RAG Document Q&A Assistance

Document Q&A Assistance is helpful for users who want quick details about any uploaded documents.It allows user to upload documents and ask questions about it.This system used Retrieval Augmented Generation to provide accurate, context-aware answers instead of hallucinated responses.

Built to demonstrate real-world LLM engineering skills including vector search, embeddings, chunking strategies, and prompt orchestration.

Features of this Project

• ✅ Upload PDF / text documents
• ✅ Intelligent document chunking
• ✅ Semantic search using embeddings
• ✅ Context-aware answers using LLM
• ✅ Reduced hallucinations with RAG architecture
• ✅ Fast retrieval from vector database
• ✅ Conversational Q&A capability

Working

User Query -> Embedding Model -> Vector Database -> Top Relevant Chunks -> LLM + Prompt Template -> Accurate Answer