python - 有没有办法可以处理 Langchain QA 检索中的上下文和一般问题？

Question

我想制作一个聊天机器人，它应该回答上下文中的问题，就我而言，是一个矢量数据库。它正在完美地做到这一点。但我也希望它能够回答矢量数据库中没有的问题。但它无法这样做。它只能从上下文中回答。

这是我为此的提示模板:

template = """Answer the question in your own words from the 
context given to you.
If questions are asked where there is no relevant context available, please answer from 
what you know.

Context: {context}
Chat history: {chat_history}

Human: {question}
Assistant:"""

我的提示如下:

prompt = PromptTemplate(
input_variables=["context", "chat_history", "question"], template=template

)

为了内存，我提供了一个初始问题:

memory.save_context({"input": "Who is the founder of India?"},
                {"output": "Gandhi"})

对于 QA 检索，我使用以下代码:

qa = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever(),
memory=memory,

chain_type_kwargs={'prompt': prompt}

)

但是当我问一个问题时:

question= "What did I ask about India?"
result = qa({"query": question})

对此没有任何答案。虽然这个问题是保存在聊天记录里的。它只能回答矢量数据库中的问题。我将非常感谢您的帮助。

Answer 1

下面是默认存储历史记录的代码，如果文档存储中没有答案，它将从 llm 获取结果。

    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,RetrievalQA
    from langchain.document_loaders import TextLoader
    from langchain.memory import ConversationBufferMemory
    from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
    from langchain.prompts import PromptTemplate

    loader = TextLoader("fatherofnation.txt")
    documents = loader.load()

    template = """Answer the question in your own words from the 
    context given to you.
    If questions are asked where there is no relevant context available, please answer from 
    what you know.

    Context: {context}

    Human: {question}
    Assistant:"""

    prompt = PromptTemplate(
    input_variables=["context",  "question"], template=template)

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

    embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")

    vectorstore = Chroma.from_documents(documents, embedding_function)

    llm = "your llm model here"

    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

    memory.save_context({"input": "Who is the founder of India?"},
                    {"output": "Gandhi"})

    qa = RetrievalQA.from_chain_type(llm, retriever=vectorstore.as_retriever(), memory=memory,chain_type_kwargs={'prompt': prompt}
    )

    # question = "Who is the father of India nation?"
    # result = qa({"query": question})
    # print(result)

    question1= "What did I ask about India?"
    result1 = qa({"query": question1})
    print(result1)

    question1= "Tell me about google in short ?"
    result1 = qa({"query": question1})
    print(result1)