AI-Powered Enterprise RAG Platform

An enterprise-grade Retrieval-Augmented Generation (RAG) platform built with React, FastAPI, LangChain, ChromaDB, and Python. The platform enables organizations to upload, process, index, and query documents using AI-powered semantic search and natural language conversations.

The platform supports multiple document formats, including PDF, DOCX, TXT, CSV, Excel, and website URLs. Uploaded documents are automatically parsed, chunked, converted into vector embeddings, and stored in ChromaDB for efficient similarity search. User queries are matched against the most relevant document chunks and combined with LLMs to generate accurate, context-aware responses.

Key Features

• Multi-format document ingestion (PDF, DOCX, TXT, CSV, Excel, URLs)

• AI-powered semantic search using vector embeddings

• LangChain-based RAG pipeline

• ChromaDB vector database integration

• FastAPI backend with React frontend

• Multi-chat sessions for each knowledge base

• Collection and document management

• Configurable chunk size, overlap, and Top-K retrieval

• Pipeline Builder for document processing workflows

• Real-time pipeline execution monitoring

• Source citations for retrieved context

• Modern enterprise dashboard

• Docker-ready deployment

Tech Stack

React • FastAPI • Python • LangChain • ChromaDB • Sentence Transformers • Groq LLM • Docker

This project demonstrates how enterprise knowledge can be transformed into an intelligent AI assistant capable of delivering accurate, context-aware answers from large document collections while maintaining high performance, scalability, and an intuitive user experience.