Capstone — BS Degree Chatbot#

Objective#

Build a production-grade RAG chatbot that answers user questions about the IIT Madras BS Data Science programme using official documentation.

Requirements#

1. Scrape academics, courses, and qualifier pages from the official IIT Madras BS program site.
2. Build an ingestion pipeline using a vector database (e.g., Qdrant) with contextual chunking.
3. Implement a hybrid retrieval strategy combining dense vector embeddings and BM25 sparse index, merged via RRF and an optional reranker.
4. Build a FastAPI backend with a /chat endpoint and a Streamlit-based UI.
5. Setup a RAGAS evaluation suite to measure retrieval and generation accuracy across different retrieval strategies.

Deliverables#

• Source code.
• Link to the live Streamlit chatbot application.
• RAGAS evaluation report comparing Naive, Hybrid, and Contextual RAG.