ih)ddlmZddlmZmZmZmZmZmZddl m Z ddl m Z ddl mZmZer ddlmZddl mZGdd eZGd d Zy ) ) annotations) TYPE_CHECKINGAnyCallableOptional TypedDictUnion)DynamicRunEvaluator) traceable)ExampleRunStringEvaluator) RunEvaluatorc4eZdZUdZded< ded< ded<y) SingleEvaluatorInputz!The input to a `StringEvaluator`.str predictionz Optional[Any] referencez Optional[str]inputN)__name__ __module__ __qualname____doc____annotations__j/var/www/html/dev/engine/venv/lib/python3.12/site-packages/langsmith/evaluation/integrations/_langchain.pyrrs+O  rrc6eZdZdZddd ddZ ddZy)LangChainStringEvaluatoraA class for wrapping a LangChain StringEvaluator. Requires the `langchain` package to be installed. Attributes: evaluator (StringEvaluator): The underlying StringEvaluator OR the name of the evaluator to load. Methods: as_run_evaluator() -> RunEvaluator: Convert the LangChainStringEvaluator to a RunEvaluator. Examples: Creating a simple LangChainStringEvaluator: >>> evaluator = LangChainStringEvaluator("exact_match") Converting a LangChainStringEvaluator to a RunEvaluator: >>> from langsmith.evaluation import LangChainStringEvaluator >>> from langchain_openai import ChatOpenAI >>> evaluator = LangChainStringEvaluator( ... "criteria", ... config={ ... "criteria": { ... "usefulness": "The prediction is useful if" ... " it is correct and/or asks a useful followup question." ... }, ... "llm": ChatOpenAI(model="gpt-4o"), ... }, ... ) >>> run_evaluator = evaluator.as_run_evaluator() >>> run_evaluator # doctest: +ELLIPSIS Customizing the LLM model used by the evaluator: >>> from langsmith.evaluation import LangChainStringEvaluator >>> from langchain_anthropic import ChatAnthropic >>> evaluator = LangChainStringEvaluator( ... "criteria", ... config={ ... "criteria": { ... "usefulness": "The prediction is useful if" ... " it is correct and/or asks a useful followup question." ... }, ... "llm": ChatAnthropic(model="claude-3-opus-20240229"), ... }, ... ) >>> run_evaluator = evaluator.as_run_evaluator() >>> run_evaluator # doctest: +ELLIPSIS Using the `evaluate` API with different evaluators: >>> def prepare_data(run: Run, example: Example): ... # Convert the evaluation data into the format expected by the evaluator ... # Only required for datasets with multiple inputs/output keys ... return { ... "prediction": run.outputs["prediction"], ... "reference": example.outputs["answer"], ... "input": str(example.inputs), ... } >>> import re >>> from langchain_anthropic import ChatAnthropic >>> import langsmith >>> from langsmith.evaluation import LangChainStringEvaluator, evaluate >>> criteria_evaluator = LangChainStringEvaluator( ... "criteria", ... config={ ... "criteria": { ... "usefulness": "The prediction is useful if it is correct" ... " and/or asks a useful followup question." ... }, ... "llm": ChatAnthropic(model="claude-3-opus-20240229"), ... }, ... prepare_data=prepare_data, ... ) >>> embedding_evaluator = LangChainStringEvaluator("embedding_distance") >>> exact_match_evaluator = LangChainStringEvaluator("exact_match") >>> regex_match_evaluator = LangChainStringEvaluator( ... "regex_match", config={"flags": re.IGNORECASE}, prepare_data=prepare_data ... ) >>> scoring_evaluator = LangChainStringEvaluator( ... "labeled_score_string", ... config={ ... "criteria": { ... "accuracy": "Score 1: Completely inaccurate\nScore 5: Somewhat accurate\nScore 10: Completely accurate" ... }, ... "normalize_by": 