Extend Power of AutoGen with Promptflow

What is Autogen?

 

What is Promptflow?

 

Promptflow is a development tool designed to streamline the entire development cycle of AI applications powered by Large Language Models (LLMs). It simplifies the process of prototyping, experimenting, iterating, and deploying your AI applications. Refer to Microsoft Documentation page for more information.

Why use Autogen with Promptflow:

 

Problem Summary:

 

We will use an existing example of Solving Complex Tasks with Nested Chats using Autogen and demonstrate how to deploy it using Promptflow. The primary goal of this workflow is to improve the quality of the LLM response by creating additional agents to provide feedback to response from the original response. In short, `user_proxy` take the input question, a `writer` agent responds to input question and a `critic` agent provide feedback to `writer` agent iteratively helping `writer` agent to improve and refine the answer provided.

 

Step1: Build chat flow

 

 

 

 

 

pf flow init --flow pf-autogen --type chat
cd pf-autogen/

 

 

 

 

 

 

from promptflow.core import tool
from promptflow.connections import AzureOpenAIConnection, CustomConnection

@tool
def my_python_tool(
    redisConnection: CustomConnection,
    question: str,
    azureOpenAiConnection: AzureOpenAIConnection,
    azureOpenAiModelName: str = "gpt-4-32k",
    autogen_workflow_id: int = 1,
) -> str:
    print("Hello Promptflow")

 

 

Click on the dropdown and select the connections created earlier.

Step:2 Create Autogen workflow

 

 

 

 

 

 

 

 

 

 

 

 

from agentchat_nestedchat import AgNestedChat
from promptflow.connections import AzureOpenAIConnection, CustomConnection
from promptflow.core import tool


@tool
def my_python_tool(
    redisConnection: CustomConnection,
    question: str,
    azureOpenAiConnection: AzureOpenAIConnection,
    azureOpenAiModelName: str = "gpt-4-32k"
    ) -> str:
    ag_workflow = AgNestedChat(config_list=OAI_CONFIG_LIST, redis_url=redis_url)
    res = ag_workflow.chat(question=question)
    return res.summary

 

 

 

Step:3 Deployment

 

 

 

Summary