Initial Python implementation with ONNX Runtime

- Multi-stage Docker build (simplified to single-stage with pre-converted ONNX)
- HTTP server with ONNX inference
- API secret authentication
- Uses pre-converted all-MiniLM-L6-v2 ONNX model from onnx-community
- Image size: ~373 MB

Generated by Mistral Vibe.
Co-Authored-By: Mistral Vibe <vibe@mistral.ai>

README.md

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+# 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