"""Creates a chain that extracts information from a passage.
    Args:
        schema: The 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. Defaults to `langchain.verbose` value.
    Returns:
        Chain that can be used to extract information from a passage.
    """
    function = _get_extraction_function(schema)
    extraction_prompt = prompt or ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE)
    output_parser = JsonKeyOutputFunctionsParser(key_name="info")
    llm_kwargs = get_llm_kwargs(function)
    chain = LLMChain(
        llm=llm,
        prompt=extraction_prompt,
        llm_kwargs=llm_kwargs,
        output_parser=output_parser,
        verbose=verbose,
    )
    return chain
[docs]def create_extraction_chain_pydantic(
    pydantic_schema: Any,
    llm: BaseLanguageModel,
    prompt: Optional[BasePromptTemplate] = None,
    verbose: bool = False,
) -> 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. Defaults to `langchain.verbose` value.
    Returns:
        Chain that can be used to extract information from a passage.
    """