Does this skill support local vector databases?

Yes, it provides guidance for integrating various vector stores including PGVector and others supported by the Spring AI ecosystem for document search.

Spring AI is an application framework that simplifies the integration of Artificial Intelligence models into Spring-based applications, providing a unified API for various AI providers.

How does this help with RAG (Retrieval-Augmented Generation)?

It provides standardized patterns for document embedding, storage in vector databases, and similarity searches to provide context-aware data to your LLM.

Can I use this for non-OpenAI models?

Absolutely, this skill includes configurations and implementation patterns for Anthropic, Azure OpenAI, and other providers compatible with the Spring AI interface.

Spring AI Assistant

Name: Spring AI Assistant
Author: partme-ai

bypartme-ai

•

Data Science & ML

Integrates advanced AI capabilities into Java applications using the official Spring AI framework.

About

The Spring AI skill provides developers with a comprehensive toolkit for building AI-powered applications within the Spring ecosystem. It facilitates seamless integration with leading Large Language Models (LLMs) like OpenAI, Anthropic, and Azure, while offering structured support for Prompt Templates, RAG (Retrieval-Augmented Generation) implementations via Vector Stores, and advanced features such as Function Calling and streaming responses. Whether you are bootstrapping a new AI project or adding intelligent features to an existing Spring Boot service, this skill offers the boilerplate code, configuration best practices, and implementation patterns needed to streamline development.

Key Features

Dynamic Prompt Template management and rendering
Declarative Function Calling and tool integration
RAG implementation patterns with Vector Store support
Multi-model integration for OpenAI, Anthropic, and Azure
37 GitHub stars
Reactive streaming response support for real-time UI

Use Cases

Automating data embedding and vector storage workflows
Integrating AI-driven chatbots into Spring Boot microservices
Building RAG-based knowledge retrieval systems in Java

About

Key Features

Dynamic Prompt Template management and rendering
Declarative Function Calling and tool integration
RAG implementation patterns with Vector Store support
Multi-model integration for OpenAI, Anthropic, and Azure
37 GitHub stars
Reactive streaming response support for real-time UI

Use Cases

Automating data embedding and vector storage workflows
Integrating AI-driven chatbots into Spring Boot microservices
Building RAG-based knowledge retrieval systems in Java