从ConversationalRetrievalChain迁移

ConversationalRetrievalChain 是一种将检索增强生成与聊天历史结合在一起的全能方式，允许您与文档进行“聊天”。

切换到LCEL实现的优势与RetrievalQA迁移指南类似：

• 更清晰的内部结构。ConversationalRetrievalChain链隐藏了一个完整的问题重述步骤，该步骤将初始查询与聊天历史进行解引用。
• 这意味着该类包含两组可配置的提示词、大型语言模型等。
• 更容易返回源文档。
• 支持可运行的方法，如流式处理和异步操作。

以下是具有自定义提示词的等效实现。 我们将使用以下摄取代码将Lilian Weng关于自主代理的博客文章加载到本地向量存储中：

共享设置

对于这两个版本，我们需要使用 WebBaseLoader 文档加载器加载数据，使用 RecursiveCharacterTextSplitter 进行分割，并将其添加到内存中的 FAISS 向量存储中。

我们还将实例化一个聊天模型以供使用。

%pip install --upgrade --quiet langchain-community langchain langchain-openai faiss-cpu beautifulsoup4

import os
from getpass import getpass

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass()

<!--IMPORTS:[{"imported": "WebBaseLoader", "source": "langchain_community.document_loaders", "docs": "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html", "title": "Migrating from ConversationalRetrievalChain"}, {"imported": "FAISS", "source": "langchain_community.vectorstores", "docs": "https://python.langchain.com/api_reference/community/vectorstores/langchain_community.vectorstores.faiss.FAISS.html", "title": "Migrating from ConversationalRetrievalChain"}, {"imported": "ChatOpenAI", "source": "langchain_openai.chat_models", "docs": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html", "title": "Migrating from ConversationalRetrievalChain"}, {"imported": "OpenAIEmbeddings", "source": "langchain_openai.embeddings", "docs": "https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html", "title": "Migrating from ConversationalRetrievalChain"}, {"imported": "RecursiveCharacterTextSplitter", "source": "langchain_text_splitters", "docs": "https://python.langchain.com/api_reference/text_splitters/character/langchain_text_splitters.character.RecursiveCharacterTextSplitter.html", "title": "Migrating from ConversationalRetrievalChain"}]-->
# Load docs
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import FAISS
from langchain_openai.chat_models import ChatOpenAI
from langchain_openai.embeddings import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = loader.load()

# Split
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)

# Store splits
vectorstore = FAISS.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())

# LLM
llm = ChatOpenAI()

旧版

Details

<!--IMPORTS:[{"imported": "ConversationalRetrievalChain", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.conversational_retrieval.base.ConversationalRetrievalChain.html", "title": "Migrating from ConversationalRetrievalChain"}, {"imported": "ChatPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html", "title": "Migrating from ConversationalRetrievalChain"}]-->
from langchain.chains import ConversationalRetrievalChain
from langchain_core.prompts import ChatPromptTemplate

condense_question_template = """
Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.

Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""

condense_question_prompt = ChatPromptTemplate.from_template(condense_question_template)

qa_template = """
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, say that you
don't know. Use three sentences maximum and keep the
answer concise.

Chat History:
{chat_history}

Other context:
{context}

Question: {question}
"""

qa_prompt = ChatPromptTemplate.from_template(qa_template)

convo_qa_chain = ConversationalRetrievalChain.from_llm(
    llm,
    vectorstore.as_retriever(),
    condense_question_prompt=condense_question_prompt,
    combine_docs_chain_kwargs={
        "prompt": qa_prompt,
    },
)

convo_qa_chain(
    {
        "question": "What are autonomous agents?",
        "chat_history": "",
    }
)

{'question': 'What are autonomous agents?',
 'chat_history': '',
 'answer': 'Autonomous agents are entities empowered with capabilities like planning, task decomposition, and memory to perform complex tasks independently. These agents can leverage tools like browsing the internet, reading documentation, executing code, and calling APIs to achieve their objectives. They are designed to handle tasks like scientific discovery and experimentation autonomously.'}

LangChain表达式 (LCEL)

Details

<!--IMPORTS:[{"imported": "create_history_aware_retriever", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.history_aware_retriever.create_history_aware_retriever.html", "title": "Migrating from ConversationalRetrievalChain"}, {"imported": "create_retrieval_chain", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html", "title": "Migrating from ConversationalRetrievalChain"}, {"imported": "create_stuff_documents_chain", "source": "langchain.chains.combine_documents", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html", "title": "Migrating from ConversationalRetrievalChain"}]-->
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain

condense_question_system_template = (
    "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."
)

condense_question_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", condense_question_system_template),
        ("placeholder", "{chat_history}"),
        ("human", "{input}"),
    ]
)
history_aware_retriever = create_history_aware_retriever(
    llm, vectorstore.as_retriever(), condense_question_prompt
)

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, say that you "
    "don't know. Use three sentences maximum and keep the "
    "answer concise."
    "\n\n"
    "{context}"
)

qa_prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        ("placeholder", "{chat_history}"),
        ("human", "{input}"),
    ]
)
qa_chain = create_stuff_documents_chain(llm, qa_prompt)

convo_qa_chain = create_retrieval_chain(history_aware_retriever, qa_chain)

convo_qa_chain.invoke(
    {
        "input": "What are autonomous agents?",
        "chat_history": [],
    }
)

{'input': 'What are autonomous agents?',
 'chat_history': [],
 'context': [Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Boiko et al. (2023) also looked into LLM-empowered agents for scientific discovery, to handle autonomous design, planning, and performance of complex scientific experiments. This agent can use tools to browse the Internet, read documentation, execute code, call robotics experimentation APIs and leverage other LLMs.\nFor example, when requested to "develop a novel anticancer drug", the model came up with the following reasoning steps:'),
  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Weng, Lilian. (Jun 2023). “LLM-powered Autonomous Agents”. Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/.'),
  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content='Fig. 1. Overview of a LLM-powered autonomous agent system.\nComponent One: Planning#\nA complicated task usually involves many steps. An agent needs to know what they are and plan ahead.\nTask Decomposition#'),
  Document(metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'}, page_content="LLM Powered Autonomous Agents | Lil'Log\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nLil'Log\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPosts\n\n\n\n\nArchive\n\n\n\n\nSearch\n\n\n\n\nTags\n\n\n\n\nFAQ\n\n\n\n\nemojisearch.app\n\n\n\n\n\n\n\n\n\n      LLM Powered Autonomous Agents\n    \nDate: June 23, 2023  |  Estimated Reading Time: 31 min  |  Author: Lilian Weng\n\n\n \n\n\nTable of Contents\n\n\n\nAgent System Overview\n\nComponent One: Planning\n\nTask Decomposition\n\nSelf-Reflection\n\n\nComponent Two: Memory\n\nTypes of Memory\n\nMaximum Inner Product Search (MIPS)")],
 'answer': 'Autonomous agents are entities that can act independently to achieve specific goals or tasks without direct human intervention. These agents have the ability to perceive their environment, make decisions, and take actions based on their programming or learning. They can perform tasks such as planning, execution, and problem-solving autonomously.'}

下一步

您现在已经了解如何将一些旧版链的现有用法迁移到LCEL。

接下来，请查看LCEL概念文档以获取更多背景信息。