How does Conversational Retrieval QA Chain different from Retrieval Qa chain

Question

After I pass the memory to the chain, they seem to perform the same. RetrievalQA chain with memory has conversation history as context. It also responses to follow-up questions similarly to ConversatonalRetrievalQA Chain. So, what is the difference?

This is my code for the RetrievalQA chain.

        qa = RetrievalQA.from_chain_type(
            llm=llm,
            chain_type="stuff",  
            retriever=retriever,
            return_source_documents=True,  
            callbacks=callback_manager,
            chain_type_kwargs={"prompt": prompt, "memory": memory},
        )

Answer 1

Key Differences

History and Context:

Retrieval QA Chain: Focuses on answering a single question based on a provided set of documents. It doesn't maintain a memory of previous interactions.

Conversational Retrieval QA Chain: Designed for multi-turn conversations. It retains the history of past questions and answers, allowing it to provide more contextually relevant responses in subsequent turns. Use Cases:

Retrieval QA Chain: Ideal for standalone question-answering systems where each question is independent.

Conversational Retrieval QA Chain: Better suited for creating chatbots, virtual assistants, or any application where a continuous dialogue is expected.

How They Work

Retrieval QA Chain:

A question is provided as input. A retriever searches through relevant documents to find passages that might contain the answer. A language model (LLM) processes the retrieved passages and the question to generate the final answer.

Conversational Retrieval QA Chain:

Leverages the Retrieval QA Chain but adds a conversational memory component. The current question, along with the conversation history (previous questions and answers), are fed into the retrieval process. The retriever takes this context into account to retrieve more relevant passages. The LLM uses the retrieved passages, the question, and the conversation context to generate an answer, ensuring continuity and relevance within the conversation.

Example Imagine a chatbot answering questions about a company's products:

Retrieval QA Chain

User Question 1: "What is the price of Product X?" (Answer provided) User Question 2: "Does it come in other colors?" (May not understand the "it" without context) Conversational Retrieval QA Chain

User Question 1: "What is the price of Product X?" (Answer provided) User Question 2: "Does it come in other colors?" (Understands "it" refers to Product X, providing a contextually accurate answer)

In Summary The Conversational Retrieval QA Chain builds upon the Retrieval QA Chain by adding the ability to handle conversational context. If you're building a system designed to have continuous conversations, the Conversational Retrieval QA Chain is likely the better choice.

Lastly, please upvote or accept my answer if it helps you in anyway.