress assign statements: {exception_class}, {exception}",
+ exception_class=e.__class__.__name__,
+ exception=e,
+ )
+ return raw_code
+
+ wrapper = cst.metadata.MetadataWrapper(tree)
+ visitor = GlobalVariableVisitor()
+ wrapper.visit(visitor)
+
+ remove_line_intervals = []
+ for stmt in visitor.assigns:
+ if stmt[2].line - stmt[1].line > total_lines:
+ remove_line_intervals.append((stmt[1].line + prefix_lines, stmt[2].line - suffix_lines))
+ return remove_lines(raw_code, remove_line_intervals)
+
+
+def stubify_code_file(
+ path: str | None,
+ raw_code: str,
+ keep_constant: bool = True,
+ keep_indent: bool = False,
+ compress_assign: bool = False,
+ total_lines: int = 30,
+ prefix_lines: int = 10,
+ suffix_lines: int = 10,
+) -> str:
+ try:
+ tree = cst.parse_module(raw_code)
+ except cst.ParserSyntaxError:
+ logger.debug("failed to stubify code file {}; will leave it as is", path)
+ return raw_code
+
+ transformer = CompressTransformer(keep_constant=keep_constant, keep_indent=True)
+ modified_tree = tree.visit(transformer)
+ code = modified_tree.code
+
+ if compress_assign:
+ code = compress_assign_stmts(
+ code,
+ total_lines=total_lines,
+ prefix_lines=prefix_lines,
+ suffix_lines=suffix_lines,
+ )
+
+ if keep_indent:
+ code = code.replace(CompressTransformer.replacement_string + "\n", "...\n")
+ code = code.replace(CompressTransformer.replacement_string, "...\n")
+ else:
+ pattern = f"\\n[ \\t]*{CompressTransformer.replacement_string}"
+ replacement = "\n..."
+ code = re.sub(pattern, replacement, code)
+
+ return code
diff --git a/imbue_tools/imbue_tools/repo_utils/subrepo_formatting.py b/imbue_tools/imbue_tools/repo_utils/subrepo_formatting.py
@@ -0,0 +1,368 @@
+import functools
+from collections import defaultdict
+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 format_subrepo_context_full(repo: Mapping[str, str]) -> str:
+ """Like get_repo_context but there's no checking for context limits (so we can use the api checks instead)
+ and the selected strategy is always the full repo contents."""
+ formatted_repo_context = format_subrepo(
+ format_all_for_agent(
+ format_subrepo_context_into_filecontexts(
+ full_repo_contents=repo,
+ path_to_format_style=defaultdict(lambda: ContextFormatStyle.FULL_FILE, {}),
+ )
+ )
+ )
+
+ repo_context_core_prompt = formatted_subrepo_to_prompt(
+ repo_context_str=formatted_repo_context,
+ is_shortened=False,
+ has_hidden_files=False,
+ template=BASE_REPO_CONTEXT_TEMPLATE,
+ )
+
+ return repo_context_core_prompt
+
+
+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/imbue_verify_config.py b/imbue_tools/imbue_tools/types/imbue_verify_config.py
@@ -0,0 +1,100 @@
+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 ImbueVerifyConfig(SerializableModel):
+ """Configuration for the imbue_verify 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 imbue_verify.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,
+ ) -> "ImbueVerifyConfig":
+ 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: ImbueVerifyConfig) -> 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/imbue_tools/imbue_tools/util_prompts/conversation_prefix.py
@@ -0,0 +1,9 @@
+CONVERSATION_PREFIX_TEMPLATE = """[ROLE=SYSTEM_CACHED]
+You will be provided a conversation history between a user and another agent. The other agent may be from any model provider or model family.
+The conversation history includes the user's messages and the agent's text-based messages, but may be missing some automated messages and tool calls/tool call results.
+Examine the conversation carefully and be prepared to answer questions about it.
+Here is the conversation history between the user and the other agent.
+{% filter indent(width=2) %}
+```
+{{ conversation_history }}
+```{% endfilter %}"""
diff --git a/imbue_tools/imbue_tools/util_prompts/goal_from_conversation.py b/imbue_tools/imbue_tools/util_prompts/goal_from_conversation.py
@@ -0,0 +1,64 @@
+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 imbue_core.sculptor.state.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
@@ -0,0 +1,43 @@
+[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 = [
+ "imbue-verify",
+]
+
+[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 }
+imbue-verify = { path = "..", editable = true }
diff --git a/imbue_tools/uv.lock b/imbue_tools/uv.lock
@@ -0,0 +1,2980 @@
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diff --git a/imbue_verify/api.py b/imbue_verify/api.py
@@ -11,7 +11,9 @@ from loguru import logger
from imbue_core.data_types import IdentifiedVerifyIssue
from imbue_core.data_types import IssueIdentificationDebugInfo
from imbue_core.sculptor.state.messages import ConversationMessageUnion
-from imbue_tools.get_conversation_history.get_conversation_history import ConversationLoadingError
+from 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
diff --git a/imbue_verify/cli/config/cli_config_test.py b/imbue_verify/cli/config/cli_config_test.py
@@ -180,7 +180,8 @@ def test_get_cli_config_file_paths_finds_git_root(tmp_path: Path) -> None:
def test_load_cli_config_file_loads_valid_toml(tmp_path: Path) -> None:
config_file = tmp_path / "config.toml"
- config_file.write_text("""
+ config_file.write_text(
+ """
[ci]
confidence_threshold = 0.9
max_workers = 4
@@ -189,7 +190,8 @@ quiet = true
[strict]
confidence_threshold = 0.6
model = "claude-4-sonnet"
-""")
+"""
+ )
result = _load_cli_config_file(config_file)
@@ -213,10 +215,12 @@ def test_load_cli_config_file_raises_on_invalid_toml(tmp_path: Path) -> None:
def test_load_cli_config_file_raises_on_invalid_schema(tmp_path: Path) -> None:
config_file = tmp_path / "config.toml"
- config_file.write_text("""
+ config_file.write_text(
+ """
[ci]
confidence_threshold = "not-a-float"
-""")
+"""
+ )
with pytest.raises(ConfigLoadError) as exc_info:
_load_cli_config_file(config_file)
@@ -226,10 +230,12 @@ confidence_threshold = "not-a-float"
def test_load_cli_config_file_raises_on_unknown_field(tmp_path: Path) -> None:
config_file = tmp_path / "config.toml"
- config_file.write_text("""
+ config_file.write_text(
+ """
[ci]
unknown_field = "value"
-""")
+"""
+ )
with pytest.raises(ConfigLoadError) as exc_info:
_load_cli_config_file(config_file)
@@ -248,10 +254,12 @@ def test_load_cli_config_loads_single_file(tmp_path: Path) -> None:
repo_path = tmp_path / "repo"
repo_path.mkdir()
config_file = repo_path / "imbue-verify.toml"
- config_file.write_text("""
+ config_file.write_text(
+ """
[ci]
confidence_threshold = 0.9
-""")
+"""
+ )
with patch.dict(os.environ, {"XDG_CONFIG_HOME": str(tmp_path / "nonexistent")}):
result = load_cli_config(repo_path=repo_path)
@@ -264,25 +272,29 @@ def test_load_cli_config_merges_global_and_project(tmp_path: Path) -> None:
xdg_config = tmp_path / "xdg"
(xdg_config / "imbue-verify").mkdir(parents=True)
global_config = xdg_config / "imbue-verify" / "config.toml"
- global_config.write_text("""
+ global_config.write_text(
