chore: update docs and region tags (#11611)

* chore: update docs and region tags

No changes to code.

* chore: updates as per linter suggestions

generative_ai/anthropic_claude_3_streaming.py

@@ -12,16 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_claude_3_streaming]
-# TODO(developer): Vertex AI SDK - uncomment below & run
-# pip3 install --upgrade --user google-cloud-aiplatform
-# gcloud auth application-default login
-# pip3 install -U 'anthropic[vertex]'
 
-from anthropic import AnthropicVertex
+def generate_text_streaming(project_id: str, region: str) -> str:
+    # [START aiplatform_claude_3_streaming]
+    # TODO(developer): Vertex AI SDK - uncomment below & run
+    # pip3 install --upgrade --user google-cloud-aiplatform
+    # gcloud auth application-default login
+    # pip3 install -U 'anthropic[vertex]'
 
+    from anthropic import AnthropicVertex
 
-def generate_text_streaming(project_id: str, region: str) -> str:
     client = AnthropicVertex(region=region, project_id=project_id)
     result = []
 
@@ -39,11 +39,9 @@ def generate_text_streaming(project_id: str, region: str) -> str:
             print(text, end="", flush=True)
             result.append(text)
 
+    # [END aiplatform_claude_3_streaming]
     return "".join(result)
 
 
-# [END aiplatform_claude_3_streaming]
-
-
 if __name__ == "__main__":
     generate_text_streaming()

generative_ai/anthropic_claude_3_unary.py

@@ -12,16 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_claude_3_unary]
-# TODO(developer): Vertex AI SDK - uncomment below & run
-# pip3 install --upgrade --user google-cloud-aiplatform
-# gcloud auth application-default login
-# pip3 install -U 'anthropic[vertex]'
 
-from anthropic import AnthropicVertex
+def generate_text(project_id: str, region: str) -> object:
+    # [START aiplatform_claude_3_unary]
+    # TODO(developer): Vertex AI SDK - uncomment below & run
+    # pip3 install --upgrade --user google-cloud-aiplatform
+    # gcloud auth application-default login
+    # pip3 install -U 'anthropic[vertex]'
 
+    from anthropic import AnthropicVertex
 
-def generate_text(project_id: str, region: str) -> object:
     client = AnthropicVertex(region=region, project_id=project_id)
     message = client.messages.create(
         model="claude-3-sonnet@20240229",
@@ -34,11 +34,9 @@ def generate_text(project_id: str, region: str) -> object:
         ],
     )
     print(message.model_dump_json(indent=2))
+    # [END aiplatform_claude_3_unary]
     return message
 
 
-# [END aiplatform_claude_3_unary]
-
-
 if __name__ == "__main__":
     generate_text()

generative_ai/code_chat.py

@@ -12,12 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_sdk_code_chat]
-from vertexai.language_models import CodeChatModel
-
 
 def write_a_function(temperature: float = 0.5) -> object:
     """Example of using Codey for Code Chat Model to write a function."""
+    # [START aiplatform_sdk_code_chat]
+    from vertexai.language_models import CodeChatModel
 
     # TODO developer - override these parameters as needed:
     parameters = {
@@ -32,10 +31,10 @@ def write_a_function(temperature: float = 0.5) -> object:
         "Please help write a function to calculate the min of two numbers", **parameters
     )
     print(f"Response from Model: {response.text}")
+    # [END aiplatform_sdk_code_chat]
 
     return response
 
 
-# [END aiplatform_sdk_code_chat]
 if __name__ == "__main__":
     write_a_function()

generative_ai/code_completion_function.py

@@ -12,12 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_sdk_code_completion_comment]
-from vertexai.language_models import CodeGenerationModel
-
 
 def complete_code_function(temperature: float = 0.2) -> object:
     """Example of using Codey for Code Completion to complete a function."""
+    # [START aiplatform_sdk_code_completion_comment]
+    from vertexai.language_models import CodeGenerationModel
 
