[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-lk-geimfari--mimesis":3,"tool-lk-geimfari--mimesis":65},[4,18,32,41,49,57],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":24,"last_commit_at":25,"category_tags":26,"status":17},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",85092,2,"2026-04-10T11:13:16",[15,16,27,28,13,29,30,14,31],"视频","插件","其他","语言模型","音频",{"id":33,"name":34,"github_repo":35,"description_zh":36,"stars":37,"difficulty_score":38,"last_commit_at":39,"category_tags":40,"status":17},5784,"funNLP","fighting41love\u002FfunNLP","funNLP 是一个专为中文自然语言处理（NLP）打造的超级资源库，被誉为\"NLP 民工的乐园”。它并非单一的软件工具，而是一个汇集了海量开源项目、数据集、预训练模型和实用代码的综合性平台。\n\n面对中文 NLP 领域资源分散、入门门槛高以及特定场景数据匮乏的痛点，funNLP 提供了“一站式”解决方案。这里不仅涵盖了分词、命名实体识别、情感分析、文本摘要等基础任务的标准工具，还独特地收录了丰富的垂直领域资源，如法律、医疗、金融行业的专用词库与数据集，甚至包含古诗词生成、歌词创作等趣味应用。其核心亮点在于极高的全面性与实用性，从基础的字典词典到前沿的 BERT、GPT-2 模型代码，再到高质量的标注数据和竞赛方案，应有尽有。\n\n无论是刚刚踏入 NLP 领域的学生、需要快速验证想法的算法工程师，还是从事人工智能研究的学者，都能在这里找到急需的“武器弹药”。对于开发者而言，它能大幅减少寻找数据和复现模型的时间；对于研究者，它提供了丰富的基准测试资源和前沿技术参考。funNLP 以开放共享的精神，极大地降低了中文自然语言处理的开发与研究成本，是中文 AI 社区不可或缺的宝藏仓库。",79857,1,"2026-04-08T20:11:31",[30,16,29],{"id":42,"name":43,"github_repo":44,"description_zh":45,"stars":46,"difficulty_score":38,"last_commit_at":47,"category_tags":48,"status":17},5773,"cs-video-courses","Developer-Y\u002Fcs-video-courses","cs-video-courses 是一个精心整理的计算机科学视频课程清单，旨在为自学者提供系统化的学习路径。它汇集了全球知名高校（如加州大学伯克利分校、新南威尔士大学等）的完整课程录像，涵盖从编程基础、数据结构与算法，到操作系统、分布式系统、数据库等核心领域，并深入延伸至人工智能、机器学习、量子计算及区块链等前沿方向。\n\n面对网络上零散且质量参差不齐的教学资源，cs-video-courses 解决了学习者难以找到成体系、高难度大学级别课程的痛点。该项目严格筛选内容，仅收录真正的大学层级课程，排除了碎片化的简短教程或商业广告，确保用户能接触到严谨的学术内容。\n\n这份清单特别适合希望夯实计算机基础的开发者、需要补充特定领域知识的研究人员，以及渴望像在校生一样系统学习计算机科学的自学者。其独特的技术亮点在于分类极其详尽，不仅包含传统的软件工程与网络安全，还细分了生成式 AI、大语言模型、计算生物学等新兴学科，并直接链接至官方视频播放列表，让用户能一站式获取高质量的教育资源，免费享受世界顶尖大学的课堂体验。",79792,"2026-04-08T22:03:59",[29,15,16,14],{"id":50,"name":51,"github_repo":52,"description_zh":53,"stars":54,"difficulty_score":10,"last_commit_at":55,"category_tags":56,"status":17},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[13,15,14,30,29],{"id":58,"name":59,"github_repo":60,"description_zh":61,"stars":62,"difficulty_score":10,"last_commit_at":63,"category_tags":64,"status":17},519,"PaddleOCR","PaddlePaddle\u002FPaddleOCR","PaddleOCR 是一款基于百度飞桨框架开发的高性能开源光学字符识别工具包。它的核心能力是将图片、PDF 等文档中的文字提取出来，转换成计算机可读取的结构化数据，让机器真正“看懂”图文内容。\n\n面对海量纸质或电子文档，PaddleOCR 解决了人工录入效率低、数字化成本高的问题。尤其在人工智能领域，它扮演着连接图像与大型语言模型（LLM）的桥梁角色，能将视觉信息直接转化为文本输入，助力智能问答、文档分析等应用场景落地。\n\nPaddleOCR 适合开发者、算法研究人员以及有文档自动化需求的普通用户。其技术优势十分明显：不仅支持全球 100 多种语言的识别，还能在 Windows、Linux、macOS 等多个系统上运行，并灵活适配 CPU、GPU、NPU 等各类硬件。