feat: gemini code samples updates (#11579)

* chore: minor code readability improvements

* feat: update region tags and imports

* move region tags & imports to inside a function

* feat: Add readme notes for Generative AI sample developers

* chore: simplify README.md

* 🦉 Updates from OwlBot post-processor

See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md

---------

Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>

generative_ai/README.md

@@ -0,0 +1,49 @@
+# Generative AI on Google Cloud
+
+Product Page: https://cloud.google.com/ai/generative-ai?hl=en
+Code samples: https://cloud.google.com/docs/samples?text=Generative%20AI
+
+## Developer Notes
+
+In developing code samples for Generative AI products, a scripting style format is recommended. It's important to wrap the code samples and region tags to be with in a function definition.
+
+This change is motivated by the desire to provide a code format that can effortlessly integrate with popular data science community tools. These tools include Colab, Jupyter Notebooks, and the IPython shell.
+
+Example:
+
+```python
+def create_hello_world_file(filename):
+    # <region tag: starts here>
+    import os
+
+    # TODO(developer): Update and uncomment below code
+    # filename = `/tmp/test.txt`
+
+    if os.path.isfile(filename):
+        print(f'Overriding content in file(name: {filename})!')
+
+    with open(filename) as fp:
+        fp.write('Hello world!')
+    # <region tag: ends here>
+```
+
+In Google Cloud documentation page, code sample is shown as below
+
+```python
+import os
+
+# TODO(developer): Update and uncomment below code
+# filename = `/tmp/test.txt`
+
+if os.path.isfile(filename):
+    print(f'Overriding content in file(name: {filename})!')
+
+with open(filename) as fp:
+    fp.write('Hello world!')
+```
+
+
+
+
+
+

generative_ai/function_calling.py

@@ -29,7 +29,7 @@ def generate_function_call(prompt: str, project_id: str, location: str) -> tuple
     vertexai.init(project=project_id, location=location)
 
     # Initialize Gemini model
-    model = GenerativeModel("gemini-1.0-pro-001")
+    model = GenerativeModel(model_name="gemini-1.0-pro-001")
 
     # Specify a function declaration and parameters for an API request
     get_current_weather_func = FunctionDeclaration(

generative_ai/function_calling_chat.py

@@ -64,7 +64,7 @@ def generate_function_call_chat(project_id: str, location: str) -> tuple:
 
     # Initialize Gemini model
     model = GenerativeModel(
-        "gemini-1.0-pro-001",
+        model_name="gemini-1.0-pro-001",
         generation_config=GenerationConfig(temperature=0),
         tools=[retail_tool],
     )

generative_ai/gemini_all_modalities.py

@@ -24,7 +24,7 @@ def analyze_all_modalities(project_id: str) -> str:
 
     vertexai.init(project=project_id, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-pro-preview-0409")
+    model = GenerativeModel(model_name="gemini-1.5-pro-preview-0409")
 
     video_file_uri = (
         "gs://cloud-samples-data/generative-ai/video/behind_the_scenes_pixel.mp4"

generative_ai/gemini_audio.py

@@ -24,7 +24,7 @@ def summarize_audio(project_id: str) -> str:
 
     vertexai.init(project=project_id, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-pro-preview-0409")
+    model = GenerativeModel(model_name="gemini-1.5-pro-preview-0409")
 
     prompt = """
     Please provide a summary for the audio.
@@ -55,7 +55,7 @@ def transcript_audio(project_id: str) -> str:
 
     vertexai.init(project=project_id, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-pro-preview-0409")
+    model = GenerativeModel(model_name="gemini-1.5-pro-preview-0409")
 
     prompt = """
     Can you transcribe this interview, in the format of timecode, speaker, caption.

generative_ai/gemini_chat_example.py

@@ -16,14 +16,15 @@
 def chat_text_example(project_id: str, location: str) -> str:
     # [START generativeaionvertexai_gemini_multiturn_chat]
     import vertexai
+
     from vertexai.generative_models import GenerativeModel, ChatSession
 
     # TODO(developer): Update and un-comment below lines
     # project_id = "PROJECT_ID"
     # location = "us-central1"
     vertexai.init(project=project_id, location=location)
 
-    model = GenerativeModel("gemini-1.0-pro")
+    model = GenerativeModel(model_name="gemini-1.0-pro-002")
     chat = model.start_chat()
 
     def get_chat_response(chat: ChatSession, prompt: str) -> str:
@@ -45,13 +46,14 @@ def get_chat_response(chat: ChatSession, prompt: str) -> str:
 def chat_stream_example(project_id: str, location: str) -> str:
     # [START generativeaionvertexai_gemini_multiturn_chat_stream]
     import vertexai
+
     from vertexai.generative_models import GenerativeModel, ChatSession
 
