Enterprise RAG Platform

Production-grade retrieval-augmented generation system with hybrid retrieval, intelligent reranking, and conversation memory.

Overview

Enterprise RAG Platform combines BM25 sparse + BGE dense embeddings with Cohere reranking to deliver accurate, context-aware answers. Features conversation memory, real-time document upload, and automated RAGAS evaluation.

📊 Key Results

Metric	Score
Answer Relevancy	0.936 ⭐
Context Precision	0.636
Indexed Chunks	7,536
Evaluated QA Pairs	355

🏗️ Architecture

Retrieval Pipeline:

• BM25 (sparse) + BGE-large embeddings (dense) with RRF fusion

• Cohere cross-encoder reranking (top-60 → top-10)

• DeBERTa-v3 NLI hallucination detection per-chunk

Generation:

• Groq Llama 3.3 70B with 3-turn conversation memory

• Automatic session management + real-time document ingestion

🚀 Tech Stack

FastAPI · Streamlit · FAISS · Qdrant · BGE Embeddings · Cohere Reranking · Groq LLM · RAGAS Evaluation · HuggingFace Spaces

⚡ Quick Start

# Install

pip install -r requirements.txt



# Run locally

uvicorn api.rag_api:app --reload --port 8000  # Terminal 1

streamlit run ui/streamlit_app.py             # Terminal 2



# Visit http://localhost:8501

📁 Project Structure

Enterprise-RAG/
├── api/                 # FastAPI backend
├── ingestion/           # PDF/DOCX parsing + 5 chunking strategies
├── retrieval/           # BM25 + FAISS + Cohere reranking
├── generation/          # Groq LLM client
├── evaluation/          # RAGAS + hallucination detection
├── ui/                  # Streamlit dashboard (3 tabs)
└── data/
    └── processed/       # Pre-indexed chunks (FAISS + BM25)

🎯 Features

✅ Hybrid retrieval (BM25 + dense embeddings)

✅ Intelligent reranking with Cohere

✅ Multi-turn conversation memory

✅ Real-time PDF/DOCX upload

✅ Automated hallucination detection

✅ Interactive evaluation dashboard

📈 Benchmark

Fixed-size chunking outperformed sentence-based by 17% on context precision. Evaluated on 50 ArXiv papers with 355 QA pairs.

🔗 Links

• Live Demo: https://sohel2309-enterprise-rag.hf.space

• Code: https://github.com/Sohel2309/Enterprise-RAG

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MIT License | Last Updated: June 2026