chore(genai): update model names in tests (#1187)

Use modern names as some previous ones are deprecated. Also parameterize
some tests

Author: mdrxy

libs/genai/tests/integration_tests/test_callbacks.py

@@ -1,11 +1,14 @@
 from typing import Any
 
+import pytest
 from langchain_core.callbacks import BaseCallbackHandler
 from langchain_core.outputs import LLMResult
 from langchain_core.prompts import PromptTemplate
 
 from langchain_google_genai import ChatGoogleGenerativeAI
 
+model_names = ["gemini-2.5-flash"]
+
 
 class StreamingLLMCallbackHandler(BaseCallbackHandler):
     def __init__(self, **kwargs: Any) -> None:
@@ -20,10 +23,14 @@ def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:
         self.generations.append(response.generations[0][0].text)
 
 
-def test_streaming_callback() -> None:
+@pytest.mark.parametrize(
+    "model_name",
+    model_names,
+)
+def test_streaming_callback(model_name: str) -> None:
     prompt_template = "Tell me details about the Company {name} with 2 bullet point?"
     cb = StreamingLLMCallbackHandler()
-    llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-001", callbacks=[cb])
+    llm = ChatGoogleGenerativeAI(model=model_name, callbacks=[cb])
     llm_chain = PromptTemplate.from_template(prompt_template) | llm
     for _t in llm_chain.stream({"name": "Google"}):
         pass

libs/genai/tests/integration_tests/test_function_call.py

@@ -2,6 +2,7 @@
 
 import json
 
+import pytest
 from langchain_core.messages import AIMessage
 from langchain_core.tools import tool
 from pydantic import BaseModel
@@ -10,8 +11,14 @@
     ChatGoogleGenerativeAI,
 )
 
+model_names = ["gemini-2.5-flash"]
 
-def test_function_call() -> None:
+
+@pytest.mark.parametrize(
+    "model_name",
+    model_names,
+)
+def test_function_call(model_name: str) -> None:
     functions = [
         {
             "name": "get_weather",
@@ -29,9 +36,7 @@ def test_function_call() -> None:
             },
         }
     ]
-    llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-001").bind(
-        functions=functions
-    )
+    llm = ChatGoogleGenerativeAI(model=model_name).bind(functions=functions)
     res = llm.invoke("what weather is today in san francisco?")
     assert res
     assert res.additional_kwargs
@@ -43,15 +48,17 @@ def test_function_call() -> None:
     assert "location" in arguments
 
 
-def test_tool_call() -> None:
+@pytest.mark.parametrize(
+    "model_name",
+    model_names,
+)
+def test_tool_call(model_name: str) -> None:
     @tool
     def search_tool(query: str) -> str:
         """Searches the web for `query` and returns the result."""
         raise NotImplementedError
 
-    llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-001").bind(
-        functions=[search_tool]
-    )
+    llm = ChatGoogleGenerativeAI(model=model_name).bind(functions=[search_tool])
     response = llm.invoke("weather in san francisco")
     assert isinstance(response, AIMessage)
     assert isinstance(response.content, str)
@@ -70,10 +77,12 @@ class MyModel(BaseModel):
     age: int
 
 
-def test_pydantic_call() -> None:
-    llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-001").bind(
-        functions=[MyModel]
-    )
+@pytest.mark.parametrize(
+    "model_name",
+    model_names,
+)
+def test_pydantic_call(model_name: str) -> None:
+    llm = ChatGoogleGenerativeAI(model=model_name).bind(functions=[MyModel])
     response = llm.invoke("my name is Erick and I am 27 years old")
     assert isinstance(response, AIMessage)
     assert isinstance(response.content, str)

libs/genai/tests/integration_tests/test_llms.py

@@ -10,7 +10,7 @@
 
 from langchain_google_genai import GoogleGenerativeAI, HarmBlockThreshold, HarmCategory
 
-model_names = ["gemini-1.5-flash-latest"]
+model_names = ["gemini-2.5-flash"]
 
 
 @pytest.mark.parametrize(
@@ -45,27 +45,43 @@ def test_google_generativeai_generate(model_name: str) -> None:
     assert len(generation_info.get("usage_metadata", {})) > 0
 
 
-async def test_google_generativeai_agenerate() -> None:
-    llm = GoogleGenerativeAI(temperature=0, model="models/gemini-2.0-flash-001")
+@pytest.mark.parametrize(
+    "model_name",
+    model_names,
+)
+async def test_google_generativeai_agenerate(model_name: str) -> None:
+    llm = GoogleGenerativeAI(temperature=0, model=model_name)
     output = await llm.agenerate(["Please say foo:"])
     assert isinstance(output, LLMResult)
 
