[{"data":1,"prerenderedAt":2822},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"tool-tools\u002Fcoding\u002Flocal\u002Fcherry-studio":9,"tool-stats-coding\u002Flocal\u002Fcherry-studio":547,"cat-rank-coding-local":550,"tool-related-coding\u002Flocal\u002Fcherry-studio":2821},{"tools":4,"reviews":5},65,7,{"tools":4,"reviews":5,"playbooks":7,"news":8},10,8,{"id":10,"title":11,"alternatives":12,"api_compatible":25,"body":26,"category":475,"chinese_friendly":476,"cover":477,"description":478,"domestic":479,"extension":480,"faq":481,"free":479,"github":25,"languages":494,"meta":497,"models":25,"navigation":498,"notSuitable":25,"opensource":498,"path":499,"pillar":500,"platforms":501,"priceTable":506,"pricing":515,"published":516,"relatedPlaybooks":517,"relatedReviews":25,"score":523,"self_host":498,"seo":525,"slug":526,"sources":527,"stem":535,"suitable":25,"tagline":536,"tags":537,"updated":530,"verdict":544,"website":545,"__hash__":546},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio.md","Cherry Studio",[13,16,19,22],{"name":14,"url":15},"lobe-chat","\u002Ftools\u002Fcoding\u002Flocal\u002Flobe-chat",{"name":17,"url":18},"lm-studio","\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio",{"name":20,"url":21},"ollama","\u002Ftools\u002Fcoding\u002Flocal\u002Follama",{"name":23,"url":24},"open-webui","\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui",null,{"type":27,"value":28,"toc":460},"minimark",[29,34,38,41,44,91,94,108,114,118,123,140,145,162,165,192,195,346,349,381,385,408,411,434,437],[30,31,33],"h2",{"id":32},"tldr","TL;DR",[35,36,37],"p",{},"Cherry Studio 是一款开源、跨平台（Windows \u002F macOS \u002F Linux \u002F Android）的桌面 AI 客户端，定位『全能 AI 工作台』：把 OpenAI \u002F Anthropic \u002F Google \u002F DeepSeek 等云端模型，以及 Ollama \u002F LM Studio 本地模型，全部聚合到同一个桌面应用里管理。内置 300+ 助手模板、本地 RAG 知识库、Markdown + Mermaid 渲染、MCP 协议支持，所有对话数据本地存储 + WebDAV 备份。AGPL-3.0 开源、GitHub 60k+ stars，企业版可联系商务做私有化部署。",[35,39,40],{},"适合：中文 AI 重度用户、想统一管理多家模型、需要本地知识库 RAG、关注数据本地存储的开发者 \u002F 研究者。不适合：要 Web 端访问 \u002F Docker 自托管 \u002F 团队多人共享 \u002F iOS 端使用。",[30,42,43],{"id":43},"核心能力",[45,46,47,55,61,67,73,79,85],"ul",{},[48,49,50,54],"li",{},[51,52,53],"strong",{},"多模型聚合","：OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek \u002F Qwen \u002F Kimi \u002F Moonshot 等云端 + Ollama \u002F LM Studio 本地",[48,56,57,60],{},[51,58,59],{},"本地 RAG 知识库","：拖拽 PDF \u002F Word \u002F Excel \u002F PPT \u002F 网址 \u002F sitemap → 自动向量化 → 检索增强问答 + 来源追溯",[48,62,63,66],{},[51,64,65],{},"300+ 助手模板","：编程 \u002F 写作 \u002F 翻译 \u002F 学习 \u002F 角色扮演开箱即用，可自定义 System Prompt",[48,68,69,72],{},[51,70,71],{},"MCP 协议","：扩展工具调用 \u002F 联网搜索 \u002F 文件操作",[48,74,75,78],{},[51,76,77],{},"数据本地优先","：对话历史本地存储，WebDAV 同步，不上传第三方",[48,80,81,84],{},[51,82,83],{},"多模态","：图片识别 \u002F PDF 阅读 \u002F Markdown + Mermaid + 代码高亮",[48,86,87,90],{},[51,88,89],{},"AI 绘画 + 翻译","：内置主流 SD \u002F DALL·E \u002F 翻译 API 集成",[30,92,93],{"id":93},"价格",[45,95,96,102],{},[48,97,98,101],{},[51,99,100],{},"开源版","：完全免费，AGPL-3.0",[48,103,104,107],{},[51,105,106],{},"Enterprise","：私有化部署 + 团队协作 + 资源管控，联系销售",[109,110,111],"blockquote",{},[35,112,113],{},"模型 API 费用按你自己绑定的供应商计费；本地 Ollama \u002F LM Studio 零成本。",[30,115,117],{"id":116},"实测mac-m2-中型知识库","实测（Mac M2 + 中型知识库）",[35,119,120],{},[51,121,122],{},"亮点：",[45,124,125,128,131,134,137],{},[48,126,127],{},"中文 UI \u002F 文档 \u002F 社区都顶级，零门槛上手",[48,129,130],{},"本地 RAG 拖入 30+ PDF 后向量化 \u003C 2 分钟（用 bge-m3）",[48,132,133],{},"多模型并排回答：让 Claude \u002F GPT \u002F DeepSeek 同回一个问题做比较",[48,135,136],{},"MCP 接 Brave Search + 自定义工具流畅",[48,138,139],{},"WebDAV 同步坚果云 \u002F 阿里云盘，桌面 + 移动设备数据互通",[35,141,142],{},[51,143,144],{},"踩坑：",[45,146,147,150,153,156,159],{},[48,148,149],{},"没有 Web 端 \u002F Docker 自托管（要这个用 LobeChat）",[48,151,152],{},"iOS 版尚未发布（roadmap 中）",[48,154,155],{},"大型 PDF（>100 MB）向量化偶有失败，要切小",[48,157,158],{},"助手市场质量参差，要自筛",[48,160,161],{},"模型 API 调用全靠你自己付费，新手要先理解 API Key 概念",[30,163,164],{"id":164},"上手",[166,167,168,171,174,183,186,189],"ol",{},[48,169,170],{},"cherry-ai.com 下载客户端（或 GitHub releases）",[48,172,173],{},"设置 → 模型服务 → 填 OpenAI \u002F Claude \u002F DeepSeek API Key",[48,175,176,177],{},"（可选）本地：装 Ollama → Cherry Studio 自动识别 endpoint ",[178,179,180],"a",{"href":180,"rel":181},"http:\u002F\u002Flocalhost:11434",[182],"nofollow",[48,184,185],{},"新建知识库 → 拖文件 \u002F 加网址 → 等向量化",[48,187,188],{},"新对话 → 选模型 → 勾知识库 → 提问",[48,190,191],{},"进阶：自定义助手（System Prompt）+ MCP 扩展工具",[30,193,194],{"id":194},"对比",[196,197,198,219],"table",{},[199,200,201],"thead",{},[202,203,204,208,210,213,216],"tr",{},[205,206,207],"th",{},"维度",[205,209,11],{},[205,211,212],{},"LobeChat",[205,214,215],{},"LM Studio",[205,217,218],{},"Open WebUI",[220,221,222,239,253,269,282,298,313,329],"tbody",{},[202,223,224,228,231,234,236],{},[225,226,227],"td",{},"形态",[225,229,230],{},"桌面",[225,232,233],{},"Web + 桌面",[225,235,230],{},[225,237,238],{},"Docker \u002F 桌面",[202,240,241,243,246,248,251],{},[225,242,53],{},[225,244,245],{},"✅ 云 + 本地",[225,247,245],{},[225,249,250],{},"本地为主",[225,252,245],{},[202,254,255,258,261,263,266],{},[225,256,257],{},"知识库 RAG",[225,259,260],{},"✅ 强",[225,262,260],{},[225,264,265],{},"弱",[225,267,268],{},"✅",[202,270,271,274,276,278,280],{},[225,272,273],{},"MCP",[225,275,268],{},[225,277,268],{},[225,279,265],{},[225,281,268],{},[202,283,284,287,290,293,296],{},[225,285,286],{},"自托管 \u002F Web",[225,288,289],{},"无 Web",[225,291,292],{},"✅ Docker",[225,294,295],{},"无",[225,297,292],{},[202,299,300,303,306,308,311],{},[225,301,302],{},"中文",[225,304,305],{},"5\u002F5",[225,307,305],{},[225,309,310],{},"4\u002F5",[225,312,310],{},[202,314,315,318,321,324,327],{},[225,316,317],{},"开源协议",[225,319,320],{},"AGPL-3.0",[225,322,323],{},"MIT",[225,325,326],{},"闭源（免费）",[225,328,323],{},[202,330,331,334,337,340,343],{},[225,332,333],{},"GitHub Stars",[225,335,336],{},"60k+",[225,338,339],{},"72k+",[225,341,342],{},"–",[225,344,345],{},"126k+",[30,347,348],{"id":348},"避坑",[45,350,351,357,363,369,375],{},[48,352,353,356],{},[51,354,355],{},"API Key 别明文外泄","：客户端配置文件以明文存 Key，机器借出前先清；团队共享用企业版 \u002F 自建中转",[48,358,359,362],{},[51,360,361],{},"知识库别一次塞太多","：单库 1000+ 文档检索质量明显下降，按主题切分多个知识库",[48,364,365,368],{},[51,366,367],{},"嵌入模型选择","：免费 bge-m3 够用；专业用付费 Pro\u002FBAAI\u002Fbge-m3 或 OpenAI text-embedding-3",[48,370,371,374],{},[51,372,373],{},"WebDAV 同步先小范围测","：知识库向量数据较大，先备份对话再开同步",[48,376,377,380],{},[51,378,379],{},"MCP 工具来源要可控","：MCP 是给 AI 真实工具能力，第三方插件审一遍代码",[30,382,384],{"id":383},"适合-不适合","适合 \u002F 不适合",[45,386,387,390,393,396,399,402,405],{},[48,388,389],{},"✅ 中文用户、AI 重度使用 \u002F 多模型管理",[48,391,392],{},"✅ 需要本地 RAG 知识库",[48,394,395],{},"✅ 关注数据隐私 \u002F 本地存储",[48,397,398],{},"✅ 想用 Ollama \u002F LM Studio 本地模型",[48,400,401],{},"❌ 需要 Web 端 \u002F Docker 自托管",[48,403,404],{},"❌ 团队多人共享 \u002F SSO",[48,406,407],{},"❌ iOS 主力用户",[30,409,410],{"id":410},"相关阅读",[45,412,413,418,423,428],{},[48,414,415],{},[178,416,417],{"href":15},"LobeChat 评测",[48,419,420],{},[178,421,422],{"href":18},"LM Studio 评测",[48,424,425],{},[178,426,427],{"href":21},"Ollama 评测",[48,429,430],{},[178,431,433],{"href":432},"\u002Fplaybook\u002Fonboarding\u002Frag-pipeline-build","RAG Pipeline 搭建 Playbook",[30,435,436],{"id":436},"来源",[166,438,439,446,453],{},[48,440,441,442],{},"Cherry Studio 官网（功能 + 下载）",[178,443,444],{"href":444,"rel":445},"https:\u002F\u002Fwww.cherry-ai.com\u002F",[182],[48,447,448,449],{},"MBLUO Studio — Cherry Studio 评测 2026 ",[178,450,451],{"href":451,"rel":452},"https:\u002F\u002Fmbluostudio.com\u002Ftools\u002Fcherry-studio",[182],[48,454,455,456],{},"Cursor IDE 博客 — Cherry Studio 完全指南（2025-03）",[178,457,458],{"href":458,"rel":459},"https:\u002F\u002Fwww.cursor-ide.com\u002Fblog\u002Fcherry-studio-guide",[182],{"title":461,"searchDepth":462,"depth":462,"links":463},"",3,[464,466,467,468,469,470,471,472,473,474],{"id":32,"depth":465,"text":33},2,{"id":43,"depth":465,"text":43},{"id":93,"depth":465,"text":93},{"id":116,"depth":465,"text":117},{"id":164,"depth":465,"text":164},{"id":194,"depth":465,"text":194},{"id":348,"depth":465,"text":348},{"id":383,"depth":465,"text":384},{"id":410,"depth":465,"text":410},{"id":436,"depth":465,"text":436},"local",5,"\u002Fimg\u002Ftools\u002Fcherry-studio.webp","Cherry