Spring Ai

What it does

The spring-ai skill enables AI agents to interact with and leverage Spring AI features. It provides functionalities like connecting to various vectorstores, integrating large language models (LLMs), and building conversational AI applications using Spring's framework. This allows for more structured and robust AI agent development compared to basic LLM interactions.

When to use it

• Building a chatbot with external knowledge: Integrate your existing data sources into a chatbot powered by an LLM, using Spring AI’s vectorstore connectors.
• Creating a question-answering system: Build a system that answers questions based on documents stored in a specific database or search index through Spring AI's retrieval capabilities.
• Developing conversational agents with memory: Utilize Spring AI to manage conversation history and context for more natural and engaging interactions.
• Integrating LLMs into existing Spring applications: Easily incorporate the power of LLMs into your current Java-based projects using Spring’s framework.

Key capabilities

• Vectorstore connectors
• LLM integration
• Conversational AI application building
• Spring Framework integration

Example prompts

• "Connect to my Pinecone vector database and retrieve relevant documents for this query."
• "Use the OpenAI LLM to answer this question based on the context provided from the document retrieval."
• "Build a conversational agent that can summarize customer support tickets using Spring AI's memory management."

Tips & gotchas

• Requires familiarity with Java and the Spring Framework for optimal integration.
• The specific vectorstores and LLMs supported will depend on available connectors within the skill.