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langchain_patterns

Implement RAG systems with LangChain4j for document question-answering.

43|11|Updated Dec 27, 2025
Search Tags:#ai development#llm#rag#knowledge base#embedding#langchain4j
One-click install
npx skills add https://github.com/vuralserhat86/antigravity-agentic-skills --skill langchain-patterns-vuralserhat86
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain_patterns
Source: https://github.com/vuralserhat86/antigravity-agentic-skills/tree/main/skills/langchain_patterns
Command: npx skills add https://github.com/vuralserhat86/antigravity-agentic-skills --skill langchain-patterns-vuralserhat86

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires dev.langchain4j:langchain4j-spring-boot-starter, dev.langchain4j:langchain4j-open-ai, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables the creation of advanced AI applications that can access and utilize external knowledge bases, significantly improving accuracy and reducing hallucinations.

Core Features & Use Cases

  • Retrieval-Augmented Generation (RAG): Implement systems that enhance LLMs with external context.
  • Document Ingestion Pipelines: Build processes to load, split, and embed documents.
  • Vector Search: Set up efficient retrieval of relevant information from embedded data.
  • Use Case: Develop a question-answering system over a collection of company policy documents, allowing employees to get accurate answers directly from the knowledge base.

Quick Start

Use the langchain_patterns skill to ingest the document located at '/path/to/your/document.txt' with metadata {'source': 'company_policies'}.

Frequently Asked Questions about langchain_patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a RAG system with LangChain4j in a Spring Boot application?▼

To build a RAG system with LangChain4j, you need Java Spring Boot and the langchain4j-spring-boot-starter dependency. This Skill provides the scripts to implement document ingestion pipelines, vector search strategies, and question-answering capabilities over your external knowledge base.

What is retrieval-augmented generation and how does it reduce LLM hallucinations?▼

Retrieval-augmented generation (RAG) enhances LLMs by providing external context from a knowledge base. By grounding responses in retrieved document embeddings, RAG significantly improves answer accuracy and reduces hallucinations compared to relying solely on the model's internal training data.

Do I need an OpenAI API key to use LangChain4j for document ingestion and vector search?▼

Yes, you need an OpenAI API key. The LangChain4j setup requires the langchain4j-open-ai dependency to access OpenAI's chat and embedding models for processing your documents and generating vector representations for the knowledge base.

Can I ingest company policy documents into an embedding store using LangChain4j?▼

Yes, you can ingest company policy documents using the provided document ingestion pipelines. The Skill allows you to load, split, and embed text files, storing them with metadata like 'source': 'company_policies' for efficient vector search retrieval.

What's the best way to set up vector search strategies for a knowledge base in Java?▼

The best way to set up vector search in Java is using LangChain4j's embedding store capabilities within a Spring Boot application. This Skill provides reference implementations to configure efficient retrieval of relevant information from your embedded document collections.

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