# Vector Service

A minimal Docker container that provides text embedding vectors using the all-MiniLM-L6-v2 model. The service accepts POST requests with text and returns a 384-dimensional embedding vector.

## Features

- **Small image size**: ~373 MB (much smaller than typical Python-based solutions)
- **Fast inference**: Uses ONNX Runtime for efficient model execution
- **API authentication**: Optional API secret protection
- **Pre-converted ONNX**: Uses ready-to-use ONNX model from HuggingFace

## Usage

### Build the container

```bash
podman build -t vector-service .
```

Or with Docker:

```bash
docker build -t vector-service .
```

The build process will:
1. Download the pre-converted ONNX model from `onnx-community/all-MiniLM-L6-v2-ONNX` on HuggingFace
2. Install only the runtime dependencies (ONNX Runtime + NumPy)
3. Create a minimal image (~373 MB)

### Run the service

Without authentication:
```bash
podman run --rm -p 8080:8080 -d vector-service
```

With API secret authentication:
```bash
podman run --rm -p 8080:8080 -e API_SECRET=your-secret-key -d vector-service
```

### Query the service

Send a POST request with JSON body:

```bash
curl -X POST http://localhost:8080/vector \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-secret-key" \
  -d '{"text": "Hello World"}'
```

Response:
```json
{
  "vector": [0.1868536774709355, 0.8120285351760685, ...]
}
```

The vector has 384 dimensions.

### Without authentication

If you didn't set `API_SECRET`, you can query without the Authorization header:

```bash
curl -X POST http://localhost:8080/vector \
  -H "Content-Type: application/json" \
  -d '{"text": "Hello World"}'
```

## Files

- `Dockerfile`: Single-stage build with pre-downloaded ONNX model
- `server.py`: HTTP server with ONNX inference

## Technical Details

### Model
- **Model**: all-MiniLM-L6-v2 (80 MB on disk)
- **Source**: Pre-converted ONNX from [onnx-community/all-MiniLM-L6-v2-ONNX](https://huggingface.co/onnx-community/all-MiniLM-L6-v2-ONNX)
- **Dimensions**: 384
- **Format**: ONNX (pre-converted)

### Dependencies
- Runtime: Python 3.11, ONNX Runtime, NumPy
- No build-time dependencies needed (uses pre-converted model)

### Image Size Breakdown
- Model files (ONNX + ONNX data + tokenizer + vocab): ~95 MB
- Python runtime and dependencies: ~278 MB
- Total: ~373 MB

## Notes

- The build will download the pre-converted ONNX model from HuggingFace (~95 MB total)
- Much faster builds since no PyTorch or model conversion is needed
- The ONNX model includes an external data file (`model.onnx_data`) which is normal for larger models
- For production use, consider adding rate limiting and HTTPS