"""Configuration for run evaluators.""" from typing import Any, Callable, Dict, List, Optional, Sequence, Union from langchain_core.embeddings import Embeddings from langchain_core.language_models import BaseLanguageModel from langchain_core.prompts import BasePromptTemplate from langsmith import RunEvaluator from langsmith.evaluation.evaluator import EvaluationResult, EvaluationResults from langsmith.schemas import Example, Run from pydantic import BaseModel, ConfigDict, Field from langchain.evaluation.criteria.eval_chain import CRITERIA_TYPE from langchain.evaluation.embedding_distance.base import ( EmbeddingDistance as EmbeddingDistanceEnum, ) from langchain.evaluation.schema import EvaluatorType, StringEvaluator from langchain.evaluation.string_distance.base import ( StringDistance as StringDistanceEnum, ) RUN_EVALUATOR_LIKE = Callable[ [Run, Optional[Example]], Union[EvaluationResult, EvaluationResults, dict] ] BATCH_EVALUATOR_LIKE = Callable[ [Sequence[Run], Optional[Sequence[Example]]], Union[EvaluationResult, EvaluationResults, dict], ] class EvalConfig(BaseModel): """Configuration for a given run evaluator. Parameters ---------- evaluator_type : EvaluatorType The type of evaluator to use. Methods ------- get_kwargs() Get the keyword arguments for the evaluator configuration. """ evaluator_type: EvaluatorType def get_kwargs(self) -> Dict[str, Any]: """Get the keyword arguments for the load_evaluator call. Returns ------- Dict[str, Any] The keyword arguments for the load_evaluator call. """ kwargs = {} for field, val in self: if field == "evaluator_type": continue elif val is None: continue kwargs[field] = val return kwargs class SingleKeyEvalConfig(EvalConfig): """Configuration for a run evaluator that only requires a single key.""" reference_key: Optional[str] = None """The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.""" prediction_key: Optional[str] = None """The key from the traced run's outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.""" input_key: Optional[str] = None """The key from the traced run's inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.""" def get_kwargs(self) -> Dict[str, Any]: kwargs = super().get_kwargs() # Filer out the keys that are not needed for the evaluator. for key in ["reference_key", "prediction_key", "input_key"]: kwargs.pop(key, None) return kwargs CUSTOM_EVALUATOR_TYPE = Union[RUN_EVALUATOR_LIKE, RunEvaluator, StringEvaluator] SINGLE_EVAL_CONFIG_TYPE = Union[EvaluatorType, str, EvalConfig] class RunEvalConfig(BaseModel): """Configuration for a run evaluation. Parameters ---------- evaluators : List[Union[EvaluatorType, EvalConfig, RunEvaluator, Callable]] Configurations for which evaluators to apply to the dataset run. Each can be the string of an :class:`EvaluatorType `, such as EvaluatorType.QA, the evaluator type string ("qa"), or a configuration for a given evaluator (e.g., :class:`RunEvalConfig.QA `). custom_evaluators : Optional[List[Union[RunEvaluator, StringEvaluator]]] Custom evaluators to apply to the dataset run. reference_key : Optional[str] The key in the dataset run to use as the reference string. If not provided, it will be inferred automatically. prediction_key : Optional[str] The key from the traced run's outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically. input_key : Optional[str] The key from the traced run's inputs dictionary to use to represent the input. If not provided, it will be inferred automatically. eval_llm : Optional[BaseLanguageModel] The language model to pass to any evaluators that use a language model. """ # noqa: E501 evaluators: List[ Union[ SINGLE_EVAL_CONFIG_TYPE, CUSTOM_EVALUATOR_TYPE, ] ] = Field(default_factory=list) """Configurations for which evaluators to apply to the dataset run. Each can be the string of an :class:`EvaluatorType `, such as `EvaluatorType.QA`, the evaluator type string ("qa"), or a configuration for a given evaluator (e.g., :class:`RunEvalConfig.QA `).""" custom_evaluators: Optional[List[CUSTOM_EVALUATOR_TYPE]] = None """Custom evaluators to apply to the dataset run.""" batch_evaluators: Optional[List[BATCH_EVALUATOR_LIKE]] = None """Evaluators that run on an aggregate/batch level. These generate 1 or more metrics that are assigned to the full test run. As a result, they are not associated with individual traces. """ reference_key: Optional[str] = None """The key in the dataset run to use as the reference string. If not provided, we will attempt to infer automatically.""" prediction_key: Optional[str] = None """The key from the traced run's outputs dictionary to use to represent the prediction. If not provided, it will be inferred automatically.""" input_key: Optional[str] = None """The key from the traced run's inputs dictionary to use to represent the input. If not provided, it will be inferred automatically.""" eval_llm: Optional[BaseLanguageModel] = None """The language model to pass to any evaluators that require one.""" model_config = ConfigDict( arbitrary_types_allowed=True, ) class Criteria(SingleKeyEvalConfig): """Configuration for a reference-free criteria evaluator. Parameters ---------- criteria : Optional[CRITERIA_TYPE] The criteria to evaluate. llm : Optional[BaseLanguageModel] The language model to use for the evaluation chain. """ criteria: Optional[CRITERIA_TYPE] = None llm: Optional[BaseLanguageModel] = None evaluator_type: EvaluatorType = EvaluatorType.CRITERIA def __init__( self, criteria: Optional[CRITERIA_TYPE] = None, **kwargs: Any ) -> None: super().