README.md

metadata

title: JanSahayak
emoji: 🙏
colorFrom: blue
colorTo: green
sdk: docker
pinned: false

🙏 JanSahayak - AI-Powered Government Schemes & Exams Assistant

Your personal AI assistant for discovering government schemes and competitive exam opportunities in India

---

🌟 Features

🤖 Multi-Agent AI System

• Profiling Agent: Extracts structured user information
• Scheme Agent: Recommends relevant government schemes
• Exam Agent: Suggests competitive exams based on qualifications
• RAG Agent: Retrieves information from curated document database

💡 Intelligent Capabilities

• ✅ Natural language understanding of user profiles
• ✅ Smart recommendations based on eligibility criteria
• ✅ RAG (Retrieval-Augmented Generation) with FAISS vectorstore
• ✅ Real-time web search via Tavily API
• ✅ PDF generation for saving recommendations
• ✅ Beautiful web interface with modern UI

---

🚀 Deploy to Hugging Face Spaces (Recommended)

Why Hugging Face Spaces?

• ✅ 16GB RAM for FREE (perfect for RAG apps!)
• ✅ Built for ML/AI applications
• ✅ Git-based deployment
• ✅ Public URL instantly
• ✅ Persistent storage

Quick Deploy Steps:

Method 1: Using HF CLI (Easiest)

# Install HF CLI
pip install huggingface_hub[cli]

# Login
huggingface-cli login

# Create Space and push
huggingface-cli repo create jansahayak --type space --space_sdk gradio
git remote add hf https://huggingface.co/spaces/YOUR_USERNAME/jansahayak
git push hf main

Method 2: Manual Setup

1. Create Space on huggingface.co/spaces

   • Click "Create new Space"
   • Name: jansahayak
   • SDK: Select "Gradio" (works with Flask)
   • Hardware: CPU basic (Free - 16GB RAM!)
   • License: MIT

2. Clone YOUR Space repo (not GitHub!)

   git clone https://huggingface.co/spaces/YOUR_USERNAME/jansahayak
   cd jansahayak
   

3. Copy your project files

   # Copy all files from your JanSahayak folder to the cloned space folder
   cp -r /path/to/JanSahayak/* .
   

4. Add Environment Variables (Space Settings → Variables and secrets)

   GROQ_API_KEY=your_groq_key
   TAVILY_API_KEY=your_tavily_key  
   HF_TOKEN=your_hf_token (optional)
   SKIP_VECTORSTORES=false
   

5. Push to Space

   git add .
   git commit -m "Initial commit"
   git push
   

Your app will be live at: https://huggingface.co/spaces/YOUR_USERNAME/jansahayak

Important Notes:

• HF Spaces uses its own Git repo (not GitHub directly)
• App runs on port 7860 by default (Flask uses 5000, update if needed)
• First deployment may take 5-10 minutes to install dependencies
• Check Space logs if deployment fails

---

🛠️ Local Development

# Clone and setup
git clone https://github.com/YOUR_USERNAME/JanSahayak.git
cd JanSahayak

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
.venv\Scripts\activate     # Windows

# Install dependencies
pip install -r requirements.txt

# Configure API keys
cp .env.example .env
# Edit .env with your keys

# Build vectorstores (optional - if you have PDFs)
python init_embeddings.py

# Run app
python app.py
# or use launcher scripts: start_web.bat (Windows) / ./start_web.sh (Linux/Mac)

Visit http://localhost:5000

---

🔑 Get API Keys

Service	URL	Free Tier	Used For
Groq	console.groq.com	✅ Yes	LLM Inference
Tavily	tavily.com	1000 searches/mo	Web Search
HuggingFace	huggingface.co/settings/tokens	✅ Yes	Model Downloads

---

💾 Adding Custom Documents

Government Schemes PDFs

1. Place PDFs in data/schemes_pdfs/
2. Run python init_embeddings.py
3. Restart app

Exam Information PDFs

1. Place PDFs in data/exams_pdfs/
2. Run python init_embeddings.py
3. Restart app

Automatically indexed and searchable via RAG!

---

🧪 Technology Stack

• Backend: Flask
• AI: LangChain + LangGraph
• LLM: Groq (Llama 3.3 70B)
• Embeddings: sentence-transformers/all-MiniLM-L6-v2
• Vector DB: FAISS (local)
• Search: Tavily API
• Frontend: HTML5 + CSS3 + JavaScript

---

📁 Project Structure

JanSahayak/
├── app.py                    # Flask web app
├── main.py                   # CLI interface
├── agents/                   # AI agents
│   ├── profiling_agent.py
│   ├── scheme_agent.py
│   ├── exam_agent.py
│   └── rag_agent.py
├── rag/                      # RAG components
│   ├── embeddings.py
│   ├── scheme_vectorstore.py
│   └── exam_vectorstore.py
├── data/                     # Documents
│   ├── schemes_pdfs/
│   └── exams_pdfs/
├── templates/                # HTML templates
└── static/                   # CSS/JS

---

🐛 Troubleshooting

Memory issues on local machine?

# Set in .env
SKIP_VECTORSTORES=true

Uses web search only (no embeddings needed)

Vectorstore errors?

rm -rf rag/scheme_index rag/exam_index
python init_embeddings.py

---

🤝 Contributing

Contributions welcome! Fork → Create branch → Submit PR

---

📜 License

MIT License

---

🙏 Acknowledgments

Built with LangChain, Groq, Tavily, and ❤️

---

Made for the people of India 🇮🇳