src/ragas/metrics/_faithfulness.py

from __future__ import annotations

import logging
import typing as t
from dataclasses import dataclass, field

import numpy as np
from pydantic import BaseModel, Field

from ragas.dataset_schema import SingleTurnSample
from ragas.metrics.base import (
    MetricOutputType,
    MetricType,
    MetricWithLLM,
    SingleTurnMetric,
)
from ragas.prompt import PydanticPrompt

if t.TYPE_CHECKING:
    from langchain_core.callbacks import Callbacks

logger = logging.getLogger(__name__)


class StatementGeneratorInput(BaseModel):
    question: str = Field(description="The question to answer")
    answer: str = Field(description="The answer to the question")


class StatementGeneratorOutput(BaseModel):
    statements: t.List[str] = Field(description="The generated statements")


class StatementGeneratorPrompt(
    PydanticPrompt[StatementGeneratorInput, StatementGeneratorOutput]
):
    instruction = "Given a question and an answer, analyze the complexity of each sentence in the answer. Break down each sentence into one or more fully understandable statements. Ensure that no pronouns are used in any statement. Format the outputs in JSON."
    input_model = StatementGeneratorInput
    output_model = StatementGeneratorOutput
    examples = [
        (
            StatementGeneratorInput(
                question="Who was Albert Einstein and what is he best known for?",
                answer="He was a German-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time. He was best known for developing the theory of relativity, he also made important contributions to the development of the theory of quantum mechanics.",
            ),
            StatementGeneratorOutput(
                statements=[
                    "Albert Einstein was a German-born theoretical physicist.",
                    "Albert Einstein is recognized as one of the greatest and most influential physicists of all time.",
                    "Albert Einstein was best known for developing the theory of relativity.",
                    "Albert Einstein also made important contributions to the development of the theory of quantum mechanics.",
                ]
            ),
        )
    ]


class StatementFaithfulnessAnswer(BaseModel):
    statement: str = Field(..., description="the original statement, word-by-word")
    reason: str = Field(..., description="the reason of the verdict")
    verdict: int = Field(..., description="the verdict(0/1) of the faithfulness.")


class NLIStatementOutput(BaseModel):
    statements: t.List[StatementFaithfulnessAnswer]


class NLIStatementInput(BaseModel):
    context: str = Field(..., description="The context of the question")
    statements: t.List[str] = Field(..., description="The statements to judge")


class NLIStatementPrompt(PydanticPrompt[NLIStatementInput, NLIStatementOutput]):
    instruction = "Your task is to judge the faithfulness of a series of statements based on a given context. For each statement you must return verdict as 1 if the statement can be directly inferred based on the context or 0 if the statement can not be directly inferred based on the context."
    input_model = NLIStatementInput
    output_model = NLIStatementOutput
    examples = [
        (
            NLIStatementInput(
                context="""John is a student at XYZ University. He is pursuing a degree in Computer Science. He is enrolled in several courses this semester, including Data Structures, Algorithms, and Database Management. John is a diligent student and spends a significant amount of time studying and completing assignments. He often stays late in the library to work on his projects.""",
                statements=[
                    "John is majoring in Biology.",
                    "John is taking a course on Artificial Intelligence.",
                    "John is a dedicated student.",
                    "John has a part-time job.",
                ],
            ),
            NLIStatementOutput(
                statements=[
                    StatementFaithfulnessAnswer(
                        statement="John is majoring in Biology.",
                        reason="John's major is explicitly mentioned as Computer Science. There is no information suggesting he is majoring in Biology.",
                        verdict=0,
                    ),
                    StatementFaithfulnessAnswer(
                        statement="John is taking a course on Artificial Intelligence.",
                        reason="The context mentions the courses John is currently enrolled in, and Artificial Intelligence is not mentioned. Therefore, it cannot be deduced that John is taking a course on AI.",
                        verdict=0,
                    ),
                    StatementFaithfulnessAnswer(
                        statement="John is a dedicated student.",
                        reason="The context states that he spends a significant amount of time studying and completing assignments. Additionally, it mentions that he often stays late in the library to work on his projects, which implies dedication.",
                        verdict=1,
                    ),
                    StatementFaithfulnessAnswer(
                        statement="John has a part-time job.",
                        reason="There is no information given in the context about John having a part-time job.",
                        verdict=0,
                    ),
                ]
            ),
        ),
        (
            NLIStatementInput(
                context="Photosynthesis is a process used by plants, algae, and certain bacteria to convert light energy into chemical energy.",
                statements=[
                    "Albert Einstein was a genius.",
                ],
            ),
            NLIStatementOutput(
                statements=[
                    StatementFaithfulnessAnswer(
                        statement="Albert Einstein was a genius.",
                        reason="The context and statement are unrelated",
                        verdict=0,
                    )
                ]
            ),
        ),
    ]


