README.md

@@ -1,54 +1,3 @@
-# OWUI-Multi-Agent-Collaboration
+# OWUI-Mult-Agent-Processing
 
-A powerful pipe function for Open WebUI that enables collaboration between multiple AI models as agents.
+Use OWUI Functions to pass request and information to multiple 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.
-
-## Features
-
-- **Multiple Agent Support**: Chain multiple language models together to work on the same task
-- **Sequential Processing**: Each agent's response is added to the conversation context for the next agent
-- **Operator Model**: A designated model synthesizes all agent responses to produce a final answer
-- **Customizable Configuration**: Choose which models to use as agents and which to use as the operator
-
-## How It Works
-
-1. User sends a prompt to the system
-2. The prompt is sequentially processed through each model in the agent list
-3. Each agent's response is appended to the conversation context
-4. The operator model (Claude 3.5 Sonnet by default) receives the enriched context with all agent responses
-5. The operator synthesizes a final response that is returned to the user
-
-## Installation
-
-1. Ensure you have Open WebUI installed
-2. Navigate to `Admin Panel` -> `Functions`
-3. `Import Functions` then select the multi-agent-collaboration.py file
-4. Enable the new Function
-
-## Configuration
-
-Users can customize their experience with these configurable options:
-
-- `agent_list`: List of model IDs to use as processing agents
-- `operator_model`: Model ID for the final operator (default: "us.anthropic.claude-3-5-sonnet-20241022-v2:0")
-
-## Example Use Cases
-
-- **Research Analysis**: Have specialized models analyze different aspects of a research question
-- **Creative Collaboration**: Use different models for idea generation, refinement, and evaluation
-- **Decision Support**: Gather perspectives from different models before making a final recommendation
-
-## Requirements
-
-- Open WebUI installation
-- API access to the models you wish to use as agents (if needed)
-
-## Limitations
-
-- Each additional agent increases token usage and processing time
-- Models must be available through your Open WebUI configuration

multi-agent-collaboration.py

@@ -1,53 +0,0 @@
-"""
-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)