+ """
[ci]
confidence_threshold = 0.8
max_workers = 2
[global-only]
model = "global-model"
-""")
+"""
+ )
repo_path = tmp_path / "repo"
repo_path.mkdir()
project_config = repo_path / "imbue-verify.toml"
- project_config.write_text("""
+ project_config.write_text(
+ """
[ci]
confidence_threshold = 0.9
[project-only]
model = "project-model"
-""")
+"""
+ )
with patch.dict(os.environ, {"XDG_CONFIG_HOME": str(xdg_config)}):
result = load_cli_config(repo_path=repo_path)
diff --git a/imbue_verify/cli/config/loader.py b/imbue_verify/cli/config/loader.py
@@ -50,7 +50,10 @@ def find_git_repo_root(start_path: Path) -> Path | None:
def _get_config_file_paths(
- global_subpath: str, global_filename: str, project_filename: str, repo_path: Path | None = None
+ global_subpath: str,
+ global_filename: str,
+ project_filename: str,
+ repo_path: Path | None = None,
) -> list[Path]:
paths = [get_xdg_config_home() / global_subpath / global_filename]
@@ -189,14 +192,14 @@ def load_cli_config(repo_path: Path | None = None) -> dict[str, CliConfigPreset]
def get_config_preset(
- config_name: str, cli_configs: dict[str, CliConfigPreset], repo_path: Path | None = None
+ config_name: str,
+ cli_configs: dict[str, CliConfigPreset],
+ repo_path: Path | None = None,
) -> CliConfigPreset:
if config_name not in cli_configs:
available = sorted(cli_configs.keys())
if available:
- raise ConfigLoadError(
- f"Configuration '{config_name}' not found. Available configs: {', '.join(available)}"
- )
+ raise ConfigLoadError(f"Configuration '{config_name}' not found. Available configs: {', '.join(available)}")
else:
paths = get_cli_config_file_paths(repo_path)
paths_list = "\n".join(f" - {p} ({'global' if i == 0 else 'project'})" for i, p in enumerate(paths))
diff --git a/imbue_verify/cli/config/loader_test.py b/imbue_verify/cli/config/loader_test.py
@@ -86,7 +86,11 @@ def test_load_single_config_file_loads_valid_config(tmp_path: Path) -> None:
"base_url": "http://localhost:8080/v1",
"api_key_env": "TEST_API_KEY",
"models": {
- "test-model": {"model_id": "test-model-v1", "context_window": 128000, "max_output_tokens": 16384}
+ "test-model": {
+ "model_id": "test-model-v1",
+ "context_window": 128000,
+ "max_output_tokens": 16384,
+ }
},
}
}
@@ -164,7 +168,12 @@ def test_load_models_config_loads_project_config(tmp_path: Path) -> None:
"project-provider": {
"base_url": "http://project:8080/v1",
"api_key_env": "PROJECT_KEY",
- "models": {"project-model": {"context_window": 128000, "max_output_tokens": 16384}},
+ "models": {
+ "project-model": {
+ "context_window": 128000,
+ "max_output_tokens": 16384,
+ }
+ },
}
}
}
@@ -188,7 +197,12 @@ def test_load_models_config_project_overrides_global(tmp_path: Path) -> None:
"name": "Global Name",
"base_url": "http://global:8080/v1",
"api_key_env": "GLOBAL_KEY",
- "models": {"global-model": {"context_window": 128000, "max_output_tokens": 16384}},
+ "models": {
+ "global-model": {
+ "context_window": 128000,
+ "max_output_tokens": 16384,
+ }
+ },
}
}
}
@@ -206,7 +220,12 @@ def test_load_models_config_project_overrides_global(tmp_path: Path) -> None:
"name": "Project Name",
"base_url": "http://project:8080/v1",
"api_key_env": "PROJECT_KEY",
- "models": {"project-model": {"context_window": 128000, "max_output_tokens": 16384}},
+ "models": {
+ "project-model": {
+ "context_window": 128000,
+ "max_output_tokens": 16384,
+ }
+ },
}
}
}
diff --git a/imbue_verify/cli/main.py b/imbue_verify/cli/main.py
@@ -13,7 +13,9 @@ from loguru import logger
from imbue_core.data_types import IssueCode
from imbue_core.log_utils import ensure_core_log_levels_configured
-from imbue_tools.get_conversation_history.get_conversation_history import parse_conversation_history
+from imbue_tools.get_conversation_history.get_conversation_history import (
+ parse_conversation_history,
+)
from imbue_tools.types.imbue_verify_config import ImbueVerifyConfig
from imbue_verify.api import find_issues
from imbue_verify.cli.config.cli_config_schema import CLI_DEFAULTS
@@ -451,8 +453,8 @@ def main(argv: list[str] | None = None) -> int:
config = ImbueVerifyConfig(
disabled_identifiers=("agentic_issue_identifier",),
language_model_generation_config=language_model_config,
- enabled_issue_codes=tuple(args.enabled_issue_codes) if args.enabled_issue_codes else None,
- disabled_issue_codes=tuple(args.disabled_issue_codes) if args.disabled_issue_codes else None,
+ enabled_issue_codes=(tuple(args.enabled_issue_codes) if args.enabled_issue_codes else None),
+ disabled_issue_codes=(tuple(args.disabled_issue_codes) if args.disabled_issue_codes else None),
temperature=args.temperature,
filter_issues_below_confidence=args.confidence_threshold,
max_identify_workers=args.max_workers,
diff --git a/imbue_verify/cli/models.py b/imbue_verify/cli/models.py
@@ -52,7 +52,9 @@ def get_builtin_models_by_provider() -> dict[str, list[str]]:
}
-def get_models_by_provider(user_config: ModelsConfig | None = None) -> dict[str, list[str]]:
+def get_models_by_provider(
+ user_config: ModelsConfig | None = None,
+) -> dict[str, list[str]]:
providers = get_builtin_models_by_provider()
if user_config:
diff --git a/imbue_verify/formatters.py b/imbue_verify/formatters.py
@@ -35,7 +35,7 @@ def issue_to_output(issue: IdentifiedVerifyIssue) -> IssueOutput:
return IssueOutput(
issue_code=str(issue.code),
- confidence=issue.confidence_score.normalized if issue.confidence_score else None,
+ confidence=(issue.confidence_score.normalized if issue.confidence_score else None),
file_path=issue.location[0].filename if issue.location else None,
line_number=issue.location[0].line_start if issue.location else None,
line_number_end=line_number_end,
diff --git a/imbue_verify/issue_identifiers/agentic_issue_collation.py b/imbue_verify/issue_identifiers/agentic_issue_collation.py
@@ -11,18 +11,24 @@ 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 to_specific_inputs_type
+from 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.imbue_verify_config import ImbueVerifyConfig
from imbue_verify.issue_identifiers.common import GeneratedIssueSchema
from imbue_verify.issue_identifiers.common import GeneratedResponseSchema
from imbue_verify.issue_identifiers.common import extract_invocation_info_from_messages
-from imbue_verify.issue_identifiers.common import format_issue_identification_guide_for_llm
+from imbue_verify.issue_identifiers.common import (
+ format_issue_identification_guide_for_llm,
+)
from imbue_verify.issue_identifiers.common import generate_issues_from_response_texts
from imbue_verify.issue_identifiers.common import generate_response_from_claude_code
from imbue_verify.issue_identifiers.common import get_claude_code_options
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE,
+)
from imbue_verify.issue_identifiers.utils import ReturnCapturingGenerator
COLLATION_PROMPT_TEMPLATE = """You are reviewing the results from parallel code analysis for potential issues.
