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How to Build Real-World AI Workflows With AutoGen: Step-by-Step Guide

Hoe om KI-werkvloeie in die regte wêreld met AutoGen te bou: stap-vir-stap-gids

অটোজেনের সাথে কীভাবে বাস্তব-বিশ্ব এআই ওয়ার্কফ্লো তৈরি করবেন: ধাপে ধাপে নির্দেশিকা

Cómo crear flujos de trabajo de IA en el mundo real con AutoGen: guía paso a paso

כיצד לבנות זרימות עבודה של AI בעולם האמיתי עם AutoGen: מדריך שלב אחר שלב

Ki jan yo bati workflows AI nan mond reyèl la ak AutoGen: Gid etap pa etap

Ինչպես կառուցել իրական աշխարհի AI աշխատանքային հոսքեր AutoGen-ով. քայլ առ քայլ ուղեցույց

AutoGen で現実世界の AI ワークフローを構築する方法: ステップバイステップ ガイド

როგორ ავაშენოთ რეალურ სამყაროში AI სამუშაო ნაკადები AutoGen-ით: ნაბიჯ-ნაბიჯ სახელმძღვანელო

Como criar fluxos de trabalho de IA do mundo real com AutoGen: guia passo a passo

AutoGen සමඟ සැබෑ-ලෝක AI කාර්ය ප්‍රවාහයන් ගොඩනඟන්නේ කෙසේද: පියවරෙන් පියවර මාර්ගෝපදේශය

Hur man bygger verkliga AI-arbetsflöden med AutoGen: Steg-för-steg-guide

Ndlela Yo Aka Mafambelo Ya Ntirho Ya AI Ya Misava Ya Xiviri Hi AutoGen: Nkongomiso Wa Goza Hi Goza

Recent advancements in led to the emergence of AI Agents. In plain terms, an agent is a mechanism that equips LLMs with a set of external tools (functions). These tools can be invoked by the model (once or multiple times in a row) based on the user’s input. Large Language Models Originally, in order to integrate a function into an LLM, it was needed to carefully construct the prompt in order to describe the function. Additionally, the output had to be parsed to detect whether the model intended to use this function. The latest release of GPT introduced function-calling capabilities that simplify the process of integrating the functions. First of all, it defines a clear and fixed structure for describing the functions. Moreover, the models themselves have been further fine-tuned to both detect when a function should be called (based on the input) and to respond with that adheres to the function signature. However, despite these improvements, the integration process still remains tedious and requires a lot of overhead. JSON In this article, we will explore how to build a simple conversational AI agent using the Agent Dingo library. The key feature of Dingo is that it allows the integration of external functions into ChatGPT by adding just a single line of code. In addition, it serves the agent with the required capabilities on an OpenAI-compatible web server. Quick Start To give a quick glimpse into the core functionality of Dingo, we will build a simple agent that is able to provide information about the current weather by querying the API. OpenWeather As the first step, the library has to be installed. pip install agent-dingo Next, we need to set the OpenAI API key. export OPENAI_API_KEY=<YOUR_KEY> Then, our Dingo agent can be instantiated as follows: from agent_dingo import AgentDingo

agent = AgentDingo() Let’s say that for getting the current weather via OpenWeather API we have a function. In order to integrate it into ChatGPT we need to add — the decorator . get_temperature a single line of code @agent.function @agent.function
def get_temperature(city: str) -> str:
    base_url = "https://api.openweathermap.org/data/2.5/weather"
    params = {"q": city, "appid": openweathermap_api_key, "units": "metric"}
    response = requests.get(base_url, params=params)
    data = response.json()
    print("get_temperature function output :", data["main"])
    return str(data) To get the output we need to call the method. chat agent.chat("What is the current weather in Linz?") As you can see the process of function integration is very intuitive and simple. Behind the scenes, we are asking ChatGPT for the current weather in Linz. The model has access to the function ( ) that was integrated by using decorator. The model decides to call function and passes city Linz as an argument. The response is parsed and we receive the following output. get_temperature agent.function get_temperature The current temperature in Linz is 30°C. As you might have noticed, we did not have to provide any descriptions of the function. This is achieved by the automatic docstring generation during the registration process (when decorator is called). agent.function In addition, it is possible to serve the agent on an OpenAI-compatible web server: from agent_dingo.wrapper import DingoWrapper
DingoWrapper(agent).serve() The server can be accessed using the python package: openai import openai

openai.api_base = "http://localhost:8080"

r = openai.ChatCompletion.create(
    model = "gpt-3.5-turbo",
    messages = [{"role": "user", "content": "What is the current weather in Linz?"}],
    temperature=0.0,
) Conclusion In this article, we explored how streamlines the process of integrating Python functions into ChatGPT in order to build a conversational AI agent. With Dingo, you no longer have to worry about manually integrating the functions or dealing with intermediate function calls. The framework is crafted to automate these tasks, allowing you to focus on writing the core functionality of your app. Agent Dingo Also published here.

Dingo: A Microframework for Building Conversational AI Agents

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