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DocuMind AI is a local-first Retrieval-Augmented Generation (RAG) application that lets users upload documents and ask grounded questions about their content.
Built with Python, Streamlit, LangChain, FAISS, SentenceTransformers, and Ollama, the app supports PDF, DOCX, and TXT files. It processes uploaded files into semantic chunks, generates embeddings, stores them in a persistent FAISS vector index, and retrieves the most relevant context for each question before generating a response with a locally hosted LLM.
Key capabilities include document ingestion, vector search, conversational context, source-file references, knowledge-base rebuilding, and Docker-based deployment. The project demonstrates an end-to-end GenAI workflow with a modular architecture designed for local experimentation and privacy-conscious document querying.
Tech stack: Python, Streamlit, LangChain, FAISS, SentenceTransformers, Ollama, Docker.
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