Building AI‑Native Applications with Spring AI: A Complete Tutorial

The article introduces the concept of AI‑native applications, where AI is an inseparable core component of the software, and contrasts it with merely AI‑augmented solutions. It then presents Spring AI, a Java library that simplifies integration with large language models (LLMs) and offers a rich set of capabilities.

Key Spring AI features are described in detail:

Support for major model providers (OpenAI, Azure, Google, etc.) and model types (chat, text‑to‑image, audio).

Chat completion APIs with both synchronous ( chatClient.call(message)) and streaming ( chatClient.stream(prompt)) modes.

Prompt templates that allow dynamic placeholder substitution, e.g.,

PromptTemplate pt = new PromptTemplate("Tell me a {adjective} joke about {topic}");

Function calling, enabling the model to invoke external Java functions; example function definitions for weather and population lookup are provided.

Output parsers that map LLM responses to Java POJOs, illustrated with a bean parser for actor filmographies.

Image generation via ImageClient and embedding via EmbeddingClient.

Vector store integration (Azure, Chroma, Milvus, PGVector, Pinecone, Qdrant, Redis, Weaviate, SimpleVectorStore) for similarity search and Retrieval‑Augmented Generation (RAG).

Practical code snippets show how to configure Maven dependencies, create Spring beans for functions and vector stores, and implement REST controllers that expose endpoints for chat, prompt, function calls, image generation, embedding, and RAG queries. A complete example builds a recruitment assistant that combines a knowledge base (candidate resumes stored in a vector store), a system prompt defining the assistant’s persona, and a function that returns a candidate’s applied position.

Finally, the article demonstrates how to assemble these components into a functional AI‑native service, emphasizing the importance of persona, knowledge base, and tools (functions) for robust, trustworthy AI applications.