diff --git a/examples/filters/langfuse_filter_pipeline.py b/examples/filters/langfuse_filter_pipeline.py
index 8c05f46..cd2c0ab 100644
--- a/examples/filters/langfuse_filter_pipeline.py
+++ b/examples/filters/langfuse_filter_pipeline.py
@@ -1,8 +1,8 @@
 """
 title: Langfuse Filter Pipeline
 author: open-webui
-date: 2025-02-20
-version: 1.5
+date: 2025-03-28
+version: 1.7
 license: MIT
 description: A filter pipeline that uses Langfuse.
 requirements: langfuse
@@ -20,6 +20,7 @@ from langfuse.api.resources.commons.errors.unauthorized_error import Unauthorize
 
 
 def get_last_assistant_message_obj(messages: List[dict]) -> dict:
+    """Retrieve the last assistant message from the message list."""
     for message in reversed(messages):
         if message["role"] == "assistant":
             return message
@@ -33,6 +34,10 @@ class Pipeline:
         secret_key: str
         public_key: str
         host: str
+        # New valve that controls whether task names are added as tags:
+        insert_tags: bool = True
+        # New valve that controls whether to use model name instead of model ID for generation
+        use_model_name_instead_of_id_for_generation: bool = False
         debug: bool = False
 
     def __init__(self):
@@ -45,18 +50,21 @@ class Pipeline:
                 "secret_key": os.getenv("LANGFUSE_SECRET_KEY", "your-secret-key-here"),
                 "public_key": os.getenv("LANGFUSE_PUBLIC_KEY", "your-public-key-here"),
                 "host": os.getenv("LANGFUSE_HOST", "https://cloud.langfuse.com"),
+                "use_model_name_instead_of_id_for_generation": os.getenv("USE_MODEL_NAME", "false").lower() == "true",
                 "debug": os.getenv("DEBUG_MODE", "false").lower() == "true",
             }
         )
 
         self.langfuse = None
-        # Keep track of the trace and the last-created generation for each chat_id
         self.chat_traces = {}
-        self.chat_generations = {}
         self.suppressed_logs = set()
+        # Dictionary to store model names for each chat
+        self.model_names = {}
+
+        # Only these tasks will be treated as LLM "generations":
+        self.GENERATION_TASKS = {"llm_response"}
 
     def log(self, message: str, suppress_repeats: bool = False):
-        """Logs messages to the terminal if debugging is enabled."""
         if self.valves.debug:
             if suppress_repeats:
                 if message in self.suppressed_logs:
@@ -96,47 +104,44 @@ class Pipeline:
                 f"Langfuse error: {e} Please re-enter your Langfuse credentials in the pipeline settings."
             )
 
+    def _build_tags(self, task_name: str) -> list:
+        """
+        Builds a list of tags based on valve settings, ensuring we always add
+        'open-webui' and skip user_response / llm_response from becoming tags themselves.
+        """
+        tags_list = []
+        if self.valves.insert_tags:
+            # Always add 'open-webui'
+            tags_list.append("open-webui")
+            # Add the task_name if it's not one of the excluded defaults
+            if task_name not in ["user_response", "llm_response"]:
+                tags_list.append(task_name)
+        return tags_list
+
     async def inlet(self, body: dict, user: Optional[dict] = None) -> dict:
-        """
-        Inlet handles the incoming request (usually a user message).
-        - If no trace exists yet for this chat_id, we create a new trace.
-        - If a trace does exist, we simply create a new generation for the new user message.
-        """
         if self.valves.debug:
             print(f"[DEBUG] Received request: {json.dumps(body, indent=2)}")
 
         self.log(f"Inlet function called with body: {body} and user: {user}")
 
         metadata = body.get("metadata", {})
+        chat_id = metadata.get("chat_id", str(uuid.uuid4()))
+        metadata["chat_id"] = chat_id
+        body["metadata"] = metadata
 
-        # ---------------------------------------------------------
-        # Prepend the system prompt from metadata to the system message:
+        # Extract and store both model name and ID if available
         model_info = metadata.get("model", {})
-        params_info = model_info.get("params", {})
-        system_prompt = params_info.get("system", "")
-
-        if system_prompt:
-            for msg in body["messages"]:
-                if msg.get("role") == "system":
-                    # Only prepend if it hasn't already been prepended:
-                    if not msg["content"].startswith("System Prompt:"):
-                        msg["content"] = f"System Prompt:\n{system_prompt}\n\n{msg['content']}"
-                    break
-        # ---------------------------------------------------------
-
-        # Fix SYSTEM MESSAGE prefix issue: Only apply for "task_generation"
-        if "chat_id" not in metadata:
-            if "task_generation" in metadata.get("type", "").lower():
-                chat_id = f"SYSTEM MESSAGE {uuid.uuid4()}"
-                self.log(f"Task Generation detected, assigned SYSTEM MESSAGE ID: {chat_id}")
-            else:
-                chat_id = str(uuid.uuid4())  # Regular chat messages
-                self.log(f"Assigned normal chat_id: {chat_id}")
-
-            metadata["chat_id"] = chat_id
-            body["metadata"] = metadata
+        model_id = body.get("model")
+        
+        # Store model information for this chat
+        if chat_id not in self.model_names:
+            self.model_names[chat_id] = {"id": model_id}
         else:
-            chat_id = metadata["chat_id"]
+            self.model_names[chat_id]["id"] = model_id
+            
+        if isinstance(model_info, dict) and "name" in model_info:
+            self.model_names[chat_id]["name"] = model_info["name"]
+            self.log(f"Stored model info - name: '{model_info['name']}', id: '{model_id}' for chat_id: {chat_id}")
 
