fix(litellm): Classify embeddings correctly (#4918)

Check the `call_type` value to distinguish embeddings from chats. The
`client` decorator sets `call_type` by introspecting the function name
and wraps all of the top-level `litellm` functions. If users import from
`litellm.llms`, embedding calls still may appear as chats, but the input
callback we provide does not have enough information in that case.

Closes #4908

Author: alexander-alderman-webb

sentry_sdk/integrations/litellm.py

@@ -48,8 +48,11 @@ def _input_callback(kwargs):
         model = full_model
         provider = "unknown"
 
-    messages = kwargs.get("messages", [])
-    operation = "chat" if messages else "embeddings"
+    call_type = kwargs.get("call_type", None)
+    if call_type == "embedding":
+        operation = "embeddings"
+    else:
+        operation = "chat"
 
     # Start a new span/transaction
     span = get_start_span_function()(
@@ -71,6 +74,7 @@ def _input_callback(kwargs):
     set_data_normalized(span, SPANDATA.GEN_AI_OPERATION_NAME, operation)
 
     # Record messages if allowed
+    messages = kwargs.get("messages", [])
     if messages and should_send_default_pii() and integration.include_prompts:
         set_data_normalized(
             span, SPANDATA.GEN_AI_REQUEST_MESSAGES, messages, unpack=False

tests/integrations/litellm/test_litellm.py

@@ -208,14 +208,15 @@ def test_embeddings_create(sentry_init, capture_events):
     )
     events = capture_events()
 
+    messages = [{"role": "user", "content": "Some text to test embeddings"}]
     mock_response = MockEmbeddingResponse()
 
     with start_transaction(name="litellm test"):
-        # For embeddings, messages would be empty
         kwargs = {
             "model": "text-embedding-ada-002",
             "input": "Hello!",
-            "messages": [],  # Empty for embeddings
+            "messages": messages,
+            "call_type": "embedding",
         }
 
         _input_callback(kwargs)