create generic llmobs decorator

ddtrace/llmobs/_llmobs.py

@@ -572,6 +572,24 @@ def retrieval(
             log.warning(SPAN_START_WHILE_DISABLED_WARNING)
         return cls._instance._start_span("retrieval", name=name, session_id=session_id, ml_app=ml_app)
 
+    @classmethod
+    def undefined_kind(
+        cls, name: Optional[str] = None, session_id: Optional[str] = None, ml_app: Optional[str] = None
+    ) -> Span:
+        """
+        Trace a generic operation. Used when the span kind is not specified, 
+
+        :param str name: The name of the traced operation. If not provided, a default value of "workflow" will be set.
+        :param str session_id: The ID of the underlying user session. Required for tracking sessions.
+        :param str ml_app: The name of the ML application that the agent is orchestrating. If not provided, the default
+                           value will be set to the value of `DD_LLMOBS_ML_APP`.
+
+        :returns: The Span object representing the traced operation.
+        """
+        if cls.enabled is False:
+            log.warning(SPAN_START_WHILE_DISABLED_WARNING)
+        return cls._instance._start_span("undefined", name=name, session_id=session_id, ml_app=ml_app)
+
     @classmethod
     def annotate(
         cls,

ddtrace/llmobs/decorators.py

@@ -253,6 +253,41 @@ def wrapper(*args, **kwargs):
 
     return decorator
 
+def _generic_decorator(operation_kind):
+    def decorator(
+        original_func: Optional[Callable] = None,
+        kind: Optional[str] = None,
+        model_name: Optional[str] = None,
+        model_provider: Optional[str] = None,
+        name: Optional[str] = None,
+        session_id: Optional[str] = None,
+        ml_app: Optional[str] = None,
+        _automatic_io_annotation: bool = True,
+    ):
+        if kind in MODEL_DECORATORS:
+            decorator = _model_decorator(kind)
+            return decorator(
+                original_func,
+                model_name,
+                model_provider,
+                name,
+                session_id,
+                ml_app,
+            )
+        else:
+            decorator = _llmobs_decorator(kind or operation_kind)
+            return decorator(
+                original_func,
+                name,
+                session_id,
+                ml_app,
+                _automatic_io_annotation,
+            )
+
+    return decorator
+
+
+MODEL_DECORATORS = ["llm", "embedding"]
 
 llm = _model_decorator("llm")
 embedding = _model_decorator("embedding")
@@ -261,3 +296,4 @@ def wrapper(*args, **kwargs):
 tool = _llmobs_decorator("tool")
 retrieval = _llmobs_decorator("retrieval")
 agent = _llmobs_decorator("agent")
+observe = _generic_decorator("undefined_kind")