Function test_generate_content_span

tests/unittests/telemetry/test_spans.py:833–975

Test native generate_content span creation with attributes and logs.

(
    mock_guess_system_name,
    mock_tracer,
    mock_otel_logger,
    monkeypatch,
    env_capture_value,
    capture_content,
    user_id,
)

)
@pytest.mark.parametrize('user_id', ['some-user-id', None])
async def test_generate_content_span(
    mock_guess_system_name,
    mock_tracer,
    mock_otel_logger,
    monkeypatch,
    env_capture_value,
    capture_content,
    user_id,
):
  """Test native generate_content span creation with attributes and logs."""
  # Arrange
  monkeypatch.setenv(
      'OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT',
      env_capture_value,
  )
  monkeypatch.setattr(
      'google.adk.telemetry.tracing._instrumented_with_opentelemetry_instrumentation_google_genai',
      lambda: False,
  )

  agent = LlmAgent(name='test_agent', model='not-a-gemini-model')
  invocation_context = await _create_invocation_context(agent)
  invocation_context.session.user_id = user_id
  system_instruction = types.Content(
      parts=[types.Part.from_text(text='You are a helpful assistant.')],
  )
  user_content1 = types.Content(role='user', parts=[types.Part(text='Hello')])
  user_content2 = types.Content(role='user', parts=[types.Part(text='World')])

  model_content = types.Content(
      role='model', parts=[types.Part(text='Response')]
  )

  llm_request = LlmRequest(
      model='some-model',
      contents=[user_content1, user_content2],
      config=types.GenerateContentConfig(system_instruction=system_instruction),
  )
  llm_response = LlmResponse(
      content=model_content,
      finish_reason=types.FinishReason.STOP,
      usage_metadata=types.GenerateContentResponseUsageMetadata(
          prompt_token_count=10,
          candidates_token_count=20,
      ),
  )

  model_response_event = mock.MagicMock()
  model_response_event.id = 'event-123'

  mock_span = (
      mock_tracer.start_as_current_span.return_value.__enter__.return_value
  )

  # Act
  async with use_inference_span(
      llm_request, invocation_context, model_response_event
  ) as gc_span: