diff --git a/litellm/integrations/opentelemetry.py b/litellm/integrations/opentelemetry.py index 77833e5de0f..8385d512379 100644 --- a/litellm/integrations/opentelemetry.py +++ b/litellm/integrations/opentelemetry.py @@ -1695,17 +1695,41 @@ class OpenTelemetry(CustomLogger): value=safe_dumps(transformed_messages), ) - if kwargs.get("system_instructions"): - transformed_system_instructions = ( - self._transform_messages_to_otel_semantic_conventions( - kwargs.get("system_instructions") + # Coalesce the different kwarg names that carry the system + # prompt depending on the call path: + # - "system_instructions" — Vertex AI Gemini chat-completion + # - "instructions" — OpenAI Responses API + # - "system" — Anthropic Messages API + # Use `is not None` rather than truthiness to avoid falsy + # values (e.g. []) falling through to the wrong kwarg. + system_instructions = ( + kwargs.get("system_instructions") + if kwargs.get("system_instructions") is not None + else ( + kwargs.get("instructions") + if kwargs.get("instructions") is not None + else kwargs.get("system") + ) + ) + if system_instructions: + if isinstance(system_instructions, str): + # Plain text system prompt — no transformation needed + self.safe_set_attribute( + span=span, + key=SpanAttributes.GEN_AI_SYSTEM_INSTRUCTIONS.value, + value=system_instructions, + ) + else: + transformed_system_instructions = ( + self._transform_messages_to_otel_semantic_conventions( + system_instructions + ) + ) + self.safe_set_attribute( + span=span, + key=SpanAttributes.GEN_AI_SYSTEM_INSTRUCTIONS.value, + value=safe_dumps(transformed_system_instructions), ) - ) - self.safe_set_attribute( - span=span, - key=SpanAttributes.GEN_AI_SYSTEM_INSTRUCTIONS.value, - value=safe_dumps(transformed_system_instructions), - ) self.safe_set_attribute( span=span, @@ -1764,6 +1788,57 @@ class OpenTelemetry(CustomLogger): value=value, ) + elif response_obj.get("output"): + # Responses API: ResponsesAPIResponse has an "output" + # list instead of "choices". Each item with + # type="message" contains a "content" list of + # OutputText objects (type="output_text"). + output_items = response_obj.get("output") + output_messages = self._transform_responses_api_output_to_otel( + output_items + ) + if output_messages: + self.safe_set_attribute( + span=span, + key=SpanAttributes.GEN_AI_OUTPUT_MESSAGES.value, + value=safe_dumps(output_messages), + ) + + # Emit per-tool-call span attributes (parity with + # the choices branch that calls _tool_calls_kv_pair). + # Convert Responses API function_call items to the + # ChatCompletionMessageToolCall format expected by + # _tool_calls_kv_pair. + tool_calls = [] + for out_item in output_items: + item_d = self._to_dict(out_item) + if item_d and item_d.get("type") == "function_call": + tool_calls.append( + { + "function": { + "name": item_d.get("name", ""), + "arguments": item_d.get("arguments", ""), + } + } + ) + if tool_calls: + kv_pairs = OpenTelemetry._tool_calls_kv_pair(tool_calls) # type: ignore + for key, value in kv_pairs.items(): + self.safe_set_attribute( + span=span, + key=key, + value=value, + ) + + # Extract finish reason from ResponsesAPIResponse.status + status = response_obj.get("status") + if status: + self.safe_set_attribute( + span=span, + key=SpanAttributes.GEN_AI_RESPONSE_FINISH_REASONS.value, + value=safe_dumps([status]), + ) + except Exception as e: self.handle_callback_failure( callback_name=self.callback_name or "opentelemetry" @@ -1859,6 +1934,78 @@ class OpenTelemetry(CustomLogger): transformed.append(transformed_msg) return transformed + @staticmethod + def _to_dict(obj) -> Optional[dict]: + """Normalize an object to a plain dict. + + Handles three forms that appear in practice: + + 1. Plain ``dict`` — returned as-is. + 2. LiteLLM's ``BaseLiteLLMOpenAIResponseObject`` — exposes a + ``.get()`` method that delegates to ``__dict__``. + 3. Raw Pydantic v2 models from the ``openai`` SDK (e.g. + ``ResponseOutputMessage``, ``ResponseOutputText``) — these do + **not** have ``.get()`` but do have ``.model_dump()``. + + Returns ``None`` for anything else so callers can skip it. + """ + if isinstance(obj, dict): + return obj + if