langchain4j-rag-implementation-patterns
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.
npx skills add majiayu000/claude-skill-registry --skill langchain4j-rag-implementation-patterns-giuseppe-trisciuogli-dev --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LangChain4j RAG Implementation Patterns ## When to Use This Skill Use this skill when: - Building knowledge-based AI applications requiring external document access - Implementing question-answering systems over large document collections - Creating AI assistants with access to company knowledge bases - Building semantic search capabilities for document repositories - Implementing chat systems that reference specific information sources - Creating AI applications requiring source attribution - Building domain-specific AI systems with curated knowledge - Implementing hybrid search combining vector similarity with traditional search - Creating AI applications requiring real-time document updates - Building multi-modal RAG systems with text, images, and other content types ## Overview Implement complete Retrieval-Augmented Generation (RAG) systems with LangChain4j. RAG enhances language models by providing relevant context from external knowledge sources, improving accuracy and reducing hallucinations. ## Instructions ### Initialize RAG Project Create a new Spring Boot project with required dependencies: **pom.xml**: ```xml <dependency> <groupId>dev.langchain4j</groupId> <artifactId
- When to Use This Skill
- Overview
- Instructions
- Initialize RAG Project
- Setup Document Ingestion
- Configure Content Retrieval
- Create RAG-Enabled AI Service
- Examples
- Basic Document Processing
- Multi-Domain Assistant
- Hierarchical RAG
- Best Practices
- Document Segmentation
- Metadata Strategy
What does the langchain4j-rag-implementation-patterns skill do?
Implement Retrieval-Augmented Generation (RAG) systems with LangChain4j. Build document ingestion pipelines, embedding stores, vector search strategies, and knowledge-enhanced AI applications. Use when creating question-answering systems over document collections or AI assistants with external knowledge bases.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill langchain4j-rag-implementation-patterns-giuseppe-trisciuogli-dev --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.