This project showcases the implementation of a Model Context Protocol (MCP) server and client using Spring Boot. It includes modules for MCP server with WebFlux and WebMvc SSE support, and an MCP client for AI-powered proposals, leveraging Spring AI tools, Ollama AI models, and PGVector for vector storage.
Added on:
Created by:
Mar 25 2025
This project showcases the implementation of a Model Context Protocol (MCP) server and client using Spring Boot. It includes modules for MCP server with WebFlux and WebMvc SSE support, and an MCP client for AI-powered proposals, leveraging Spring AI tools, Ollama AI models, and PGVector for vector storage.
Added on:
Created by:
Mar 25 2025
lucas deng
Featured
What is Spring AI Example?
This MCP implementation provides a comprehensive framework for developing and deploying AI-enhanced applications within a Spring ecosystem. The MCP server module supports real-time data streaming via SSE, enabling dynamic AI interactions. The client module facilitates making AI-powered proposals, equipped with tools annotation and contextual configurations. The architecture emphasizes best practices in AI tool integration, using Spring Boot's capabilities for scalability, real-time updates, and AI model deployment, making it suitable for developers building intelligent, reactive applications.
Who will use Spring AI Example?
- AI developers
- Spring Boot practitioners
- MCP protocol implementers
- Research and Data scientists
How to use the Spring AI Example?
- Step 1: Clone the repository
- Step 2: Configure the environment and dependencies
- Step 3: Run the MCP server module
- Step 4: Set up the proposal-agent client
- Step 5: Make AI proposals and observe real-time updates
Spring AI Example's Core Features & Benefits
The Core Features
- MCP server with WebFlux and WebMvc SSE support
- AI tool integration with annotations
- Real-time data streaming via SSE
- Vector store integration with PGVector
- Spring AI model deployment and management
The Benefits
- Supports real-time AI interaction and proposal generation
- Flexible and scalable with Spring Boot
- Easy tool annotation and contextual configuration
- Leverages advanced AI models and vector storage
- Suitable for intelligent application development
Spring AI Example's Main Use Cases & Applications
- AI-powered proposal systems
- Real-time data streaming applications
- Research projects involving AI model integration
FAQs of Spring AI Example
Developer
- lucasdengcn
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