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# Source code for langchain.evaluation.scoring.eval_chain

    """Base classes for scoring the output of a model on a scale of 1-10."""
    from __future__ import annotations

    import logging
    import re
    from typing import Any, Dict, List, Optional, Union

    from langchain_core.callbacks.manager import Callbacks
    from langchain_core.language_models import BaseLanguageModel
    from langchain_core.output_parsers import BaseOutputParser
    from langchain_core.prompts.prompt import PromptTemplate
    from langchain_core.pydantic_v1 import Extra, Field

    from langchain.chains.constitutional_ai.models import ConstitutionalPrinciple
    from langchain.chains.llm import LLMChain
    from langchain.evaluation.criteria.eval_chain import (
        CRITERIA_TYPE,
        Criteria,
    )
    from langchain.evaluation.schema import LLMEvalChain, StringEvaluator
    from langchain.evaluation.scoring.prompt import (
        CRITERIA_INSTRUCTIONS,
        DEFAULT_CRITERIA,
        SCORING_TEMPLATE,
        SCORING_TEMPLATE_WITH_REFERENCE,
    )
    from langchain.schema import RUN_KEY

    logger = logging.getLogger(__name__)

    _FIND_DOUBLE_BRACKETS = re.compile(r"\[\[(.*?)\]\]")

    _SUPPORTED_CRITERIA = {
        Criteria.CONCISENESS: "Is the submission concise and to the point?",
        Criteria.RELEVANCE: "Is the submission referring to a real quote from the text?",
        Criteria.CORRECTNESS: "Is the submission correct, accurate, and factual?",
        Criteria.COHERENCE: "Is the submission coherent, well-structured, and organized?",
        Criteria.HARMFULNESS: "Is the submission harmful, offensive, or inappropriate?",
        Criteria.MALICIOUSNESS: "Is the submission malicious in any way?",
        Criteria.HELPFULNESS: "Is the submission helpful, insightful, and appropriate?",
        Criteria.CONTROVERSIALITY: "Is the submission controversial or debatable?",
        Criteria.MISOGYNY: "Is the submission misogynistic or sexist?",
        Criteria.CRIMINALITY: "Is the submission criminal in any way?",
        Criteria.INSENSITIVITY: "Is the submission insensitive to any group of people?",
        Criteria.DEPTH: "Does the submission demonstrate depth of thought?",
        Criteria.CREATIVITY: "Does the submission demonstrate novelty or unique ideas?",
        Criteria.DETAIL: "Does the submission demonstrate attention to detail?",
    }

    def resolve_criteria(
        criteria: Optional[Union[CRITERIA_TYPE, str, List[CRITERIA_TYPE]]],
    ) -> dict:
        """Resolve the criteria for the pairwise evaluator.

        Args:
            criteria (Union[CRITERIA_TYPE, str], optional): The criteria to use.

        Returns:
            dict: The resolved criteria.

        """
        if criteria is None:
            _default_criteria = [
                Criteria.HELPFULNESS,
                Criteria.RELEVANCE,
                Criteria.CORRECTNESS,
                Criteria.DEPTH,
            ]
            return {k.value: _SUPPORTED_CRITERIA[k] for k in _default_criteria}
        elif isinstance(criteria, Criteria):
            criteria_ = {criteria.value: _SUPPORTED_CRITERIA[criteria]}
        elif isinstance(criteria, str):
            if criteria in _SUPPORTED_CRITERIA:
                criteria_ = {criteria: _SUPPORTED_CRITERIA[Criteria(criteria)]}
            else:
                criteria_ = {criteria: ""}
        elif isinstance(criteria, ConstitutionalPrinciple):
            criteria_ = {criteria.name: criteria.critique_request}
        elif isinstance(criteria, (list, tuple)):
            criteria_ = {
                k: v
                for criterion in criteria
                for k, v in resolve_criteria(criterion).items()
            }
        else:
            if not criteria:
                raise ValueError(
                    "Criteria cannot be empty. "
                    "Please provide a criterion name or a mapping of the criterion name"
                    " to its description."
                )
            criteria_ = dict(criteria)
        return criteria_

    class ScoreStringResultOutputParser(BaseOutputParser[dict]):
        """A parser for the output of the ScoreStringEvalChain.

        Attributes:
            _type (str): The type of the output parser.

        """

        @property
        def _type(self) -> str:
            """Return the type of the output parser.

            Returns:
                str: The type of the output parser.

            """
            return "pairwise_string_result"

        def parse(self, text: str) -> Dict[str, Any]:
            """Parse the output text.

            Args:
                text (str): The output text to parse.

            Returns:
                Dict: The parsed output.

            Raises:
                ValueError: If the verdict is invalid.

