xiaoyaosearch
小遥搜索,听懂你的话、看懂你的图,用AI找到本地任何文件。让搜索像聊天一样简单。XiaoyaoSearch: Understands your words, reads your images, finds any local file with AI. Making search as easy as chatting.
Stars: 738
XiaoyaoSearch is a cross-platform local desktop application designed for knowledge workers, content creators, and developers. It integrates AI models to support various input methods such as voice, text, and image to intelligently search local files. The application is free for non-commercial use, provides source code and development documentation, and ensures privacy by running locally without uploading data to the cloud. It features modern interface design using Electron, Vue 3, and TypeScript.
README:
English Version | 简体中文
小遥搜索是一款专为知识工作者、内容创作者和技术开发者设计的跨平台本地桌面应用(Windows/MacOS/Linux)。通过集成的AI模型,支持语音输入(30秒内)、文本输入、图片输入等多种方式,将用户的查询转换为语义进行智能搜索,实现对本地文件的深度检索。
- 本项目非商业使用完全免费,允许修改和分发(需保留版权声明和协议);商业目的需授权,详细见小遥搜索软件授权协议
- 本项目完全通过Vibe Coding实现,提供所有源码及开发文档(上下文)供大家交流学习
- 🎤 多模态输入:支持语音录音、文本输入、图片上传
- 🔍 深度检索:支持视频(mp4、avi)、音频(mp3、wav)、文档(txt、markdown、office、pdf)的内容和文件名搜索
- 🧠 AI增强:集成BGE-M3、FasterWhisper、CN-CLIP、OLLAMA等先进AI模型
- ⚡ 高性能:基于Faiss向量搜索和Whoosh全文搜索的混合检索架构
- 🔒 隐私安全:本地运行,数据不上传云端,支持隐私模式
- 🎨 现代界面:基于Electron + Vue 3 + TypeScript的现代化桌面应用
前端技术
- 框架: Electron + Vue 3 + TypeScript
- UI库: Ant Design Vue
- 状态管理: Pinia
- 构建工具: Vite
后端技术
- 框架: Python 3.10 + FastAPI + Uvicorn
- AI模型: BGE-M3 + FasterWhisper + CN-CLIP + Ollama
- 搜索引擎: Faiss (向量搜索) + Whoosh (全文搜索)
- 数据库: SQLite + 索引文件
xiaoyaosearch/
├── backend/ # 后端服务 (Python FastAPI)
│ ├── app/ # 应用核心代码
│ │ ├── api/ # API路由层
│ │ ├── core/ # 核心配置
│ │ ├── models/ # 数据模型
│ │ ├── services/ # 业务服务
│ │ ├── schemas/ # 数据模式
│ │ └── utils/ # 工具函数
│ ├── requirements.txt # Python依赖
│ ├── main.py # 应用入口
│ └── .env # 环境变量
├── frontend/ # 前端应用 (Electron + Vue3)
│ ├── src/ # 源代码
│ │ ├── main/ # Electron主进程
│ │ ├── preload/ # 预加载脚本
│ │ └── renderer/ # Vue渲染进程
│ ├── out/ # 构建输出
│ ├── dist-electron/ # 打包输出
│ ├── resources/ # 应用资源
│ ├── package.json # Node.js依赖
│ └── electron-builder.yml # 打包配置
├── docs/ # 项目文档
│ ├── 00-mrd.md # 市场调研
│ ├── 01-prd.md # 产品需求
│ ├── 02-原型.md # 产品原型
│ ├── 03-技术方案.md # 技术方案
│ ├── 04-开发任务清单.md # 开发任务
│ ├── 05-开发排期表.md # 开发排期
│ ├── 开发进度.md # 进度跟踪
│ ├── 接口文档.md # API文档
│ ├── 数据库设计文档.md # 数据库设计
│ └── 高保真原型/ # UI原型
├── data/ # 数据目录
│ ├── database/ # SQLite数据库
│ ├── indexes/ # 搜索索引
│ │ ├── faiss/ # 向量索引
│ │ └── whoosh/ # 全文索引
│ ├── models/ # 模型文件
│ └── logs/ # 日志文件
├── .claude/ # Claude助手配置
├── LICENSE # 软件授权协议(中文版)
├── LICENSE_EN # 软件授权协议(英文版)
├── README.md # 项目说明(中文版)
└── README_EN.md # 项目说明(英文版)
适用人群:非开发者、希望快速体验小遥搜索的用户 支持平台:仅支持 Windows 部署难度:⭐ 简单(一键安装)
从百度网盘下载最新的 Windows 整合包:
请选择最新版本下载(如 XiaoyaoSearch-Windows-v1.1.1.zip)
1. 解压整合包
将下载的压缩包解压到任意目录(建议不要包含中文路径)
2. 运行环境准备脚本
双击运行 scripts/setup.bat,脚本会自动完成以下操作:
- 解压 Python 嵌入式运行时
- 安装后端 Python 依赖
- 安装前端 Node 依赖
- 生成配置文件
- 创建数据目录
3. 安装 Ollama
