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Google ML and AI - How to Use OpenAPI Tools with Google ADK Agents

The Machine Learning Engineer via YouTube

Overview

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Learn to build a conversational assistant using the Google Agent Development Kit (ADK) that integrates with REST APIs through OpenAPI specifications. Discover how to create an intelligent agent that can interact with a read-only SQL API using natural language queries, enabling users to list available tables, examine table schemas, retrieve sample data rows, and execute parameterized SELECT statements. Explore the implementation of OpenAI models through the ADK's LiteLLM adapter and understand how to combine ADK agents with REST API integrations defined by OpenAPI specifications. Master the process of leveraging schema.json files to automatically generate tools that bridge conversational AI with database operations, creating a seamless natural language interface for data exploration and querying.

Syllabus

Google ML and AI: How to use OpenAPI tools with Google ADK agents #datascience #machinelearning

Taught by

The Machine Learning Engineer

Reviews

5.0 rating, based on 2 Class Central reviews

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  • Nihmath Farsha
    I completed this course 100%, and I must say it was an excellent learning experience. Every concept was explained clearly and in a logical order, which made it easy to follow from start to finish. I didn't face any confusion at any stage. The instructor's teaching style is engaging and practical, and the examples used really helped in understanding the real-world application of the topics. I took notes along the way, and now I feel much more confident in this subject. This course is definitely useful for beginners and even for those who want a structured revision. I highly recommend it to anyone serious about learning. Worth every minute. In sha Allah, I will also explore other courses from the same instructor."
  • Profile image for Immanuel
    Immanuel
    This course does a competent job introducing Google’s Agent Developer Kit and how OpenAPI specs integrate with it. It focuses on the practical workflow: creating a tool, wiring it into an agent, and watching the model call the API automatically. The explanations are simple enough for beginners but still relevant for intermediate developers who want to build agents that perform real actions instead of just generating text.

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