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fix: GenAI Client(evals) - Improve LiteLLM model completion response parsing #5813

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Merged
copybara-service merged 1 commit into from
Sep 17, 2025
Merged

fix: GenAI Client(evals) - Improve LiteLLM model completion response parsing #5813

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118 changes: 111 additions & 7 deletions tests/unit/vertexai/genai/test_evals.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1002,11 +1002,8 @@ def test_run_inference_with_litellm_string_prompt_format(
assert call_kwargs["model"] == "gpt-4o"
assert call_kwargs["messages"] == expected_messages
assert "response" in result_dataset.eval_dataset_df.columns
response_content = json.loads(result_dataset.eval_dataset_df["response"][0])
assert (
response_content["choices"][0]["message"]["content"]
== "LiteLLM is a library..."
)
response_content = result_dataset.eval_dataset_df["response"][0]
assert response_content == "LiteLLM is a library..."

def test_run_inference_with_litellm_openai_request_format(
self,
Expand Down Expand Up @@ -1077,8 +1074,8 @@ def test_run_inference_with_litellm_openai_request_format(
assert call_kwargs["model"] == "gpt-4o"
assert call_kwargs["messages"] == expected_messages
assert "response" in result_dataset.eval_dataset_df.columns
response_content = json.loads(result_dataset.eval_dataset_df["response"][0])
assert response_content["choices"][0]["message"]["content"] == "Hello there"
response_content = result_dataset.eval_dataset_df["response"][0]
assert response_content == "Hello there"

def test_run_inference_with_unsupported_model_string(
self,
Expand Down Expand Up @@ -1106,6 +1103,113 @@ def test_run_inference_with_litellm_import_error(self, mock_api_client_fixture):
):
evals_module.run_inference(model="gpt-4o", src=prompt_df)

@mock.patch.object(_evals_common, "_run_litellm_inference")
@mock.patch.object(_evals_common, "_is_gemini_model")
@mock.patch.object(_evals_common, "_is_litellm_model")
@mock.patch.object(_evals_common, "_is_litellm_vertex_maas_model")
@mock.patch.object(_evals_utils, "EvalDatasetLoader")
def test_run_inference_with_litellm_parsing(
self,
mock_eval_dataset_loader,
mock_is_litellm_vertex_maas_model,
mock_is_litellm_model,
mock_is_gemini_model,
mock_run_litellm_inference,
):
"""Tests the parsing logic for LiteLLM responses within _run_inference_internal."""
mock_is_gemini_model.return_value = False
mock_is_litellm_model.return_value = True
mock_is_litellm_vertex_maas_model.return_value = False

mock_df = pd.DataFrame(
{
"prompt": [
"prompt1",
"prompt2",
"prompt3",
"prompt4",
"prompt5",
"prompt6",
]
}
)
mock_eval_dataset_loader.return_value.load.return_value = mock_df.to_dict(
orient="records"
)

raw_responses = [
{ # Successful response
"choices": [{"message": {"content": "LiteLLM response 1"}}]
},
{"error": "LiteLLM call failed: API key error"}, # Response with error key
{"id": "test-id-3"}, # Missing choices
{"choices": [{"index": 0}]}, # Missing message
{"choices": [{"message": {"role": "assistant"}}]}, # Missing content
"Invalid JSON string", # Non-dict response
]
mock_run_litellm_inference.return_value = raw_responses
# fmt: off
with mock.patch("vertexai._genai._evals_common.litellm") as mock_litellm:
# fmt: on
mock_litellm.utils.get_valid_models.return_value = ["gpt-4o"]
inference_result = self.client.evals.run_inference(
model="gpt-4o",
src=mock_df,
)

expected_responses = [
"LiteLLM response 1",
json.dumps({"error": "LiteLLM call failed: API key error"}),
json.dumps(
{
"error": "LiteLLM response missing 'choices'",
"details": {"id": "test-id-3"},
}
),
json.dumps(
{
"error": "LiteLLM response missing 'message' in first choice",
"details": {"choices": [{"index": 0}]},
}
),
json.dumps(
{
"error": "LiteLLM response missing 'content' in message",
"details": {"choices": [{"message": {"role": "assistant"}}]},
}
),
json.dumps(
{
"error": "Invalid LiteLLM response format",
"details": "Invalid JSON string",
}
),
]

expected_df = pd.DataFrame(
{
"prompt": [
"prompt1",
"prompt2",
"prompt3",
"prompt4",
"prompt5",
"prompt6",
],
"response": expected_responses,
}
)

pd.testing.assert_frame_equal(
inference_result.eval_dataset_df.reset_index(drop=True),
expected_df.reset_index(drop=True),
check_dtype=False,
)
mock_run_litellm_inference.assert_called_once()
_, call_kwargs = mock_run_litellm_inference.call_args
assert call_kwargs["model"] == "gpt-4o"
pd.testing.assert_frame_equal(call_kwargs["prompt_dataset"], mock_df)


class TestMetricPromptBuilder:
"""Unit tests for the MetricPromptBuilder class."""
Expand Down
59 changes: 57 additions & 2 deletions vertexai/_genai/_evals_common.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -496,8 +496,63 @@ def _run_inference_internal(
raw_responses = _run_litellm_inference(
model=processed_model_id, prompt_dataset=prompt_dataset
)
responses = [json.dumps(resp) for resp in raw_responses]

processed_llm_responses = []
for response_dict in raw_responses:
if not isinstance(response_dict, dict):
processed_llm_responses.append(
json.dumps(
{
"error": "Invalid LiteLLM response format",
"details": str(response_dict),
}
)
)
continue

if "error" in response_dict:
processed_llm_responses.append(json.dumps(response_dict))
continue

if (
"choices" in response_dict
and isinstance(response_dict["choices"], list)
and len(response_dict["choices"]) > 0
):
first_choice = response_dict["choices"][0]
if "message" in first_choice and isinstance(
first_choice["message"], dict
):
message = first_choice["message"]
if "content" in message and isinstance(message["content"], str):
processed_llm_responses.append(message["content"])
else:
processed_llm_responses.append(
json.dumps(
{
"error": "LiteLLM response missing 'content' in message",
"details": response_dict,
}
)
)
else:
processed_llm_responses.append(
json.dumps(
{
"error": "LiteLLM response missing 'message' in first choice",
"details": response_dict,
}
)
)
else:
processed_llm_responses.append(
json.dumps(
{
"error": "LiteLLM response missing 'choices'",
"details": response_dict,
}
)
)
responses = processed_llm_responses
else:
raise TypeError(
f"Unsupported model type: {type(model)}. Expecting string (model"
Expand Down
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