Lightweight AI Chat Interface with Plugin System | 轻量 AI 聊天界面与插件系统
Fast, Lightweight, Extensible | 快速、轻量、可扩展
Many developers, AI hardware vendors, and users just need a simple, lightweight application that can quickly demonstrate their model capabilities. That's why we created ChatRaw - a minimal, ready-to-use chat interface that deploys in seconds. No complex configuration, no heavy dependencies—just a clean, fast AI chat experience.
Fast, Lightweight, Convenient
- Ultra Lightweight - ~60MB memory footprint, optimized binary embedding storage
- Instant Startup - Ready in seconds with connection pooling for fast API calls
- Custom Branding - Freely customize name, logo, and theme
- Universal API Support - Works with any OpenAI-compatible API (Ollama, vLLM, LocalAI, LM Studio, etc.)
- Document Parsing - Native PDF, DOCX, TXT, MD parsing as chat context
- Vision AI Ready - Multimodal image understanding with auto-compression
- Thinking Mode - Support for reasoning models (DeepSeek-R1, Qwen, o1, etc.)
- Responsive Design - Optimized for desktop, tablet, and mobile with touch-friendly UI
- One-Click Copy - Copy AI responses instantly (text only, no metadata)
- Bilingual UI - English & Chinese with one-click switch
- Zero Registration - Settings auto-saved locally
- One-Click Deploy - Docker deployment in 30 seconds
Multi-Model Configuration
- Supports unlimited chat, embedding, and reranking models
- Automatic API key rotation to bypass rate limits
- Built-in endpoint validation and testing
Thinking Mode
- Deep reasoning for supported models
- Collapsible thought process display
Custom Branding
- Customize interface: name, Logo, subtitle, avatar, and theme colors
Document & Image Support
- Upload documents (PDF, DOCX, TXT, MD) as chat context. AI can read and reference document content
- Attach images for multimodal understanding. Automatically compressed to WebP format (~2MB)
Flexible, Free, Community-Driven
ChatRaw features a complete plugin system to extend functionality:
- Lightweight RAG Demo — Knowledge base retrieval
- Bocha Search — Web / AI search
- Tavily Search — Web search with AI answers
- Excel Parser — Parse .xlsx/.xls for chat
- CSV Parser — Parse CSV/TSV for chat
- Enhanced Web Parsing — Parse web pages (browser / Firecrawl / Jina)
- Multi-Model Manager — Manage and switch models
- Markdown Renderer Plus — Math (KaTeX), Mermaid, code copy, offline
- Toolbar Extension Demo — Demo plugin showcasing UI Extension API
Plugins can add custom buttons to the input toolbar with active/loading states, overflow menu for many buttons, and fullscreen modal for complex interactions.
- Complete development documentation
- Rich hook system (including new UI Extension API)
- Custom settings UI
- One-click packaging and distribution
Plugin Development Guide: Plugins/README.md
Note: Performance tests conducted using Google Lighthouse on localhost deployment
| Desktop | Mobile |
|---|---|
 |
 |
| Lighthouse Report | Lighthouse Report |
Desktop: Performance 100 | Accessibility 100 | Best Practices 100 | SEO 100
Mobile: Performance 96 | Accessibility 93 | Best Practices 100 | SEO 100
Prerequisites: Docker installed.
Docker images are published to Docker Hub and GitHub Container Registry. To get the latest image (not a cached old one), always run docker pull with the tag you want before creating the container. Use :latest for the current release, or a version tag (e.g. v2.1.2) from Releases for a fixed version.
Supported platforms: linux/amd64 (Intel/AMD), linux/arm64 (Apple Silicon, Raspberry Pi 4/5).
Run these commands in a terminal. Data is stored in a Docker volume chatraw-data.
# 1. Pull the latest image (run this again whenever you want to update)
docker pull massif01/chatraw:latest
# 2. Start the container (creates volume chatraw-data if needed)
docker run -d -p 51111:51111 -v chatraw-data:/app/data --name chatraw massif01/chatraw:latest- Access: http://localhost:51111
- To access LAN services (e.g. local LLM at 192.168.x.x), use host network instead:
docker run -d --network host -v chatraw-data:/app/data -e PORT=51111 --name chatraw massif01/chatraw:latest
Clone the project and run from the repo root. The included docker-compose.yml uses host network so the app can reach LAN services (e.g. 192.168.x.x) without extra config.
# 1. Clone the repository
git clone https://github.com/massif-01/ChatRaw.git
cd ChatRaw
# 2. Pull the latest image and start the service
docker compose pull
docker compose up -d- Access: http://localhost:51111 (or http://<your-ip>:51111 from other devices).
Requirements: Python 3.12+
# Clone repository
git clone https://github.com/massif-01/ChatRaw.git
cd ChatRaw/backend
# Install dependencies
pip install -r requirements.txt
# Run
python main.pyAccess: http://localhost:51111
| Source | Pull command |
|---|---|
| Docker Hub | docker pull massif01/chatraw:latest |
| GitHub Container Registry | docker pull ghcr.io/massif-01/chatraw:latest |
Use the same tag for a specific version, e.g. massif01/chatraw:v2.1.2 (see Releases).
# Stop and remove the current container
docker stop chatraw && docker rm chatraw
# Pull the latest image (important: otherwise the old image is reused)
docker pull massif01/chatraw:latest
# Start again (same volume keeps your data)
docker run -d -p 51111:51111 -v chatraw-data:/app/data --name chatraw massif01/chatraw:latestcd ChatRaw
git pull origin main
docker compose pull
docker compose up -dcd ChatRaw
git pull origin main
cd backend
pip install -r requirements.txt --upgrade
python main.py- RAG is now a plugin: install Lightweight RAG Demo from Plugin Market if you need it.
- Default theme is light (changeable in Settings).
- Chat history and settings are preserved.
- Open http://localhost:51111
- Click the Settings button in the bottom-left corner
- Go to Model Settings
- Add your API configuration:
- API Base URL (e.g.,
https://api.openai.com/v1) - Model ID (e.g.,
gpt-4) - API Key
- API Base URL (e.g.,
- Click Verify to test the connection
- Click Save
In Settings → Interface, you can customize:
- Application name and logo
- User and AI avatars
- Theme mode (light/dark)
- Click the Plugins button in the bottom-left corner
- Browse the Plugin Market tab
- Click Install on any plugin
- After installation, enable the plugin in the Installed tab
- Developers: Quickly test and demo your AI models
- AI Hardware Vendors: Showcase device capabilities with a ready-to-use interface
- Researchers: Experiment with RAG, embeddings, and reranking
- Students: Learn AI applications hands-on
- Enterprises: Internal AI tools and knowledge bases
Contributions are welcome! Please submit issues or pull requests.
- Fork this repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Apache License 2.0
© 2026 ChatRaw by massif-01, RMinte® AI Technology Co., Ltd.
- GitHub: https://github.com/massif-01/ChatRaw
- Docker Hub: https://hub.docker.com/r/massif01/chatraw
- Plugin Development: Plugins/README.md
- Issue Tracker: https://github.com/massif-01/ChatRaw/issues
很多开发者、AI 硬件厂商，甚至是用户只需要一个简洁轻量，能够快速展示自己模型使用的应用，于是我们提供了极简、开箱即用的聊天界面，秒级部署。无需复杂配置，无重型依赖——只需一个干净、快速的 AI 聊天体验。
快速、轻量、便捷
- 极致轻量 - 内存占用约 60MB，优化的二进制向量存储
- 极速启动 - 秒级启动，连接池加速 API 调用
- 自定义品牌 - 自由定制名称、Logo 和主题
- 通用 API 支持 - 兼容任意 OpenAI 兼容 API（Ollama、vLLM、LocalAI、LM Studio 等）
- 文档解析 - 原生支持 PDF、DOCX、TXT、MD 解析作为聊天上下文
- 视觉 AI 就绪 - 多模态图片理解，自动压缩
- 思考模式 - 支持推理模型（DeepSeek-R1、Qwen、o1 等）
- 响应式设计 - 完美适配桌面、平板和移动设备，触控友好
- 一键复制 - 一键复制 AI 回复内容（纯文本，不含元数据）
- 双语界面 - 中英文一键切换
- 零注册 - 设置本地自动保存
- 一键部署 - Docker 30 秒部署
多模型配置
- 支持无限数量的聊天、嵌入和重排模型
- 自动 API Key 轮换以绕过速率限制
- 内置端点验证和测试
思考模式
- 为支持的模型启用深度推理
- 可折叠的思考过程显示
自定义品牌
- 自定义界面：名称、Logo、副标题、头像和主题颜色
文档与图片支持
- 上传文档（PDF、DOCX、TXT、MD）作为聊天上下文。AI 可以阅读和引用文档内容
- 附加图片进行多模态理解。自动压缩为 WebP 格式（约 2MB）
灵活、自由、社区驱动
ChatRaw 拥有完整的插件系统以扩展功能：
- 轻量 RAG 演示 — 知识库检索
- 博查搜索 — Web / AI 搜索
- Tavily 搜索 — Web 搜索 + AI 答案
- Excel 解析器 — 解析 .xlsx/.xls 供对话使用
- CSV 解析器 — 解析 CSV/TSV 供对话使用
- 增强网页解析 — 解析网页（浏览器 / Firecrawl / Jina）
- 多模型管理 — 管理并切换模型
- Markdown 渲染增强 — 数学公式、Mermaid、代码复制，离线可用
- 工具栏扩展演示 — 展示 UI 扩展 API 的演示插件
