Agentic MCP Weather System

# ⚡ Quick Setup Guide - Weather Intelligence System ## 🐳 **Docker Setup (Recommended - Production Ready)** ### Prerequisites - Docker 20.10+ with Docker Compose - 8GB+ RAM (for AI models) - 15GB+ disk space - Internet connection ### One-Command Setup ```bash git clone <your-repo-url> weather cd weather chmod +x *.sh ./start-docker.sh ``` **✅ System Ready!** - 🌐 **Chat Interface**: http://localhost:8501 (Primary) - 🔧 **API Server**: http://localhost:8000 - 🤖 **AI Models**: http://localhost:11434 - 📚 **API Docs**: http://localhost:8000/docs --- ## 🐍 **Local Python Setup (Development)** ### Prerequisites - Python 3.13+ - Git - Ollama (for AI features) ### Step 1: Install Ollama ```bash # macOS brew install ollama # Linux/WSL curl -fsSL https://ollama.ai/install.sh | sh # Start Ollama service ollama serve & # Install required models (4GB+ each) ollama pull llama3 ollama pull phi3 # Verify installation ollama list ``` ### Step 2: Python Environment #### Using uv (Recommended) ```bash git clone <your-repo-url> weather cd weather curl -LsSf https://astral.sh/uv/install.sh | sh uv sync # Installs all dependencies automatically ``` #### Using pip ```bash git clone <your-repo-url> weather cd weather python -m venv .venv source .venv/bin/activate # Linux/macOS # .venv\Scripts\activate # Windows pip install -r requirements.txt ``` ### Step 3: Start Services #### Option A: Full System (Streamlit + API + Agents) ```bash # Terminal 1: Ensure Ollama is running ollama serve # Terminal 2: Start API server python main.py server # Terminal 3: Start Streamlit interface streamlit run streamlit_app.py --server.port 8501 ``` #### Option B: API Only ```bash ollama serve & python main.py start ``` ### Step 4: Verify Installation ```bash # Check system health python main.py validate # Test API endpoints curl http://localhost:8000/health # Open Streamlit interface (if running) # http://localhost:8501 ``` ## 🔧 **Configuration** ### Environment Variables - **Development**: `.env` file is pre-configured - **Production**: Copy `.env.example` to `.env.production` and customize ### Key Settings ```bash # Server Configuration SERVER_HOST=0.0.0.0 SERVER_PORT=8000 ENVIRONMENT=development # AI Integration OLLAMA_HOST=http://localhost:11434 OLLAMA_MODEL=llama3 # Security (Production) API_KEY_REQUIRED=false RATE_LIMIT_PER_MINUTE=300 ``` ## 🧪 **First Test** ### Using Streamlit (Easiest) 1. Open http://localhost:8501 2. Type: `"What's the weather in San Francisco?"` 3. Watch the multi-agent system respond! ### Using API ```bash curl -X POST http://localhost:8000/tools/get_weather \ -H "Content-Type: application/json" \ -d '{"city": "London"}' ``` ### Using Python ```python from agent_coordination_hub import AgentCoordinationHub hub = AgentCoordinationHub() result = await hub.process_request("Weather in Tokyo with travel tips") print(result) ``` ## ❓ **Troubleshooting** ### Ollama Issues ```bash # Check if Ollama is running curl http://localhost:11434/api/version # Restart Ollama service pkill ollama && ollama serve & # Re-download models if needed ollama pull llama3 ``` ### Port Conflicts ```bash # Check what's using ports lsof -i :8000 # API server lsof -i :8501 # Streamlit lsof -i :11434 # Ollama # Use different ports if needed SERVER_PORT=8080 streamlit run streamlit_app.py --server.port 8502 ``` ### Memory Issues - Ensure 8GB+ RAM available - Close other applications - Use `ollama pull phi3` (smaller model) instead of llama3 --- ## 🚀 **Next Steps** 1. **Explore the Interface**: Try the Streamlit chat at http://localhost:8501 2. **Test Agent Coordination**: Ask complex queries that require multiple agents 3. **Set Up Alerts**: Configure proactive weather monitoring 4. **Review Documentation**: Check AGENT_COORDINATION_GUIDE.md for advanced features 5. **Production Deployment**: See DEPLOYMENT.md for production setup That's it! 🚀