         required_keys = ["model", "messages"]
         missing_keys = [key for key in required_keys if key not in body]
@@ -146,100 +151,108 @@ class Pipeline:
             raise ValueError(error_message)
 
         user_email = user.get("email") if user else None
+        # Defaulting to 'user_response' if no task is provided
+        task_name = metadata.get("task", "user_response")
+
+        # Build tags
+        tags_list = self._build_tags(task_name)
 
-        # Check if we already have a trace for this chat
         if chat_id not in self.chat_traces:
-            # Create a new trace and generation
-            self.log(f"Creating new chat trace for chat_id: {chat_id}")
+            self.log(f"Creating new trace for chat_id: {chat_id}")
 
             trace_payload = {
-                "name": f"filter:{__name__}",
+                "name": f"chat:{chat_id}",
                 "input": body,
                 "user_id": user_email,
-                "metadata": {"chat_id": chat_id},
+                "metadata": metadata,
                 "session_id": chat_id,
             }
 
+            if tags_list:
+                trace_payload["tags"] = tags_list
+
             if self.valves.debug:
                 print(f"[DEBUG] Langfuse trace request: {json.dumps(trace_payload, indent=2)}")
 
             trace = self.langfuse.trace(**trace_payload)
+            self.chat_traces[chat_id] = trace
+        else:
+            trace = self.chat_traces[chat_id]
+            self.log(f"Reusing existing trace for chat_id: {chat_id}")
+            if tags_list:
+                trace.update(tags=tags_list)
 
+        # Update metadata with type
+        metadata["type"] = task_name
+        metadata["interface"] = "open-webui"
+
+        # If it's a task that is considered an LLM generation
+        if task_name in self.GENERATION_TASKS:
+            # Determine which model value to use based on the use_model_name valve
+            model_id = self.model_names.get(chat_id, {}).get("id", body["model"])
+            model_name = self.model_names.get(chat_id, {}).get("name", "unknown")
+            
+            # Pick primary model identifier based on valve setting
+            model_value = model_name if self.valves.use_model_name_instead_of_id_for_generation else model_id
+            
+            # Add both values to metadata regardless of valve setting
+            metadata["model_id"] = model_id
+            metadata["model_name"] = model_name
+            
             generation_payload = {
-                "name": chat_id,
-                "model": body["model"],
+                "name": f"{task_name}:{str(uuid.uuid4())}",
+                "model": model_value,
                 "input": body["messages"],
-                "metadata": {"interface": "open-webui"},
+                "metadata": metadata,
             }
+            if tags_list:
+                generation_payload["tags"] = tags_list
 
             if self.valves.debug:
                 print(f"[DEBUG] Langfuse generation request: {json.dumps(generation_payload, indent=2)}")
 
-            generation = trace.generation(**generation_payload)
-
-            self.chat_traces[chat_id] = trace
-            self.chat_generations[chat_id] = generation
-            self.log(f"Trace and generation objects successfully created for chat_id: {chat_id}")
-
+            trace.generation(**generation_payload)
         else:
-            # Re-use existing trace but create a new generation for each new message
-            self.log(f"Re-using existing chat trace for chat_id: {chat_id}")
-            trace = self.chat_traces[chat_id]
-
-            new_generation_payload = {
-                "name": f"{chat_id}:{str(uuid.uuid4())}",
-                "model": body["model"],
+            # Otherwise, log it as an event
+            event_payload = {
+                "name": f"{task_name}:{str(uuid.uuid4())}",
+                "metadata": metadata,
                 "input": body["messages"],
-                "metadata": {"interface": "open-webui"},
             }
-            if self.valves.debug:
-                print(f"[DEBUG] Langfuse new_generation request: {json.dumps(new_generation_payload, indent=2)}")
+            if tags_list:
+                event_payload["tags"] = tags_list
 
