Source code for langchain.evaluation.loading

"""Loading datasets and evaluators."""
from typing import Any, Dict, List, Optional, Sequence, Type, Union

from langchain_core.language_models import BaseLanguageModel

from langchain.chains.base import Chain
from langchain.evaluation.agents.trajectory_eval_chain import TrajectoryEvalChain
from langchain.evaluation.comparison import PairwiseStringEvalChain
from langchain.evaluation.comparison.eval_chain import LabeledPairwiseStringEvalChain
from langchain.evaluation.criteria.eval_chain import (
    CriteriaEvalChain,
    LabeledCriteriaEvalChain,
)
from langchain.evaluation.embedding_distance.base import (
    EmbeddingDistanceEvalChain,
    PairwiseEmbeddingDistanceEvalChain,
)
from langchain.evaluation.exact_match.base import ExactMatchStringEvaluator
from langchain.evaluation.parsing.base import (
    JsonEqualityEvaluator,
    JsonValidityEvaluator,
)
from langchain.evaluation.parsing.json_distance import JsonEditDistanceEvaluator
from langchain.evaluation.parsing.json_schema import JsonSchemaEvaluator
from langchain.evaluation.qa import ContextQAEvalChain, CotQAEvalChain, QAEvalChain
from langchain.evaluation.regex_match.base import RegexMatchStringEvaluator
from langchain.evaluation.schema import EvaluatorType, LLMEvalChain, StringEvaluator
from langchain.evaluation.scoring.eval_chain import (
    LabeledScoreStringEvalChain,
    ScoreStringEvalChain,
)
from langchain.evaluation.string_distance.base import (
    PairwiseStringDistanceEvalChain,
    StringDistanceEvalChain,
)


[docs]def load_dataset(uri: str) -> List[Dict]: """Load a dataset from the `LangChainDatasets on HuggingFace <https://huggingface.co/LangChainDatasets>`_. Args: uri: The uri of the dataset to load. Returns: A list of dictionaries, each representing a row in the dataset. **Prerequisites** .. code-block:: shell pip install datasets Examples -------- .. code-block:: python from langchain.evaluation import load_dataset ds = load_dataset("llm-math") """ try: from datasets import load_dataset except ImportError: raise ImportError( "load_dataset requires the `datasets` package." " Please install with `pip install datasets`" ) dataset = load_dataset(f"LangChainDatasets/{uri}") return [d for d in dataset["train"]]
_EVALUATOR_MAP: Dict[ EvaluatorType, Union[Type[LLMEvalChain], Type[Chain], Type[StringEvaluator]] ] = { EvaluatorType.QA: QAEvalChain, EvaluatorType.COT_QA: CotQAEvalChain, EvaluatorType.CONTEXT_QA: ContextQAEvalChain, EvaluatorType.PAIRWISE_STRING: PairwiseStringEvalChain, EvaluatorType.SCORE_STRING: ScoreStringEvalChain, EvaluatorType.LABELED_PAIRWISE_STRING: LabeledPairwiseStringEvalChain, EvaluatorType.LABELED_SCORE_STRING: LabeledScoreStringEvalChain, EvaluatorType.AGENT_TRAJECTORY: TrajectoryEvalChain, EvaluatorType.CRITERIA: CriteriaEvalChain, EvaluatorType.LABELED_CRITERIA: LabeledCriteriaEvalChain, EvaluatorType.STRING_DISTANCE: StringDistanceEvalChain, EvaluatorType.PAIRWISE_STRING_DISTANCE: PairwiseStringDistanceEvalChain, EvaluatorType.EMBEDDING_DISTANCE: EmbeddingDistanceEvalChain, EvaluatorType.PAIRWISE_EMBEDDING_DISTANCE: PairwiseEmbeddingDistanceEvalChain, EvaluatorType.JSON_VALIDITY: JsonValidityEvaluator, EvaluatorType.JSON_EQUALITY: JsonEqualityEvaluator, EvaluatorType.JSON_EDIT_DISTANCE: JsonEditDistanceEvaluator, EvaluatorType.JSON_SCHEMA_VALIDATION: JsonSchemaEvaluator, EvaluatorType.REGEX_MATCH: RegexMatchStringEvaluator, EvaluatorType.EXACT_MATCH: ExactMatchStringEvaluator, }
[docs]def load_evaluator( evaluator: EvaluatorType, *, llm: Optional[BaseLanguageModel] = None, **kwargs: Any, ) -> Union[Chain, StringEvaluator]: """Load the requested evaluation chain specified by a string. Parameters ---------- evaluator : EvaluatorType The type of evaluator to load. llm : BaseLanguageModel, optional The language model to use for evaluation, by default None **kwargs : Any Additional keyword arguments to pass to the evaluator. Returns ------- Chain The loaded evaluation chain. Examples -------- >>> from langchain.evaluation import load_evaluator, EvaluatorType >>> evaluator = load_evaluator(EvaluatorType.QA) """ if evaluator not in _EVALUATOR_MAP: raise ValueError( f"Unknown evaluator type: {evaluator}" f"\nValid types are: {list(_EVALUATOR_MAP.keys())}" ) evaluator_cls = _EVALUATOR_MAP[evaluator] if issubclass(evaluator_cls, LLMEvalChain): try: try: from langchain_openai import ChatOpenAI except ImportError: try: from langchain_community.chat_models.openai import ChatOpenAI except ImportError: raise ImportError( "Could not import langchain_openai or fallback onto " "langchain_community. Please install langchain_openai " "or specify a language model explicitly. " "It's recommended to install langchain_openai AND " "specify a language model explicitly." ) llm = llm or ChatOpenAI( # type: ignore[call-arg] model="gpt-4", model_kwargs={"seed": 42}, temperature=0 ) except Exception as e: raise ValueError( f"Evaluation with the {evaluator_cls} requires a " "language model to function." " Failed to create the default 'gpt-4' model." " Please manually provide an evaluation LLM" " or check your openai credentials." ) from e return evaluator_cls.from_llm(llm=llm, **kwargs) else: return evaluator_cls(**kwargs)
[docs]def load_evaluators( evaluators: Sequence[EvaluatorType], *, llm: Optional[BaseLanguageModel] = None, config: Optional[dict] = None, **kwargs: Any, ) -> List[Union[Chain, StringEvaluator]]: """Load evaluators specified by a list of evaluator types. Parameters ---------- evaluators : Sequence[EvaluatorType] The list of evaluator types to load. llm : BaseLanguageModel, optional The language model to use for evaluation, if none is provided, a default ChatOpenAI gpt-4 model will be used. config : dict, optional A dictionary mapping evaluator types to additional keyword arguments, by default None **kwargs : Any Additional keyword arguments to pass to all evaluators. Returns ------- List[Chain] The loaded evaluators. Examples -------- >>> from langchain.evaluation import load_evaluators, EvaluatorType >>> evaluators = [EvaluatorType.QA, EvaluatorType.CRITERIA] >>> loaded_evaluators = load_evaluators(evaluators, criteria="helpfulness") """ loaded = [] for evaluator in evaluators: _kwargs = config.get(evaluator, {}) if config else {} loaded.append(load_evaluator(evaluator, llm=llm, **{**kwargs, **_kwargs})) return loaded