__init__(criteria=criteria, **kwargs) # type: ignore[call-arg] class LabeledCriteria(SingleKeyEvalConfig): """Configuration for a labeled (with references) criteria evaluator. Parameters ---------- criteria : Optional[CRITERIA_TYPE] The criteria to evaluate. llm : Optional[BaseLanguageModel] The language model to use for the evaluation chain. """ criteria: Optional[CRITERIA_TYPE] = None llm: Optional[BaseLanguageModel] = None evaluator_type: EvaluatorType = EvaluatorType.LABELED_CRITERIA def __init__( self, criteria: Optional[CRITERIA_TYPE] = None, **kwargs: Any ) -> None: super().__init__(criteria=criteria, **kwargs) # type: ignore[call-arg] class EmbeddingDistance(SingleKeyEvalConfig): """Configuration for an embedding distance evaluator. Parameters ---------- embeddings : Optional[Embeddings] The embeddings to use for computing the distance. distance_metric : Optional[EmbeddingDistanceEnum] The distance metric to use for computing the distance. """ evaluator_type: EvaluatorType = EvaluatorType.EMBEDDING_DISTANCE embeddings: Optional[Embeddings] = None distance_metric: Optional[EmbeddingDistanceEnum] = None model_config = ConfigDict( arbitrary_types_allowed=True, ) class StringDistance(SingleKeyEvalConfig): """Configuration for a string distance evaluator. Parameters ---------- distance : Optional[StringDistanceEnum] The string distance metric to use. """ evaluator_type: EvaluatorType = EvaluatorType.STRING_DISTANCE distance: Optional[StringDistanceEnum] = None """The string distance metric to use. damerau_levenshtein: The Damerau-Levenshtein distance. levenshtein: The Levenshtein distance. jaro: The Jaro distance. jaro_winkler: The Jaro-Winkler distance. """ normalize_score: bool = True """Whether to normalize the distance to between 0 and 1. Applies only to the Levenshtein and Damerau-Levenshtein distances.""" class QA(SingleKeyEvalConfig): """Configuration for a QA evaluator. Parameters ---------- prompt : Optional[BasePromptTemplate] The prompt template to use for generating the question. llm : Optional[BaseLanguageModel] The language model to use for the evaluation chain. """ evaluator_type: EvaluatorType = EvaluatorType.QA llm: Optional[BaseLanguageModel] = None prompt: Optional[BasePromptTemplate] = None class ContextQA(SingleKeyEvalConfig): """Configuration for a context-based QA evaluator. Parameters ---------- prompt : Optional[BasePromptTemplate] The prompt template to use for generating the question. llm : Optional[BaseLanguageModel] The language model to use for the evaluation chain. """ evaluator_type: EvaluatorType = EvaluatorType.CONTEXT_QA llm: Optional[BaseLanguageModel] = None prompt: Optional[BasePromptTemplate] = None class CoTQA(SingleKeyEvalConfig): """Configuration for a context-based QA evaluator. Parameters ---------- prompt : Optional[BasePromptTemplate] The prompt template to use for generating the question. llm : Optional[BaseLanguageModel] The language model to use for the evaluation chain. """ evaluator_type: EvaluatorType = EvaluatorType.CONTEXT_QA llm: Optional[BaseLanguageModel] = None prompt: Optional[BasePromptTemplate] = None class JsonValidity(SingleKeyEvalConfig): """Configuration for a json validity evaluator. Parameters ---------- """ evaluator_type: EvaluatorType = EvaluatorType.JSON_VALIDITY class JsonEqualityEvaluator(EvalConfig): """Configuration for a json equality evaluator. Parameters ---------- """ evaluator_type: EvaluatorType = EvaluatorType.JSON_EQUALITY class ExactMatch(SingleKeyEvalConfig): """Configuration for an exact match string evaluator. Parameters ---------- ignore_case : bool Whether to ignore case when comparing strings. ignore_punctuation : bool Whether to ignore punctuation when comparing strings. ignore_numbers : bool Whether to ignore numbers when comparing strings. """ evaluator_type: EvaluatorType = EvaluatorType.EXACT_MATCH ignore_case: bool = False ignore_punctuation: bool = False ignore_numbers: bool = False class RegexMatch(SingleKeyEvalConfig): """Configuration for a regex match string evaluator. Parameters ---------- flags : int The flags to pass to the regex. Example: re.IGNORECASE. """ evaluator_type: EvaluatorType = EvaluatorType.REGEX_MATCH flags: int = 0 class ScoreString(SingleKeyEvalConfig): """Configuration for a score string evaluator. This is like the criteria evaluator but it is configured by default to return a score on the scale from 1-10. It is recommended to normalize these scores by setting `normalize_by` to 10. Parameters ---------- criteria : Optional[CRITERIA_TYPE] The criteria to evaluate. llm : Optional[BaseLanguageModel] The language model to use for the evaluation chain. normalize_by: Optional[int] = None If you want to normalize the score, the denominator to use. If not provided, the score will be between 1 and 10 (by default). prompt : Optional[BasePromptTemplate] """ evaluator_type: EvaluatorType = EvaluatorType.SCORE_STRING criteria: Optional[CRITERIA_TYPE] = None llm: Optional[BaseLanguageModel] = None normalize_by: Optional[float] = None prompt: Optional[BasePromptTemplate] = None def __init__( self, criteria: Optional[CRITERIA_TYPE] = None, normalize_by: Optional[float] = None, **kwargs: Any, ) -> None: super().__init__(criteria=criteria, normalize_by=normalize_by, **kwargs) # type: ignore[call-arg] class LabeledScoreString(ScoreString): evaluator_type: EvaluatorType = EvaluatorType.LABELED_SCORE_STRING