Reality2byte
diff --git a/genai/embeddings/generate_embeddings_with_lower_dimension.py b/genai/embeddings/generate_embeddings_with_lower_dimension.py
@@ -0,0 +1,71 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# [START aiplatform_genai_embeddings_specify_lower_dimension]
+import os
+
+from google import genai
+
+# TODO (Developer) Set environment variables
+PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
+LOCATION_ID = "global"
+
+# Supported dimensions: 128, 256, 512, 1408 (or up to 3072 for gemini-embedding-2)
+EMBEDDING_DIMENSION = 128
+IMAGE_URI = "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
+EMBEDDING_MODEL = "gemini-embedding-2"
+CONTEXTUAL_TEXT = "Colosseum"
+
+
+def generate_embeddings_with_lower_dimension() -> genai.types.EmbedContentResponse:
+    """Generates multimodal embeddings (image + text) with custom lower dimensionality
+
+    using the modern google-genai SDK.
+    """
+
+    client = genai.Client(
+        vertexai=True,
+        project=PROJECT_ID,
+        location=LOCATION_ID,
+    )
+
+    image_part = genai.types.Part.from_uri(
+        file_uri=IMAGE_URI,
+        mime_type="image/png",
+    )
+
+    text_part = genai.types.Part.from_text(text=CONTEXTUAL_TEXT)
+
+    contents = genai.types.Content(parts=[image_part, text_part])
+
+    config = genai.types.EmbedContentConfig(output_dimensionality=EMBEDDING_DIMENSION)
+
+    response = client.models.embed_content(
+        model=EMBEDDING_MODEL,
+        contents=[contents],
+        config=config,
+    )
+
+    if response.embeddings:
+
+        embeddings = response.embeddings[0].values
+
+        print(f"Embeddings (dim={len(embeddings)}): {embeddings[:3]}...\n")
+
+    print(response)
+
+    return response
+
+
+# [END aiplatform_genai_embeddings_specify_lower_dimension]
diff --git a/genai/embeddings/multimodal_example.py b/genai/embeddings/multimodal_example.py
@@ -0,0 +1,68 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# [START aiplatform_genai_multimodal_embedding_image_video_text]
+
+import os
+
+from google import genai
+
+# Environment configuration
+PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT", "your-project-id")
+LOCATION_ID = "global"
+
+EMBEDDING_MODEL = "gemini-embedding-2"
+IMAGE_URI = "gs://cloud-samples-data/vertex-ai/llm/prompts/landmark1.png"
+VIDEO_URI = "gs://cloud-samples-data/vertex-ai-vision/highway_vehicles.mp4"
+CONTEXTUAL_TEXT = "Cars on Highway"
+
+
+def get_image_video_text_embeddings() -> genai.types.EmbedContentResponse:
+    """Generates multimodal embeddings from image, video, and text using the google-genai SDK."""
+
+    client = genai.Client(
+        vertexai=True,
+        project=PROJECT_ID,
+        location=LOCATION_ID,
+    )
+
+    image_part = genai.types.Part.from_uri(
+        file_uri=IMAGE_URI,
+        mime_type="image/png",
+    )
+
+    video_part = genai.types.Part.from_uri(
+        file_uri=VIDEO_URI,
+        mime_type="video/mp4",
+    )
+
+    content = genai.types.Content(
+        parts=[image_part, video_part, genai.types.Part.from_text(text=CONTEXTUAL_TEXT)]
+    )
+
+    # Joint/Interleaved Multimodal Embedding (Image + Video + Text in same vector space)
+    response = client.models.embed_content(model=EMBEDDING_MODEL, contents=content)
+
+    if response.embeddings:
+
+        vector = response.embeddings[0].values
+
+        print(f"Embeddings ({len(vector)} dims): {vector[:3]}...")
+
+    print(response)
+
+    return response
+
+
+# [END aiplatform_genai_multimodal_embedding_image_video_text]
diff --git a/genai/embeddings/test_embeddings_examples.py b/genai/embeddings/test_embeddings_examples.py
@@ -20,9 +20,12 @@
 
 import code_retrieval_example
 import embeddings_docretrieval_with_txt
+import generate_embeddings_with_lower_dimension
 import model_tuning_example
 import multimodal_embedding_image
 import multimodal_embedding_video
+import multimodal_example
+
 
 os.environ["GOOGLE_GENAI_USE_ENTERPRISE"] = "True"
 os.environ["GOOGLE_CLOUD_LOCATION"] = "global"
@@ -45,6 +48,16 @@ def test_model_tuning_example() -> None:
     assert response
 
 
+def test_multimodal_example() -> None:
+    response = multimodal_example.get_image_video_text_embeddings()
+    assert response
+
+
+def test_generate_embeddings_with_lower_dimension() -> None:
+    response = generate_embeddings_with_lower_dimension.generate_embeddings_with_lower_dimension()
+    assert response
+
+
 def test_multimodal_embedding_image() -> None:
     response = multimodal_embedding_image.embed_content()
     assert response