@@ -96,7 +102,9 @@ def _get_collation_prompt(
return prompt
-def _convert_parsed_issues_to_combined_string(all_parsed_issues: Iterable[GeneratedIssueSchema]) -> str:
+def _convert_parsed_issues_to_combined_string(
+ all_parsed_issues: Iterable[GeneratedIssueSchema],
+) -> str:
"""Convert all parsed issues from all issue types to a combined string for collation prompt."""
combined_issues = []
@@ -142,7 +150,8 @@ def collate_issues_with_agent(
issue_generator_debug_info = issue_generator_with_capture.return_value
options = get_claude_code_options(
- cwd=project_context.repo_path, model_name=config.language_model_generation_config.model_name
+ cwd=project_context.repo_path,
+ model_name=config.language_model_generation_config.model_name,
)
combined_issues_string = _convert_parsed_issues_to_combined_string(all_issues)
collation_prompt = _get_collation_prompt(
diff --git a/imbue_verify/issue_identifiers/base.py b/imbue_verify/issue_identifiers/base.py
@@ -7,7 +7,9 @@ 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 to_specific_inputs_type
+from 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.imbue_verify_config import ImbueVerifyConfig
from imbue_verify.issue_identifiers.common import GeneratedIssueSchema
@@ -26,7 +28,10 @@ class IssueIdentifier(SerializableModel, abc.ABC, Generic[T]):
@abc.abstractmethod
def identify_issues(
- self, identifier_inputs: T, project_context: ProjectContext, config: ImbueVerifyConfig
+ self,
+ identifier_inputs: T,
+ project_context: ProjectContext,
+ config: ImbueVerifyConfig,
) -> Generator[GeneratedIssueSchema, None, IssueIdentificationDebugInfo]:
"""
Identify issues given the identifier inputs.
diff --git a/imbue_verify/issue_identifiers/common.py b/imbue_verify/issue_identifiers/common.py
@@ -33,12 +33,16 @@ 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_core.sculptor.telemetry import send_exception_to_posthog
-from imbue_core.sculptor.telemetry_constants import SculptorPosthogEvent
-from imbue_tools.llm_output_parsing.parse_model_json_response import ResponseParsingError
-from imbue_tools.llm_output_parsing.parse_model_json_response import parse_model_json_response
+from imbue_tools.llm_output_parsing.parse_model_json_response import (
+ ResponseParsingError,
+)
+from imbue_tools.llm_output_parsing.parse_model_json_response import (
+ parse_model_json_response,
+)
from imbue_tools.repo_utils.project_context import ProjectContext
-from imbue_verify.issue_identifiers.identification_guides import IssueIdentificationGuide
+from imbue_verify.issue_identifiers.identification_guides import (
+ IssueIdentificationGuide,
+)
from imbue_verify.issue_identifiers.utils import ReturnCapturingGenerator
@@ -80,17 +84,15 @@ class GeneratedResponseSchema(SerializableModel):
issues: list[GeneratedIssueSchema] = Field(default_factory=list, description="List of identified issues")
-def generate_issues_from_response_texts(response_texts: Iterable[str]) -> Generator[GeneratedIssueSchema, None, None]:
+def generate_issues_from_response_texts(
+ response_texts: Iterable[str],
+) -> Generator[GeneratedIssueSchema, None, None]:
"""Generate IssueIdentifierResult objects from LLM response text."""
for response_text in response_texts:
try:
parsed_data = parse_model_json_response(response_text, GeneratedResponseSchema)
- except ResponseParsingError as e:
- send_exception_to_posthog(
- SculptorPosthogEvent.FAILED_TO_PARSE_LLM_RESPONSE_WHEN_GENERATING_ISSUES,
- e,
- message=f"Failed to parse response text: {response_text}",
- )
+ except ResponseParsingError:
+ logger.warning(f"Failed to parse response text: {response_text}")
continue
for raw_issue in parsed_data.issues:
@@ -136,13 +138,9 @@ def convert_generated_issue_to_identified_issue(
repo_path = project_context.repo_path
assert repo_path is not None
issue_location_path = issue_location_path.relative_to(repo_path)
- except ValueError as e:
+ except ValueError:
issue_location_path = None
- send_exception_to_posthog(
- SculptorPosthogEvent.INVALID_FILE_PATH_FROM_LLM_IN_ISSUE_LOCATION,
- e,
- message=f"Invalid location '{issue_location}', skipping line range detection.",
- )
+ logger.warning(f"Invalid location '{issue_location}', skipping line range detection.")
issue_code_part = issue_data.code_part
if issue_location_path and issue_code_part:
contents = project_context.file_contents_by_path.get(issue_location_path.as_posix())
@@ -155,11 +153,7 @@ def convert_generated_issue_to_identified_issue(
code_part_repr=repr(issue_code_part),
)
else:
- send_exception_to_posthog(
- SculptorPosthogEvent.INVALID_FILE_PATH_FROM_LLM_IN_ISSUE_LOCATION,
- KeyError(issue_location),
- message=f"Unknown location '{issue_location}', skipping line range detection.",
- )
+ logger.warning(f"Unknown location '{issue_location}', skipping line range detection.")
# Convert severity (1-5) to normalized score (0-1)
severity_normalized = (issue_data.severity - 1) / 4.0 # Map 1-5 to 0-1
@@ -193,7 +187,9 @@ def convert_to_issue_identifier_result(
generator_with_capture = ReturnCapturingGenerator(generator)
for issue_data in generator_with_capture:
issue = convert_generated_issue_to_identified_issue(
- issue_data=issue_data, project_context=project_context, enabled_issue_codes=enabled_issue_codes
+ issue_data=issue_data,
+ project_context=project_context,
+ enabled_issue_codes=enabled_issue_codes,
)
if issue:
yield IssueIdentifierResult(issue=issue, passes_filtration=issue_data.passes_filtration)
@@ -277,7 +273,9 @@ def extract_invocation_info_from_costed_response(
)
-def extract_invocation_info_from_messages(messages: list[AgentMessage]) -> InvocationInfo:
+def extract_invocation_info_from_messages(
+ messages: list[AgentMessage],
+) -> InvocationInfo:
"""Extract invocation information from Agent messages."""
