chain.py — langchain Source File

Source Code

"""Load summarizing chains."""

from collections.abc import Mapping
from typing import Any, Protocol

from langchain_core.callbacks import Callbacks
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate

from langchain_classic.chains.combine_documents.base import BaseCombineDocumentsChain
from langchain_classic.chains.combine_documents.map_reduce import (
    MapReduceDocumentsChain,
)
from langchain_classic.chains.combine_documents.reduce import ReduceDocumentsChain
from langchain_classic.chains.combine_documents.refine import RefineDocumentsChain
from langchain_classic.chains.combine_documents.stuff import StuffDocumentsChain
from langchain_classic.chains.llm import LLMChain
from langchain_classic.chains.summarize import (
    map_reduce_prompt,
    refine_prompts,
    stuff_prompt,
)


class LoadingCallable(Protocol):
    """Interface for loading the combine documents chain."""

    def __call__(
        self,
        llm: BaseLanguageModel,
        **kwargs: Any,
    ) -> BaseCombineDocumentsChain:
        """Callable to load the combine documents chain."""


def _load_stuff_chain(
    llm: BaseLanguageModel,
    *,
    prompt: BasePromptTemplate = stuff_prompt.PROMPT,
    document_variable_name: str = "text",
    verbose: bool | None = None,
    **kwargs: Any,
) -> StuffDocumentsChain:
    llm_chain = LLMChain(llm=llm, prompt=prompt, verbose=verbose)
    """Load a StuffDocumentsChain for summarization.

    Args:
        llm: Language Model to use in the chain.
        prompt: Prompt template that controls how the documents are formatted and
            passed into the LLM.
        document_variable_name: Variable name in the prompt template where the
            document text will be inserted.
        verbose: Whether to log progress and intermediate steps.
        **kwargs: Additional keyword arguments passed to the StuffDocumentsChain.

    Returns:
        A StuffDocumentsChain that takes in documents, formats them with the
        given prompt, and runs the chain on the provided LLM.
    """
    return StuffDocumentsChain(
// ... (167 more lines)