Hands-On Workshop: Building a RAG-Powered Q&A Bot

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Ensure the environment is properly configured for the project by following these steps:

• Spin up a Python virtual environment (venv) for isolation.

• Install necessary dependencies using pip:

  bash

  pip install langchain faiss-cpu openai dotenv fastapi uvicorn


• Create a .env file to store your API key for OpenAI integration.

Integrate document ingestion and text embedding with the following process:

• Write a script to load sample text/PDFs from the ./docs/ directory.

• Chunk the texts into manageable segments (e.g., 500 tokens per chunk).

• Use OpenAIEmbeddings to generate embeddings and store them in FAISS for efficient retrieval.

Set up the vector store and implement a retrieval function:

• Initialize the FAISS index in your code for storing and searching vectors.

• Implement the retrieve(query): function using index.similarity_search.

• Perform quick tests by printing out retrieved chunks for sample queries.

Define a QA chain that utilizes the retrieved context for answering questions:

• Create a prompt template that dynamically injects the retrieved context into the model.

• Integrate the LLMChain (or RetrievalQA from LangChain) to process the query.
  Example code:

  docs = retrieve(user_question)
answer = llm_chain.predict(context=docs, question=user_question)


• Test the system by asking 2–3 different questions to check if it retrieves accurate answers.

Expose the solution via an API:

• Scaffold a FastAPI app with a /qa POST endpoint.

• Integrate the retrieve and llm_chain inside this endpoint for real-time querying.

• Test the API via curl or Postman to ensure functionality.

Ensure the system is robust and extendable:

• Handle cases where no results are found: return a message like “No context found.”

• Experiment with different chunk sizes and k-values in retrieval for performance tuning.

• Conduct a performance check: Measure latency for sample queries and optimize as necessary.