Update to RunOnDataset helper functions to accept evaluator callbacks (#6629)

Also improve docstrings and update the tracing datasets notebook to
focus on "debug, evaluate, monitor"

langchain/callbacks/tracers/evaluation.py

@@ -0,0 +1,84 @@
+"""A tracer that runs evaluators over completed runs."""
+from concurrent.futures import Future, ThreadPoolExecutor, wait
+from typing import Any, Optional, Sequence, Set, Union
+from uuid import UUID
+
+from langchainplus_sdk import LangChainPlusClient, RunEvaluator
+
+from langchain.callbacks.tracers.base import BaseTracer
+from langchain.callbacks.tracers.schemas import Run
+
+
+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 : LangChainPlusClient, optional
+        The LangChainPlusClient 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.
+
+    Attributes
+    ----------
+    example_id : Union[UUID, None]
+        The example ID associated with the runs.
+    client : LangChainPlusClient
+        The LangChainPlusClient 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.
+    """
+
+    name = "evaluator_callback_handler"
+
+    def __init__(
+        self,
+        evaluators: Sequence[RunEvaluator],
+        max_workers: Optional[int] = None,
+        client: Optional[LangChainPlusClient] = None,
+        example_id: Optional[Union[UUID, str]] = None,
+        **kwargs: Any
+    ) -> None:
+        super().__init__(**kwargs)
+        self.example_id = (
+            UUID(example_id) if isinstance(example_id, str) else example_id
+        )
+        self.client = client or LangChainPlusClient()
+        self.evaluators = evaluators
+        self.executor = ThreadPoolExecutor(
+            max_workers=max(max_workers or len(evaluators), 1)
+        )
+        self.futures: Set[Future] = set()
+
+    def _persist_run(self, run: Run) -> None:
+        """Run the evaluator on the run.
+
+        Parameters
+        ----------
+        run : Run
+            The run to be evaluated.
+
+        """
+        run_ = run.copy()
+        run_.reference_example_id = self.example_id
+        for evaluator in self.evaluators:
+            self.futures.add(
+                self.executor.submit(self.client.evaluate_run, run_, evaluator)
+            )
+
+    def wait_for_futures(self) -> None:
+        """Wait for all futures to complete."""
+        futures = list(self.futures)
+        wait(futures)
+        for future in futures:
+            self.futures.remove(future)

langchain/callbacks/tracers/run_collector.py

@@ -1,20 +1,52 @@
 """A tracer that collects all nested runs in a list."""
-from typing import Any, List
+
+from typing import Any, List, Optional, Union
+from uuid import UUID
 
 from langchain.callbacks.tracers.base import BaseTracer
 from langchain.callbacks.tracers.schemas import Run
 
 
 class RunCollectorCallbackHandler(BaseTracer):
-    """A tracer that collects all nested runs in a list.
+    """
+    A tracer that collects all nested runs in a list.
+
+    This tracer is useful for inspection and evaluation purposes.
 
-    Useful for inspection and for evaluation."""
+    Parameters
+    ----------
+    example_id : Optional[Union[UUID, str]], default=None
+        The ID of the example being traced. It can be either a UUID or a string.
+    """
 
     name = "run-collector_callback_handler"
 
-    def __init__(self, **kwargs: Any) -> None:
+    def __init__(
+        self, example_id: Optional[Union[UUID, str]] = None, **kwargs: Any
+    ) -> None:
+        """
+        Initialize the RunCollectorCallbackHandler.
+
+        Parameters
+        ----------
+        example_id : Optional[Union[UUID, str]], default=None
+            The ID of the example being traced. It can be either a UUID or a string.
+        """
         super().__init__(**kwargs)
+        self.example_id = (
+            UUID(example_id) if isinstance(example_id, str) else example_id
+        )
         self.traced_runs: List[Run] = []
 
     def _persist_run(self, run: Run) -> None:
-        self.traced_runs.append(run)
+        """
+        Persist a run by adding it to the traced_runs list.
+
+        Parameters
+        ----------
+        run : Run
+            The run to be persisted.
+        """
+        run_ = run.copy()
+        run_.reference_example_id = self.example_id
+        self.traced_runs.append(run_)

langchain/client/runner_utils.py

@@ -1,4 +1,5 @@
-"""Utilities for running LLMs/Chains over datasets."""
+"""Utilities for running language models or Chains over datasets."""
+
 from __future__ import annotations
 
 import asyncio
@@ -13,15 +14,18 @@ from typing import (
     Iterator,
     List,
     Optional,
+    Sequence,
     Union,
 )
 
