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feat(tracing): Adds more OpenTelemetry convention attributes: gen_ai.request.max_tokens, gen_ai.request.top_p and gen_ai.response.finish_reasons
Fixes #1234 PiperOrigin-RevId: 795082815
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Copybara-Service
parent
9cfe43334a
commit
9ba8eec220
@@ -81,9 +81,57 @@ async def _create_invocation_context(
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@pytest.mark.asyncio
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async def test_trace_call_llm_function_response_includes_part_from_bytes(
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async def test_trace_call_llm(monkeypatch, mock_span_fixture):
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"""Test trace_call_llm sets all telemetry attributes correctly with normal content."""
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monkeypatch.setattr(
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'opentelemetry.trace.get_current_span', lambda: mock_span_fixture
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)
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agent = LlmAgent(name='test_agent')
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invocation_context = await _create_invocation_context(agent)
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llm_request = LlmRequest(
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contents=[
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types.Content(
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role='user',
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parts=[types.Part(text='Hello, how are you?')],
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),
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],
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config=types.GenerateContentConfig(
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system_instruction='You are a helpful assistant.',
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top_p=0.95,
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max_output_tokens=1024,
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),
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)
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llm_response = LlmResponse(
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turn_complete=True,
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finish_reason=types.FinishReason.STOP,
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usage_metadata=types.GenerateContentResponseUsageMetadata(
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total_token_count=100,
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prompt_token_count=50,
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candidates_token_count=50,
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),
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)
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trace_call_llm(invocation_context, 'test_event_id', llm_request, llm_response)
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expected_calls = [
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mock.call('gen_ai.system', 'gcp.vertex.agent'),
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mock.call('gen_ai.request.top_p', 0.95),
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mock.call('gen_ai.request.max_tokens', 1024),
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mock.call('gen_ai.usage.input_tokens', 50),
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mock.call('gen_ai.usage.output_tokens', 50),
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mock.call('gen_ai.response.finish_reasons', ['stop']),
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]
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assert mock_span_fixture.set_attribute.call_count == 12
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mock_span_fixture.set_attribute.assert_has_calls(
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expected_calls, any_order=True
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)
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@pytest.mark.asyncio
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async def test_trace_call_llm_with_binary_content(
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monkeypatch, mock_span_fixture
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):
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"""Test trace_call_llm handles binary content serialization correctly."""
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monkeypatch.setattr(
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'opentelemetry.trace.get_current_span', lambda: mock_span_fixture
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)
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@@ -123,11 +171,14 @@ async def test_trace_call_llm_function_response_includes_part_from_bytes(
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llm_response = LlmResponse(turn_complete=True)
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trace_call_llm(invocation_context, 'test_event_id', llm_request, llm_response)
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# Verify basic telemetry attributes are set
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expected_calls = [
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mock.call('gen_ai.system', 'gcp.vertex.agent'),
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]
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assert mock_span_fixture.set_attribute.call_count == 7
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mock_span_fixture.set_attribute.assert_has_calls(expected_calls)
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# Verify binary content is replaced with '<not serializable>' in JSON
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llm_request_json_str = None
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for call_obj in mock_span_fixture.set_attribute.call_args_list:
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if call_obj.args[0] == 'gcp.vertex.agent.llm_request':
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@@ -141,38 +192,6 @@ async def test_trace_call_llm_function_response_includes_part_from_bytes(
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assert llm_request_json_str.count('<not serializable>') == 2
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@pytest.mark.asyncio
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async def test_trace_call_llm_usage_metadata(monkeypatch, mock_span_fixture):
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monkeypatch.setattr(
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'opentelemetry.trace.get_current_span', lambda: mock_span_fixture
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)
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agent = LlmAgent(name='test_agent')
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invocation_context = await _create_invocation_context(agent)
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llm_request = LlmRequest(
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config=types.GenerateContentConfig(system_instruction=''),
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)
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llm_response = LlmResponse(
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turn_complete=True,
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usage_metadata=types.GenerateContentResponseUsageMetadata(
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total_token_count=100,
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prompt_token_count=50,
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candidates_token_count=50,
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),
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)
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trace_call_llm(invocation_context, 'test_event_id', llm_request, llm_response)
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expected_calls = [
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mock.call('gen_ai.system', 'gcp.vertex.agent'),
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mock.call('gen_ai.usage.input_tokens', 50),
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mock.call('gen_ai.usage.output_tokens', 50),
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]
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assert mock_span_fixture.set_attribute.call_count == 9
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mock_span_fixture.set_attribute.assert_has_calls(
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expected_calls, any_order=True
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)
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def test_trace_tool_call_with_scalar_response(
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monkeypatch, mock_span_fixture, mock_tool_fixture, mock_event_fixture
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):
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