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Spring AI is an official Spring Framework project that applies familiar Spring design patterns to Artificial Intelligence and Machine Learning development. It provides a portable API that abstracts interactions with various AI providers, allowing developers to switch between models with minimal code changes. Key Architectural Pillars
https://livebook.manning.com/book/spring-ai-in-action This interactive version includes the ability to run and experiment with code snippets directly in your browser.
: Interacting with various LLMs like OpenAI, Anthropic, and DeepSeek.
| Repository | Description | Key Technologies | | :--------- | :---------- | :--------------- | | | A clean, minimal RAG implementation using Spring AI, OpenAI, vector store, and Tika document reader. Great for understanding core RAG concepts. | OpenAI, Tika, Vector Store | | rajadilipkolli/ai-playground | Comprehensive comparison playground between LangChain4j and Spring AI. Includes multiple embedding stores (Neo4j, OpenSearch, PGVector) and RAG implementations with various LLMs (Ollama, OpenAI). Excellent for framework comparison. | LangChain4j, PGVector, Neo4j, OpenSearch, Redis | | aDudko/spring-ai-with-openai-and-milvus-in-action | Recommendation service based on user documents. Uses Milvus vector database and includes Docker Compose infrastructure. Full integration with MinIO and Swagger API documentation. | Milvus, OpenAI, MinIO, Docker Compose, Tika | | richthibault/springaidemo | Spring Boot application demonstrating integration with Azure OpenAI ChatGPT, Perplexity.ai, and Pinecone for RAG. Includes Thymeleaf templates and an Ajax-based chat interface. | Azure OpenAI, Perplexity.ai, Pinecone | spring ai in action pdf github link
The most reliable source of truth is the official Spring AI documentation and its corresponding GitHub repository.
The book assumes you are already comfortable with Spring Boot but does not require a background in data science or artificial intelligence. It focuses on keeping your development stack entirely within the Java/JVM ecosystem, avoiding the need to switch to Python or other languages commonly associated with AI.
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Navigate to the spring-ai-examples directory (clone separately from the experimental repo). Run the simple-chat example:
Mastering Spring AI: Your Ultimate Guide and Repository Resource
Spring AI natively supports RAG pipelines. The framework automates the process of ingestion (reading PDFs, Markdown, or JSON), ETL processing (splitting text into manageable tokens), creating embeddings, storing them in a vector database, and retrieving relevant context during a chat session to minimize hallucinations. Spring AI in Action: A Practical Code Example : Interacting with various LLMs like OpenAI, Anthropic,
You can build sophisticated AI apps using familiar Spring Boot starters.
git clone https://github.com/spring-projects/spring-ai.git cd spring-ai
@GetMapping("/ai/generate")public Map generate(@RequestParam(value = "message", defaultValue = "Tell me a joke") String message) return Map.of("generation", chatClient.prompt().user(message).call().content());
