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

### Standard HF API Usage
```bash
# 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!**