vector-service/README.md
Christoph Haas 111af2d66e
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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.
2026-09-13 22:10:01 +02:00

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# Vector Service
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.
## Model
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`:
- **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).
## API
`POST /vector`
```bash
curl -X POST http://localhost:8080/vector \
-H "Content-Type: application/json" \
-d '{"text": "sqlite editor", "type": "query"}'
```
```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 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
```bash
docker run --rm -p 8080:8080 -e API_SECRET=your-secret-key vector-service
```
## Technical details
- **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`