Open WebUI

Open WebUI is a feature-rich self-hosted AI platform that supports fully offline operation. Through APIYI, you can integrate various mainstream large language models in Open WebUI.

Quick Deployment

Docker Quick Start

docker run -d -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:main

Docker Compose Deployment

version: '3.6'

services:
  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: open-webui
    ports:
      - "3000:8080"
    volumes:
      - open-webui:/app/backend/data
    environment:
      - OPENAI_API_BASE_URL=https://api.apiyi.com
      - OPENAI_API_KEY=Your APIYI key
    restart: unless-stopped

volumes:
  open-webui:

Configure APIYI

Method 1: Environment Variable Configuration

Set environment variables during deployment:

docker run -d -p 3000:8080 \
  -e OPENAI_API_BASE_URL=https://api.apiyi.com \
  -e OPENAI_API_KEY=Your APIYI key \
  -v open-webui:/app/backend/data \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

Method 2: Interface Configuration

Access Open WebUI admin interface
Go to Settings > Connections
Configure in OpenAI API section:
- API Base URL: https://api.apiyi.com/v1
- API Key: Enter your APIYI key
Click save configuration

Configuration Key Points

API Base URL needs to include the /v1 suffix
API Key can be obtained from APIYI Console
Recommended to use environment variable method for easier management and updates

Supported Models

Open WebUI supports 400+ mainstream AI models through APIYI.

See the models we recommend right now

Latest model recommendations, performance comparisons, and scenario-based guidance — covering writing, programming, fast responses, image generation, video generation, and more.

Why don’t we list specific models here?AI models are updated at a rapid pace. To make sure you always get accurate recommendations, we maintain the model list, performance data, and usage guidance in one place: the Model Recommendations page.

Core Features

RAG (Retrieval-Augmented Generation)

Open WebUI supports document upload and knowledge base features:

Document Upload
- Supports PDF, TXT, DOCX and other formats
- Automatic vectorization storage
- Supports multilingual documents
Knowledge Base Management
- Create specialized knowledge bases
- Document classification and tagging
- Intelligent retrieval matching

OpenAI Compatible API

Open WebUI provides a complete OpenAI compatible API:

# Chat completion
curl -X POST "http://localhost:3000/api/chat/completions" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer Your APIYI key" \
  -d '{
    "model": "gpt-4-turbo",
    "messages": [
      {"role": "user", "content": "Hello, world!"}
    ]
  }'

Tool Integration

Supports external tools and plugins:

Web search
Code execution
Image generation
Document processing

Advanced Configuration

Multi-Model Configuration

Configure multiple model sources in docker-compose.yml:

environment:
  - OPENAI_API_BASE_URL=https://api.apiyi.com
  - OPENAI_API_KEY=Your APIYI key
  - ENABLE_OPENAI_API=true
  - ENABLE_OLLAMA_API=false

User Permission Management

environment:
  - ENABLE_SIGNUP=false
  - DEFAULT_USER_ROLE=user
  - WEBHOOK_URL=Your webhook address

Data Persistence

volumes:
  - open-webui:/app/backend/data
  - ./uploads:/app/backend/data/uploads
  - ./vector_db:/app/backend/data/vector_db

API Integration Examples

Python Integration

import requests

# Open WebUI API endpoint
api_url = "http://localhost:3000/api/chat/completions"

# Request configuration
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer Your APIYI key"
}

data = {
    "model": "gpt-4-turbo",
    "messages": [
        {"role": "user", "content": "Explain the basic principles of quantum computing"}
    ],
    "stream": False
}

# Send request
response = requests.post(api_url, headers=headers, json=data)
result = response.json()
print(result["choices"][0]["message"]["content"])

JavaScript Integration

const apiUrl = 'http://localhost:3000/api/chat/completions';

const requestData = {
  model: 'gpt-4-turbo',
  messages: [
    { role: 'user', content: 'Write a simple Python function' }
  ]
};

fetch(apiUrl, {
  method: 'POST',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer Your APIYI key'
  },
  body: JSON.stringify(requestData)
})
.then(response => response.json())
.then(data => {
  console.log(data.choices[0].message.content);
});

Troubleshooting

Common Issues

Connection Failed

Check if API Base URL is correct: https://api.apiyi.com/v1
Verify API Key validity
Confirm firewall settings

Model Unavailable

Check account balance
Confirm model is within service scope
Check APIYI service status

Upload Failed

Check file format support
Confirm sufficient storage space
Verify file size limits

Log Debugging

Enable debug mode:

docker logs -f open-webui

View detailed logs:

environment:
  - LOG_LEVEL=DEBUG
  - WEBUI_DEBUG=true

Best Practices

Performance Optimization

Model Selection
- Pick a model that matches the complexity of the task
- See the Model Recommendations page for up-to-date guidance on choosing a model
Caching Strategy
- Enable conversation caching
- Set reasonable cache expiration time
- Regularly clean unused cache
Resource Management
- Monitor memory usage
- Set reasonable concurrent limits
- Regularly backup user data

Security Configuration

environment:
  - ENABLE_ADMIN_EXPORT=false
  - ENABLE_ADMIN_CHAT_ACCESS=false
  - JWT_EXPIRES_IN=7d

Monitoring Alerts

Integrate monitoring system:

environment:
  - ENABLE_WEBHOOKS=true
  - WEBHOOK_URL=https://your-monitoring-url

Need more help? Please check Open WebUI Official Documentation or visit APIYI Official Website.

Overview

Conversational AI

Programming

Community Contributions

AI Agent

Engineering

Translation

Quick Deployment

Docker Quick Start

Docker Compose Deployment

Configure APIYI

Method 1: Environment Variable Configuration

Method 2: Interface Configuration

Supported Models

See the models we recommend right now

Core Features

RAG (Retrieval-Augmented Generation)

OpenAI Compatible API

Tool Integration

Advanced Configuration

Multi-Model Configuration

User Permission Management

Data Persistence

API Integration Examples

Python Integration

JavaScript Integration

Troubleshooting

Common Issues

Log Debugging

Best Practices

Performance Optimization

Security Configuration

Monitoring Alerts

​Quick Deployment

​Docker Quick Start

​Docker Compose Deployment

​Configure APIYI

​Method 1: Environment Variable Configuration

​Method 2: Interface Configuration

​Supported Models

See the models we recommend right now

​Core Features

​RAG (Retrieval-Augmented Generation)

​OpenAI Compatible API

​Tool Integration

​Advanced Configuration

​Multi-Model Configuration

​User Permission Management

​Data Persistence

​API Integration Examples

​Python Integration

​JavaScript Integration

​Troubleshooting

​Common Issues

​Log Debugging

​Best Practices

​Performance Optimization

​Security Configuration

​Monitoring Alerts

Quick Deployment

Docker Quick Start

Docker Compose Deployment

Configure APIYI

Method 1: Environment Variable Configuration

Method 2: Interface Configuration

Supported Models

Core Features

RAG (Retrieval-Augmented Generation)

OpenAI Compatible API

Tool Integration

Advanced Configuration

Multi-Model Configuration

User Permission Management

Data Persistence

API Integration Examples

Python Integration

JavaScript Integration

Troubleshooting

Common Issues

Log Debugging

Best Practices

Performance Optimization

Security Configuration

Monitoring Alerts