你需要在链“return_source_documents”中添加参数，如下所示

qa = ConversationalRetrievalChain.from_llm(
    llm,
    retriever,
    condense_question_prompt=CUSTOM_QUESTION_PROMPT,
    memory=memory,
    return_source_documents=True
)

query = "what are cars made of?"
result = qa({"question": query})

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结果你会得到你的源文档沿着的相似性分数
获取所有相关文件

answer = result.get("answer")

docs = result.get("source_documents", [])

型

为了解决这个问题，我不得不将链类型更改为RetrievalQA并引入代理和工具。

import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.agents import AgentExecutor, Tool,initialize_agent
from langchain.agents.types import AgentType

os.environ['OPENAI_API_KEY'] = ''

system_message = """
"You are the XYZ bot."
"This is conversation with a human. Answer the questions you get based on the knowledge you have."
"If you don't know the answer, just say that you don't, don't try to make up an answer."
"""

llm = ChatOpenAI(
    model_name="gpt-3.5-turbo",  # Name of the language model
    temperature=0  # Parameter that controls the randomness of the generated responses
)

embeddings = OpenAIEmbeddings()

docs = [
    "Buildings are made out of brick",
    "Buildings are made out of wood",
    "Buildings are made out of stone",
    "Buildings are made out of atoms",
    "Buildings are made out of building materials",
    "Cars are made out of metal",
    "Cars are made out of plastic",
  ]

vectorstore = FAISS.from_texts(docs, embeddings)

retriever = vectorstore.as_retriever()

memory = ConversationBufferMemory(memory_key="chat_history", input_key='input', return_messages=True, output_key='output')

qa = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=vectorstore.as_retriever(),
        verbose=True,
        return_source_documents=True
    )

tools = [
        Tool(
            name="doc_search_tool",
            func=qa,
            description=(
               "This tool is used to retrieve information from the knowledge base"
            )
        )
    ]

agent = initialize_agent(
        agent = AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
        tools=tools,
        llm=llm,
        memory=memory,
        return_source_documents=True,
        return_intermediate_steps=True,
        agent_kwargs={"system_message": system_message}
        )

query1 = "what are buildings made of?"
result1 = agent(query1)

query2 = "who are you?"
result2 = agent(query2)

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如果结果访问源，则它将具有键"intermediate_steps"的值，然后源文档可以通过result1["intermediate_steps"][0][1]["source_documents"]访问
否则，当查询不需要源时，result2["intermediate_steps"]将为空。

很抱歉问到这里，我不能添加评论。我的问题是：当你添加代理时，你的答案是否变短了？你能解决这个问题吗？

这帮助我获得了源文档的URL：

for x in range(len(response["source_documents"][0].metadata)):
    print(response["source_documents"][x].metadata)

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还有这个版本来更好地格式化它：

for x in range(len(response["source_documents"][0].metadata)):
        raw_dict = response["source_documents"][x].metadata
        print("Page number:", raw_dict['page'], "Filename:", raw_dict['source'])

型