"""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 langchain_core.pydantic_v1 import BaseModel, Field
from langsmith import RunEvaluator
from langsmith.evaluation.evaluator import EvaluationResult, EvaluationResults
from langsmith.schemas import Example, Run
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],
]
[docs]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
[docs] 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
[docs]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."""
[docs] 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]
[docs]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 <langchain.evaluation.schema.EvaluatorType>`, such
as EvaluatorType.QA, the evaluator type string ("qa"), or a configuration for a
given evaluator (e.g., :class:`RunEvalConfig.QA <langchain.smith.evaluation.config.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 <langchain.evaluation.schema.EvaluatorType>`, such
as `EvaluatorType.QA`, the evaluator type string ("qa"), or a configuration for a
given evaluator
(e.g.,
:class:`RunEvalConfig.QA <langchain.smith.evaluation.config.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."""
class Config:
arbitrary_types_allowed = True
[docs] 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]
[docs] 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]
[docs] 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
class Config:
arbitrary_types_allowed = True
[docs] 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."""
[docs] 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
[docs] 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
[docs] 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
[docs] class JsonValidity(SingleKeyEvalConfig):
"""Configuration for a json validity evaluator.
Parameters
----------
"""
evaluator_type: EvaluatorType = EvaluatorType.JSON_VALIDITY
[docs] class JsonEqualityEvaluator(EvalConfig):
"""Configuration for a json equality evaluator.
Parameters
----------
"""
evaluator_type: EvaluatorType = EvaluatorType.JSON_EQUALITY
[docs] 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
[docs] 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
[docs] 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]
[docs] class LabeledScoreString(ScoreString):
evaluator_type: EvaluatorType = EvaluatorType.LABELED_SCORE_STRING
