langchain4j-ai-services-patterns

# LangChain4j AI Services Patterns This skill provides guidance for building declarative AI Services with LangChain4j using interface-based patterns, annotations for system and user messages, memory management, tools integration, and advanced AI application patterns that abstract away low-level LLM interactions. ## Overview LangChain4j AI Services define AI functionality using Java interfaces with annotations, providing type-safe, declarative AI with minimal boilerplate. ## When to Use Use this skill when: - Building declarative AI services with minimal boilerplate using Java interfaces - Creating type-safe conversational AI with memory management - Implementing AI agents with function/tool calling capabilities - Designing AI services returning structured data (enums, POJOs, lists) - Integrating RAG patterns declaratively ## Instructions Follow these steps to create declarative AI Services with LangChain4j: ### 1. Define AI Service Interface Create a Java interface with method signatures for AI interactions: ```java interface Assistant { String chat(String userMessage); } ``` ### 2. Add Annotations for System and User Messages Use `@SystemMessage` and `@UserMessage` annotations to define prompts: ```java interface CustomerSupportBot { @SystemMessage("You are a helpful customer support agent for TechCorp") String handleInquiry(String customerMessage); @UserMessage("Analyze sentiment: {{it}}") Sentiment analyzeSentiment(String feedback); } ``` ### 3. Create AI Service Instance Use `AiServices` builder or create to instantiate the service: ```java // Simple creation Assistant assistant = AiServices.create(Assistant.class, chatModel); // Or with builder for advanced configuration Assistant assistant = AiServices.builder(Assistant.class) .chatModel(chatModel) .build(); ``` ### 4. Configure Memory for Multi-turn Conversations Add memory management using `@MemoryId` for multi-user scenarios: ```java interface MultiUserAssistant { String chat(@MemoryId String userId, String userMessage); } Assistant assistant = AiServices.builder(MultiUserAssistant.class) .chatModel(model) .chatMemoryProvider(userId -> MessageWindowChatMemory.withMaxMessages(10)) .build(); ``` ### 5. Integrate Tools for Function Calling Register tools using `@Tool` annotation to enable AI function execution: ```java class Calculator { @Tool("Add two numbers") double add(double a, double b) { return a + b; } } interface MathGenius { String ask(String question); } MathGenius mathGenius = AiServices.builder(MathGenius.class) .chatModel(model) .tools(new Calculator()) .build(); ``` ### 6. Validate and Test Test AI services with concrete validation patterns: ```java // 1. Test with sample inputs String response = assistant.chat("Hello, how are you?"); assert response != null && !response.isEmpty(); // 2. Validate structured outputs with assertions Sentiment result = bot.analyzeSentiment("Great product!"); assert result == Sentiment.POSITIVE; // 3. Log tool calls with side effects for audit MathGenius math = AiServices.builder(MathGenius.class) .chatModel(model) .tools(new Calculator()) .build(); // 4. Test memory isolation between users String userA = assistant.chat("User A message", "session-a"); String userB = assistant.chat("User B message", "session-b"); assert !userA.equals(userB); // Verify memory isolation ``` ## Examples See [examples.md](references/examples.md) for comprehensive practical examples including: - Basic chat interfaces - Stateful assistants with memory - Multi-user scenarios - Structured output extraction - Tool calling and function execution - Streaming responses - Error handling - RAG integration - Production patterns ## API Reference Complete API documentation, annotations, interfaces, and configuration patterns are available in [references.md](references/references.md). ## Best Practices 1. **Use type-safe interfaces** instead of string-based prompts 2. **Implement proper memory management** with appropriate limits 3. **Design clear tool descriptions** with parameter documentation 4. **Handle errors gracefully** with custom error handlers 5. **Use structured output** for predictable responses 6. **Implement validation** for user inputs 7. **Monitor performance** for production deployments ## Dependencies ```xml <!-- Maven --> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j</artifactId> <version>1.8.0</version> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> <version>1.8.0</version> </dependency> ``` ```gradle // Gradle implementation 'dev.langchain4j:langchain4j:1.8.0' implementation 'dev.langchain4j:langchain4j-open-ai:1.8.0' ``` ## References - [LangChain4j Documentation](https://langchain4j.com/docs/) - [LangChain4j AI Services - API References](references/references.md) - [LangChain4j AI Services - Practical Examples](references/examples.md) ## Constraints and Warnings - AI Services rely on LLM responses which are non-deterministic; tests should account for variability. - Memory providers store conversation history; ensure proper cleanup for multi-user scenarios. - Tool execution can be expensive; implement rate limiting and timeout handling. - Never pass sensitive data (API keys, passwords) in system or user messages. - Large context windows can lead to high token costs; implement message pruning strategies. - Streaming responses require proper error handling for partial failures. - AI-generated outputs should be validated before use in production systems. - Be cautious with tools that have side effects; AI models may call them unexpectedly. - Token limits vary by model; ensure prompts and context fit within model constraints.