a `g)@sddlmZddlmZmZmZmZmZmZddl m Z ddl m Z ddl mZmZerpddlmZddl 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__rrz/var/www/html/cobodadashboardai.evdpl.com/venv/lib/python3.9/site-packages/langsmith/evaluation/integrations/_langchain.pyrs rc@s8eZdZdZddddddddd Zd d d d ZdS)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_datazUnion[StringEvaluator, str]zOptional[dict]zBOptional[Callable[[Run, Optional[Example]], SingleEvaluatorInput]]) evaluatorrrcCshddlm}t||r||_n@t|trLddlm}||fi|pBi|_ntdt|||_ dS)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 isinstancerrZlangchain.evaluationr NotImplementedErrortype _prepare_data)selfrrrrr rrr__init__s    z!LangChainStringEvaluator.__init__r)returncsjjr dnd}jjrdnd}d||dtdddd d fd d tjjd ddddd fdd }tjjd ddddd fdd }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) Nr zOptional[Example]r)runexampler(cs|jr*t|jdkr*tdjdjjr`|r`|jr`t|jdkr`tdjdjjr|r|jrt|jdkrtdjdttt |j jjr|r|jrtt |j ndjjr|r|jrtt |j nddS)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_inputssb     zKLangChainStringEvaluator.as_run_evaluator..prepare_evaluator_inputs)namedictcsBjdur||n ||}jjfi|}djji|SNkey)r%rZevaluate_stringsevaluation_namer*r+Z eval_inputsresultsr7r&rrevaluates   z;LangChainStringEvaluator.as_run_evaluator..evaluatecsHjdur||n ||}jjfi|IdH}djji|Sr:)r%rZaevaluate_stringsr<r=r?rr aevaluates   z.aevaluate)N)N)N)rr1r0r r<r )r&Z input_strZ reference_strr@rAr)r6r7r&ras_run_evaluators* 5   z)LangChainStringEvaluator.as_run_evaluator)rrrrr'rBrrrrrs wrN) __future__rtypingrrrrrrZlangsmith.evaluation.evaluatorr Zlangsmith.run_helpersr Zlangsmith.schemasr r r!rrrrrrrrs