README.md

metadata

title: ACCESS EMBEDDING
emoji: 🐨
colorFrom: green
colorTo: red
sdk: docker
pinned: false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

title: ACCESS SBERT Embedding Service colorFrom: blue colorTo: indigo sdk: docker app_port: 7860 pinned: false

ACCESS SBERT Embedding Service

FastAPI service for generating SBERT embeddings for ACCESS semantic context search.

The service only converts text into normalized sentence embeddings. It does not store ACCESS data, query PostgreSQL, generate HEAT suggestions, or perform business workflow logic.

Model

Default model:

sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

Expected embedding dimension:

384

Endpoints

• GET /health
• POST /embed
• POST /embed/batch

Environment Variables

MODEL_NAME=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
EMBEDDING_API_KEY=change-this-secret
MAX_TEXT_LENGTH=3000
MAX_BATCH_SIZE=64

EMBEDDING_API_KEY is optional for local development. Configure it for deployed Spaces.

Local Run

python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --host 0.0.0.0 --port 7860 --reload

Health Check

curl http://localhost:7860/health

Expected response:

{
  "status": "ok",
  "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
  "dimension": 384
}

Single Embedding

curl -X POST http://localhost:7860/embed \
  -H "Content-Type: application/json" \
  -H "X-API-Key: change-this-secret" \
  -d "{\"text\":\"Saldo saya terpotong tapi tiket tidak muncul.\"}"

Batch Embedding

curl -X POST http://localhost:7860/embed/batch \
  -H "Content-Type: application/json" \
  -H "X-API-Key: change-this-secret" \
  -d "{\"texts\":[\"Saldo terpotong tapi tiket belum muncul.\",\"Refund pembatalan tiket belum diterima.\"]}"

Tests

pytest

The first embedding test run downloads and loads the SBERT model, so it can be slow.

Docker

docker build -t access-sbert-embedding-service .
docker run --rm -p 7860:7860 -e EMBEDDING_API_KEY=change-this-secret access-sbert-embedding-service

ACCESS Backend Integration

ACCESS Backend should call this service over HTTP, then store/search embeddings in PostgreSQL + pgvector.

Backend env shape:

EMBEDDING_ENABLED=true
EMBEDDING_SERVICE_URL=https://<space-url>/embed
EMBEDDING_BATCH_SERVICE_URL=https://<space-url>/embed/batch
EMBEDDING_API_KEY=<same-secret>
EMBEDDING_MODEL=sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
EMBEDDING_DIMENSION=384
EMBEDDING_TIMEOUT_MS=10000

Failure rules:

• If embedding fails during preview, ACCESS Backend should keep HEAT suggestions working.
• If embedding fails during reference/case indexing, backend should keep source data saved and backfill embeddings later.