# langchain4j-rag-implementation-patterns

A coding-agent skill that scaffolds LangChain4j RAG pipelines: document ingestion, embedding stores, vector search and knowledge-base answers.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 3700 installs · ★ 363
- Author: giuseppe-trisciuoglio
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/giuseppe-trisciuoglio/developer-kit/tree/HEAD/plugins/developer-kit-java/skills/langchain4j-rag-implementation-patterns/SKILL.md
- Updated: 2026-09-10
- HTML: https://affpapa.org/en/nevibecode/skills/langchain4j-rag-implementation-patterns

## What it does

The skill hands your agent working code for RAG systems in Java with LangChain4j. It builds a Spring Boot setup with `langchain4j-spring-boot-starter` and `langchain4j-open-ai`, a document ingestion service, an embedding store and a content retriever. Validation is baked in: after ingestion the agent compares embedding count against segment count, then runs a sample query to confirm retrieval actually returns matches.

The skill is limited to `Read`, `Write` and `Bash`, so the agent writes files and runs builds without reaching for anything else. You need `OPENAI_API_KEY` in your environment — the `text-embedding-3-small` embedding model reads it from there. The retriever ships with `maxResults(5)` and `minScore(0.7)`. If your documents score lower on similarity, that threshold will return nothing, so lower it before blaming the pipeline.

Use it for chat-with-documents systems, Q&A over PDFs or text files, or semantic search across a document repository. Examples cover a basic pipeline, a multi-domain assistant with `@MemoryId`, and hierarchical RAG. Worth installing if you write Java and would rather not have the agent invent the project structure from scratch.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-rag-implementation-patterns -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/langchain4j-rag-implementation-patterns/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/langchain4j-rag-implementation-patterns/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-rag-implementation-patterns -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/langchain4j-rag-implementation-patterns/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-rag-implementation-patterns -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/langchain4j-rag-implementation-patterns/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install giuseppe-trisciuoglio/developer-kit langchain4j-rag-implementation-patterns
```

2. **Manually: put the files in place**
- File: `.github/skills/langchain4j-rag-implementation-patterns/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-rag-implementation-patterns -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/giuseppe-trisciuoglio/developer-kit --path plugins/developer-kit-java/skills/langchain4j-rag-implementation-patterns
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/langchain4j-rag-implementation-patterns/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add giuseppe-trisciuoglio/developer-kit --skill langchain4j-rag-implementation-patterns -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/giuseppe-trisciuoglio/developer-kit/tree/HEAD/plugins/developer-kit-java/skills/langchain4j-rag-implementation-patterns/SKILL.md

## When to use it
- Build a document ingestion pipeline with segment splitting
- Set up an embedding store and vector search over it
- Create an assistant that answers from a knowledge base and cites sources
- Verify that retrieval finds the right fragments after ingestion
- Add semantic search across a repository of PDFs and text files

## Example prompts
- Build a LangChain4j RAG service: load PDFs, split into segments, use InMemoryEmbeddingStore
- Configure a ContentRetriever with minScore 0.7 and wire it into an AI service that answers from documents
- Add a check that compares embedding count with segment count after ingest

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 3176e22bcf78a273.

BEGIN THIRD-PARTY FILE 3176e22bcf78a273
````text
---
name: langchain4j-rag-implementation-patterns
description: 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.
allowed-tools: Read, Write, Bash
---

# 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.

```java
@Configuration
````
END THIRD-PARTY FILE 3176e22bcf78a273

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