fix output type semconv value and run ruff format

Author: Nik-Reddy

instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/__init__.py

@@ -162,9 +162,7 @@ def _instrument(self, **kwargs):
         wrap_function_wrapper(
             module="openai.resources.embeddings",
             name="Embeddings.create",
-            wrapper=embeddings_create(
-                tracer, instruments, latest_experimental_enabled
-            ),
+            wrapper=embeddings_create(tracer, instruments, latest_experimental_enabled),
         )
 
         wrap_function_wrapper(

instrumentation-genai/opentelemetry-instrumentation-openai-v2/src/opentelemetry/instrumentation/openai_v2/utils.py

@@ -161,9 +161,7 @@ def choice_to_event(choice, capture_content):
     if choice.message:
         message = {
             "role": (
-                choice.message.role
-                if choice.message and choice.message.role
-                else None
+                choice.message.role if choice.message and choice.message.role else None
             )
         }
         tool_calls = extract_tool_calls(choice.message, capture_content)
@@ -237,14 +235,10 @@ def get_llm_request_attributes(
     if operation_name == GenAIAttributes.GenAiOperationNameValues.CHAT.value:
         attributes.update(
             {
-                GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE: kwargs.get(
-                    "temperature"
-                ),
+                GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE: kwargs.get("temperature"),
                 GenAIAttributes.GEN_AI_REQUEST_TOP_P: kwargs.get("p")
                 or kwargs.get("top_p"),
-                GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS: kwargs.get(
-                    "max_tokens"
-                ),
+                GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS: kwargs.get("max_tokens"),
                 GenAIAttributes.GEN_AI_REQUEST_PRESENCE_PENALTY: kwargs.get(
                     "presence_penalty"
                 ),
@@ -258,16 +252,12 @@ def get_llm_request_attributes(
         if (choice_count := kwargs.get("n")) is not None:
             # Only add non default, meaningful values
             if isinstance(choice_count, int) and choice_count != 1:
-                attributes[GenAIAttributes.GEN_AI_REQUEST_CHOICE_COUNT] = (
-                    choice_count
-                )
+                attributes[GenAIAttributes.GEN_AI_REQUEST_CHOICE_COUNT] = choice_count
 
         if (stop_sequences := kwargs.get("stop")) is not None:
             if isinstance(stop_sequences, str):
                 stop_sequences = [stop_sequences]
-            attributes[GenAIAttributes.GEN_AI_REQUEST_STOP_SEQUENCES] = (
-                stop_sequences
-            )
+            attributes[GenAIAttributes.GEN_AI_REQUEST_STOP_SEQUENCES] = stop_sequences
 
         request_response_format_attr_key = (
             GenAIAttributes.GEN_AI_OUTPUT_TYPE
@@ -278,14 +268,10 @@ def get_llm_request_attributes(
             # response_format may be string, object with a string in the `type` key,
             # or a type (e.g. Pydantic model class used with parse())
             if isinstance(response_format, type):
-                attributes[request_response_format_attr_key] = "json_schema"
+                attributes[request_response_format_attr_key] = "json"
             elif isinstance(response_format, Mapping):
-                if (
-                    response_format_type := response_format.get("type")
-                ) is not None:
-                    attributes[request_response_format_attr_key] = (
-                        response_format_type
-                    )
+                if (response_format_type := response_format.get("type")) is not None:
+                    attributes[request_response_format_attr_key] = response_format_type
             else:
                 attributes[request_response_format_attr_key] = response_format
 
@@ -306,10 +292,7 @@ def get_llm_request_attributes(
         )
 
     # Add embeddings-specific attributes
-    elif (
-        operation_name
-        == GenAIAttributes.GenAiOperationNameValues.EMBEDDINGS.value
-    ):
+    elif operation_name == GenAIAttributes.GenAiOperationNameValues.EMBEDDINGS.value:
         # Add embedding dimensions if specified
         if (dimensions := kwargs.get("dimensions")) is not None:
             # TODO: move to GEN_AI_EMBEDDINGS_DIMENSION_COUNT when 1.39.0 is baseline
@@ -339,16 +322,12 @@ def create_chat_invocation(
     # pylint: disable=too-many-branches
 
     llm_invocation = LLMInvocation(request_model=kwargs.get("model", ""))
-    llm_invocation.provider = (
-        GenAIAttributes.GenAiProviderNameValues.OPENAI.value
-    )
+    llm_invocation.provider = GenAIAttributes.GenAiProviderNameValues.OPENAI.value
     llm_invocation.temperature = get_value(kwargs.get("temperature"))
     llm_invocation.top_p = get_value(kwargs.get("p") or kwargs.get("top_p"))
     llm_invocation.max_tokens = get_value(kwargs.get("max_tokens"))
     llm_invocation.presence_penalty = get_value(kwargs.get("presence_penalty"))
-    llm_invocation.frequency_penalty = get_value(
-        kwargs.get("frequency_penalty")
-    )
+    llm_invocation.frequency_penalty = get_value(kwargs.get("frequency_penalty"))
     llm_invocation.seed = get_value(kwargs.get("seed"))
     if (stop_sequences := get_value(kwargs.get("stop"))) is not None:
         if isinstance(stop_sequences, str):
@@ -365,28 +344,22 @@ def create_chat_invocation(
     if (choice_count := get_value(kwargs.get("n"))) is not None:
         # Only add non default, meaningful values
         if isinstance(choice_count, int) and choice_count != 1:
-            attributes[GenAIAttributes.GEN_AI_REQUEST_CHOICE_COUNT] = (
-                choice_count
-            )
+            attributes[GenAIAttributes.GEN_AI_REQUEST_CHOICE_COUNT] = choice_count
 
