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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.
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Resolve: an AI support agent that answers from a company’s own policies, looks up orders, and escalates weak matches to a human instead of guessing. A human approves replies before they go out.
Live demo: support-resolver.vercel.app
For the human approval step, I'd show the exact policy passage and its effective date beside the draft. Does Resolve flag a reply for re-review if the policy changes while it waits for approval?
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When a CRM-to-database sync fails halfway, how do you handle it: idempotent retries, or a reconciliation pass afterward? That's usually where integrations start to drift.
An AI agent answers questions from real store data. A check layer blocks numbers that AI makes up.
For that check layer, I'd include a test where the answer uses a real store number with the wrong metric or date range. How do you tie each numerical claim back to the source result and its filters?