feat(generative-ai): Update Gemini Flask and Pro model names

generative_ai/batch_predict/gemini_batch_predict.py

@@ -47,7 +47,7 @@ def batch_predict_gemini_createjob(
 
     # Submit a batch prediction job with Gemini model
     batch_prediction_job = BatchPredictionJob.submit(
-        source_model="gemini-1.5-flash-001",
+        source_model="gemini-1.5-flash-002",
         input_dataset=input_uri,
         output_uri_prefix=output_uri,
     )

generative_ai/chat_completions/chat_completions_credentials_refresher.py

@@ -58,7 +58,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     )
 
     response = client.chat.completions.create(
-        model="google/gemini-1.5-flash-001",
+        model="google/gemini-1.5-flash-002",
         messages=[{"role": "user", "content": "Why is the sky blue?"}],
     )
 

generative_ai/chat_completions/chat_completions_function_calling_config.py

@@ -68,7 +68,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     messages.append({"role": "user", "content": "What is the weather in Boston?"})
 
     response = client.chat.completions.create(
-        model="google/gemini-1.5-flash-001",
+        model="google/gemini-1.5-flash-002",
         messages=messages,
         tools=tools,
         tool_choice="auto",

generative_ai/chat_completions/chat_completions_non_streaming_image.py

@@ -38,7 +38,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     )
 
     response = client.chat.completions.create(
-        model="google/gemini-1.5-flash-001",
+        model="google/gemini-1.5-flash-002",
         messages=[
             {
                 "role": "user",

generative_ai/chat_completions/chat_completions_non_streaming_text.py

@@ -38,7 +38,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     )
 
     response = client.chat.completions.create(
-        model="google/gemini-1.5-flash-001",
+        model="google/gemini-1.5-flash-002",
         messages=[{"role": "user", "content": "Why is the sky blue?"}],
     )
 

generative_ai/chat_completions/chat_completions_streaming_image.py

@@ -38,7 +38,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     )
 
     response = client.chat.completions.create(
-        model="google/gemini-1.5-flash-001",
+        model="google/gemini-1.5-flash-002",
         messages=[
             {
                 "role": "user",

generative_ai/chat_completions/chat_completions_streaming_text.py

@@ -38,7 +38,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     )
 
     response = client.chat.completions.create(
-        model="google/gemini-1.5-flash-001",
+        model="google/gemini-1.5-flash-002",
         messages=[{"role": "user", "content": "Why is the sky blue?"}],
         stream=True,
     )

generative_ai/context_caching/create_context_cache.py

@@ -46,7 +46,7 @@ def create_context_cache() -> str:
     ]
 
     cached_content = caching.CachedContent.create(
-        model_name="gemini-1.5-pro-001",
+        model_name="gemini-1.5-pro-002",
         system_instruction=system_instruction,
         contents=contents,
         ttl=datetime.timedelta(minutes=60),

generative_ai/controlled_generation/example_01.py

@@ -39,7 +39,7 @@ def generate_content() -> str:
         },
     }
 
-    model = GenerativeModel("gemini-1.5-pro-001")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     response = model.generate_content(
         "List a few popular cookie recipes",

generative_ai/controlled_generation/example_02.py

@@ -46,7 +46,7 @@ def generate_content() -> str:
         - "Quite good, but a bit too sweet for my taste." Rating: 1, Flavor: Mango Tango
     """
 
-    model = GenerativeModel("gemini-1.5-pro-001")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     response = model.generate_content(
         prompt,

generative_ai/controlled_generation/example_03.py

@@ -58,7 +58,7 @@ def generate_content() -> str:
         Finally, Saturday rounds off the week with sunny skies, a temperature of 80°F, and a humidity level of 40%. Winds will be gentle at 8 km/h.
     """
 
-    model = GenerativeModel("gemini-1.5-pro-001")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     response = model.generate_content(
         prompt,

generative_ai/controlled_generation/example_04.py

@@ -69,7 +69,7 @@ def generate_content() -> str:
         It has large questionable stains on it.
     """
 
