stserve 0.1.0

Light-weight server exposing sentence-transformer models for embeddings

Sentence Transformer Server

Lightweight FastAPI wrapper that exposes any sentence-transformers model over HTTP for easy embedding generation.

Quick Start

1. Install the package from PyPI (Python 3.13+):

   pip install stserve
   

2. Launch the server with the default MiniLM model (choose your preferred runner):

   stserve
   # or via uvx
   uvx stserve
   

3. Request embeddings from another terminal:

   curl -X POST http://127.0.0.1:8501/embed \
     -H "Content-Type: application/json" \
     -d '{"texts": ["hello world", "how are you?"]}'
   

Configuration

• --model: sentence-transformers model name or local path (defaults to all-MiniLM-L6-v2).
• --device: target Torch device; auto-detects CUDA.
• --batch-size: batches encode calls for throughput.
• --normalize: toggles L2 normalization on embeddings.
• --show-progress: prints encode progress in the server logs.
• --host / --port: Uvicorn bind address (defaults 127.0.0.1:8501).

Example with GPU and normalization:

uvx stserve --model sentence-transformers/all-MiniLM-L12-v2 --device cuda --normalize

API

• POST /embed: Body { "texts": [str, ...] } → { "embeddings": [[float, ...], ...] }.
• GET /health: Returns current configuration (model, device, batch size, etc.).

Development Notes

• The CLI entry point is stserve.app:main.
• Embedding work happens in a thread pool so the event loop stays responsive.
• CUDA usage requires PyTorch with GPU support.