Spring AI demos: OpenAI & Ollama chat, RAG with pgvector, image generation, text-to-speech, MCP server/client (Java 25, Spring Boot 4, Spring AI 2)
A collection of hands-on demo applications built with Spring AI, Spring Boot 4 and Java 25, showing how to integrate LLMs (OpenAI and Ollama), build a RAG pipeline, generate images and audio, and wire models to external tools through MCP (Model Context Protocol).
Each app is an independent Spring Boot project with its own Maven wrapper, so you can run any of them on its own.
Technology Version ------ Java 25 Spring Boot 4.1.x Spring AI 2.0.x Models OpenAI (gpt-4o-mini), Ollama (qwen3:4b) Embeddings Ollama nomic-embed-text Vector Store PostgreSQL + pgvector MCP Streamable HTTP (@McpTool)
1. springai-openai — OpenAI Chat Playground Free chat, expert personas, code generation/explanation, and structured (typed) outputs with Spring AI's ChatClient.
Key endpoints (AiController): - GET /greeting — one-line hello - POST /chat, POST /chat-expert — free chat, Java/Spring expert persona - POST /generate-code, POST /explain-code — code generation and explanation - POST /chat-format, POST /analyze — structured summaries and analysis - GET /city-info?city=, POST /classify-typed — typed records (CityInfo, TicketClassification) - POST /generate-code-json, POST /explain-code-json, POST /expert-architect-json — typed JSON outputs - GET /chat-metadata?prompt= — answer plus raw ChatResponse metadata
Requires: OPENAIAPIKEY (see spring.config.import=optional:file:.env[.properties]).
2. springai-ollama — Ollama Chat Playground (local, no cloud) The same feature set as the OpenAI app, backed by a local Ollama server with qwen3:4b. No API keys, fully offline.
From the project README.
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