spring-ai-java-docker-mcp-rag

# Sample: Spring AI with Docker Model Runner and MCP A Spring Boot application that provides an AI-powered dog adoption service using: - Docker Model Runner for AI/ML capabilities - Spring AI for conversation management - PostgreSQL with pgvector for vector storage - Two services: - Adoptions service: Handles dog adoption inquiries - Scheduling service: MCP Server that manages adoption appointments _Note:_ This is a fork of the [sample app in AWS Samples](https://github.com/aws-samples/Sample-Model-Context-Protocol-Demos/tree/main) <p align="center"> <img src="welcome-home-prancer.jpg" width="480"> </p> ## Architecture ```mermaid sequenceDiagram actor User participant Controller as ConversationalController participant Memory as ChatMemory participant RAG as QuestionAnswerAdvisor participant Vector as VectorStore participant Chat as ChatClient participant MCP as MCPSyncClient participant AI as Docker Model Runner User->>Controller: POST /{id}/inquire alt New conversation Controller->>Memory: computeIfAbsent(id) Memory-->>Controller: Create new PromptChatMemoryAdvisor end par RAG Process Controller->>RAG: Process question RAG->>Vector: Search relevant context Vector-->>RAG: Return matching embeddings RAG-->>Controller: Return augmented prompt and Memory Management Controller->>Memory: Get conversation history Memory-->>Controller: Return chat context end Controller->>Chat: prompt().user(question) Chat->>MCP: Synchronous tool callback MCP-->>Chat: Return tool results Chat->>AI: Send augmented prompt + context AI-->>Chat: Generate response Chat-->>Controller: Return content Controller->>Memory: Store conversation Controller-->>User: Return response Note over RAG,Vector: Retrieval Augmented Generation Note over Memory: Maintains conversation state Note over MCP: Handles scheduled operations ``` ## Setup To run locally you will need: - JDK 23 or higher - Docker Desktop with Model Runner enabled 1. Pull the models used in this sample application into Docker Model Runner: ```shell docker model pull ai/mxbai-embed-large docker model pull ai/qwen2.5:7B-Q4_K_M ``` Build the Scheduling MCP Server as a Docker container: ``` cd scheduling && ./mvnw spring-boot:build-image && cd .. ``` ## Running This sample includes tests and a "test" main application which will start the dependency services (postgres with pgvector and the scheduling MCP server) in Docker with Testcontainers. First make sure you are in the `adoptions` directory: ``` cd adoptions ``` Run the tests: ``` ./mvnw test ``` Run the "adoptions" server: ``` ./mvnw spring-boot:test-run ``` With the server started you can now make requests to the server. In IntelliJ, open the `resources/client.http` file and run the two requests. Or via `curl`: ``` curl -X POST --location "http://localhost:8080/2/inquire" \ -H "Content-Type: application/x-www-form-urlencoded" \ -d 'question=Do you have any neurotic dogs?' ``` ``` curl -X POST --location "http://localhost:8080/2/inquire" \ -H "Content-Type: application/x-www-form-urlencoded" \ -d 'question=fantastic. when could i schedule an appointment to adopt Prancer, from the London location?' ```