Context-Aware Document Q&A
Retrieval-augmented generation over PDFs and web content, with a Streamlit interface.
A retrieval-augmented generation system for querying documents in natural language.
Pipeline. Documents (PDFs or scraped web content) are chunked and embedded with sentence-transformers, indexed in a FAISS vector store, and retrieved at query time to condition a Gemini call. A Streamlit front end handles upload and chat.
«FILL: The decisions worth writing about. Chunk size and overlap — what did you settle on and why? How many chunks do you retrieve? Did you try reranking? Where does it still fail — questions spanning multiple documents, tables, anything needing numbers?
Retrieval quality is where every RAG system actually lives or dies, and a page that discusses it honestly reads completely differently from one that lists the stack.»
«TODO: deploy on Streamlit Community Cloud (free) and link it. It’s already a Streamlit app, so this is close to zero work and turns the page into something someone can try.»