10, ... "llm": ChatAnthropic(model="claude-3-opus-20240229"), ... }, ... prepare_data=prepare_data, ... ) >>> string_distance_evaluator = LangChainStringEvaluator( ... "string_distance", ... config={"distance_metric": "levenshtein"}, ... prepare_data=prepare_data, ... ) >>> from langsmith import Client >>> client = Client() >>> results = evaluate( ... lambda inputs: {"prediction": "foo"}, ... data=client.list_examples(dataset_name="Evaluate Examples", limit=1), ... evaluators=[ ... embedding_evaluator, ... criteria_evaluator, ... exact_match_evaluator, ... regex_match_evaluator, ... scoring_evaluator, ... string_distance_evaluator, ... ], ... ) # doctest: +ELLIPSIS View the evaluation results for experiment:... N)config prepare_datacddlm}t||r||_||_ yt|tr ddlm}||fi|xsi|_||_ ytdt|)a(Initialize a LangChainStringEvaluator. See: https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.schema.StringEvaluator.html#langchain-evaluation-schema-stringevaluator Args: evaluator (StringEvaluator): The underlying StringEvaluator. rr)load_evaluatorzUnsupported evaluator type: N) langchain.evaluation.schemar isinstance evaluatorrlangchain.evaluationr$NotImplementedErrortype _prepare_data)selfr'r!r"rr$s r__init__z!LangChainStringEvaluator.__init__sk @ i 1&DN* 3 ' ;+IH&,BHDN*&(DT)_DU&VW Wrcrjjrdnd}jjrdnd}d||dt d d fd tjjd d fd }tjjd d fd }t ||S) zConvert the LangChainStringEvaluator to a RunEvaluator. This is the object used in the LangSmith `evaluate` API. Returns: RunEvaluator: The converted RunEvaluator. z) "input": example.inputs['input'],z0 "reference": example.outputs['expected']z] def prepare_data(run, example): return { "prediction": run.outputs['my_output'],zL } evaluator = LangChainStringEvaluator(..., prepare_data=prepare_data) c |jr3t|jdkDrtdjdjjrA|r?|jr3t|jdkDrtdjdjj rA|r?|j r3t|j dkDrtdjdttt|jjjjr:|r8|jr,tt|jjndjj r?|r=|j r1tt|j jSdS)Nz Evaluator z{ only supports a single prediction key. Please ensure that the run has a single output. Or initialize with a prepare_data: z nly supports a single reference key. Please ensure that the example has a single output. Or create a custom evaluator yourself: zy only supports a single input key. Please ensure that the example has a single input. Or initialize with a prepare_data: )rrr) outputslen ValueErrorr'requires_referencerequires_inputinputsrnextitervalues)runexamplecustomization_error_strr,s rprepare_evaluator_inputszKLangChainStringEvaluator.as_run_evaluator..prepare_evaluator_inputss{{s3;;/!3   01<//111OO(1,   01@//1--NN'!+   01<//1(S[[%7%7%9 :;99##OO goo44678 55'gnngnn33567  r)namecj ||nj||}jjdi|}djji|SNkeyr)r+r'evaluate_stringsevaluation_namer;r< eval_inputsresultsr>r,s revaluatez;LangChainStringEvaluator.as_run_evaluator..evaluatesg%%-)g6''W5  6dnn55D DG4>>99EWE ErcKj ||nj||}jjdi|d{}djji|S7wrA)r+r'aevaluate_stringsrDrEs r aevaluatez.aevaluatest%%-)g6''W5  =DNN<<K{KKG4>>99EWE ELsAA+ A) A+)N)r;r r<Optional[Example]returnr)r;r r<rLrMdict)r'r6r5r rDr )r, input_str reference_strrHrKr=r>s` @@ras_run_evaluatorz)LangChainStringEvaluator.as_run_evaluators~~,, ; ~~00 B  '01>ykJ# 373 3 03  !3  3 j 66 7 F 8 F 66 7 F 8 F#8Y77r)r'zUnion[StringEvaluator, str]r!zOptional[dict]r"zBOptional[Callable[[Run, Optional[Example]], SingleEvaluatorInput]])rMr)rrrrr-rQrrrr r sCqn"& *.* *  *:f8 f8rr N) __future__rtypingrrrrrr langsmith.evaluation.evaluatorr langsmith.run_helpersr langsmith.schemasr r r%rrrr rrrrWs8"KK>+*;;9w8w8r