langchain4j-ai-services-patterns_skill

Overview

This skill provides patterns and utilities to build declarative, type-safe AI Services in Java using LangChain4j. It uses interface-based definitions, annotations for system and user messages, memory management, and tool integration to remove boilerplate around prompt construction and response parsing. The result is concise, maintainable AI-powered features that integrate with existing Java applications.

How this skill works

Define plain Java interfaces whose methods represent AI interactions and annotate them with system/user message templates, memory identifiers, or tool metadata. A builder generates implementations that orchestrate chat models, manage per-user memory windows, call registered tools (functions), and parse structured outputs into enums, POJOs, or collections. The patterns support streaming responses, RAG integration, error handlers, and validation hooks to make production-ready services.

When to use it

• When you want type-safe AI endpoints with minimal prompt boilerplate in Java
• Building conversational agents that require per-user or windowed memory
• Integrating LLM-driven function calling or external tools from Java code
• Implementing structured outputs (POJOs, enums, lists) returned directly from the model
• Creating multi-agent systems or persona-specific services via annotations

Best practices

• Model AI interactions as interfaces to keep behavior explicit and testable
• Limit memory windows and prune history to control token costs and privacy exposure
• Document tool parameters clearly and add rate limiting/timeouts for side-effecting tools
• Validate and sanitize AI-generated structured data before using it in business logic
• Add custom error handlers and retries for streaming or external tool failures

Example use cases

• Customer support assistant defined as an interface with @SystemMessage and @UserMessage templates
• Multi-tenant chat service using @MemoryId to provide isolated conversation context per user
• Math or utility agent that exposes Java methods as @Tool functions callable by the model
• RAG-powered answer service that integrates a vector store and returns structured POJO results
• Streaming assistant that emits partial responses to a reactive client while handling partial failures