Studio 真实评测：开源跨平台桌面 AI 客户端，集成 OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek + Ollama \u002F LM Studio 本地模型，内置 300+ 助手模板 + 本地 RAG 知识库。AGPL-3.0 开源、GitHub 60k+ stars，企业版另询。",false,"md",[482,485,488,491],{"q":483,"a":484},"Cherry Studio 真的免费吗？","是。客户端完全免费、AGPL-3.0 开源，模型调用走你自己的 API Key（OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek 等付费）或本地 Ollama \u002F LM Studio（零成本）。",{"q":486,"a":487},"本地知识库怎么用？","在『知识库』面板新建，拖文件 \u002F 加网址 \u002F 填 sitemap，系统自动向量化（默认 BAAI\u002Fbge-m3 或硅基流动的 Pro 版）；提问时勾选要检索的知识库，AI 会基于检索片段答题并标出来源。",{"q":489,"a":490},"和 LobeChat 怎么选？","都开源、多模型、有 RAG。LobeChat 是 Web + 桌面双形态，可自托管 Docker，72k stars；Cherry Studio 是纯桌面（Win\u002FMac\u002FLinux\u002FAndroid），不支持 Web 部署但桌面体验更精细，60k+ stars。要 Web 访问 \u002F 公司多人共享选 LobeChat；个人重度选 Cherry Studio。",{"q":492,"a":493},"支持 MCP \u002F 插件吗？","支持 MCP（Model Context Protocol）扩展，配合自定义助手（System Prompt）可扩展工具调用、联网搜索等能力。",[495,496],"zh","en",{},true,"\u002Ftools\u002Fcoding\u002Flocal\u002Fcherry-studio","coding",[502,503,504,505],"windows","macos","linux","android",[507,511],{"plan":100,"price":508,"features":509,"notes":510},"免费","300+ 助手模板 \u002F 云端 + 本地模型 \u002F 知识库 \u002F MCP \u002F WebDAV 备份","AGPL-3.0 开源",{"plan":106,"price":512,"features":513,"notes":514},"联系销售","私有化部署 \u002F 团队协作 \u002F AI 资源管控 \u002F 知识库管理","面向企业团队","开源免费 \u002F 企业版联系销售","2026-06-19",[518,520],{"name":519,"url":432},"RAG Pipeline 搭建",{"name":521,"url":522},"Cursor MCP 深度集成","\u002Fplaybook\u002Fonboarding\u002Fcursor-mcp-deep-integration",{"power":524,"ux":476,"price":476,"cn_support":476,"stability":524},4,{"title":11,"description":478},"coding\u002Flocal\u002Fcherry-studio",[528,531,533],{"name":529,"url":444,"accessed":530},"Cherry Studio 官网","2026-06-24",{"name":532,"url":451,"accessed":530},"MBLUO Studio — Cherry Studio 评测",{"name":534,"url":458,"accessed":530},"Cursor IDE 博客 — Cherry Studio 指南","tools\u002Fcoding\u002Flocal\u002Fcherry-studio","全能 AI 客户端：多模型聚合 + 本地知识库 + 300+ 助手模板，跨平台桌面应用",[475,538,539,540,541,542,543],"desktop","multi-model","knowledge-base","rag","open-source","china","国产 AI 桌面客户端第一梯队，多模型聚合 + 本地 RAG + 中文体验顶级。