Spring AI Is All You Need • Christian Tzolov • GOTO 2024

Spring AI provides portable abstractions across different AI model providers (OpenAI, Azure, Anthropic, etc.) and vector stores, making it easier to switch implementations

Two key integration patterns for using generative AI in applications:

• Prompt engineering/system messages to align responses with specific goals
• Retrieval Augmented Generation (RAG) to provide domain-specific context

Models have important limitations to consider:

• They are stateless by default
• Limited context window size for input
• No knowledge of company-specific data unless explicitly provided
• Can hallucinate/generate incorrect information

Function calling allows models to interact with backend systems:

• Models can be taught to call specific functions when needed
• Spring AI handles parameter mapping and response formatting
• Enables integration with existing business logic

Vector stores and embedding models enable semantic search:

• Convert documents into vector representations
• Find relevant content based on semantic similarity
• Support for different implementations (PGVector, Chroma, etc.)

Spring AI includes support for:

• Multiple chat models and embedding models
• Document loading and splitting
• Vector store implementations
• Chat memory management
• Structured output formatting
• Observability and metrics

Key benefits of Spring framework integration:

• Familiar Spring programming model
• Easy dependency configuration
• Enterprise integration patterns
• Production-ready features

Recommended approach for implementation:

• Start with basic prompt engineering
• Add RAG for domain knowledge
• Implement function calling for backend integration
• Consider output formatting needs
• Add evaluation and quality controls