hasattr(obj, "get"): + # BaseLiteLLMOpenAIResponseObject duck-type + return obj # type: ignore[return-value] + if hasattr(obj, "model_dump"): + # Raw Pydantic v2 model (e.g. openai SDK types) + return obj.model_dump() # type: ignore[union-attr] + return None + + def _transform_responses_api_output_to_otel(self, output: List) -> List[dict]: + """ + Transform Responses API output items into OTEL GenAI 1.38 format. + + The Responses API returns output as a list of items, each with a + ``type`` field. Message items (``type="message"``) contain a + ``content`` list of ``OutputText`` objects with ``type="output_text"`` + and ``text`` fields. + + Items may be plain dicts, LiteLLM wrapper objects (with ``.get()``), + or raw Pydantic v2 models from the ``openai`` SDK (with + ``.model_dump()``). We normalize each item to a dict via + ``_to_dict`` before processing. + + This method converts them to the same ``{"role": ..., "parts": [...]}`` + format used by ``_transform_choices_to_otel_semantic_conventions``. + """ + transformed = [] + for raw_item in output: + item = self._to_dict(raw_item) + if item is None: + continue + if item.get("type") == "message": + role = item.get("role", "assistant") + parts = [] + for raw_content in item.get("content", []): + content = self._to_dict(raw_content) + if content is None: + continue + if content.get("type") == "output_text": + text = content.get("text", "") + if text: + parts.append({"type": "text", "content": text}) + if parts: + transformed.append({"role": role, "parts": parts}) + elif item.get("type") == "function_call": + # Surface tool calls from Responses API output + part: dict = { + "type": "tool_call", + "name": item.get("name", ""), + "arguments": item.get("arguments", ""), + } + if item.get("call_id"): + part["id"] = item["call_id"] + transformed.append({"role": "assistant", "parts": [part]}) + return transformed + def set_raw_request_attributes(self, span: Span, kwargs, response_obj): try: # Only set provider-specific raw payload attributes on this span. diff --git a/tests/test_litellm/integrations/test_opentelemetry.py b/tests/test_litellm/integrations/test_opentelemetry.py index 962806c5b52..aeb6d826f11 100644 --- a/tests/test_litellm/integrations/test_opentelemetry.py +++ b/tests/test_litellm/integrations/test_opentelemetry.py @@ -3042,3 +3042,630 @@ class TestResponseIdFallback(unittest.TestCase): otel.set_attributes(mock_span, kwargs, response_obj) mock_span.set_attribute.assert_any_call("litellm.call_id", call_id) + + + +class TestOpenTelemetryResponsesAPI(unittest.TestCase): + """ + Tests for Responses API (/v1/responses) OTel span attributes. + + The Responses API uses ``output`` (list of output items) instead of + ``choices``, ``instructions`` instead of ``system_instructions``, and + ``status`` instead of per-choice ``finish_reason``. + + See: https://github.com/BerriAI/litellm/issues/25840 + """ + + def _base_kwargs(self, **overrides): + """Return minimal kwargs for set_attributes with Responses API defaults.""" + kwargs = { + "model": "gpt-4o", + "messages": [{"role": "user", "content": "What is 2+2?"}], + "optional_params": {}, + "litellm_params": {"custom_llm_provider": "openai"}, + "standard_logging_object": { + "id": "resp_abc123", + "call_type": "responses", + "metadata": {}, + }, + } + kwargs.update(overrides) + return kwargs + + def _responses_api_response_obj(self, text="The answer is 4.", status="completed"): + """Return a dict mimicking ResponsesAPIResponse with a message output.""" + return { + "id": "resp_abc123", + "model": "gpt-4o", + "status": status, + "output": [ + { + "type": "message", + "role": "assistant", + "content": [ + { + "type": "output_text", + "text": text, + } + ], + } + ], + "usage": { + "prompt_tokens": 10, + "completion_tokens": 20, + "total_tokens": 30, + }, + } + + def _get_attr(self, mock_span, attr_name): + """Extract the value set for a specific attribute name, or None.""" + calls = [ + call + for call in mock_span.set_attribute.call_args_list + if call[0][0] == attr_name + ] + if not calls: + return None + return calls[0][0][1] + + # ------------------------------------------------------------------ + # gen_ai.output.messages + # ------------------------------------------------------------------ + + def test_output_messages_populated_for_responses_api(self): + """gen_ai.output.messages must be set when response has output items.