feat(generative-ai): update samples to Gemini Flash (GoogleCloudPlatform#11879)

Author: irataxy

generative_ai/function_calling.py

@@ -33,7 +33,7 @@ def generate_function_call(project_id: str) -> GenerationResponse:
  vertexai.init(project=project_id, location="us-central1")
 
  # Initialize Gemini model
- model = GenerativeModel(model_name="gemini-1.0-pro-001")
+ model = GenerativeModel(model_name="gemini-1.5-flash-001")
 
  # 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.py

@@ -66,7 +66,7 @@ def generate_function_call_chat(project_id: str) -> ChatSession:
 
  # Initialize Gemini model
  model = GenerativeModel(
- model_name="gemini-1.0-pro-001",
+ model_name="gemini-1.5-flash-001",
  generation_config=GenerationConfig(temperature=0),
  tools=[retail_tool],
  )

generative_ai/gemini_chat_example.py

@@ -24,7 +24,7 @@ def chat_text_example(project_id: str) -> str:
 
  vertexai.init(project=project_id, location="us-central1")
 
- model = GenerativeModel(model_name="gemini-1.0-pro-002")
+ model = GenerativeModel(model_name="gemini-1.5-flash-001")
 
  chat = model.start_chat()
 
@@ -55,7 +55,7 @@ def chat_stream_example(project_id: str) -> str:
 
  vertexai.init(project=project_id, location="us-central1")
 
- model = GenerativeModel(model_name="gemini-1.0-pro-002")
+ model = GenerativeModel(model_name="gemini-1.5-flash-001")
 
  chat = model.start_chat()
 

generative_ai/gemini_count_token_example.py

@@ -26,7 +26,7 @@ def count_tokens(project_id: str) -> GenerationResponse:
 
  vertexai.init(project=project_id, location="us-central1")
 
- model = GenerativeModel(model_name="gemini-1.0-pro-002")
+ model = GenerativeModel(model_name="gemini-1.5-flash-001")
 
  prompt = "Why is the sky blue?"
 

generative_ai/gemini_pro_config_example.py

@@ -25,7 +25,7 @@ def generate_text(project_id: str) -> None:
 
  vertexai.init(project=project_id, location="us-central1")
 
- model = GenerativeModel(model_name="gemini-1.0-pro-vision-001")
+ model = GenerativeModel(model_name="gemini-1.5-flash-001")
 
  # Load example image from local storage
  encoded_image = base64.b64encode(open("scones.jpg", "rb").read()).decode("utf-8")

generative_ai/gemini_rapid_evaluation.py

@@ -65,7 +65,7 @@ def create_evaluation_task(project_id: str) -> EvalResult:
  ],
  )
 
- model = GenerativeModel("gemini-1.0-pro")
+ model = GenerativeModel("gemini-1.5-flash-001")
 
  prompt_template = (
  "Instruction: {instruction}. Article: {context}. Summary: {response}"

generative_ai/gemini_safety_config_example.py

@@ -26,7 +26,7 @@ def generate_text(project_id: str) -> str:
 
  vertexai.init(project=project_id, location="us-central1")
 
- model = generative_models.GenerativeModel(model_name="gemini-1.0-pro-vision-001")
+ model = generative_models.GenerativeModel(model_name="gemini-1.5-flash-001")
 
  # Generation config
  generation_config = generative_models.GenerationConfig(

generative_ai/gemini_tuning.py

@@ -31,7 +31,7 @@ def gemini_tuning_basic(project_id: str) -> sft.SupervisedTuningJob:
  vertexai.init(project=project_id, location="us-central1")
 
  sft_tuning_job = sft.train(
- source_model="gemini-1.0-pro-002",
+ source_model="gemini-1.5-flash-001",
  train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl",
  )
 
@@ -62,7 +62,7 @@ def gemini_tuning_advanced(project_id: str) -> sft.SupervisedTuningJob:
  vertexai.init(project=project_id, location="us-central1")
 
  sft_tuning_job = sft.train(
- source_model="gemini-1.0-pro-002",
+ source_model="gemini-1.5-flash-001",
  train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl",
  # The following parameters are optional
  validation_dataset="gs://cloud-samples-data/ai-platform/generative_ai/sft_validation_data.jsonl",

generative_ai/rag.py

@@ -318,7 +318,7 @@ def generate_content_with_rag(
  )
 
  rag_model = GenerativeModel(
- model_name="gemini-1.0-pro-002", tools=[rag_retrieval_tool]
+ model_name="gemini-1.5-flash-001", tools=[rag_retrieval_tool]
  )
  response = rag_model.generate_content("Why is the sky blue?")
  print(response.text)
@@ -392,7 +392,7 @@ def quickstart(
  )
  # Create a gemini-pro model instance
  rag_model = GenerativeModel(
- model_name="gemini-1.0-pro-002", tools=[rag_retrieval_tool]
+ model_name="gemini-1.5-flash-001", tools=[rag_retrieval_tool]
  )
 
  # Generate response