GenAI Utils | Add more SemConv Attributes

util/opentelemetry-util-genai/CHANGELOG.md

@@ -7,6 +7,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## Unreleased
 
+- Add more Semconv attributes to LLMInvocation spans. 
+  ([https://github.com/open-telemetry/opentelemetry-python-contrib/pull/3862](#3862))
+
 ## Version 0.2b0 (2025-10-14)
 
 - Add jsonlines support to fsspec uploader

util/opentelemetry-util-genai/pyproject.toml

@@ -25,8 +25,8 @@ classifiers = [
   "Programming Language :: Python :: 3.13",
 ]
 dependencies = [
-  "opentelemetry-instrumentation ~= 0.57b0",
-  "opentelemetry-semantic-conventions ~= 0.57b0",
+  "opentelemetry-instrumentation ~= 0.58b0",
+  "opentelemetry-semantic-conventions ~= 0.58b0",
   "opentelemetry-api>=1.31.0",
 ]
 

util/opentelemetry-util-genai/src/opentelemetry/util/genai/handler.py

@@ -61,7 +61,7 @@
 from __future__ import annotations
 
 from contextlib import contextmanager
-from typing import Iterator, Optional
+from typing import Iterator
 
 from opentelemetry import context as otel_context
 from opentelemetry.semconv._incubating.attributes import (
@@ -93,7 +93,7 @@ def __init__(self, tracer_provider: TracerProvider | None = None):
             __name__,
             __version__,
             tracer_provider,
-            schema_url=Schemas.V1_36_0.value,
+            schema_url=Schemas.V1_37_0.value,
         )
 
     def start_llm(
@@ -132,6 +132,7 @@ def fail_llm(  # pylint: disable=no-self-use
             # TODO: Provide feedback that this invocation was not started
             return invocation
 
+        _apply_finish_attributes(invocation.span, invocation)
         _apply_error_attributes(invocation.span, error)
         # Detach context and end span
         otel_context.detach(invocation.context_token)
@@ -140,7 +141,7 @@ def fail_llm(  # pylint: disable=no-self-use
 
     @contextmanager
     def llm(
-        self, invocation: Optional[LLMInvocation] = None
+        self, invocation: LLMInvocation | None = None
     ) -> Iterator[LLMInvocation]:
         """Context manager for LLM invocations.
 
@@ -169,7 +170,7 @@ def get_telemetry_handler(
     """
     Returns a singleton TelemetryHandler instance.
     """
-    handler: Optional[TelemetryHandler] = getattr(
+    handler: TelemetryHandler | None = getattr(
         get_telemetry_handler, "_default_handler", None
     )
     if handler is None:

util/opentelemetry-util-genai/src/opentelemetry/util/genai/span_utils.py

@@ -12,8 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
+
 from dataclasses import asdict
-from typing import List
+from typing import Any
 
 from opentelemetry.semconv._incubating.attributes import (
     gen_ai_attributes as GenAI,
@@ -60,32 +62,13 @@ def _apply_common_span_attributes(
         # TODO: clean provider name to match GenAiProviderNameValues?
         span.set_attribute(GenAI.GEN_AI_PROVIDER_NAME, invocation.provider)
 
-    if invocation.output_messages:
-        span.set_attribute(
-            GenAI.GEN_AI_RESPONSE_FINISH_REASONS,
-            [gen.finish_reason for gen in invocation.output_messages],
-        )
-
-    if invocation.response_model_name is not None:
-        span.set_attribute(
-            GenAI.GEN_AI_RESPONSE_MODEL, invocation.response_model_name
-        )
-    if invocation.response_id is not None:
-        span.set_attribute(GenAI.GEN_AI_RESPONSE_ID, invocation.response_id)
-    if invocation.input_tokens is not None:
-        span.set_attribute(
-            GenAI.GEN_AI_USAGE_INPUT_TOKENS, invocation.input_tokens
-        )
-    if invocation.output_tokens is not None:
-        span.set_attribute(
-            GenAI.GEN_AI_USAGE_OUTPUT_TOKENS, invocation.output_tokens
-        )
+    _apply_response_attributes(span, invocation)
 
 
 def _maybe_set_span_messages(
     span: Span,
-    input_messages: List[InputMessage],
-    output_messages: List[OutputMessage],
+    input_messages: list[InputMessage],
+    output_messages: list[OutputMessage],
 ) -> None:
     if not is_experimental_mode() or get_content_capturing_mode() not in (
         ContentCapturingMode.SPAN_ONLY,
@@ -112,6 +95,8 @@ def _apply_finish_attributes(span: Span, invocation: LLMInvocation) -> None:
     _maybe_set_span_messages(
         span, invocation.input_messages, invocation.output_messages
     )
+    _apply_request_attributes(span, invocation)
+    _apply_response_attributes(span, invocation)
     span.set_attributes(invocation.attributes)
 
 
@@ -122,7 +107,75 @@ def _apply_error_attributes(span: Span, error: Error) -> None:
         span.set_attribute(ErrorAttributes.ERROR_TYPE, error.type.__qualname__)
 