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + kwargs = self._base_kwargs() + response_obj = self._responses_api_response_obj(text="The answer is 4.") + + otel.set_attributes(span=mock_span, kwargs=kwargs, response_obj=response_obj) + + raw = self._get_attr(mock_span, "gen_ai.output.messages") + self.assertIsNotNone(raw, "gen_ai.output.messages should be set") + + parsed = json.loads(raw) + self.assertIsInstance(parsed, list) + self.assertEqual(len(parsed), 1) + self.assertEqual(parsed[0]["role"], "assistant") + self.assertIn("parts", parsed[0]) + self.assertEqual(parsed[0]["parts"][0]["type"], "text") + self.assertEqual(parsed[0]["parts"][0]["content"], "The answer is 4.") + + def test_output_messages_with_multiple_content_items(self): + """Multiple output_text items in a single message should all appear as parts.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + response_obj = { + "id": "resp_multi", + "model": "gpt-4o", + "status": "completed", + "output": [ + { + "type": "message", + "role": "assistant", + "content": [ + {"type": "output_text", "text": "First paragraph."}, + {"type": "output_text", "text": "Second paragraph."}, + ], + } + ], + } + + otel.set_attributes( + span=mock_span, kwargs=self._base_kwargs(), response_obj=response_obj + ) + + raw = self._get_attr(mock_span, "gen_ai.output.messages") + parsed = json.loads(raw) + self.assertEqual(len(parsed[0]["parts"]), 2) + self.assertEqual(parsed[0]["parts"][0]["content"], "First paragraph.") + self.assertEqual(parsed[0]["parts"][1]["content"], "Second paragraph.") + + def test_output_messages_with_function_call(self): + """function_call output items should appear as tool_call parts.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + response_obj = { + "id": "resp_fc", + "model": "gpt-4o", + "status": "completed", + "output": [ + { + "type": "function_call", + "name": "get_weather", + "call_id": "call_abc", + "arguments": '{"location": "SF"}', + } + ], + } + + otel.set_attributes( + span=mock_span, kwargs=self._base_kwargs(), response_obj=response_obj + ) + + raw = self._get_attr(mock_span, "gen_ai.output.messages") + parsed = json.loads(raw) + self.assertEqual(len(parsed), 1) + self.assertEqual(parsed[0]["role"], "assistant") + self.assertEqual(parsed[0]["parts"][0]["type"], "tool_call") + self.assertEqual(parsed[0]["parts"][0]["name"], "get_weather") + self.assertEqual(parsed[0]["parts"][0]["arguments"], '{"location": "SF"}') + self.assertEqual(parsed[0]["parts"][0]["id"], "call_abc") + + def test_output_messages_mixed_message_and_function_call(self): + """Mixed output with both message and function_call items.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + response_obj = { + "id": "resp_mixed", + "model": "gpt-4o", + "status": "completed", + "output": [ + { + "type": "message", + "role": "assistant", + "content": [ + {"type": "output_text", "text": "Let me check the weather."}, + ], + }, + { + "type": "function_call", + "name": "get_weather", + "call_id": "call_xyz", + "arguments": "{}", + }, + ], + } + + otel.set_attributes( + span=mock_span, kwargs=self._base_kwargs(), response_obj=response_obj + ) + + raw = self._get_attr(mock_span, "gen_ai.output.messages") + parsed = json.loads(raw) + self.assertEqual(len(parsed), 2) + self.assertEqual(parsed[0]["role"], "assistant") + self.assertEqual(parsed[0]["parts"][0]["content"], "Let me check the weather.") + self.assertEqual(parsed[1]["parts"][0]["type"], "tool_call") + + def test_output_messages_empty_text_skipped(self): + """Output items with empty text should not produce parts.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + response_obj = { + "id": "resp_empty", + "model": "gpt-4o", + "status": "completed", + "output": [ + { + "type": "message", + "role": "assistant", + "content": [{"type": "output_text", "text": ""}], + } + ], + } + + otel.set_attributes( + span=mock_span, kwargs=self._base_kwargs(), response_obj=response_obj + ) + + # No output messages should be set since the text is empty + raw = self._get_attr(mock_span, "gen_ai.output.messages") + self.assertIsNone(raw, "Empty output text should not produce gen_ai.output.messages") + + def test_choices_still_work(self): + """Existing choices-based responses must still work (no regression).""" + otel = OpenTelemetry() + mock_span = MagicMock() + + kwargs = { + "model": "gpt-4", + "messages": [{"role": "user", "content": "Hello"}], + "optional_params": {}, + "litellm_params": {"custom_llm_provider": "openai"}, + "standard_logging_object": { + "id": "test-id", + "call_type": "completion", + "metadata": {}, + }, + } + + response_obj = { + "id": "chatcmpl-123", + "model": "gpt-4", + "choices": [ + { + "finish_reason": "stop", + "message": {"role": "assistant", "content": "Hi there!"}, + } + ], + "usage": {"prompt_tokens": 5, "completion_tokens": 10, "total_tokens": 15}, + } + + otel.set_attributes(span=mock_span, kwargs=kwargs, response_obj=response_obj) + + raw = self._get_attr(mock_span, "gen_ai.output.messages") + parsed = json.loads(raw) + self.assertEqual(parsed[0]["parts"][0]["content"], "Hi there!") + self.assertEqual(parsed[0]["finish_reason"], "stop") + + # ------------------------------------------------------------------ + # gen_ai.response.finish_reasons + # ------------------------------------------------------------------ + + def test_finish_reasons_from_status(self): + """gen_ai.response.finish_reasons should use ResponsesAPIResponse.status.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + otel.set_attributes( + span=mock_span, + kwargs=self._base_kwargs(), + response_obj=self._responses_api_response_obj(status="completed"), + ) + + raw = self._get_attr(mock_span, "gen_ai.response.finish_reasons") + self.assertIsNotNone(raw) + parsed = json.loads(raw) + self.assertEqual(parsed, ["completed"]) + + def test_finish_reasons_incomplete_status(self): + """Non-completed status values should still be captured.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + otel.set_attributes( + span=mock_span, + kwargs=self._base_kwargs(), + response_obj=self._responses_api_response_obj(status="incomplete"), + ) + + raw = self._get_attr(mock_span, "gen_ai.response.finish_reasons") + parsed = json.loads(raw) + self.assertEqual(parsed, ["incomplete"]) + + # ------------------------------------------------------------------ + # gen_ai.system_instructions + # ------------------------------------------------------------------ + + def test_system_instructions_from_instructions_kwarg(self): + """Responses API passes system prompt as kwargs['instructions'].""" + otel = OpenTelemetry() + mock_span = MagicMock() + + kwargs = self._base_kwargs(instructions="You are a math tutor.") + response_obj = self._responses_api_response_obj() + + otel.set_attributes(span=mock_span, kwargs=kwargs, response_obj=response_obj) + + value = self._get_attr(mock_span, "gen_ai.system_instructions") + self.assertEqual(value, "You are a math tutor.") + + def test_system_instructions_from_system_kwarg(self): + """Anthropic Messages API passes system prompt as kwargs['system'].""" + otel = OpenTelemetry() + mock_span = MagicMock() + + kwargs = self._base_kwargs(system="You are a helpful assistant.") + response_obj = self._responses_api_response_obj() + + otel.set_attributes(span=mock_span, kwargs=kwargs, response_obj=response_obj) + + value = self._get_attr(mock_span, "gen_ai.system_instructions") + self.assertEqual(value, "You are a helpful assistant.") + + def test_system_instructions_from_system_instructions_kwarg(self): + """Vertex AI Gemini path uses kwargs['system_instructions'] (existing behavior).""" + otel = OpenTelemetry() + mock_span = MagicMock() + + kwargs = self._base_kwargs( + system_instructions=[{"role": "system", "content": "Be concise."}] + ) + response_obj = self._responses_api_response_obj() + + otel.set_attributes(span=mock_span, kwargs=kwargs, response_obj=response_obj) + + raw = self._get_attr(mock_span, "gen_ai.system_instructions") + self.assertIsNotNone(raw) + parsed = json.loads(raw) + self.assertEqual(parsed[0]["role"], "system") + self.assertIn("parts", parsed[0]) + + def test_system_instructions_precedence(self): + """system_instructions takes precedence over instructions and system.