langchain[patch]: deprecate QAGenerationChain (#23730)

libs/langchain/langchain/chains/qa_generation/base.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import json
 from typing import Any, Dict, List, Optional
 
+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
@@ -14,8 +15,53 @@ from langchain.chains.llm import LLMChain
 from langchain.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"  # noqa: E501
+    ),
+    removal="1.0",
+)
 class QAGenerationChain(Chain):
-    """Base class for question-answer generation chains."""
+    """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.
+
+        .. code-block:: python
+
+            from langchain.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
+
+            llm = ChatOpenAI()
+            text_splitter = RecursiveCharacterTextSplitter(chunk_overlap=500)
+            split_text = RunnableLambda(
+                lambda x: text_splitter.create_documents([x])
+            )
+
+            chain = RunnableParallel(
+                text=RunnablePassthrough(),
+                questions=(
+                    split_text | RunnableEach(bound=prompt | llm | JsonOutputParser())
+                )
+            )
+    """
 
     llm_chain: LLMChain
     """LLM Chain that generates responses from user input and context."""