langchain4j-spring-boot-integration_skill

Overview

This skill provides integration patterns for embedding LangChain4j into Spring Boot applications. It delivers auto-configuration, declarative AI service support, dependency injection, and examples for chat models, embeddings, RAG, and tool registration. Use it to quickly wire AI providers into Spring Boot microservices with production-ready patterns.

How this skill works

The starter auto-configures LangChain4j components from application properties and exposes interface-based AI services via an @AiService annotation. It wires chat models, embedding models, and embedding stores as Spring beans, supports multiple providers through explicit wiring, and registers tools and streaming endpoints automatically. Developers can override beans or supply manual @Configuration for advanced setups.

When to use it

• Embedding LangChain4j into an existing Spring Boot application
• Building AI-powered microservices with declarative AI services
• Configuring multiple AI providers (OpenAI, Azure, Ollama) in one app
• Implementing RAG systems with embedding stores and document retrieval
• Adding observability, retries, and production-ready error handling for AI components

Best practices

• Prefer property-based configuration and environment variables for secrets
• Use explicit wiring when multiple chat models are present
• Add metrics, health checks, and request logging for observability
• Implement retries, exponential backoff, and graceful fallbacks for provider failures
• Limit tokens and monitor usage to control cost; validate AI outputs before acting on them

Example use cases

• Declarative customer support assistant defined as an @AiService and injected into services
• RAG assistant using PgVector or Pinecone embedding store for knowledge-augmented answers
• Tool-enabled assistants where Spring components annotated with @Tool are auto-registered for reasoning
• Multi-provider setup where OpenAI handles embeddings and a different provider handles chat responses
• Streaming chat endpoints implemented with Reactor for low-latency conversational UIs