Implement API for Q&A Using RetrievalQAChain

To recap, we have generated the vector embeddings using OpenAIEmbeddings earlier. Now, we will use the RetrievalQAChain to get answers for the user inputs using the embeddings.

Use RetrievalQAChain to get answers from LLM

Now comes the most important part. We have the vector embeddings created and now we need to do two things:

• Retrieve the documents from a set of documents based on the input question.

• Generate a final answer based on the retrieved documents.

Langchain provides a chained workflow of the above two steps. We will use RetrievalQAChain that will take care of the above steps, a nice prompt, and finally return us the response. Let's discuss RetrievalQAChain in detail.

RetrievalQAChain is a component in LangChain designed for question-answering tasks. It tackles the challenge of finding the most relevant information before feeding it to an LLM for answer generation. Below is a simple flow that is followed by RetrievalQAChain:

• The user poses a question.

• RetrievalQAChain utilizes the BaseRetriever to search through a document corpus (for example, our PDF document) and identify the most relevant sections/pages from the documents.

• These retrieved passages are then passed on to the BaseLanguageModel (for example, GPT-3).

• The LLM analyzes the context provided by the retrieved text and generates the answer to the question.

RetrievalQAChain improves the accuracy of question answering tasks by ensuring the LLM focuses on the most pertinent information and reduces the amount of data the LLM needs to process, making the system more efficient.

Let's jump into the code.