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + kwargs = self._base_kwargs( + system_instructions="From Gemini", + instructions="From Responses API", + system="From Anthropic", + ) + response_obj = self._responses_api_response_obj() + + otel.set_attributes(span=mock_span, kwargs=kwargs, response_obj=response_obj) + + # system_instructions (string) should win — it's checked first + value = self._get_attr(mock_span, "gen_ai.system_instructions") + self.assertEqual(value, "From Gemini") + + def test_no_system_instructions_when_absent(self): + """No gen_ai.system_instructions attr when none of the kwargs are set.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + kwargs = self._base_kwargs() + response_obj = self._responses_api_response_obj() + + otel.set_attributes(span=mock_span, kwargs=kwargs, response_obj=response_obj) + + value = self._get_attr(mock_span, "gen_ai.system_instructions") + self.assertIsNone(value) + + +class TestTransformResponsesAPIOutput(unittest.TestCase): + """ + Unit tests for _transform_responses_api_output_to_otel. + """ + + def test_message_with_output_text(self): + otel = OpenTelemetry() + output = [ + { + "type": "message", + "role": "assistant", + "content": [{"type": "output_text", "text": "Hello!"}], + } + ] + result = otel._transform_responses_api_output_to_otel(output) + self.assertEqual(len(result), 1) + self.assertEqual(result[0]["role"], "assistant") + self.assertEqual(result[0]["parts"], [{"type": "text", "content": "Hello!"}]) + + def test_function_call_item(self): + otel = OpenTelemetry() + output = [ + { + "type": "function_call", + "name": "search", + "call_id": "call_1", + "arguments": '{"q": "test"}', + } + ] + result = otel._transform_responses_api_output_to_otel(output) + self.assertEqual(len(result), 1) + self.assertEqual(result[0]["role"], "assistant") + self.assertEqual(result[0]["parts"][0]["type"], "tool_call") + self.assertEqual(result[0]["parts"][0]["name"], "search") + self.assertEqual(result[0]["parts"][0]["id"], "call_1") + + def test_function_call_without_call_id(self): + otel = OpenTelemetry() + output = [ + { + "type": "function_call", + "name": "search", + "arguments": "{}", + } + ] + result = otel._transform_responses_api_output_to_otel(output) + self.assertNotIn("id", result[0]["parts"][0]) + + def test_unknown_type_ignored(self): + otel = OpenTelemetry() + output = [{"type": "reasoning", "content": "thinking..."}] + result = otel._transform_responses_api_output_to_otel(output) + self.assertEqual(result, []) + + def test_non_dict_items_ignored(self): + otel = OpenTelemetry() + output = ["not a dict", 42, None] + result = otel._transform_responses_api_output_to_otel(output) + self.assertEqual(result, []) + + def test_empty_output(self): + otel = OpenTelemetry() + result = otel._transform_responses_api_output_to_otel([]) + self.assertEqual(result, []) + + def test_message_with_empty_text_skipped(self): + otel = OpenTelemetry() + output = [ + { + "type": "message", + "role": "assistant", + "content": [{"type": "output_text", "text": ""}], + } + ] + result = otel._transform_responses_api_output_to_otel(output) + self.assertEqual(result, []) + + def test_message_default_role(self): + """Messages without explicit role should default to assistant.""" + otel = OpenTelemetry() + output = [ + { + "type": "message", + "content": [{"type": "output_text", "text": "Hi"}], + } + ] + result = otel._transform_responses_api_output_to_otel(output) + self.assertEqual(result[0]["role"], "assistant") + + + def test_pydantic_like_objects_accepted(self): + """Items with .get() but not isinstance(dict) should be accepted.""" + + class FakeOutputItem: + """Mimics BaseLiteLLMOpenAIResponseObject duck-typing.""" + + def __init__(self, data): + self._data = data + + def get(self, key, default=None): + return self._data.get(key, default) + + class FakeContent: + def __init__(self, data): + self._data = data + + def get(self, key, default=None): + return self._data.get(key, default) + + otel = OpenTelemetry() + output = [ + FakeOutputItem( + { + "type": "message", + "role": "assistant", + "content": [ + FakeContent({"type": "output_text", "text": "Pydantic works!"