for message in messages:
if isinstance(message, AgentResultMessage):
diff --git a/imbue_verify/issue_identifiers/common_test.py b/imbue_verify/issue_identifiers/common_test.py
@@ -7,19 +7,33 @@ 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 ResponseParsingError
-from imbue_tools.llm_output_parsing.parse_model_json_response import parse_model_json_response
+from imbue_tools.llm_output_parsing.parse_model_json_response import (
+ ResponseParsingError,
+)
+from 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 imbue_verify.issue_identifiers.common import GeneratedResponseSchema
-from imbue_verify.issue_identifiers.common import convert_generated_issue_to_identified_issue
-from imbue_verify.issue_identifiers.common import format_issue_identification_guide_for_llm
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_CODES_FOR_CORRECTNESS_CHECK
-from imbue_verify.issue_identifiers.identification_guides import IssueIdentificationGuide
+from imbue_verify.issue_identifiers.common import (
+ convert_generated_issue_to_identified_issue,
+)
+from imbue_verify.issue_identifiers.common import (
+ format_issue_identification_guide_for_llm,
+)
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_CODES_FOR_CORRECTNESS_CHECK,
+)
+from imbue_verify.issue_identifiers.identification_guides import (
+ IssueIdentificationGuide,
+)
def _parse_issues(
- valid_response: str, project_context: ProjectContext, enabled_issue_codes: Iterable[IssueCode]
+ valid_response: str,
+ project_context: ProjectContext,
+ enabled_issue_codes: Iterable[IssueCode],
) -> list[IdentifiedVerifyIssue]:
issues = []
try:
@@ -28,7 +42,9 @@ def _parse_issues(
return []
for parsed_issue in issue_data.issues:
issue = convert_generated_issue_to_identified_issue(
- issue_data=parsed_issue, project_context=project_context, enabled_issue_codes=tuple(enabled_issue_codes)
+ issue_data=parsed_issue,
+ project_context=project_context,
+ enabled_issue_codes=tuple(enabled_issue_codes),
)
if issue:
issues.append(issue)
@@ -87,8 +103,8 @@ def test_parse_response_with_leading_and_trailing_text() -> None:
)
response_text = "Some leading text\n```json\n" + valid_response + "\n```\nSome trailing text"
- with expect_exact_logged_errors(["Unknown location"]):
- issues = _parse_issues(response_text, project_context, ISSUE_CODES_FOR_CORRECTNESS_CHECK)
+ # Note: This logs a warning about "Unknown location" since test.py isn't in the project context
+ issues = _parse_issues(response_text, project_context, ISSUE_CODES_FOR_CORRECTNESS_CHECK)
issue = only(issues)
assert issue.code == IssueCode.LOGIC_ERROR
assert issue.description == "Infinite loop detected"
@@ -119,7 +135,8 @@ def test_parse_issues_invalid_json() -> None:
def test_parse_issues_with_markdown_formatting() -> None:
project_context = BaseProjectContext(
- file_contents_by_path=FrozenDict({"test.py": "x = 1"}), cached_prompt_prefix="test"
+ file_contents_by_path=FrozenDict({"test.py": "x = 1"}),
+ cached_prompt_prefix="test",
)
markdown_response = (
@@ -161,7 +178,11 @@ def test_parse_issues_invalid_severity() -> None:
)
with expect_exact_logged_errors(["Response does not match the expected schema"]):
- issues = _parse_issues(invalid_severity_response, project_context, ISSUE_CODES_FOR_CORRECTNESS_CHECK)
+ issues = _parse_issues(
+ invalid_severity_response,
+ project_context,
+ ISSUE_CODES_FOR_CORRECTNESS_CHECK,
+ )
assert len(issues) == 0 # Should be skipped due to invalid severity
@@ -187,9 +208,7 @@ def test_parse_issues_unknown_issue_code() -> None:
def test_parse_issues_missing_required_fields() -> None:
- project_context = BaseProjectContext(
- file_contents_by_path=FrozenDict(), cached_prompt_prefix="[ROLE=SYSTEM]\ntest"
- )
+ project_context = BaseProjectContext(file_contents_by_path=FrozenDict(), cached_prompt_prefix="[ROLE=SYSTEM]\ntest")
# Missing required field 'confidence'
missing_field_response = json.dumps(
@@ -211,9 +230,7 @@ def test_parse_issues_missing_required_fields() -> None:
def test_parse_issues_invalid_confidence() -> None:
- project_context = BaseProjectContext(
- file_contents_by_path=FrozenDict(), cached_prompt_prefix="[ROLE=SYSTEM]\ntest"
- )
+ project_context = BaseProjectContext(file_contents_by_path=FrozenDict(), cached_prompt_prefix="[ROLE=SYSTEM]\ntest")
invalid_confidence_response = json.dumps(
{
@@ -229,14 +246,19 @@ def test_parse_issues_invalid_confidence() -> None:
)
with expect_exact_logged_errors(["Response does not match the expected schema"]):
- issues = _parse_issues(invalid_confidence_response, project_context, ISSUE_CODES_FOR_CORRECTNESS_CHECK)
+ issues = _parse_issues(
+ invalid_confidence_response,
+ project_context,
+ ISSUE_CODES_FOR_CORRECTNESS_CHECK,
+ )
assert len(issues) == 0 # Should be skipped due to invalid confidence
def test_parse_issues_with_line_ranges() -> None:
code_content = "def hello():\n print('world')\n return True"
project_context = BaseProjectContext(
- file_contents_by_path=FrozenDict({"test.py": code_content}), cached_prompt_prefix="[ROLE=SYSTEM]\ntest"
+ file_contents_by_path=FrozenDict({"test.py": code_content}),
+ cached_prompt_prefix="[ROLE=SYSTEM]\ntest",
)
response_with_location = json.dumps(
@@ -261,9 +283,7 @@ def test_parse_issues_with_line_ranges() -> None:
def test_parse_issues_malformed_response_structure() -> None:
- project_context = BaseProjectContext(
- file_contents_by_path=FrozenDict(), cached_prompt_prefix="[ROLE=SYSTEM]\ntest"
- )
+ project_context = BaseProjectContext(file_contents_by_path=FrozenDict(), cached_prompt_prefix="[ROLE=SYSTEM]\ntest")
# Test with non-dict response
non_dict_response = json.dumps(["not", "a", "dict"])
diff --git a/imbue_verify/issue_identifiers/harnesses/agentic.py b/imbue_verify/issue_identifiers/harnesses/agentic.py
@@ -31,11 +31,15 @@ from imbue_verify.issue_identifiers.base import IssueIdentifier
from imbue_verify.issue_identifiers.common import GeneratedIssueSchema
from imbue_verify.issue_identifiers.common import GeneratedResponseSchema
from imbue_verify.issue_identifiers.common import extract_invocation_info_from_messages
-from imbue_verify.issue_identifiers.common import format_issue_identification_guide_for_llm
+from imbue_verify.issue_identifiers.common import (
+ format_issue_identification_guide_for_llm,
+)
from imbue_verify.issue_identifiers.common import generate_issues_from_response_texts
from imbue_verify.issue_identifiers.common import generate_response_from_claude_code
from imbue_verify.issue_identifiers.harnesses.base import IssueIdentifierHarness
-from imbue_verify.issue_identifiers.identification_guides import IssueIdentificationGuide
+from imbue_verify.issue_identifiers.identification_guides import (
+ IssueIdentificationGuide,
+)
PROMPT_TEMPLATE = """You are analyzing a code repository for potential issues. The repository files are available in {{ repo_path }}.
