One of the most powerful applications of Spring AI is RAG. RAG allows you to augment an AI model's knowledge with your own private data. This is achieved by:
Embedding Generation: Converting data into numerical vectors using an Embedding Model. Storage: Saving these vectors in a Vector Database. spring ai in action pdf github link
@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());}} One of the most powerful applications of Spring AI is RAG
Document Ingestion: Loading your data (PDFs, text files, database records). Storage: Saving these vectors in a Vector Database
Vector Database Integration: Seamlessly connect with popular vector databases like Pinecone, Milvus, Redis, and Weaviate for Retrieval-Augmented Generation (RAG).
Spring AI is a project designed to streamline the integration of AI functionalities into Spring-based applications. It provides a high-level API that abstracts the complexities of interacting with various AI model providers, such as OpenAI, Azure OpenAI, Google Vertex AI, and Amazon Bedrock. Drawing inspiration from established Spring patterns like the Strategy pattern and the Template pattern, Spring AI offers a familiar and consistent development experience. Key Features of Spring AI
Official Spring AI GitHub Repository: github.comThis repository contains the source code, samples, and the latest issues being tracked by the development team.