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SKILL.md
npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-spring-boot-integration
Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.
npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-spring-boot-integration
Integrate LangChain4j with Spring Boot using declarative AI Services, auto-configuration, and Spring Boot starters. Configure AI model beans, set up chat memory, implement RAG pipelines with Spring Data, and build production-ready AI applications.
Use this skill when:
@Bean annotationsLangChain4j Spring Boot integration provides declarative AI Services through Spring Boot starters, enabling automatic configuration of AI components based on properties. Combine Spring dependency injection with LangChain4j's AI capabilities using interface-based definitions with annotations.
Or using YAML:
After setup, verify the configuration:
LangChain4jSpringBootAutoConfiguration activationCustomerSupportAssistant in Spring contextassistant.handleInquiry("test") and verify a response is returnedProperty-Based Configuration: Configure AI models through application.properties for different providers.
Manual Bean Configuration: For advanced configurations, define beans manually:
Multiple Providers: Use explicit wiring when configuring multiple AI providers:
Basic AI Service: Create interfaces with @AiService annotation and define methods with message templates.
Streaming AI Service: Implement streaming responses using Project Reactor:
Chat Memory: Set up conversation memory with Spring context:
Embedding Stores: Configure embedding stores for RAG pipelines with Spring Data:
Document Ingestion: Use ContentInjector and DocumentSplitter for processing documents.
Content Retrieval: Configure EmbeddingStoreContentRetriever for knowledge augmentation.
Spring Component Tools: Define tools as Spring components:
For more examples (including RAG configurations, streaming assistants, and multi-provider setups), refer to references/examples.md.
For detailed API references and advanced configurations:
# application.properties
langchain4j.open-ai.chat-model.api-key=${OPENAI_API_KEY}
langchain4j.open-ai.chat-model.model-name=gpt-4o-mini
langchain4j.open-ai.chat-model.temperature=0.7
langchain4j.open-ai.chat-model.timeout=PT60S
langchain4j.open-ai.chat-model.max-tokens=1000langchain4j:
open-ai:
chat-model:
api-key: ${OPENAI_API_KEY}
model-name: gpt-4o-mini
temperature: 0.7
timeout: 60s
max-tokens: 1000import dev.langchain4j.service.spring.AiService;
@AiService
public interface CustomerSupportAssistant {
@SystemMessage("You are a helpful customer support agent for TechCorp.")
String handleInquiry(String customerMessage);
@UserMessage("Translate to {{language}}: {{text}}")
String translate(String text, String language);
}@SpringBootApplication
@ComponentScan(basePackages = {
"com.yourcompany",
"dev.langchain4j.service.spring"
})
public class Application {
public static void main(String[] args) {
SpringApplication.run(Application.class, args);
}
}@Service
public class CustomerService {
private final CustomerSupportAssistant assistant;
public CustomerService(CustomerSupportAssistant assistant) {
this.assistant = assistant;
}
public String processCustomerQuery(String query) {
return assistant.handleInquiry(query);
}
}@Configuration
public class AiConfig {
@Bean
public ChatModel chatModel(@Value("${OPENAI_API_KEY}") String apiKey) {
return OpenAiChatModel.builder()
.apiKey(apiKey)
.modelName("gpt-4o-mini")
.temperature(0.7)
.build();
}
}@AiService(wiringMode = WiringMode.EXPLICIT)
interface MultiProviderAssistant {
@AiServiceAnnotation
ChatModel openAiModel;
@AiServiceAnnotation
ChatModel azureModel;
}@AiService
public interface StreamingAssistant {
@SystemMessage("You are a helpful assistant.")
Flux<String> chatStream(String message);
}@AiService
public interface ConversationalAssistant {
@SystemMessage("You are a helpful assistant with memory.")
String chat(@MemoryId String userId, String message);
}@Configuration
public class RagConfig {
@Bean
public EmbeddingStore<TextSegment> embeddingStore() {
return PgVectorEmbeddingStore.builder()
.host("localhost")
.port(5432)
.database("vectordb")
.table("embeddings")
.dimension(1536)
.build();
}
@Bean
public EmbeddingModel embeddingModel() {
return OpenAiEmbeddingModel.withApiKey(System.getenv("OPENAI_API_KEY"));
}
}
@AiService
public interface RagAssistant {
String answer(@UserMessage("Question: {{question}}") String question);
}@Component
public class Calculator {
@Tool("Calculate the sum of two numbers")
public double add(double a, double b) {
return a + b;
}
}
@AiService
public interface MathAssistant {
String solve(String problem);
}@AiService
public interface ChatAssistant {
@SystemMessage("You are a helpful assistant.")
String chat(String message);
}@AiService
public interface ConversationalAssistant {
@SystemMessage("You are a helpful assistant with memory of conversations.")
String chat(@MemoryId String userId, String message);
}@Component
public class WeatherService {
@Tool("Get weather for a city")
public String getWeather(String city) {
return "Sunny, 22°C in " + city;
}
}
@AiService
public interface WeatherAssistant {
String getWeatherForCity(String city);
}