@@ -240,7 +244,10 @@ class _AgenticIssueIdentifier(IssueIdentifier[CommitInputs]):
return prompt
def identify_issues(
- self, identifier_inputs: CommitInputs, project_context: ProjectContext, config: ImbueVerifyConfig
+ self,
+ identifier_inputs: CommitInputs,
+ project_context: ProjectContext,
+ config: ImbueVerifyConfig,
) -> Generator[GeneratedIssueSchema, None, IssueIdentificationDebugInfo]:
assert project_context.repo_path is not None, "Project context must have a valid repo_path, got None"
diff --git a/imbue_verify/issue_identifiers/harnesses/base.py b/imbue_verify/issue_identifiers/harnesses/base.py
@@ -4,7 +4,9 @@ from typing import TypeVar
from imbue_tools.get_conversation_history.input_data_types import IdentifierInputs
from imbue_verify.issue_identifiers.base import IssueIdentifier
-from imbue_verify.issue_identifiers.identification_guides import IssueIdentificationGuide
+from imbue_verify.issue_identifiers.identification_guides import (
+ IssueIdentificationGuide,
+)
T = TypeVar("T", bound=IdentifierInputs)
diff --git a/imbue_verify/issue_identifiers/harnesses/conversation_single_prompt.py b/imbue_verify/issue_identifiers/harnesses/conversation_single_prompt.py
@@ -18,7 +18,9 @@ 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 format_conversation_history_for_prompt
+from 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.imbue_verify_config import ImbueVerifyConfig
@@ -26,11 +28,17 @@ from imbue_tools.util_prompts.conversation_prefix import CONVERSATION_PREFIX_TEM
from imbue_verify.issue_identifiers.base import IssueIdentifier
from imbue_verify.issue_identifiers.common import GeneratedIssueSchema
from imbue_verify.issue_identifiers.common import GeneratedResponseSchema
-from imbue_verify.issue_identifiers.common import extract_invocation_info_from_costed_response
-from imbue_verify.issue_identifiers.common import format_issue_identification_guide_for_llm
+from imbue_verify.issue_identifiers.common import (
+ extract_invocation_info_from_costed_response,
+)
+from imbue_verify.issue_identifiers.common import (
+ format_issue_identification_guide_for_llm,
+)
from imbue_verify.issue_identifiers.common import generate_issues_from_response_texts
from imbue_verify.issue_identifiers.harnesses.base import IssueIdentifierHarness
-from imbue_verify.issue_identifiers.identification_guides import IssueIdentificationGuide
+from imbue_verify.issue_identifiers.identification_guides import (
+ IssueIdentificationGuide,
+)
PROMPT_TEMPLATE = (
CONVERSATION_PREFIX_TEMPLATE
@@ -94,7 +102,10 @@ class _ConversationSinglePromptIssueIdentifier(IssueIdentifier[ConversationInput
)
def identify_issues(
- self, identifier_inputs: ConversationInputs, project_context: ProjectContext, config: ImbueVerifyConfig
+ self,
+ identifier_inputs: ConversationInputs,
+ project_context: ProjectContext,
+ config: ImbueVerifyConfig,
) -> Generator[GeneratedIssueSchema, None, IssueIdentificationDebugInfo]:
language_model = build_language_model_from_config(config.language_model_generation_config)
language_model_params = LanguageModelGenerationParams(
diff --git a/imbue_verify/issue_identifiers/harnesses/conversation_single_prompt_test.py b/imbue_verify/issue_identifiers/harnesses/conversation_single_prompt_test.py
@@ -9,9 +9,15 @@ from imbue_core.sculptor.state.messages import LLMModel
from imbue_core.sculptor.state.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 IdentifierInputsMissingError
-from imbue_verify.issue_identifiers.harnesses.conversation_single_prompt import ConversationSinglePromptHarness
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE
+from imbue_tools.get_conversation_history.input_data_types import (
+ IdentifierInputsMissingError,
+)
+from imbue_verify.issue_identifiers.harnesses.conversation_single_prompt import (
+ ConversationSinglePromptHarness,
+)
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE,
+)
def test_to_required_inputs() -> None:
diff --git a/imbue_verify/issue_identifiers/harnesses/single_prompt.py b/imbue_verify/issue_identifiers/harnesses/single_prompt.py
@@ -23,11 +23,17 @@ from imbue_tools.types.imbue_verify_config import ImbueVerifyConfig
from imbue_verify.issue_identifiers.base import IssueIdentifier
from imbue_verify.issue_identifiers.common import GeneratedIssueSchema
from imbue_verify.issue_identifiers.common import GeneratedResponseSchema
-from imbue_verify.issue_identifiers.common import extract_invocation_info_from_costed_response
-from imbue_verify.issue_identifiers.common import format_issue_identification_guide_for_llm
+from imbue_verify.issue_identifiers.common import (
+ extract_invocation_info_from_costed_response,
+)
+from imbue_verify.issue_identifiers.common import (
+ format_issue_identification_guide_for_llm,
+)
from imbue_verify.issue_identifiers.common import generate_issues_from_response_texts
from imbue_verify.issue_identifiers.harnesses.base import IssueIdentifierHarness
-from imbue_verify.issue_identifiers.identification_guides import IssueIdentificationGuide
+from imbue_verify.issue_identifiers.identification_guides import (
+ IssueIdentificationGuide,
+)
USER_REQUEST_PREFIX_TEMPLATE = """{{cached_prompt_prefix}}
[ROLE=USER_CACHED]
@@ -134,7 +140,7 @@ class _SinglePromptIssueIdentifier(IssueIdentifier[CommitInputs]):
"include_request_and_diff": True,
"cached_prompt_prefix": project_context.cached_prompt_prefix,
"cache_full_prompt": config.cache_full_prompt,
- "extra_context": escape_prompt_markers(config.extra_context) if config.extra_context else None,
+ "extra_context": (escape_prompt_markers(config.extra_context) if config.extra_context else None),
"commit_message": escape_prompt_markers(identifier_inputs.goal),
"unified_diff": escape_prompt_markers(identifier_inputs.diff),
"guides": formatted_guides,
@@ -143,7 +149,10 @@ class _SinglePromptIssueIdentifier(IssueIdentifier[CommitInputs]):
)
def identify_issues(
- self, identifier_inputs: CommitInputs, project_context: ProjectContext, config: ImbueVerifyConfig
+ self,
+ identifier_inputs: CommitInputs,
+ project_context: ProjectContext,
+ config: ImbueVerifyConfig,
) -> Generator[GeneratedIssueSchema, None, IssueIdentificationDebugInfo]:
prompt = self._get_prompt(project_context, config, identifier_inputs)
language_model = build_language_model_from_config(config.language_model_generation_config)
diff --git a/imbue_verify/issue_identifiers/harnesses/single_prompt_test.py b/imbue_verify/issue_identifiers/harnesses/single_prompt_test.py
@@ -17,13 +17,19 @@ 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 IdentifierInputsMissingError
+from imbue_tools.get_conversation_history.input_data_types import (
+ IdentifierInputsMissingError,
+)
from imbue_tools.repo_utils.project_context import BaseProjectContext
from imbue_tools.types.imbue_verify_config import ImbueVerifyConfig
from imbue_verify.issue_identifiers.base import IssueIdentifier
from imbue_verify.issue_identifiers.harnesses.single_prompt import SinglePromptHarness
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_CODES_FOR_CORRECTNESS_CHECK
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_CODES_FOR_CORRECTNESS_CHECK,
+)
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE,
+)
from imbue_verify.issue_identifiers.utils import ReturnCapturingGenerator
@@ -75,7 +81,10 @@ def test_to_required_inputs() -> None:
# Should support inputs where the commit message and diff are present
combined_inputs = IdentifierInputs(
- maybe_goal="test", maybe_diff="test", maybe_files=("test.py",), maybe_conversation_history=()
+ maybe_goal="test",
+ maybe_diff="test",
+ maybe_files=("test.py",),
+ maybe_conversation_history=(),
)
cmi = identifier.to_required_inputs(combined_inputs)
assert isinstance(cmi, CommitInputs)
@@ -104,7 +113,8 @@ def test_get_prompt_structure() -> None:
cached_prompt_prefix="[ROLE=SYSTEM]\nSystem context here",
)
commit_inputs = CommitInputs(
- maybe_goal="Add hello world function", maybe_diff="+def hello():\n+ print('hello')"
+ maybe_goal="Add hello world function",
+ maybe_diff="+def hello():\n+ print('hello')",
)
config = ImbueVerifyConfig()
diff --git a/imbue_verify/issue_identifiers/issue_deduplication.py b/imbue_verify/issue_identifiers/issue_deduplication.py
@@ -16,10 +16,16 @@ from imbue_tools.repo_utils.context_utils import escape_prompt_markers
from imbue_tools.types.imbue_verify_config import ImbueVerifyConfig
from imbue_verify.issue_identifiers.common import GeneratedIssueSchema
from imbue_verify.issue_identifiers.common import GeneratedResponseSchema
-from imbue_verify.issue_identifiers.common import extract_invocation_info_from_costed_response
-from imbue_verify.issue_identifiers.common import format_issue_identification_guide_for_llm
+from imbue_verify.issue_identifiers.common import (
+ extract_invocation_info_from_costed_response,
+)
+from imbue_verify.issue_identifiers.common import (
+ format_issue_identification_guide_for_llm,
+)
from imbue_verify.issue_identifiers.common import generate_issues_from_response_texts
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE,
+)
from imbue_verify.issue_identifiers.utils import ReturnCapturingGenerator
DEDUPLICATION_PROMPT_TEMPLATE = """[ROLE=USER]
@@ -90,7 +96,9 @@ def _get_deduplication_prompt(
return prompt
-def _convert_parsed_issues_to_combined_string(all_parsed_issues: Iterable[GeneratedIssueSchema]) -> str:
+def _convert_parsed_issues_to_combined_string(
+ all_parsed_issues: Iterable[GeneratedIssueSchema],
+) -> str:
"""Convert all parsed issues from all issue types to a combined string for the deduplication prompt."""