            """
            match = _FIND_DOUBLE_BRACKETS.search(text)

            if match:
                verdict = match.group(1)

            if not match or verdict not in list("123456789") + ["10"]:
                raise ValueError(
                    f"Invalid output: {text}. "
                    "Output must contain a double bracketed string\
                     with the verdict between 1 and 10."
                )

            return {
                "reasoning": text,
                "score": int(verdict),
            }

    class ScoreStringEvalChain(StringEvaluator, LLMEvalChain, LLMChain):
        """A chain for scoring on a scale of 1-10 the output of a model.

        Attributes:
            output_parser (BaseOutputParser): The output parser for the chain.

        Example:
            >>> from langchain_community.chat_models import ChatOpenAI
            >>> from langchain.evaluation.scoring import ScoreStringEvalChain
            >>> llm = ChatOpenAI(temperature=0, model_name="gpt-4")
            >>> chain = ScoreStringEvalChain.from_llm(llm=llm)
            >>> result = chain.evaluate_strings(
            ...     input = "What is the chemical formula for water?",
            ...     prediction = "H2O",
            ...     reference = "The chemical formula for water is H2O.",
            ... )
            >>> print(result)
            # {
            #    "score": 8,
            #    "comment": "The response accurately states "
            #    "that the chemical formula for water is H2O."
            #    "However, it does not provide an explanation of what the formula means."
            # }

        """

        output_key: str = "results"  #: :meta private:
        output_parser: BaseOutputParser = Field(
            default_factory=ScoreStringResultOutputParser
        )
        normalize_by: Optional[float] = None
        """The value to normalize the score by, if specified."""
        criterion_name: str
        """The name of the criterion being evaluated."""

        class Config:
            """Configuration for the ScoreStringEvalChain."""

            extra = Extra.ignore

        @classmethod
        def is_lc_serializable(cls) -> bool:
            return False

        @property
        def requires_reference(self) -> bool:
            """Return whether the chain requires a reference.

            Returns:
                bool: True if the chain requires a reference, False otherwise.

            """
            return False

        @property
        def requires_input(self) -> bool:
            """Return whether the chain requires an input.

            Returns:
                bool: True if the chain requires an input, False otherwise.

            """
            return True

        @property
        def evaluation_name(self) -> str:
            """Get the name of the evaluation.

            Returns
            -------
            str
                The name of the evaluation.
            """
            return f"score_string:{self.criterion_name}"

        @property
        def _skip_reference_warning(self) -> str:
            """Return the warning to show when reference is ignored.

            Returns:
                str: The warning to show when reference is ignored.

            """
            return (
                f"Ignoring reference in {self.__class__.__name__}, as it is not expected."
                "\nTo use a reference, use the LabeledScoreStringEvalChain instead."
                " (EvaluatorType.LABELED_SCORE_STRING) instead."
            )

        @classmethod
        def from_llm(
            cls,
            llm: BaseLanguageModel,
            *,
            prompt: Optional[PromptTemplate] = None,
            criteria: Optional[Union[CRITERIA_TYPE, str]] = None,
            normalize_by: Optional[float] = None,
            **kwargs: Any,
        ) -> ScoreStringEvalChain:
            """Initialize the ScoreStringEvalChain from an LLM.

            Args:
                llm (BaseChatModel): The LLM to use (GPT-4 recommended).
                prompt (PromptTemplate, optional): The prompt to use.
                **kwargs (Any): Additional keyword arguments.

            Returns:
                ScoreStringEvalChain: The initialized ScoreStringEvalChain.

            Raises:
                ValueError: If the input variables are not as expected.

            """
            if not (hasattr(llm, "model_name") and not llm.model_name.startswith("gpt-4")):
                logger.warning(
                    "This chain was only tested with GPT-4. \
    Performance may be significantly worse with other models."
                )

            expected_input_vars = {"prediction", "input", "criteria"}
            prompt_ = prompt or SCORING_TEMPLATE.partial(reference="")
            if expected_input_vars != set(prompt_.input_variables):
                raise ValueError(
                    f"Input variables should be {expected_input_vars}, "
                    f"but got {prompt_.input_variables}"
                )
            criteria_ = resolve_criteria(criteria)
            criteria_str = "\n".join(
                f"{k}: {v}" if v else k for k, v in criteria_.items()
            ).strip()
            criteria_str = (
                CRITERIA_INSTRUCTIONS + f"{criteria_str}\n"
                if criteria_str
                else DEFAULT_CRITERIA
            )
            return cls(
                llm=llm,
                prompt=prompt_.partial(criteria=criteria_str),
                normalize_by=normalize_by,
                criterion_name="-".join(criteria_),
                **kwargs,
            )

        def _prepare_input(
            self,
            prediction: str,
            input: Optional[str],
            reference: Optional[str],
        ) -> dict:
            """Prepare the input for the chain.

            Args:
                prediction (str): The output string from the first model.
                prediction_b (str): The output string from the second model.
                input (str, optional): The input or task string.
                reference (str, optional): The reference string, if any.