     # TODO(developer): Update and un-comment below lines
     # project_id = "PROJECT_ID"
     # location = "us-central1"
     vertexai.init(project=project_id, location=location)
-    model = GenerativeModel("gemini-1.0-pro")
+    model = GenerativeModel(model_name="gemini-1.0-pro-002")
     chat = model.start_chat()
 
     def get_chat_response(chat: ChatSession, prompt: str) -> str:

generative_ai/gemini_count_token_example.py

@@ -13,17 +13,17 @@
 # limitations under the License.
 
 
-# [START generativeaionvertexai_gemini_token_count]
-import vertexai
-from vertexai.generative_models import GenerativeModel
+def generate_text(project_id: str, location: str) -> str:
+    # [START generativeaionvertexai_gemini_token_count]
+    import vertexai
 
+    from vertexai.generative_models import GenerativeModel
 
-def generate_text(project_id: str, location: str) -> str:
     # Initialize Vertex AI
     vertexai.init(project=project_id, location=location)
 
     # Load the model
-    model = GenerativeModel("gemini-1.0-pro")
+    model = GenerativeModel(model_name="gemini-1.0-pro-002")
 
     # prompt tokens count
     print(model.count_tokens("why is sky blue?"))
@@ -33,7 +33,5 @@ def generate_text(project_id: str, location: str) -> str:
 
     # response tokens count
     print(response._raw_response.usage_metadata)
+    # [END generativeaionvertexai_gemini_token_count]
     return response.text
-
-
-# [END generativeaionvertexai_gemini_token_count]

generative_ai/gemini_grounding_example.py

@@ -12,29 +12,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START generativeaionvertexai_gemini_grounding_with_web]
-# [START generativeaionvertexai_gemini_grounding_with_vais]
 
-import vertexai
-from vertexai.preview.generative_models import (
-    GenerationConfig,
-    GenerationResponse,
-    GenerativeModel,
-    grounding,
-    Tool,
-)
-
-# [END generativeaionvertexai_gemini_grounding_with_vais]
+from vertexai.preview.generative_models import GenerationResponse
 
 
 def generate_text_with_grounding_web(
     project_id: str, location: str
 ) -> GenerationResponse:
+    # [START generativeaionvertexai_gemini_grounding_with_web]
+    import vertexai
+    from vertexai.preview.generative_models import grounding
+    from vertexai.generative_models import GenerationConfig, GenerativeModel, Tool
+
     # Initialize Vertex AI
     vertexai.init(project=project_id, location=location)
 
     # Load the model
-    model = GenerativeModel(model_name="gemini-1.0-pro")
+    model = GenerativeModel(model_name="gemini-1.0-pro-002")
 
     # Use Google Search for grounding
     tool = Tool.from_google_search_retrieval(grounding.GoogleSearchRetrieval())
@@ -54,17 +48,20 @@ def generate_text_with_grounding_web(
     return response
 
 
-# [START generativeaionvertexai_gemini_grounding_with_vais]
-
-
 def generate_text_with_grounding_vertex_ai_search(
     project_id: str, location: str, data_store_path: str
 ) -> GenerationResponse:
+    # [START generativeaionvertexai_gemini_grounding_with_vais]
+    import vertexai
+
+    from vertexai.preview.generative_models import grounding
+    from vertexai.generative_models import GenerationConfig, GenerativeModel, Tool
+
     # Initialize Vertex AI
     vertexai.init(project=project_id, location=location)
 
     # Load the model
-    model = GenerativeModel(model_name="gemini-1.0-pro")
+    model = GenerativeModel(model_name="gemini-1.0-pro-002")
 
     # Use Vertex AI Search data store
     # Format: projects/{project_id}/locations/{location}/collections/default_collection/dataStores/{data_store_id}

generative_ai/gemini_guide_example.py

@@ -12,20 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START generativeaionvertexai_gemini_get_started]
-# TODO(developer): Vertex AI SDK - uncomment below & run
-# pip3 install --upgrade --user google-cloud-aiplatform
-# gcloud auth application-default login
 
-import vertexai
-from vertexai.generative_models import GenerativeModel, Part
+def generate_text(project_id: str, location: str) -> str:
+    # [START generativeaionvertexai_gemini_get_started]
+    # TODO(developer): Vertex AI SDK - uncomment below & run
+    # pip3 install --upgrade --user google-cloud-aiplatform
+    # gcloud auth application-default login
 
