Can I use this with Spring Boot?

Absolutely. This skill is specifically optimized for Java developers using Spring Boot and the LangChain4j starter ecosystem.

How does it handle large documents?

It implements document segmentation strategies like recursive splitting to break large texts into manageable chunks for efficient embedding and context-window retrieval.

What is RAG in the context of LangChain4j?

RAG (Retrieval-Augmented Generation) is a technique that provides LLMs with specific, external context from your own documents to improve accuracy and reduce hallucinations.

Does this skill support vector databases?

Yes, it provides patterns for connecting to various embedding stores and performing vector similarity searches to retrieve relevant document segments.

LangChain4j RAG Patterns

Name: LangChain4j RAG Patterns
Author: giuseppe-trisciuoglio

bygiuseppe-trisciuoglio

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126

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Data Science & ML

Implements Retrieval-Augmented Generation (RAG) systems using LangChain4j to build AI applications with external knowledge access.

This skill provides production-ready implementation patterns for building Retrieval-Augmented Generation (RAG) systems within Java and Spring Boot environments using the LangChain4j framework. It streamlines the creation of complex document ingestion pipelines, embedding management, and vector search strategies, enabling developers to build AI assistants that can accurately reference internal company documentation, technical manuals, or vast knowledge bases. By providing structured patterns for semantic search, hybrid retrieval, and source attribution, this skill helps reduce AI hallucinations and ensures that generated responses are grounded in verified data.

Key Features

01Context-aware AI service configuration with source attribution

02Automated document ingestion and recursive splitting pipelines

03Seamless embedding store and vector search integration

04126 GitHub stars

05Hybrid search combining vector similarity with keyword matching

06Advanced metadata filtering for multi-tenant and domain-specific search

Use Cases

01Implementing automated customer support systems with access to technical documentation

02Building company-specific knowledge base assistants and AI wikis

03Creating semantic search engines for large-scale enterprise document repositories

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add giuseppe-trisciuoglio/developer-kit langchain4j-rag-implementation-patterns

For use in Claude.ai and ChatGPT

Download Skill

Key Features

01Context-aware AI service configuration with source attribution

02Automated document ingestion and recursive splitting pipelines

03Seamless embedding store and vector search integration

04126 GitHub stars

05Hybrid search combining vector similarity with keyword matching

06Advanced metadata filtering for multi-tenant and domain-specific search

Use Cases

01Implementing automated customer support systems with access to technical documentation

02Building company-specific knowledge base assistants and AI wikis

03Creating semantic search engines for large-scale enterprise document repositories

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add giuseppe-trisciuoglio/developer-kit langchain4j-rag-implementation-patterns

For use in Claude.ai and ChatGPT

Download Skill