📄 RAG Document Intelligence System

Upload any PDF → ask questions in plain English → get accurate, source-attributed answers instantly.

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🌐 Live Demo

👉 https://rag-chatbot-danjpg2yc4taeeerwzsvyp.streamlit.app/

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🚀 What it does

1. Upload any PDF document
2. Ask questions in plain English
3. Get accurate answers with source page references — no hallucinations

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⚙️ Architecture

PDF Upload → Text Chunking → HuggingFace Embeddings
          → FAISS Vector Store → LLaMA3 (Groq) → Source-attributed Answer

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🛠 Tech Stack

Layer	Technology
LLM	LLaMA3-8b via Groq
Embeddings	sentence-transformers/all-MiniLM-L6-v2
Vector Store	FAISS
Orchestration	LangChain
PDF Parsing	PyMuPDF
Backend API	FastAPI (port 7860)
Frontend	Streamlit (port 8501)

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📊 Performance

• ⚡ Sub-10s response latency
• 💰 $0 API cost — fully open source stack
• 🎯 100% source attribution — eliminates hallucinations
• 🔧 Configurable top-k retrieval (1–10 sources)

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🔧 Run Locally

git clone https://github.com/Ayesha037/rag-chatbot
cd rag-chatbot

pip install -r requirements.txt

# Add your Groq API key
cp .env.example .env
# Edit .env → set GROQ_API_KEY=gsk_...

# Terminal 1 — backend
python main.py

# Terminal 2 — frontend
streamlit run frontend/app.py

Open http://localhost:8501

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🐳 Docker

docker build -t rag-chatbot .
docker run --env-file .env -p 7860:7860 -p 8501:8501 rag-chatbot

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📄 API Endpoints

Method	Path	Description
POST	/upload	Upload & index a PDF
POST	/query	Ask a question
GET	/documents	List indexed docs
GET	/health	Health check
GET	/reset	Clear all docs
GET	/docs	Swagger UI

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📁 Project Structure

rag-chatbot/
├── main.py              # FastAPI backend
├── frontend/
│   └── app.py           # Streamlit frontend (all-in-one for cloud deploy)
├── requirements.txt
├── Dockerfile
└── .env.example

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Built by Mohammad Ayesha Summaiyya