code: """A tracer that runs evaluators over completed runs."""
  from __future__ import annotations
  
  import logging
  from concurrent.futures import Future, ThreadPoolExecutor
  from typing import Any, Dict, List, Optional, Sequence, Set, Union
  from uuid import UUID
  
  import langsmith
  from langsmith.evaluation.evaluator import EvaluationResult
  
  from langchain.callbacks import manager
  from langchain.callbacks.tracers import langchain as langchain_tracer
  from langchain.callbacks.tracers.base import BaseTracer
  from langchain.callbacks.tracers.schemas import Run
  
  logger = logging.getLogger(__name__)
  
  
  class EvaluatorCallbackHandler(BaseTracer):
      """A tracer that runs a run evaluator whenever a run is persisted.
  
      Parameters
      ----------
      evaluators : Sequence[RunEvaluator]
          The run evaluators to apply to all top level runs.
      max_workers : int, optional
          The maximum number of worker threads to use for running the evaluators.
          If not specified, it will default to the number of evaluators.
      client : LangSmith Client, optional
          The LangSmith client instance to use for evaluating the runs.
          If not specified, a new instance will be created.
      example_id : Union[UUID, str], optional
          The example ID to be associated with the runs.
      project_name : str, optional
          The LangSmith project name to be organize eval chain runs under.
  
      Attributes
      ----------
      example_id : Union[UUID, None]
          The example ID associated with the runs.
      client : Client
          The LangSmith client instance used for evaluating the runs.
      evaluators : Sequence[RunEvaluator]
          The sequence of run evaluators to be executed.
      executor : ThreadPoolExecutor
          The thread pool executor used for running the evaluators.
      futures : Set[Future]
          The set of futures representing the running evaluators.
      skip_unfinished : bool
          Whether to skip runs that are not finished or raised
          an error.
      project_name : Optional[str]
          The LangSmith project name to be organize eval chain runs under.
      """
  
      name = "evaluator_callback_handler"
  
      def __init__(
          self,
          evaluators: Sequence[langsmith.RunEvaluator],
          max_workers: Optional[int] = None,
          client: Optional[langsmith.Client] = None,
          example_id: Optional[Union[UUID, str]] = None,
          skip_unfinished: bool = True,
          project_name: Optional[str] = "evaluators",
          **kwargs: Any,
      ) -> None:
          super().__init__(**kwargs)
          self.example_id = (
              UUID(example_id) if isinstance(example_id, str) else example_id
          )
          self.client = client or langchain_tracer.get_client()
          self.evaluators = evaluators
          self.max_workers = max_workers or len(evaluators)
          self.futures: Set[Future] = set()
          self.skip_unfinished = skip_unfinished
          self.project_name = project_name
          self.logged_eval_results: Dict[str, List[EvaluationResult]] = {}
  
      def _evaluate_in_project(self, run: Run, evaluator: langsmith.RunEvaluator) -> None:
          """Evaluate the run in the project.
  
          Parameters
          ----------
          run : Run
              The run to be evaluated.
          evaluator : RunEvaluator
              The evaluator to use for evaluating the run.
  
          """
          try:
              if self.project_name is None:
                  eval_result = self.client.evaluate_run(run, evaluator)
              with manager.tracing_v2_enabled(
                  project_name=self.project_name, tags=["eval"], client=self.client
              ):
                  eval_result = self.client.evaluate_run(run, evaluator)
          except Exception as e:
              logger.error(
                  f"Error evaluating run {run.id} with "
                  f"{evaluator.__class__.__name__}: {e}",
                  exc_info=True,
              )
              raise e
          example_id = str(run.reference_example_id)
          self.logged_eval_results.setdefault(example_id, []).append(eval_result)
  
      def _persist_run(self, run: Run) -> None:
          """Run the evaluator on the run.
  
          Parameters
          ----------
          run : Run
              The run to be evaluated.
  
          """
          if self.skip_unfinished and not run.outputs:
              logger.debug(f"Skipping unfinished run {run.id}")
              return
          run_ = run.copy()
          run_.reference_example_id = self.example_id
          if self.max_workers > 0:
              with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
                  list(
                      executor.map(
                          self._evaluate_in_project,
                          [run_ for _ in range(len(self.evaluators))],
                          self.evaluators,
                      )
                  )
          else:
              for evaluator in self.evaluators:
                  self._evaluate_in_project(run_, evaluator)
  
apis: langchain.callbacks.tracers.langchain.get_client langchain.callbacks.manager.tracing_v2_enabled
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