added reduced dimensionanility for multimodal embeddings (#476)

libs/vertexai/langchain_google_vertexai/embeddings.py

@@ -459,7 +459,10 @@ def embed_query(self, text: str) -> List[float]:
         return self.embed([text], 1, "RETRIEVAL_QUERY")[0]
 
     def embed_image(
-        self, image_path: str, contextual_text: Optional[str] = None
+        self,
+        image_path: str,
+        contextual_text: Optional[str] = None,
+        dimensions: Optional[int] = None,
     ) -> List[float]:
         """Embed an image.
 
@@ -479,5 +482,5 @@ def embed_image(
         image = Image(bytes_image)
         result: MultiModalEmbeddingResponse = self.instance[
             "get_embeddings_with_retry"
-        ](image=image, contextual_text=contextual_text)
+        ](image=image, contextual_text=contextual_text, dimension=dimensions)
         return result.image_embedding

libs/vertexai/tests/integration_tests/test_embeddings.py

@@ -77,10 +77,19 @@ def test_langchain_google_vertexai_large_batches() -> None:
 
 
 @pytest.mark.release
-def test_langchain_google_vertexai_image_embeddings(tmp_image) -> None:
+@pytest.mark.parametrize(
+    "dim, expected_dim",
+    [(None, 1408), (512, 512)],
+)
+def test_langchain_google_vertexai_image_embeddings(
+    dim, expected_dim, tmp_image
+) -> None:
     model = VertexAIEmbeddings(model_name="multimodalembedding")
-    output = model.embed_image(tmp_image)
-    assert len(output) == 1408
+    kwargs = {}
+    if dim:
+        kwargs["dimensions"] = dim
+    output = model.embed_image(tmp_image, **kwargs)
+    assert len(output) == expected_dim
 
 
 @pytest.mark.release