Ng)ddlmZddlmZmZmZmZmZmZddl m Z ddl m Z ddl mZmZer ddlmZddl mZGdd eZGd d Zd S) ) annotations) TYPE_CHECKINGAnyCallableOptional TypedDictUnion)DynamicRunEvaluator) traceable)ExampleRunStringEvaluator) RunEvaluatorc6eZdZUdZded< ded< ded<dS) SingleEvaluatorInputz!The input to a `StringEvaluator`.str predictionz Optional[Any] referencez Optional[str]inputN)__name__ __module__ __qualname____doc____annotations__h/var/www/html/ai-engine/env/lib/python3.11/site-packages/langsmith/evaluation/integrations/_langchain.pyrrs?++OOO rrc*eZdZdZddddd Zdd 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_data evaluatorUnion[StringEvaluator, str]r!Optional[dict]r"BOptional[Callable[[Run, Optional[Example]], SingleEvaluatorInput]]cddlm}t||r||_nKt|trddlm}||fi|pi|_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 isinstancer#rlangchain.evaluationr(NotImplementedErrortype _prepare_data)selfr#r!r"rr(s r__init__z!LangChainStringEvaluator.__init__s @????? i 1 1 X&DNN  3 ' ' X ; ; ; ; ; ;+^IHH&,BHHDNN%&VT)__&V&VWW W)rreturnrcDjjrdnd}jjrdnd}d||dt ddfd tjjddfd }tjjddfd }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]r1rc 8|jr2t|jdkrtdjdjjr;|r9|jr2t|jdkrtdjdjjr;|r9|jr2t|jdkrtdjdttt|j jjr<|r:|jr3tt|j ndjjr<|r:|jr3tt|j ndS)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)r4r5customization_error_strr/s rprepare_evaluator_inputszKLangChainStringEvaluator.as_run_evaluator..prepare_evaluator_inputss{ s3;//!33 111/111  O ((1,, 111/11-  N ''!++ 111/11(S[%7%7%9%9 : :;;9$$O Dgo446677888 5:AFMnDgn335566777 r)namedictcj ||n||}jjdi|}djji|SNkeyr)r.r#evaluate_stringsevaluation_namer4r5 eval_inputsresultsrCr/s revaluatez;LangChainStringEvaluator.as_run_evaluator..evaluatesl%-)(g666''W55  6dn5DD DDG4>9EWE ErcKj ||n||}jjdi|d{V}djji|SrG)r.r#aevaluate_stringsrJrKs r aevaluatez.aevaluates%-)(g666''W55  =DN<KK{KKKKKKKKG4>9EWE Er)N)r4r r5r6r1r)r4r r5r6r1rE)r#r=r<r rJr )r/ input_str reference_strrNrQrBrCs` @@ras_run_evaluatorz)LangChainStringEvaluator.as_run_evaluatorsK~,  : : ~0  A A  #1>#@I### 373 3 3 3 3 3 3  3 j 6 7 7 7 F F F F F F F 8 7 F 6 7 7 7 F F F F F F F 8 7 F#8Y777r)r#r$r!r%r"r&)r1r)rrrrr0rTrrrr r scqqn"& ******:f8f8f8f8f8f8rr N) __future__rtypingrrrrrr langsmith.evaluation.evaluatorr langsmith.run_helpersr langsmith.schemasr r r)rrrr rrrrZs""""""KKKKKKKKKKKKKKKK>>>>>>++++++********<;;;;;;;;;;;;9w8w8w8w8w8w8w8w8w8w8r