Spring Boot AI

Name: Spring Boot AI
Author: rogervinas

@rogervinas

代码仓库主页

关于 Spring Boot AI

🍀 🤖 Spring Boot AI - Evaluation Testing

#mcp #rag #pgvector #ollama #mcp-server #mcp-client #spring-boot-ai #spring-boot-ai-mcp

基本信息

分类

其他

运行时

kotlin

传输方式

stdio

发布者

rogervinas

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "spring-boot-ai": {
      "command": "docker",
      "args": [
        "compose",
        "-f",
        "docker-compose-vectordb.yml",
        "up",
        "-d"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Spring Boot AI?

Spring Boot AI is a sample application demonstrating how to build an AI agent using Spring AI, the Model Context Protocol (MCP), and Retrieval Augmented Generation (RAG). It shows how to create tools (Weather, Booking), integrate with AI models (Ollama, Gemini, Bedrock), and add chat memory and vector store context.

How to use Spring Boot AI?

Configure the application for your chosen AI provider (Ollama, Gemini, or Bedrock) via Spring profiles. Run the vector database (PGVector) and start the MCP server and chat server. Send requests to the REST endpoint POST /{chatId}/chat?question=... to interact with the agent.

Key features of Spring Boot AI

Local and remote MCP tools for weather and booking
RAG with PGVector vector store
Chat memory via MessageChatMemoryAdvisor
Support for Ollama, Gemini, and AWS Bedrock
AI Model Evaluation for testing

Use cases of Spring Boot AI

Building an AI-powered travel booking assistant
Learning how to integrate MCP tools into a Spring Boot application
Experimenting with different AI models and embedding providers

FAQ from Spring Boot AI

What AI models does Spring Boot AI support?

It supports Google Gemini, AWS Bedrock, and local Ollama models, switchable via Spring profiles.

How are tools implemented in Spring Boot AI?

Tools are created as Spring Beans annotated with @Tool and @ToolParam, and registered as MethodToolCallbackProvider.

Does Spring Boot AI use a vector store?

Yes, it uses PGVector for RAG – the vector store is loaded with sample city data at startup.

What transport does the MCP server use?

The remote Booking MCP server uses SSE transport, configured via spring.ai.mcp.client.sse.connections.booking-tool.url.

Can I test the AI agent?

Yes, the project includes AI Model Evaluation using Spring AI’s testing support.

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