FynnCloud-Embeddings

Multimodal embedding service for FynnCloud. Runs jina-clip-v2 behind a FastAPI server to generate 1024-dimensional vectors from text and images.

Used by the backend for semantic file search (pgvector cosine similarity).

Running locally

python -m venv venv && source venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload

The model weights get downloaded on first launch (~2 GB).

Docker

docker build -t fynncloud-embeddings .
docker run -p 8000:8000 fynncloud-embeddings

The Dockerfile bakes the model weights into the image so there's no download at runtime.

API

POST /api/embed

{
  "texts": ["hello world"],
  "image_urls": ["https://example.com/photo.jpg"],
  "images_base64": ["iVBORw0KGgo..."]
}

All fields are optional, but at least one must be provided. Returns:

{
  "embeddings": [[0.012, -0.034, ...]],
  "model": "jinaai/jina-clip-v2",
  "dimensions": 1024
}

GET /health

Returns server status.

Config

Environment variables:

Variable	Default	Description
MODEL_NAME	jinaai/jina-clip-v2	HuggingFace model ID
PORT	8000	Server port
WORKERS	1	Uvicorn worker count (each loads a full model copy)
LOG_LEVEL	info	Logging level