combined_issues = []
diff --git a/imbue_verify/issue_identifiers/issue_evaluation.py b/imbue_verify/issue_identifiers/issue_evaluation.py
@@ -11,20 +11,34 @@ 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 format_conversation_history_for_prompt
+from 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 ResponseParsingError
-from imbue_tools.llm_output_parsing.parse_model_json_response import parse_model_json_response
+from imbue_tools.llm_output_parsing.parse_model_json_response import (
+ ResponseParsingError,
+)
+from 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.imbue_verify_config import DEFAULT_CONFIDENCE_THRESHOLD
from imbue_tools.types.imbue_verify_config import ImbueVerifyConfig
from imbue_tools.util_prompts.conversation_prefix import CONVERSATION_PREFIX_TEMPLATE
from imbue_verify.issue_identifiers.common import GeneratedIssueSchema
-from imbue_verify.issue_identifiers.common import extract_invocation_info_from_costed_response
-from imbue_verify.issue_identifiers.common import format_issue_identification_guide_for_llm
-from imbue_verify.issue_identifiers.harnesses.single_prompt import USER_REQUEST_PREFIX_TEMPLATE
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE
+from imbue_verify.issue_identifiers.common import (
+ extract_invocation_info_from_costed_response,
+)
+from imbue_verify.issue_identifiers.common import (
+ format_issue_identification_guide_for_llm,
+)
+from imbue_verify.issue_identifiers.harnesses.single_prompt import (
+ USER_REQUEST_PREFIX_TEMPLATE,
+)
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE,
+)
from imbue_verify.issue_identifiers.utils import ReturnCapturingGenerator
CODE_BASED_CRITERIA = (
diff --git a/imbue_verify/issue_identifiers/registry.py b/imbue_verify/issue_identifiers/registry.py
@@ -18,22 +18,36 @@ 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 IdentifierInputsMissingError
+from imbue_tools.get_conversation_history.input_data_types import (
+ IdentifierInputsMissingError,
+)
from imbue_tools.repo_utils.project_context import ProjectContext
from imbue_tools.types.imbue_verify_config import ImbueVerifyConfig
from imbue_tools.types.imbue_verify_config import get_enabled_issue_codes
-from imbue_verify.issue_identifiers.agentic_issue_collation import collate_issues_with_agent
+from imbue_verify.issue_identifiers.agentic_issue_collation import (
+ collate_issues_with_agent,
+)
from imbue_verify.issue_identifiers.base import IssueIdentifier
from imbue_verify.issue_identifiers.common import GeneratedIssueSchema
from imbue_verify.issue_identifiers.common import convert_to_issue_identifier_result
from imbue_verify.issue_identifiers.harnesses.agentic import AgenticHarness
from imbue_verify.issue_identifiers.harnesses.base import IssueIdentifierHarness
-from imbue_verify.issue_identifiers.harnesses.conversation_single_prompt import ConversationSinglePromptHarness
+from imbue_verify.issue_identifiers.harnesses.conversation_single_prompt import (
+ ConversationSinglePromptHarness,
+)
from imbue_verify.issue_identifiers.harnesses.single_prompt import SinglePromptHarness
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_CODES_FOR_BATCHED_COMMIT_CHECK
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_CODES_FOR_CONVERSATION_HISTORY_CHECK
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_CODES_FOR_CORRECTNESS_CHECK
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_CODES_FOR_BATCHED_COMMIT_CHECK,
+)
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_CODES_FOR_CONVERSATION_HISTORY_CHECK,
+)
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_CODES_FOR_CORRECTNESS_CHECK,
+)
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE,
+)
from imbue_verify.issue_identifiers.issue_deduplication import deduplicate_issues
from imbue_verify.issue_identifiers.issue_evaluation import filter_issues
from imbue_verify.issue_identifiers.utils import ReturnCapturingGenerator
@@ -89,7 +103,9 @@ def _convert_all_to_enum(
return tuple(results)
-def _get_enabled_identifier_names(config: ImbueVerifyConfig) -> set[IssueIdentifierType]:
+def _get_enabled_identifier_names(
+ config: ImbueVerifyConfig,
+) -> set[IssueIdentifierType]:
all_names = get_all_valid_identifier_names()
explicitly_enabled = _convert_all_to_enum(config.enabled_identifiers or tuple(), all_names, IssueIdentifierType)
explicitly_disabled = _convert_all_to_enum(config.disabled_identifiers or tuple(), all_names, IssueIdentifierType)
@@ -120,9 +136,7 @@ def _build_identifiers(
(
combined_name,
harness.make_issue_identifier(
- identification_guides=tuple(
- ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE[code] for code in issue_codes
- )
+ identification_guides=tuple(ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE[code] for code in issue_codes)
),
)
)
@@ -131,7 +145,8 @@ def _build_identifiers(
def _generate_with_name_in_debug_info(
- name: str, generator: Generator[GeneratedIssueSchema, None, IssueIdentificationDebugInfo]
+ name: str,
+ generator: Generator[GeneratedIssueSchema, None, IssueIdentificationDebugInfo],
) -> Generator[GeneratedIssueSchema, None, tuple[str, IssueIdentificationDebugInfo]]:
generator_with_capture = ReturnCapturingGenerator(generator)
for result in generator_with_capture:
@@ -142,7 +157,7 @@ def _generate_with_name_in_debug_info(
def _combine_issue_generator_debug_info(
generator: Generator[GeneratedIssueSchema, None, tuple[tuple[str, IssueIdentificationDebugInfo], ...]],
) -> Generator[GeneratedIssueSchema, None, IssueIdentificationDebugInfo]:
- collected_debug_info: tuple[tuple[str, IssueIdentificationDebugInfo], ...] = yield from generator
+ collected_debug_info: tuple[tuple[str, IssueIdentificationDebugInfo], ...] = (yield from generator)
updated_llm_responses = []
for identifier_name, debug_info in collected_debug_info:
@@ -178,7 +193,11 @@ def run(
compatible_enabled_identifier_names.append(identifier_name)
detectable_issue_codes.update(identifier.enabled_issue_codes)
except IdentifierInputsMissingError as e:
- logger.debug("skipping identifier {} because of missing inputs: {}", identifier_name, e)
+ logger.debug(
+ "skipping identifier {} because of missing inputs: {}",
+ identifier_name,
+ e,
+ )
continue
