DEPLOYMENT.md

🧠 Hugging Face Inference Endpoint - Ready for Deplo2. Set app_port: 7860 in README.md headerment

✅ What's Been Created

The inference_point directory now contains a complete lightweight LLM inference endpoint that:

• 🎯 Standard HF API: Provides Hugging Face-compatible REST API on port 8000
• 🔌 MCP Integration: Can connect to external MCP servers for enhanced capabilities
• 🐳 Docker Ready: Fully containerized for Hugging Face Spaces deployment
• ⚡ Lightweight: Minimal dependencies for fast startup and inference

🚀 Quick Start

Local Testing

cd inference_point
python app.py
# API available at http://localhost:7860

Standard HF API Usage

# Basic inference
curl -X POST http://localhost:7860/ \
  -H "Content-Type: application/json" \
  -d '{"inputs": "Hello world"}'

# With parameters
curl -X POST http://localhost:7860/ \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": "Tell me about product 123",
    "parameters": {
      "max_new_tokens": 150,
      "mcp_server_url": "https://your-mcp-server.hf.space"
    }
  }'

📁 Files Created

• app.py - Main FastAPI application with HF-compatible endpoints
• Dockerfile - Container configuration for HF Spaces
• requirements.txt - Lightweight dependencies (FastAPI, httpx, uvicorn)
• README.md - Comprehensive documentation for HF Spaces
• start.sh - Startup script with health checks
• test_api.sh - Complete API testing script
• .env.example - Environment configuration template

🌐 Deployment Options

1. Hugging Face Spaces

1. Create new Space with Docker SDK
2. Upload all files from inference_point/
3. Set app_port: 8000 in README.md header
4. Configure MCP_SERVER_URL environment variable

2. Hugging Face Inference Endpoints

1. Use this Docker setup for custom inference endpoints
2. Configure auto-scaling and instance types
3. Set environment variables for MCP integration

🔗 Architecture

HTTP Request → Lightweight LLM → [Optional MCP Server] → HF-Compatible Response

Key Features:

• Auto-detection: Automatically detects when to use MCP tools based on keywords
• Fallback: Works perfectly without MCP server (direct responses)
• Configurable: Can connect to different MCP servers per request
• Standard Format: Returns responses in standard Hugging Face format

📊 API Endpoints

• POST / - Main inference endpoint (HF compatible)
• GET /health - Health check and MCP status
• GET /info - Model and service information
• GET / - API information and usage examples

🎯 Next Steps

1. Deploy to HF Spaces: Upload to new Docker-based Space
2. Connect MCP Server: Set MCP_SERVER_URL to your MCP server Space
3. Scale as Needed: Use HF Inference Endpoints for production loads
4. Integrate: Use standard HF API calls from your applications

✨ Hugging Face Compliant

The inference endpoint is now fully HF compliant using port 7860 (HF standard) and can be deployed immediately to Hugging Face Spaces or Inference Endpoints. It provides a clean separation between the lightweight LLM inference and the MCP server, allowing independent scaling and deployment.

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🚀 Lightweight, Fast, and HF-Compatible!