Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.
Provides Retrieval-Augmented Generation (RAG) implementation patterns with LangChain4j for Java. Generates document ingestion pipelines, embedding stores, vector search, and semantic search capabilities. Use when building chat-with-documents systems, document Q&A over PDFs or text files, AI assistants with knowledge bases, semantic search over document repositories, or knowledge-enhanced AI applications with source attribution.
allowed-tools
Read, Write, Bash
LangChain4j RAG Implementation Patterns
Overview
Implements RAG systems with LangChain4j: document ingestion pipelines, embedding stores, and vector search for chat-with-documents and knowledge-enhanced AI applications.
When to Use This Skill
Building chat-with-documents systems or document Q&A over PDFs, text files, or web pages
Creating AI assistants with access to company knowledge bases or external sources
Implementing semantic search or hybrid search over document repositories
Building domain-specific AI with curated knowledge and source attribution
Instructions
Initialize RAG Project
Create a new Spring Boot project with required dependencies:
interfaceKnowledgeAssistant {
@SystemMessage("""
You are a knowledgeable assistant with access to a comprehensive knowledge base.
When answering questions:
1. Use the provided context from the knowledge base
2. If information is not in the context, clearly state this
3. Provide accurate, helpful responses
4. When possible, reference specific sources
5. If the context is insufficient, ask for clarification
""")
String answerQuestion(String question);
}
@Service@RequiredArgsConstructorpublicclassKnowledgeService {
privatefinal KnowledgeAssistant assistant;
publicKnowledgeService(ChatModel chatModel, ContentRetriever contentRetriever) {
this.assistant = AiServices.builder(KnowledgeAssistant.class)
.chatModel(chatModel)
.contentRetriever(contentRetriever)
.build();
}
public String answerQuestion(String question) {
return assistant.answerQuestion(question);
}
}
Examples
Basic Document Processing
publicclassBasicRAGExample {
publicstaticvoidmain(String[] args) {
varembeddingStore=newInMemoryEmbeddingStore<TextSegment>();
varembeddingModel= OpenAiEmbeddingModel.builder()
.apiKey(System.getenv("OPENAI_API_KEY"))
.modelName("text-embedding-3-small")
.build();
varingestor= EmbeddingStoreIngestor.builder()
.embeddingModel(embeddingModel)
.embeddingStore(embeddingStore)
.build();
ingestor.ingest(Document.from("Spring Boot is a framework for building Java applications with minimal configuration."));
varretriever= EmbeddingStoreContentRetriever.builder()
.embeddingStore(embeddingStore)
.embeddingModel(embeddingModel)
.build();
}
}
Multi-Domain Assistant
interfaceMultiDomainAssistant {
@SystemMessage("""
You are an expert assistant with access to multiple knowledge domains:
- Technical documentation
- Company policies
- Product information
- Customer support guides
Tailor your response based on the type of question and available context.
Always indicate which domain the information comes from.
""")
String answerQuestion(@MemoryId String userId, String question);
}
