Project Title:- Custom Enterprise RAG Assistant | Chat with Documents & PDFs

Project Overview: DocuMind is an enterprise-grade Retrieval-Augmented Generation (RAG) assistant designed to eliminate manual data extraction and prevent AI hallucinations. It converts static corporate files (PDFs, reports, resumes, contracts) into an interactive, grounded knowledge engine that provides answers backed by direct page-level citations.

The Problem Solved: * Manual Data Hunting: Eliminates hours spent reading through dense, complex documentation.

AI Hallucinations: Constrains LLM outputs strictly to uploaded context, ensuring reliable, factual data.

Lack of Auditability: Provides exact file names and page references for compliance and verification.

Key Technical Features: * Dynamic Indexing: Fast chunking and local vector embedding using Hugging Face models (all-MiniLM-L6-v2).

High-Accuracy Vector Search: ChromaDB integration for persistent vector storage and low-latency similarity retrieval.

Decoupled Architecture: Asynchronous FastAPI backend paired with a clean, responsive Streamlit chat frontend.

Contextual Synthesis: Powered by Google Gemini (gemini-2.5-flash) for cost-effective inference.

Tech Stack: Python, FastAPI, LangChain, ChromaDB, Hugging Face, Google Gemini, Streamlit.