forked from phoenix/litellm-mirror
fix medllm test
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1 changed files with 10 additions and 6 deletions
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@ -706,24 +706,28 @@ def test_vertex_ai_completion_cost():
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print("calculated_input_cost: {}".format(calculated_input_cost))
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print("calculated_input_cost: {}".format(calculated_input_cost))
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@pytest.mark.skip(reason="new test - WIP, working on fixing this")
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def test_vertex_ai_medlm_completion_cost():
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def test_vertex_ai_medlm_completion_cost():
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"""Test for medlm completion cost."""
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"""Test for medlm completion cost."""
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with pytest.raises(Exception) as e:
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with pytest.raises(Exception) as e:
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model="vertex_ai/medlm-medium"
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model = "vertex_ai/medlm-medium"
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messages = [{"role": "user", "content": "Test MedLM completion cost."}]
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messages = [{"role": "user", "content": "Test MedLM completion cost."}]
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predictive_cost = completion_cost(model=model, messages=messages, custom_llm_provider="vertex_ai")
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predictive_cost = completion_cost(
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model=model, messages=messages, custom_llm_provider="vertex_ai"
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)
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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litellm.model_cost = litellm.get_model_cost_map(url="")
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model="vertex_ai/medlm-medium"
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model = "vertex_ai/medlm-medium"
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messages = [{"role": "user", "content": "Test MedLM completion cost."}]
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messages = [{"role": "user", "content": "Test MedLM completion cost."}]
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predictive_cost = completion_cost(model=model, messages=messages, custom_llm_provider="vertex_ai")
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predictive_cost = completion_cost(
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model=model, messages=messages, custom_llm_provider="vertex_ai"
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)
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assert predictive_cost > 0
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assert predictive_cost > 0
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model="vertex_ai/medlm-large"
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model = "vertex_ai/medlm-large"
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messages = [{"role": "user", "content": "Test MedLM completion cost."}]
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messages = [{"role": "user", "content": "Test MedLM completion cost."}]
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predictive_cost = completion_cost(model=model, messages=messages)
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predictive_cost = completion_cost(model=model, messages=messages)
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assert predictive_cost > 0
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assert predictive_cost > 0
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