langchain4j

🥇Gold

LangChain4j is a powerful open-source Java library that streamlines the integration of large language models (LLMs) into Java applications. With a unified API, it simplifies implementing retrieval-augmented generation (RAG), tool calling, and agent functionalities, making it ideal for enterprise-level solutions.

10,5731,9360Updated 3w ago

Intermediate15 minutes to implementdevelopment

Saves ~240 min per use

Quick InstallView Source

claude install langchain4j/langchain4j

Works with:

ChatGPT

Overview

About This Skill

Use Cases

Integrate LLMs into enterprise Java applications for enhanced user interactions.

Implement retrieval-augmented generation (RAG) to improve data retrieval accuracy and efficiency.

Create intelligent agents that automate routine tasks and decision-making processes.

Develop chatbots that provide real-time customer support using natural language understanding.

Best For

GrowthRevOps

Setup & Installation

Quick Install

Terminal

claude install langchain4j/langchain4j

Alternative Install (Git Clone)

git clone https://github.com/langchain4j/langchain4j

Requirements

Claude Code or compatible AI agent
Works with: ChatGPT

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Using LangChain4j, how can I implement a retrieval-augmented generation (RAG) system for [COMPANY] in the [INDUSTRY] sector? Please provide a step-by-step guide on how to set this up with [DATA] as a reference.

Example Output

### Implementation of RAG System for TechCorp

To implement a retrieval-augmented generation (RAG) system for TechCorp in the technology sector, follow these steps:

1. **Data Preparation**: Gather relevant documents and data from your internal databases and external sources. For instance, use customer feedback, product manuals, and FAQs.
2. **Integration with LangChain4j**: Utilize the LangChain4j library to connect your Java application with the LLM. Use the unified API to streamline this process.
3. **Develop RAG Functionality**: Create a method that retrieves relevant documents based on user queries and feeds them into the LLM for context-aware responses.
4. **Testing**: Run tests with various user queries to ensure that the RAG system returns accurate and helpful information.

By following these steps, TechCorp can enhance its customer support with a sophisticated AI-driven solution that provides timely and relevant responses.