### Summary
This PR adds **SigLIP** (Sigmoid Loss Image Pretraining) as a new
embedding model in the LanceDB embedding registry. SigLIP improves
image-text alignment performance using sigmoid-based contrastive loss
and offers robust zero-shot generalization.
Fixes#2498
### What’s Implemented
#### 1. `SigLIP` Embedding Class
* Added `SigLIP` support under `python/lancedb/embeddings/siglip.py`
* Implements:
* `compute_source_embeddings`
* `_batch_generate_embeddings`
* Normalization logic
* Batch-wise progress logging for image embedding
#### 2. Registry Integration
* Registered `SigLIP` in `embeddings/__init__.py`
* `SigLIP` now usable via `connect(..., embedding="siglip")`
#### 3. Evaluation Benchmark Support
* Added SigLIP to `test_embeddings_slow.py` for side-by-side
benchmarking with OpenCLIP and ImageBind
### New Test Methods
#### `test_siglip`
* End-to-end test to verify embeddings table creation and vector shape
for SigLIP
#### `test_siglip_vs_openclip_vs_imagebind_benchmark_full`
* Benchmarks:
* **Recall\@1 / 5 / 10**
* **mAP (Mean Average Precision)**
* **Embedding & Search Latency**
* Dimensionality reporting
### Notes
* SigLIP outputs 768D embeddings (vs 512D for OpenCLIP)
* Benchmark shows competitive performance despite higher dimensionality
* I'm still new to contributing to open-source and learning as I go.
Please feel free to suggest any improvements — I'm happy to make
changes!
This PR adds ColPali support with ColPaliEmbeddings class (tagged
"colpali") using ColQwen2.5 for multi-vector text/image embeddings. Also
added MultiVector Pydantic type to handle the vector lists.
I've added some integration test for the embedding model and some unit
test for the new Pydantic type. Could be a template for other ColPali
variants as well. or until transformers🤗 starts supporting it.
Still `TODO`:
- [ ] Documentation
- [ ] Add an example
_Could also allow Image as query, but didn't work well when testing it._
[ColPali-Engine](https://github.com/illuin-tech/colpali) version:
0.3.9.dev17+g3faee24
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Introduced support for ColPali-based multimodal multi-vector
embeddings for both text and images.
- Added a new embedding class for generating multi-vector embeddings,
configurable for various model and processing options.
- Added a new Pydantic type for multi-vector embeddings, supporting
validation and schema generation for lists of fixed-dimension vectors.
- **Bug Fixes**
- Ensured proper asynchronous index creation in query tests for improved
reliability.
- **Tests**
- Added integration tests for ColPali embeddings, including
text-to-image search and validation of multi-vector fields.
- Added comprehensive tests for the new multi-vector Pydantic type,
covering schema, validation, and default value behavior.
- **Chores**
- Updated optional dependencies to include the ColPali engine.
- Added utility to check for availability of flash attention support.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
* Replaces Python implementation of Remote SDK with Rust one.
* Drops dependency on `attrs` and `cachetools`. Makes `requests` an
optional dependency used only for embeddings feature.
* Adds dependency on `nest-asyncio`. This was required to get hybrid
search working.
* Deprecate `request_thread_pool` parameter. We now use the tokio
threadpool.
* Stop caching the `schema` on a remote table. Schema is mutable and
there's no mechanism in place to invalidate the cache.
* Removed the client-side resolution of the vector column. We should
already be resolving this server-side.
This changes `lancedb` from a "pure python" setuptools project to a
maturin project and adds a rust lancedb dependency.
The async python client is extremely minimal (only `connect` and
`Connection.table_names` are supported). The purpose of this PR is to
get the infrastructure in place for building out the rest of the async
client.
Although this is not technically a breaking change (no APIs are
changing) it is still a considerable change in the way the wheels are
built because they now include the native shared library.
