update

python/python/lancedb/embeddings/gemini_text.py

@@ -4,7 +4,7 @@
 
 import os
 from functools import cached_property
-from typing import List, Union
+from typing import List, Optional, Union
 
 import numpy as np
 
@@ -46,10 +46,11 @@ class GeminiText(TextEmbeddingFunction):
 
     Parameters
     ----------
-    name: str, default "models/embedding-001"
+    name: str, default "models/text-embedding-004"
         The name of the model to use. See the Gemini documentation for a list of
         available models.
-
+    dims: int, optional
+        The dimension of the embedding, otherwise it will be inferred.
     query_task_type: str, default "retrieval_query"
         Sets the task type for the queries.
     source_task_type: str, default "retrieval_document"
@@ -77,9 +78,10 @@ class GeminiText(TextEmbeddingFunction):
 
     """
 
-    name: str = "models/embedding-001"
+    name: str = "models/text-embedding-004"
     query_task_type: str = "retrieval_query"
     source_task_type: str = "retrieval_document"
+    dims: Optional[int] = None
 
     if PYDANTIC_VERSION.major < 2:  # Pydantic 1.x compat
 
@@ -89,9 +91,18 @@ class GeminiText(TextEmbeddingFunction):
         model_config = dict()
         model_config["ignored_types"] = (cached_property,)
 
-    def ndims(self):
+    @cached_property
-        # TODO: fix hardcoding
+    def _model(self):
-        return 768
+        return self.client.get_model(self.name)
+
+    def ndims(self) -> int:
+        if self.dims:
+            return self.dims
+        if hasattr(self._model, "output_dimensionality"):
+            return self._model.output_dimensionality
+        # Fallback for older versions of the library
+        # or models that don't have the attribute
+        return len(self.generate_embeddings(["lancedb"])[0])
 
     def compute_query_embeddings(self, query: str, *args, **kwargs) -> List[np.array]:
         return self.compute_source_embeddings(query, task_type=self.query_task_type)
@@ -119,6 +130,8 @@ class GeminiText(TextEmbeddingFunction):
         ):  # Provide a title to use existing API design
             title = "Embedding of a document"
             kwargs["title"] = title
+        if self.dims:
+            kwargs["output_dimensionality"] = self.dims
 
         return [
             self.client.embed_content(model=self.name, content=text, **kwargs)[
@@ -131,6 +144,8 @@ class GeminiText(TextEmbeddingFunction):
     def client(self):
         genai = attempt_import_or_raise("google.generativeai", "google.generativeai")
 
-        if not os.environ.get("GOOGLE_API_KEY"):
+        api_key = os.environ.get("GOOGLE_API_KEY")
+        if not api_key:
             api_key_not_found_help("google")
+        genai.configure(api_key=api_key)
         return genai

python/python/tests/test_embeddings_slow.py

@@ -308,7 +308,7 @@ def test_instructor_embedding(tmp_path):
     os.environ.get("GOOGLE_API_KEY") is None, reason="GOOGLE_API_KEY not set"
 )
 def test_gemini_embedding(tmp_path):
-    model = get_registry().get("gemini-text").create(max_retries=0)
+    model = get_registry().get("gemini-text").create(max_retries=0, dims=512)
 
     class TextModel(LanceModel):
         text: str = model.SourceField()
@@ -319,7 +319,7 @@ def test_gemini_embedding(tmp_path):
     tbl = db.create_table("test", schema=TextModel, mode="overwrite")
 
     tbl.add(df)
-    assert len(tbl.to_pandas()["vector"][0]) == model.ndims()
+    assert len(tbl.to_pandas()["vector"][0]) == model.ndims() == 512
     assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"