ChatRaw

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

English | 中文

English

📸 Interface Preview

Main Chat Interface

Model Configuration

Plugin Market

🌟 Why ChatRaw?

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.

📦 Part 1: Core Features

Fast, Lightweight, Convenient

Core Highlights

🪶 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
🌍 Bilingual UI - English & Chinese with one-click switch
🔒 Zero Registration - Settings auto-saved locally
🐳 One-Click Deploy - Docker deployment in 30 seconds

Key Features

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)

🔌 Part 2: Extension Plugins

Flexible, Free, Community-Driven

ChatRaw features a complete plugin system to extend functionality:

Official Plugins

🧠 Lightweight RAG Demo

Knowledge base management and retrieval
Embedding model configuration
Reranking model optimization
Document chunking and vectorization

🔍 Bocha Search

Web search
AI-powered intelligent search
Agent search mode
Semantic reranking

📊 Excel Parser

Support for .xlsx, .xls, .xlsm formats
Automatic table recognition
One-click install, no configuration needed

📋 CSV Parser

CSV/TSV file parsing
Multiple output formats
Lightweight implementation

Plugin Development

Complete development documentation
Rich hook system
Custom settings UI
One-click packaging and distribution

📖 Plugin Development Guide: Plugins/README.md

🚀 Quick Start

Option 1: Docker (Recommended)

# Pull image
docker pull massif01/chatraw:2.0.0

# Run container
docker run -d -p 51111:51111 -v chatraw-data:/app/data massif01/chatraw:2.0.0

Or use docker-compose:

docker-compose up -d

Access: http://localhost:51111

Option 2: From Source

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.py

Access: http://localhost:51111

🐳 Docker Multi-Platform Support

Supports the following platforms:

✅ linux/amd64 (Intel/AMD x86_64)
✅ linux/arm64 (Apple Silicon M-series, ARM64 servers, Raspberry Pi 4/5)

# Docker Hub
docker pull massif01/chatraw:latest

# GitHub Container Registry
docker pull ghcr.io/massif-01/chatraw:latest

🔄 Update Guide

Updating to v2.0.0

If you're upgrading from v1.x:

Docker Users:

# Stop and remove old container
docker stop chatraw && docker rm chatraw

# Pull new image
docker pull massif01/chatraw:2.0.0

# Run new container (data persists in volume)
docker run -d -p 51111:51111 -v chatraw-data:/app/data massif01/chatraw:2.0.0

Or with docker-compose:

# Pull new image
docker-compose pull

# Restart services
docker-compose up -d

Source Code Users:

cd ChatRaw
git pull origin main
cd backend
pip install -r requirements.txt --upgrade
python main.py

Important Changes in v2.0.0:

⚠️ RAG functionality has been moved to a plugin
Install the "Lightweight RAG Demo" plugin from Plugin Market if you need RAG features
Default theme changed to light mode (can be changed in Settings)
All chat history and settings are automatically preserved

🛠️ Configuration

Initial Setup

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
Click Verify to test the connection
Click Save

Custom Branding

In Settings → Interface, you can customize:

Application name and logo
User and AI avatars
Theme mode (light/dark)

Install Plugins

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

📝 Use Cases

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

🤝 Contributing

Contributions are welcome! Please submit issues or pull requests.

Development Guidelines

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

📄 License

Apache License 2.0

🔗 Links

🌐 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

⭐ Star History

If you find ChatRaw useful, please give it a star! ⭐

中文

📸 界面展示

主聊天界面

模型配置

插件市场

🌟 为什么选择 ChatRaw？

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。

📦 第一部分：核心功能

快速、轻量、便捷

核心亮点

🪶 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
🌍 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

主要功能

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）

🔌 第二部分：扩展插件

灵活、自由、社区驱动

ChatRaw 拥有完整的插件系统以扩展功能：

官方插件

🧠 轻量 RAG 演示

知识库管理与检索
嵌入模型配置
重排模型优化
文档切片与向量化

🔍 博查搜索

Web 通搜
AI 智能搜索
Agent 搜索模式
语义重排

📊 Excel 解析器

支持 .xlsx, .xls, .xlsm 格式
自动表格识别
一键安装，无需配置

📋 CSV 解析器

CSV/TSV 文件解析
多种输出格式
轻量级实现

插件开发

完整的开发文档
丰富的 Hook 系统
自定义设置界面
一键打包分发

📖 插件开发指南: Plugins/README.md

🚀 快速开始

方式一：Docker（推荐）

# 拉取镜像
docker pull massif01/chatraw:2.0.0

# 运行容器
docker run -d -p 51111:51111 -v chatraw-data:/app/data massif01/chatraw:2.0.0

或使用 docker-compose：

docker-compose up -d

访问：http://localhost:51111

方式二：源码部署

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

访问：http://localhost:51111

🐳 Docker 多平台支持

支持以下平台：

✅ linux/amd64 (Intel/AMD x86_64)
✅ linux/arm64 (Apple Silicon M 系列、ARM64 服务器、树莓派 4/5)

# Docker Hub
docker pull massif01/chatraw:latest

# GitHub Container Registry
docker pull ghcr.io/massif-01/chatraw:latest

🔄 更新指南

升级到 v2.0.0

如果你正在从 v1.x 升级：

Docker 用户：

# 停止并移除旧容器
docker stop chatraw && docker rm chatraw

# 拉取新镜像
docker pull massif01/chatraw:2.0.0

# 运行新容器（数据持久化在卷中）
docker run -d -p 51111:51111 -v chatraw-data:/app/data massif01/chatraw:2.0.0

或使用 docker-compose：

# 拉取新镜像
docker-compose pull

# 重启服务
docker-compose up -d

源码用户：

cd ChatRaw
git pull origin main
cd backend
pip install -r requirements.txt --upgrade
python main.py

v2.0.0 重要变更：

⚠️ RAG 功能已迁移至插件
如需使用 RAG 功能，请从插件市场安装"轻量 RAG 演示"插件
默认主题改为亮色模式（可在设置中更改）
所有对话历史和设置会自动保留

🛠️ 配置说明

初始设置

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
点击验证测试连接
点击保存

自定义品牌

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）

安装插件

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

📝 使用场景

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

🤝 贡献

欢迎贡献！请提交 issue 或 pull request。

开发指南

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

📄 开源协议