Build a custom AI agent/chatbot in Java (hands-on tutorial)

Introduction

Welcome to this live coding tutorial on how to build your own AI agent in Java. In this practical and hands-on session, we will briefly cover the theory of AI applications before diving into the actual coding. This tutorial is designed for both newcomers and experienced developers who might want to refresh their minds on the concepts surrounding AI-powered applications.

Theory Behind AI Applications

To kick things off, let's consider a real-world scenario involving AI. Recently, I had to navigate a complex medical report for my dog, Nova. The report was filled with dense jargon, so I turned to ChatGPT for help. I provided it with instructions to interpret the medical jargon in an easy-to-understand manner. The response was a concise summary that alleviated our concerns regarding Nova's health. This highlighted the potential of AI to assist us if used correctly.

However, despite their capabilities, large language models (LLMs) can struggle with specific context-related issues. This presents challenges when building AI applications since real-world problems often require context that the AI may not possess. To enhance the effectiveness of AI applications, developers must provide the necessary contextual information.

As a case study, we'll build a simulated customer support agent for an airline. The application will allow users to inquire about cancellation policies, booking details, modifications, and cancellations. Here’s the approach we'll take:

1. LLM as a CPU: Think of the LLM as a CPU, powerful but generic. Enhancements will be modules similar to RAM (context window) and Vector Store for persistent information.
2. Agent Autonomy: Our AI will progress from a simple chatbot to a capable assistant, allowing for retrieval-augmented generation (RAG) and tool utilization for a co-pilot setting.
3. Framework Usage: We'll utilize tools like LangChain for J (a Java port of the popular Python library) and Hilla (a full-stack framework) to simplify our development process.

Application Structure

The user interface will have a chat feature integrated with a live view of bookings. Key functionalities will include fetching cancellation policies, booking details, and the ability to modify existing bookings.

Building the AI Agent

Project Setup

For our project, we will create a Spring Boot application as the back end, integrating LangChain for J to handle AI functionalities. Here’s a step-by-step breakdown of the components we'll create:

1. OpenAI API: Integrate OpenAI as our LLM, using the API key configured in our properties file.
2. Assistant Service: Create a service to handle user requests and retrieve contextual responses.
3. Token Management: Implement a system message to maintain context by keeping a history of messages exchanged.

Enhancing with Contextual Awareness

The AI assistant will be initialized with guidelines to maintain a friendly demeanor and understand its role as a customer support agent. As we build the application, it is critical to ensure that the assistant can reference the chat history to authenticate user requests.

Integration with Business Logic

Next, we will go beyond just answering questions. The AI will be equipped to run specific functions such as:

• Fetching booking details
• Modifying bookings
• Processing cancellations

In this section, we will create a "Booking Tools" class, which contains methods to interact with our flight service.

Conclusion

In this demo, we've built a working AI agent capable of engaging users in conversation while providing access to their booking details. This implementation applies core AI concepts that enhance user experience without diving deep into the underlying mathematics or infrastructure.

The full project, including various versions using different libraries for further exploration, is available on my GitHub. Check out the repository for details!

Keyword

AI, Java, Chatbot, LangChain for J, Spring Boot, OpenAI, Context-Aware, Airline Customer Support, Booking, Cancellation, Tool Integration.

FAQ

1. What is the purpose of this tutorial?

   • This tutorial aims to guide developers on how to build a custom AI chatbot in Java, specifically for handling customer inquiries within an airline context.

2. What tools are used in building the AI agent?

   • We utilize LangChain for J, Spring Boot, and Hilla to streamline the development process for our AI application.

3. How does the AI handle user queries?

   • The AI processes user queries using a large language model and maintains context through a token management system, enabling it to provide meaningful responses.

4. Can the AI agent perform actions beyond answering questions?

   • Yes, the AI can interact with backend services to fetch or manipulate booking details, allowing for a more dynamic and interactive experience.

5. Where can I find the complete source code?

   • The complete project source code is available on GitHub and can be explored in different branches that present alternative implementations using various libraries.