-    model = GenerativeModel("gemini-1.5-pro-001")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     response = model.generate_content(
         prompt,

generative_ai/controlled_generation/example_05.py

@@ -26,7 +26,7 @@ def generate_content() -> str:
     # PROJECT_ID = "your-project-id"
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     prompt = """
         List a few popular cookie recipes using this JSON schema:

generative_ai/controlled_generation/example_06.py

@@ -39,7 +39,7 @@ def generate_content() -> str:
         },
     }
 
-    model = GenerativeModel("gemini-1.5-pro-001")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     response = model.generate_content(
         [

generative_ai/controlled_generation/response_mime_type.py

@@ -24,7 +24,7 @@ def generate_content(project_id: str) -> str:
     # project_id = "PROJECT_ID"
     vertexai.init(project=project_id, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     prompt = """
         List a few popular cookie recipes using this JSON schema:

generative_ai/controlled_generation/response_schema.py

@@ -37,7 +37,7 @@ def generate_content(project_id: str) -> str:
         },
     }
 
-    model = GenerativeModel("gemini-1.5-pro-001")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     response = model.generate_content(
         "List a few popular cookie recipes",
@@ -84,7 +84,7 @@ def generate_content2(project_id: str) -> str:
         - "Quite good, but a bit too sweet for my taste." Rating: 1, Flavor: Mango Tango
     """
 
-    model = GenerativeModel("gemini-1.5-pro-001")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     response = model.generate_content(
         prompt,
@@ -142,7 +142,7 @@ def generate_content3(project_id: str) -> str:
         Finally, Saturday rounds off the week with sunny skies, a temperature of 80°F, and a humidity level of 40%. Winds will be gentle at 8 km/h.
     """
 
-    model = GenerativeModel("gemini-1.5-pro-001")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     response = model.generate_content(
         prompt,
@@ -218,7 +218,7 @@ def generate_content4(project_id: str) -> str:
         It has large questionable stains on it.
     """
 
-    model = GenerativeModel("gemini-1.5-pro-001")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     response = model.generate_content(
         prompt,
@@ -257,7 +257,7 @@ def generate_content6(project_id: str) -> str:
         },
     }
 
-    model = GenerativeModel("gemini-1.5-pro-001")
+    model = GenerativeModel("gemini-1.5-pro-002")
 
     response = model.generate_content(
         [

generative_ai/function_calling.py

@@ -37,7 +37,7 @@ def generate_function_call() -> GenerationResponse:
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
     # Initialize Gemini model
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     # Define the user's prompt in a Content object that we can reuse in model calls
     user_prompt_content = Content(
@@ -168,7 +168,7 @@ def generate_function_call_advanced() -> GenerationResponse:
     )
 
     model = GenerativeModel(
-        model_name="gemini-1.5-flash-001",
+        model_name="gemini-1.5-flash-002",
         tools=[retail_tool],
         tool_config=retail_tool_config,
     )

generative_ai/function_calling/advanced_example.py

@@ -79,7 +79,7 @@ def generate_function_call_advanced() -> GenerationResponse:
     )
 
     model = GenerativeModel(
-        model_name="gemini-1.5-flash-001",
+        model_name="gemini-1.5-flash-002",
         tools=[retail_tool],
         tool_config=retail_tool_config,
     )

generative_ai/function_calling/basic_example.py

@@ -38,7 +38,7 @@ def generate_function_call() -> GenerationResponse:
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
     # Initialize Gemini model
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     # Define the user's prompt in a Content object that we can reuse in model calls
     user_prompt_content = Content(

generative_ai/function_calling/chat_function_calling_config.py

@@ -71,7 +71,7 @@ def generate_text() -> object:
     messages.append({"role": "user", "content": "What is the weather in Boston?"})
 
     response = client.chat.completions.create(
-        model="google/gemini-1.5-flash-001",
+        model="google/gemini-1.5-flash-002",
         messages=messages,
         tools=tools,
         tool_choice="auto",

generative_ai/gemini_all_modalities.py

@@ -25,7 +25,7 @@ def analyze_all_modalities() -> str:
     # TODO (developer): update project_id
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     video_file_uri = (
         "gs://cloud-samples-data/generative-ai/video/behind_the_scenes_pixel.mp4"

generative_ai/gemini_audio.py

@@ -26,7 +26,7 @@ def summarize_audio() -> str:
     # TODO (developer): update project_id
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     prompt = """
     Please provide a summary for the audio.
@@ -56,7 +56,7 @@ def transcript_audio() -> str:
     # TODO (developer): update project & location
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     prompt = """
     Can you transcribe this interview, in the format of timecode, speaker, caption.

