Dealing with comments

Author: shuningc

util/opentelemetry-util-genai/pyproject.toml

@@ -26,8 +26,8 @@ classifiers = [
   "Programming Language :: Python :: 3.14",
 ]
 dependencies = [
-  "opentelemetry-instrumentation ~= 0.60b1",
-  "opentelemetry-semantic-conventions ~= 0.60b1",
+  "opentelemetry-instrumentation ~= 0.61b0.dev",
+  "opentelemetry-semantic-conventions ~= 0.61b0.dev",
   "opentelemetry-api>=1.31.0",
 ]
 

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

@@ -225,6 +225,30 @@ def llm(
             raise
         self.stop_llm(invocation)
 
+    @contextmanager
+    def embedding(
+        self, invocation: EmbeddingInvocation | None = None
+    ) -> Iterator[EmbeddingInvocation]:
+        """Context manager for Embedding invocations.
+
+        Only set data attributes on the invocation object, do not modify the span or context.
+
+        Starts the span on entry. On normal exit, finalizes the invocation and ends the span.
+        If an exception occurs inside the context, marks the span as error, ends it, and
+        re-raises the original exception.
+        """
+        if invocation is None:
+            invocation = EmbeddingInvocation()
+        self.start_embedding(invocation)
+        try:
+            yield invocation
+        except Exception as exc:
+            self.fail_embedding(
+                invocation, Error(message=str(exc), type=type(exc))
+            )
+            raise
+        self.stop_embedding(invocation)
+
     def start_embedding(
         self, invocation: EmbeddingInvocation
     ) -> EmbeddingInvocation:
@@ -249,11 +273,13 @@ def stop_embedding(
             return invocation
 
         span = invocation.span
-        _apply_embedding_finish_attributes(span, invocation)
-        self._record_embedding_metrics(invocation, span)
-        # Detach context and end span
-        otel_context.detach(invocation.context_token)
-        span.end()
+        try:
+            _apply_embedding_finish_attributes(span, invocation)
+            self._record_embedding_metrics(invocation, span)
+        finally:
+            # Detach context and end span even if finishing fails
+            otel_context.detach(invocation.context_token)
+            span.end()
         return invocation
 
     def fail_embedding(
@@ -265,13 +291,17 @@ def fail_embedding(
             return invocation
 
         span = invocation.span
-        _apply_embedding_finish_attributes(invocation.span, invocation)
-        _apply_error_attributes(invocation.span, error)
-        error_type = getattr(error.type, "__qualname__", None)
-        self._record_embedding_metrics(invocation, span, error_type=error_type)
-        # Detach context and end span
-        otel_context.detach(invocation.context_token)
-        span.end()
+        try:
+            _apply_embedding_finish_attributes(invocation.span, invocation)
+            _apply_error_attributes(invocation.span, error)
+            error_type = getattr(error.type, "__qualname__", None)
+            self._record_embedding_metrics(
+                invocation, span, error_type=error_type
+            )
+        finally:
+            # Detach context and end span even if finishing fails
+            otel_context.detach(invocation.context_token)
+            span.end()
         return invocation
 
 

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

@@ -264,32 +264,18 @@ class EmbeddingInvocation(GenAIInvocation):
     and context_token attributes are set by the TelemetryHandler.
     """
 
-    operation_name: str = field(
-        default=GenAI.GenAiOperationNameValues.EMBEDDINGS.value,
-        metadata={"semconv": GenAI.GEN_AI_OPERATION_NAME},
-    )
-    provider: str | None = None  # e.g., azure.ai.openai, openai, aws.bedrock
+    operation_name: str = GenAI.GenAiOperationNameValues.EMBEDDINGS.value
 
-    request_model: str | None = field(
-        default=None,
-        metadata={"semconv": GenAI.GEN_AI_REQUEST_MODEL},
-    )
+    provider: str | None = None  # e.g., azure.ai.openai, openai, aws.bedrock
 
+    request_model: str | None = None
     server_address: str | None = None
     server_port: int | None = None
-    error_type: str | None = None
 
     # encoding_formats can be multi-value -> combinational cardinality risk.
     # Keep on spans/events only.
-    encoding_formats: list[str] = field(
-        default_factory=list,
-        metadata={"semconv": GenAI.GEN_AI_REQUEST_ENCODING_FORMATS},
-    )
-
-    input_tokens: int | None = field(
-        default=None,
-        metadata={"semconv": GenAI.GEN_AI_USAGE_INPUT_TOKENS},
-    )
+    encoding_formats: list[str] | None = None
+    input_tokens: int | None = None
     dimension_count: int | None = None
 
     attributes: dict[str, Any] = field(default_factory=_new_str_any_dict)
@@ -305,6 +291,7 @@ class EmbeddingInvocation(GenAIInvocation):
     Additional attributes to set on metrics. Must be of a low cardinality.
     These attributes will not be set on spans or events.
     """
+    monotonic_start_s: float | None = None
 
 
 @dataclass