siglip2-toolkit

Local-first toolkit around Google's SigLIP 2 models: text/image embeddings, zero-shot and fine-tuned classification, incremental corpus export, and retrieval math.

It is not a vector database: it produces embeddings and portable exports, and stays agnostic about where you store or search your vectors.

Co-developed by GIA-UH and Syalia S.R.L..

Documentation source: https://github.com/gia-uh/siglip2-toolkit/tree/main/docs (build the static site with the commands in Development; the included GitHub Actions workflow can publish it to GitHub Pages).

Installation

pip install siglip2-toolkit             # library + CLI
pip install "siglip2-toolkit[server]"   # + REST server (FastAPI)

Model management (the toolkit's own cache)

Models live in the toolkit's own cache, independent from HuggingFace's cache. The only command that uses the network is models download; everything else works with what you already have locally.

siglip2 models                       # your cache (★ model in use, origin, size)
siglip2 models --hf                  # SigLIP-2 catalog on HuggingFace: ✓ have it / ☁ downloadable
siglip2 models current               # the model in use (+ path, origin; --json)
siglip2 models use <id>              # set the model in use (persistent)
siglip2 models download <org/name> [--revision REV]   # into the toolkit cache (network)
siglip2 models import <path> [--name ALIAS]           # copy a local model into the cache
siglip2 models delete <id>           # delete from the cache (refuses the model in use)

Configuration lives in <home>/config.json (default home ~/.local/share/siglip2-toolkit/, created on first use):

{
  "cache_dir": "~/.local/share/siglip2-toolkit/models",
  "current": "google/siglip2-base-patch16-naflex",
  "device": "auto"
}

• cache_dir: where the cache lives; change it to relocate it (e.g. another disk).
• current: the model in use.
• device: auto (CUDA → MPS → CPU), or cuda, cpu, mps, cuda:1, … Any command's --device overrides it for that run.

There are no environment variables; for non-default installations pass --home DIR (global option, before the command).

--model accepts either an identifier from your cache or a path to a local model directory. The implicit default is google/siglip2-base-patch16-naflex: on a machine that does not have it yet, the first command tells you the exact models download to run. The toolkit never picks a model for you.

Incremental exports refuse to run when the existing export was produced by a different model: mixing embedding spaces would silently corrupt the vectors.

Examples and documentation

• Documentation — installation, quickstart, models, library guide, CLI, REST, export format, recipes, troubleshooting and the API reference.
• examples/ — runnable scripts for embeddings, export/inspect, search, classification and the REST client.

Three ways to use it

1. As a library (primary interface)

from siglip2_toolkit import Embedder, models

# Resolve from your cache (download/import first) or pass a local path.
resolved = models.resolve_model("google/siglip2-base-patch16-naflex")
embedder = Embedder(model=resolved.path, model_id=resolved.identifier, device="auto")

vectors = embedder.embed_texts(["a wedding at sunset"])
results = embedder.classify(["photo.jpg"], labels=["beach", "mountain", "wedding"])

from siglip2_toolkit.filters import field, matches

matches({"width": 2400}, field("width") >= 2000)  # -> True

2. As a CLI

siglip2 models download google/siglip2-base-patch16-naflex
siglip2 models use google/siglip2-base-patch16-naflex
siglip2 export -i /path/to/photos -o embeddings.jsonl --incremental
siglip2 classify --image photo.jpg --labels "beach,mountain,wedding"

3. As a REST service (optional extra)

pip install "siglip2-toolkit[server]"
siglip2 serve --host 0.0.0.0 --port 8000

Endpoints: /v1/embed/text, /v1/embed/image, /v1/classify, /v1/health, /v1/info.

Highlights

• Library-first design: the CLI and the REST server are thin adapters over the same public API.
• Zero-shot classification: prompt-based labels using the model's calibrated SigLIP logits, plus fine-tuned classification heads (ImageClassifier).
• Own model cache: downloads and imports live in the toolkit cache (independent from HuggingFace's); only models download uses the network.
• GPU-ready: device: "auto" picks CUDA when available (FP16; FP32 on CPU); override per command with --device.
• Incremental export: new, modified and deleted files are detected (modification detection requires metadata).
• Resumable runs: interrupted exports continue where they left off.
• Optional metadata: rich file metadata (EXIF, dimensions, timestamps, thumbnails) — or ids and vectors only, your choice.
• Versioned export format (jsonl / pkl) with an embedding-space manifest.
• Retrieval math: top-k scoring, RRF fusion, similarity-drop filtering and serializable metadata filters, composable with any store.

Status: first preview (0.0.4, October 2026). The public API and the export format may still change before 1.0; pin the version you depend on.

Development

Managed with uv:

uv sync --all-extras     # dev + server extras
uv run pytest
uv run siglip2 --help
uv lock                   # refresh uv.lock
uv build                  # wheel + sdist

CPU-only development (avoids the CUDA torch wheels):

uv venv --python 3.12
uv pip install --torch-backend cpu -e ".[dev,server]"

License

MIT — see LICENSE.