langchain.smith.evaluation.runner_utils.arun_on_dataset

async langchain.smith.evaluation.runner_utils.arun_on_dataset(client: Optional[Client], dataset_name: str, llm_or_chain_factory: Union[Callable[[], Union[Chain, Runnable]], BaseLanguageModel, Callable[[dict], Any], Runnable, Chain], *, evaluation: Optional[RunEvalConfig] = None, concurrency_level: int = 5, project_name: Optional[str] = None, project_metadata: Optional[Dict[str, Any]] = None, verbose: bool = False, tags: Optional[List[str]] = None, **kwargs: Any) → Dict[str, Any]

Run the Chain or language model on a dataset and store traces to the specified project name.

Parameters

• 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 independent calls on each example without carrying over state.

• evaluation – Configuration for evaluators to run on the results of the chain

• concurrency_level – The number of async tasks to run concurrently.

• project_name – Name of the project to store the traces in. Defaults to {dataset_name}-{chain class name}-{datetime}.

• project_metadata – Optional metadata to add to the project. Useful for storing information the test variant. (prompt version, model version, etc.)

• client – LangSmith client to use to access the dataset and to log feedback and run traces.

• verbose – Whether to print progress.

• tags – Tags to add to each run in the project.

Returns

A dictionary containing the run’s project name and the resulting model outputs.

For the (usually faster) async version of this function, see arun_on_dataset().

Examples

from langsmith import Client
from langchain_community.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.smith import smith_eval.RunEvalConfig, run_on_dataset

# Chains may have memory. Passing in a constructor function lets the
# evaluation framework avoid cross-contamination between runs.
def construct_chain():
    llm = ChatOpenAI(temperature=0)
    chain = LLMChain.from_string(
        llm,
        "What's the answer to {your_input_key}"
    )
    return chain

# Load off-the-shelf evaluators via config or the EvaluatorType (string or enum)
evaluation_config = smith_eval.RunEvalConfig(
    evaluators=[
        "qa",  # "Correctness" against a reference answer
        "embedding_distance",
        smith_eval.RunEvalConfig.Criteria("helpfulness"),
        smith_eval.RunEvalConfig.Criteria({
            "fifth-grader-score": "Do you have to be smarter than a fifth grader to answer this question?"
        }),
    ]
)

client = Client()
await arun_on_dataset(
    client,
    "<my_dataset_name>",
    construct_chain,
    evaluation=evaluation_config,
)

You can also create custom evaluators by subclassing the StringEvaluator or LangSmith’s RunEvaluator classes.

from typing import Optional
from langchain.evaluation import StringEvaluator

class MyStringEvaluator(StringEvaluator):

    @property
    def requires_input(self) -> bool:
        return False

    @property
    def requires_reference(self) -> bool:
        return True

    @property
    def evaluation_name(self) -> str:
        return "exact_match"

    def _evaluate_strings(self, prediction, reference=None, input=None, **kwargs) -> dict:
        return {"score": prediction == reference}

evaluation_config = smith_eval.RunEvalConfig(
    custom_evaluators = [MyStringEvaluator()],
)

await arun_on_dataset(
    client,
    "<my_dataset_name>",
    construct_chain,
    evaluation=evaluation_config,
)

Examples using arun_on_dataset

• LangSmith Walkthrough