作为一个轻量级且社区活跃的开源项目，PaddleOCR 既能满足快速集成的需求，也能支撑前沿的视觉语言研究，是处理文字识别任务的理想选择。",75309,"2026-04-10T11:12:54",[30,15,14,29],{"id":66,"github_repo":67,"name":68,"description_en":69,"description_zh":70,"ai_summary_zh":70,"readme_en":71,"readme_zh":72,"quickstart_zh":73,"use_case_zh":74,"hero_image_url":75,"owner_login":76,"owner_name":77,"owner_avatar_url":78,"owner_bio":79,"owner_company":80,"owner_location":80,"owner_email":81,"owner_twitter":80,"owner_website":82,"owner_url":83,"languages":84,"stars":96,"forks":97,"last_commit_at":98,"license":99,"difficulty_score":38,"env_os":100,"env_gpu":101,"env_ram":102,"env_deps":103,"category_tags":107,"github_topics":108,"view_count":24,"oss_zip_url":80,"oss_zip_packed_at":80,"status":17,"created_at":128,"updated_at":129,"faqs":130,"releases":131},6199,"lk-geimfari\u002Fmimesis","mimesis","Mimesis is a fast Python library for generating fake data in multiple languages.","Mimesis 是一款专为 Python 打造的高性能假数据生成库，旨在帮助开发者快速构建逼真的测试数据集。在软件开发、系统测试或原型设计过程中，往往需要大量多样化且符合逻辑的模拟数据（如姓名、地址、金融信息等），手动编写既耗时又难以覆盖多语言场景。Mimesis 完美解决了这一痛点，它支持全球 46 种语言区域（Locale），能够根据特定文化背景生成地道的本地化数据。\n\n这款工具特别适合后端工程师、测试人员以及数据研究人员使用。其核心优势在于极致的运行速度，被公认为 Python 生态中最快的数据生成方案之一。除了基础的个人身份信息，Mimesis 还提供了强大的基于 Schema 的生成功能，支持生成具有复杂关联关系的结构化数据，轻松应对数据库填充等高级需求。此外，它具备完整的类型提示支持，能为开发者提供智能的代码自动补全体验，让数据构造过程更加直观流畅。无论是进行单元测试、压力测试，还是为演示项目准备样本数据，Mimesis 都能以简洁的 API 和灵活的扩展性，助你高效完成任务。","\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flk-geimfari_mimesis_readme_6b03b0a557ab.png\" width=\"300\" alt=\"Mimesis\">\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n    \u003Cem>Mimesis: The Fake Data Generator\u003C\u002Fem>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis\u002Factions\u002Fworkflows\u002Ftest.yml?query=branch%3Amaster\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis\u002Factions\u002Fworkflows\u002Ftest.yml\u002Fbadge.svg?branch=master\" alt=\"Test\">\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fmimesis.name\u002Fen\u002Flatest\u002F\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flk-geimfari_mimesis_readme_13d664e1afd7.png\" alt=\"Coverage\">\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fmimesis\u002F\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fmimesis?color=bright-green\" alt=\"Package version\">\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fmimesis\u002F\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fmimesis\" alt=\"Package version\">\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fmimesis\u002F\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13%20%7C%203.14%20%7C%20pypy-brightgreen\" alt=\"Supported Python versions\">\n\u003C\u002Fa>\n\u003C\u002Fp>\n\n---\n\n**Documentation**: \u003Ca href=\"https:\u002F\u002Fmimesis.name\u002F\" target=\"_blank\">https:\u002F\u002Fmimesis.name\u002F\u003C\u002Fa>\n\n---\n\nMimesis ([\u002Fmɪˈmiːsɪs](https:\u002F\u002Fmimesis.name\u002Fmaster\u002Fabout.html#what-does-name-mean)) is a robust data generator for\nPython that can produce a wide range of fake data in various languages.\n\nThe key features are:\n\n- **Multilingual**: Supports 46 different locales.\n- **Extensibility**: Supports custom data providers and custom field handlers.\n- **Ease of use**: Features a simple design and clear documentation for straightforward data generation.\n- **Performance**: Widely recognized as the fastest data generator among Python solutions.