-    if (
-        response_format := get_value(kwargs.get("response_format"))
-    ) is not None:
+    if (response_format := get_value(kwargs.get("response_format"))) is not None:
         # response_format may be string, object with a string in the `type` key,
         # or a type (e.g. Pydantic model class used with parse())
         if isinstance(response_format, type):
-            attributes[GenAIAttributes.GEN_AI_OUTPUT_TYPE] = "json_schema"
+            attributes[GenAIAttributes.GEN_AI_OUTPUT_TYPE] = "json"
         elif isinstance(response_format, Mapping):
             if (
                 response_format_type := get_value(response_format.get("type"))
             ) is not None:
-                attributes[GenAIAttributes.GEN_AI_OUTPUT_TYPE] = (
-                    response_format_type
-                )
+                attributes[GenAIAttributes.GEN_AI_OUTPUT_TYPE] = response_format_type
         else:
-            attributes[
-                GenAIAttributes.GEN_AI_OPENAI_REQUEST_RESPONSE_FORMAT
-            ] = response_format
+            attributes[GenAIAttributes.GEN_AI_OPENAI_REQUEST_RESPONSE_FORMAT] = (
+                response_format
+            )
 
     # service_tier can be passed directly or in extra_body (in SDK 1.26.0 it's via extra_body)
     service_tier = get_value(kwargs.get("service_tier"))
@@ -416,16 +389,13 @@ def get_value(v: Any):
 def handle_span_exception(span, error: BaseException):
     span.set_status(Status(StatusCode.ERROR, str(error)))
     if span.is_recording():
-        span.set_attribute(
-            ErrorAttributes.ERROR_TYPE, type(error).__qualname__
-        )
+        span.set_attribute(ErrorAttributes.ERROR_TYPE, type(error).__qualname__)
     span.end()
 
 
 def _is_text_part(content: Any) -> bool:
     return isinstance(content, str) or (
-        isinstance(content, Iterable)
-        and all(isinstance(part, str) for part in content)
+        isinstance(content, Iterable) and all(isinstance(part, str) for part in content)
     )
 
 
@@ -476,9 +446,7 @@ def extract_tool_calls_new(tool_calls) -> list[ToolCallRequest]:
                     arguments = arguments_str
 
         # TODO: support custom
-        parts.append(
-            ToolCallRequest(id=call_id, name=func_name, arguments=arguments)
-        )
+        parts.append(ToolCallRequest(id=call_id, name=func_name, arguments=arguments))
     return parts
 
 

instrumentation-genai/opentelemetry-instrumentation-openai-v2/tests/test_structured_outputs.py

@@ -62,7 +62,7 @@ def test_structured_output_with_content(
         if latest_experimental_enabled
         else GenAIAttributes.GEN_AI_OPENAI_REQUEST_RESPONSE_FORMAT
     )
-    assert spans[0].attributes[output_type_attr_key] == "json_schema"
+    assert spans[0].attributes[output_type_attr_key] == "json"
 
     if latest_experimental_enabled:
         assert_messages_attribute(
@@ -71,18 +71,14 @@ def test_structured_output_with_content(
         )
         assert_messages_attribute(
             spans[0].attributes["gen_ai.output.messages"],
-            format_simple_expected_output_message(
-                response.choices[0].message.content
-            ),
+            format_simple_expected_output_message(response.choices[0].message.content),
         )
     else:
         logs = log_exporter.get_finished_logs()
         assert len(logs) == 2
 
         user_message = {"content": STRUCTURED_OUTPUT_PROMPT[0]["content"]}
-        assert_message_in_logs(
-            logs[0], "gen_ai.user.message", user_message, spans[0]
-        )
+        assert_message_in_logs(logs[0], "gen_ai.user.message", user_message, spans[0])
 
         choice_event = {
             "index": 0,
@@ -92,9 +88,7 @@ def test_structured_output_with_content(
                 "content": response.choices[0].message.content,
             },
         }
-        assert_message_in_logs(
-            logs[1], "gen_ai.choice", choice_event, spans[0]
-        )
+        assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0])
 
 
 def test_structured_output_no_content(
@@ -126,7 +120,7 @@ def test_structured_output_no_content(
         if latest_experimental_enabled
         else GenAIAttributes.GEN_AI_OPENAI_REQUEST_RESPONSE_FORMAT
     )
-    assert spans[0].attributes[output_type_attr_key] == "json_schema"
+    assert spans[0].attributes[output_type_attr_key] == "json"
 
     logs = log_exporter.get_finished_logs()
     if latest_experimental_enabled:
@@ -143,6 +137,4 @@ def test_structured_output_no_content(
             "finish_reason": "stop",
             "message": {"role": "assistant"},
         }
-        assert_message_in_logs(
-            logs[1], "gen_ai.choice", choice_event, spans[0]
-        )
+        assert_message_in_logs(logs[1], "gen_ai.choice", choice_event, spans[0])