CODEWITHSUNDEEP
pythonopenai-apilangchainchatgpt-apiazure-openai
Usman Afridi
Usman Afridi

Reputation: 189

Is there a way I can handle context and general questions in Langchain QA Retrieval?

I want to make a chatbot, that should answer questions from the context, in my case, a vector database. It is doing that perfectly. But I also want it to answer questions, which are not in the vector database. But it is unable to do so. It only is able to answer from the context.

This is the prompt template I have for this:

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:"""

My prompt is as follows:

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

)

For the memory, I provided an initial question:

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

For the QA Retrieval, I am using the following code:

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

chain_type_kwargs={'prompt': prompt}

)

But when I ask about a question:

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

It doesn't have any answer for that. Although this question is stored in the chat history. It is only able to answer questions from the vector database. I will greatly appreciate a help in this.

Upvotes: 2

Views: 6177

Answers (1)

ZKS
ZKS

Reputation: 2876

Below is the code that stores history by default, if there is no answer in doc store, it will fetch result from llm.

    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)

Upvotes: 3

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