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Your First Spring AI 1.0 Application
- Spring AI has been introduced in the Spring Initializr, making it an exciting time to be a Java and Spring developer, especially in the AI space.
- AI integration with Spring-based workloads is essential, focusing on system prompts, stateless models, collaborating with isolated sandbox models, utilizing prompt stuffing wisely, and dealing with chat model hallucinations using evaluators.
- Spring AI simplifies AI development for Spring developers by offering portable service abstractions, Spring Boot starters, configuration properties, and supporting technologies like virtual threads, GraalVM native images, and Micrometer.
- The article discusses building an application to facilitate dog adoptions using Spring Initializr, including dependencies like PgVector, GraalVM Native Support, Actuator, Data JDBC, JDBC Chat Memory, PostgresML, Devtools, and Web.
- Instructions are provided for setting up the PostgreSQL database and specifying configurations in the application.properties file.
- The article covers implementing features like a ChatClient for fielding user questions, utilizing chat memory for persistence, and using advisors for pre- and post-processing model requests.
- The usage of retrieval augmented generation (RAG) with vector stores, structured output, local tool calling, and integrating with Model Context Protocol (MCP) for remote HTTP-based services are discussed.
- Further topics include securing the application with Spring Security, ensuring scalability with virtual threads, creating GraalVM native images, Dockerizing the application, and leveraging observability for monitoring system resources.
- The article concludes by highlighting the production readiness of the AI application developed using Spring AI, Claude, and PostgresSQL, and suggests exploring Spring Initializr to begin developing similar applications.
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