Build generative AI agents with Vertex AI Agent Builder and Flutter

Summary

At Google I/O, the Dart and Flutter team introduced a generative AI-powered Flutter app that enhances user photographs by providing deeper insights. The app uses the Gemini API to identify subjects in photos and offer detailed descriptions. It also features an AI agent for interactive learning and a chat interface for user inquiries. The talk covered building the app with Vertex AI for Firebase Dart SDK, integrating AI agents with Vertex AI agent Builder for external data integration, and optimizing the Flutter app for multiple platforms. The result is an adaptive, cross-platform app that enriches user experience through generative AI.

Takeaways

• 📸 Khan, a developer relations engineer at Google, introduced a generative AI-powered Flutter app that enhances photos by providing detailed information about the subjects within them.
• 🌐 The app leverages the Gemini API, Google's advanced AI model, which supports multimodal understanding, including text, images, videos, and audio.
• 🔍 Users can interact with the app by selecting a photo, which the app uses to identify the subject and provide a description, as well as answer follow-up questions through an AI chat interface.
• 🛍️ The app can also identify Google merchandise, providing information on pricing and offering direct links for purchase.
• 🛠️ The talk covered the technical aspects of building the app, including using the Vertex AI for Firebase Dart SDK to send prompts and photos to the Gemini API.
• 🤖 The importance of AI agents was discussed, with the introduction of Vertex AI Agent Builder for creating chat agents that can reason and orchestrate tasks with external tools.
• 🔗 The concept of 'rag' (retrieval augmented generation) was explained, highlighting how it connects language models with external data sources to provide up-to-date and accurate information.
• 📚 The app development process included creating a data model, implementing camera and image selection features, and using various Flutter packages to enhance functionality.
• 🎨 Flutter's capabilities for building adaptive and responsive apps across multiple platforms were demonstrated, ensuring a consistent user experience on different devices.
• 🔄 The talk emphasized the efficiency of Flutter's hot reload feature, which allows for rapid development cycles and a great developer experience.
• 🌟 The combination of Flutter and Google Cloud provides developers with powerful tools to build, scale, and reach more users with their applications.

Q & A

What is the main theme of the talk at Google I/O?

The main theme of the talk is about building a generative AI-powered Flutter app that helps users learn more about their photographs using the Gemini API and Google Cloud's Vertex AI.

Who are the speakers at the Google I/O talk?

The speakers are Khan, a developer relations engineer on the Dart and Flutter team at Google, and Cass, a developer advocate for the Cloud AI team.

What does the app developed in the talk allow users to do?

The app allows users to select a photo, identify the subject within the photo, get a description of the subject, and chat with an AI agent to ask questions and learn more about the subject.

How does the app identify the subject in a photo?

The app uses the Gemini API to identify the subject in the photo. It sends a prompt along with the user's photo to the API, which then returns the subject and a description.

What is the role of the Vertex AI for Firebase Dart SDK in the app?

The Vertex AI for Firebase Dart SDK is used to call the Gemini API. It handles the configuration and setup needed to communicate with the API, making the process easier for the developers.

Why is the Gemini 1.5 Pro model significant according to the talk?

The Gemini 1.5 Pro model is significant because it features a mixture of expert (MoE) architecture for efficiency and lower latency, supports a large context window of up to 2 million tokens, and has multimodal understanding, capable of processing text, images, videos, and audio.

What is the purpose of the AI agent in the generative AI app?

The AI agent serves to provide a chat interface for users to interact with, ask questions, and receive more information about the subject of their photos. It uses reasoning and orchestration to dynamically integrate with various external tools and data sources.

How does the app handle user requests for information not available in Wikipedia?

The app uses an AI agent that can access multiple data sources. For example, if the user asks about a Google merchandise item, the agent can use the Google Shop data set to provide information such as pricing and a purchase link.

What is the significance of the 'tell me more' feature in the app?

The 'tell me more' feature enhances user engagement by providing a chat interface where users can ask questions about the subject in their photo. It allows for a more interactive and informative experience.

How does the app ensure it works well across different platforms and devices?

The app uses Flutter, which allows for a single code base to run on multiple platforms. It also implements responsive and adaptive design principles to optimize the user interface and experience for different devices like mobile phones, tablets, and desktops.

What are some of the Flutter packages used in the app development?

Some of the Flutter packages used include 'permission_handler' for device permissions, 'image_picker' for selecting images and launching the camera, 'image_gallery_saver' for saving images, and 'flutter_chat_ui' for the chat interface.

How does the app handle the potential issue of outdated or 'hallucinated' information from the AI?

The app addresses this by using an AI agent that can connect to external data sources, ensuring that the information provided is up-to-date and accurate. This approach, known as retrieval-augmented generation (RAG), combines the AI model with a retrieval system to fetch the latest information.

What is the role of Vertex AI Agent Builder in building the app?

Vertex AI Agent Builder provides a suite of tools for building AI agents with orchestration and grounding capabilities. It allows developers to easily design, deploy, and manage AI agents that can reason and interact with various external tools and data sources.

How does the app ensure a consistent and less random output from the Gemini API?

The app sets the temperature to zero when calling the Gemini API. This minimizes randomness and ensures more consistent output from the API for each request.

What is the importance of the JSON format support in Gemini 1.5 Pro model?

The JSON format support in the Gemini 1.5 Pro model allows for easier extraction of information by the Flutter app. It enables developers to use Dart's pattern matching to efficiently parse and utilize the data returned by the API.

What are some considerations for deploying a generative AI app in a production environment?

When deploying a generative AI app in production, considerations include ensuring the information provided is up-to-date, grounding the model with external data sources for accuracy, and handling user requests in a way that maps to the appropriate functions and tools.

How does the app provide a good developer experience while building with Flutter?

The app provides a good developer experience by leveraging Flutter's capabilities for writing a single code base that runs on multiple platforms, using official packages for common functionalities, and adhering to best practices for responsive and adaptive design.