Does this skill support Spring Boot integration?

Yes, it includes specific configurations, dependency examples, and service patterns designed for seamless integration with Spring Boot applications.

Can I use this with local embedding models?

While the examples often use OpenAI, the patterns provided are modular and can be adapted for any embedding model or vector store supported by LangChain4j.

What is hybrid search in these patterns?

Hybrid search combines the semantic power of vector embeddings with traditional keyword-based full-text search to provide more relevant and comprehensive results.

What is RAG in the context of LangChain4j?

Retrieval-Augmented Generation (RAG) is a technique used to provide LLMs with specific, retrieved documents from your own data sources to improve answer accuracy and reduce hallucinations.

How does this handle large document collections?

The skill implements recursive document splitting and metadata strategies to ensure large documents are broken into manageable, context-rich chunks for efficient retrieval.

LangChain4j RAG Implementation Patterns

Name: LangChain4j RAG Implementation Patterns
Author: pur3v4d3r

bypur3v4d3r

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Ciencia de Datos y ML

Implements robust Retrieval-Augmented Generation (RAG) systems using the LangChain4j framework to enhance Java-based AI applications with external knowledge.

Acerca de

This skill provides standardized patterns for building end-to-end RAG pipelines within the LangChain4j ecosystem, enabling developers to create context-aware AI assistants that reference specific document collections. It covers everything from document ingestion and recursive splitting to advanced vector search strategies, hybrid search implementations, and hierarchical retrieval. Whether you are building a customer support bot or a complex technical documentation assistant, this skill offers production-ready Java patterns for grounding LLM responses in verified data sources while maintaining high performance and source attribution.

Características Principales

Vector store integration for semantic search and embedding management
Hierarchical retrieval strategies for complex multi-domain knowledge bases
RAG-enabled AI service configuration for Spring Boot applications
Hybrid search patterns combining vector similarity with keyword matching
End-to-end document ingestion and recursive text splitting pipelines
1 GitHub stars

Casos de Uso

Developing AI assistants requiring strict source attribution to reduce hallucinations
Creating customer support bots with real-time access to product manuals
Building question-answering systems over internal company wikis or documentation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add pur3v4d3r/pur3-pkb-codebase langchain4j-rag-implementation-patterns

For use in Claude.ai and ChatGPT

Download Skill

GitHub