Add code snippet for embeddings normalization

generative_ai/embeddings/normalize_embeddings.py

@@ -0,0 +1,39 @@
+# Copyright 2025 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.
+
+import numpy as np
+
+
+def normalize_embedding(embedding_np: np.ndarray) -> np.ndarray:
+    """
+    Normalizes an embedding array to have a magnitude (L2 norm) of 1.
+
+    Args:
+        embedding_np: The input NumPy array to be normalized.
+
+    Returns:
+        The normalized NumPy array with a magnitude of 1.
+        Returns the original array if its magnitude is 0.
+    """
+    # Calculate the L2 norm (magnitude) of the array
+    norm = np.linalg.norm(embedding_np)
+
+    # Avoid division by zero if the array is all zeros
+    #
+    # An all-zeros embedding array does not exist in theroy
+    if norm == 0:
+        return embedding_np
+
+    # Divide the array by its norm to normalize it
+    return embedding_np / norm

generative_ai/embeddings/test_embeddings_examples.py

@@ -31,6 +31,7 @@
 import multimodal_example
 import multimodal_image_example
 import multimodal_video_example
+import normalize_embeddings
 
 
 @backoff.on_exception(backoff.expo, ResourceExhausted, max_time=10)
@@ -97,6 +98,22 @@ def test_code_embed_text() -> None:
     assert [len(e) for e in embeddings] == [dimensionality or 768] * len(texts)
 
 
+@backoff.on_exception(backoff.expo, ResourceExhausted, max_time=10)
+def test_embedding_normalization() -> None:
+    import numpy as np
+
+    embedding_value = [0.01] * 256
+    embedding_np = np.linalg.norm(np.array(embedding_value))
+    assert np.isclose(np.linalg.norm(embedding_np), 0.16)
+
+    normalized_embedding_np = normalize_embeddings.normalize_embedding(embedding_np)
+    assert np.isclose(np.linalg.norm(normalized_embedding_np), 1)
+
+    invalid_embedding_np = np.linalg.norm(np.array([0]))
+    normalized_embedding_np = normalize_embeddings.normalize_embedding(invalid_embedding_np)
+    assert np.isclose(np.linalg.norm(normalized_embedding_np), 0)
+
+
 @backoff.on_exception(backoff.expo, FailedPrecondition, max_time=300)
 def dispose(tuning_job) -> None:  # noqa: ANN001
     if tuning_job._status.name == "PIPELINE_STATE_RUNNING":