# 2. Collation for agentic identifiers
@@ -192,7 +211,10 @@ def run(
identifier.enabled_issue_codes,
)
except IdentifierInputsMissingError as e:
- logger.warning("collate_issues_with_agent requires commit message and diff, skipping: {}", e)
+ logger.warning(
+ "collate_issues_with_agent requires commit message and diff, skipping: {}",
+ e,
+ )
continue
else:
collated_issues_generator = identified_issues_generator
@@ -222,7 +244,9 @@ def run(
# 4. Deduplicate issues across all identifiers
if config.enable_deduplication:
deduplicated_generator = deduplicate_issues(
- multiplexed_generators_with_combined_debug_info, config, tuple(detectable_issue_codes)
+ multiplexed_generators_with_combined_debug_info,
+ config,
+ tuple(detectable_issue_codes),
)
else:
deduplicated_generator = multiplexed_generators_with_combined_debug_info
diff --git a/imbue_verify/issue_identifiers/test_prompt_lengths.py b/imbue_verify/issue_identifiers/test_prompt_lengths.py
@@ -5,7 +5,9 @@ from imbue_tools.get_conversation_history.input_data_types import CommitInputs
from imbue_tools.repo_utils.project_context import BaseProjectContext
from imbue_tools.types.imbue_verify_config import ImbueVerifyConfig
from imbue_verify.issue_identifiers import registry
-from imbue_verify.issue_identifiers.identification_guides import ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE
+from imbue_verify.issue_identifiers.identification_guides import (
+ ISSUE_IDENTIFICATION_GUIDES_BY_ISSUE_CODE,
+)
from imbue_verify.repo_utils import IMBUE_VERIFY_MAX_PROMPT_TOKENS
EMPTY_PROJECT_CONTEXT = BaseProjectContext(file_contents_by_path=FrozenDict(), cached_prompt_prefix="")
@@ -53,6 +55,6 @@ def test_prompt_lengths() -> None:
)
prompt = extract_prompt(identifier)
num_tokens = _estimate_tokens(prompt)
- assert num_tokens <= IMBUE_VERIFY_MAX_PROMPT_TOKENS, (
- f"Prompt for {identifier_name} exceeds IMBUE_VERIFY_MAX_PROMPT_TOKENS. Consider increasing IMBUE_VERIFY_MAX_PROMPT_TOKENS or shortening the prompt. "
- )
+ assert (
+ num_tokens <= IMBUE_VERIFY_MAX_PROMPT_TOKENS
+ ), f"Prompt for {identifier_name} exceeds IMBUE_VERIFY_MAX_PROMPT_TOKENS. Consider increasing IMBUE_VERIFY_MAX_PROMPT_TOKENS or shortening the prompt. "
diff --git a/imbue_verify/issue_identifiers/utils.py b/imbue_verify/issue_identifiers/utils.py
@@ -20,7 +20,10 @@ def xml_post_escape(input_string: str, element_to_post_escape: str) -> str:
element_to_post_escape -- the element to post-escape (e.g. "code_part")
"""
- pattern = re.compile(f"<({element_to_post_escape})>(.*?)</({element_to_post_escape})>", re.IGNORECASE | re.DOTALL)
+ pattern = re.compile(
+ f"<({element_to_post_escape})>(.*?)</({element_to_post_escape})>",
+ re.IGNORECASE | re.DOTALL,
+ )
return re.sub(
pattern,
lambda x: f"<{x.group(1)}>{escape(x.group(2))}</{x.group(3)}>",
@@ -77,7 +80,8 @@ class GeneratorDoneSentinel:
def _run_and_queue_generator(
- generator: Generator[IterT, None, ReturnT], output_queue: queue.Queue[IterT | GeneratorDoneSentinel]
+ generator: Generator[IterT, None, ReturnT],
+ output_queue: queue.Queue[IterT | GeneratorDoneSentinel],
) -> ReturnT:
try:
generator_with_capture = ReturnCapturingGenerator(generator)
@@ -101,7 +105,12 @@ def multiplex_generators(
with ThreadPoolExecutor(max_workers=max_workers) as executor:
output_queue: queue.Queue[IterT | GeneratorDoneSentinel] = queue.Queue()
futures = [
- executor.submit(contextvars.copy_context().run, _run_and_queue_generator, gen, output_queue=output_queue)
+ executor.submit(
+ contextvars.copy_context().run,
+ _run_and_queue_generator,
+ gen,
+ output_queue=output_queue,
+ )
for gen in generators
]
diff --git a/imbue_verify/repo_utils_test.py b/imbue_verify/repo_utils_test.py
@@ -15,7 +15,11 @@ def test_get_code_to_check(simple_test_git_repo: Path) -> None:
"""Test that get_code_to_check correctly handles staged, unstaged, and untracked files"""
repo_path = simple_test_git_repo
first_commit = subprocess.run(
- ["git", "rev-parse", "HEAD"], cwd=repo_path, capture_output=True, text=True, check=True
+ ["git", "rev-parse", "HEAD"],
+ cwd=repo_path,
+ capture_output=True,
+ text=True,
+ check=True,
).stdout.strip()
# Create an untracked file
@@ -67,7 +71,11 @@ def test_get_code_to_check(simple_test_git_repo: Path) -> None:
def test_build_context(simple_test_git_repo: Path, snapshot: SnapshotAssertion) -> None:
first_commit = subprocess.run(
- ["git", "rev-parse", "HEAD"], cwd=simple_test_git_repo, capture_output=True, text=True, check=True
+ ["git", "rev-parse", "HEAD"],
+ cwd=simple_test_git_repo,
+ capture_output=True,
+ text=True,
+ check=True,
).stdout.strip()
git_hash, diff, _diff_no_binary = get_code_to_check(first_commit, repo_path=simple_test_git_repo)
project_context = LazyProjectContext.build(
diff --git a/imbue_verify/telemetry.py b/imbue_verify/telemetry.py
@@ -1,96 +0,0 @@
-from datetime import datetime
-
-from imbue_core.agents.configs import LanguageModelGenerationConfig
-from imbue_core.sculptor.state.messages import ConversationMessageUnion
-from imbue_tools.capabilities_data_logging.common import get_current_user_name
-from imbue_tools.capabilities_data_logging.data_types import CommandType
-from imbue_tools.capabilities_data_logging.data_types import ImbueVerifyEvent
-from imbue_tools.capabilities_data_logging.data_types import LoggedFeatureType
-from imbue_tools.repo_utils.context_prefix import SubrepoContext
-from imbue_tools.repo_utils.context_prefix import SubrepoContextWithFormattedContext
-from imbue_tools.types.imbue_verify_config import ImbueVerifyConfig
-
-
-def prune_context(context: SubrepoContext | None) -> SubrepoContext | None:
- if isinstance(context, SubrepoContextWithFormattedContext):
- return SubrepoContext(
- repo_context_files=context.repo_context_files,
- subrepo_context_strategy_label=context.subrepo_context_strategy_label,
- )
- return context
-
-
-# TODO quick and dirty "new" event distinguished only by feature_name. should add more actual event types
-async def create_imbue_verify_exception_event(
- base_commit: str,
- diff: str,
- goal: str,
- config: ImbueVerifyConfig,
- exception_name: str | None = None,
- created_at: datetime | None = None,
-) -> ImbueVerifyEvent:
- """
- Log the repo state and goal to a local file.