 
-def test_generativeai_stream() -> None:
-    llm = GoogleGenerativeAI(temperature=0, model="gemini-1.5-flash-latest")
+@pytest.mark.parametrize(
+    "model_name",
+    model_names,
+)
+def test_generativeai_stream(model_name: str) -> None:
+    llm = GoogleGenerativeAI(temperature=0, model=model_name)
     outputs = list(llm.stream("Please say foo:"))
     assert isinstance(outputs[0], str)
 
 
-def test_generativeai_get_num_tokens_gemini() -> None:
-    llm = GoogleGenerativeAI(temperature=0, model="gemini-1.5-flash-latest")
+@pytest.mark.parametrize(
+    "model_name",
+    model_names,
+)
+def test_generativeai_get_num_tokens_gemini(model_name: str) -> None:
+    llm = GoogleGenerativeAI(temperature=0, model=model_name)
     output = llm.get_num_tokens("How are you?")
     assert output == 4
 
 
-def test_safety_settings_gemini() -> None:
+@pytest.mark.parametrize(
+    "model_name",
+    model_names,
+)
+def test_safety_settings_gemini(model_name: str) -> None:
     # test with blocked prompt
-    llm = GoogleGenerativeAI(temperature=0, model="gemini-1.5-flash-latest")
+    llm = GoogleGenerativeAI(temperature=0, model=model_name)
     output = llm.generate(prompts=["how to make a bomb?"])
     assert isinstance(output, LLMResult)
     assert len(output.generations[0]) > 0
@@ -88,7 +104,7 @@ def test_safety_settings_gemini() -> None:
 
     # test  with safety filters on instantiation
     llm = GoogleGenerativeAI(
-        model="gemini-1.5-flash-latest",
+        model=model_name,
         safety_settings=safety_settings,
         temperature=0,
     )

libs/genai/tests/integration_tests/test_standard.py

@@ -24,7 +24,7 @@ def chat_model_class(self) -> type[BaseChatModel]:
     @property
     def chat_model_params(self) -> dict:
         return {
-            "model": "models/gemini-2.5-flash",
+            "model": "gemini-2.5-flash",
             "rate_limiter": rate_limiter,
         }
 
@@ -107,7 +107,7 @@ def chat_model_class(self) -> type[BaseChatModel]:
     @property
     def chat_model_params(self) -> dict:
         return {
-            "model": "models/gemini-2.5-pro",
+            "model": "gemini-2.5-pro",
             "rate_limiter": rate_limiter,
         }
 

libs/genai/tests/integration_tests/test_tools.py

@@ -25,7 +25,7 @@ def test_multiple_tools() -> None:
     tools = [check_weather, check_live_traffic, check_tennis_score]
 
     model = ChatGoogleGenerativeAI(
-        model="gemini-2.0-flash-001",
+        model="gemini-2.5-flash",
     )
 
     model_with_tools = model.bind_tools(tools)

libs/genai/tests/unit_tests/__snapshots__/test_standard.ambr

@@ -18,7 +18,7 @@
       }),
       'max_output_tokens': 100,
       'max_retries': 2,
-      'model': 'models/gemini-1.5-pro-001',
+      'model': 'models/gemini-2.5-flash',
       'n': 1,
       'stop': list([
       ]),
@@ -49,7 +49,7 @@
       }),
       'max_output_tokens': 100,
       'max_retries': 2,
-      'model': 'models/gemini-1.0-pro-001',
+      'model': 'models/gemini-2.5-flash',
       'n': 1,
       'stop': list([
       ]),

libs/genai/tests/unit_tests/test_chat_models.py

@@ -44,7 +44,7 @@
 def test_integration_initialization() -> None:
     """Test chat model initialization."""
     llm = ChatGoogleGenerativeAI(
-        model="gemini-nano",
+        model="gemini-2.5-flash",
         google_api_key=SecretStr("..."),
         top_k=2,
         top_p=1,
@@ -54,27 +54,27 @@ def test_integration_initialization() -> None:
     ls_params = llm._get_ls_params()
     assert ls_params == {
         "ls_provider": "google_genai",
-        "ls_model_name": "gemini-nano",
+        "ls_model_name": "gemini-2.5-flash",
         "ls_model_type": "chat",
         "ls_temperature": 0.7,
     }
 
     llm = ChatGoogleGenerativeAI(
-        model="gemini-nano",
+        model="gemini-2.5-flash",
         google_api_key=SecretStr("..."),
         max_output_tokens=10,
     )
     ls_params = llm._get_ls_params()
     assert ls_params == {
         "ls_provider": "google_genai",
-        "ls_model_name": "gemini-nano",
+        "ls_model_name": "gemini-2.5-flash",
         "ls_model_type": "chat",
         "ls_temperature": 0.7,
         "ls_max_tokens": 10,
     }
 
