feat: update gemini samples imports (GoogleCloudPlatform#11205)

* feat: update VertexAI Gemini samples imports

* feat: update function_calling.py imports

* feat: update model name

* feat: update requirements.txt

* 🦉 Updates from OwlBot post-processor

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

* fix: update tests cases

Update validation checks for new Gemini-1.0 reponses

---------

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

Author: msampathkumar

generative_ai/function_calling.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 # [START aiplatform_gemini_function_calling]
-from vertexai.preview.generative_models import (
+from vertexai.generative_models import (
     FunctionDeclaration,
     GenerativeModel,
     Tool,
@@ -22,7 +22,7 @@
 
 def generate_function_call(prompt: str) -> str:
     # Load the Vertex AI Gemini API to use function calling
-    model = GenerativeModel("gemini-pro")
+    model = GenerativeModel("gemini-1.0-pro")
 
     # Specify a function declaration and parameters for an API request
     get_current_weather_func = FunctionDeclaration(
@@ -56,4 +56,4 @@ def generate_function_call(prompt: str) -> str:
 # [END aiplatform_gemini_function_calling]
 
 if __name__ == "__main__":
-    generate_function_call("What is the weather like in Boston?")
+    print(generate_function_call("What is the weather like in Boston?"))

generative_ai/gemini_chat_example.py

@@ -16,14 +16,14 @@
 def chat_text_example(project_id: str, location: str) -> str:
     # [START aiplatform_gemini_multiturn_chat]
     import vertexai
-    from vertexai.preview.generative_models import GenerativeModel, ChatSession
+    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-pro")
+    model = GenerativeModel("gemini-1.0-pro")
     chat = model.start_chat()
 
     def get_chat_response(chat: ChatSession, prompt: str) -> str:
@@ -45,13 +45,13 @@ def get_chat_response(chat: ChatSession, prompt: str) -> str:
 def chat_stream_example(project_id: str, location: str) -> str:
     # [START aiplatform_gemini_multiturn_chat_stream]
     import vertexai
-    from vertexai.preview.generative_models import GenerativeModel, ChatSession
+    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-pro")
+    model = GenerativeModel("gemini-1.0-pro")
     chat = model.start_chat()
 
     def get_chat_response(chat: ChatSession, prompt: str) -> str:

generative_ai/gemini_count_token_example.py

@@ -15,15 +15,15 @@
 
 # [START aiplatform_gemini_token_count]
 import vertexai
-from vertexai.preview.generative_models import GenerativeModel
+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-pro")
+    model = GenerativeModel("gemini-1.0-pro")
 
     # prompt tokens count
     print(model.count_tokens("why is sky blue?"))

generative_ai/gemini_guide_example.py

@@ -18,14 +18,14 @@
 # gcloud auth application-default login
 
 import vertexai
-from vertexai.preview.generative_models import GenerativeModel, Part
+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-pro-vision")
+    multimodal_model = GenerativeModel("gemini-1.0-pro-vision")
     # Query the model
     response = multimodal_model.generate_content(
         [

generative_ai/gemini_multi_image_example.py

@@ -23,7 +23,7 @@ def generate_text_multimodal(project_id: str, location: str) -> str:
     import http.client
     import typing
     import urllib.request
-    from vertexai.preview.generative_models import GenerativeModel, Image
+    from vertexai.generative_models import GenerativeModel, Image
 
     # create helper function
     def load_image_from_url(image_url: str) -> Image:
@@ -44,7 +44,7 @@ def load_image_from_url(http://github.com/image_url:%20str) -> Image:
     )
 
     # Pass multimodal prompt
-    model = GenerativeModel("gemini-pro-vision")
+    model = GenerativeModel("gemini-1.0-pro-vision")
     response = model.generate_content(
         [
             landmark1,

generative_ai/gemini_pro_basic_example.py

@@ -14,7 +14,7 @@
 
 # [START aiplatform_gemini_pro_example]
 import vertexai
-from vertexai.preview.generative_models import GenerativeModel, Part
+from vertexai.generative_models import GenerativeModel, Part
 
 
 def generate_text(project_id: str, location: str) -> None:

generative_ai/gemini_pro_config_example.py

@@ -16,15 +16,15 @@
 import base64
 
 import vertexai
-from vertexai.preview.generative_models import GenerativeModel, Part
+from vertexai.generative_models import 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-pro-vision")
+    model = GenerativeModel("gemini-1.0-pro-vision")
 
     # Load example image from local storage
     encoded_image = base64.b64encode(open("scones.jpg", "rb").read()).decode("utf-8")

generative_ai/gemini_safety_config_example.py

@@ -15,15 +15,15 @@
 import vertexai
 
 # [START aiplatform_gemini_safety_settings]
-from vertexai.preview import generative_models
+from vertexai import generative_models
 
 
 def generate_text(project_id: str, location: str, image: str) -> str:
     # Initialize Vertex AI
     vertexai.init(project=project_id, location=location)
 
     # Load the model
-    model = generative_models.GenerativeModel("gemini-pro-vision")
+    model = generative_models.GenerativeModel("gemini-1.0-pro-vision")
 
     # Generation config
     config = {"max_output_tokens": 2048, "temperature": 0.4, "top_p": 1, "top_k": 32}

generative_ai/gemini_single_turn_video_example.py

@@ -16,14 +16,14 @@
 # [START aiplatform_gemini_single_turn_video]
 import vertexai
 
-from vertexai.preview.generative_models import GenerativeModel, Part
+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
-    vision_model = GenerativeModel("gemini-pro-vision")
+    vision_model = GenerativeModel("gemini-1.0-pro-vision")
     # Generate text
     response = vision_model.generate_content(
         [

generative_ai/requirements.txt

@@ -1,4 +1,4 @@
 pandas==1.3.5; python_version == '3.7'
 pandas==2.0.1; python_version > '3.7'
-google-cloud-aiplatform[pipelines]==1.38.0
+google-cloud-aiplatform[pipelines]==1.42.0
 google-auth==2.17.3