-from langchainplus_sdk import LangChainPlusClient
+from langchainplus_sdk import LangChainPlusClient, RunEvaluator
 from langchainplus_sdk.schemas import Example
 
 from langchain.base_language import BaseLanguageModel
 from langchain.callbacks.base import BaseCallbackHandler
 from langchain.callbacks.manager import Callbacks
+from langchain.callbacks.tracers.base import BaseTracer
+from langchain.callbacks.tracers.evaluation import EvaluatorCallbackHandler
 from langchain.callbacks.tracers.langchain import LangChainTracer
 from langchain.chains.base import Chain
 from langchain.chat_models.base import BaseChatModel
@@ -41,11 +45,21 @@ MODEL_OR_CHAIN_FACTORY = Union[Callable[[], Chain], BaseLanguageModel]
 
 
 class InputFormatError(Exception):
-    """Raised when input format is invalid."""
+    """Raised when the input format is invalid."""
 
 
 def _get_prompts(inputs: Dict[str, Any]) -> List[str]:
-    """Get prompts from inputs."""
+    """
+    Get prompts from inputs.
+
+    Args:
+        inputs: The input dictionary.
+
+    Returns:
+        A list of prompts.
+    Raises:
+        InputFormatError: If the input format is invalid.
+    """
     if not inputs:
         raise InputFormatError("Inputs should not be empty.")
 
@@ -83,7 +97,17 @@ def _get_prompts(inputs: Dict[str, Any]) -> List[str]:
 
 
 def _get_messages(inputs: Dict[str, Any]) -> List[List[BaseMessage]]:
-    """Get Chat Messages from inputs."""
+    """
+    Get Chat Messages from inputs.
+
+    Args:
+        inputs: The input dictionary.
+
+    Returns:
+        A list of chat messages.
+    Raises:
+        InputFormatError: If the input format is invalid.
+    """
     if not inputs:
         raise InputFormatError("Inputs should not be empty.")
 
@@ -112,13 +136,25 @@ def _get_messages(inputs: Dict[str, Any]) -> List[List[BaseMessage]]:
 async def _arun_llm(
     llm: BaseLanguageModel,
     inputs: Dict[str, Any],
-    langchain_tracer: Optional[LangChainTracer],
     *,
     tags: Optional[List[str]] = None,
+    callbacks: Callbacks = None,
 ) -> Union[LLMResult, ChatResult]:
-    callbacks: Optional[List[BaseCallbackHandler]] = (
-        [langchain_tracer] if langchain_tracer else None
-    )
+    """
+    Asynchronously run the language model.
+
+    Args:
+        llm: The language model to run.
+        inputs: The input dictionary.
+        tags: Optional tags to add to the run.
+        callbacks: Optional callbacks to use during the run.
+
+    Returns:
+        The LLMResult or ChatResult.
+    Raises:
+        ValueError: If the LLM type is unsupported.
+        InputFormatError: If the input format is invalid.
+    """
     if isinstance(llm, BaseLLM):
         try:
             llm_prompts = _get_prompts(inputs)
@@ -152,18 +188,32 @@ async def _arun_llm_or_chain(
     example: Example,
     llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
     n_repetitions: int,
-    langchain_tracer: Optional[LangChainTracer],
     *,
     tags: Optional[List[str]] = None,
+    callbacks: Optional[List[BaseCallbackHandler]] = None,
 ) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]:
-    """Run the chain asynchronously."""
-    if langchain_tracer is not None:
-        previous_example_id = langchain_tracer.example_id
-        langchain_tracer.example_id = example.id
-        callbacks: Optional[List[BaseCallbackHandler]] = [langchain_tracer]
+    """
+    Asynchronously run the Chain or language model.
+
+    Args:
+        example: The example to run.
+        llm_or_chain_factory: The Chain or language model constructor to run.
+        n_repetitions: The number of times to run the model on each example.
+        tags: Optional tags to add to the run.
+        callbacks: Optional callbacks to use during the run.
+
+    Returns:
+        A list of outputs.
+    """
+    if callbacks:
+        previous_example_ids = [
+            getattr(tracer, "example_id", None) for tracer in callbacks
+        ]
+        for tracer in callbacks:
+            if hasattr(tracer, "example_id"):
+                tracer.example_id = example.id
     else:
-        previous_example_id = None
-        callbacks = None
+        previous_example_ids = None
     outputs = []
     for _ in range(n_repetitions):
         try:
@@ -171,8 +221,8 @@ async def _arun_llm_or_chain(
                 output: Any = await _arun_llm(
                     llm_or_chain_factory,
                     example.inputs,
-                    langchain_tracer,
                     tags=tags,
+                    callbacks=callbacks,
                 )
             else:
                 chain = llm_or_chain_factory()
@@ -183,15 +233,19 @@ async def _arun_llm_or_chain(
         except Exception as e:
             logger.warning(f"Chain failed for example {example.id}. Error: {e}")
             outputs.append({"Error": str(e)})
-    if langchain_tracer is not None:
-        langchain_tracer.example_id = previous_example_id
+    if callbacks and previous_example_ids:
+        for example_id, tracer in zip(previous_example_ids, callbacks):
+            if hasattr(tracer, "example_id"):
+                tracer.example_id = example_id
     return outputs
 