\n- **Data variety**: Includes various data providers designed for different use cases.\n- **Schema-based generators**: Offers schema-based data generators to effortlessly produce data of any complexity.\n- **Relational data**: Supports generating relational data with references between schemas for complex data structures.\n- **Intuitive**: Great editor support. Fully typed, thus autocompletion almost everywhere.\n\n## Installation\n\n> [!WARNING]\n> In Mimesis 20.0.0, I’m going to completely rework the current schema-based generation implementation. There will be no backward compatibility with the existing implementation.\n\n> [!IMPORTANT]\n> To work with Mimesis on Python versions 3.8 and 3.9, the final compatible version is Mimesis 11.1.0. Install this specific version to ensure compatibility.\n\n> [!WARNING]\n> Starting from version 19.0.0, Mimesis has dropped support for builtin providers.\n\n\nTo install mimesis, use pip:\n\n```\n~ pip install mimesis\n```\n\n## Documentation\n\nYou can find the complete documentation on the [Read the Docs](https:\u002F\u002Fmimesis.name\u002F).\n\nIt is divided into several sections:\n\n-  [About Mimesis](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fabout.html)\n-  [Quickstart](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fquickstart.html)\n-  [Locales](https:\u002F\u002Fmimesis.name\u002Flatest\u002Flocales.html)\n-  [Data Providers](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fproviders.html)\n-  [Structured Data Generation](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fschema.html)\n-  [Random and Seed](https:\u002F\u002Fmimesis.name\u002Flatest\u002Frandom_and_seed.html)\n-  [Integration with factory_boy](https:\u002F\u002Fmimesis.name\u002Flatest\u002Ffactory_plugin.html)\n-  [API Reference](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fapi.html)\n-  [Changelog](https:\u002F\u002Fmimesis.name\u002Flatest\u002Findex.html#changelog)\n\nYou can improve it by sending pull requests to this repository.\n\n## Usage\n\nThe library is exceptionally user-friendly, and it only requires you to import a **Data Provider** object that\ncorresponds to the desired data type.\n\nFor instance, the [Person](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fapi.html#person) provider can be imported to access personal information,\nincluding name, surname, email, and other related fields:\n\n```python\nfrom mimesis import Person\nfrom mimesis.locales import Locale\n\nperson = Person(Locale.EN)\n\nperson.full_name()\n# Output: 'Brande Sears'\n\nperson.email(domains=['example.com'])\n# Output: 'roccelline1878@example.com'\n\nperson.email(domains=['mimesis.name'], unique=True)\n# Output: 'f272a05d39ec46fdac5be4ac7be45f3f@mimesis.name'\n\nperson.telephone(mask='1-4##-8##-5##3')\n# Output: '1-436-896-5213'\n```\n\n## License\n\nMimesis is licensed under the MIT License. See [LICENSE](https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis\u002Fblob\u002Fmaster\u002FLICENSE) for more information.