在文档上进行聊天并保留聊天历史记录　（Chat Over Documents with Chat History）

本文档介绍了如何使用ConversationalRetrievalChain来设置一个链条，通过文档进行聊天并保留聊天历史记录。这个链条和RetrievalQAChain的唯一区别在于它允许传入聊天历史记录，这可以用于进行后续问题的提问。

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import ConversationalRetrievalChain

加载文档。你可以将其替换为你想要的任何类型的数据加载器。

from langchain.document_loaders import TextLoader
loader = TextLoader("../../state_of_the_union.txt")
documents = loader.load()

如果你有多个加载器要合并，可以这样做：

# loaders = [....]
# docs = []
# for loader in loaders:
#     docs.extend(loader.load())

然后，我们将文档拆分、创建嵌入并将它们放入向量存储中。这样我们就可以在它们上进行语义搜索了。

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)

Using embedded DuckDB without persistence: data will be transient

现在我们可以创建一个内存对象，用于跟踪输入/输出并保存对话。

from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

现在我们可以初始化ConversationalRetrievalChain。

qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), memory=memory)

query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query})

result["answer"]

" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."

query = "Did he mention who she suceeded"
result = qa({"question": query})

result['answer']

' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'

传递聊天历史记录

在上面的例子中，我们使用了一个内存对象来跟踪聊天历史记录。我们也可以直接将聊天历史记录传递进来。为了做到这一点，我们需要初始化一个没有任何内存对象的链条。

qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever())

下面是一个没有聊天历史记录的提问的例子。

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})

result["answer"]

" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."

以下是一个带有一些聊天历史记录的提问的例子。

chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
result = qa({"question": query, "chat_history": chat_history})

result['answer']

' Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.'

使用不同的模型来压缩问题

该链条有两个步骤。首先，它将当前问题和聊天历史记录压缩为一个独立的问题。这是为了创建一个独立的向量以用于检索。然后，它进行检索，然后使用单独的模型进行检索增强生成来回答问题。LangChain的声明性特性的一部分优势在于您可以轻松地为每个调用使用一个单独的语言模型。这在将一个更便宜、更快速的模型用于压缩问题的简单任务，然后使用一个更昂贵的模型来回答问题时非常有用。以下是一个这样做的例子。

from langchain.chat_models import ChatOpenAI

qa = ConversationalRetrievalChain.from_llm(
    ChatOpenAI(temperature=0, model="gpt-4"),
    vectorstore.as_retriever(),
    condense_question_llm = ChatOpenAI(temperature=0, model='gpt-3.5-turbo'),
)

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})

chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
result = qa({"question": query, "chat_history": chat_history})

返回源文档

您还可以轻松地从ConversationalRetrievalChain中返回源文档。这在您想要检查返回的文档时非常有用。

qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})

result['source_documents'][0]

Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../state_of_the_union.txt'})

带有 search_distance 的 ConversationalRetrievalChain

如果您正在使用支持按搜索距离进行过滤的向量数据库，您可以添加一个阈值参数。

vectordbkwargs = {"search_distance": 0.9}

qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), return_source_documents=True)
chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history, "vectordbkwargs": vectordbkwargs})

ConversationalRetrievalChain with map_reduce

我们还可以在 ConversationalRetrievalChain 链中使用不同类型的 Combine Document Chains。

from langchain.chains import LLMChain
from langchain.chains.question_answering import load_qa_chain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT

llm = OpenAI(temperature=0)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(llm, chain_type="map_reduce")

chain = ConversationalRetrievalChain(
    retriever=vectorstore.as_retriever(),
    question_generator=question_generator,
    combine_docs_chain=doc_chain,
)

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})

result['answer']

" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."

使用带有源文档的 ConversationalRetrievalChain 进行问答

您还可以将此链与带有源文档的问答链结合使用。

from langchain.chains.qa_with_sources import load_qa_with_sources_chain

llm = OpenAI(temperature=0)
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_with_sources_chain(llm, chain_type="map_reduce")

chain = ConversationalRetrievalChain(
    retriever=vectorstore.as_retriever(),
    question_generator=question_generator,
    combine_docs_chain=doc_chain,
)

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = chain({"question": query, "chat_history": chat_history})

result['answer']

" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, from a family of public school educators and police officers, a consensus builder, and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \nSOURCES: ../../state_of_the_union.txt"

带有流式输出到 stdout 的 ConversationalRetrievalChain

在此示例中，链的输出将逐个标记地流式传输到 stdout。

from langchain.chains.llm import LLMChain
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT, QA_PROMPT
from langchain.chains.question_answering import load_qa_chain

# 使用流式 LLM 构建一个 ConversationalRetrievalChain 来合并文档
# 并使用单独的非流式 LLM 生成问题
llm = OpenAI(temperature=0)
streaming_llm = OpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()], temperature=0)

question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT)

qa = ConversationalRetrievalChain(
    retriever=vectorstore.as_retriever(), combine_docs_chain=doc_chain, question_generator=question_generator)

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})

 The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.

chat_history = [(query, result["answer"])]
query = "Did he mention who she suceeded"
result = qa({"question": query, "chat_history": chat_history})

 Ketanji Brown Jackson succeeded Justice Stephen Breyer on the United States Supreme Court.

get_chat_history 函数

您还可以指定一个 get_chat_history 函数，用于格式化 chat_history 字符串。

def get_chat_history(inputs) -> str:
    res = []
    for human, ai in inputs:
        res.append(f"Human:{human}\nAI:{ai}")
    return "\n".join(res)
qa = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0), vectorstore.as_retriever(), get_chat_history=get_chat_history)

chat_history = []
query = "What did the president say about Ketanji Brown Jackson"
result = qa({"question": query, "chat_history": chat_history})

result['answer']

" The president said that Ketanji Brown Jackson is one of the nation's top legal minds, a former top litigator in private practice, a former federal public defender, and from a family of public school educators and police officers. He also said that she is a consensus builder and has received a broad range of support from the Fraternal Order of Police to former judges appointed by Democrats and Republicans."