base.py — langchain Source File

Source Code

from __future__ import annotations

import json
from typing import Any

from langchain_core._api import deprecated
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_text_splitters import RecursiveCharacterTextSplitter, TextSplitter
from pydantic import Field
from typing_extensions import override

from langchain_classic.chains.base import Chain
from langchain_classic.chains.llm import LLMChain
from langchain_classic.chains.qa_generation.prompt import PROMPT_SELECTOR


@deprecated(
    since="0.2.7",
    alternative=(
        "example in API reference with more detail: "
        "https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_generation.base.QAGenerationChain.html"
    ),
    removal="1.0",
)
class QAGenerationChain(Chain):
    """Base class for question-answer generation chains.

    This class is deprecated. See below for an alternative implementation.

    Advantages of this implementation include:

    - Supports async and streaming;
    - Surfaces prompt and text splitter for easier customization;
    - Use of JsonOutputParser supports JSONPatch operations in streaming mode,
        as well as robustness to markdown.

        ```python
        from langchain_classic.chains.qa_generation.prompt import (
            CHAT_PROMPT as prompt,
        )

        # Note: import PROMPT if using a legacy non-chat model.
        from langchain_core.output_parsers import JsonOutputParser
        from langchain_core.runnables import (
            RunnableLambda,
            RunnableParallel,
            RunnablePassthrough,
        )
        from langchain_core.runnables.base import RunnableEach
        from langchain_openai import ChatOpenAI
        from langchain_text_splitters import RecursiveCharacterTextSplitter

        model = ChatOpenAI()
        text_splitter = RecursiveCharacterTextSplitter(chunk_overlap=500)
        split_text = RunnableLambda(lambda x: text_splitter.create_documents([x]))

        chain = RunnableParallel(
            text=RunnablePassthrough(),
// ... (68 more lines)