\n","\u003Cp align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flk-geimfari_mimesis_readme_6b03b0a557ab.png\" width=\"300\" alt=\"Mimesis\">\n  \u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n    \u003Cem>Mimesis：虚假数据生成器\u003C\u002Fem>\n\u003C\u002Fp>\n\n\u003Cp align=\"center\">\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis\u002Factions\u002Fworkflows\u002Ftest.yml?query=branch%3Amaster\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis\u002Factions\u002Fworkflows\u002Ftest.yml\u002Fbadge.svg?branch=master\" alt=\"测试\">\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fmimesis.name\u002Fen\u002Flatest\u002F\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flk-geimfari_mimesis_readme_13d664e1afd7.png\" alt=\"覆盖率\">\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fmimesis\u002F\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fmimesis?color=bright-green\" alt=\"软件包版本\">\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fmimesis\u002F\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fmimesis\" alt=\"软件包下载量\">\n\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fmimesis\u002F\" target=\"_blank\">\n    \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpython-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13%20%7C%203.14%20%7C%20pypy-brightgreen\" alt=\"支持的Python版本\">\n\u003C\u002Fa>\n\u003C\u002Fp>\n\n---\n\n**文档**: \u003Ca href=\"https:\u002F\u002Fmimesis.name\u002F\" target=\"_blank\">https:\u002F\u002Fmimesis.name\u002F\u003C\u002Fa>\n\n---\n\nMimesis（[\u002Fmɪˈmiːsɪs](https:\u002F\u002Fmimesis.name\u002Fmaster\u002Fabout.html#what-does-name-mean)）是一款功能强大的 Python 数据生成工具，能够以多种语言生成各类虚假数据。\n\n其主要特性包括：\n\n- **多语言支持**：支持 46 种不同的本地化环境。\n- **可扩展性**：支持自定义数据提供者和自定义字段处理器。\n- **易用性**：设计简洁、文档清晰，便于快速生成数据。\n- **高性能**：被广泛认为是 Python 生态中最高效的的数据生成工具。\n- **数据多样性**：内置多种适用于不同场景的数据提供者。\n- **基于模式的生成器**：提供基于模式的数据生成器，轻松生成任意复杂度的数据。\n- **关系型数据**：支持生成包含模式间引用的关系型数据，适用于复杂的数据结构。\n- **直观友好**：对编辑器支持良好。完全类型化，因此几乎在所有地方都支持自动补全。\n\n## 安装\n\n> [!警告]\n> 在 Mimesis 20.0.0 版本中，我将彻底重构当前基于模式的生成实现。新版本将不向后兼容现有实现。\n\n> [!重要提示]\n> 若要在 Python 3.8 和 3.9 上使用 Mimesis，最后兼容的版本是 Mimesis 11.1.0。请安装此特定版本以确保兼容性。\n\n> [!警告]\n> 自 19.0.0 版本起，Mimesis 已不再支持内置数据提供者。\n\n要安装 mimesis，请使用 pip：\n\n```\n~ pip install mimesis\n```\n\n## 文档\n\n完整的文档可在 [Read the Docs](https:\u002F\u002Fmimesis.name\u002F) 上找到。\n\n文档分为以下几个部分：\n\n-  [关于 Mimesis](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fabout.html)\n-  [快速入门](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fquickstart.html)\n-  [本地化环境](https:\u002F\u002Fmimesis.name\u002Flatest\u002Flocales.html)\n-  [数据提供者](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fproviders.html)\n-  [结构化数据生成](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fschema.html)\n-  [随机数与种子](https:\u002F\u002Fmimesis.name\u002Flatest\u002Frandom_and_seed.html)\n-  [与 factory_boy 的集成](https:\u002F\u002Fmimesis.name\u002Flatest\u002Ffactory_plugin.html)\n-  [API 参考](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fapi.html)\n-  [变更日志](https:\u002F\u002Fmimesis.name\u002Flatest\u002Findex.html#changelog)\n\n您可以通过向本仓库提交 Pull Request 来改进文档。