ref(langchain): Revert input truncation

Author: alexander-alderman-webb

sentry_sdk/integrations/langchain.py

@@ -14,7 +14,6 @@
     get_start_span_function,
     normalize_message_roles,
     set_data_normalized,
-    truncate_and_annotate_messages,
 )
 from sentry_sdk.consts import OP, SPANDATA
 from sentry_sdk.integrations import DidNotEnable, Integration
@@ -377,17 +376,12 @@ def on_llm_start(
                     }
                     for prompt in prompts
                 ]
-                scope = sentry_sdk.get_current_scope()
-                messages_data = truncate_and_annotate_messages(
-                    normalized_messages, span, scope
+                set_data_normalized(
+                    span,
+                    SPANDATA.GEN_AI_REQUEST_MESSAGES,
+                    normalized_messages,
+                    unpack=False,
                 )
-                if messages_data is not None:
-                    set_data_normalized(
-                        span,
-                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
-                        messages_data,
-                        unpack=False,
-                    )
 
     def on_chat_model_start(
         self: "SentryLangchainCallback",
@@ -457,17 +451,12 @@ def on_chat_model_start(
                             self._normalize_langchain_message(message)
                         )
                 normalized_messages = normalize_message_roles(normalized_messages)
-                scope = sentry_sdk.get_current_scope()
-                messages_data = truncate_and_annotate_messages(
-                    normalized_messages, span, scope
+                set_data_normalized(
+                    span,
+                    SPANDATA.GEN_AI_REQUEST_MESSAGES,
+                    normalized_messages,
+                    unpack=False,
                 )
-                if messages_data is not None:
-                    set_data_normalized(
-                        span,
-                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
-                        messages_data,
-                        unpack=False,
-                    )
 
     def on_chat_model_end(
         self: "SentryLangchainCallback",
@@ -979,17 +968,12 @@ def new_invoke(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
                     and integration.include_prompts
                 ):
                     normalized_messages = normalize_message_roles([input])
-                    scope = sentry_sdk.get_current_scope()
-                    messages_data = truncate_and_annotate_messages(
-                        normalized_messages, span, scope
+                    set_data_normalized(
+                        span,
+                        SPANDATA.GEN_AI_REQUEST_MESSAGES,
+                        normalized_messages,
+                        unpack=False,
                     )
-                    if messages_data is not None:
-                        set_data_normalized(
-                            span,
-                            SPANDATA.GEN_AI_REQUEST_MESSAGES,
-                            messages_data,
-                            unpack=False,
-                        )
 
                 output = result.get("output")
                 if (
@@ -1041,17 +1025,12 @@ def new_stream(self: "Any", *args: "Any", **kwargs: "Any") -> "Any":
             and integration.include_prompts
         ):
             normalized_messages = normalize_message_roles([input])
-            scope = sentry_sdk.get_current_scope()
-            messages_data = truncate_and_annotate_messages(
-                normalized_messages, span, scope
+            set_data_normalized(
+                span,
+                SPANDATA.GEN_AI_REQUEST_MESSAGES,
+                normalized_messages,
+                unpack=False,
             )
-            if messages_data is not None:
-                set_data_normalized(
-                    span,
-                    SPANDATA.GEN_AI_REQUEST_MESSAGES,
-                    messages_data,
-                    unpack=False,
-                )
 
         # Run the agent
         result = f(self, *args, **kwargs)

tests/integrations/langchain/test_langchain.py

@@ -1290,84 +1290,6 @@ def test_langchain_message_role_normalization_units():
     assert normalized[5] == "string message"  # String message unchanged
 
 
-def test_langchain_message_truncation(sentry_init, capture_items):
-    """Test that large messages are truncated properly in Langchain integration."""
-    from langchain_core.outputs import LLMResult, Generation
-
-    sentry_init(
-        integrations=[LangchainIntegration(include_prompts=True)],
-        traces_sample_rate=1.0,
-        send_default_pii=True,
-    )
-    items = capture_items("transaction", "span")
-
-    callback = SentryLangchainCallback(max_span_map_size=100, include_prompts=True)
-
-    run_id = "12345678-1234-1234-1234-123456789012"
-    serialized = {"_type": "openai-chat", "model_name": "gpt-3.5-turbo"}
-
-    large_content = (
-        "This is a very long message that will exceed our size limits. " * 1000
-    )
-    prompts = [
-        "small message 1",
-        large_content,
-        large_content,
-        "small message 4",
-        "small message 5",
-    ]
-
-    with start_transaction():
-        callback.on_llm_start(
-            serialized=serialized,
-            prompts=prompts,
-            run_id=run_id,
-            name="my_pipeline",
-            invocation_params={
-                "temperature": 0.7,
-                "max_tokens": 100,
-                "model": "gpt-3.5-turbo",
-            },
-        )
-
-        response = LLMResult(
-            generations=[[Generation(text="The response")]],
-            llm_output={
-                "token_usage": {
-                    "total_tokens": 25,
-                    "prompt_tokens": 10,
-                    "completion_tokens": 15,
-                }
-            },
-        )
-        callback.on_llm_end(response=response, run_id=run_id)
-
-    tx = next(item.payload for item in items if item.type == "transaction")
-    assert tx["type"] == "transaction"
-
-    spans = [item.payload for item in items if item.type == "span"]
-    llm_spans = [
-        span
-        for span in spans
-        if span["attributes"].get("sentry.op") == "gen_ai.text_completion"
-    ]
-    assert len(llm_spans) > 0
-
-    llm_span = llm_spans[0]
-    assert llm_span["attributes"]["gen_ai.operation.name"] == "text_completion"
-    assert llm_span["attributes"][SPANDATA.GEN_AI_PIPELINE_NAME] == "my_pipeline"
-
-    assert SPANDATA.GEN_AI_REQUEST_MESSAGES in llm_span["attributes"]
-    messages_data = llm_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])
-    assert tx["_meta"]["spans"]["0"]["data"]["gen_ai.request.messages"][""]["len"] == 5
-
-
 @pytest.mark.parametrize(
     "send_default_pii, include_prompts",
     [