generative_ai/gemini_chat_example.py

@@ -26,7 +26,7 @@ def chat_text_example() -> str:
     # TODO (developer): update project_id
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     chat = model.start_chat()
 
@@ -56,7 +56,7 @@ def chat_stream_example() -> str:
     # TODO (developer): update project_id
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     chat = model.start_chat()
 

generative_ai/gemini_count_token_example.py

@@ -26,7 +26,7 @@ def count_tokens() -> GenerationResponse:
     # TODO (developer): update project_id
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     prompt = "Why is the sky blue?"
     # Prompt tokens count
@@ -55,7 +55,7 @@ def count_tokens_multimodal() -> GenerationResponse:
     # TODO (developer): update project_id
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     contents = [
         Part.from_uri(

generative_ai/gemini_grounding_example.py

@@ -32,7 +32,7 @@ def generate_text_with_grounding_web() -> GenerationResponse:
     # TODO (developer): update project_id
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     # Use Google Search for grounding
     tool = Tool.from_google_search_retrieval(grounding.GoogleSearchRetrieval())
@@ -68,7 +68,7 @@ def generate_text_with_grounding_vertex_ai_search(
     # TODO (developer): update project_id
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     # TODO(developer): Update project id, location, and data store id for your Vertex AI Search data store.
     # data_store_id = "DATA_STORE_ID"

generative_ai/gemini_guide_example.py

@@ -26,7 +26,7 @@ def generate_text() -> str:
     # PROJECT_ID = "your-project-id"
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     response = model.generate_content(
         [

generative_ai/gemini_multi_image_example.py

@@ -39,7 +39,7 @@ def generate_text_multimodal() -> str:
         mime_type="image/png",
     )
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
     response = model.generate_content(
         [
             image_file1,

generative_ai/gemini_pdf_example.py

@@ -25,7 +25,7 @@ def analyze_pdf() -> str:
     # TODO(developer): Update project_id and location
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     prompt = """
     You are a very professional document summarization specialist.

generative_ai/gemini_pro_basic_example.py

@@ -25,7 +25,7 @@ def generate_text() -> None:
     # TODO(developer): Update project_id and location
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     image_file = Part.from_uri(
         "gs://cloud-samples-data/generative-ai/image/scones.jpg", "image/jpeg"

generative_ai/gemini_pro_config_example.py

@@ -26,7 +26,7 @@ def generate_text() -> None:
     # TODO(developer): Update project_id and location
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     # 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

@@ -32,7 +32,7 @@ def generate_text() -> str:
     # TODO(developer): Update project
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     # Safety config
     safety_config = [

generative_ai/gemini_single_turn_video_example.py

@@ -25,7 +25,7 @@ def generate_text() -> str:
     # TODO(developer): Update project_id and location
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    vision_model = GenerativeModel("gemini-1.5-flash-001")
+    vision_model = GenerativeModel("gemini-1.5-flash-002")
 
     # Generate text
     response = vision_model.generate_content(

generative_ai/gemini_system_instruction.py

@@ -23,7 +23,7 @@ def set_system_instruction(project_id: str) -> str:
     vertexai.init(project=project_id, location="us-central1")
 
     model = GenerativeModel(
-        model_name="gemini-1.5-flash-001",
+        model_name="gemini-1.5-flash-002",
         system_instruction=[
             "You are a helpful language translator.",
             "Your mission is to translate text in English to French.",

generative_ai/gemini_text_input_example.py

@@ -25,7 +25,7 @@ def generate_from_text_input() -> str:
     # PROJECT_ID = "your-project-id"
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     response = model.generate_content(
         "What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"

generative_ai/gemini_video_audio.py

@@ -26,7 +26,7 @@ def analyze_video_with_audio() -> str:
     # PROJECT_ID = "your-project-id"
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-1.5-flash-001")
+    model = GenerativeModel("gemini-1.5-flash-002")
 
     prompt = """
     Provide a description of the video.