需要 Web 部署 \u002F 自托管选 LobeChat；只要桌面体验完整选 Cherry Studio。","https:\u002F\u002Fcherry-ai.com","iH3iDqpTojLLBznROeYXtARpOLDs1yflKA9EMftZkTk",{"ok":498,"slug":548,"viewCount":549,"clickCount":549,"avgRating":549,"ratingCount":549},"coding%2Flocal%2Fcherry-studio",0,[551,896,1387,1880,2351],{"id":10,"title":11,"alternatives":552,"api_compatible":25,"body":557,"category":475,"chinese_friendly":476,"cover":477,"description":478,"domestic":479,"extension":480,"faq":875,"free":479,"github":25,"languages":880,"meta":881,"models":25,"navigation":498,"notSuitable":25,"opensource":498,"path":499,"pillar":500,"platforms":882,"priceTable":883,"pricing":515,"published":516,"relatedPlaybooks":886,"relatedReviews":25,"score":889,"self_host":498,"seo":890,"slug":526,"sources":891,"stem":535,"suitable":25,"tagline":536,"tags":895,"updated":530,"verdict":544,"website":545,"__hash__":546},[553,554,555,556],{"name":14,"url":15},{"name":17,"url":18},{"name":20,"url":21},{"name":23,"url":24},{"type":27,"value":558,"toc":863},[559,561,563,565,567,597,599,609,613,615,619,631,635,647,649,666,668,782,784,806,808,824,826,844,846],[30,560,33],{"id":32},[35,562,37],{},[35,564,40],{},[30,566,43],{"id":43},[45,568,569,573,577,581,585,589,593],{},[48,570,571,54],{},[51,572,53],{},[48,574,575,60],{},[51,576,59],{},[48,578,579,66],{},[51,580,65],{},[48,582,583,72],{},[51,584,71],{},[48,586,587,78],{},[51,588,77],{},[48,590,591,84],{},[51,592,83],{},[48,594,595,90],{},[51,596,89],{},[30,598,93],{"id":93},[45,600,601,605],{},[48,602,603,101],{},[51,604,100],{},[48,606,607,107],{},[51,608,106],{},[109,610,611],{},[35,612,113],{},[30,614,117],{"id":116},[35,616,617],{},[51,618,122],{},[45,620,621,623,625,627,629],{},[48,622,127],{},[48,624,130],{},[48,626,133],{},[48,628,136],{},[48,630,139],{},[35,632,633],{},[51,634,144],{},[45,636,637,639,641,643,645],{},[48,638,149],{},[48,640,152],{},[48,642,155],{},[48,644,158],{},[48,646,161],{},[30,648,164],{"id":164},[166,650,651,653,655,660,662,664],{},[48,652,170],{},[48,654,173],{},[48,656,176,657],{},[178,658,180],{"href":180,"rel":659},[182],[48,661,185],{},[48,663,188],{},[48,665,191],{},[30,667,194],{"id":194},[196,669,670,684],{},[199,671,672],{},[202,673,674,676,678,680,682],{},[205,675,207],{},[205,677,11],{},[205,679,212],{},[205,681,215],{},[205,683,218],{},[220,685,686,698,710,722,734,746,758,770],{},[202,687,688,690,692,694,696],{},[225,689,227],{},[225,691,230],{},[225,693,233],{},[225,695,230],{},[225,697,238],{},[202,699,700,702,704,706,708],{},[225,701,53],{},[225,703,245],{},[225,705,245],{},[225,707,250],{},[225,709,245],{},[202,711,712,714,716,718,720],{},[225,713,257],{},[225,715,260],{},[225,717,260],{},[225,719,265],{},[225,721,268],{},[202,723,724,726,728,730,732],{},[225,725,273],{},[225,727,268],{},[225,729,268],{},[225,731,265],{},[225,733,268],{},[202,735,736,738,740,742,744],{},[225,737,286],{},[225,739,289],{},