 
 async def _gather_with_concurrency(
     n: int,
-    initializer: Callable[[], Coroutine[Any, Any, Optional[LangChainTracer]]],
-    *async_funcs: Callable[[Optional[LangChainTracer], Dict], Coroutine[Any, Any, Any]],
+    initializer: Callable[[], Coroutine[Any, Any, Any]],
+    *async_funcs: Callable[
+        [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any]
+    ],
 ) -> List[Any]:
     """
     Run coroutines with a concurrency limit.
@@ -207,37 +261,42 @@ async def _gather_with_concurrency(
     semaphore = asyncio.Semaphore(n)
     job_state = {"num_processed": 0}
 
-    tracer_queue: asyncio.Queue[Optional[LangChainTracer]] = asyncio.Queue()
+    callback_queue: asyncio.Queue[Sequence[BaseCallbackHandler]] = asyncio.Queue()
     for _ in range(n):
-        tracer_queue.put_nowait(await initializer())
+        callback_queue.put_nowait(await initializer())
 
     async def run_coroutine_with_semaphore(
         async_func: Callable[
-            [Optional[LangChainTracer], Dict], Coroutine[Any, Any, Any]
+            [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any]
         ]
     ) -> Any:
         async with semaphore:
-            tracer = await tracer_queue.get()
+            callbacks = await callback_queue.get()
             try:
-                result = await async_func(tracer, job_state)
+                result = await async_func(callbacks, job_state)
             finally:
-                tracer_queue.put_nowait(tracer)
+                callback_queue.put_nowait(callbacks)
             return result
 
     results = await asyncio.gather(
         *(run_coroutine_with_semaphore(function) for function in async_funcs)
     )
-    while tracer_queue:
+    while callback_queue:
         try:
-            tracer = tracer_queue.get_nowait()
+            callbacks = callback_queue.get_nowait()
         except asyncio.QueueEmpty:
             break
-        if tracer:
-            tracer.wait_for_futures()
+        for callback in callbacks:
+            if isinstance(callback, (LangChainTracer, EvaluatorCallbackHandler)):
+                callback.wait_for_futures()
     return results
 
 
-async def _tracer_initializer(project_name: Optional[str]) -> Optional[LangChainTracer]:
+async def _callbacks_initializer(
+    project_name: Optional[str],
+    client: LangChainPlusClient,
+    run_evaluators: Sequence[RunEvaluator],
+) -> List[BaseTracer]:
     """
     Initialize a tracer to share across tasks.
 
@@ -247,11 +306,19 @@ async def _tracer_initializer(project_name: Optional[str]) -> Optional[LangChain
     Returns:
         A LangChainTracer instance with an active project.
     """
+    callbacks: List[BaseTracer] = []
     if project_name:
-        tracer = LangChainTracer(project_name=project_name)
-        return tracer
-    else:
-        return None
+        callbacks.append(LangChainTracer(project_name=project_name))
+    if run_evaluators:
+        callbacks.append(
+            EvaluatorCallbackHandler(
+                client=client,
+                evaluators=run_evaluators,
+                # We already have concurrency, don't want to overload the machine
+                max_workers=1,
+            )
+        )
+    return callbacks
 
 
 async def arun_on_examples(
@@ -262,13 +329,16 @@ async def arun_on_examples(
     num_repetitions: int = 1,
     project_name: Optional[str] = None,
     verbose: bool = False,
+    client: Optional[LangChainPlusClient] = None,
     tags: Optional[List[str]] = None,
+    run_evaluators: Optional[Sequence[RunEvaluator]] = None,
 ) -> Dict[str, Any]:
     """
-    Run the chain on examples and store traces to the specified project name.
+    Asynchronously run the chain on examples and store traces
+        to the specified project name.
 
