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Spring AI course examples for Spring Boot apps

This repository is the companion code for a Spring AI course. It collects worked examples for LLM providers, embeddings, RAG, chat memory, and MCP inside Spring Boot projects. The code is split by section so you can follow along lesson by lesson.

158 stars•200 forks•Java•Updated 2mo ago

Who it's for

Builders who want to learn Spring AI by trying course projects in Spring Boot.

What it delivers

You can follow a guided path from Spring Boot app code to AI features like chat, RAG, and MCP.

What it does

Provider examples

Examples for OpenAI, Ollama, AWS Bedrock, and Docker-based model runners in `section01`.

RAG and vector store demos

Projects that show retrieval-augmented generation patterns, including Qdrant-based setups.

MCP examples

Client and server samples for Model Context Protocol in `section07`, including stdio and remote server setups.

Chat memory and sampling demos

Section folders for Spring AI chat memory, elicitation, logging, progress, tool filtering, and sampling.

Postman collection

`SpringAI.postman_collection.json` for testing course APIs and demo endpoints.

README

From Java Dev to AI Engineer: Spring AI Fast Track

🌱 Spring AI Course – Resources & Reference Links

Welcome to the official GitHub repository for the Spring AI Course. This course helps you build intelligent applications using the Spring AI framework and integrate powerful LLMs like OpenAI into your Spring Boot apps.

Below are some carefully curated reference links and tools used throughout the course. Bookmark this information for quick access during development and exploration.

📘 Official Documentation

Spring AI Official Documentation
The core reference for understanding Spring AI modules, configuration, and supported AI providers.
OpenAI Platform Docs
Learn how to use OpenAI's APIs including ChatGPT, GPT-4, embeddings, and more.

🤖 AI Providers & Runtimes

Ollama
Run open-source large language models (LLMs) locally on your machine with simple commands.
AWS Bedrock
Access foundation models from various providers via a fully managed AWS service.
Docker Desktop
Essential for running local AI model runtimes and Docker Compose setups used in the course.
Docker Model Runner
Use Docker’s official tool for running and managing AI models locally.

📚 Foundational Papers & Tools

Attention Is All You Need (Transformer Paper)
The seminal research paper that introduced the Transformer architecture behind modern LLMs.
OpenAI Tokenizer Tool
Visualize how OpenAI tokenizes input prompts and estimate token usage.

📦 Vector Store & MCP

Qdrant Vector Database
An open-source vector store used in Retrieval-Augmented Generation (RAG) demos with Spring AI.
Model Context Protocol (MCP)
A protocol for connecting AI clients and servers in a decoupled and extensible way.

📊 Observability & Monitoring Tools

Prometheus
Monitoring and alerting toolkit for collecting Spring Boot and AI app metrics.
Micrometer
Java metrics collection library used with Spring Boot to expose observability data.
OpenTelemetry
Industry-standard framework for distributed tracing and telemetry data.
Grafana
Visualization tool for creating dashboards from Prometheus and other data sources.
Jaeger Tracing
Distributed tracing platform used to trace and monitor AI request flows.

📎 Stay Connected

📬 For questions or issues, raise a GitHub issue or connect with the course instructor

Happy Learning! 🚀

Files in the repo

Repository payload•13 top-level entries

section_10— 1 item
section_11— 1 item
section01— 6 items
section02— 1 item
section04— 1 item
section05— 1 item
section06— 1 item
section07— 8 items
section08— 1 item
section09— 1 item
.gitignore
README.md— The README
SpringAI.postman_collection.json

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