Switch to bge-small-en-v1.5 for asymmetric query/passage embeddings

Replace all-MiniLM-L6-v2 with bge-small-en-v1.5, which separates
short search queries from package descriptions via the retrieval
instruction. Requests now default to query mode and opt out with
type=passage for documents. Uses the export's pooled, L2-normalized
sentence_embedding output instead of manual mean pooling.

README.md

@@ -1,100 +1,64 @@
 # 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.
+A minimal Go container that provides text embedding vectors using the
+[bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) model
+(384 dimensions). The service accepts POST requests and returns an embedding
+vector, ready to be consumed by pgvector.
 
-## Features
+## Model
 
-- **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
+bge-small-en-v1.5 uses **asymmetric** embeddings: queries and passages are
+embedded differently, which is what lets a short search query align with a
+package description. To support this, the request carries a `type`:
 
-## Usage
+- **default (omitted)** — treated as a **query**: the text is prefixed with
+  the bge retrieval instruction
+  (`Represent this sentence for searching relevant passages: `). This keeps
+  existing callers that send a bare `{"text": ...}` working unchanged.
+- `"passage"` — embedded as-is (used for documents/descriptions).
 
-### Build the container
+## API
+
+`POST /vector`
 
 ```bash
-podman build -t vector-service .
+curl -X POST http://localhost:8080/vector \
+  -H "Content-Type: application/json" \
+  -d '{"text": "sqlite editor", "type": "query"}'
 ```
 
-Or with Docker:
+```json
+{ "vector": [0.023, -0.118, ...] }
+```
+
+Endpoints:
+
+- `POST /vector` — embed text.
+- `GET /ok` — health check.
+- `GET /version` — build revision.
+
+Optional Bearer auth via the `API_SECRET` environment variable.
+
+## Build
 
 ```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)
+The build downloads the pre-converted ONNX model
+(`onnx-community/bge-small-en-v1.5-ONNX`) and the ONNX Runtime shared library,
+then compiles the Go binary.
 
-### 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:
+## Run
 
 ```bash
-curl -X POST http://localhost:8080/vector \
-  -H "Content-Type: application/json" \
-  -H "Authorization: Bearer your-secret-key" \
-  -d '{"text": "Hello World"}'
+docker run --rm -p 8080:8080 -e API_SECRET=your-secret-key vector-service
 ```
 
-Response:
-```json
-{
-  "vector": [0.1868536774709355, 0.8120285351760685, ...]
-}
-```
+## Technical details
 
-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
+- **Model**: bge-small-en-v1.5 (ONNX), 384 dimensions
+- **Pooling/normalization**: the export's `sentence_embedding` output is
+  mean-pooled and L2-normalized
+- **Runtime**: Go + ONNX Runtime (no Python)
+- **Files**: `main.go` (server), `Dockerfile`