     ChatGoogleGenerativeAI(
-        model="gemini-nano",
+        model="gemini-2.5-flash",
         api_key=SecretStr("..."),
         top_k=2,
         top_p=1,
@@ -86,13 +86,13 @@ def test_integration_initialization() -> None:
         with warnings.catch_warnings():
             warnings.simplefilter("ignore", UserWarning)
             llm = ChatGoogleGenerativeAI(
-                model="gemini-nano",
+                model="gemini-2.5-flash",
                 google_api_key=SecretStr("..."),
                 safety_setting={
                     "HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_LOW_AND_ABOVE"
                 },  # Invalid arg
             )
-        assert llm.model == "models/gemini-nano"
+        assert llm.model == "models/gemini-2.5-flash"
         mock_warning.assert_called_once()
         call_args = mock_warning.call_args[0][0]
         assert "Unexpected argument 'safety_setting'" in call_args
@@ -105,14 +105,14 @@ def test_initialization_inside_threadpool() -> None:
     with ThreadPoolExecutor() as executor:
         executor.submit(
             ChatGoogleGenerativeAI,
-            model="gemini-nano",
+            model="gemini-2.5-flash",
             google_api_key=SecretStr("secret-api-key"),
         ).result()
 
 
 def test_initalization_without_async() -> None:
     chat = ChatGoogleGenerativeAI(
-        model="gemini-nano",
+        model="gemini-2.5-flash",
         google_api_key=SecretStr("secret-api-key"),
     )
     assert chat.async_client is None
@@ -121,7 +121,7 @@ def test_initalization_without_async() -> None:
 def test_initialization_with_async() -> None:
     async def initialize_chat_with_async_client() -> ChatGoogleGenerativeAI:
         model = ChatGoogleGenerativeAI(
-            model="gemini-nano",
+            model="gemini-2.5-flash",
             google_api_key=SecretStr("secret-api-key"),
         )
         _ = model.async_client
@@ -133,7 +133,7 @@ async def initialize_chat_with_async_client() -> ChatGoogleGenerativeAI:
 
 def test_api_key_is_string() -> None:
     chat = ChatGoogleGenerativeAI(
-        model="gemini-nano",
+        model="gemini-2.5-flash",
         google_api_key=SecretStr("secret-api-key"),
     )
     assert isinstance(chat.google_api_key, SecretStr)
@@ -143,7 +143,7 @@ def test_api_key_masked_when_passed_via_constructor(
     capsys: pytest.CaptureFixture,
 ) -> None:
     chat = ChatGoogleGenerativeAI(
-        model="gemini-nano",
+        model="gemini-2.5-flash",
         google_api_key=SecretStr("secret-api-key"),
     )
     print(chat.google_api_key, end="")  # noqa: T201
@@ -349,7 +349,7 @@ def test_additional_headers_support(headers: Optional[dict[str, str]]) -> None:
         mock_client,
     ):
         chat = ChatGoogleGenerativeAI(
-            model="gemini-pro",
+            model="gemini-2.5-flash",
             google_api_key=param_secret_api_key,
             client_options=param_client_options,
             transport=param_transport,
@@ -387,7 +387,7 @@ def test_default_metadata_field_alias() -> None:
     # error
     # This is the main issue: LangSmith Playground passes None to default_metadata_input
     chat1 = ChatGoogleGenerativeAI(
-        model="gemini-pro",
+        model="gemini-2.5-flash",
         google_api_key=SecretStr("test-key"),
         default_metadata_input=None,
     )
@@ -398,7 +398,7 @@ def test_default_metadata_field_alias() -> None:
     # Test with empty list for default_metadata_input (should not cause validation
     # error)
     chat2 = ChatGoogleGenerativeAI(
-        model="gemini-pro",
+        model="gemini-2.5-flash",
         google_api_key=SecretStr("test-key"),
         default_metadata_input=[],
     )
@@ -407,7 +407,7 @@ def test_default_metadata_field_alias() -> None:
 
     # Test with tuple for default_metadata_input (should not cause validation error)
     chat3 = ChatGoogleGenerativeAI(
-        model="gemini-pro",
+        model="gemini-2.5-flash",
         google_api_key=SecretStr("test-key"),
         default_metadata_input=[("X-Test", "test")],
     )
@@ -716,7 +716,7 @@ def test_parse_response_candidate(raw_candidate: dict, expected: AIMessage) -> N
 
 
 def test_serialize() -> None:
-    llm = ChatGoogleGenerativeAI(model="gemini-pro-1.5", google_api_key="test-key")
+    llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", google_api_key="test-key")
     serialized = dumps(llm)
     llm_loaded = loads(
         serialized,