Summary

This project, java-ai-tech/spring-ai-summary, offers a collection of modular examples built upon the native Spring AI framework. It aims to accelerate developer understanding and adoption of Spring AI by providing clear code demonstrations and detailed documentation for core functionalities. The project is ideal for developers, from beginners to experienced engineers, looking to quickly grasp Spring AI's capabilities and integrate them into AI applications, while also staying abreast of the latest practices.

How It Works

The project structures its content into distinct modules, each showcasing a specific aspect of Spring AI. It follows a learn-by-doing approach, guiding users through practical implementations of chat applications, tool calling, vector database integrations, and advanced patterns like Retrieval Augmented Generation (RAG) and Agents. This modular design allows users to focus on specific features and progressively build their understanding of the framework's architecture and capabilities.

Quick Start & Requirements

• Environment: Spring Boot 3.3.6, Spring AI 1.0.0, JDK 21+, Maven 3.6+ (mvnd recommended), Docker (for services like Milvus).
• Installation: Clone the repository (git clone https://github.com/java-ai-tech/spring-ai-summary.git), navigate into the directory, and compile using Maven (mvn clean compile -DskipTests).
• Configuration: API keys for LLM providers must be configured in module-specific application.yml or application.properties files, preferably using environment variables for security.
• Running Examples: Execute specific modules (e.g., spring-ai-chat-deepseek) and test via HTTP clients like curl.
• Links: Official Spring AI Documentation, Project Wiki for API key guides.

Highlighted Details

• Modular examples cover chat, tool calling, vector databases, RAG, and agent patterns.
• Demonstrates integration with various LLM providers, including DeepSeek.
• Includes examples for monitoring AI-related metrics like token usage.

Maintenance & Community

The project is independently developed and maintained by glmapper. Contributions via Pull Requests and Issues are welcomed. A community WeChat group is available for discussion (scan QR code, mention "Spring AI"). Further resources and updates are shared via the "磊叔的技术博客" WeChat public account.

Licensing & Compatibility

The project is licensed under the MIT License. However, it is explicitly designated for learning and research purposes only and is not suitable for direct use in production environments. Users must also adhere to the terms of service for any underlying AI models used.

Limitations & Caveats

This project is strictly for educational and research purposes and should not be deployed in production. Some provided learning resources may be based on older versions of Spring AI and might not be fully compatible with the latest releases. Users are responsible for obtaining and managing their own API keys and adhering to model provider terms.