a i.dZddlmZmZmZmZmZddlmZddl m Z ddl m Z m Z mZddlmZmZGddeZGd d eZd eeefd efd ZGddeZdS)zIContains the `LLMEvaluator` class for building LLM-as-a-judge evaluators.)AnyCallableOptionalUnioncast) BaseModel) warn_beta)EvaluationResultEvaluationResults RunEvaluator)ExampleRuncfeZdZUdZeed<eeed<eed<dZeed<dZ e eed<dS) CategoricalScoreConfigz&Configuration for a categorical score.keychoices descriptionFinclude_explanationNexplanation_description) __name__ __module__ __qualname____doc__str__annotations__listrboolrrC:\Users\Dell Inspiron 16\Desktop\tws\AgrotaPowerBi\back-agrota-powerbi\mcp-client-agrota\venv\Lib\site-packages\langsmith/evaluation/llm_evaluator.pyrr sc00 HHH #Y %%%%-1Xc]11111rrcleZdZUdZeed<dZeed<dZeed<eed<dZ e ed <d Z e eed <d S) ContinuousScoreConfigz%Configuration for a continuous score.rrminmaxrFrNr) rrrrrrr#floatr%rrrrrrr r"r"sm// HHHCNNNCNNN %%%%-1Xc]11111rr" score_configreturnci}t|tr+d|jdd|jdd|d<nJt|tr&d|j|jd|jd |jd d |d<ntd |jrd|j d n|j d|d<|j |j d||jrddgndgdS)Nstringz%The score for the evaluation, one of z, .)typeenumrscorenumberz&The score for the evaluation, between z and z , inclusive.)r,minimummaximumrz9Invalid score type. Must be 'categorical' or 'continuous'zThe explanation for the score.)r,r explanationobject)titlerr, propertiesrequired) isinstancerrjoinr"r#r% ValueErrorrrrr)r'r5s r _create_score_json_schemar:!sL"$J, 677V (2yy-..222  7 L"7 8 8 V#'#'EEE&2&6EEE   7TUUU'  7?10!9 % % =!!#/ (4(H WWm $ $wi   rc~eZdZdZdddddeeeeeeffdeee fde e e e e gefd ed ef d Zedd d edeeeeeeffdeee fde e e e e geffdZdeeeeeeffdeee fde e e e e gefdefdZe dde de e deeeffdZe dde de e deeeffdZde de e defdZdedeeeffdZdS) LLMEvaluatorzA class for building LLM-as-a-judge evaluators. .. deprecated:: 0.5.0 LLMEvaluator is deprecated. Use openevals instead: https://github.com/langchain-ai/openevals Nzgpt-4oopenai) map_variables model_namemodel_providerprompt_templater'r>r?r@c ddlm}n"#t$r}td|d}~wwxYw|d||d|} ||||| dS)aInitialize the `LLMEvaluator`. Args: prompt_template (Union[str, List[Tuple[str, str]]): The prompt template to use for the evaluation. If a string is provided, it is assumed to be a human / user message. score_config (Union[CategoricalScoreConfig, ContinuousScoreConfig]): The configuration for the score, either categorical or continuous. map_variables (Optional[Callable[[Run, Example], dict]], optional): A function that maps the run and example to the variables in the prompt. If `None`, it is assumed that the prompt only requires 'input', 'output', and 'expected'. model_name (Optional[str], optional): The model to use for the evaluation. model_provider (Optional[str], optional): The model provider to use for the evaluation. r)init_chat_modelzmLLMEvaluator requires langchain to be installed. Please install langchain by running `pip install langchain`.N)modelr@r)langchain.chat_modelsrC ImportError _initialize) selfrAr'r>r?r@kwargsrCe chat_models r __init__zLLMEvaluator.__init__Ts8           O   %_ ^  ?E   , zRRRRRs (#()r>rDc`||}||||||S)aCreate an `LLMEvaluator` instance from a `BaseChatModel` instance. Args: model (BaseChatModel): The chat model instance to use for the evaluation. prompt_template (Union[str, List[Tuple[str, str]]): The prompt template to use for the evaluation. If a string is provided, it is assumed to be a system message. score_config (Union[CategoricalScoreConfig, ContinuousScoreConfig]): The configuration for the score, either categorical or continuous. map_variables (Optional[Callable[[Run, Example]], dict]], optional): A function that maps the run and example to the variables in the prompt. If `None`, it is assumed that the prompt only requires 'input', 'output', and 'expected'. Returns: LLMEvaluator: An instance of `LLMEvaluator`. )__new__rG)clsrDrAr'r>instances r from_modelzLLMEvaluator.from_models48;;s##_lM5QQQrrKc\ ddlm}ddlm}n"#t$r}t d|d}~wwxYwt ||rt |dstdt |tr| d|fg|_ n| ||_ t|j j hd z r|std ||_ ||_t|j|_||j}|j |z|_dS) aShared initialization code for `__init__` and `from_model`. 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