Build an Advanced RAG Chatbot with Hybrid Retrieval SystemBuild an Advanced RAG Chatbot with Hybrid Retrieval System
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Ahamed Shahmi
• May 23
Built an agentic RAG chatbot enabling natural language conversations over 25 landmark AI/ML research papers from ArXiv, with source-attributed answers and page-level citations.
Designed a 5-category intent classification system using zero-temperature Gemini to route messages - eliminating unnecessary vector.
Implemented hybrid retrieval combining FAISS vector search (60%) and BM25 keyword search (40%) using LangChain EnsembleRetriever, improving recall for both semantic queries and exact terminology.
Architected a two-layer memory system - short-term session memory and long-term cross-session memory, injected into the system prompt for personalised responses.
Deployed FastAPI backend on AWS EC2 with Ngrok HTTPS tunnel and React frontend on Vercel.
Technologies: Python, FastAPI, LangChain, HuggingFace Transformers, FAISS, BM25, Google Gemini, SQLite, React, AWS EC2, Vercel.
AI DevelopmentAI Engineer AWSFastAPILangChainAI Chatbot Development
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