     Args:
-        examples: Examples to run the model or chain over
+        examples: Examples to run the model or chain over.
         llm_or_chain_factory: Language model or Chain constructor to run
             over the dataset. The Chain constructor is used to permit
             independent calls on each example without carrying over state.
@@ -277,24 +347,35 @@ async def arun_on_examples(
             This is useful when testing success rates or generating confidence
             intervals.
         project_name: Project name to use when tracing runs.
+            Defaults to {dataset_name}-{chain class name}-{datetime}.
         verbose: Whether to print progress.
-        tags: Tags to add to the traces.
+        client: Client to use to read the dataset. If not provided, a new
+            client will be created using the credentials in the environment.
+        tags: Tags to add to each run in the project.
+        run_evaluators: Evaluators to run on the results of the chain.
 
     Returns:
         A dictionary mapping example ids to the model outputs.
     """
+    project_name = _get_project_name(project_name, llm_or_chain_factory, None)
+    client_ = client or LangChainPlusClient()
+    client_.create_project(project_name, mode="eval")
+
     results: Dict[str, List[Any]] = {}
+    evaluation_handler = EvaluatorCallbackHandler(
+        evaluators=run_evaluators or [], client=client_
+    )
 
     async def process_example(
-        example: Example, tracer: Optional[LangChainTracer], job_state: dict
+        example: Example, callbacks: List[BaseCallbackHandler], job_state: dict
     ) -> None:
         """Process a single example."""
         result = await _arun_llm_or_chain(
             example,
             llm_or_chain_factory,
             num_repetitions,
-            tracer,
             tags=tags,
+            callbacks=callbacks,
         )
         results[str(example.id)] = result
         job_state["num_processed"] += 1
@@ -307,9 +388,15 @@ async def arun_on_examples(
 
     await _gather_with_concurrency(
         concurrency_level,
-        functools.partial(_tracer_initializer, project_name),
+        functools.partial(
+            _callbacks_initializer,
+            project_name=project_name,
+            client=client_,
+            run_evaluators=run_evaluators or [],
+        ),
         *(functools.partial(process_example, e) for e in examples),
     )
+    evaluation_handler.wait_for_futures()
     return results
 
 
@@ -320,7 +407,21 @@ def run_llm(
     *,
     tags: Optional[List[str]] = None,
 ) -> Union[LLMResult, ChatResult]:
-    """Run the language model on the example."""
+    """
+    Run the language model on the example.
+
+    Args:
+        llm: The language model to run.
+        inputs: The input dictionary.
+        callbacks: The callbacks to use during the run.
+        tags: Optional tags to add to the run.
+
+    Returns:
+        The LLMResult or ChatResult.
+    Raises:
+        ValueError: If the LLM type is unsupported.
+        InputFormatError: If the input format is invalid.
+    """
     if isinstance(llm, BaseLLM):
         try:
             llm_prompts = _get_prompts(inputs)
@@ -350,18 +451,32 @@ def run_llm_or_chain(
     example: Example,
     llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
     n_repetitions: int,
-    langchain_tracer: Optional[LangChainTracer] = None,
     *,
     tags: Optional[List[str]] = None,
+    callbacks: Optional[List[BaseCallbackHandler]] = None,
 ) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]:
-    """Run the chain synchronously."""
-    if langchain_tracer is not None:
-        previous_example_id = langchain_tracer.example_id
-        langchain_tracer.example_id = example.id
-        callbacks: Optional[List[BaseCallbackHandler]] = [langchain_tracer]
+    """
+    Run the Chain or language model synchronously.
+
+    Args:
+        example: The example to run.
+        llm_or_chain_factory: The Chain or language model constructor to run.
+        n_repetitions: The number of times to run the model on each example.
+        tags: Optional tags to add to the run.
+        callbacks: Optional callbacks to use during the run.
+
+    Returns:
+        A list of outputs.
+    """
+    if callbacks:
+        previous_example_ids = [
+            getattr(tracer, "example_id", None) for tracer in callbacks
+        ]
+        for tracer in callbacks:
+            if hasattr(tracer, "example_id"):
+                tracer.example_id = example.id
     else:
-        previous_example_id = None
-        callbacks = None
+        previous_example_ids = None
     outputs = []
     for _ in range(n_repetitions):
         try:
@@ -376,8 +491,10 @@ def run_llm_or_chain(
         except Exception as e:
             logger.warning(f"Chain failed for example {example.id}. Error: {e}")
             outputs.append({"Error": str(e)})
-    if langchain_tracer is not None:
-        langchain_tracer.example_id = previous_example_id
+    if callbacks and previous_example_ids:
+        for example_id, tracer in zip(previous_example_ids, callbacks):
+            if hasattr(tracer, "example_id"):
+                tracer.example_id = example_id
     return outputs
 
