run_on_dataset

Examples:

from langsmith import Client
from langchain_openai import ChatOpenAI
from langchain_classic.chains import LLMChain
from langchain_classic.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():
    model = ChatOpenAI(temperature=0)
    chain = LLMChain.from_string(
        model,
        "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()
run_on_dataset(
    client,
    dataset_name="<my_dataset_name>",
    llm_or_chain_factory=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_classic.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()],
)

run_on_dataset(
    client,
    dataset_name="<my_dataset_name>",
    llm_or_chain_factory=construct_chain,
    evaluation=evaluation_config,
)