Self-hostable web service to turn a string of text into a vector for embedding. Consider it all-minilm-l6-v2-as-a-service.
Christoph Haas
111af2d66e
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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. |
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| .forgejo/workflows | ||
| Dockerfile | ||
| go.mod | ||
| go.sum | ||
| main.go | ||
| README.md | ||
| server.py | ||
Vector Service
A minimal Go container that provides text embedding vectors using the 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
curl -X POST http://localhost:8080/vector \
-H "Content-Type: application/json" \
-d '{"text": "sqlite editor", "type": "query"}'
{ "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
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
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_embeddingoutput is mean-pooled and L2-normalized - Runtime: Go + ONNX Runtime (no Python)
- Files:
main.go(server),Dockerfile