}), + ], + } + ) + ] + result = otel._transform_responses_api_output_to_otel(output) + self.assertEqual(len(result), 1) + self.assertEqual(result[0]["parts"][0]["content"], "Pydantic works!") + + +class TestSystemInstructionsPrecedence(unittest.TestCase): + """Tests for the is-not-None precedence in system_instructions coalescing.""" + + def _get_attr(self, mock_span, attr_name): + calls = [ + call + for call in mock_span.set_attribute.call_args_list + if call[0][0] == attr_name + ] + if not calls: + return None + return calls[0][0][1] + + def _base_kwargs(self, **overrides): + kwargs = { + "model": "gpt-4o", + "messages": [{"role": "user", "content": "Hi"}], + "optional_params": {}, + "litellm_params": {"custom_llm_provider": "openai"}, + "standard_logging_object": { + "id": "test-id", + "call_type": "responses", + "metadata": {}, + }, + } + kwargs.update(overrides) + return kwargs + + def test_empty_list_system_instructions_does_not_fallthrough(self): + """An empty list for system_instructions should NOT fall through to instructions.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + kwargs = self._base_kwargs( + system_instructions=[], + instructions="Should not be used", + ) + response_obj = {"id": "r1", "model": "gpt-4o"} + + otel.set_attributes(span=mock_span, kwargs=kwargs, response_obj=response_obj) + + # system_instructions is [] (falsy but not None), so it wins. + # Since it's an empty list, no attribute should be set (nothing to transform). + value = self._get_attr(mock_span, "gen_ai.system_instructions") + # The empty list is truthy for `is not None` but produces empty + # transformed output — the attribute should NOT contain "Should not be used". + if value is not None: + self.assertNotIn("Should not be used", str(value)) + + +class TestResponsesAPIToolCallSpanAttributes(unittest.TestCase): + """Tests for per-tool-call span attributes on Responses API function_call items.""" + + def _base_kwargs(self): + return { + "model": "gpt-4o", + "messages": [{"role": "user", "content": "What is the weather?"}], + "optional_params": {}, + "litellm_params": {"custom_llm_provider": "openai"}, + "standard_logging_object": { + "id": "resp_tc", + "call_type": "responses", + "metadata": {}, + }, + } + + def test_per_tool_call_attributes_emitted(self): + """function_call output items should produce per-tool-call span attributes.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + response_obj = { + "id": "resp_tc", + "model": "gpt-4o", + "status": "completed", + "output": [ + { + "type": "function_call", + "name": "get_weather", + "call_id": "call_abc", + "arguments": '{"location": "SF"}', + } + ], + } + + otel.set_attributes(span=mock_span, kwargs=self._base_kwargs(), response_obj=response_obj) + + # Verify per-tool-call attributes were set (same format as choices branch) + attr_names = [call[0][0] for call in mock_span.set_attribute.call_args_list] + tool_call_attrs = [a for a in attr_names if "function_call" in a] + self.assertTrue(len(tool_call_attrs) > 0, "Per-tool-call span attributes should be emitted") + + # Verify the name attribute specifically + mock_span.set_attribute.assert_any_call( + "gen_ai.completion.0.function_call.name", "get_weather" + ) + mock_span.set_attribute.assert_any_call( + "gen_ai.completion.0.function_call.arguments", '{"location": "SF"}' + ) + + def test_multiple_tool_calls_indexed(self): + """Multiple function_call items should be indexed correctly.""" + otel = OpenTelemetry() + mock_span = MagicMock() + + response_obj = { + "id": "resp_tc2", + "model": "gpt-4o", + "status": "completed", + "output": [ + { + "type": "function_call", + "name": "get_weather", + "call_id": "call_1", + "arguments": "{}", + }, + { + "type": "function_call", + "name": "get_time", + "call_id": "call_2", + "arguments": "{}", + }, + ], + } + + otel.set_attributes(span=mock_span, kwargs=self._base_kwargs(), response_obj=response_obj) + + mock_span.set_attribute.assert_any_call( + "gen_ai.completion.0.function_call.name", "get_weather" + ) + mock_span.set_attribute.assert_any_call( + "gen_ai.completion.1.function_call.name", "get_time" + )