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Basic Information

This repository is a hands‚Äëon teaching and demo project that shows how to build AI-enabled applications with SpringAI. It collects example implementations and end‚Äëto‚Äëend code for streaming responses (SSE), agent smart bodies, function call handling, embeddings and vector databases, retrieval‚Äëaugmented generation (RAG) including a graph RAG variant, historical message management, and basic image generation and understanding flows. The project contains a Java Spring backend and a Node.js front end, configuration examples, and instructions to run a local stack with MySQL, Redis‚ÄëStack (vector support) and Neo4j. It is intended for developers who want practical examples of integrating SpringAI features rather than a packaged production service.

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Features
The codebase demonstrates key SpringAI capabilities with runnable examples. It includes SSE streaming for real‚Äëtime outputs, agent implementations and FunctionCall examples, embedding creation and storage using a vector capable Redis‚ÄëStack, RAG retrieval plus a Neo4j‚Äëbacked graph RAG example, and message history handling. There are samples for image generation and image understanding. The repository provides environment requirements (Java 17, Node 18+, MySQL 8, Redis‚ÄëStack, Neo4j 5+), Docker commands for Redis and Neo4j, front‚Äëend build and run scripts, and guidance to configure API keys and application.yml for local execution.
Use Cases
This project helps developers learn and prototype SpringAI integrations by providing complete, reproducible examples and deployment notes. Users can run the backend and front end to explore streaming responses, agent orchestration, function calling patterns, embedding vectors and vector search, and both document and graph‚Äëbased RAG workflows. The included Docker snippets and configuration instructions accelerate setting up Redis‚ÄëStack for vector queries and Neo4j for graph RAG. It is useful as a teaching aid, a starter template for custom projects, and a reference for implementing image understanding and generation alongside conversational and retrieval features. The README also lists contact options for paid help and customization.

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