langchain: deprecate LLMChain, RetrievalQA, and ConversationalRetrievalChain (#20751)

docs/docs/use_cases/question_answering/chat_history.ipynb

@@ -403,16 +403,13 @@
    "outputs": [],
    "source": [
     "import bs4\n",
-    "from langchain import hub\n",
     "from langchain.chains import create_history_aware_retriever, create_retrieval_chain\n",
     "from langchain.chains.combine_documents import create_stuff_documents_chain\n",
     "from langchain_chroma import Chroma\n",
     "from langchain_community.chat_message_histories import ChatMessageHistory\n",
     "from langchain_community.document_loaders import WebBaseLoader\n",
     "from langchain_core.chat_history import BaseChatMessageHistory\n",
-    "from langchain_core.output_parsers import StrOutputParser\n",
     "from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
-    "from langchain_core.runnables import RunnablePassthrough\n",
     "from langchain_core.runnables.history import RunnableWithMessageHistory\n",
     "from langchain_openai import ChatOpenAI, OpenAIEmbeddings\n",
     "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",

libs/langchain/langchain/chains/conversational_retrieval/base.py

@@ -7,6 +7,7 @@ from abc import abstractmethod
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
 
+from langchain_core._api import deprecated
 from langchain_core.callbacks import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -233,9 +234,84 @@ class BaseConversationalRetrievalChain(Chain):
         super().save(file_path)
 
 
+@deprecated(
+    since="0.1.17",
+    alternative=(
+        "create_history_aware_retriever together with create_retrieval_chain "
+        "(see example in docstring)"
+    ),
+    removal="0.3.0",
+)
 class ConversationalRetrievalChain(BaseConversationalRetrievalChain):
     """Chain for having a conversation based on retrieved documents.
 
+    This class is deprecated. See below for an example implementation using
+    `create_retrieval_chain`. Additional walkthroughs can be found at
+    https://python.langchain.com/docs/use_cases/question_answering/chat_history
+
+        .. code-block:: python
+
+            from langchain.chains import (
+                create_history_aware_retriever,
+                create_retrieval_chain,
+            )
+            from langchain.chains.combine_documents import create_stuff_documents_chain
+            from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+            from langchain_openai import ChatOpenAI
+
+
+            retriever = ...  # Your retriever
+
+            llm = ChatOpenAI()
+
+            # Contextualize question
+            contextualize_q_system_prompt = (
+                "Given a chat history and the latest user question "
+                "which might reference context in the chat history, "
+                "formulate a standalone question which can be understood "
+                "without the chat history. Do NOT answer the question, just "
+                "reformulate it if needed and otherwise return it as is."
+            )
+            contextualize_q_prompt = ChatPromptTemplate.from_messages(
+                [
+                    ("system", contextualize_q_system_prompt),
+                    MessagesPlaceholder("chat_history"),
+                    ("human", "{input}"),
+                ]
+            )
+            history_aware_retriever = create_history_aware_retriever(
+                llm, retriever, contextualize_q_prompt
+            )
+
+            # Answer question
+            qa_system_prompt = (
+                "You are an assistant for question-answering tasks. Use "
+                "the following pieces of retrieved context to answer the "
+                "question. If you don't know the answer, just say that you "
+                "don't know. Use three sentences maximum and keep the answer "
+                "concise."
+                "\n\n"
+                "{context}"
+            )
+            qa_prompt = ChatPromptTemplate.from_messages(
+                [
+                    ("system", qa_system_prompt),
+                    MessagesPlaceholder("chat_history"),
+                    ("human", "{input}"),
+                ]
+            )
+            # Below we use create_stuff_documents_chain to feed all retrieved context
+            # into the LLM. Note that we can also use StuffDocumentsChain and other
+            # instances of BaseCombineDocumentsChain.
+            question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
+            rag_chain = create_retrieval_chain(
+                history_aware_retriever, question_answer_chain
+            )
+
+            # Usage:
+            chat_history = []  # Collect chat history here (a sequence of messages)
+            rag_chain.invoke({"input": query, "chat_history": chat_history})
+
     This chain takes in chat history (a list of messages) and new questions,
     and then returns an answer to that question.
     The algorithm for this chain consists of three parts:

libs/langchain/langchain/chains/llm.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 import warnings
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Union, cast
 
+from langchain_core._api import deprecated
 from langchain_core.callbacks import (
     AsyncCallbackManager,
     AsyncCallbackManagerForChainRun,
@@ -34,9 +35,31 @@ from langchain_core.utils.input import get_colored_text
 from langchain.chains.base import Chain
 
 
+@deprecated(
+    since="0.1.17",
+    alternative="RunnableSequence, e.g., `prompt | llm`",
+    removal="0.3.0",
+)
 class LLMChain(Chain):
     """Chain to run queries against LLMs.
 
+    This class is deprecated. See below for an example implementation using
+    LangChain runnables:
+
+        .. code-block:: python
+
+            from langchain_core.prompts import PromptTemplate
+            from langchain_openai import OpenAI
+
+            prompt_template = "Tell me a {adjective} joke"
+            prompt = PromptTemplate(
+                input_variables=["adjective"], template=prompt_template
+            )
+            llm = OpenAI()
+            chain = prompt | llm
+
+            chain.invoke("your adjective here")
+
     Example:
         .. code-block:: python
 

libs/langchain/langchain/chains/retrieval_qa/base.py

@@ -6,6 +6,7 @@ import warnings
 from abc import abstractmethod
 from typing import Any, Dict, List, Optional
 
+from langchain_core._api import deprecated
 from langchain_core.callbacks import (
     AsyncCallbackManagerForChainRun,
     CallbackManagerForChainRun,
@@ -194,9 +195,41 @@ class BaseRetrievalQA(Chain):
             return {self.output_key: answer}
 
 
+@deprecated(since="0.1.17", alternative="create_retrieval_chain", removal="0.3.0")
 class RetrievalQA(BaseRetrievalQA):
     """Chain for question-answering against an index.
 
+    This class is deprecated. See below for an example implementation using
+    `create_retrieval_chain`:
+
+        .. code-block:: python
+
+            from langchain.chains import create_retrieval_chain
+            from langchain.chains.combine_documents import create_stuff_documents_chain
+            from langchain_core.prompts import ChatPromptTemplate
+            from langchain_openai import ChatOpenAI
+
+
+            retriever = ...  # Your retriever
+            llm = ChatOpenAI()
+
+            system_prompt = (
+                "Use the given context to answer the question. "
+                "If you don't know the answer, say you don't know. "
+                "Use three sentence maximum and keep the answer concise."
+                "\n\n{context}"
+            )
+            prompt = ChatPromptTemplate.from_messages(
+                [
+                    ("system", system_prompt),
+                    ("human", "{input}"),
+                ]
+            )
+            question_answer_chain = create_stuff_documents_chain(llm, prompt)
+            chain = create_retrieval_chain(retriever, question_answer_chain)
+
+            chain.invoke({"input": query})
+
     Example:
         .. code-block:: python