name observation process

chat_3.py

@@ -95,7 +95,7 @@ class Chat:
             history_str_parts.append(f"{role}: {msg.content}")
         return "\n".join(history_str_parts)
 
-    @observe()
+    @observe(name="ClassifyInput")
     def classify_input(self, user_input: str) -> str:
         history_content_list = [msg.content for msg in self.history] 
         return classify_input_type(user_input, history=history_content_list)
@@ -103,7 +103,7 @@ class Chat:
     def format_docs(self, docs: list) -> str:
         return "\n\n".join(doc.page_content for doc in docs)
 
-    @observe()
+    @observe(name="GetRetrieverandPrompt")
     def get_retriever_and_prompt(self, lang_code: str):
         """
         Returns the appropriate retriever and RAG prompt based on the language.
@@ -139,7 +139,7 @@ class Chat:
             
         return retriever, prompt_template
 
-    @observe()
+    @observe(name="Non-Rag-Flow")
     def call_non_rag(self, user_input: str, input_lang: str) -> str:
         try:
             if hasattr(NON_RAG_PROMPT, "format_messages"): 
@@ -157,13 +157,13 @@ class Chat:
             print(f"Error during Non-RAG LLM call: {e}")
             return "Sorry, I had trouble processing your general request."
 
-    @observe()
+    @observe(name="DetectLang")
     def _observe_detect_language(self, user_input: str) -> str:
         """Wraps the detect_language call for Langfuse observation."""
         return detect_language(user_input)
 
     # If the main chat method itself should be a trace, uncomment @observe() below
-    # @observe() 
+    @observe("MainChatFlow")
     def chat(self, user_input: str) -> str:
         # print(f"\n\n-- USER INPUT: {user_input} --")
         try:
@@ -194,10 +194,23 @@ class Chat:
 
         self.append_history(AIMessage(content=ai_response_content))
         
-        # print(f"AI:::: {ai_response_content}")
+        # # print(f"AI:::: {ai_response_content}")
+        # return ai_response_content
+        
+        try:
+            full_conversation = "\n".join(
+                f"{'User' if isinstance(m, HumanMessage) else 'AI'}: {m.content}" 
+                for m in self.history
+            )
+            self.langfuse_handler.update_current_trace(metadata={"full_conversation": full_conversation})
+        except Exception as e:
+            print(f"Failed to update Langfuse metadata: {e}")
+
         return ai_response_content
 
-    @observe()
+
+
+    @observe("Rag-Flow")
     def call_rag_v2(self, user_input: str, input_lang: str, history_for_rewrite: list) -> str:
         try:
             retriever, selected_rag_prompt = self.get_retriever_and_prompt(input_lang)
@@ -238,7 +251,7 @@ class Chat:
             print(f"Error during RAG LLM call: {e}")
             return "Sorry, I encountered an error while generating the response."
 
-    @observe()
+    @observe("RewriteQuery")
     def _rewrite_query_if_needed_v2(self, user_input: str, history_list: list) -> str:
         if not history_list:
             # self.langfuse_handler.trace(name="rewrite_query_skipped_no_history", input={"user_input": user_input}, output=user_input)