langchain4j-rag-implementation-patterns
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.
npx skills add majiayu000/claude-skill-registry --skill langchain4j-rag-implementation-patterns-giuseppe-trisciuogli-dev-giuseppe-trisciuogli-developer-kit --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 ## 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: **pom.xml**: ```xml <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-spring-boot-starter</artifactId> <version>1.8.0</version> </dependency> <dependency> <groupId>dev.langchain4j</groupId> <artifactId>langchain4j-open-ai</artifactId> <version>1.8.0</version> </dependency> ``` ### Setup Document Ingestion Configure document loading and processing with validation: **Validation Checkpoint**: After ingestion, verify embedding count matches segment count and test retrieval with a sample query. ``
- Overview
- When to Use This Skill
- 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?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill langchain4j-rag-implementation-patterns-giuseppe-trisciuogli-dev-giuseppe-trisciuogli-developer-kit --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.