LangChain4j RAG Implementation Patterns

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:

<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.