插件可以在输入框工具栏添加自定义按钮，支持激活态/加载态、按钮溢出折叠菜单，以及全屏模态框实现复杂交互。
- 完整的开发文档
- 丰富的 Hook 系统（包含全新 UI 扩展 API）
- 自定义设置界面
- 一键打包分发
插件开发指南: Plugins/README.md
说明: 使用 Google Lighthouse 对本地部署进行性能测试
| 桌面端 | 移动端 |
|---|---|
|
|
| Lighthouse 测试报告 | Lighthouse 测试报告 |
桌面端: 性能 100 | 无障碍 100 | 最佳做法 100 | SEO 100
移动端: 性能 96 | 无障碍 93 | 最佳做法 100 | SEO 100
前置条件：已安装 Docker。
镜像发布在 Docker Hub 和 GitHub Container Registry。若想用最新镜像（避免用到本地缓存的旧镜像），在创建容器前请先执行一次 docker pull。使用 :latest 表示当前最新版本；如需固定版本，可使用 Releases 中的版本号标签（如 v2.1.2）。
支持平台：linux/amd64（Intel/AMD）、linux/arm64（Apple Silicon、树莓派 4/5）。
在终端依次执行。数据保存在 Docker 卷 chatraw-data 中。
# 1. 拉取最新镜像（每次要更新时重新执行此命令）
docker pull massif01/chatraw:latest
# 2. 启动容器（若卷 chatraw-data 不存在会自动创建）
docker run -d -p 51111:51111 -v chatraw-data:/app/data --name chatraw massif01/chatraw:latest- 访问：http://localhost:51111
- 如需访问局域网服务（例如本机 LLM 在 192.168.x.x），可改用 host 网络：
docker run -d --network host -v chatraw-data:/app/data -e PORT=51111 --name chatraw massif01/chatraw:latest
克隆项目后在仓库根目录执行。项目内的 docker-compose.yml 使用 host 网络，可直接访问局域网服务（如 192.168.x.x），无需额外配置。
# 1. 克隆仓库
git clone https://github.com/massif-01/ChatRaw.git
cd ChatRaw
# 2. 拉取最新镜像并启动服务
docker compose pull
docker compose up -d- 访问：http://localhost:51111（或本机 IP http://<你的IP>:51111 从其他设备访问）。
环境要求：Python 3.12+
# 克隆仓库
git clone https://github.com/massif-01/ChatRaw.git
cd ChatRaw/backend
# 安装依赖
pip install -r requirements.txt
# 运行
python main.py| 来源 | 拉取命令 |
|---|---|
| Docker Hub | docker pull massif01/chatraw:latest |
| GitHub Container Registry | docker pull ghcr.io/massif-01/chatraw:latest |
需要固定版本时使用相同标签格式，例如 massif01/chatraw:v2.1.2，版本号见 Releases。
# 停止并删除当前容器
docker stop chatraw && docker rm chatraw
# 拉取最新镜像（重要：否则会继续用旧镜像）
docker pull massif01/chatraw:latest
# 再次启动（使用同一卷，数据保留）
docker run -d -p 51111:51111 -v chatraw-data:/app/data --name chatraw massif01/chatraw:latestcd ChatRaw
git pull origin main
docker compose pull
docker compose up -dcd ChatRaw
git pull origin main
cd backend
pip install -r requirements.txt --upgrade
python main.py- RAG 已改为插件：需要 RAG 时请在插件市场中安装 轻量 RAG 演示。
- 默认主题为亮色（可在设置中修改）。
- 对话历史与设置会保留。
- 打开 http://localhost:51111
- 点击左下角的设置按钮
- 进入模型设置
- 添加你的 API 配置：
- API Base URL（例如：
https://api.openai.com/v1） - Model ID（例如：
gpt-4） - API Key
- API Base URL（例如：
- 点击验证测试连接
- 点击保存
在设置 → 界面中，你可以自定义：
- 应用名称和 Logo
- 用户和 AI 头像
- 主题模式（亮色/暗色）
- 点击左下角的插件按钮
- 浏览插件市场标签页
- 点击任意插件的安装按钮
- 安装后，在已安装标签页中启用插件
- 开发者：快速测试和演示你的 AI 模型
- AI 硬件厂商：用即插即用的界面展示设备能力
- 研究人员：实验 RAG、嵌入和重排技术
- 学生：动手学习 AI 应用
- 企业：内部 AI 工具和知识库
欢迎贡献！请提交 issue 或 pull request。
- Fork 本仓库
- 创建你的特性分支 (
git checkout -b feature/AmazingFeature) - 提交你的更改 (
git commit -m 'Add some AmazingFeature') - 推送到分支 (
git push origin feature/AmazingFeature) - 打开一个 Pull Request
Apache License 2.0
© 2026 ChatRaw by massif-01, RMinte® AI Technology Co., Ltd.
- GitHub: https://github.com/massif-01/ChatRaw
- Docker Hub: https://hub.docker.com/r/massif01/chatraw
- 插件开发: Plugins/README.md
- 问题反馈: https://github.com/massif-01/ChatRaw/issues