How Spring AI Made My RAG Pipeline Surprisingly Elegant
Building a modern RAG app became much simpler with Spring AI advisors, hybrid retrieval, streaming chat responses and memory management....
Read now ➤Spring AI Tutorial
Before getting started with Spring AI, it is important to understand how it fits into the broader Spring ecosystem. Spring AI is designed to simplify the integration of AI capabilities into modern applications by providing abstractions over popular AI models and services such as OpenAI, vector databases, and embedding models.
With Spring AI, developers can easily build intelligent applications like chatbots, knowledge assistants, and recommendation systems without worrying about low-level API complexities. It offers seamless integration with Spring Boot, supports prompt templating, retrieval-augmented generation (RAG), and enables structured interactions with large language models.
In this section, we will explore a series of hand-written tutorials covering core concepts and real-world implementations of Spring AI. From building simple chat applications to advanced AI-powered systems using vector search and embeddings, these tutorials will help you get hands-on experience and master Spring AI step by step.
Table of Contents
- How Spring AI Made My RAG Pipeline Surprisingly Elegant
- Build a Production-Grade RAG Application with Spring AI, Elasticsearch and Ollama
- Build AI Usage Analytics in Spring Boot 4 with Spring AI Advisors
- Build a Spring AI RAG Application with PGVector and Ollama
- Spring AI Tool Calling Example: Build a Structured Travel Planner API
- Build a Monitoring System for Token Usage and Cost in Spring AI
- Building Smart Customer Support with Spring AI
- Build an AI-Powered Document Search App (RAG) with Spring AI, Ollama & ChromaDB
- Streaming AI Responses with SSE in Spring AI (ChatClient)
- Building a Stateful AI Chat Application with Spring AI, Memory & Semantic Cache
- Building an AI Knowledge Assistant with Spring AI, Ollama & RAG (Step-by-Step Guide)
- Semantic Caching with Redis, LSH & Elasticsearch in Spring Boot
- Building Your Own AI-Powered Search Engine with Spring Boot and Elasticsearch
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Build a Production-Grade RAG Application with Spring AI, Elasticsearch and Ollama
Learn how to build a production-grade Retrieval Augmented Generation (RAG) application using Spring AI, Elasticsearch and Ollama. Implement hybrid search, query rewriting, metadata indexing, citations and vector search for Indian recipe recommendations....
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Build AI Usage Analytics in Spring Boot 4 with Spring AI Advisors
Learn how to track Spring AI token usage in Spring Boot 4 using Spring AI Advisors and PostgreSQL. Capture prompt tokens, completion tokens, latency, cost analytics, and AI usage metrics for OpenAI and local LLMs....
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Build a Spring AI RAG Application with PGVector and Ollama
Learn how to build a Retrieval-Augmented Generation (RAG) application using Spring AI, PGVector, PostgreSQL and Ollama. In this tutorial, we will ingest PDF documents, generate embeddings using Ollama, store vectors in PostgreSQL using pgvector and retrieve contextual answers using Spring AI....
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Spring AI Tool Calling Example: Build a Structured Travel Planner API
Learn how to implement tool calling in Spring AI 2.0 using ToolCallbacks. Build a travel planner API with DTO binding, validation, and production-ready patterns....
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Build a Monitoring System for Token Usage and Cost in Spring AI
Build a production-ready monitoring system to track LLM token usage, latency, and cost in Spring AI using Spring Boot, Micrometer, Prometheus, and Actuator....
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Building Smart Customer Support with Spring AI
Build a production-grade RAG-based customer support automation system using Spring AI, Elasticsearch, and Ollama with hybrid search, query rewriting, and reranking....
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Build an AI-Powered Document Search App (RAG) with Spring AI, Ollama & ChromaDB
Step-by-step guide to building an AI-powered document search app using Spring AI, Ollama, and ChromaDB. Learn how to implement RAG, ingest documents, generate embeddings, and perform semantic search efficiently....
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Streaming AI Responses with SSE in Spring AI (ChatClient)
Streaming AI Responses with SSE in Spring AI...
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Building a Stateful AI Chat Application with Spring AI, Memory & Semantic Cache
Learn how to build a stateful AI chat application using Spring AI. Implement chat memory, semantic caching with Redis, and RAG-based question answering step by step....
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Building an AI Knowledge Assistant with Spring AI, Ollama & RAG (Step-by-Step Guide)
Learn how to build an AI Knowledge Assistant using Spring AI, Ollama, and RAG. Step-by-step Java tutorial covering document ingestion, embeddings, and vector storage....
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Semantic Caching with Redis, LSH & Elasticsearch in Spring Boot
Learn how to build a production-grade semantic caching architecture using Redis, LSH hashing and Elasticsearch with Spring Boot. Includes architecture, performance optimization and full implementation....
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Building Your Own AI-Powered Search Engine with Spring Boot and Elasticsearch
Learn how to build an AI-powered semantic search engine using Spring Boot and Elasticsearch. Generate embeddings, index documents, and perform vector search using Elasticsearch ML models....
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