update link

Author: lievan

ddtrace/llmobs/_llmobs.py

@@ -119,7 +119,7 @@ def enable(
         _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.
@@ -289,7 +289,7 @@ def llm(
         session_id: Optional[str] = None,
         ml_app: Optional[str] = None,
     ) -> Span:
-        print("[✧ LLM Observability] LLM ✨: {} running ...".format(name))
+        print("[✧ LLMObs] LLM ✨: {} running ...".format(name), flush=True)
         """
         Trace an invocation call to an LLM where inputs and outputs are represented as text.
 
@@ -327,7 +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("[✧ LLM Observability] Tool 🔧: {} running ...".format(name))
+        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)
@@ -344,14 +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("[✧ LLM Observability] Task 📌: {} running...".format(name))
+        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("[✧ LLM Observability] Agent 🤖: {} running ...".format(name))
+        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.
 
@@ -370,7 +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("[✧ LLM Observability] Workflow 🔗: {} running ...".format(name))
+        print("[✧ LLMObs] Workflow 🔗: {} running ...".format(name), flush=True)
         """
         Trace a predefined or static sequence of operations.
 
@@ -428,7 +428,7 @@ def embedding(
     def retrieval(
         cls, name: Optional[str] = None, session_id: Optional[str] = None, ml_app: Optional[str] = None
     ) -> Span:
-        print("[✧ LLM Observability] Retrieval 🔎: {} running ...".format(name))
+        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

@@ -105,26 +105,16 @@ def _llmobs_span_event(self, span: Span) -> Dict[str, Any]:
         parent_id = str(_get_llmobs_parent_id(span) or "undefined")
         span._meta.pop(PARENT_ID_KEY, None)
 
-        name = _get_span_name(span)
-        if span_kind == "llm":
-            print("[✧ LLM Observability] LLM ✨: {} finished in {} seconds!".format(name, span.duration))
-        elif span_kind == "workflow":
-            print("[✧ LLM Observability] Workflow 🔗: {} finished in {} seconds!".format(name, span.duration))
-        elif span_kind == "agent":
-            print("[✧ LLM Observability] Agent 🤖: {} finished in {} seconds!".format(name, span.duration))
-            url = """
-            View your agent run:
-            https://app.datadoghq.com/llm/traces?query=%40event_type%3Aspan%20%40parent_id%3Aundefined%20%40trace_id%3A{}%20&agg_m=count&agg_m_source=base&agg_t=count&fromUser=false&llmPanels=%5B%7B%22t%22%3A%22sampleDetailPanel%22%2C%22rEID%22%3A%22AgAAAZDMT2fSc-LOggAAAAAAAAAYAAAAAEFaRE1UMS1vQUFBMl9fZXBadnc3QUFBQQAAACQAAAAAMDE5MGNjNGYtODc3MC00YmY0LTg5NGItZmFiNTY1NDk1ZjE0%22%7D%5D&sidepanelTab=trace&viz=stream
+        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.trace_id
+                span_name=span.name,
+                span_duration=span.duration,
             )
-            print(url)
-        elif span_kind == "tool":
-            print("[✧ LLM Observability] Tool 🔧: {} finished in {} seconds!".format(name, span.duration))
-        elif span_kind == "task":
-            print("[✧ LLM Observability] Task 📌: {} finished in {} seconds!".format(name, span.duration))
-        elif span_kind == "retrieval":
-            print("[✧ LLM Observability] Retrieval 🔎: {} finished in {} seconds!".format(name, span.duration))
+            print(url, flush=True)
 
         return {
             "trace_id": "{:x}".format(span.trace_id),