 
@@ -388,48 +505,74 @@ def run_on_examples(
     num_repetitions: int = 1,
     project_name: Optional[str] = None,
     verbose: bool = False,
+    client: Optional[LangChainPlusClient] = None,
     tags: Optional[List[str]] = None,
+    run_evaluators: Optional[Sequence[RunEvaluator]] = None,
 ) -> Dict[str, Any]:
-    """Run the chain on examples and store traces to the specified project name.
+    """
+    Run the Chain or language model on examples and store
+    traces to the specified project name.
 
     Args:
-        examples: Examples to run model or chain over.
+        examples: Examples to run the model or chain over.
         llm_or_chain_factory: Language model or Chain constructor to run
             over the dataset. The Chain constructor is used to permit
             independent calls on each example without carrying over state.
-        concurrency_level: Number of async workers to run in parallel.
         num_repetitions: Number of times to run the model on each example.
             This is useful when testing success rates or generating confidence
             intervals.
-        project_name: Project name to use when tracing runs.
+        project_name: Name of the project to store the traces in.
+            Defaults to {dataset_name}-{chain class name}-{datetime}.
         verbose: Whether to print progress.
-        tags: Tags to add to the run traces.
+        client: Client to use to access the dataset. If None, a new client
+            will be created using the credentials in the environment.
+        tags: Tags to add to each run in the project.
+        run_evaluators: Evaluators to run on the results of the chain.
+
     Returns:
         A dictionary mapping example ids to the model outputs.
     """
     results: Dict[str, Any] = {}
-    tracer = LangChainTracer(project_name=project_name) if project_name else None
+    project_name = _get_project_name(project_name, llm_or_chain_factory, None)
+    client_ = client or LangChainPlusClient()
+    client_.create_project(project_name, mode="eval")
+    tracer = LangChainTracer(project_name=project_name)
+    evalution_handler = EvaluatorCallbackHandler(
+        evaluators=run_evaluators or [], client=client_
+    )
+    callbacks: List[BaseCallbackHandler] = [tracer, evalution_handler]
     for i, example in enumerate(examples):
         result = run_llm_or_chain(
             example,
             llm_or_chain_factory,
             num_repetitions,
-            langchain_tracer=tracer,
             tags=tags,
+            callbacks=callbacks,
         )
         if verbose:
             print(f"{i+1} processed", flush=True, end="\r")
         results[str(example.id)] = result
-    if tracer:
-        tracer.wait_for_futures()
+    tracer.wait_for_futures()
+    evalution_handler.wait_for_futures()
     return results
 
 
 def _get_project_name(
     project_name: Optional[str],
     llm_or_chain_factory: MODEL_OR_CHAIN_FACTORY,
-    dataset_name: str,
+    dataset_name: Optional[str],
 ) -> str:
+    """
+    Get the project name.
+
+    Args:
+        project_name: The project name if manually specified.
+        llm_or_chain_factory: The Chain or language model constructor.
+        dataset_name: The dataset name.
+
+    Returns:
+        The project name.
+    """
     if project_name is not None:
         return project_name
     current_time = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
@@ -437,7 +580,8 @@ def _get_project_name(
         model_name = llm_or_chain_factory.__class__.__name__
     else:
         model_name = llm_or_chain_factory().__class__.__name__
-    return f"{dataset_name}-{model_name}-{current_time}"
+    dataset_prefix = f"{dataset_name}-" if dataset_name else ""
+    return f"{dataset_prefix}{model_name}-{current_time}"
 
 
 async def arun_on_dataset(
@@ -450,12 +594,13 @@ async def arun_on_dataset(
     verbose: bool = False,
     client: Optional[LangChainPlusClient] = None,
     tags: Optional[List[str]] = None,
+    run_evaluators: Optional[Sequence[RunEvaluator]] = None,
 ) -> Dict[str, Any]:
     """
-    Run the chain on a dataset and store traces to the specified project name.
+    Asynchronously run the Chain or language model on a dataset
+    and store traces to the specified project name.
 
