How to Build and Deploy a Dify LLM Application Platform on CentOS

Detailed Guide to Building a Dify LLM Application Development Platform

1. Introduction

What is Dify?

Dify is an open‑source large language model (LLM) application development platform that simplifies and accelerates the creation and deployment of generative AI applications. It combines Backend‑as‑a‑Service (BaaS) and LLMOps concepts to provide a complete AI development environment.

Dify Application Scenarios

1. Content Creation and Generation

Automated Writing: Generate articles, reports, press releases, etc., suitable for media, advertising, and marketing industries.

Creative Inspiration: Provide ideas and material for designers, writers, and artists.

2. Intelligent Customer Service and Dialogue Systems

Chatbots: Build highly intelligent chatbots for customer service, online education, entertainment, and more.

Virtual Assistants: Offer personal or enterprise assistants for scheduling, email replies, information queries, etc.

3. Data Analysis and Prediction

Data Interpretation: Analyze complex datasets and generate understandable reports and insights.

Predictive Analytics: Build models for market trend forecasting, sales prediction, and other use cases.

4. Business Automation and Process Optimization

Automated Tasks: Automate repetitive tasks such as data entry, email sending, and report generation.

Process Optimization: Analyze workflows and suggest improvements to reduce costs and increase efficiency.

5. Personalization and Marketing

User Profiling: Analyze user behavior to build profiles for personalized recommendations.

Content Recommendation: Recommend relevant content, products, or services based on interests and history.

2. Core Features and Characteristics

Core Functions

Multi‑Model Integration and Support:

Dify supports hundreds of proprietary and open‑source LLMs, including GPT, Claude, Llama, and any model compatible with the OpenAI API.

Users can select appropriate models per project and switch or update them easily.

Prompt Orchestration and IDE:

Provides an intuitive interface for defining, adjusting, and optimizing prompts to improve AI interaction.

Built‑in Prompt IDE supports prompt creation, model performance comparison, and adding features like text‑to‑speech.

RAG Pipeline and Retrieval‑Augmented Generation:

Includes a high‑quality RAG engine that extracts and retrieves information from documents to enhance AI accuracy.

Users can define agents based on LLM function calls or ReAct, adding pre‑built or custom tools for complex tasks.

Agent Framework:

Allows creation of conversational agents that decompose tasks, reason, and invoke tools.

Provides over 50 built‑in tools such as Google Search, DALL·E, Stable Diffusion, and WolframAlpha.

Workflow Management and Orchestration:

Supports defining complex workflows with sequential execution, conditional logic, and parallel processing.

Helps manage and optimize AI application performance.

Model Management and Optimization:

Enables model upload, configuration, training, and fine‑tuning.

Enterprise LLMOps support allows continuous iteration and optimization.

Key Characteristics

Low‑Code/No‑Code Development:

Enables non‑technical users to participate in AI application definition and data operations.

Modular design and visual interface allow rapid building and deployment.

Cloud Service and Self‑Hosted Options:

Offers a cloud service for immediate use without self‑deployment.

Supports self‑hosting via Docker Compose or local source deployment.

Powerful Integration and Deployment:

Zero‑code embedding into third‑party systems for instant intelligent Q&A.

Rich API and SDK for seamless integration into custom applications.

Data Security and Privacy:

RAG pipeline ensures private data protection.

Advanced encryption and security protocols prevent data leakage.

Usability and Scalability:

Clean, intuitive UI makes onboarding easy.

Extensible plugin system meets growing user needs.

3. Preparation Work

Operating System

We use CentOS 7.9 for the tutorial, but any Linux distribution or Windows can be used.

Configuration requirements:

CPU >= 2 cores

RAM >= 4 GiB

Image Preparation

Dify installation relies on Docker images. Pulling them online can be slow, so it is recommended to prepare offline images in advance.

Install Docker

Add Alibaba mirror source:

`curl -o /etc/yum.repos.d/CentOS-Base.repo http://mirrors.aliyun.com/repo/Centos-7.repo`

Install dependencies:

`yum install -y yum-utils device-mapper-persistent-data lvm2`

Configure Docker yum repository:

`yum-config-manager --add-repo http://mirrors.aliyun.com/docker-ce/linux/centos/docker-ce.repo`

Install Docker: `yum install docker-ce -y` Configure Docker registry mirrors (add the following JSON to /etc/docker/daemon.json):

{
  "registry-mirrors": [
    "https://docker.m.daocloud.io",
    "https://noohub.ru",
    "https://huecker.io",
    "https://dockerhub.timeweb.cloud",
    "https://0c105db5188026850f80c001def654a0.mirror.swr.myhuaweicloud.com",
    "https://5tqw56kt.mirror.aliyuncs.com",
    "https://docker.1panel.live",
    "http://mirrors.ustc.edu.cn/",
    "http://mirror.azure.cn/",
    "https://hub.rat.dev/",
    "https://docker.ckyl.me/",
    "https://docker.chenby.cn",
    "https://docker.hpcloud.cloud",
    "https://docker.m.daocloud.io"
  ]
}

Start Docker:

`systemctl start docker`

Install Docker‑Compose

Download the Docker‑Compose binary (e.g., Release v2.33.1 from GitHub) and rename it: `mv docker-compose-linux-x86_64 docker-compose` Add execution permission and move it to /usr/bin:

`chmod +x docker-compose`
`mv docker-compose /usr/bin/`

Verify the installation:

`docker-compose --version`

4. Install Dify

Installation Steps

Clone the repository:

`git clone https://github.com/langgenius/dify.git`

Enter the Docker directory: `cd dify/docker/` Copy the example environment file: `cp .env.example .env` Start the services: `docker-compose up -d` Check container status:

`docker ps`

Access Test

Open a browser and navigate to the server address; if the Dify UI loads, the installation succeeded.