feat(llmobs): llmobs dev mode

ddtrace/llmobs/_llmobs.py

@@ -115,10 +115,11 @@ def enable(
         api_key: Optional[str] = None,
         env: Optional[str] = None,
         service: Optional[str] = None,
+        dev_mode: bool = True,
         _tracer: Optional[ddtrace.Tracer] = None,
     ) -> None:
         """
-        Enable LLM Observability tracing.
+        Enable LLMObs tracing.
 
         :param str ml_app: The name of your ml application.
         :param bool integrations_enabled: Set to `true` to enable LLM integrations.
@@ -288,6 +289,7 @@ def llm(
         session_id: Optional[str] = None,
         ml_app: Optional[str] = None,
     ) -> Span:
+        print("[✧ LLMObs] LLM ✨: {} running ...".format(name), flush=True)
         """
         Trace an invocation call to an LLM where inputs and outputs are represented as text.
 
@@ -325,6 +327,7 @@ def tool(cls, name: Optional[str] = None, session_id: Optional[str] = None, ml_a
 
         :returns: The Span object representing the traced operation.
         """
+        print("[✧ LLMObs] Tool 🔧: {} running ...".format(name), flush=True)
         if cls.enabled is False:
             log.warning(SPAN_START_WHILE_DISABLED_WARNING)
         return cls._instance._start_span("tool", name=name, session_id=session_id, ml_app=ml_app)
@@ -341,12 +344,14 @@ def task(cls, name: Optional[str] = None, session_id: Optional[str] = None, ml_a
 
         :returns: The Span object representing the traced operation.
         """
+        print("[✧ LLMObs] Task 📌: {} running...".format(name), flush=True)
         if cls.enabled is False:
             log.warning(SPAN_START_WHILE_DISABLED_WARNING)
         return cls._instance._start_span("task", name=name, session_id=session_id, ml_app=ml_app)
 
     @classmethod
     def agent(cls, name: Optional[str] = None, session_id: Optional[str] = None, ml_app: Optional[str] = None) -> Span:
+        print("[✧ LLMObs] Agent 🤖: {} running ...".format(name), flush=True)
         """
         Trace a dynamic workflow in which an embedded language model (agent) decides what sequence of actions to take.
 
@@ -365,6 +370,7 @@ def agent(cls, name: Optional[str] = None, session_id: Optional[str] = None, ml_
     def workflow(
         cls, name: Optional[str] = None, session_id: Optional[str] = None, ml_app: Optional[str] = None
     ) -> Span:
+        print("[✧ LLMObs] Workflow 🔗: {} running ...".format(name), flush=True)
         """
         Trace a predefined or static sequence of operations.
 
@@ -422,6 +428,7 @@ def embedding(
     def retrieval(
         cls, name: Optional[str] = None, session_id: Optional[str] = None, ml_app: Optional[str] = None
     ) -> Span:
+        print("[✧ LLMObs] Retrieval 🔎: {} running ...".format(name), flush=True)
         """
         Trace a vector search operation involving a list of documents being returned from an external knowledge base.
 

ddtrace/llmobs/_trace_processor.py

@@ -68,6 +68,7 @@ def submit_llmobs_span(self, span: Span) -> None:
     def _llmobs_span_event(self, span: Span) -> Dict[str, Any]:
         """Span event object structure."""
         span_kind = span._meta.pop(SPAN_KIND)
+
         meta: Dict[str, Any] = {"span.kind": span_kind, "input": {}, "output": {}}
         if span_kind in ("llm", "embedding") and span.get_tag(MODEL_NAME) is not None:
             meta["model_name"] = span._meta.pop(MODEL_NAME)
@@ -103,6 +104,18 @@ def _llmobs_span_event(self, span: Span) -> Dict[str, Any]:
         span.set_tag_str(SESSION_ID, session_id)
         parent_id = str(_get_llmobs_parent_id(span) or "undefined")
         span._meta.pop(PARENT_ID_KEY, None)
+
+        if parent_id == "undefined":
+            url = """[✧ LLMObs] Trace with root span name "{span_name}" finished in {span_duration} seconds 🎉!
+
+        View your trace at:
+        https://dd.datad0g.com/llm/traces?query=%40ml_app%3Aai-chat
+            """.format(
+                span_name=span.name,
+                span_duration=span.duration,
+            )
+            print(url, flush=True)
+
         return {
             "trace_id": "{:x}".format(span.trace_id),
             "span_id": str(span.span_id),