# Technical Notes - ht-docker-ai

## Architecture

This project uses **Ollama** as the runtime framework for serving AI models. This provides:

- Automatic model download and caching
- Unified REST API (compatible with OpenAI format)
- Built-in quantization support
- GPU/CPU auto-detection

## Model Details

### MiniCPM-V 4.5

- **Source**: OpenBMB (https://github.com/OpenBMB/MiniCPM-V)
- **Base Models**: Qwen3-8B + SigLIP2-400M
- **Total Parameters**: 8B
- **Ollama Model Name**: `minicpm-v`

### VRAM Usage

| Mode | VRAM Required |
|------|---------------|
| Full precision (bf16) | 18GB |
| int4 quantized | 9GB |
| GGUF (CPU) | 8GB RAM |

## Container Startup Flow

1. `docker-entrypoint.sh` starts Ollama server in background
2. Waits for server to be ready
3. Checks if model already exists in volume
4. Pulls model if not present
5. Keeps container running

## Volume Persistence

Mount `/root/.ollama` to persist downloaded models:

```bash
-v ollama-data:/root/.ollama
```

Without this volume, the model will be re-downloaded on each container start (~5GB download).

## API Endpoints

All endpoints follow the Ollama API specification:

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/api/tags` | GET | List available models |
| `/api/generate` | POST | Generate completion |
| `/api/chat` | POST | Chat completion |
| `/api/pull` | POST | Pull a model |
| `/api/show` | POST | Show model info |

## GPU Detection

The GPU variant uses Ollama's automatic GPU detection. For CPU-only mode, we set:

```dockerfile
ENV CUDA_VISIBLE_DEVICES=""
```

This forces Ollama to use CPU inference even if GPU is available.

## Health Checks

Both variants include Docker health checks:

```dockerfile
HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
    CMD curl -f http://localhost:11434/api/tags || exit 1
```

CPU variant has longer `start-period` (120s) due to slower startup.

## PaddleOCR

### Overview

PaddleOCR is a standalone OCR service using PaddlePaddle's PP-OCRv4 model. It provides:

- Text detection and recognition
- Multi-language support
- FastAPI REST API
- GPU and CPU variants

### Docker Images

| Tag | Description |
|-----|-------------|
| `paddleocr` | GPU variant (default) |
| `paddleocr-gpu` | GPU variant (alias) |
| `paddleocr-cpu` | CPU-only variant |

### API Endpoints

| Endpoint | Method | Description |
|----------|--------|-------------|
| `/health` | GET | Health check with model info |
| `/ocr` | POST | OCR with base64 image (JSON body) |
| `/ocr/upload` | POST | OCR with file upload (multipart form) |

### Request/Response Format

**POST /ocr (JSON)**
```json
{
  "image": "<base64-encoded-image>",
  "language": "en"  // optional
}
```

**POST /ocr/upload (multipart)**
- `img`: image file
- `language`: optional language code

**Response**
```json
{
  "success": true,
  "results": [
    {
      "text": "Invoice #12345",
      "confidence": 0.98,
      "box": [[x1,y1], [x2,y2], [x3,y3], [x4,y4]]
    }
  ]
}
```

### Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| `OCR_LANGUAGE` | `en` | Default language for OCR |
| `SERVER_PORT` | `5000` | Server port |
| `SERVER_HOST` | `0.0.0.0` | Server host |
| `CUDA_VISIBLE_DEVICES` | (auto) | Set to `-1` for CPU-only |

### Performance

- **GPU**: ~1-3 seconds per page
- **CPU**: ~10-30 seconds per page

### Supported Languages

Common language codes: `en` (English), `ch` (Chinese), `de` (German), `fr` (French), `es` (Spanish), `ja` (Japanese), `ko` (Korean)

---

## Adding New Models

To add a new model variant:

1. Create `Dockerfile_<modelname>`
2. Set `MODEL_NAME` environment variable
3. Update `build-images.sh` with new build target
4. Add documentation to `readme.md`

## Troubleshooting

### Model download hangs

Check container logs:
```bash
docker logs -f <container-name>
```

The model download is ~5GB and may take several minutes.

### Out of memory

- GPU: Use int4 quantized version or add more VRAM
- CPU: Increase container memory limit: `--memory=16g`

### API not responding

1. Check if container is healthy: `docker ps`
2. Check logs for errors: `docker logs <container>`
3. Verify port mapping: `curl localhost:11434/api/tags`

## CI/CD Integration

Build and push using npmci:

```bash
npmci docker login
npmci docker build
npmci docker push code.foss.global
```

## Related Resources

- [Ollama Documentation](https://ollama.ai/docs)
- [MiniCPM-V GitHub](https://github.com/OpenBMB/MiniCPM-V)
- [Ollama API Reference](https://github.com/ollama/ollama/blob/main/docs/api.md)