灵境万象 - 企业智能知识库 🌟

基于 Dify 的企业定制版本 | Enterprise Customization Based on Dify

灵境万象 (Lingjing Wanxiang) - 智能知识，触手可及

这是专为企业定制的 Dify 版本，具有完整的品牌定制、本地化支持和企业级功能。

✨ 定制特性

• 🎨 精美 UI - 紫蓝渐变主题，现代化界面设计
• 🏢 企业定制 - 完整的品牌定制方案
• 🤖 本地大模型 - 支持 Ollama，数据完全自主可控
• 📚 智能知识库 - 强大的 RAG 检索能力
• 🔧 高度可定制 - 配置化设计，易于二次开发
• 🐳 容器化部署 - 一键启动，开箱即用
• 🔍 智能端口检测 - 自动检测并避免端口冲突，支持多项目并存
• 🚀 一键部署脚本 - Docker 和 K8s 双模式，自动化构建和部署

🚀 快速开始

Docker 本地部署

# 1. 启动 Ollama (本地模型)
ollama serve
ollama pull qwen:latest

# 2. 一键启动灵境万象（自动检测端口冲突）
./start-lingjing.sh

# 3. 访问系统
# 系统会自动检测端口冲突并分配可用端口
# 查看实际端口: cat .env.ports
# 默认访问地址:
#   Web: http://localhost:3002
#   API: http://localhost:5001/docs
#   Nginx: http://localhost:8089
#   Qdrant: http://localhost:6333/dashboard

端口配置说明

灵境万象提供智能端口检测功能，自动避免端口冲突：

# 手动检测并分配端口
./check-ports.sh

# 检测结果会保存到 .env.ports 文件
# Docker Compose 会自动使用该配置文件

默认端口：

• Web 前端: 3002
• API 后端: 5001
• PostgreSQL: 5433
• Redis: 6380
• Qdrant: 6333/6334
• Nginx HTTP: 8089
• Nginx HTTPS: 8445

💡 如遇端口冲突，系统会自动分配下一个可用端口，并显示占用端口的进程信息

Kubernetes 生产部署

方式一：一键部署（推荐）

cd k8s

# 完整部署（包含镜像构建、推送和K8s部署）
./deploy-k3s.sh

# 或使用独立脚本
./build-push-deploy.sh    # 构建、推送到Harbor并部署到K8s

# 查看部署状态
./deploy-k3s.sh status

# 查看日志
./deploy-k3s.sh logs api
./deploy-k3s.sh logs web

# 清理部署
./deploy-k3s.sh cleanup

访问地址：

• HTTP: http://zhishiku.omniverseai.net/
• HTTPS: https://zhishiku.omniverseai.net/ (自签名证书)

方式二：手动部署

# 1. 配置 Kubernetes 集群
cd k8s

# 2. 创建命名空间和密钥
kubectl create namespace zhishiku
kubectl apply -f secrets.yaml
kubectl apply -f configmap.yaml

# 3. 部署数据库和存储服务
kubectl apply -f postgres.yaml
kubectl apply -f redis.yaml
kubectl apply -f qdrant.yaml

# 4. 部署应用服务
kubectl apply -f api.yaml
kubectl apply -f worker.yaml
kubectl apply -f web.yaml
kubectl apply -f plugin-daemon.yaml

# 5. 部署 Ingress
kubectl apply -f ingress.yaml

# 6. 查看部署状态
kubectl get pods -n zhishiku
kubectl get ingress -n zhishiku

注意:

• 插件功能需要 plugin-daemon 服务正常运行，确保 PLUGIN_DAEMON_KEY 在 secrets 中正确配置
• 镜像存储在本地 Harbor: harbor.omniverseai.net:8080/ljwx-dify
• 详细的 K8s 部署文档请参考 k8s/README.md

📖 文档

• 📘 详细部署指南
• 🔧 定制总结文档
• 💡 使用说明

---

关于 Dify

📌 Introducing Dify Workflow File Upload: Recreate Google NotebookLM Podcast

Dify Cloud · Self-hosting · Documentation · Dify edition overview

Dify is an open-source platform for developing LLM applications. Its intuitive interface combines agentic AI workflows, RAG pipelines, agent capabilities, model management, observability features, and more—allowing you to quickly move from prototype to production.

Quick start

Before installing Dify, make sure your machine meets the following minimum system requirements:

• CPU >= 2 Core
• RAM >= 4 GiB

The easiest way to start the Dify server is through Docker Compose. Before running Dify with the following commands, make sure that Docker and Docker Compose are installed on your machine:

cd dify
cd docker
cp .env.example .env
docker compose up -d

After running, you can access the Dify dashboard in your browser at http://localhost/install and start the initialization process.

Seeking help

Please refer to our FAQ if you encounter problems setting up Dify. Reach out to the community and us if you are still having issues.

If you'd like to contribute to Dify or do additional development, refer to our guide to deploying from source code

Key features

1. Workflow: Build and test powerful AI workflows on a visual canvas, leveraging all the following features and beyond.

2. Comprehensive model support: Seamless integration with hundreds of proprietary / open-source LLMs from dozens of inference providers and self-hosted solutions, covering GPT, Mistral, Llama3, and any OpenAI API-compatible models. A full list of supported model providers can be found here.