     Args:
-        client: Client to use to read the dataset.
         dataset_name: Name of the dataset to run the chain on.
         llm_or_chain_factory: Language model or Chain constructor to run
             over the dataset. The Chain constructor is used to permit
@@ -469,7 +614,8 @@ async def arun_on_dataset(
         verbose: Whether to print progress.
         client: Client to use to read the dataset. If not provided, a new
             client will be created using the credentials in the environment.
-        tags: Tags to add to each run in the sesssion.
+        tags: Tags to add to each run in the project.
+        run_evaluators: Evaluators to run on the results of the chain.
 
     Returns:
         A dictionary containing the run's project name and the resulting model outputs.
@@ -478,7 +624,6 @@ async def arun_on_dataset(
     project_name = _get_project_name(project_name, llm_or_chain_factory, dataset_name)
     dataset = client_.read_dataset(dataset_name=dataset_name)
     examples = client_.list_examples(dataset_id=str(dataset.id))
-
     results = await arun_on_examples(
         examples,
         llm_or_chain_factory,
@@ -486,7 +631,9 @@ async def arun_on_dataset(
         num_repetitions=num_repetitions,
         project_name=project_name,
         verbose=verbose,
+        client=client_,
         tags=tags,
+        run_evaluators=run_evaluators,
     )
     return {
         "project_name": project_name,
@@ -503,8 +650,11 @@ def run_on_dataset(
     verbose: bool = False,
     client: Optional[LangChainPlusClient] = None,
     tags: Optional[List[str]] = None,
+    run_evaluators: Optional[Sequence[RunEvaluator]] = None,
 ) -> Dict[str, Any]:
-    """Run the chain on a dataset and store traces to the specified project name.
+    """
+    Run the Chain or language model on a dataset and store traces
+    to the specified project name.
 
     Args:
         dataset_name: Name of the dataset to run the chain on.
@@ -520,7 +670,8 @@ def run_on_dataset(
         verbose: Whether to print progress.
         client: Client to use to access the dataset. If None, a new client
             will be created using the credentials in the environment.
-        tags: Tags to add to each run in the sesssion.
+        tags: Tags to add to each run in the project.
+        run_evaluators: Evaluators to run on the results of the chain.
 
     Returns:
         A dictionary containing the run's project name and the resulting model outputs.
@@ -536,6 +687,8 @@ def run_on_dataset(
         project_name=project_name,
         verbose=verbose,
         tags=tags,
+        run_evaluators=run_evaluators,
+        client=client_,
     )
     return {
         "project_name": project_name,

langchain/evaluation/run_evaluators/implementations.py

@@ -117,10 +117,12 @@ def get_qa_evaluator(
             choices_map={"CORRECT": 1, "INCORRECT": 0},
         ),
     )
+    tags = kwargs.pop("tags", [])
     return RunEvaluatorChain(
         eval_chain=eval_chain,
         input_mapper=input_mapper,
         output_parser=output_parser,
+        tags=tags + [evaluation_name],
         **kwargs,
     )
 
@@ -174,6 +176,7 @@ def get_criteria_evaluator(
             choices_map={"Y": 1, "N": 0}, evaluation_name=evaluation_name
         ),
     )
+    tags = kwargs.pop("tags", [])
     eval_chain = CriteriaEvalChain.from_llm(
         llm=llm, criteria=criteria_, prompt=prompt, **kwargs
     )
@@ -181,6 +184,7 @@ def get_criteria_evaluator(
         eval_chain=eval_chain,
         input_mapper=input_mapper,
         output_parser=parser,
+        tags=tags + [evaluation_name],
         **kwargs,
     )
 
@@ -303,9 +307,11 @@ def get_trajectory_evaluator(
         TrajectoryEvalOutputParser(evaluation_name=evaluation_name),
     )
     eval_chain = LLMChain(llm=llm, prompt=prompt, **kwargs)
+    tags = kwargs.pop("tags", [])
     return RunEvaluatorChain(
         eval_chain=eval_chain,
         input_mapper=input_mapper,
         output_parser=parser,
+        tags=tags + [evaluation_name],
         **kwargs,
     )

tests/unit_tests/client/test_runner_utils.py

@@ -169,8 +169,8 @@ async def test_arun_on_dataset(monkeypatch: pytest.MonkeyPatch) -> None:
         example: Example,
         llm_or_chain: Union[BaseLanguageModel, Chain],
         n_repetitions: int,
-        tracer: Any,
         tags: Optional[List[str]] = None,
+        callbacks: Optional[Any] = None,
     ) -> List[Dict[str, Any]]:
         return [
             {"result": f"Result for example {example.id}"} for _ in range(n_repetitions)