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.
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....
Read now ➤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....
Read now ➤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....
Read now ➤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....
Read now ➤Streaming AI Responses with SSE in Spring AI...
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