chore(generative_ai): update model references to stable gemini models (#14117)

generative_ai/chat_completions/chat_completions_credentials_refresher.py

@@ -55,7 +55,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     )
 
     response = client.chat.completions.create(
-        model="google/gemini-2.0-flash-001",
+        model="google/gemini-1.5-flash",
         messages=[{"role": "user", "content": "Why is the sky blue?"}],
     )
 

generative_ai/chat_completions/chat_completions_function_calling_basic.py

@@ -68,7 +68,7 @@ def generate_text() -> object:
     messages.append({"role": "user", "content": "What is the weather in Boston?"})
 
     response = client.chat.completions.create(
-        model="google/gemini-2.0-flash-001",
+        model="google/gemini-1.5-flash",
         messages=messages,
         tools=tools,
     )

generative_ai/chat_completions/chat_completions_function_calling_config.py

@@ -68,7 +68,7 @@ def generate_text() -> object:
     messages.append({"role": "user", "content": "What is the weather in Boston, MA?"})
 
     response = client.chat.completions.create(
-        model="google/gemini-2.0-flash-001",
+        model="google/gemini-1.5-flash",
         messages=messages,
         tools=tools,
         tool_choice="auto",

generative_ai/chat_completions/chat_completions_non_streaming_image.py

@@ -36,7 +36,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     )
 
     response = client.chat.completions.create(
-        model="google/gemini-2.0-flash-001",
+        model="google/gemini-1.5-flash",
         messages=[
             {
                 "role": "user",

generative_ai/chat_completions/chat_completions_non_streaming_text.py

@@ -35,7 +35,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     )
 
     response = client.chat.completions.create(
-        model="google/gemini-2.0-flash-001",
+        model="google/gemini-1.5-flash",
         messages=[{"role": "user", "content": "Why is the sky blue?"}],
     )
 

generative_ai/chat_completions/chat_completions_streaming_image.py

@@ -35,7 +35,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     )
 
     response = client.chat.completions.create(
-        model="google/gemini-2.0-flash-001",
+        model="google/gemini-1.5-flash",
         messages=[
             {
                 "role": "user",

generative_ai/chat_completions/chat_completions_streaming_text.py

@@ -35,7 +35,7 @@ def generate_text(project_id: str, location: str = "us-central1") -> object:
     )
 
     response = client.chat.completions.create(
-        model="google/gemini-2.0-flash-001",
+        model="google/gemini-1.5-flash",
         messages=[{"role": "user", "content": "Why is the sky blue?"}],
         stream=True,
     )

generative_ai/evaluation/pairwise_summarization_quality.py

@@ -52,11 +52,11 @@ def evaluate_output() -> EvalResult:
     eval_dataset = pd.DataFrame({"prompt": [prompt]})
 
     # Baseline model for pairwise comparison
-    baseline_model = GenerativeModel("gemini-2.0-flash-lite-001")
+    baseline_model = GenerativeModel("gemini-1.5-flash")
 
     # Candidate model for pairwise comparison
     candidate_model = GenerativeModel(
-        "gemini-2.0-flash-001", generation_config={"temperature": 0.4}
+        "gemini-1.5-pro", generation_config={"temperature": 0.4}
     )
 
     prompt_template = MetricPromptTemplateExamples.get_prompt_template(

generative_ai/function_calling/chat_function_calling_basic.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-2.0-flash-001",
+        model="google/gemini-1.5-flash",
         messages=messages,
         tools=tools,
     )

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, MA?"})
 
     response = client.chat.completions.create(
-        model="google/gemini-2.0-flash-001",
+        model="google/gemini-1.5-flash",
         messages=messages,
         tools=tools,
         tool_choice="auto",

generative_ai/labels/labels_example.py

@@ -28,7 +28,7 @@ def generate_content() -> GenerationResponse:
     # PROJECT_ID = "your-project-id"
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
-    model = GenerativeModel("gemini-2.0-flash-001")
+    model = GenerativeModel("gemini-1.5-flash")
 
     prompt = "What is Generative AI?"
     response = model.generate_content(

generative_ai/model_tuning/supervised_advanced_example.py

@@ -40,7 +40,7 @@ def gemini_tuning_advanced() -> sft.SupervisedTuningJob:
     # vertexai.init(encryption_spec_key_name="your-kms-key")
 
