Use gen_ai.embeddings.dimension.count

Author: drewby

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

@@ -173,9 +173,9 @@ def traced_method(wrapped, instance, args, kwargs):
             )
         }
 
-        # Using a custom attribute "gen_ai.embeddings.dimensions". Will propose to semantic conventions.
+        # Using a custom attribute "gen_ai.embeddings.dimension.count". Will propose to semantic conventions.
         if "dimensions" in kwargs and kwargs["dimensions"] is not None:
-            span_attributes["gen_ai.embeddings.dimensions"] = kwargs[
+            span_attributes["gen_ai.embeddings.dimension.count"] = kwargs[
                 "dimensions"
             ]
 
@@ -240,7 +240,7 @@ async def traced_method(wrapped, instance, args, kwargs):
 
         # Set embeddings dimensions if specified in the request
         if "dimensions" in kwargs and kwargs["dimensions"] is not None:
-            span_attributes["gen_ai.embeddings.dimensions"] = kwargs[
+            span_attributes["gen_ai.embeddings.dimension.count"] = kwargs[
                 "dimensions"
             ]
 
@@ -303,10 +303,10 @@ def _record_metrics(
         ],
     }
 
-    if "gen_ai.embeddings.dimensions" in span_attributes:
-        common_attributes["gen_ai.embeddings.dimensions"] = span_attributes[
-            "gen_ai.embeddings.dimensions"
-        ]
+    if "gen_ai.embeddings.dimension.count" in span_attributes:
+        common_attributes["gen_ai.embeddings.dimension.count"] = (
+            span_attributes["gen_ai.embeddings.dimension.count"]
+        )
 
     if error_type:
         common_attributes["error.type"] = error_type
@@ -425,7 +425,7 @@ def _set_embeddings_response_attributes(
         if getattr(first_embedding, "embedding", None):
             set_span_attribute(
                 span,
-                "gen_ai.embeddings.dimensions",
+                "gen_ai.embeddings.dimension.count",
                 len(first_embedding.embedding),
             )
 

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

@@ -242,7 +242,9 @@ def get_llm_request_attributes(
     ):
         # Add embedding dimensions if specified
         if "dimensions" in kwargs and kwargs["dimensions"] is not None:
-            attributes["gen_ai.embeddings.dimensions"] = kwargs["dimensions"]
+            attributes["gen_ai.embeddings.dimension.count"] = kwargs[
+                "dimensions"
+            ]
 
         # Add encoding format if specified
         # Using a custom attribute "gen_ai.embeddings.encoding_format". Will propose to semantic conventions.

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

@@ -146,7 +146,9 @@ async def test_async_embeddings_with_dimensions(
     assert_embedding_attributes(spans[0], model_name, response)
 
     # Verify dimensions attribute is set correctly
-    assert spans[0].attributes["gen_ai.embeddings.dimensions"] == dimensions
+    assert (
+        spans[0].attributes["gen_ai.embeddings.dimension.count"] == dimensions
+    )
 
     # Verify actual embedding dimensions match the requested dimensions
     assert len(response.data[0].embedding) == dimensions
@@ -283,10 +285,10 @@ def assert_embedding_attributes(
     # Assert embeddings-specific attributes
     if (
         hasattr(span, "attributes")
-        and "gen_ai.embeddings.dimensions" in span.attributes
+        and "gen_ai.embeddings.dimension.count" in span.attributes
     ):
         # If dimensions were specified, verify that they match the actual dimensions
-        assert span.attributes["gen_ai.embeddings.dimensions"] == len(
+        assert span.attributes["gen_ai.embeddings.dimension.count"] == len(
             response.data[0].embedding
         )
 

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

@@ -144,7 +144,9 @@ def test_embeddings_with_dimensions(
     assert_embedding_attributes(spans[0], model_name, response)
 
     # Verify dimensions attribute is set correctly
-    assert spans[0].attributes["gen_ai.embeddings.dimensions"] == dimensions
+    assert (
+        spans[0].attributes["gen_ai.embeddings.dimension.count"] == dimensions
+    )
 
     # Verify actual embedding dimensions match the requested dimensions
     assert len(response.data[0].embedding) == dimensions
@@ -166,9 +168,10 @@ def test_embeddings_with_dimensions(
 
     # Verify the dimensions attribute is present in metrics
     for point in duration_metric.data.data_points:
-        if "gen_ai.embeddings.dimensions" in point.attributes:
+        if "gen_ai.embeddings.dimension.count" in point.attributes:
             assert (
-                point.attributes["gen_ai.embeddings.dimensions"] == dimensions
+                point.attributes["gen_ai.embeddings.dimension.count"]
+                == dimensions
             )
             break
     else:
@@ -415,10 +418,10 @@ def assert_embedding_attributes(
     # Assert embeddings-specific attributes
     if (
         hasattr(span, "attributes")
-        and "gen_ai.embeddings.dimensions" in span.attributes
+        and "gen_ai.embeddings.dimension.count" in span.attributes
     ):
         # If dimensions were specified, verify that they match the actual dimensions
-        assert span.attributes["gen_ai.embeddings.dimensions"] == len(
+        assert span.attributes["gen_ai.embeddings.dimension.count"] == len(
             response.data[0].embedding
         )