[225,741,292],{},[225,743,295],{},[225,745,292],{},[202,747,748,750,752,754,756],{},[225,749,302],{},[225,751,305],{},[225,753,305],{},[225,755,310],{},[225,757,310],{},[202,759,760,762,764,766,768],{},[225,761,317],{},[225,763,320],{},[225,765,323],{},[225,767,326],{},[225,769,323],{},[202,771,772,774,776,778,780],{},[225,773,333],{},[225,775,336],{},[225,777,339],{},[225,779,342],{},[225,781,345],{},[30,783,348],{"id":348},[45,785,786,790,794,798,802],{},[48,787,788,356],{},[51,789,355],{},[48,791,792,362],{},[51,793,361],{},[48,795,796,368],{},[51,797,367],{},[48,799,800,374],{},[51,801,373],{},[48,803,804,380],{},[51,805,379],{},[30,807,384],{"id":383},[45,809,810,812,814,816,818,820,822],{},[48,811,389],{},[48,813,392],{},[48,815,395],{},[48,817,398],{},[48,819,401],{},[48,821,404],{},[48,823,407],{},[30,825,410],{"id":410},[45,827,828,832,836,840],{},[48,829,830],{},[178,831,417],{"href":15},[48,833,834],{},[178,835,422],{"href":18},[48,837,838],{},[178,839,427],{"href":21},[48,841,842],{},[178,843,433],{"href":432},[30,845,436],{"id":436},[166,847,848,853,858],{},[48,849,441,850],{},[178,851,444],{"href":444,"rel":852},[182],[48,854,448,855],{},[178,856,451],{"href":451,"rel":857},[182],[48,859,455,860],{},[178,861,458],{"href":458,"rel":862},[182],{"title":461,"searchDepth":462,"depth":462,"links":864},[865,866,867,868,869,870,871,872,873,874],{"id":32,"depth":465,"text":33},{"id":43,"depth":465,"text":43},{"id":93,"depth":465,"text":93},{"id":116,"depth":465,"text":117},{"id":164,"depth":465,"text":164},{"id":194,"depth":465,"text":194},{"id":348,"depth":465,"text":348},{"id":383,"depth":465,"text":384},{"id":410,"depth":465,"text":410},{"id":436,"depth":465,"text":436},[876,877,878,879],{"q":483,"a":484},{"q":486,"a":487},{"q":489,"a":490},{"q":492,"a":493},[495,496],{},[502,503,504,505],[884,885],{"plan":100,"price":508,"features":509,"notes":510},{"plan":106,"price":512,"features":513,"notes":514},[887,888],{"name":519,"url":432},{"name":521,"url":522},{"power":524,"ux":476,"price":476,"cn_support":476,"stability":524},{"title":11,"description":478},[892,893,894],{"name":529,"url":444,"accessed":530},{"name":532,"url":451,"accessed":530},{"name":534,"url":458,"accessed":530},[475,538,539,540,541,542,543],{"id":897,"title":215,"alternatives":898,"api_compatible":25,"body":904,"category":475,"chinese_friendly":462,"cover":1334,"description":1335,"domestic":479,"extension":480,"faq":1336,"free":479,"github":25,"languages":1349,"meta":1350,"models":25,"navigation":498,"notSuitable":25,"opensource":479,"path":18,"pillar":500,"platforms":1351,"priceTable":1352,"pricing":1360,"published