jL7dZddlmZddlmZmZmZddlmZddl m Z m Z ddl m Z ddlmZddlmZdd lmZdd lmZmZdd lmZd efd ZGddeeZGddeZGddeeZGddeeZy)z:String distance evaluators based on the RapidFuzz library.)Enum)AnyCallableOptional) Callbacks)AsyncCallbackManagerForChainRunCallbackManagerForChainRun)pre_init)Field)override)Chain)PairwiseStringEvaluatorStringEvaluator)RUN_KEYreturncb ddl}|jS#t$r}d}t||d}~wwxYw)z Load the RapidFuzz library. Raises: ImportError: If the rapidfuzz library is not installed. Returns: Any: The rapidfuzz.distance module. rNzyPlease install the rapidfuzz library to use the FuzzyMatchStringEvaluator.Please install it with `pip install rapidfuzz`.) rapidfuzz ImportErrordistance)remsgs s/var/www/html/Fitness-lenito-AI-main/venv/lib/python3.12/site-packages/langchain/evaluation/string_distance/base.py_load_rapidfuzzrsC&    & > #A% &s .).c(eZdZdZdZdZdZdZdZdZ y) StringDistancea7Distance metric to use. Attributes: DAMERAU_LEVENSHTEIN: The Damerau-Levenshtein distance. LEVENSHTEIN: The Levenshtein distance. JARO: The Jaro distance. JARO_WINKLER: The Jaro-Winkler distance. HAMMING: The Hamming distance. INDEL: The Indel distance. damerau_levenshtein levenshteinjaro jaro_winklerhammingindelN) __name__ __module__ __qualname____doc__DAMERAU_LEVENSHTEIN LEVENSHTEINJARO JARO_WINKLERHAMMINGINDELrrr)s( 0K D!LG Er-rceZdZUdZeej Zeed<edZ e ed< e de e efde e effdZedee fd Zd e e efde e effd Zed d de de defdZedefdZde de defdZy)_RapidFuzzChainMixinz staticmethodrrMrOfloatrSr,r-rr/r/=sF$^-H-HIHnI!$/OT/J 4S> d38n  T#Y d38n c3h >C c t   D       ! ! ! !r-r/ceZdZdZedefdZedefdZedee fdZ ede fdZ dde e e fd eede e e ffd Z dde e e fd eede e e ffd Zedddddd d de dee dee dedeee dee e e fdede de fdZedddddd d de dee dee dedeee dee e e fdede de fdZy)StringDistanceEvalChainakCompute string distances between the prediction and the reference. Examples ---------- >>> from langchain.evaluation import StringDistanceEvalChain >>> evaluator = StringDistanceEvalChain() >>> evaluator.evaluate_strings( prediction="Mindy is the CTO", reference="Mindy is the CEO", ) Using the `load_evaluator` function: >>> from langchain.evaluation import load_evaluator >>> evaluator = load_evaluator("string_distance") >>> evaluator.evaluate_strings( prediction="The answer is three", reference="three", ) rcy)z8 This evaluator does not require input. Fr,r8s rrequires_inputz&StringDistanceEvalChain.requires_inputs r-cy)z> This evaluator does not require a reference. Tr,r8s rrequires_referencez*StringDistanceEvalChain.requires_references r-c ddgS)^ Get the input keys. Returns: List[str]: The input keys. reference predictionr,r8s r input_keysz"StringDistanceEvalChain.input_keyss\**r-c4|jjdS)b Get the evaluation name. Returns: str: The evaluation name. _distancervaluer8s revaluation_namez'StringDistanceEvalChain.evaluation_names--%%&i00r-Ninputs run_managerc6d|j|d|diS)a^ Compute the string distance between the prediction and the reference. Args: inputs (Dict[str, Any]): The input values. run_manager (Optional[CallbackManagerForChainRun]): The callback manager. Returns: Dict[str, Any]: The evaluation results containing the score. r7rbrcrSr9rkrls r_callzStringDistanceEvalChain._calls& ,,VK-@&BVWXXr-c>Kd|j|d|diSw)a} Asynchronously compute the string distance between the prediction and the reference. Args: inputs (Dict[str, Any]): The input values. run_manager (Optional[AsyncCallbackManagerForChainRun]: The callback manager. Returns: Dict[str, Any]: The evaluation results containing the score. r7rbrcrnros r_acallzStringDistanceEvalChain._acalls*",,VK-@&BVWXXF)rbinput callbackstagsmetadatainclude_run_inforcrbrtrurvrwrxkwargsc D|||d||||} |j| S)a Evaluate the string distance between the prediction and the reference. Args: prediction (str): The prediction string. reference (Optional[str], optional): The