chore: update deprecated embedding model id (text-embedding-004 to gemini-embedding-001)

PiperOrigin-RevId: 872936859

Author: speedstorm1

google/genai/tests/batches/test_create_with_bigquery.py

@@ -24,8 +24,8 @@
 
 _GEMINI_MODEL = 'gemini-2.5-flash'
 _GEMINI_MODEL_FULL_NAME = 'publishers/google/models/gemini-2.5-flash'
-_EMBEDDING_MODEL = 'text-embedding-004'
-_EMBEDDING_MODEL_FULL_NAME = 'publishers/google/models/text-embedding-004'
+_EMBEDDING_MODEL = 'gemini-embedding-001'
+_EMBEDDING_MODEL_FULL_NAME = 'publishers/google/models/gemini-embedding-001'
 _DISPLAY_NAME = 'test_batch'
 _GENERATE_CONTENT_BQ_INPUT_FILE = (
     'bq://vertex-sdk-dev.unified_genai_tests_batches.generate_content_requests'
@@ -56,7 +56,7 @@
     pytest_helper.TestTableItem(
         name='test_union_embedding_with_bigquery',
         parameters=types._CreateBatchJobParameters(
-            model=_EMBEDDING_MODEL_FULL_NAME,
+            model='publishers/google/models/text-embedding-004',
             src=_EMBEDDING_BQ_INPUT_FILE,
             config={
                 'display_name': _DISPLAY_NAME,

google/genai/tests/batches/test_create_with_gcs.py

@@ -24,8 +24,8 @@
 
 _GEMINI_MODEL = 'gemini-2.5-flash'
 _GEMINI_MODEL_FULL_NAME = 'publishers/google/models/gemini-2.5-flash'
-_EMBEDDING_MODEL = 'text-embedding-004'
-_EMBEDDING_MODEL_FULL_NAME = 'publishers/google/models/text-embedding-004'
+_EMBEDDING_MODEL = 'gemini-embedding-001'
+_EMBEDDING_MODEL_FULL_NAME = 'publishers/google/models/gemini-embedding-001'
 _DISPLAY_NAME = 'test_batch'
 
 _GENERATE_CONTENT_GCS_INPUT_FILE = (
@@ -53,7 +53,7 @@
     pytest_helper.TestTableItem(
         name='test_union_embedding_with_gcs',
         parameters=types._CreateBatchJobParameters(
-            model=_EMBEDDING_MODEL,
+            model='text-embedding-004',
             src=_EMBEDDING_GCS_INPUT_FILE,
             config={
                 'display_name': _DISPLAY_NAME,

google/genai/tests/models/test_embed_content.py

@@ -33,17 +33,10 @@ def _get_bytes_from_file(relative_path: str) -> bytes:
     return file.read()
 
 test_table: list[pytest_helper.TestTableItem] = [
-    pytest_helper.TestTableItem(
-        name='test_single_text',
-        parameters=types.EmbedContentParameters(
-            model='text-embedding-004',
-            contents=t.t_contents('What is your name?'),
-        ),
-    ),
     pytest_helper.TestTableItem(
         name='test_multi_texts_with_config',
         parameters=types.EmbedContentParameters(
-            model='text-embedding-004',
+            model='gemini-embedding-001',
             contents=[
                 t.t_content('What is your name?'),
                 t.t_content('I am a model.'),
@@ -61,7 +54,7 @@ def _get_bytes_from_file(relative_path: str) -> bytes:
     pytest_helper.TestTableItem(
         name='test_single_text_with_mime_type_not_supported_in_mldev',
         parameters=types.EmbedContentParameters(
-            model='text-embedding-004',
+            model='gemini-embedding-001',
             contents=t.t_contents('What is your name?'),
             config={
                 'output_dimensionality': 10,
@@ -73,7 +66,7 @@ def _get_bytes_from_file(relative_path: str) -> bytes:
     pytest_helper.TestTableItem(
         name='test_single_text_with_auto_truncate_not_supported_in_mldev',
         parameters=types.EmbedContentParameters(
-            model='text-embedding-004',
+            model='gemini-embedding-001',
             contents=t.t_contents('What is your name?'),
             config={
                 'output_dimensionality': 10,
@@ -201,7 +194,7 @@ def _get_bytes_from_file(relative_path: str) -> bytes:
 @pytest.mark.asyncio
 async def test_async(client):
   response = await client.aio.models.embed_content(
-      model='text-embedding-004',
+      model='gemini-embedding-001',
       contents='What is your name?',
       config={'output_dimensionality': 10},
   )

google/genai/tests/shared/models/test_embed.py

@@ -24,8 +24,11 @@
     pytest_helper.TestTableItem(
         name='test_embed',
         parameters=genai_types.EmbedContentParameters(
-            model='text-embedding-004',
+            model='gemini-embedding-001',
             contents='Hello world!',
+            config={
+                'output_dimensionality': 10,
+            },
         ),
     ),
 ]