spring-ai-alibaba-examples
LocalAbout spring-ai-alibaba-examples
A collection of Spring AI and Spring AI Alibaba example modules, aimed at developers learning how to build AI applications with these frameworks. It ranges from basic chat to MCP, RAG, image generation, and tool-calling demos.
Key features
- 01Examples from basic to advanced usage
- 02Chat and image-generation demos
- 03MCP and RAG examples
- 04Tool-calling examples
- 05Quickstart commands and configuration references
Use cases
- 01Explore Spring AI chat integrations
- 02Run image-generation examples
- 03Study MCP or RAG demos
- 04Learn tool-calling patterns
MCP support
Includes an MCP demo module for clients to explore MCP usage; the README does not specify what it exposes to MCP clients.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMESpring AI Alibaba Examples
Spring AI Alibaba Repo: https://github.com/alibaba/spring-ai-alibaba
Spring AI Alibaba Website: https://java2ai.com
Spring AI Alibaba Website Repo: https://github.com/springaialibaba/spring-ai-alibaba-website
English | 中文
介绍
此仓库中包含许多 Example 模块项目来介绍 Spring AI 和 Spring AI Alibaba 从基础到高级的各种用法和 AI 项目的最佳实践。
更详细的介绍请参阅每个子项目中的 README.md 和 Spring AI Alibaba 官网。
参与建设
欢迎任何形式的代码贡献。
Quickstart Matrix (Community Contribution)
| module | purpose | command | required services | env vars | env template | entry |
|---|---|---|---|---|---|---|
| spring-ai-alibaba-helloworld | basic chat and advisor examples | mvn -pl spring-ai-alibaba-helloworld spring-boot:run | none | AI_DASHSCOPE_API_KEY | — | README |
| spring-ai-alibaba-chat-example/dashscope-chat | DashScope chat basics | mvn -pl spring-ai-alibaba-chat-example/dashscope-chat spring-boot:run | none | AI_DASHSCOPE_API_KEY | — | README |
| spring-ai-alibaba-image-example/dashscope-image | DashScope image generation | mvn -pl spring-ai-alibaba-image-example/dashscope-image spring-boot:run | none | AI_DASHSCOPE_API_KEY | — | README |
| spring-ai-alibaba-mcp-example | MCP demo | mvn -pl spring-ai-alibaba-mcp-example spring-boot:run | none/local mcp tool | model api key | .env.example | README |
| spring-ai-alibaba-rag-example | RAG demo | mvn -pl spring-ai-alibaba-rag-example spring-boot:run | vector db (optional by profile) | model api key, embedding model | .env.example | README |
| spring-ai-alibaba-tool-calling-example | tool calling | mvn -pl spring-ai-alibaba-tool-calling-example spring-boot:run | none | model api key, map api key | .env.example | README |
常用配置键速查
| 配置键 | 常见模块 | 说明 |
|---|---|---|
AI_DASHSCOPE_API_KEY | spring-ai-alibaba-helloworld、DashScope chat/image、tool calling、evaluation、很多 graph/rag 示例 | DashScope 兼容模型最常见的 API Key |
OPENAI_API_KEY | spring-ai-alibaba-chat-example/openai-chat、spring-ai-alibaba-chat-example/vllm-chat | OpenAI 兼容接口示例常用 |
AI_OPENAI_API_KEY | spring-ai-alibaba-image-example/openai-image | OpenAI 图片生成示例使用 |
AI_DEEPSEEK_API_KEY | spring-ai-alibaba-chat-example/deepseek-chat、spring-ai-alibaba-mem0-example | DeepSeek 相关示例使用 |
MINIMAX_API_KEY | spring-ai-alibaba-chat-example/minimax-chat | MiniMax 模型示例使用 |
ZHIPUAI_API_KEY | spring-ai-alibaba-chat-example/zhipuai-chat | 智谱模型示例使用 |
BAIDU_MAP_API_KEY | spring-ai-alibaba-tool-calling-example | 地图工具调用示例需要 |
常见启动问题 / Troubleshooting
AI_DASHSCOPE_API_KEY未设置 / MissingAI_DASHSCOPE_API_KEY: 先确认环境变量已在当前 shell 或 IDE 中生效,再重新启动示例。- 端口被占用 / Port already in use: 检查对应模块
application.yml中的server.port,释放端口或改端口后重试。 - 本地依赖未启动 / Required local services not running: RAG、MCP、向量库或 Docker 相关示例通常需要先启动对应的中间件或容器。
- 模块里暂时没有
.env.example/ No.env.examplein a module yet: 优先查看该模块 README 和src/main/resources/application.yml,确认真实的变量名和依赖服务。
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.