Avoid embedding warnings

python/python/lancedb/embeddings/colpali.py

@@ -9,6 +9,8 @@ import numpy as np
 import io
 import warnings
 
+from pydantic import Field
+
 from ..util import attempt_import_or_raise
 from .base import EmbeddingFunction
 from .registry import register
@@ -26,7 +28,7 @@ class ColPaliEmbeddings(EmbeddingFunction):
 
     Parameters
     ----------
-    model_name : str
+    colpali_model_name : str
         The name of the model to use (e.g., "Metric-AI/ColQwen2.5-3b-multilingual-v1.0")
         Supports models based on these engines:
         - ColPali: "vidore/colpali-v1.3" and others
@@ -57,7 +59,10 @@ class ColPaliEmbeddings(EmbeddingFunction):
         useful for large models that do not fit in memory.
     """
 
-    model_name: str = "Metric-AI/ColQwen2.5-3b-multilingual-v1.0"
+    colpali_model_name: str = Field(
+        default="Metric-AI/ColQwen2.5-3b-multilingual-v1.0",
+        validation_alias="model_name",
+    )
     device: str = "auto"
     dtype: str = "bfloat16"
     use_token_pooling: bool = True
@@ -107,7 +112,7 @@ class ColPaliEmbeddings(EmbeddingFunction):
             self._processor,
             self._token_pooler,
         ) = self._load_model(
-            self.model_name,
+            self.colpali_model_name,
             dtype,
             device,
             self.pooling_strategy,

python/python/lancedb/embeddings/siglip.py

@@ -10,7 +10,7 @@ import urllib.parse as urlparse
 import numpy as np
 import pyarrow as pa
 from tqdm import tqdm
-from pydantic import PrivateAttr
+from pydantic import Field, PrivateAttr
 
 from ..util import attempt_import_or_raise
 from .base import EmbeddingFunction
@@ -24,7 +24,10 @@ if TYPE_CHECKING:
 
 @register("siglip")
 class SigLipEmbeddings(EmbeddingFunction):
-    model_name: str = "google/siglip-base-patch16-224"
+    siglip_model_name: str = Field(
+        default="google/siglip-base-patch16-224",
+        validation_alias="model_name",
+    )
     device: str = "cpu"
     batch_size: int = 64
     normalize: bool = True
@@ -39,8 +42,10 @@ class SigLipEmbeddings(EmbeddingFunction):
         transformers = attempt_import_or_raise("transformers")
         self._torch = attempt_import_or_raise("torch")
 
-        self._processor = transformers.AutoProcessor.from_pretrained(self.model_name)
-        self._model = transformers.SiglipModel.from_pretrained(self.model_name)
+        self._processor = transformers.AutoProcessor.from_pretrained(
+            self.siglip_model_name
+        )
+        self._model = transformers.SiglipModel.from_pretrained(self.siglip_model_name)
         self._model.to(self.device)
         self._model.eval()
         self._ndims = None