update the README.md

README.md

@@ -1,3 +1,54 @@
-# OWUI-Mult-Agent-Processing
+# OWUI-Multi-Agent-Collaboration
 
-Use OWUI Functions to pass request and information to multiple Agents
+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.
+
+## 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

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+"""
+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)