Generative AI
ResearchRAG
Document-Based Retrieval-Augmented Generation System
- Built an end-to-end RAG application for asking questions across uploaded TXT, PDF and DOCX research documents.
- Implemented document extraction, chunking, Gemini embeddings, semantic retrieval with Qdrant and LLM-powered answer generation.
- Built a FastAPI backend and React frontend with document upload, duplicate detection, document management and question answering.
- Deployed the frontend on Vercel and backend on Render with production environment configuration and CORS support.
PythonFastAPIReactGoogle GeminiQdrantRAGEmbeddingsVercelRender