Building a Generative AI Application using Vertex AI and PaLM API

Introduction

• Topic: Building a generative AI application using Vertex AI and PaLM API on Google Cloud.
• Overview of generative AI as a trending topic.
• Tools: Vertex AI and PaLM API.

Lecture Outline

1. Introduction to Generative AI

   • Definition and basics of Generative AI.
   • Differences from traditional AI/ML services.
   • Current popular tools based on Generative AI.

2. Generative AI Studio on Google Cloud

   • Overview of the Generative AI Studio in Google Cloud.
   • Demonstration on the Google Cloud console.
   • Available options in the Generative AI Studio.

3. Using Python SDK for Vertex AI PaLM API

   • Introduction to PaLM API for building generative applications.
   • Creating a simple Python chat application using the PaLM API.
   • Transitioning to a web application using Python Flask and PaLM API.
   • Deployment on the web.

4. Deployment using Docker and Cloud Run

   • Creating a Docker container for the generative AI app.
   • Deploying the Docker container on Cloud Run.

Detailed Content

What is Generative AI?

• Generative AI focuses on creating new content (text, images, audio, video).
• Example: Learning ABCD and then generating content, similar to human intelligence.
• Tools: ChatGPT and Google Bard.

How Generative AI Works

• Involves input data and a large language model (LLM).
• Provides output based on trained data.
• Difference from traditional AI: Generative AI can create new content while traditional AI usually predicts or classifies based on training data.

Generative AI Studio on Google Cloud

• Utilized to create and test generative AI applications.
• Allows customization of generative AI models.
• Example: Building a chatbot using Google’s LLM PaLM API.

Using Python SDK for Vertex AI PaLM API

• PaLM API: Pathways Language Model from Google.
• Create a new chat session with Vertex AI model using Python.
• Example provided with code snippets and explanations using the Python SDK.

Creating a Web Application with Flask

• Transition from a terminal chat application to a web application using Flask.
• Flask setup: Importing libraries, creating chat sessions, and integrating with the web interface.
• Example of code for the Flask application to be deployed.

Deployment on Cloud Run

• Building a Docker container for the Flask application.
• Steps to push code to GitHub and create Cloud Run service.
• Integration with continuous deployment pipeline (Cloud Build).
• Live demonstration of the deployed application.

Practical Examples

• Direct interaction with Generative AI Studio, using the PaLM API, and deploying on Cloud Run.
• Specific queries and responses to demonstrate chatbot functionality.

Summary and Conclusion

• Building and deploying a generative AI application from scratch using Google Cloud tools.
• Various steps from basic introduction to deployment covered in the lecture.

Additional Resources

• GitHub repository with source code for the examples provided.
• Previous video links for related content on Vertex AI, Docker, Cloud Run, etc.