     # TODO developer - override these parameters as needed:
     parameters = {
@@ -31,10 +30,10 @@ def complete_code_function(temperature: float = 0.2) -> object:
     )
 
     print(f"Response from Model: {response.text}")
+    # [END aiplatform_sdk_code_completion_comment]
 
     return response
 
 
-# [END aiplatform_sdk_code_completion_comment]
 if __name__ == "__main__":
     complete_code_function()

generative_ai/code_completion_test_function.py

@@ -12,12 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_sdk_code_completion_test_function]
-from vertexai.language_models import CodeGenerationModel
-
 
 def complete_test_function(temperature: float = 0.2) -> object:
     """Example of using Codey for Code Completion to complete a test function."""
+    # [START aiplatform_sdk_code_completion_test_function]
+    from vertexai.language_models import CodeGenerationModel
 
     # TODO developer - override these parameters as needed:
     parameters = {
@@ -34,10 +33,10 @@ def test_empty_input_string()""",
     )
 
     print(f"Response from Model: {response.text}")
+    # [END aiplatform_sdk_code_completion_test_function]
 
     return response
 
 
-# [END aiplatform_sdk_code_completion_test_function]
 if __name__ == "__main__":
     complete_test_function()

generative_ai/grounding.py

@@ -12,15 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_sdk_grounding]
 from typing import Optional
 
-import vertexai
-from vertexai.language_models import (
-    GroundingSource,
-    TextGenerationModel,
-    TextGenerationResponse,
-)
+from vertexai.language_models import TextGenerationResponse
 
 
 def grounding(
@@ -30,7 +24,12 @@ def grounding(
     data_store_id: Optional[str],
 ) -> TextGenerationResponse:
     """Grounding example with a Large Language Model"""
+    # [START aiplatform_sdk_grounding]
+    import vertexai
 
+    from vertexai.language_models import GroundingSource, TextGenerationModel
+
+    # TODO(developer): Update values for project_id, location
     vertexai.init(project=project_id, location=location)
 
     # TODO developer - override these parameters as needed:
@@ -43,6 +42,7 @@ def grounding(
 
     model = TextGenerationModel.from_pretrained("text-bison@002")
 
+    # TODO(developer): Update values for data_store_location, data_store_id
     if data_store_id and data_store_location:
         # Use Vertex AI Search data store
         grounding_source = GroundingSource.VertexAISearch(

generative_ai/ideation.py

@@ -12,20 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_sdk_ideation]
-import vertexai
-from vertexai.language_models import TextGenerationModel
-
 
 def interview(
     temperature: float,
     project_id: str,
     location: str,
 ) -> str:
     """Ideation example with a Large Language Model"""
+    # [START aiplatform_sdk_ideation]
+    import vertexai
+
+    from vertexai.language_models import TextGenerationModel
 
+    # TODO(developer): Update values for project_id, location & temperature
     vertexai.init(project=project_id, location=location)
-    # TODO developer - override these parameters as needed:
     parameters = {
         "temperature": temperature,  # Temperature controls the degree of randomness in token selection.
         "max_output_tokens": 256,  # Token limit determines the maximum amount of text output.

generative_ai/list_tuned_code_generation_models.py

@@ -12,26 +12,27 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_sdk_list_tuned_code_generation_models]
-
-import vertexai
-from vertexai.language_models import CodeGenerationModel
-
 
 def list_tuned_code_generation_models(
     project_id: str,
     location: str,
 ) -> None:
     """List tuned models."""
+    # [START aiplatform_sdk_list_tuned_code_generation_models]
+
+    import vertexai
+    from vertexai.preview.language_models import CodeGenerationModel
+
+    # TODO(developer): Update values for project_id & location
     vertexai.init(project=project_id, location=location)
     model = CodeGenerationModel.from_pretrained("code-bison@001")
     tuned_model_names = model.list_tuned_model_names()
     print(tuned_model_names)
+    # [END aiplatform_sdk_list_tuned_code_generation_models]
 
     return tuned_model_names
 
 
-# [END aiplatform_sdk_list_tuned_code_generation_models]
 if __name__ == "__main__":
     import google.auth
 

generative_ai/list_tuned_models.py

@@ -12,26 +12,26 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_sdk_list_tuned_models]
-
-import vertexai
-from vertexai.language_models import TextGenerationModel
-
 
 def list_tuned_models(
     project_id: str,
     location: str,
 ) -> None:
     """List tuned models."""
+    # [START aiplatform_sdk_list_tuned_models]
+    import vertexai
+
+    from vertexai.language_models import TextGenerationModel
 
