Update to add Event Emitter calls and updated README.md

README.md

@@ -2,11 +2,9 @@
 
 A powerful pipe function for Open WebUI that enables collaboration between multiple AI models as agents.
 
-[TOC]
-
 ## Overview
 
-OWUI-Multi-Agent-Processing (v0.5.3) allows you to leverage the strengths of different language models by having them work together on the same prompt. The system functions as a pipeline where each agent processes the input sequentially, and a final operator model synthesizes all the agent responses.
+OWUI-Multi-Agent-Processing (v0.5.6) allows you to leverage the strengths of different language models by having them work together on the same prompt. The system functions as a pipeline where each agent processes the input sequentially, and a final operator model synthesizes all the agent responses.
 
 ## Features
 

multi-agent-collaboration.py

@@ -1,16 +1,41 @@
 """
 title: Multi Agent Collaboration System for Open WebUI
 Description: Allows for Multiple Models to act as Agents in collaboration
-version: 0.5.3
+version: 0.5.6
 """
 
 from pydantic import BaseModel, Field
 from fastapi import Request
-from typing import Optional
+from typing import Callable, Any
 from open_webui.models.users import Users
 from open_webui.utils.chat import generate_chat_completion
 
 
+class EventEmitter:
+    def __init__(self, event_emitter: Callable[[dict], Any] = None):
+        self.event_emitter = event_emitter
+
+    async def emit(self, description="Unknown state", status="in_progress", done=False):
+        """
+        Send a status event to the event emitter.
+
+        :param description: Event description
+        :param status: Event status
+        :param done: Whether the event is complete
+        """
+        if self.event_emitter:
+            await self.event_emitter(
+                {
+                    "type": "status",
+                    "data": {
+                        "status": status,
+                        "description": description,
+                        "done": done,
+                    },
+                }
+            )
+
+
 class Pipe:
 
     class UserValves(BaseModel):
@@ -26,7 +51,22 @@ class Pipe:
     def __init__(self):
         pass
 
-    async def pipe(self, body: dict, __user__: dict, __request__: Request) -> str:
+    async def pipe(
+        self,
+        body: dict,
+        __user__: dict,
+        __request__: Request,
+        __event_emitter__: Callable[[dict], Any] = None,
+    ) -> str:
+        """
+        :param __user__: User dictionary containing the user ID
+        :param __event_emitter__: Optional event emitter for tracking status"
+        :param body: body dictionary containing the prompt and messages and other settings sent to the LLM(s)
+        :param __request__: User Request object
+        :return: The final response from the operator model after processing through all agents
+        """
+        # Initialize Event Emitter
+        emitter = EventEmitter(__event_emitter__)
         # Use the unified endpoint with the updated signature
         user = Users.get_user_by_id(__user__["id"])
         agents = __user__["valves"].agent_list
@@ -38,6 +78,10 @@ class Pipe:
                 # Temporarily change the model to the agent model
                 body["model"] = agent_model
                 print(f"Model being use: {agent_model}")
+                message = f"Processing request through agent model {agent_model}"
+                await emitter.emit(
+                    description=message, status="agent_processing", done=True
+                )
                 response = await generate_chat_completion(__request__, body, user)
                 # Add Agent response as context
                 body["messages"].append(
@@ -49,5 +93,8 @@ class Pipe:
             # set Operator for final processing
             body["model"] = operator_model
             print(f"Model being use: {operator_model}")
-            #print(f"Body Response: {body['messages']}")
+            message = f"Final Response from {operator_model}"
+            await emitter.emit(
+                description=message, status="final_processing", done=True
+            )
         return await generate_chat_completion(__request__, body, user)