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 spring ai in action pdf github link
First, you need to add the necessary dependencies to your pom.xml: org.springframework.aispring-ai-openai-spring-boot-starter Configuration Configure your OpenAI API key in application.properties: spring.ai.openai.api-key=${OPENAI_API_KEY} Implementing the Service Now, create a simple controller to handle chat requests: @RestControllerpublic class ChatController { private final ChatClient chatClient; such as OpenAI
Spring AI provides the VectorStore interface and various DocumentReader implementations to make this process straightforward. Resources: Spring AI in Action PDF and GitHub Link Google Vertex AI
The most reliable source of truth is the official Spring AI documentation and its corresponding GitHub repository.
Embedding Generation: Converting data into numerical vectors using an Embedding Model. Storage: Saving these vectors in a Vector Database.
Spring AI in Action: A Deep Dive into Integrating Generative AI with Java