     sft_tuning_job = sft.train(
-        source_model="gemini-2.0-flash-001",
+        source_model="gemini-1.5-flash",
         # 1.5 and 2.0 models use the same JSONL format
         train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
         # The following parameters are optional

generative_ai/model_tuning/supervised_example.py

@@ -32,7 +32,7 @@ def gemini_tuning_basic() -> sft.SupervisedTuningJob:
     vertexai.init(project=PROJECT_ID, location="us-central1")
 
     sft_tuning_job = sft.train(
-        source_model="gemini-2.0-flash-001",
+        source_model="gemini-1.5-flash",
         # 1.5 and 2.0 models use the same JSONL format
         train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
     )

generative_ai/prompts/prompt_create.py

@@ -39,7 +39,7 @@ def prompt_create() -> Prompt:
             {"movie1": "The Lion King", "movie2": "Frozen"},
             {"movie1": "Inception", "movie2": "Interstellar"},
         ],
-        model_name="gemini-2.0-flash-001",
+        model_name="gemini-1.5-flash",
         system_instruction="You are a movie critic. Answer in a short sentence.",
         # generation_config=GenerationConfig, # Optional,
         # safety_settings=SafetySetting, # Optional,

generative_ai/prompts/prompt_delete.py

@@ -35,7 +35,7 @@ def delete_prompt() -> None:
             {"movie1": "The Lion King", "movie2": "Frozen"},
             {"movie1": "Inception", "movie2": "Interstellar"},
         ],
-        model_name="gemini-2.0-flash-001",
+        model_name="gemini-1.5-flash",
         system_instruction="You are a movie critic. Answer in a short sentence.",
 
     )

generative_ai/prompts/prompt_get.py

@@ -33,7 +33,7 @@ def get_prompt() -> Prompt:
     prompt = Prompt(
         prompt_name="meteorologist",
         prompt_data="How should I dress for weather in August?",
-        model_name="gemini-2.0-flash-001",
+        model_name="gemini-1.5-flash",
         system_instruction="You are a meteorologist. Answer in a short sentence.",
 
     )

generative_ai/prompts/prompt_list_version.py

@@ -32,7 +32,7 @@ def list_prompt_version() -> list:
     prompt = Prompt(
         prompt_name="zoologist",
         prompt_data="Which animal is the fastest on earth?",
-        model_name="gemini-2.0-flash-001",
+        model_name="gemini-1.5-flash",
         system_instruction="You are a zoologist. Answer in a short sentence.",
     )
     # Save Prompt to online resource.

generative_ai/prompts/prompt_restore_version.py

@@ -32,7 +32,7 @@
 #     prompt = Prompt(
 #         prompt_name="zoologist",
 #         prompt_data="Which animal is the fastest on earth?",
-#         model_name="gemini-2.0-flash-001",
+#         model_name="gemini-1.5-flash",
 #         system_instruction="You are a zoologist. Answer in a short sentence.",
 #     )
 #     # Save Prompt to online resource.

generative_ai/prompts/prompt_template.py

@@ -38,7 +38,7 @@ def prompt_template_example() -> list[GenerationResponse]:
     # define prompt template
     prompt = Prompt(
         prompt_data="Do {animal} {activity}?",
-        model_name="gemini-2.0-flash-001",
+        model_name="gemini-1.5-flash",
         variables=variables,
         system_instruction="You are a helpful zoologist"
         # generation_config=generation_config, # Optional

generative_ai/provisioned_throughput/provisioned_throughput_with_txt.py

@@ -33,7 +33,7 @@ def generate_content() -> str:
         request_metadata=[("x-vertex-ai-llm-request-type", "shared")],
     )
 
-    model = GenerativeModel("gemini-2.0-flash-001")
+    model = GenerativeModel("gemini-1.5-flash")
 
     response = model.generate_content(
         "What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"

generative_ai/rag/generate_content_example.py

@@ -53,7 +53,7 @@ def generate_content_with_rag(
     genai_client = genai.Client(enterprise=True, project=PROJECT_ID, location="us-central1")
 
     response = genai_client.models.generate_content(
-        model="gemini-2.5-pro",
+        model="gemini-2.5-flash",
         contents="Why is the sky blue?",
         config=genai_types.GenerateContentConfig(
             tools=[rag_retrieval_tool]

generative_ai/text_generation/text_example01.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-2.0-flash-001")
+    model = GenerativeModel("gemini-1.5-flash")
 
     response = model.generate_content(
         "What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"