Langchain RAG with conversation memory

API
1

Hi, I want to use Pinecone database to limit gpt-3.5 knowledge. I found a good example here, which I want to use.

(https://github.com/pinecone-io/examples/blob/master/learn/generation/langchain/handbook/05-langchain-retrieval-augmentation.ipynb)

Help me to finish the code from example with RetrievalQAWithSourcesChain, so that chatbot can memorize the conversation.

2

Welcome!

Are you trying to find ChatGPT?

chat.openai.com

3

GPT4 do not provide working solution.

4

I found a solution to my problem.

from langchain.memory import ConversationBufferMemory  

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, input_key="question", output_key="answer")

qa_with_sources = RetrievalQAWithSourcesChain.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(),
    memory=memory
)
query = "Who was Benito Mussolini?"
res = qa_with_sources.invoke(query)
print(res)

# To continue the conversation, use the same qa_with_sources instance
query2 = "At what time he was in power?"
res2 = qa_with_sources.invoke(query2)
print(res2)
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