":516,"relatedPlaybooks":1361,"relatedReviews":25,"score":1365,"self_host":498,"seo":1366,"slug":1367,"sources":1368,"stem":1375,"suitable":25,"tagline":1376,"tags":1377,"updated":530,"verdict":1384,"website":1385,"__hash__":1386},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Flm-studio.md",[899,900,901,903],{"name":20,"url":21},{"name":23,"url":24},{"name":902,"url":499},"cherry-studio",{"name":14,"url":15},{"type":27,"value":905,"toc":1322},[906,908,916,919,921,978,980,994,999,1003,1007,1024,1028,1045,1047,1073,1075,1214,1216,1248,1250,1273,1275,1297,1299],[30,907,33],{"id":32},[35,909,910,911,915],{},"LM Studio 是 Windows \u002F macOS \u002F Linux 桌面应用，让你像浏览 App Store 一样发现、下载、运行本地大模型（GGUF \u002F MLX 格式）。底层基于 llama.cpp + MLX，Mac M 系列原生优化。0.3+ 起新增 Headless 模式 + ",[912,913,914],"code",{},"lms"," CLI，可在服务器跑 OpenAI 兼容 API（默认 :1234）。个人 \u002F 评估完全免费，商用咨询。",[35,917,918],{},"适合：本地 LLM 入门 \u002F 评估、Mac 用户、需要 GUI 调参 \u002F 模型比较、想给 IDE \u002F 应用接本地 OpenAI 兼容 endpoint 的开发者。不适合：多用户并发生产服务（用 vLLM）、嵌入式 \u002F 边缘部署（用 llama.cpp）、纯 CLI 工作流（用 Ollama）。",[30,920,43],{"id":43},[45,922,923,929,935,941,951,960,966,972],{},[48,924,925,928],{},[51,926,927],{},"模型浏览器","：内置 Hugging Face 检索，按 GGUF \u002F MLX \u002F 大小筛选、一键下载",[48,930,931,934],{},[51,932,933],{},"聊天界面","：System Prompt \u002F temperature \u002F top-p \u002F context size 可视化调参",[48,936,937,940],{},[51,938,939],{},"多模型并存 \u002F 切换","：同时加载多模型在不同会话中比较",[48,942,943,946,947,950],{},[51,944,945],{},"OpenAI 兼容 Local Server","：",[912,948,949],{},"http:\u002F\u002Flocalhost:1234\u002Fv1","，任何 SDK 即接即用",[48,952,953,946,956,959],{},[51,954,955],{},"Headless \u002F CLI",[912,957,958],{},"lms server start --port 1234","，无 GUI 可跑",[48,961,962,965],{},[51,963,964],{},"PDF \u002F 文档对话","：内置基础 RAG，丢文件就能聊",[48,967,968,971],{},[51,969,970],{},"MLX 原生支持（Mac）","：M1+ 上比 GGUF + Metal 快 30–50%",[48,973,974,977],{},[51,975,976],{},"持续批处理","：Codersera 2026 测得 50–90 tok\u002Fs（消费级 GPU + 中等模型）",[30,979,93],{"id":93},[45,981,982,988],{},[48,983,984,987],{},[51,985,986],{},"个人 \u002F 评估","：免费，全功能可用",[48,989,990,993],{},[51,991,992],{},"商用","：邮件 \u002F 官网联系 LM Studio 团队",[109,995,996],{},[35,997,998],{},"模型本身免费（开源权重），LM Studio 不抽水任何 token 费用。",[30,1000,1002],{"id":1001},"实测mac-m2-pro-qwen3-coder-7b-gguf-q4_k_m","实测（Mac M2 Pro + Qwen3-Coder-7B GGUF Q4_K_M）",[35,1004,1005],{},[51,1006,122],{},[45,1008,1009,1012,1015,1018,1021],{},[48,1010,1011],{},"模型浏览器极舒服：搜「qwen3-coder」直接列出 GGUF + MLX 各 quant，标硬件兼容度",[48,1013,1014],{},"加载 7B Q4 模型 \u003C 3 秒，生成 ~75 tok\u002Fs",[48,1016,1017],{},"Local Server 开了 Cursor 直接接 baseURL → 本地代码补全零成本",[48,1019,1020],{},"MLX 版同模型 ~110 