reference string. input (Optional[str], optional): The input string. callbacks (Callbacks, optional): The callbacks to use. kwargs: Additional keyword arguments. Returns: dict: The evaluation results containing the score. rcrbrkrurvrwrxr> r9rcrbrtrurvrwrxryr;s r_evaluate_stringsz)StringDistanceEvalChain._evaluate_stringss54",9E-  ##F++r-crK|j||d||||d{} |j| S7w)a Asynchronously evaluate the string distance between the prediction and the reference. Args: prediction (str): The prediction string. reference (Optional[str], optional): The reference string. input (Optional[str], optional): The input string. callbacks (Callbacks, optional): The callbacks to use. kwargs: Additional keyword arguments. Returns: dict: The evaluation results containing the score. r{r|Nacallr>r~s r_aevaluate_stringsz*StringDistanceEvalChain._aevaluate_strings,sL6zz",9E- "  ##F++ 757N)r"r#r$r%rWrUr]r_rGrVrdrjr=rrr rprrrr rrrr,r-rr[r[s;, D +DI++111=AYS#XY89Y c3h Y*BFYS#XY=>Y c3h Y& $(##$(-1!&!,!,C= !, } !,  !,tCy!!,4S>*!,!,!, !,!,F $(##$(-1!&!,!,C= !, } !,  !,tCy!!,4S>*!,!,!, !,!,r-r[cFeZdZdZedeefdZedefdZ dde ee fde e de ee ffdZ dde ee fde ede ee ffd Zdddd d d ed edede eede e ee fdede de fdZdddd d d ed edede eede e ee fdede de fdZy)PairwiseStringDistanceEvalChainz6Compute string edit distances between two predictions.rc ddgS)rarc prediction_br,r8s rrdz*PairwiseStringDistanceEvalChain.input_keysTsn--r-c6d|jjdS)rf pairwise_rgrhr8s rrjz/PairwiseStringDistanceEvalChain.evaluation_name^s4==../y99r-Nrkrlc6d|j|d|diS)aN Compute the string distance between two predictions. Args: inputs (Dict[str, Any]): The input values. run_manager (CallbackManagerForChainRun , optional): The callback manager. Returns: Dict[str, Any]: The evaluation results containing the score. r7rcrrnros rrpz%PairwiseStringDistanceEvalChain._callhs*" T(( )=vn?UV  r-c>Kd|j|d|diSw)ab Asynchronously compute the string distance between two predictions. Args: inputs (Dict[str, Any]): The input values. run_manager (AsyncCallbackManagerForChainRun , optional): The callback manager. Returns: Dict[str, Any]: The evaluation results containing the score. r7rcrrnros rrrz&PairwiseStringDistanceEvalChain._acall|s." T(( )=vn?UV  rsF)rurvrwrxrcrrurvrwrxryc D|||d||||}|j|S)a$ Evaluate the string distance between two predictions. Args: prediction (str): The first prediction string. prediction_b (str): The second prediction string. callbacks (Callbacks, optional): The callbacks to use. tags (List[str], optional): Tags to apply to traces. metadata (Dict[str, Any], optional): Metadata to apply to traces. kwargs: Additional keyword arguments. Returns: dict: The evaluation results containing the score. rcrr|r} r9rcrrurvrwrxryr;s r_evaluate_string_pairsz6PairwiseStringDistanceEvalChain._evaluate_string_pairss52",lK-  ##F++r-crK|j||d||||d{}|j|S7w)a3 Asynchronously evaluate the string distance between two predictions. Args: prediction (str): The first prediction string. prediction_b (str): The second prediction string. callbacks (Callbacks, optional): The callbacks to use. tags (List[str], optional): Tags to apply to traces. metadata (Dict[str, Any], optional): Metadata to apply to traces. kwargs: Additional keyword arguments. Returns: dict: The evaluation results containing the score. rr|Nrrs r_aevaluate_string_pairsz7PairwiseStringDistanceEvalChain._aevaluate_string_pairssL2zz",lK- "  ##F++ rr)r"r#r$r%rWrGrVrdrjr=rrr rprrrrrUrrr,r-rrrQs@ .DI..:::=A S#X 89  c3h  .BF S#X =>  c3h  2 $$(-1!& , , ,  , tCy! ,4S>* , , ,  ,N $$(-1!& , , ,  , tCy! ,4S>* , , ,  ,r-rN)r%enumrtypingrrrlangchain_core.callbacksr langchain_core.callbacks.managerrr langchain_core.utilsr pydanticr typing_extensionsr langchain.chains.baser langchain.evaluation.schemarrlangchain.schemarrrVrr/r[rr,r-rrsz@**.*&'P$*S$(j!5j!Zd,o/Cd,NA,&=?SA,r-