\n\n## 使用方法\n\n该库非常易于使用，只需导入与所需数据类型相对应的 **数据提供者** 对象即可。\n\n例如，可以导入 [Person](https:\u002F\u002Fmimesis.name\u002Flatest\u002Fapi.html#person) 提供者来获取个人信息，包括姓名、姓氏、电子邮件等字段：\n\n```python\nfrom mimesis import Person\nfrom mimesis.locales import Locale\n\nperson = Person(Locale.EN)\n\nperson.full_name()\n# 输出: 'Brande Sears'\n\nperson.email(domains=['example.com'])\n# 输出: 'roccelline1878@example.com'\n\nperson.email(domains=['mimesis.name'], unique=True)\n# 输出: 'f272a05d39ec46fdac5be4ac7be45f3f@mimesis.name'\n\nperson.telephone(mask='1-4##-8##-5##3')\n# 输出: '1-436-896-5213'\n```\n\n## 许可证\n\nMimesis 采用 MIT 许可证授权。更多信息请参阅 [LICENSE](https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis\u002Fblob\u002Fmaster\u002FLICENSE) 文件。","# Mimesis 快速上手指南\n\nMimesis 是一个强大的 Python 假数据生成库，支持 46 种语言环境，以高性能和丰富的数据类型著称，适用于测试数据构造、原型开发等场景。\n\n## 环境准备\n\n- **操作系统**：跨平台支持（Linux, macOS, Windows）\n- **Python 版本**：\n  - 推荐版本：Python 3.10 - 3.14 或 PyPy\n  - 兼容版本：若需使用 Python 3.8 或 3.9，请安装 `mimesis==11.1.0`\n- **依赖管理**：确保已安装 `pip`\n\n## 安装步骤\n\n使用 pip 直接安装最新版：\n\n```bash\npip install mimesis\n```\n\n> **注意**：从 v19.0.0 起，内置提供者（builtin providers）已被移除；从 v20.0.0 起，基于 Schema 的生成机制将重构且不向后兼容。\n\n## 基本使用\n\nMimesis 的使用非常直观，只需导入对应的数据提供者（Provider）即可生成特定类型的假数据。以下以生成个人信息为例：\n\n```python\nfrom mimesis import Person\nfrom mimesis.locales import Locale\n\n# 初始化提供者，指定语言环境（此处为英文）\nperson = Person(Locale.EN)\n\n# 生成全名\nprint(person.full_name())\n# 输出示例: 'Brande Sears'\n\n# 生成指定域名的邮箱\nprint(person.email(domains=['example.com']))\n# 输出示例: 'roccelline1878@example.com'\n\n# 生成唯一邮箱\nprint(person.email(domains=['mimesis.name'], unique=True))\n# 输出示例: 'f272a05d39ec46fdac5be4ac7be45f3f@mimesis.name'\n\n# 生成符合掩码格式的电话号码\nprint(person.telephone(mask='1-4##-8##-5##3'))\n# 输出示例: '1-436-896-5213'\n```\n\n通过切换 `Locale` 参数（如 `Locale.ZH`），可轻松生成中文或其他语言的假数据。更多数据类型（如地址、金融、网络等）请参考官方文档中的数据提供者章节。","某电商初创团队正在开发新的推荐系统，急需构建包含十万级用户画像的测试数据集以验证算法性能。\n\n### 没有 mimesis 时\n- 开发人员需手动编写复杂的随机字符串生成逻辑，代码冗长且难以维护，极易出现逻辑漏洞。\n- 生成的测试数据缺乏真实感（如邮箱格式错误、姓名与国籍不匹配），导致算法在真实环境中表现偏差。\n- 难以快速切换不同国家地区的测试场景，硬编码本地化数据耗时费力，严重拖慢国际化测试进度。\n- 构造具有关联关系的复杂数据（如用户与其订单、地址的对应）需要大量自定义脚本，出错率高且效率低下。\n\n### 使用 mimesis 后\n- 仅需几行代码即可调用内置的 Person、Address 等提供者，瞬间生成结构清晰、格式规范的高质量假数据。\n- 利用其支持 46 种语言区域的特性，一键切换生成符合特定国家习惯的真实姓名、电话及地址，大幅提升测试覆盖率。\n- 通过基于 Schema 的生成器，轻松定义并批量产出带有复杂引用关系的嵌套数据，完美模拟真实业务场景。\n- 凭借极高的运行性能，在数秒内完成百万级数据量的构建，让团队能将精力集中于核心算法优化而非数据准备。\n\nmimesis 将繁琐的数据构造工作转化为简单的配置调用，让开发者能以最低成本获取高保真测试数据，显著加速产品迭代周期。