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

About This Course

Building an intelligent Q&A bot powered by retrieval-augmented generation (RAG) is a powerful way to enable sophisticated question-answering systems. This hands-on course is designed to help you build, deploy, and optimize a RAG-based Q&A bot using tools like FastAPI, LangChain, FAISS, and OpenAI.

Participants will learn the end-to-end process of creating a Q&A bot, from setting up the development environment to integrating the language model and document embedding for efficient vector retrieval. You will also gain skills in optimizing performance, ensuring your bot handles real-time queries effectively. This course is ideal for developers and AI enthusiasts who want to explore building AI-driven Q&A systems with modern tools.

Aim

The aim of this course is to equip participants with the skills to build, deploy, and optimize a RAG-powered Q&A bot, covering the complete workflow from document ingestion, vector retrieval, to the integration of language models via FastAPI.

Course Objectives

By the end of this course, participants will:

• Be able to set up a Python environment and install the required dependencies for the project

• Learn to ingest documents and generate embeddings for efficient vector retrieval

• Implement a vector store and retrieval function for fast querying

• Build a QA chain that uses the retrieved context for answering questions

• Expose the bot’s capabilities via a FastAPI endpoint for real-time querying

• Gain hands-on experience with testing, debugging, and performance optimization

Course Structure

Module 1: Environment Setup & Document Ingestion

• Setting Up the Development Environment:

  • Spin up a Python virtual environment for isolation

  • Install necessary dependencies using pip (langchain, faiss-cpu, openai, dotenv, fastapi, uvicorn)

  • Configure the environment by creating a .env file for storing your OpenAI API key

• Document Ingestion & Embedding:

  • Write a script to load text or PDF documents from the ./docs/ directory

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

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

• Hands-On Session:

  • Preprocess and ingest sample documents to prepare them for the next steps

Module 2: Vector Store, Retrieval, and QA Chain Implementation

• Setting Up the Vector Store:

  • Initialize FAISS for storing and searching document vectors

  • Implement the retrieve(query) function for efficient document retrieval

• QA Chain Implementation:

  • Define a QA chain that retrieves relevant context for answering questions

  • Integrate LLMChain or RetrievalQA from LangChain to process the query

• Hands-On Session:

  • Test the QA system by asking 2–3 different questions and verifying the quality of responses

Module 3: API Endpoint, Testing, and Performance Optimization

• Building the API Endpoint:

  • Scaffold a FastAPI app and expose the bot’s functionality via a /qa POST endpoint

  • Integrate the retrieve function and LLMChain inside this endpoint for real-time querying

• Testing and Debugging:

  • Test the system using curl or Postman to ensure proper functionality

  • Handle edge cases where no results are found (e.g., return a message like “No context found”)

• Performance Optimization:

  • Experiment with different chunk sizes and k-values to optimize retrieval speed

  • Measure latency for sample queries and optimize as needed

• Hands-On Session:

  • Complete a performance check and make necessary adjustments to the system for improved query handling

Who Should Enrol?

• Developers, data scientists, and AI enthusiasts with a basic understanding of Python programming and machine learning concepts

• Those looking to gain hands-on experience building AI-driven Q&A systems

• Individuals familiar with APIs, natural language processing (NLP), and vector databases like FAISS (helpful but not required)

• Students and researchers looking to expand their knowledge in AI models, generative systems, and real-time deployment