3. Prompt IDE: Intuitive interface for crafting prompts, comparing model performance, and adding additional features such as text-to-speech to a chat-based app.

4. RAG Pipeline: Extensive RAG capabilities that cover everything from document ingestion to retrieval, with out-of-box support for text extraction from PDFs, PPTs, and other common document formats.

5. Agent capabilities: You can define agents based on LLM Function Calling or ReAct, and add pre-built or custom tools for the agent. Dify provides 50+ built-in tools for AI agents, such as Google Search, DALL·E, Stable Diffusion and WolframAlpha.

6. LLMOps: Monitor and analyze application logs and performance over time. You could continuously improve prompts, datasets, and models based on production data and annotations.

7. Backend-as-a-Service: All of Dify's offerings come with corresponding APIs, so you could effortlessly integrate Dify into your own business logic.

Using Dify

• Cloud
  We host a Dify Cloud service for anyone to try with zero setup. It provides all the capabilities of the self-deployed version, and includes 200 free GPT-4 calls in the sandbox plan.

• Self-hosting Dify Community Edition
  Quickly get Dify running in your environment with this starter guide. Use our documentation for further references and more in-depth instructions.

• Dify for enterprise / organizations
  We provide additional enterprise-centric features. Send us an email to discuss your enterprise needs.
  

  For startups and small businesses using AWS, check out Dify Premium on AWS Marketplace and deploy it to your own AWS VPC with one click. It's an affordable AMI offering with the option to create apps with custom logo and branding.

Staying ahead

Star Dify on GitHub and be instantly notified of new releases.

Advanced Setup

Custom configurations

If you need to customize the configuration, please refer to the comments in our .env.example file and update the corresponding values in your .env file. Additionally, you might need to make adjustments to the docker-compose.yaml file itself, such as changing image versions, port mappings, or volume mounts, based on your specific deployment environment and requirements. After making any changes, please re-run docker-compose up -d. You can find the full list of available environment variables here.

Customizing Suggested Questions

You can now customize the "Suggested Questions After Answer" feature to better fit your use case. For example, to generate longer, more technical questions:

# In your .env file
SUGGESTED_QUESTIONS_PROMPT='Please help me predict the five most likely technical follow-up questions a developer would ask. Focus on implementation details, best practices, and architecture considerations. Keep each question between 40-60 characters. Output must be JSON array: ["question1","question2","question3","question4","question5"]'
SUGGESTED_QUESTIONS_MAX_TOKENS=512
SUGGESTED_QUESTIONS_TEMPERATURE=0.3

See the Suggested Questions Configuration Guide for detailed examples and usage instructions.

Metrics Monitoring with Grafana

Import the dashboard to Grafana, using Dify's PostgreSQL database as data source, to monitor metrics in granularity of apps, tenants, messages, and more.

• Grafana Dashboard by @bowenliang123

Deployment with Kubernetes

If you'd like to configure a highly-available setup, there are community-contributed Helm Charts and YAML files which allow Dify to be deployed on Kubernetes.

• Helm Chart by @LeoQuote
• Helm Chart by @BorisPolonsky
• Helm Chart by @magicsong
• YAML file by @Winson-030
• YAML file by @wyy-holding
• 🚀 NEW! YAML files (Supports Dify v1.6.0) by @Zhoneym

Using Terraform for Deployment

Deploy Dify to Cloud Platform with a single click using terraform

Azure Global

• Azure Terraform by @nikawang

Google Cloud

• Google Cloud Terraform by @sotazum

Using AWS CDK for Deployment

Deploy Dify to AWS with CDK

AWS

• AWS CDK by @KevinZhao (EKS based)
• AWS CDK by @tmokmss (ECS based)

Using Alibaba Cloud Computing Nest

Quickly deploy Dify to Alibaba cloud with Alibaba Cloud Computing Nest

Using Alibaba Cloud Data Management

One-Click deploy Dify to Alibaba Cloud with Alibaba Cloud Data Management

Deploy to AKS with Azure Devops Pipeline

One-Click deploy Dify to AKS with Azure Devops Pipeline Helm Chart by @LeoZhang

Contributing

For those who'd like to contribute code, see our Contribution Guide. At the same time, please consider supporting Dify by sharing it on social media and at events and conferences.

We are looking for contributors to help translate Dify into languages other than Mandarin or English. If you are interested in helping, please see the i18n README for more information, and leave us a comment in the global-users channel of our Discord Community Server.

Community & contact

• GitHub Discussion. Best for: sharing feedback and asking questions.
• GitHub Issues. Best for: bugs you encounter using Dify.AI, and feature proposals. See our Contribution Guide.
• Discord. Best for: sharing your applications and hanging out with the community.
• X(Twitter). Best for: sharing your applications and hanging out with the community.

Contributors

Star history

Security disclosure

To protect your privacy, please avoid posting security issues on GitHub. Instead, report issues to [email protected], and our team will respond with detailed answer.

License

This repository is licensed under the Dify Open Source License, based on Apache 2.0 with additional conditions.