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flk-geimfari_mimesis_fc54d617.png","lk-geimfari","Isaak","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Flk-geimfari_b0e5cd0a.jpg","Backend Engineer",null,"hey@isaak.dev","https:\u002F\u002Fisaak.dev","https:\u002F\u002Fgithub.com\u002Flk-geimfari",[85,89,93],{"name":86,"color":87,"percentage":88},"Python","#3572A5",99.8,{"name":90,"color":91,"percentage":92},"Makefile","#427819",0.1,{"name":94,"color":95,"percentage":92},"Shell","#89e051",4797,359,"2026-04-08T16:41:14","MIT","","不需要 GPU","未说明",{"notes":104,"python":105,"dependencies":106},"这是一个纯 Python 实现的假数据生成库，无重型依赖。注意：从 19.0.0 版本起不再支持内置提供者（builtin providers）；20.0.0 版本将重构基于模式的生成功能且不向后兼容。","3.10, 3.11, 3.12, 3.13, 3.14, pypy (Python 3.8 和 3.9 需使用版本 11.1.0)",[],[29,16],[68,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127],"fake","data","generator","fixtures","dummy","schema","testing","python","json-generator","mock","synthetic-data","datascience","dataframe","pandas","syntetic","polars","factory","factory-boy","relational","2026-03-27T02:49:30.150509","2026-04-10T20:47:27.977231",[],[132,137,142,147,152,157,162,167,172,177,182,187,192,197,202,207,212,217,222,227],{"id":133,"version":134,"summary_zh":135,"released_at":136},188995,"v19.1.0","- 为 `Person` 提供程序的 `phone_number()` 方法添加了对 E.164 电话号码格式的支持（#1690，由 @rodrigobnogueira 提交）\n- 向 `Address` 提供程序添加了 `secondary_address()` 方法（#1691，由 @rodrigobnogueira 提交）","2026-01-11T09:14:35",{"id":138,"version":139,"summary_zh":140,"released_at":141},188996,"v19.0.0","* 新增了用于各种数据转换的多项关键功能。完整列表请参阅 `mimesis\u002Fkeys.py`。\n* 从包中移除了 `pytest` 插件。请改用项目特定的 fixture 和 `Field` 类。\n* 从包中移除了 `builtins` 模块。请改用自定义字段处理器。\n* `Cryptographic` 提供者现在使用可种子化的随机数生成器，而非 `secrets` 模块。详情请参阅 #1656。\n* 向 `Cryptographic` 提供者添加了 `jwt`、`api_key` 和 `certificate_fingerprint` 方法。\n* 添加了用于生成关系型数据的 `SchemaBuilder`。\n* 向 `Internet` 提供者添加了 `ip_v4_cidr()`、`ip_v6_cidr()` 和 `cloud_region()` 方法。\n","2025-12-28T11:28:54",{"id":143,"version":144,"summary_zh":145,"released_at":146},188997,"v18.0.0","### 新增内容\n\n- 在多个阿拉伯语国家新增了对阿拉伯语的支持。\n\nMimesis 目前支持以下国家\u002F地区的数据：\n\n- 🇦🇪 阿联酋\n- 🇩🇿 阿尔及利亚\n- 🇪🇬 埃及\n- 🇯🇴 约旦\n- 🇴🇲 阿曼\n- 🇸🇾 叙利亚\n- 🇾🇪 也门\n\n### 贡献者\n\n特别感谢 @linuxscout、@yah04dev、@WatheqAlshowaiter 和 @zayedalsaidi。","2024-09-13T22:25:26",{"id":148,"version":149,"summary_zh":150,"released_at":151},188998,"v17.0.0","- 添加了 `mimesis.enums.DurationUnit` 枚举。\n- 为 `Datetime` 提供者添加了 `.duration()` 方法，用于生成随机时长。\n","2024-06-01T21:50:08",{"id":153,"version":154,"summary_zh":155,"released_at":156},188999,"v16.0.0","- 修复 Windows 系统中的文件权限。\n- 从数据集中移除了不安全和不当的词汇。详见 #1511。\n","2024-04-04T00:50:56",{"id":158,"version":159,"summary_zh":160,"released_at":161},189000,"15.1.0","- 修复了 `factory_boy` 插件中的少量问题。","2024-02-27T23:38:12",{"id":163,"version":164,"summary_zh":165,"released_at":166},189001,"v15.0.0","- 已将 `mimesis-factory` 集成到 Mimesis 本身中。更多信息请参阅 [mimesis-factory#246](https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis-factory\u002Fissues\u002F246>) 和 [mimesis#1494](https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis\u002Fissues\u002F1494>)。\n","2024-02-27T22:58:04",{"id":168,"version":169,"summary_zh":170,"released_at":171},189002,"v14.0.0","- 修复了 `Locale.HR` 区域设置中的街道后缀。