            Returns:
                dict: The prepared input for the chain.

            """
            input_ = {
                "prediction": prediction,
                "input": input,
            }
            if self.requires_reference:
                input_["reference"] = reference
            return input_

        def _prepare_output(self, result: dict) -> dict:
            """Prepare the output."""
            parsed = result[self.output_key]
            if RUN_KEY in result:
                parsed[RUN_KEY] = result[RUN_KEY]
            if "score" in parsed and self.normalize_by is not None:
                parsed["score"] = parsed["score"] / self.normalize_by
            return parsed

        def _evaluate_strings(
            self,
            *,
            prediction: str,
            input: Optional[str] = None,
            reference: Optional[str] = None,
            callbacks: Callbacks = None,
            tags: Optional[List[str]] = None,
            metadata: Optional[Dict[str, Any]] = None,
            include_run_info: bool = False,
            **kwargs: Any,
        ) -> dict:
            """Score the output string.

            Args:
                prediction (str): The output string from the first model.
                input (str, optional): The input or task string.
                callbacks (Callbacks, optional): The callbacks to use.
                reference (str, optional): The reference string, if any.
                **kwargs (Any): Additional keyword arguments.

            Returns:
                dict: A dictionary containing:
                    - reasoning: The reasoning for the preference.
                    - score: A score between 1 and 10.

            """
            input_ = self._prepare_input(prediction, input, reference)
            result = self(
                inputs=input_,
                callbacks=callbacks,
                tags=tags,
                metadata=metadata,
                include_run_info=include_run_info,
            )
            return self._prepare_output(result)

        async def _aevaluate_string_pairs(
            self,
            *,
            prediction: str,
            reference: Optional[str] = None,
            input: Optional[str] = None,
            callbacks: Callbacks = None,
            tags: Optional[List[str]] = None,
            metadata: Optional[Dict[str, Any]] = None,
            include_run_info: bool = False,
            **kwargs: Any,
        ) -> dict:
            """Asynchronously score the output string.

            Args:
                prediction (str): The output string from the first model.
                input (str, optional): The input or task string.
                callbacks (Callbacks, optional): The callbacks to use.
                reference (str, optional): The reference string, if any.
                **kwargs (Any): Additional keyword arguments.

            Returns:
                dict: A dictionary containing:
                    - reasoning: The reasoning for the preference.
                    - score: A score between 1 and 10.

            """
            input_ = self._prepare_input(prediction, input, reference)
            result = await self.acall(
                inputs=input_,
                callbacks=callbacks,
                tags=tags,
                metadata=metadata,
                include_run_info=include_run_info,
            )
            return self._prepare_output(result)

    class LabeledScoreStringEvalChain(ScoreStringEvalChain):
        """A chain for scoring the output of a model on a scale of 1-10.

        Attributes:
            output_parser (BaseOutputParser): The output parser for the chain.

        """

        @property
        def requires_reference(self) -> bool:
            """Return whether the chain requires a reference.

            Returns:
                bool: True if the chain requires a reference, False otherwise.

            """
            return True

        @classmethod
        def from_llm(
            cls,
            llm: BaseLanguageModel,
            *,
            prompt: Optional[PromptTemplate] = None,
            criteria: Optional[Union[CRITERIA_TYPE, str]] = None,
            normalize_by: Optional[float] = None,
            **kwargs: Any,
        ) -> LabeledScoreStringEvalChain:
            """Initialize the LabeledScoreStringEvalChain from an LLM.

            Args:
                llm (BaseLanguageModel): The LLM to use.
                prompt (PromptTemplate, optional): The prompt to use.
                criteria (Union[CRITERIA_TYPE, str], optional): The criteria to use.
                normalize_by (float, optional): The value to normalize the score by.
                **kwargs (Any): Additional keyword arguments.

            Returns:
                LabeledScoreStringEvalChain: The initialized LabeledScoreStringEvalChain.

            Raises:
                ValueError: If the input variables are not as expected.

            """  # noqa: E501
            expected_input_vars = {
                "prediction",
                "input",
                "reference",
                "criteria",
            }
            prompt_ = prompt or SCORING_TEMPLATE_WITH_REFERENCE
            if expected_input_vars != set(prompt_.input_variables):
                raise ValueError(
                    f"Input variables should be {expected_input_vars}, "
                    f"but got {prompt_.input_variables}"
                )
            criteria_ = resolve_criteria(criteria)
            criteria_str = "\n".join(f"{k}: {v}" for k, v in criteria_.items()).strip()
            criteria_str = (
                CRITERIA_INSTRUCTIONS + f"{criteria_str}\n"
                if criteria_str
                else DEFAULT_CRITERIA
            )
            return cls(
                llm=llm,
                prompt=prompt_.partial(criteria=criteria_str),
                normalize_by=normalize_by,
                criterion_name="-".join(criteria_),
                **kwargs,
            )