-            new_generation = trace.generation(**new_generation_payload)
-            self.chat_generations[chat_id] = new_generation
+            if self.valves.debug:
+                print(f"[DEBUG] Langfuse event request: {json.dumps(event_payload, indent=2)}")
+
+            trace.event(**event_payload)
 
         return body
 
     async def outlet(self, body: dict, user: Optional[dict] = None) -> dict:
-        """
-        Outlet handles the response body (usually the assistant message).
-        It will finalize/end the generation created for the user request.
-        """
         self.log(f"Outlet function called with body: {body}")
 
         chat_id = body.get("chat_id")
+        metadata = body.get("metadata", {})
+        # Defaulting to 'llm_response' if no task is provided
+        task_name = metadata.get("task", "llm_response")
 
-        # If no trace or generation exist, attempt to register again
-        if chat_id not in self.chat_traces or chat_id not in self.chat_generations:
-            self.log(f"[WARNING] No matching chat trace found for chat_id: {chat_id}, attempting to re-register.")
+        # Build tags
+        tags_list = self._build_tags(task_name)
+
+        if chat_id not in self.chat_traces:
+            self.log(f"[WARNING] No matching trace found for chat_id: {chat_id}, attempting to re-register.")
+            # Re-run inlet to register if somehow missing
             return await self.inlet(body, user)
 
         trace = self.chat_traces[chat_id]
-        generation = self.chat_generations[chat_id]
 
-        # Get the last assistant message from the conversation
         assistant_message = get_last_assistant_message(body["messages"])
         assistant_message_obj = get_last_assistant_message_obj(body["messages"])
 
-        # ---------------------------------------------------------
-        # If the outlet contains a sources array, append it after the "System Prompt:"
-        # section in the system message:
-        if assistant_message_obj and "sources" in assistant_message_obj and assistant_message_obj["sources"]:
-            for msg in body["messages"]:
-                if msg.get("role") == "system":
-                    if msg["content"].startswith("System Prompt:"):
-                        # Format the sources nicely
-                        sources_str = "\n\n".join(
-                            json.dumps(src, indent=2) for src in assistant_message_obj["sources"]
-                        )
-                        msg["content"] += f"\n\nSources:\n{sources_str}"
-                    break
-        # ---------------------------------------------------------
-
-        # Extract usage if available
         usage = None
         if assistant_message_obj:
-            info = assistant_message_obj.get("info", {})
+            info = assistant_message_obj.get("usage", {})
             if isinstance(info, dict):
                 input_tokens = info.get("prompt_eval_count") or info.get("prompt_tokens")
                 output_tokens = info.get("eval_count") or info.get("completion_tokens")
@@ -251,20 +264,58 @@ class Pipeline:
                     }
                     self.log(f"Usage data extracted: {usage}")
 
-        # Optionally update the trace with the final assistant output
+        # Update the trace output with the last assistant message
         trace.update(output=assistant_message)
 
-        # End the generation with the final assistant message and updated conversation
-        generation_payload = {
-            "input": body["messages"],  # include the entire conversation
-            "metadata": {"interface": "open-webui"},
-            "usage": usage,
-        }
+        metadata["type"] = task_name
+        metadata["interface"] = "open-webui"
 
-        if self.valves.debug:
-            print(f"[DEBUG] Langfuse generation end request: {json.dumps(generation_payload, indent=2)}")
+        if task_name in self.GENERATION_TASKS:
+            # Determine which model value to use based on the use_model_name valve
+            model_id = self.model_names.get(chat_id, {}).get("id", body.get("model"))
+            model_name = self.model_names.get(chat_id, {}).get("name", "unknown")
+            
+            # Pick primary model identifier based on valve setting
+            model_value = model_name if self.valves.use_model_name_instead_of_id_for_generation else model_id
+            
+            # Add both values to metadata regardless of valve setting
+            metadata["model_id"] = model_id
+            metadata["model_name"] = model_name
+            
+            # If it's an LLM generation
+            generation_payload = {
+                "name": f"{task_name}:{str(uuid.uuid4())}",
+                "model": model_value,   # <-- Use model name or ID based on valve setting
+                "input": body["messages"],
+                "metadata": metadata,
+                "usage": usage,
+            }
+            if tags_list:
+                generation_payload["tags"] = tags_list
 
-        generation.end(**generation_payload)
-        self.log(f"Generation ended for chat_id: {chat_id}")
+            if self.valves.debug:
+                print(f"[DEBUG] Langfuse generation end request: {json.dumps(generation_payload, indent=2)}")
+
+            trace.generation().end(**generation_payload)
+            self.log(f"Generation ended for chat_id: {chat_id}")
+        else:
+            # Otherwise log as an event
+            event_payload = {
+                "name": f"{task_name}:{str(uuid.uuid4())}",
+                "metadata": metadata,
+                "input": body["messages"],
+            }
+            if usage:
+                # If you want usage on event as well
+                event_payload["metadata"]["usage"] = usage
+
+            if tags_list:
+                event_payload["tags"] = tags_list
+
+            if self.valves.debug:
+                print(f"[DEBUG] Langfuse event end request: {json.dumps(event_payload, indent=2)}")
+
+            trace.event(**event_payload)
+            self.log(f"Event logged for chat_id: {chat_id}")
 
         return body