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https://github.com/hwchase17/langchain
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langchain[patch]: deprecate QAGenerationChain (#23730)
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@ -3,6 +3,7 @@ from __future__ import annotations
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import json
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from typing import Any, Dict, List, Optional
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from langchain_core._api import deprecated
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from langchain_core.callbacks import CallbackManagerForChainRun
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from langchain_core.language_models import BaseLanguageModel
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from langchain_core.prompts import BasePromptTemplate
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@ -14,8 +15,53 @@ from langchain.chains.llm import LLMChain
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from langchain.chains.qa_generation.prompt import PROMPT_SELECTOR
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@deprecated(
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since="0.2.7",
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alternative=(
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"example in API reference with more detail: "
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"https://api.python.langchain.com/en/latest/chains/langchain.chains.qa_generation.base.QAGenerationChain.html" # noqa: E501
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),
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removal="1.0",
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)
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class QAGenerationChain(Chain):
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"""Base class for question-answer generation chains."""
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"""Base class for question-answer generation chains.
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This class is deprecated. See below for an alternative implementation.
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Advantages of this implementation include:
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- Supports async and streaming;
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- Surfaces prompt and text splitter for easier customization;
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- Use of JsonOutputParser supports JSONPatch operations in streaming mode,
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as well as robustness to markdown.
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.. code-block:: python
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from langchain.chains.qa_generation.prompt import CHAT_PROMPT as prompt
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# Note: import PROMPT if using a legacy non-chat model.
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from langchain_core.output_parsers import JsonOutputParser
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from langchain_core.runnables import (
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RunnableLambda,
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RunnableParallel,
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RunnablePassthrough,
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)
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from langchain_core.runnables.base import RunnableEach
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from langchain_openai import ChatOpenAI
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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llm = ChatOpenAI()
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text_splitter = RecursiveCharacterTextSplitter(chunk_overlap=500)
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split_text = RunnableLambda(
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lambda x: text_splitter.create_documents([x])
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)
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chain = RunnableParallel(
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text=RunnablePassthrough(),
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questions=(
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split_text | RunnableEach(bound=prompt | llm | JsonOutputParser())
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)
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)
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"""
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llm_chain: LLMChain
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"""LLM Chain that generates responses from user input and context."""
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