LangChain Lab LangChain Lab

Declarative AI services

Test text analysis, summarization, and sentiment classification using annotated interfaces.
Compare LangChain4j's approach with our custom implementations.

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🚀 LangChain4j AI Operations
Declarative AI services with type-safe interfaces
Enter any text you want to process with AI
🦜 Understanding LangChain4j
🎯 Key Features
  • Type-Safe AI Services: Define interfaces with annotations
  • Prompt Templates: Automatic variable injection with {{placeholders}}
  • Multi-Provider Support: OpenAI, Anthropic, HuggingFace, local models
  • Spring Boot Integration: Auto-configuration and dependency injection
💡 Architecture Benefits
  • Declarative Approach: Focus on what, not how
  • Production Ready: Built-in retry, error handling, observability
  • Extensible: Custom tools, memory, and retrieval augmentation
  • Enterprise Features: RAG, agents, function calling
📚 Code Example:
interface AssistantService {
    @UserMessage("Analyze this text: {{text}}")
    fun analyzeText(text: String): String
}

val assistant = AiServices.builder(AssistantService::class.java)
    .chatLanguageModel(chatModel)
    .build()

val result = assistant.analyzeText("Your text here")