ref(litellm): Revert input truncation

sentry_sdk/integrations/litellm.py

@@ -6,8 +6,6 @@
 from sentry_sdk.ai.utils import (
     get_start_span_function,
     set_data_normalized,
-    truncate_and_annotate_messages,
-    truncate_and_annotate_embedding_inputs,
 )
 from sentry_sdk.consts import SPANDATA
 from sentry_sdk.integrations import DidNotEnable, Integration
@@ -83,36 +81,22 @@ def _input_callback(kwargs: "Dict[str, Any]") -> None:
             # For embeddings, look for the 'input' parameter
             embedding_input = kwargs.get("input")
             if embedding_input:
-                scope = sentry_sdk.get_current_scope()
-                # Normalize to list format
                 input_list = (
                     embedding_input
                     if isinstance(embedding_input, list)
                     else [embedding_input]
                 )
-                messages_data = truncate_and_annotate_embedding_inputs(
-                    input_list, span, scope
-                )
-                if messages_data is not None:
+                if input_list is not None:
                     set_data_normalized(
-                        span,
-                        SPANDATA.GEN_AI_EMBEDDINGS_INPUT,
-                        messages_data,
-                        unpack=False,
+                        span, SPANDATA.GEN_AI_EMBEDDINGS_INPUT, input_list, unpack=False
                     )
         else:
             # For chat, look for the 'messages' parameter
             messages = kwargs.get("messages", [])
             if messages:
-                scope = sentry_sdk.get_current_scope()
-                messages_data = truncate_and_annotate_messages(messages, span, scope)
-                if messages_data is not None:
-                    set_data_normalized(
-                        span,
-                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
-                        messages_data,
-                        unpack=False,
-                    )
+                set_data_normalized(
+                    span, SPANDATA.GEN_AI_REQUEST_MESSAGES, messages, unpack=False
+                )
 
     # Record other parameters
     params = {

tests/integrations/litellm/test_litellm.py

@@ -1382,59 +1382,3 @@ def test_integration_setup(sentry_init):
     assert _input_callback in (litellm.input_callback or [])
     assert _success_callback in (litellm.success_callback or [])
     assert _failure_callback in (litellm.failure_callback or [])
-
-
-def test_litellm_message_truncation(sentry_init, capture_items):
-    """Test that large messages are truncated properly in LiteLLM integration."""
-    sentry_init(
-        integrations=[LiteLLMIntegration(include_prompts=True)],
-        traces_sample_rate=1.0,
-        send_default_pii=True,
-    )
-    items = capture_items("transaction", "span")
-
-    large_content = (
-        "This is a very long message that will exceed our size limits. " * 1000
-    )
-    messages = [
-        {"role": "user", "content": "small message 1"},
-        {"role": "assistant", "content": large_content},
-        {"role": "user", "content": large_content},
-        {"role": "assistant", "content": "small message 4"},
-        {"role": "user", "content": "small message 5"},
-    ]
-    mock_response = MockCompletionResponse()
-
-    with start_transaction(name="litellm test"):
-        kwargs = {
-            "model": "gpt-3.5-turbo",
-            "messages": messages,
-        }
-
-        _input_callback(kwargs)
-        _success_callback(
-            kwargs,
-            mock_response,
-            datetime.now(),
-            datetime.now(),
-        )
-
-    spans = [item.payload for item in items if item.type == "span"]
-    chat_spans = [
-        span for span in spans if span["attributes"].get("sentry.op") == OP.GEN_AI_CHAT
-    ]
-    assert len(chat_spans) > 0
-
-    chat_span = chat_spans[0]
-    assert SPANDATA.GEN_AI_REQUEST_MESSAGES in chat_span["attributes"]
-
-    messages_data = chat_span["attributes"][SPANDATA.GEN_AI_REQUEST_MESSAGES]
-    assert isinstance(messages_data, str)
-
-    parsed_messages = json.loads(messages_data)
-    assert isinstance(parsed_messages, list)
-    assert len(parsed_messages) == 1
-    assert "small message 5" in str(parsed_messages[0])
-
-    tx = next(item.payload for item in items if item.type == "transaction")
-    assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5