双击运行 runtime\ollama\OllamaSetup.exe,按提示完成安装。
安装完成后,打开命令行运行:
ollama serve
ollama pull qwen2.5:1.5b4. 下载 AI 模型
从百度网盘下载默认模型:
- 链接:https://pan.baidu.com/s/1jRcTztvjf8aiExUh6oayVg
- 提取码:ycr5
将模型解压到对应目录:
-
data\models\embedding\BAAI\bge-m3\- 嵌入模型 -
data\models\cn-clip\- 视觉模型 -
data\models\faster-whisper\- 语音识别模型
5. 启动应用
双击运行 scripts/startup.bat,脚本会:
- 启动后端服务
- 启动前端服务
详细文档:整合包部署指南
适用人群:开发者、希望参与项目贡献的用户 支持平台:Windows / macOS / Linux 部署难度:⭐⭐⭐ 需要开发环境
- 操作系统: Windows / macOS / Linux
- Python: 3.10.11+(https://www.python.org/downloads/)
- Node.js: 21.x+(https://nodejs.org/en/download)
- 内存: 建议16GB 以上
- 显卡: 建议RTX3060 6GB以上
1. 克隆项目
git clone https://github.com/dtsola/xiaoyaosearch.git
cd xiaoyaosearch2. 后端部署
# 进入后端目录
cd backend
# 安装依赖包(默认CPU版本的推理引擎)
pip install -r requirements.txt
# 安装faster-whisper
pip install faster-whisper
# 启用CUDA(可选,注意:cuda版本需根据环境确定)
pip uninstall torch torchaudio torchvision
pip install torch==2.1.0+cu121 torchaudio==2.1.0+cu121 torchvision==0.16.0+cu121 --index-url https://download.pytorch.org/whl/cu121
安装ffmpeg: https://ffmpeg.org/download.html
安装ollama: https://ollama.com/
配置 .env 文件:
# 数据配置
FAISS_INDEX_PATH=../data/indexes/faiss
WHOOSH_INDEX_PATH=../data/indexes/whoosh
DATABASE_PATH=../data/database/xiaoyao_search.db
# API配置
API_HOST=127.0.0.1
API_PORT=8000
API_RELOAD=true
# 日志配置
LOG_LEVEL=info
LOG_FILE=../data/logs/app.log准备模型: 系统默认模型说明:
- ollama:qwen2.5:1.5b
- 嵌入模型:BAAI/bge-m3
- 语音识别模型:Systran/faster-whisper-base
- 视觉模型:OFA-Sys/chinese-clip-vit-base-patch16
注意:建议先准备默认模型,先成功启动应用后,再更换模型。
ollama模型: ollama pull qwen2.5:1.5b (根据情况自行选择)
所有模型下载地址:(百度盘) 链接: https://pan.baidu.com/s/1jRcTztvjf8aiExUh6oayVg?pwd=ycr5 提取码: ycr5
嵌入模型:
- 模型根目录:data/models/embedding
- 将下载的模型直接解压放入到根目录即可,以下是对应关系
- data/models/embedding/BAAI/bge-m3
- data/models/embedding/BAAI/bge-small-zh
- data/models/embedding/BAAI/bge-large-zh
语音识别模型:
- 模型根目录:data/models/faster-whisper
- 将下载的模型直接解压放入到根目录即可,以下是对应关系
- data/models/faster-whisper/Systran/faster-whisper-base
- data/models/faster-whisper/Systran/faster-whisper-small
- data/models/faster-whisper/Systran/faster-whisper-medium
- data/models/faster-whisper/Systran/faster-whisper-large-v3
视觉模型:
- 模型根目录:data/models/cn-clip
- 将下载的模型直接解压放入到根目录即可,以下是对应关系
- data/models/cn-clip/OFA-Sys/chinese-clip-vit-base-patch16
- data/models/cn-clip/OFA-Sys/chinese-clip-vit-large-patch14
启动后端服务:
# 使用内置配置启动
python main.py
# 或使用uvicorn启动
uvicorn main:app --host 127.0.0.1 --port 8000 --reload# 进入前端目录
cd frontend
# 安装依赖
npm install
# 启动开发服务器
npm run dev感谢你对小遥搜索的关注!我们欢迎任何形式的贡献,无论是代码、文档、Bug 修复还是新功能建议。
步骤 1:Fork 项目
- 访问 xiaoyaosearch 仓库
- 点击右上角的 "Fork" 按钮,将项目 Fork 到你的 GitHub 账户
步骤 2:克隆到本地
git clone https://github.com/<你的用户名>/xiaoyaosearch.git
cd xiaoyaosearch步骤 3:创建功能分支
git checkout -b feature/你的功能名称
# 或
git checkout -b fix/问题描述步骤 4:进行开发
- 按照项目代码规范进行开发
- 确保代码有适当的注释
- 运行测试确保功能正常
步骤 5:提交代码
git add .