 
+def _apply_request_attributes(span: Span, invocation: LLMInvocation) -> None:
+    """Attach GenAI request semantic convention attributes to the span."""
+    attributes: dict[str, Any] = {}
+    if invocation.temperature is not None:
+        attributes[GenAI.GEN_AI_REQUEST_TEMPERATURE] = invocation.temperature
+    if invocation.top_p is not None:
+        attributes[GenAI.GEN_AI_REQUEST_TOP_P] = invocation.top_p
+    if invocation.frequency_penalty is not None:
+        attributes[GenAI.GEN_AI_REQUEST_FREQUENCY_PENALTY] = (
+            invocation.frequency_penalty
+        )
+    if invocation.presence_penalty is not None:
+        attributes[GenAI.GEN_AI_REQUEST_PRESENCE_PENALTY] = (
+            invocation.presence_penalty
+        )
+    if invocation.max_tokens is not None:
+        attributes[GenAI.GEN_AI_REQUEST_MAX_TOKENS] = invocation.max_tokens
+    if invocation.stop_sequences is not None:
+        attributes[GenAI.GEN_AI_REQUEST_STOP_SEQUENCES] = (
+            invocation.stop_sequences
+        )
+    if invocation.seed is not None:
+        attributes[GenAI.GEN_AI_REQUEST_SEED] = invocation.seed
+    if attributes:
+        span.set_attributes(attributes)
+
+
+def _apply_response_attributes(span: Span, invocation: LLMInvocation) -> None:
+    """Attach GenAI response semantic convention attributes to the span."""
+    attributes: dict[str, Any] = {}
+
+    finish_reasons: list[str] | None
+    if invocation.finish_reasons is not None:
+        finish_reasons = invocation.finish_reasons
+    elif invocation.output_messages:
+        finish_reasons = [
+            message.finish_reason
+            for message in invocation.output_messages
+            if message.finish_reason
+        ]
+    else:
+        finish_reasons = None
+
+    if finish_reasons:
+        # De-duplicate finish reasons
+        unique_finish_reasons = sorted(set(finish_reasons))
+        if unique_finish_reasons:
+            attributes[GenAI.GEN_AI_RESPONSE_FINISH_REASONS] = (
+                unique_finish_reasons
+            )
+
+    if invocation.response_model_name is not None:
+        attributes[GenAI.GEN_AI_RESPONSE_MODEL] = (
+            invocation.response_model_name
+        )
+    if invocation.response_id is not None:
+        attributes[GenAI.GEN_AI_RESPONSE_ID] = invocation.response_id
+    if invocation.input_tokens is not None:
+        attributes[GenAI.GEN_AI_USAGE_INPUT_TOKENS] = invocation.input_tokens
+    if invocation.output_tokens is not None:
+        attributes[GenAI.GEN_AI_USAGE_OUTPUT_TOKENS] = invocation.output_tokens
+
+    if attributes:
+        span.set_attributes(attributes)
+
+
 __all__ = [
     "_apply_finish_attributes",
     "_apply_error_attributes",
+    "_apply_request_attributes",
+    "_apply_response_attributes",
 ]

util/opentelemetry-util-genai/src/opentelemetry/util/genai/types.py

@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import annotations
 
 from contextvars import Token
 from dataclasses import dataclass, field
 from enum import Enum
-from typing import Any, Dict, List, Literal, Optional, Type, Union
+from typing import Any, Literal, Type, Union
 
 from typing_extensions import TypeAlias
 
@@ -41,14 +42,14 @@ class ContentCapturingMode(Enum):
 class ToolCall:
     arguments: Any
     name: str
-    id: Optional[str]
+    id: str | None
     type: Literal["tool_call"] = "tool_call"
 
 
 @dataclass()
 class ToolCallResponse:
     response: Any
-    id: Optional[str]
+    id: str | None
     type: Literal["tool_call_response"] = "tool_call_response"
 
 
@@ -76,18 +77,18 @@ class InputMessage:
 class OutputMessage:
     role: str
     parts: list[MessagePart]
-    finish_reason: Union[str, FinishReason]
+    finish_reason: str | FinishReason
 
 
-def _new_input_messages() -> List[InputMessage]:
+def _new_input_messages() -> list[InputMessage]:
     return []
 
 
-def _new_output_messages() -> List[OutputMessage]:
+def _new_output_messages() -> list[OutputMessage]:
     return []
 
 
-def _new_str_any_dict() -> Dict[str, Any]:
+def _new_str_any_dict() -> dict[str, Any]:
     return {}
 
 
@@ -100,20 +101,28 @@ class LLMInvocation:
     """
 
     request_model: str
-    context_token: Optional[ContextToken] = None
-    span: Optional[Span] = None
-    input_messages: List[InputMessage] = field(
+    context_token: ContextToken | None = None
+    span: Span | None = None
+    input_messages: list[InputMessage] = field(
         default_factory=_new_input_messages
     )
-    output_messages: List[OutputMessage] = field(
+    output_messages: list[OutputMessage] = field(
         default_factory=_new_output_messages
     )
-    provider: Optional[str] = None
-    response_model_name: Optional[str] = None
-    response_id: Optional[str] = None
-    input_tokens: Optional[int] = None
-    output_tokens: Optional[int] = None
-    attributes: Dict[str, Any] = field(default_factory=_new_str_any_dict)
+    provider: str | None = None
+    response_model_name: str | None = None
+    response_id: str | None = None
+    finish_reasons: list[str] | None = None
+    input_tokens: int | None = None
+    output_tokens: int | None = None
+    attributes: dict[str, Any] = field(default_factory=_new_str_any_dict)
+    temperature: float | None = None
+    top_p: float | None = None
+    frequency_penalty: float | None = None
+    presence_penalty: float | None = None
+    max_tokens: int | None = None
+    stop_sequences: list[str] | None = None
+    seed: int | None = None
 
 
 @dataclass