+    import vertexai
+    from vertexai.generative_models import GenerativeModel, Part
 
-def generate_text(project_id: str, location: str) -> str:
     # Initialize Vertex AI
     vertexai.init(project=project_id, location=location)
     # Load the model
-    multimodal_model = GenerativeModel("gemini-1.0-pro-vision")
+    multimodal_model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
     # Query the model
     response = multimodal_model.generate_content(
         [

generative_ai/gemini_multi_image_example.py

@@ -12,19 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import vertexai
-
 
 def generate_text_multimodal(project_id: str, location: str) -> str:
-    # Initialize Vertex AI
-    vertexai.init(project=project_id, location=location)
-
     # [START generativeaionvertexai_gemini_single_turn_multi_image]
     import http.client
     import typing
     import urllib.request
+    import vertexai
+
     from vertexai.generative_models import GenerativeModel, Image
 
+    # Initialize Vertex AI
+    vertexai.init(project=project_id, location=location)
+
     # create helper function
     def load_image_from_url(image_url: str) -> Image:
         with urllib.request.urlopen(image_url) as response:
@@ -44,7 +44,7 @@ def load_image_from_url(image_url: str) -> Image:
     )
 
     # Pass multimodal prompt
-    model = GenerativeModel("gemini-1.0-pro-vision")
+    model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
     response = model.generate_content(
         [
             landmark1,

generative_ai/gemini_pdf_example.py

@@ -15,16 +15,16 @@
 
 def analyze_pdf(project_id: str) -> str:
     # [START generativeaionvertexai_gemini_pdf]
-
     import vertexai
+
     from vertexai.generative_models import GenerativeModel, Part
 
     # TODO(developer): Update and un-comment below lines
     # project_id = "PROJECT_ID"
 
     vertexai.init(project=project_id, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-pro-preview-0409")
+    model = GenerativeModel(model_name="gemini-1.5-pro-preview-0409")
 
     prompt = """
     Your are a very professional document summarization specialist.
@@ -37,6 +37,6 @@ def analyze_pdf(project_id: str) -> str:
 
     response = model.generate_content(contents)
     print(response.text)
-
     # [END generativeaionvertexai_gemini_pdf]
+
     return response.text

generative_ai/gemini_pro_basic_example.py

@@ -12,17 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START generativeaionvertexai_gemini_pro_example]
-import vertexai
-from vertexai.generative_models import GenerativeModel, Part
-
 
 def generate_text(project_id: str, location: str) -> None:
+    # [START generativeaionvertexai_gemini_pro_example]
+    import vertexai
+
+    from vertexai.generative_models import GenerativeModel, Part
+
     # Initialize Vertex AI
     vertexai.init(project=project_id, location=location)
 
     # Load the model
-    model = GenerativeModel(model_name="gemini-pro-vision")
+    model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
 
     # Load example image
     image_url = "gs://generativeai-downloads/images/scones.jpg"
@@ -32,7 +33,5 @@ def generate_text(project_id: str, location: str) -> None:
     response = model.generate_content([image_content, "what is this image?"])
     print(response)
 
+    # [END generativeaionvertexai_gemini_pro_example]
     return response.text
-
-
-# [END generativeaionvertexai_gemini_pro_example]

generative_ai/gemini_pro_config_example.py

@@ -12,19 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# [START generativeaionvertexai_gemini_pro_config_example]
-import base64
 
-import vertexai
-from vertexai.generative_models import GenerationConfig, GenerativeModel, Part
+def generate_text(project_id: str, location: str) -> None:
+    # [START generativeaionvertexai_gemini_pro_config_example]
+    import base64
+    import vertexai
 
+    from vertexai.generative_models import GenerationConfig, GenerativeModel, Part
 
-def generate_text(project_id: str, location: str) -> None:
     # Initialize Vertex AI
     vertexai.init(project=project_id, location=location)
 
     # Load the model
-    model = GenerativeModel("gemini-1.0-pro-vision")
+    model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
 
     # Load example image from local storage
     encoded_image = base64.b64encode(open("scones.jpg", "rb").read()).decode("utf-8")
@@ -42,7 +42,6 @@ def generate_text(project_id: str, location: str) -> None:
         [image_content, "what is this image?"], generation_config=config
     )
     print(response.text)
-    return response.text
+    # [END generativeaionvertexai_gemini_pro_config_example]
 
-
-# [END generativeaionvertexai_gemini_pro_config_example]
+    return response.text