Spring AI for Your Organization
Spring AI for Your Organization
GCP Vertex AI Edition
About the Book
This book is for Spring developers who want to explore GCP Vertex AI. It's perfect if you're looking to:
- Learn Google Vertex AI
- Understand vector databases
- Help your company become AI-driven
The guide is designed for developers who want to quickly start with generative AI.
By the end, you'll be able to:
- Build chatbots that can handle any type of company data (PDFs, logs, emails, audio, video, images)
- Use the newest features of Spring AI
- Create and launch an API for interacting with these chatbots
This will help make your organization's data more accessible and useful.
About the Author
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Table of Contents
- 1:1 Introduction
- 1.1 Evolution of AI Frameworks in Java
- 1.2 Emergence of Spring AI
- 1.3 Introduction to Generative AI
- 1.4 Overview of Spring AI
- 1.5 Introduction to Google Cloud’s Vertex AI
- 1.6 Goals of the Book (What You Will Learn)
- 1.7 Who Should Read This Book
- 1.8 Prerequisites
- 1.9 What’s Next
- 2:Creating ChatBot with Spring AI
- 2.1 Chapter Highlights:
- 2.2 How to Initialize the project
- 2.3 Creating a RESTful Chat Endpoint
- 2.4 Connecting to Vertex AI Gemini
- 2.5 Implementing the Chat Endpoint
- 2.6 How to Test the Application
- 2.7 Configuring Chat Options for Vertex AI
- 2.8 How to Test the Application
- 2.9 Understanding API Interaction with Vertex AI
- 2.10 How to Deploy in GCP Cloud Run
- 2.11 Review Questions
- 2.12 Answers to Review Questions
- 2.13 What We Learned
- 3:Building Context-Aware Chatbots with Spring AI: Memory Management and Conversational Continuity
- 3.1 Chapter Highlights
- 3.2 The Challenge of Contextual Conversation
- 3.3 Understanding LLMs and Contextual Conversations
- 3.4 Implementing Conversation History in Spring AI
- 3.5 Managing ChatBot Conversations with Session IDs
- 3.6 Updating the ChatBot Endpoint for Conversational Context
- 3.7 Testing the Enhanced ChatBot with Session History
- 3.8 Simplifying Chat History Management with Advisors
- 3.9 Creating a DELETE Endpoint to Reset Chat Session History
- 3.10 Understanding System Prompts in Chatbot Responses
- 3.11 What We Learned
- 4:Creating Structural Data Bots
- 4.1 Chapter Highlights:
- 4.2 Make Inventory to be queried by Natural Language
- 4.3 Update Dependencies
- 4.4 Setting up an SQL database in GCP Cloud SQL
- 4.6 Getting Ready for Natural Language Processing
- 4.7 From Natural Queries to SQL
- 4.8 JSON Conversion of SQL Results
- 4.9 SQL to Natural Language Response
- 4.10 Bringing It All Together: The Endpoint
- 4.11 Testing the Endpoint
- 4.12 Exercise: Implement it using CSV instead of JSON
- 4.13 Improving SQL Query Flexibility for User Queries
- 4.14 Handling multiple questions
- 4.15 What We Learned
- 5:LLM Function Calling with Spring AI
- 5.1 Chapter Highlights:
- 5.2 Understanding LLM Function Calls
- 5.3 Streamlining the SQL Workflow with LLM Function Calls
- 5.4 Testing
- 5.5 Review Questions
- 5.6 Answers to Review Questions
- 5.7 Expanding System Integration through LLM Functions
- 5.8 What We Learned
- 6:Building Chatbots with Text and PDF Files
- 6.1 Chapter Highlights:
- 6.2 Use Case: Answering Questions About Vacuum Cleaners based on text file
- 6.3 Use Case: Answering Questions About Laptop based on pdf manual
- 7:Building Chatbots with Multimedia Capabilities
- 7.1 Chapter Highlights:
- 7.2 Use Case: Answering Questions About Coupons from an Image
- 7.3 Invoking the Chatbot with Image Data
- 7.4 Testing
- 7.5 Use Case: Answering Questions based on a Customer Care Audio
- 7.6 Testing
- 7.7 Use Case: Answering Questions from a Vaccum Cleaner Advertisement video
- 7.8 Invoking the Chatbot with Video Data
- 7.9 Testing
- 8:Using RAG to Make LLMs Smarter with Internal Data
- 8.1 Integrating Internal Data with LLM
- 8.2 Chapter Highlights:
- 8.3 How to Create Embeddings
- 8.4 Vector Database
- 8.5 Making Similar Documents as Conversational
- 8.6 Exercise: Modify Chatbot to Search All Records When Department Is Not Specified
- 9:Building Internal Knowledge based assistant
- 9.1 Chapter Highlights:
- 9.2 Integrating Google Cloud Storage Bucket
- 9.3 Building a Chatbot with Internal Documents
- 9.4 Adding File References on Chat
- 10:PDF Documents & Image Embeddings
- 10.1 Saving PDF Document Pages as Image Embeddings
- 10.2 Updating Chat Endpoint to use Image embeddings
- 11:Accessing other models in GCP Model Garden
- 11.1 Setting Up GCP
- 11.2 Setting Up Gradle and Configuration
- 11.3 Setting Up Authentication Tokens for Llama Requests
- 12:Whats Next
- 1:1 Introduction
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