@dataclass
class Faithfulness(MetricWithLLM, SingleTurnMetric):
    name: str = "faithfulness"
    _required_columns: t.Dict[MetricType, t.Set[str]] = field(
        default_factory=lambda: {
            MetricType.SINGLE_TURN: {
                "user_input",
                "response",
                "retrieved_contexts",
            }
        }
    )
    output_type: t.Optional[MetricOutputType] = MetricOutputType.CONTINUOUS
    nli_statements_prompt: PydanticPrompt = field(default_factory=NLIStatementPrompt)
    statement_generator_prompt: PydanticPrompt = field(
        default_factory=StatementGeneratorPrompt
    )
    max_retries: int = 1

    async def _create_verdicts(
        self, row: t.Dict, statements: t.List[str], callbacks: Callbacks
    ) -> NLIStatementOutput:
        assert self.llm is not None, "llm must be set to compute score"

        contexts_str: str = "\n".join(row["retrieved_contexts"])
        verdicts = await self.nli_statements_prompt.generate(
            data=NLIStatementInput(context=contexts_str, statements=statements),
            llm=self.llm,
            callbacks=callbacks,
        )

        return verdicts

    async def _create_statements(
        self, row: t.Dict, callbacks: Callbacks
    ) -> StatementGeneratorOutput:
        assert self.llm is not None, "llm is not set"

        text, question = row["response"], row["user_input"]

        prompt_input = StatementGeneratorInput(question=question, answer=text)
        statements = await self.statement_generator_prompt.generate(
            llm=self.llm,
            data=prompt_input,
            callbacks=callbacks,
        )

        return statements

    def _compute_score(self, answers: NLIStatementOutput):
        # check the verdicts and compute the score
        faithful_statements = sum(
            1 if answer.verdict else 0 for answer in answers.statements
        )
        num_statements = len(answers.statements)
        if num_statements:
            score = faithful_statements / num_statements
        else:
            logger.warning("No statements were generated from the answer.")
            score = np.nan

        return score

    async def _single_turn_ascore(
        self, sample: SingleTurnSample, callbacks: Callbacks
    ) -> float:
        row = sample.to_dict()
        return await self._ascore(row, callbacks)

    async def _ascore(self, row: t.Dict, callbacks: Callbacks) -> float:
        """
        returns the NLI score for each (q, c, a) pair
        """
        assert self.llm is not None, "LLM is not set"

        statements = await self._create_statements(row, callbacks)
        statements = statements.statements
        if statements == []:
            return np.nan

        verdicts = await self._create_verdicts(row, statements, callbacks)
        return self._compute_score(verdicts)


@dataclass
class FaithfulnesswithHHEM(Faithfulness):
    name: str = "faithfulness_with_hhem"
    device: str = "cpu"
    batch_size: int = 10

    def __post_init__(self):
        try:
            from transformers import AutoModelForSequenceClassification  # type: ignore
        except ImportError:
            raise ImportError(
                "Huggingface transformers must be installed to use this feature, try `pip install transformers`"
            )
        self.nli_classifier = AutoModelForSequenceClassification.from_pretrained(
            "vectara/hallucination_evaluation_model", trust_remote_code=True
        )
        self.nli_classifier.to(self.device)
        super().__post_init__()

    def _create_pairs(
        self, row: t.Dict, statements: t.List[str]
    ) -> t.List[t.Tuple[str, str]]:
        """
        create pairs of (question, answer) from the row
        """
        premise = "\n".join(row["retrieved_contexts"])
        pairs = [(premise, statement) for statement in statements]
        return pairs

    def _create_batch(
        self, pairs: t.List[t.Tuple[str, str]]
    ) -> t.Generator[t.List[t.Tuple[str, str]], None, None]:
        length_of_pairs = len(pairs)
        for ndx in range(0, length_of_pairs, self.batch_size):
            yield pairs[ndx : min(ndx + self.batch_size, length_of_pairs)]

    async def _ascore(self, row: t.Dict, callbacks: Callbacks) -> float:
        """
        returns the NLI score for each (q, c, a) pair
        """
        assert self.llm is not None, "LLM is not set"

        statements = await self._create_statements(row, callbacks)
        statements = statements.statements
        if statements == []:
            return np.nan

        scores = []
        pairs = self._create_pairs(row, statements)
        for input_pairs in self._create_batch(pairs):  # to avoid OOM
            batch_scores = (
                self.nli_classifier.predict(input_pairs).cpu().detach().round()
            )
            # convert tensor to list of floats
            scores.extend(batch_scores.tolist())

        return sum(scores) / len(scores)


faithfulness = Faithfulness()