Source code for langchain.chains.openai_functions.citation_fuzzy_match
from typing import Iterator, List
from langchain.chains.llm import LLMChain
from langchain.chains.openai_functions.utils import get_llm_kwargs
from langchain.output_parsers.openai_functions import (
    PydanticOutputFunctionsParser,
)
from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain.pydantic_v1 import BaseModel, Field
from langchain.schema.language_model import BaseLanguageModel
from langchain.schema.messages import HumanMessage, SystemMessage
[docs]class FactWithEvidence(BaseModel):
    """Class representing a single statement.
    Each fact has a body and a list of sources.
    If there are multiple facts make sure to break them apart
    such that each one only uses a set of sources that are relevant to it.
    """
    fact: str = Field(..., description="Body of the sentence, as part of a response")
    substring_quote: List[str] = Field(
        ...,
        description=(
            "Each source should be a direct quote from the context, "
            "as a substring of the original content"
        ),
    )
    def _get_span(self, quote: str, context: str, errs: int = 100) -> Iterator[str]:
        import regex
        minor = quote
        major = context
        errs_ = 0
        s = regex.search(f"({minor}){{e<={errs_}}}", major)
        while s is None and errs_ <= errs:
            errs_ += 1
            s = regex.search(f"({minor}){{e<={errs_}}}", major)
        if s is not None:
            yield from s.spans()