langchain/evaluation/qa/generate_chain.py

"""LLM Chain for generating examples for question answering."""
from __future__ import annotations

from typing import Any

from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers import BaseLLMOutputParser
from langchain_core.pydantic_v1 import Field

from langchain.chains.llm import LLMChain
from langchain.evaluation.qa.generate_prompt import PROMPT
from langchain.output_parsers.regex import RegexParser

_QA_OUTPUT_PARSER = RegexParser(
    regex=r"QUESTION: (.*?)\n+ANSWER: (.*)", output_keys=["query", "answer"]
)


class QAGenerateChain(LLMChain):
    """LLM Chain for generating examples for question answering."""

    output_parser: BaseLLMOutputParser = Field(default=_QA_OUTPUT_PARSER)
    output_key: str = "qa_pairs"

    @classmethod
    def from_llm(cls, llm: BaseLanguageModel, **kwargs: Any) -> QAGenerateChain:
        """Load QA Generate Chain from LLM."""
        return cls(llm=llm, prompt=PROMPT, **kwargs)