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test(ci): refresh retired OpenAI tool-call models (#43676)
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3 changed files with 9 additions and 5 deletions
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@ -690,7 +690,7 @@ def test_langfuse_logging_tool_calling():
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]
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response = litellm.completion(
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model="gpt-3.5-turbo-1106",
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model="gpt-6-luna",
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messages=messages,
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tools=tools,
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tool_choice="auto", # auto is default, but we'll be explicit
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@ -698,6 +698,8 @@ def test_langfuse_logging_tool_calling():
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print("\nLLM Response1:\n", response)
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response_message = response.choices[0].message
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tool_calls = response.choices[0].message.tool_calls
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assert response.choices[0].message.tool_calls
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assert all(call.function.name == "get_current_weather" for call in response.choices[0].message.tool_calls)
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# test_langfuse_logging_tool_calling()
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@ -39,7 +39,7 @@ def get_current_weather(location, unit="fahrenheit"):
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@pytest.mark.parametrize(
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"model",
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[
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"gpt-3.5-turbo-1106",
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"gpt-6-luna",
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"mistral/mistral-large-latest",
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"claude-haiku-4-5-20251001",
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"gemini/gemini-2.5-flash-lite",
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@ -386,7 +386,7 @@ def test_parallel_function_call_stream():
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}
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]
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response = litellm.completion(
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model="gpt-3.5-turbo-1106",
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model="gpt-6-luna",
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messages=messages,
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tools=tools,
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stream=True,
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@ -435,7 +435,7 @@ def test_parallel_function_call_stream():
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) # extend conversation with function response
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print(f"messages: {messages}")
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second_response = litellm.completion(
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model="gpt-3.5-turbo-1106", messages=messages, temperature=0.2, seed=22
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model="gpt-6-luna", messages=messages, temperature=0.2, seed=22, reasoning_effort="none"
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) # get a new response from the model where it can see the function response
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print("second response\n", second_response)
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return second_response
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@ -83,13 +83,15 @@ def test_lunary_with_tools():
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]
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response = litellm.completion(
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model="gpt-3.5-turbo-1106",
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model="gpt-6-luna",
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messages=messages,
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tools=tools,
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tool_choice="auto", # auto is default, but we'll be explicit
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)
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response_message = response.choices[0].message
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assert response.choices[0].message.tool_calls
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assert all(call.function.name == "get_current_weather" for call in response.choices[0].message.tool_calls)
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print("\nLLM Response:\n", response.choices[0].message)
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