- """
- # TODO: we should really be passing in the generation config not relying on these defaults
- generation_config = LanguageModelGenerationConfig(model_name=config.language_model_generation_config.model_name)
- event = ImbueVerifyEvent(
- task_description=goal,
- feature_name=LoggedFeatureType.VERIFY_EXCEPTION,
- generation_config=generation_config,
- user_id=get_current_user_name(),
- organization_id="Imbue",
- git_hash=base_commit,
- diff=diff,
- command_type=CommandType.IMBUE_VERIFY,
- imbue_verify_config=config,
- exception_name=exception_name,
- # If created_at is provided, use it, otherwise use the current time as measured by the event's constructor.
- # pyre-fixme[6]: pyre can't check unpacking untyped dict
- **(dict(created_at=created_at) if created_at else {}),
- )
- return event
-
-
-async def create_imbue_verify_issues_found_event(
- base_commit: str,
- diff: str,
- goal: str,
- config: ImbueVerifyConfig,
- created_at: datetime | None = None,
- git_url: str | None = None,
- subrepo_context: SubrepoContext | None = None,
- instruction_context: SubrepoContext | None = None,
- conversation_history: tuple[ConversationMessageUnion, ...] | None = None,
-) -> ImbueVerifyEvent:
- """
- Log the repo state and goal to a local file.
- """
- # TODO: this is kind of hacky but we don't want to log the formatted context as well since it's a lot to log
- pruned_subrepo_context = prune_context(subrepo_context)
- pruned_instruction_context = prune_context(instruction_context)
- # TODO: we should really be passing in the generation config not relying on these defaults
- generation_config = LanguageModelGenerationConfig(model_name=config.language_model_generation_config.model_name)
- if git_url:
- assert "https://oauth2:" not in git_url, f"Expected no oauth2 in url, {git_url=}"
-
- event = ImbueVerifyEvent(
- task_description=goal,
- feature_name=LoggedFeatureType.COMMAND_RUN,
- # TODO: maybe this should actually become something like a CodeGenerationConfig since that also has info about the context
- generation_config=generation_config,
- user_id=get_current_user_name(),
- organization_id="Imbue",
- git_hash=base_commit,
- diff=diff,
- command_type=CommandType.IMBUE_VERIFY,
- git_url=git_url,
- subrepo_context=pruned_subrepo_context,
- instruction_context=pruned_instruction_context,
- conversation_history=conversation_history,
- imbue_verify_config=config,
- # If created_at is provided, use it, otherwise use the current time as measured by the event's constructor.
- # pyre-fixme[6]: pyre can't check unpacking untyped dict
- **(dict(created_at=created_at) if created_at else {}),
- )
- return event
diff --git a/imbue_verify/telemetry_test.py b/imbue_verify/telemetry_test.py
@@ -1,36 +0,0 @@
-from datetime import datetime
-from datetime import timezone
-
-from imbue_tools.repo_utils.context_prefix import SubrepoContext
-from imbue_tools.types.imbue_verify_config import ImbueVerifyConfig
-from imbue_verify.telemetry import create_imbue_verify_issues_found_event
-
-
-async def test_create_imbue_verify_issues_found_event_with_created_at() -> None:
- now = datetime.now(timezone.utc) # noqa: F821
- event = await create_imbue_verify_issues_found_event(
- config=ImbueVerifyConfig(),
- base_commit="",
- diff="",
- goal="",
- subrepo_context=SubrepoContext(
- subrepo_context_strategy_label="",
- repo_context_files=tuple(),
- ),
- created_at=now,
- )
- assert event.created_at == now
-
-
-async def test_without_created_at() -> None:
- event = await create_imbue_verify_issues_found_event(
- config=ImbueVerifyConfig(),
- base_commit="",
- diff="",
- goal="",
- subrepo_context=SubrepoContext(
- subrepo_context_strategy_label="",
- repo_context_files=tuple(),
- ),
- )
- assert isinstance(event.created_at, datetime)
diff --git a/pyproject.toml b/pyproject.toml
@@ -31,5 +31,14 @@ package-data.imbue_verify = ["py.typed"]
include = ["imbue_verify*"]
[tool.uv.sources]
-imbue_core = { path = "../generally_intelligent/imbue_core", editable = true }
-imbue_tools = { path = "../generally_intelligent/imbue_tools", editable = true }
+imbue_core = { path = "./imbue_core", editable = true }
+imbue_tools = { path = "./imbue_tools", editable = true }
+
+[dependency-groups]
+dev = [
+ "black",
+]
+
+[tool.black]
+line-length = 120
+target-version = ['py311']
diff --git a/uv.lock b/uv.lock
@@ -2,7 +2,9 @@ version = 1
revision = 3
requires-python = ">=3.11"
resolution-markers = [
- "python_full_version >= '3.12'",
+ "python_full_version >= '3.14'",
+ "python_full_version == '3.13.*'",
+ "python_full_version == '3.12.*'",
"python_full_version < '3.12'",
]
@@ -208,15 +210,6 @@ wheels = [
]
[[package]]
-name = "backoff"
-version = "2.2.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/47/d7/5bbeb12c44d7c4f2fb5b56abce497eb5ed9f34d85701de869acedd602619/backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba", size = 17001, upload-time = "2022-10-05T19:19:32.061Z" }
-wheels = [
- { url = "https://files.pythonhosted.org/packages/df/73/b6e24bd22e6720ca8ee9a85a0c4a2971af8497d8f3193fa05390cbd46e09/backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8", size = 15148, upload-time = "2022-10-05T19:19:30.546Z" },
-]
-
-[[package]]
name = "black"
version = "25.12.0"
source = { registry = "https://pypi.org/simple" }
@@ -704,15 +697,6 @@ wheels = [
]
[[package]]
-name = "fsspec"
-version = "2026.1.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/d5/7d/5df2650c57d47c57232af5ef4b4fdbff182070421e405e0d62c6cdbfaa87/fsspec-2026.1.0.tar.gz", hash = "sha256:e987cb0496a0d81bba3a9d1cee62922fb395e7d4c3b575e57f547953334fe07b", size = 310496, upload-time = "2026-01-09T15:21:35.562Z" }
-wheels = [
- { url = "https://files.pythonhosted.org/packages/01/c9/97cc5aae1648dcb851958a3ddf73ccd7dbe5650d95203ecb4d7720b4cdbf/fsspec-2026.1.0-py3-none-any.whl", hash = "sha256:cb76aa913c2285a3b49bdd5fc55b1d7c708d7208126b60f2eb8194fe1b4cbdcc", size = 201838, upload-time = "2026-01-09T15:21:34.041Z" },
-]
-
-[[package]]
name = "google-auth"
version = "2.48.0"
source = { registry = "https://pypi.org/simple" }
@@ -805,35 +789,6 @@ wheels = [
]
[[package]]
-name = "hf-xet"
-version = "1.2.0"
-source = { registry = "https://pypi.org/simple" }
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