spring-ai-examples/agents/reflection/README.md

Spring AI Reflection Agent Application

This project demonstrates the use of Spring AI to create a self-improving code generation system. The Reflection Agent uses two ChatClient instances in an iterative loop - one for generation and one for critique - to produce high-quality Java code.

It is based on the code in the repository https://github.com/neural-maze/agentic_patterns

The application implements a reflection-based system where the Reflection Agent:

• Uses a generation ChatClient to create code based on user prompts
• Uses a critique ChatClient to review the generated code
• Iteratively improves the code by feeding critique back to the generation ChatClient
• Continues this loop until the critique ChatClient is satisfied with the quality

Prerequisites

• Java 17 or higher
• Maven

This examples uses OpenAI as the model provider.

Before using the AI commands, make sure you have a developer token from OpenAI.

Create an account at OpenAI Signup and generate the token at API Keys.

The Spring AI project defines a configuration property named spring.ai.openai.api-key that you should set to the value of the API key obtained from OpenAI.

Exporting an environment variable is one way to set that configuration property:

export SPRING_AI_OPENAI_API_KEY=<INSERT KEY HERE>

Running the Application

1. Clone the repository
2. Navigate to the project directory
3. Run the application using Maven wrapper: ./mvnw spring-boot:run

Project Structure

Main Components

• Application.java: The main Spring Boot application that provides the command-line interface
• ReflectionAgent.java: The core component that manages the iteration between generation and critique

How It Works

Initial Setup

The Reflection Agent creates two ChatClient instances:

• generateChatClient: For generating Java code based on user requests
• critiqueChatClient: For reviewing and critiquing the generated code

Generation Process

• User inputs a request
• The generation ChatClient creates initial code
• The critique ChatClient reviews the code
• If improvements are needed, the generation ChatClient creates a revised version
• This continues for up to maxIterations or until the critique ChatClient approves (<OK>)

ChatClient Configurations

• Generation ChatClient system prompt:

You are a Java programmer tasked with generating high quality
Java code. Your task is to generate the best content possible
for the user's request.

• Critique ChatClient system prompt:

You are tasked with generating critique and recommendations for
the user's generated content. If the user content has something
wrong or something to be improved, output a list of
recommendations and critiques.

Example Run

In this sample run, the user requested a JUnit 5 test for a Person class. See the file JacksonTestAgent.md for the actual output.

Initial Generation

• The generation ChatClient created a basic Person class and test class
• Included serialization/deserialization functionality
• Implemented basic test cases

Critique Phase

The critique `ChatClient identified several improvements:

• Better error handling
• Improved code readability
• Need for edge case testing
• Better test structure
• Expanded test coverage
• Enhanced Java class structure
• Modern Java feature usage

Final Result

The generation ChatClient created improved code with:

• Separated test methods for better modularity
• Enhanced error handling with detailed messages
• Added edge case testing for null values
• Improved code structure and readability
• Better test coverage

MergeSort

The file AgentMergeSort.md shows a similar run to create a merge sort algorithm. There is also a JUnit test of the code that was generated to show it works.