feat(embeddings): add siglip embedding support to lancedb (#2499)

###  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
![WhatsApp Image 2025-07-10 at 18 00
27_a3368163](https://github.com/user-attachments/assets/e5582ee1-80a3-43d7-a7a1-26ceecce9f4d)


####  `test_siglip_vs_openclip_vs_imagebind_benchmark_full`

* Benchmarks:

  * **Recall\@1 / 5 / 10**
  * **mAP (Mean Average Precision)**
  * **Embedding & Search Latency**
  * Dimensionality reporting
![WhatsApp Image 2025-07-10 at 18 12
13_22c67a84](https://github.com/user-attachments/assets/455bf30f-62b7-4684-a3f3-ad52e2a1ffe5)


###  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!

python/python/tests/test_embeddings_slow.py

@@ -4,7 +4,6 @@
 import importlib
 import io
 import os
-
 import lancedb
 import numpy as np
 import pandas as pd
@@ -12,7 +11,6 @@ import pyarrow as pa
 import pytest
 from lancedb.embeddings import get_registry
 from lancedb.pydantic import LanceModel, Vector, MultiVector
-import requests
 
 # These are integration tests for embedding functions.
 # They are slow because they require downloading models
@@ -98,9 +96,34 @@ def test_basic_text_embeddings(alias, tmp_path):
     assert not np.allclose(actual.vector, actual.vector2)
 
 
-@pytest.mark.slow
-def test_openclip(tmp_path):
+@pytest.fixture(scope="module")
+def test_images():
     import requests
+
+    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
+    uris = [
+        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
+        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
+        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
+    ]
+    image_bytes = [requests.get(uri).content for uri in uris]
+    return labels, uris, image_bytes
+
+
+@pytest.fixture(scope="module")
+def query_image_bytes():
+    import requests
+
+    query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
+    image_bytes = requests.get(query_image_uri).content
+    return image_bytes
+
+
+@pytest.mark.slow
+def test_openclip(tmp_path, test_images, query_image_bytes):
     from PIL import Image
 
     db = lancedb.connect(tmp_path)
@@ -114,20 +137,12 @@ def test_openclip(tmp_path):
         vector: Vector(func.ndims()) = func.VectorField()
         vec_from_bytes: Vector(func.ndims()) = func.VectorField()
 
+    labels, uris, image_bytes_list = test_images
     table = db.create_table("images", schema=Images)
-    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
-    uris = [
-        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
-        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
-        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
-        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
-        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
-        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
-    ]
-    # get each uri as bytes
-    image_bytes = [requests.get(uri).content for uri in uris]
     table.add(
-        pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
+        pd.DataFrame(
+            {"label": labels, "image_uri": uris, "image_bytes": image_bytes_list}
+        )
     )
 
     # text search
@@ -146,9 +161,7 @@ def test_openclip(tmp_path):
     assert np.allclose(actual.vector, frombytes.vector)
 
     # image search
-    query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
-    image_bytes = requests.get(query_image_uri).content
-    query_image = Image.open(io.BytesIO(image_bytes))
+    query_image = Image.open(io.BytesIO(query_image_bytes))
     actual = (
         table.search(query_image, vector_column_name="vector")
         .limit(1)
@@ -524,6 +537,8 @@ def test_voyageai_embedding_function():
     os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
 )
 def test_voyageai_multimodal_embedding_function():
+    import requests
+
     voyageai = (
         get_registry().get("voyageai").create(name="voyage-multimodal-3", max_retries=0)
     )
@@ -639,3 +654,71 @@ def test_colpali(tmp_path):
     assert len(first_row["image_vectors"][0]) == func.ndims(), (
         "Vector dimension mismatch"
     )
+
+
+@pytest.mark.slow
+def test_siglip(tmp_path, test_images, query_image_bytes):
+    from PIL import Image
+
+    labels, uris, image_bytes = test_images
+
+    db = lancedb.connect(tmp_path)
+    registry = get_registry()
+    func = registry.get("siglip").create(max_retries=0)
+
+    class Images(LanceModel):
+        label: str
+        image_uri: str = func.SourceField()
+        image_bytes: bytes = func.SourceField()
+        vector: Vector(func.ndims()) = func.VectorField()
+        vec_from_bytes: Vector(func.ndims()) = func.VectorField()
+
+    table = db.create_table("images", schema=Images)
+
+    table.add(
+        pd.DataFrame(
+            {
+                "label": labels,
+                "image_uri": uris,
+                "image_bytes": image_bytes,
+            }
+        )
+    )
+
+    # Text search
+    actual = (
+        table.search("man's best friend", vector_column_name="vector")
+        .limit(1)
+        .to_pydantic(Images)[0]
+    )
+    assert actual.label == "dog"
+
+    frombytes = (
+        table.search("man's best friend", vector_column_name="vec_from_bytes")
+        .limit(1)
+        .to_pydantic(Images)[0]
+    )
+    assert actual.label == frombytes.label
+    assert np.allclose(actual.vector, frombytes.vector)
+
+    # Image search
+    query_image = Image.open(io.BytesIO(query_image_bytes))
+    actual = (
+        table.search(query_image, vector_column_name="vector")
+        .limit(1)
+        .to_pydantic(Images)[0]
+    )
+    assert actual.label == "dog"
+
+    other = (
+        table.search(query_image, vector_column_name="vec_from_bytes")
+        .limit(1)
+        .to_pydantic(Images)[0]
+    )
+    assert actual.label == other.label
+
+    arrow_table = table.search().select(["vector", "vec_from_bytes"]).to_arrow()
+    assert np.allclose(
+        arrow_table["vector"].combine_chunks().values.to_numpy(),
+        arrow_table["vec_from_bytes"].combine_chunks().values.to_numpy(),
+    )