\n- 将 `pytest-mimesis` 纳入 Mimesis 项目本身。详见 #1473。\n","2024-01-31T14:16:38",{"id":173,"version":174,"summary_zh":175,"released_at":176},189003,"v13.1.0","版本 13.1.0\n--------------\n\n- 修复了 `Generic` 的类型提示。参见 #1471\n- 在 `Person` 提供者中添加了 `birthdate()` 方法。参见 #1470。\n- 由于实用性不高，已从 `Person` 提供者中移除 `age()` 和 `work_experience()` 方法。如需生成随机整数，请使用 `person.random.randint()`。\n","2024-01-24T17:07:15",{"id":178,"version":179,"summary_zh":180,"released_at":181},189004,"v13.0.0","对克罗地亚同胞们来说是个好消息！Mimesis 目前已支持克罗地亚语（``Locale.HR``）。非常感谢 @CerealKiller0807 的贡献！","2024-01-19T12:12:40",{"id":183,"version":184,"summary_zh":185,"released_at":186},189005,"v12.1.1","- Fixed minimal required version of Python.","2024-01-17T10:44:07",{"id":188,"version":189,"summary_zh":190,"released_at":191},189006,"v12.1.0","- Methods ``gender_code()`` and ``gender_symbol()`` have been added for the ``Person`` provider.\r\n- The methods ``gender()`` and ``sex()`` no longer accept arguments like ``iso5218`` and ``symbol``. Please use ``gender_code()`` and ``gender_symbol()`` instead.\r\n- Added a stub for ``mimesis.providers.generic.py``, enabling type hints for ``Generic``.\r\n","2024-01-07T14:40:15",{"id":193,"version":194,"summary_zh":195,"released_at":196},189007,"v12.0.0","- Python 3.8 and 3.9 are no longer supported.\r\n- Added support for [field aliases](https:\u002F\u002Fmimesis.name\u002Fen\u002Fmaster\u002Fschema.html#using-field-aliases).\r\n- Added the method `calver` for `Development`.\r\n- Added the method `stage` for `Development`.\r\n- Added the method `country_emoji_flag` for `Address`.\r\n- Removed the method `hashtags` from the `Internet` provider. Use the `words` method from the `Text` provider instead.\r\n- Removed the `providers` parameter for `Field` and `Fieldset`. Use [custom field handlers](https:\u002F\u002Fmimesis.name\u002Fen\u002Fmaster\u002Fschema.html#custom-field-handlers) instead.\r\n- Removed the parameters `pre_release` and `calver` for `Development.version`. Use the `stage` and `calver` methods instead.\r\n- Moved the method `emoji` from the `Internet` provider to the `Text` provider.\r\n- Moved the method `dsn` from the `Development` provider to the `Internet` provider.\r\n- The `Text().emoji()` method now supports the `category` parameter and [`EmojiCategory`](https:\u002F\u002Fmimesis.name\u002Fen\u002Fmaster\u002Fapi.html#mimesis.enums.EmojyCategory) enum. It also returns an emoji instead of an emoji shortcut string.\r\n- Added the decorator `@handle` for `Field` and `Fieldset` to register custom fields.\r\n- Renamed `register_field` to `register_handler` for `Field` and `Fieldset`.\r\n- Renamed `register_fields` to `register_handlers` for `Field` and `Fieldset`.\r\n- Renamed `unregister_field` to `unregister_handler` for `Field` and `Fieldset`.\r\n- Renamed `unregister_fields` to `unregister_handlers` for `Field` and `Fieldset`.