AskDocs.

A retrieval-augmented generation system that lets users query uploaded documents in natural language and get accurate, cited answers — not hallucinated summaries. Built the full ingestion pipeline: PDF parsing with layout-aware chunking, semantic embedding via LangChain, and vector storage in Pinecone with metadata filtering. The retrieval layer uses hybrid search (dense + sparse) with reranking to surface the most relevant chunks before passing them to the LLM. Every response includes source citations with page numbers so users can verify answers against the original document. Designed the system to handle documents up to 200 pages without degrading response quality.