dtsola/xiaoyaosearch

• highreadme#1

  Strengthen README's opening sentence to emphasize AI-powered semantic desktop search

  Why:

  CURRENT

  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。

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  小遥搜索是一款先进的AI驱动本地桌面搜索应用，专为知识工作者、内容创作者和技术开发者设计。它通过多模态AI（语音、文本、图片）实现对本地文件的深度语义检索，让搜索像聊天一样简单。
  
  XiaoyaoSearch is an advanced AI-powered local desktop search application designed for knowledge workers, content creators, and tech developers. It enables deep semantic retrieval of local files through multimodal AI (voice, text, images), making search as easy as chatting.

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• mediumcomparison#2

  Add a 'Why XiaoyaoSearch?' or 'Comparison' section to the README

  Why:

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  ## 💡 为什么选择小遥搜索？
  
  与传统文件搜索工具（如Everything、DocFetcher）或通用AI助手（如Copilot）不同，小遥搜索专注于本地文件的深度语义检索。我们提供：
  
  - **多模态AI输入**：支持语音、文本、图片，实现更自然的查询方式。
  - **AI驱动的深度语义理解**：超越关键词匹配，理解查询意图和文件内容。
  - **隐私优先的本地运行**：数据默认不上传，保障您的信息安全。
  - **跨平台桌面应用**：提供Windows、macOS、Linux原生体验。
  
  ## 💡 Why Choose XiaoyaoSearch?
  
  XiaoyaoSearch stands apart from traditional file search tools (like Everything, DocFetcher) or general AI assistants (like Copilot) by focusing on deep semantic retrieval of local files. We offer:
  
  - **Multimodal AI Input**: Supports voice, text, and images for more natural queries.
  - **AI-Driven Deep Semantic Understanding**: Goes beyond keyword matching to understand query intent and file content.
  - **Privacy-First Local Operation**: Data is not uploaded by default, ensuring your information security.
  - **Cross-Platform Desktop Application**: Provides a native experience on Windows, macOS, and Linux.

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• lowlicense#3

  Explicitly state the license type(s) in the README's 'Important Notes' section

  Why:

  CURRENT

  - 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](LICENSE)

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  - 本项目遵循[小遥搜索软件授权协议](LICENSE)（非商业使用免费，允许修改和分发；商业目的需授权），请查阅[LICENSE](LICENSE)文件了解具体条款。
  
  - This project follows the [XiaoyaoSearch Software License Agreement](LICENSE) (free for non-commercial use, modification and distribution allowed; commercial use requires authorization). Please refer to the [LICENSE](LICENSE) file for specific terms.

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I use AI to find local files using natural language and image content?

  you: not recommended
  AI recommended (in order):

  1. Everything
  2. DocFetcher
  3. Microsoft Windows Search
  4. PowerToys Run
  5. Tesseract
  6. ABBYY FineReader
  7. DEVONthink
  8. Tesseract OCR
  9. Whoosh
  10. Elasticsearch
  11. Google Desktop Search
  12. Google Drive
  13. Microsoft OneDrive
  14. Dropbox
  15. Eagle
  16. Pixave

  AI recommended 16 alternatives but never named dtsola/xiaoyaosearch. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  Recommendations for AI-powered desktop search to find content within local documents and media?

  you: not recommended
  AI recommended (in order):

  1. Copilot
  2. Raycast
  3. Alfred
  4. Everything
  5. DocFetcher
  6. DEVONthink

  AI recommended 6 alternatives but never named dtsola/xiaoyaosearch. This is the gap to close.

  Show full AI answer

• Metadata completeness

  pass

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of dtsola/xiaoyaosearch?

  pass
  ✓AI named dtsola/xiaoyaosearch explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts dtsola/xiaoyaosearch in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named dtsola/xiaoyaosearch explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo dtsola/xiaoyaosearch solve, and who is the primary audience?

  pass
  ✓AI named dtsola/xiaoyaosearch explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?