+    # TODO(developer): Update values for project_id & location
     vertexai.init(project=project_id, location=location)
     model = TextGenerationModel.from_pretrained("text-bison@002")
     tuned_model_names = model.list_tuned_model_names()
     print(tuned_model_names)
+    # [END aiplatform_sdk_list_tuned_models]
 
     return tuned_model_names
 
 
-# [END aiplatform_sdk_list_tuned_models]
 if __name__ == "__main__":
     list_tuned_models()

generative_ai/multimodal_embedding_image.py

@@ -12,15 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_sdk_multimodal_embedding_image]
 from typing import Optional
 
-import vertexai
-from vertexai.vision_models import (
-    Image,
-    MultiModalEmbeddingModel,
-    MultiModalEmbeddingResponse,
-)
+from vertexai.vision_models import MultiModalEmbeddingResponse
 
 
 def get_image_embeddings(
@@ -37,7 +31,11 @@ def get_image_embeddings(
         image_path: Path to image (local or Google Cloud Storage) to generate embeddings for.
         contextual_text: Text to generate embeddings for.
     """
+    # [START aiplatform_sdk_multimodal_embedding_image]
+    import vertexai
+    from vertexai.vision_models import Image, MultiModalEmbeddingModel
 
+    # TODO(developer): Update values for project_id, location, image_path & contextual_text
     vertexai.init(project=project_id, location=location)
 
     model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding")

generative_ai/multimodal_embedding_image_video_text.py

@@ -12,17 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_sdk_multimodal_embedding_image_video_text]
 from typing import Optional
 
-import vertexai
-from vertexai.vision_models import (
-    Image,
-    MultiModalEmbeddingModel,
-    MultiModalEmbeddingResponse,
-    Video,
-    VideoSegmentConfig,
-)
+from vertexai.vision_models import MultiModalEmbeddingResponse, VideoSegmentConfig
 
 
 def get_image_video_text_embeddings(
@@ -47,7 +39,13 @@ def get_image_video_text_embeddings(
         video_segment_config: Define specific segments to generate embeddings for.
             https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-multimodal-embeddings#video-best-practices
     """
+    # [START aiplatform_sdk_multimodal_embedding_image_video_text]
+    import vertexai
 
+    from vertexai.vision_models import Image, MultiModalEmbeddingModel, Video
+
+    # TODO(developer): Update values for project_id, location,
+    #            image_path, video_path, contextual_text, video_segment_config
     vertexai.init(project=project_id, location=location)
 
     model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding")

generative_ai/multimodal_embedding_video.py

@@ -12,16 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START aiplatform_sdk_multimodal_embedding_video]
 from typing import Optional
 
-import vertexai
-from vertexai.vision_models import (
-    MultiModalEmbeddingModel,
-    MultiModalEmbeddingResponse,
-    Video,
-    VideoSegmentConfig,
-)
+from vertexai.vision_models import MultiModalEmbeddingResponse, VideoSegmentConfig
 
 
 def get_video_embeddings(
@@ -44,7 +37,13 @@ def get_video_embeddings(
         video_segment_config: Define specific segments to generate embeddings for.
             https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-multimodal-embeddings#video-best-practices
     """
+    # [START aiplatform_sdk_multimodal_embedding_video]
+    import vertexai
 
+    from vertexai.vision_models import MultiModalEmbeddingModel, Video
+
+    # TODO(developer): Update values for project_id, location,
+    #               video_path, contextual_text, dimension, video_segment_config
     vertexai.init(project=project_id, location=location)
 
     model = MultiModalEmbeddingModel.from_pretrained("multimodalembedding")