feat(langchain): Update LLM span operation to gen_ai.generate_text (#5796)

Update LangChain integration to use the new `gen_ai.generate_text`
operation for LLM call spans, aligning with OpenTelemetry semantic
conventions.

**Changes:**
- Changed LLM span operation from `gen_ai.pipeline` to
`gen_ai.generate_text`
- Updated span naming to include the model identifier: `generate_text
{model}` instead of generic "Langchain LLM call"
- Removed unnecessary docstring from callback method
- Updated test assertions to validate the new operation and naming
convention

This change improves observability by using more specific operation
types that accurately reflect the semantic nature of LLM generation
calls, and includes the model identifier for better span context.

Related to SDK-669. Replaces some of the changes introduced in #5705
(this is being broken down into 2 parts due to upcoming changes in the
langchain test suite)

Co-authored-by: Claude Haiku 4.5 <noreply@anthropic.com>

Author: ericapisani

sentry_sdk/integrations/langchain.py

@@ -351,7 +351,6 @@ def on_llm_start(
         metadata: "Optional[Dict[str, Any]]" = None,
         **kwargs: "Any",
     ) -> "Any":
-        """Run when LLM starts running."""
         with capture_internal_exceptions():
             if not run_id:
                 return
@@ -369,8 +368,8 @@ def on_llm_start(
             watched_span = self._create_span(
                 run_id,
                 parent_run_id,
-                op=OP.GEN_AI_PIPELINE,
-                name=kwargs.get("name") or "Langchain LLM call",
+                op=OP.GEN_AI_GENERATE_TEXT,
+                name=f"generate_text {model}".strip(),
                 origin=LangchainIntegration.origin,
             )
             span = watched_span.span

tests/integrations/langchain/test_langchain.py

@@ -851,12 +851,14 @@ def test_langchain_integration_with_langchain_core_only(sentry_init, capture_eve
         assert tx["type"] == "transaction"
 
         llm_spans = [
-            span for span in tx.get("spans", []) if span.get("op") == "gen_ai.pipeline"
+            span
+            for span in tx.get("spans", [])
+            if span.get("op") == "gen_ai.generate_text"
         ]
         assert len(llm_spans) > 0
 
         llm_span = llm_spans[0]
-        assert llm_span["description"] == "Langchain LLM call"
+        assert llm_span["description"] == "generate_text gpt-3.5-turbo"
         assert llm_span["data"]["gen_ai.request.model"] == "gpt-3.5-turbo"
         assert (
             llm_span["data"]["gen_ai.response.text"]
@@ -1062,7 +1064,7 @@ def test_langchain_message_truncation(sentry_init, capture_events):
     assert tx["type"] == "transaction"
 
     llm_spans = [
-        span for span in tx.get("spans", []) if span.get("op") == "gen_ai.pipeline"
+        span for span in tx.get("spans", []) if span.get("op") == "gen_ai.generate_text"
     ]
     assert len(llm_spans) > 0
 
@@ -1776,7 +1778,7 @@ def test_langchain_response_model_extraction(
     assert tx["type"] == "transaction"
 
     llm_spans = [
-        span for span in tx.get("spans", []) if span.get("op") == "gen_ai.pipeline"
+        span for span in tx.get("spans", []) if span.get("op") == "gen_ai.generate_text"
     ]
     assert len(llm_spans) > 0