\r\n- Renamed `unregister_all_fields` to `unregister_all_handlers` for `Field` and `Fieldset`.","2024-01-07T11:45:54",{"id":198,"version":199,"summary_zh":200,"released_at":201},189008,"v11.1.0","**What's changed**:\r\n\r\n- Added validation for custom field names.","2023-08-19T20:40:36",{"id":203,"version":204,"summary_zh":205,"released_at":206},189009,"v11.0.0","## What's Changed\r\n\r\n* Custom field handlers by @lk-geimfari in https:\u002F\u002Fgithub.com\u002Flk-geimfari\u002Fmimesis\u002Fpull\u002F1399\r\n","2023-08-19T11:47:52",{"id":208,"version":209,"summary_zh":210,"released_at":211},189010,"v10.2.1","- Fix order of imports","2023-08-06T18:17:33",{"id":213,"version":214,"summary_zh":215,"released_at":216},189011,"v10.2.0","Version 10.2.0\r\n--------------\r\n\r\n**Added**:\r\n\r\n- Improved imports\r\n- Added a new method ``system_quality_attribute()`` for ``Development``.","2023-08-06T18:04:14",{"id":218,"version":219,"summary_zh":220,"released_at":221},189012,"v10.1.0","**Added**:\r\n\r\n- Added a new enum ``TimestampFormat`` for the ``timestamp()`` method.\r\n\r\n**Updated**:\r\n\r\n- The method ``timestamp()`` for ``Datetime()`` now expects one of the following timestamp formats: `TimestampFormat.POSIX`, `TimestampFormat.RFC_3339`, or `TimestampFormat.ISO_8601`. This method no longer accepts the ``posix`` parameter.\r\n- The ``datetime()`` method now has default parameters start and end set to the current year.\r\n","2023-05-26T18:25:24",{"id":223,"version":224,"summary_zh":225,"released_at":226},189013,"v10.0.0","**Updated**:\r\n\r\n- ``romanize()`` is a key function now. See docs for more information.\r\n\r\n\r\n**Removed**:\r\n\r\n- Removed method ``swear_word()`` of ``Text()``. This method is inappropriate and lacks practical utility.\r\n\r\n","2023-05-20T11:47:32",{"id":228,"version":229,"summary_zh":230,"released_at":231},189014,"v9.0.0","**Updated**:\r\n\r\n- Key functions now may accept additional argument ``random``.\r\n\r\n**Removed**:\r\n\r\n- The ``loop`` method for the ``Schema``, which was considered deprecated and unsafe, has been removed.\r\n- The ``iterations`` parameter for all methods of ``Schema`` has been removed. Instead, you now have to specify the number of iterations on instantiation of ``Schema`` passing the ``iterations`` parameter.\r\n- The ``iterator`` method for ``Schema`` has been removed. Instead, you can now use an instance of ``Schema`` directly as an iterator.\r\n- The multiplication is no longer supported for ``Schema``. Instead, you can use the ``iterations`` parameter on instantiation of ``Schema``.\r\n\r\n**Added**:\r\n\r\n- Add [``weighted_choice()``](https:\u002F\u002Fmimesis.name\u002Fen\u002Fv9.0.0\u002Fapi.html#mimesis.random.Random.weighted_choice) method for [``Random()``](https:\u002F\u002Fmimesis.name\u002Fen\u002Fv9.0.0\u002Fapi.html#random-object). See docs for more information.\r\n- Add module [``keys``](https:\u002F\u002Fmimesis.name\u002Fen\u002Fv9.0.0\u002Fapi.html#module-mimesis.keys) for generating key functions.","2023-04-30T12:40:02"]