git commit -m "feat(scope): 简洁描述你的改动"提交格式规范:
-
feat: 新功能 -
fix: Bug 修复 -
docs: 文档更新 -
style: 代码格式调整 -
refactor: 代码重构 -
perf: 性能优化 -
test: 测试相关 -
chore: 构建/工具链相关
步骤 6:推送到 GitHub
git push origin feature/你的功能名称步骤 7:创建 Pull Request
- 访问你 Fork 的仓库页面
- 点击 "Compare & pull request" 按钮
- 填写 PR 描述:
- 标题:简洁说明改动内容
- 描述:详细说明改动原因、实现方式、测试结果
- 等待维护者审核
如果你发现了 Bug 或有功能建议:
- 访问 Issues 页面
- 点击 "New Issue"
- 选择合适的 Issue 模板
- 详细描述问题或建议
- 组件命名:PascalCase(如
SearchPanel.vue) - 变量/函数:camelCase(如
searchResults) - 常量:UPPER_SNAKE_CASE(如
MAX_FILE_SIZE) - 代码注释:使用中文
- 文件命名:snake_case(如
search_service.py) - 类名:PascalCase(如
SearchService) - 函数/变量:snake_case(如
search_files) - 常量:UPPER_SNAKE_CASE(如
MAX_RESULTS) - 代码注释:使用中文
- ✅ 遵循项目的代码规范
- ✅ 保持代码简洁,避免过度设计
- ✅ 添加适当的错误处理
- ✅ 确保代码有适当的测试
- ✅ 更新相关文档
- 💬 微信:dtsola(请备注 "小遥搜索贡献")
- 📧 邮件:通过官网 https://www.dtsola.com 联系
- 📺 B站:https://space.bilibili.com/736015
- 📝 你的名字将出现在项目贡献者列表中
- 🌟 你的改动将帮助成千上万的用户
- 🤝 加入独立开发者社区,交流学习
- 🎁 优秀贡献者可获得项目周边礼品
让我们一起打造更好的本地搜索体验! 🚀
感谢以下人员为本项目做出的贡献:
- @jidingliu - 提交代码及项目宣传
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LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.
daily-poetry-image
Daily Chinese ancient poetry and AI-generated images powered by Bing DALL-E-3. GitHub Action triggers the process automatically. Poetry is provided by Today's Poem API. The website is built with Astro.
exif-photo-blog
EXIF Photo Blog is a full-stack photo blog application built with Next.js, Vercel, and Postgres. It features built-in authentication, photo upload with EXIF extraction, photo organization by tag, infinite scroll, light/dark mode, automatic OG image generation, a CMD-K menu with photo search, experimental support for AI-generated descriptions, and support for Fujifilm simulations. The application is easy to deploy to Vercel with just a few clicks and can be customized with a variety of environment variables.
SillyTavern
SillyTavern is a user interface you can install on your computer (and Android phones) that allows you to interact with text generation AIs and chat/roleplay with characters you or the community create. SillyTavern is a fork of TavernAI 1.2.8 which is under more active development and has added many major features. At this point, they can be thought of as completely independent programs.
Twitter-Insight-LLM
This project enables you to fetch liked tweets from Twitter (using Selenium), save it to JSON and Excel files, and perform initial data analysis and image captions. This is part of the initial steps for a larger personal project involving Large Language Models (LLMs).
AISuperDomain
Aila Desktop Application is a powerful tool that integrates multiple leading AI models into a single desktop application. It allows users to interact with various AI models simultaneously, providing diverse responses and insights to their inquiries. With its user-friendly interface and customizable features, Aila empowers users to engage with AI seamlessly and efficiently. Whether you're a researcher, student, or professional, Aila can enhance your AI interactions and streamline your workflow.
ChatGPT-On-CS
This project is an intelligent dialogue customer service tool based on a large model, which supports access to platforms such as WeChat, Qianniu, Bilibili, Douyin Enterprise, Douyin, Doudian, Weibo chat, Xiaohongshu professional account operation, Xiaohongshu, Zhihu, etc. You can choose GPT3.5/GPT4.0/ Lazy Treasure Box (more platforms will be supported in the future), which can process text, voice and pictures, and access external resources such as operating systems and the Internet through plug-ins, and support enterprise AI applications customized based on their own knowledge base.
obs-localvocal
LocalVocal is a live-streaming AI assistant plugin for OBS that allows you to transcribe audio speech into text and perform various language processing functions on the text using AI / LLMs (Large Language Models). It's privacy-first, with all data staying on your machine, and requires no GPU, cloud costs, network, or downtime.











