create_extraction_chain_pydantic() — langchain Function Reference

Source Code

libs/langchain/langchain_classic/chains/openai_functions/extraction.py lines 145–190

def create_extraction_chain_pydantic(
    pydantic_schema: Any,
    llm: BaseLanguageModel,
    prompt: BasePromptTemplate | None = None,
    verbose: bool = False,  # noqa: FBT001,FBT002
) -> Chain:
    """Creates a chain that extracts information from a passage using Pydantic schema.

    Args:
        pydantic_schema: The Pydantic schema of the entities to extract.
        llm: The language model to use.
        prompt: The prompt to use for extraction.
        verbose: Whether to run in verbose mode. In verbose mode, some intermediate
            logs will be printed to the console.

    Returns:
        Chain that can be used to extract information from a passage.
    """

    class PydanticSchema(BaseModel):
        info: list[pydantic_schema]

    if hasattr(pydantic_schema, "model_json_schema"):
        openai_schema = pydantic_schema.model_json_schema()
    else:
        openai_schema = pydantic_schema.schema()

    openai_schema = _resolve_schema_references(
        openai_schema,
        openai_schema.get("definitions", {}),
    )

    function = _get_extraction_function(openai_schema)
    extraction_prompt = prompt or ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE)
    output_parser = PydanticAttrOutputFunctionsParser(
        pydantic_schema=PydanticSchema,
        attr_name="info",
    )
    llm_kwargs = get_llm_kwargs(function)
    return LLMChain(
        llm=llm,
        prompt=extraction_prompt,
        llm_kwargs=llm_kwargs,
        output_parser=output_parser,
        verbose=verbose,
    )