adding MultiAgent Collaboration Function

multi-agent-collaboration.py

@@ -0,0 +1,53 @@
+"""
+title: Multi Agent Collaboration System for Open WebUI
+Description: Allows for Multiple Models to act as Agents in collaboration
+version: 0.5.3
+"""
+
+from pydantic import BaseModel, Field
+from fastapi import Request
+from typing import Optional
+from open_webui.models.users import Users
+from open_webui.utils.chat import generate_chat_completion
+
+
+class Pipe:
+
+    class UserValves(BaseModel):
+        agent_list: list = (
+            Field(default=[], description="List of Models to process as agents"),
+        )
+        operator_model: str = Field(
+            default="us.anthropic.claude-3-5-sonnet-20241022-v2:0",
+            description="Default Operator Model to use",
+        )
+        pass
+
+    def __init__(self):
+        pass
+
+    async def pipe(self, body: dict, __user__: dict, __request__: Request) -> str:
+        # Use the unified endpoint with the updated signature
+        user = Users.get_user_by_id(__user__["id"])
+        agents = __user__["valves"].agent_list
+        operator_model = __user__["valves"].operator_model
+        number_of_agents = len(agents)
+        if number_of_agents > 0:
+            # Process through each agent in the list
+            for agent_model in agents:
+                # Temporarily change the model to the agent model
+                body["model"] = agent_model
+                print(f"Model being use: {agent_model}")
+                response = await generate_chat_completion(__request__, body, user)
+                # Add Agent response as context
+                body["messages"].append(
+                    {
+                        "role": "assistant",
+                        "content": f"{response} \n (Provided by Agent: {agent_model})",
+                    }
+                )
+            # set Operator for final processing
+            body["model"] = operator_model
+            print(f"Model being use: {operator_model}")
+            #print(f"Body Response: {body['messages']}")
+        return await generate_chat_completion(__request__, body, user)