Using Structured Data/Structured Output - Google Agent Development Kit for Beginners (Part 4) скачать в хорошем качестве

Using Structured Data/Structured Output - Google Agent Development Kit for Beginners (Part 4)

Welcome back to the Google Agent Development Kit (ADK) tutorial series! In this fourth part (Part 4), we dive deep into leveraging structured output within your AI agents for more reliable and predictable responses. Following up on our multi-modal agents from Part 3 (   • Working with Tools & Function calling - Go...  ), this video introduces how to use libraries like Pydantic to define clear input and output schemas for your agents. This ensures your AI agent team returns data in a specific JSON format, making it easy for your applications (frontend, backend) to parse and react to the results programmatically. You'll learn how to build a Sequential Agent team specifically designed for a consultation application flow. We'll create: 👉🏽 A Problem Analyzer Agent: Takes the user's raw query and identifies the core problem, recommending the most suitable consultant type (Psychologist, Personal Trainer, Financial Advisor, etc.) and providing a concise summary. This agent utilizes a structured output defined with Pydantic. 👉🏽 An Advice Generator Agent: Receives the structured output from the Problem Analyzer and generates initial guidance, key questions for the user to consider, and actionable steps, also returning this information in a structured JSON format using Pydantic schemas. This pattern is incredibly useful for building complex AI workflows where different agents need to exchange information in a standardized way, powering applications like automated consultation services. We'll demonstrate the agent team working and inspect the structured JSON output using the ADK web UI. If you're a developer looking to build advanced AI applications with predictable data outputs and robust workflows using Google ADK and Python, this tutorial is for you! Important AI Disclaimer: The AI-generated guidance provided by these agents is for informational purposes only and should not be considered a substitute for professional advice. Always consult with a qualified professional for your specific needs. Resources: 👉🏽 Get the Code : https://github.com/AhsanAyaz/ai-agent... (Check the main branch for the final code) 👉🏽 Explore Google ADK Documentation: https://google.github.io/adk-docs/ Subscribe for more videos and more AI Content: https://blog.codewithahsan.dev/#/port... Let me know what you think of using structured output with AI agents and what kinds of consultation workflows you're excited to build in the comments below! Updated Timeline: 00:00 - Introduction & Series Context 00:16 - What is Structured Output and Why is it Useful? 00:54 - Structured Data in Google ADK Docs 01:36 - The AI Consultation Agent Workflow We'll Build 01:58 - Example Consultant Types Database 02:49 - Let's Code: Project Setup and Dependencies 03:40 - Creating Pydantic Models for Structured Data 05:02 - Defining Consultant Types Enum 05:30 - Building the Problem Analysis Schema 06:10 - Crafting Instructions for the Problem Analyzer Agent 07:40 - Crafting Instructions for the Advice Generator Agent 09:53 - Building the Consultation Response Schema 10:26 - Creating the Sequential Root Agent 10:52 - Running and Testing the Structured Output Agent 12:25 - Analyzing the Structured JSON Output 13:15 - Real-World Application & Next Steps Teaser 13:51 - Outro & Call to Action Hashtags: