o +i*@sddlmZddlmZmZmZmZmZmZddl m Z ddl m Z ddl mZmZer8ddlmZddl mZGdd d eZGd d d Zd S) ) annotations) TYPE_CHECKINGAnyCallableOptional TypedDictUnion)DynamicRunEvaluator) traceable)ExampleRunStringEvaluator) RunEvaluatorc@s.eZdZUdZded< ded< ded<dS) SingleEvaluatorInputz!The input to a `StringEvaluator`.str predictionz Optional[Any] referencez Optional[str]inputN)__name__ __module__ __qualname____doc____annotations__rri/var/www/html/psymed-ai/venv/lib/python3.10/site-packages/langsmith/evaluation/integrations/_langchain.pyrs rc@s,eZdZdZddddd d ZdddZdS)LangChainStringEvaluatoratA 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: !!! example "Creating a simple `LangChainStringEvaluator`" ```python evaluator = LangChainStringEvaluator("exact_match") ``` !!! example "Converting a `LangChainStringEvaluator` to a `RunEvaluator`" ```python 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 ``` !!! example "Customizing the LLM model used by the evaluator" ```python 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 ``` !!! example "Using the `evaluate` API with different evaluators" ```python 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 ``` N)config prepare_data evaluatorUnion[StringEvaluator, str]rOptional[dict]rBOptional[Callable[[Run, Optional[Example]], SingleEvaluatorInput]]cCshddlm}t||r||_n t|tr&ddlm}||fi|p!i|_n tdt|||_ dS)zInitialize a `LangChainStringEvaluator`. Args: evaluator (StringEvaluator): The underlying `StringEvaluator`. rr )load_evaluatorzUnsupported evaluator type: N) langchain.evaluation.schemar isinstancerrlangchain.evaluationr#NotImplementedErrortype _prepare_data)selfrrrrr#rrr__init__s     z!LangChainStringEvaluator.__init__returnrcsjjrdnd}jjrdnd}d||dt ddfd d tjjdddfdd }tjjdddfdd }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) Nrunr exampleOptional[Example]r,rcs|jrt|jdkrtdjdjjr0|r0|jr0t|jdkr0tdjdjjrK|rK|jrKt|jdkrKtdjdttt |j jjrf|rf|jrftt |j ndjjr{|r{|jr{tt |j dSddS)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 ValueErrorrrequires_referencerequires_inputinputsrnextitervalues)r.r/)customization_error_strr*rrprepare_evaluator_inputss`   zKLangChainStringEvaluator.as_run_evaluator..prepare_evaluator_inputs)namedictcsBjdur ||n||}jjdi|}djji|SNkeyr)r)revaluate_stringsevaluation_namer.r/ eval_inputsresultsr<r*rrevaluates  z;LangChainStringEvaluator.as_run_evaluator..evaluatecsJjdur ||n||}jjdi|IdH}djji|Sr?)r)raevaluate_stringsrBrCrFrr aevaluates  z.aevaluate)N)r.r r/r0r,r)r.r r/r0r,r>)rr6r5r rBr )r* input_str reference_strrGrIr)r;r<r*ras_run_evaluators*  5  z)LangChainStringEvaluator.as_run_evaluator)rr rr!rr")r,r)rrrrr+rLrrrrrsrN) __future__rtypingrrrrrrlangsmith.evaluation.evaluatorr langsmith.run_helpersr langsmith.schemasr r r$rrrrrrrrs