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🚀 START HERE - Graph RAG Chatbot

Welcome! This is your complete Graph-based RAG chatbot with knowledge graph visualization.

What You Have 📦

A production-ready Flask application that:

  • 📤 Accepts PDF, CSV, TXT documents
  • 📊 Builds and visualizes knowledge graphs
  • 💬 Answers questions using RAG (Retrieval-Augmented Generation)
  • 🎨 Beautiful responsive UI (mobile-friendly)
  • 🐳 Docker-ready for deployment
  • 🤗 Hugging Face Spaces compatible

Get Started in 3 Steps ⚡

Step 1: Get API Key (2 minutes) 

# Visit: https://console.groq.com
# Sign up → Create API key → Copy it

Step 2: Start the App (Choose One)

Option A: Docker Compose (Recommended) 

cp .env.example .env
# Edit .env and paste your API key

docker-compose up -d
# Open: http://localhost:7860

Option B: Python (Local) 

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

export GROQ_API_KEY="your_key"  # Windows: set GROQ_API_KEY=your_key
python app.py
# Open: http://localhost:7860

Option C: Hugging Face Spaces (Cloud)

See space_config.md for detailed steps

Step 3: Use the App

  1. Upload a document (PDF, CSV, or TXT)
  2. Wait for "ready" status
  3. Ask a question in the chat
  4. View the knowledge graph!

File Guide 📚

File Purpose Read When
QUICKSTART.md 5-minute quick start You want to run it NOW
README.md Complete documentation You want detailed info
TESTING.md Testing guide + test cases You want to test features
space_config.md Hugging Face deployment You want to deploy to cloud
DEPLOYMENT_CHECKLIST.md Production checklist You're going live
PROJECT_STRUCTURE.md Architecture & file details You want to customize

Common Questions 🤔

Q: Where do I get the API key?

A: Visit https://console.groq.com, sign up, and create an API key. It's free!

Q: Do I need Docker?

A: No! Run locally with Python or use HF Spaces (no setup needed).

Q: Can I change the UI?

A: Yes! Edit templates/index.html. It's plain HTML/CSS/JS.

Q: How do I deploy to production?

A: See DEPLOYMENT_CHECKLIST.md or space_config.md for HF Spaces.

Q: What file types are supported?

A: PDF, CSV, TXT. More can be added by editing app.py.

Q: Can multiple users use it?

A: Yes! Each browser session is independent. Add authentication if needed.

Q: How big can files be?

A: Up to 50MB. Change MAX_CONTENT_LENGTH in app.py if needed.

Q: Is my data private?

A: Files are stored on your server only. Never sent to Groq (except queries).

Q: What's a knowledge graph?

A: It visualizes relationships between documents and concepts as an interactive network diagram.

Q: How does RAG work?

A: Your question is matched to relevant document chunks, then an AI generates an answer based on those chunks.

Project Files 📁 

your-project/
├── app.py                    # Main application
├── templates/index.html      # Frontend UI
├── requirements.txt          # Dependencies
├── Dockerfile               # Docker image
├── docker-compose.yml       # Docker setup
├── .env.example            # Config template
├── README.md               # Full documentation
├── QUICKSTART.md           # 5-min guide
├── TESTING.md              # Testing guide
├── space_config.md         # HF Spaces guide
├── DEPLOYMENT_CHECKLIST.md # Production guide
├── PROJECT_STRUCTURE.md    # Architecture
└── data/                   # Storage (created automatically)

Troubleshooting 🔧

"GROQ_API_KEY not found" 

# Windows
echo GROQ_API_KEY=your_key >> .env

# Mac/Linux
echo "GROQ_API_KEY=your_key" >> .env

"Port 7860 is already in use" 

# Option 1: Use different port
PORT=8000 python app.py

# Option 2: Find and stop the process using 7860
lsof -i :7860  # Mac/Linux
netstat -ano | findstr :7860  # Windows

"Docker not found"

Install Docker from https://docker.com

"Graph doesn't load"

  • Ensure document status is "ready"
  • Wait 3-5 seconds after upload
  • Check browser console (F12)

"Chat not responding"

  • Verify GROQ_API_KEY is set
  • Check document is "ready" status
  • Ensure internet connectivity

Next Steps 🎯

  1. Run locally: Execute one of the 3 options above
  2. Test features: Upload a sample CSV or PDF
  3. Explore code: Look at app.py to understand the flow
  4. Customize: Edit templates/index.html for styling
  5. Deploy: Push to HF Spaces or your server (see guides)

Feature Highlights ✨

Feature Status Notes
📤 Upload Documents ✅ PDF, CSV, TXT
📊 Knowledge Graphs ✅ Auto-generated, visualized
💬 RAG Chat ✅ Semantic search + LLM
🎨 Beautiful UI ✅ Modern, responsive
🚀 Fast ✅ Async processing
📱 Mobile ✅ Fully responsive
🐳 Docker Ready ✅ One command to run
🤗 HF Spaces Ready ✅ Easy cloud deploy

Technology Stack 🛠️

  • Backend: Flask (Python)
  • LLM: Groq Mixtral 8x7b
  • Embeddings: SentenceTransformers
  • Graphs: NetworkX + Matplotlib
  • Frontend: HTML + CSS + JavaScript
  • Deployment: Docker + Docker Compose

Performance 📊

Operation Time
App startup (cold) 30-60s (model download)
App startup (warm) 2-3s
Upload small file 5-10s
Upload large file 30-60s
Query response 2-5s
Graph visualization <1s

Architecture 🏗️ 

User Browser
    ↓
HTML/CSS/JS Frontend
    ↓
Flask Backend (Python)
    ├→ Document Processing
    ├→ Knowledge Graph Building
    ├→ Embedding Generation
    └→ RAG Query Processing
    ↓
Groq API (LLM)

Support & Help 💬

  1. Quick questions: Check this file (START_HERE.md)
  2. Detailed help: See README.md
  3. Testing: See TESTING.md
  4. Deployment: See DEPLOYMENT_CHECKLIST.md or space_config.md
  5. Architecture: See PROJECT_STRUCTURE.md
  6. Issues: Check code comments in app.py

Your Next Action 👉

Pick your favorite option above and run it now!

# Fastest way:
docker-compose up -d

# Or if you have Python 3.8+:
python app.py

# Or deploy to cloud:
# See space_config.md

Success Indicators ✅

Once running, you should see:

  • ✅ Blue gradient homepage
  • ✅ Upload zone with drag-and-drop
  • ✅ Chat section on the right
  • ✅ "Knowledge Graph" tab visible
  • ✅ No error messages

Ready? Let's go! 🚀

Key Files Explained in 30 Seconds

File What It Does
app.py Flask app + AI logic
index.html The web interface users see
Dockerfile Containerizes the app for deployment
requirements.txt Lists all Python packages needed
docker-compose.yml Easy way to run with Docker
.env.example Template for configuration

One More Thing... 🎁

This project is production-ready. You can:

  • Deploy to Hugging Face Spaces (free, cloud)
  • Deploy to AWS, Azure, GCP (paid cloud)
  • Run on your own server
  • Run locally for testing
  • Customize to your needs

Everything you need is included! 🎉

Questions? Read the relevant guide file above. Ready to start? Run Docker Compose or Python. Want to deploy? Check DEPLOYMENT_CHECKLIST.md.

Created: June 27, 2024 Version: 1.0.0 Status: Production Ready ✅