o +i[.@sdZddlmZmZmZmZmZddlmZddl m Z ddl m Z m Z mZddlmZmZGdddeZGd d d eZd eeefd efd dZGdddeZdS)zIContains the `LLMEvaluator` class for building LLM-as-a-judge evaluators.)AnyCallableOptionalUnioncast) BaseModel) warn_beta)EvaluationResultEvaluationResults RunEvaluator)ExampleRunc@sJeZdZUdZeed<eeed<eed<dZeed<dZ e eed<dS) CategoricalScoreConfigz&Configuration for a categorical score.keychoices descriptionFinclude_explanationNexplanation_description) __name__ __module__ __qualname____doc__str__annotations__listrboolrrrr_/var/www/html/psymed-ai/venv/lib/python3.10/site-packages/langsmith/evaluation/llm_evaluator.pyr s   rc@sVeZdZUdZeed<dZeed<dZeed<eed<dZ e ed <d Z e eed <d S) ContinuousScoreConfigz%Configuration for a continuous score.rrminmaxrFrNr) rrrrrrrfloatr!rrrrrrrrrs    r score_configreturncCsi}t|trd|jdd|jdd|d<nt|tr3d|j|jd|jd |jd d |d<ntd |jrId|j durBd n|j d|d<|j |j d||jrXddgdSdgdS)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) isinstancerrjoinrrr! ValueErrorrrrr)r#r0rrr_create_score_json_schema!sB        r5c@seZdZdZdddddeeeeeeffdeee fde e e e e gefd ed ef d d Zedd dedeeeeeeffdeee fde e e e e geffddZdeeeeeeffdeee fde e e e e gefdefddZe d de de e deeeffddZe d de de e deeeffddZde de e defddZdedeeeffddZdS)! LLMEvaluatorz/A class for building LLM-as-a-judge evaluators.Nzgpt-4oopenai) map_variables model_namemodel_providerprompt_templater#r8r9r:c Ks\zddlm}Wnty}ztd|d}~ww|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_modelsr< ImportError _initialize) selfr;r#r8r9r:kwargsr<e chat_modelrrr__init__Os zLLMEvaluator.__init__)r8r=cCs||}||||||S)aCreate an `LLMEvaluator` instance from a `BaseChatModel` instance. 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Please install langchain-core by running `pip install langchain-core`.Nwith_structured_outputzRchat_model must be an instance of BaseLanguageModel and support structured output.human>inputoutputexpectedzrmap_inputs must be provided if the prompt template contains variables other than 'input', 'output', and 'expected')*langchain_core.language_models.chat_modelsrJlangchain_core.promptsrKr?r2hasattrr4r from_messagespromptsetinput_variablesr8r#r5 score_schemarLrunnable)rAr;r#r8rDrJrKrCrrrr@s>     zLLMEvaluator._initializerunexampler$cCs(|||}tt|j|}||S)zEvaluate a run.)_prepare_variablesrdictrYinvoke _parse_outputrArZr[ variablesrOrrr evaluate_runs  zLLMEvaluator.evaluate_runcs0|||}tt|j|IdH}||S)zAsynchronously evaluate a run.N)r\rr]rYainvoker_r`rrr aevaluate_runs  zLLMEvaluator.aevaluate_runcCs$|jr |||Si}d|jjvr2t|jdkrtdt|jdkr'tdt|jd|d<d|jjvr`|js?tdt|jdkrJtdt|jdkrUtdt|jd|d<d |jjvr|rk|jsotd t|jdkrztd t|jdkrtd t|jd|d <|S) z'Prepare variables for model invocation.rNrzHNo input keys are present in run.inputs but the prompt requires 'input'.r zWMultiple input keys are present in run.inputs. 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