Source code for langchain_core.tracers.langchain

"""A Tracer implementation that records to LangChain endpoint."""

from __future__ import annotations

import logging
from concurrent.futures import ThreadPoolExecutor
from datetime import datetime, timezone
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
from uuid import UUID

from langsmith import Client
from langsmith import utils as ls_utils
from tenacity import (
    Retrying,
    retry_if_exception_type,
    stop_after_attempt,
    wait_exponential_jitter,
)

from langchain_core.env import get_runtime_environment
from langchain_core.load import dumpd
from langchain_core.tracers.base import BaseTracer
from langchain_core.tracers.schemas import Run

if TYPE_CHECKING:
    from langchain_core.messages import BaseMessage

logger = logging.getLogger(__name__)
_LOGGED = set()
_CLIENT: Optional[Client] = None
_EXECUTOR: Optional[ThreadPoolExecutor] = None


[docs]def log_error_once(method: str, exception: Exception) -> None: """Log an error once. Args: method: The method that raised the exception. exception: The exception that was raised. """ global _LOGGED if (method, type(exception)) in _LOGGED: return _LOGGED.add((method, type(exception))) logger.error(exception)
[docs]def wait_for_all_tracers() -> None: """Wait for all tracers to finish.""" global _CLIENT if _CLIENT is not None and _CLIENT.tracing_queue is not None: _CLIENT.tracing_queue.join()
[docs]def get_client() -> Client: """Get the client.""" global _CLIENT if _CLIENT is None: _CLIENT = Client() return _CLIENT
def _get_executor() -> ThreadPoolExecutor: """Get the executor.""" global _EXECUTOR if _EXECUTOR is None: _EXECUTOR = ThreadPoolExecutor() return _EXECUTOR def _run_to_dict(run: Run) -> dict: return { **run.dict(exclude={"child_runs", "inputs", "outputs"}), "inputs": run.inputs.copy() if run.inputs is not None else None, "outputs": run.outputs.copy() if run.outputs is not None else None, }
[docs]class LangChainTracer(BaseTracer): """Implementation of the SharedTracer that POSTS to the LangChain endpoint."""
[docs] def __init__( self, example_id: Optional[Union[UUID, str]] = None, project_name: Optional[str] = None, client: Optional[Client] = None, tags: Optional[List[str]] = None, **kwargs: Any, ) -> None: """Initialize the LangChain tracer. Args: example_id: The example ID. project_name: The project name. Defaults to the tracer project. client: The client. Defaults to the global client. tags: The tags. Defaults to an empty list. kwargs: Additional keyword arguments. """ super().__init__(**kwargs) self.example_id = ( UUID(example_id) if isinstance(example_id, str) else example_id ) self.project_name = project_name or ls_utils.get_tracer_project() self.client = client or get_client() self.tags = tags or [] self.latest_run: Optional[Run] = None
[docs] def on_chat_model_start( self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, name: Optional[str] = None, **kwargs: Any, ) -> Run: """Start a trace for an LLM run. Args: serialized: The serialized model. messages: The messages. run_id: The run ID. tags: The tags. Defaults to None. parent_run_id: The parent run ID. Defaults to None. metadata: The metadata. Defaults to None. name: The name. Defaults to None. kwargs: Additional keyword arguments. Returns: Run: The run. """ start_time = datetime.now(timezone.utc) if metadata: kwargs.update({"metadata": metadata}) chat_model_run = Run( id=run_id, parent_run_id=parent_run_id, serialized=serialized, inputs={"messages": [[dumpd(msg) for msg in batch] for batch in messages]}, extra=kwargs, events=[{"name": "start", "time": start_time}], start_time=start_time, run_type="llm", tags=tags, name=name, # type: ignore[arg-type] ) self._start_trace(chat_model_run) self._on_chat_model_start(chat_model_run) return chat_model_run
def _persist_run(self, run: Run) -> None: run_ = run.copy() run_.reference_example_id = self.example_id self.latest_run = run_
[docs] def get_run_url(self) -> str: """Get the LangSmith root run URL. Returns: str: The LangSmith root run URL. Raises: ValueError: If no traced run is found. ValueError: If the run URL cannot be found. """ if not self.latest_run: raise ValueError("No traced run found.") # If this is the first run in a project, the project may not yet be created. # This method is only really useful for debugging flows, so we will assume # there is some tolerace for latency. for attempt in Retrying( stop=stop_after_attempt(5), wait=wait_exponential_jitter(), retry=retry_if_exception_type(ls_utils.LangSmithError), ): with attempt: return self.client.get_run_url( run=self.latest_run, project_name=self.project_name ) raise ValueError("Failed to get run URL.")
def _get_tags(self, run: Run) -> List[str]: """Get combined tags for a run.""" tags = set(run.tags or []) tags.update(self.tags or []) return list(tags) def _persist_run_single(self, run: Run) -> None: """Persist a run.""" run_dict = _run_to_dict(run) run_dict["tags"] = self._get_tags(run) extra = run_dict.get("extra", {}) extra["runtime"] = get_runtime_environment() run_dict["extra"] = extra try: self.client.create_run(**run_dict, project_name=self.project_name) except Exception as e: # Errors are swallowed by the thread executor so we need to log them here log_error_once("post", e) raise def _update_run_single(self, run: Run) -> None: """Update a run.""" try: run_dict = _run_to_dict(run) run_dict["tags"] = self._get_tags(run) self.client.update_run(run.id, **run_dict) except Exception as e: # Errors are swallowed by the thread executor so we need to log them here log_error_once("patch", e) raise def _on_llm_start(self, run: Run) -> None: """Persist an LLM run.""" if run.parent_run_id is None: run.reference_example_id = self.example_id self._persist_run_single(run) def _on_chat_model_start(self, run: Run) -> None: """Persist an LLM run.""" if run.parent_run_id is None: run.reference_example_id = self.example_id self._persist_run_single(run) def _on_llm_end(self, run: Run) -> None: """Process the LLM Run.""" self._update_run_single(run) def _on_llm_error(self, run: Run) -> None: """Process the LLM Run upon error.""" self._update_run_single(run) def _on_chain_start(self, run: Run) -> None: """Process the Chain Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id self._persist_run_single(run) def _on_chain_end(self, run: Run) -> None: """Process the Chain Run.""" self._update_run_single(run) def _on_chain_error(self, run: Run) -> None: """Process the Chain Run upon error.""" self._update_run_single(run) def _on_tool_start(self, run: Run) -> None: """Process the Tool Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id self._persist_run_single(run) def _on_tool_end(self, run: Run) -> None: """Process the Tool Run.""" self._update_run_single(run) def _on_tool_error(self, run: Run) -> None: """Process the Tool Run upon error.""" self._update_run_single(run) def _on_retriever_start(self, run: Run) -> None: """Process the Retriever Run upon start.""" if run.parent_run_id is None: run.reference_example_id = self.example_id self._persist_run_single(run) def _on_retriever_end(self, run: Run) -> None: """Process the Retriever Run.""" self._update_run_single(run) def _on_retriever_error(self, run: Run) -> None: """Process the Retriever Run upon error.""" self._update_run_single(run)
[docs] def wait_for_futures(self) -> None: """Wait for the given futures to complete.""" if self.client is not None and self.client.tracing_queue is not None: self.client.tracing_queue.join()