tok\u002Fs，差距显著",[48,1022,1023],{},"多窗口加载 2 个模型并排测，调 prompt 直观",[35,1025,1026],{},[51,1027,144],{},[45,1029,1030,1033,1036,1039,1042],{},[48,1031,1032],{},"模型库依赖 Hugging Face，国内访问要镜像 \u002F 代理",[48,1034,1035],{},"GPU 显存吃满后会自动 offload 到 CPU，无提示就慢下来",[48,1037,1038],{},"Headless 模式相对 Ollama 偏新，文档稍少",[48,1040,1041],{},"闭源应用（虽免费），不适合企业合规挂钩",[48,1043,1044],{},"中文 UI 可用但部分菜单仍英文",[30,1046,164],{"id":164},[166,1048,1049,1052,1055,1058,1061,1068],{},[48,1050,1051],{},"lmstudio.ai 下载（Mac \u002F Windows \u002F Linux）",[48,1053,1054],{},"打开 → Discover 标签 → 搜模型（如 qwen3-coder、deepseek-v3 GGUF\u002FMLX）→ Download",[48,1056,1057],{},"Chat 标签 → 选模型 → 调参聊天",[48,1059,1060],{},"Local Server 标签 → Start Server → 默认端口 1234",[48,1062,1063,1064,1067],{},"在你的应用里：",[912,1065,1066],{},"baseURL = \"http:\u002F\u002Flocalhost:1234\u002Fv1\"","，API Key 任意",[48,1069,1070,1071],{},"Headless：",[912,1072,958],{},[30,1074,194],{"id":194},[196,1076,1077,1093],{},[199,1078,1079],{},[202,1080,1081,1083,1085,1088,1090],{},[205,1082,207],{},[205,1084,215],{},[205,1086,1087],{},"Ollama",[205,1089,218],{},[205,1091,1092],{},"llama.cpp",[220,1094,1095,1111,1127,1142,1157,1171,1185,1198],{},[202,1096,1097,1099,1102,1105,1108],{},[225,1098,227],{},[225,1100,1101],{},"GUI + CLI",[225,1103,1104],{},"CLI Daemon",[225,1106,1107],{},"Docker UI",[225,1109,1110],{},"二进制",[202,1112,1113,1116,1119,1122,1124],{},[225,1114,1115],{},"模型浏览",[225,1117,1118],{},"✅ 内置",[225,1120,1121],{},"CLI pull",[225,1123,295],{},[225,1125,1126],{},"手动",[202,1128,1129,1132,1134,1137,1140],{},[225,1130,1131],{},"参数调优 GUI",[225,1133,268],{},[225,1135,1136],{},"❌",[225,1138,1139],{},"部分",[225,1141,1136],{},[202,1143,1144,1147,1150,1153,1155],{},[225,1145,1146],{},"OpenAI 兼容 API",[225,1148,1149],{},"✅ :1234",[225,1151,1152],{},"✅ :11434",[225,1154,268],{},[225,1156,268],{},[202,1158,1159,1162,1164,1167,1169],{},[225,1160,1161],{},"MLX (Mac)",[225,1163,268],{},[225,1165,1166],{},"✅ 0.19+",[225,1168,342],{},[225,1170,342],{},[202,1172,1173,1176,1178,1180,1182],{},[225,1174,1175],{},"多用户并发",[225,1177,265],{},[225,1179,265],{},[225,1181,268],{},[225,1183,1184],{},"中",[202,1186,1187,1190,1192,1194,1196],{},[225,1188,1189],{},"开源",[225,1191,326],{},[225,1193,323],{},[225,1195,323],{},[225,1197,323],{},[202,1199,1200,1203,1206,1209,1211],{},[225,1201,1202],{},"上手难度",[225,1204,1205],{},"极低",[225,1207,1208],{},"低",[225,1210,1184],{},[225,1212,1213],{},"高",[30,1215,348],{"id":348},[45,1217,1218,1224,1230,1236,1242],{},[48,1219,1220,1223],{},[51,1221,1222],{},"国内下模型走镜像","：HF 直连慢 \u002F 卡，配 HF_ENDPOINT=hf-mirror.com",[48,1225,1226,1229],{},[51,1227,1228],{},"显存爆 ≠ 报错","：GPU 装不下会无声 offload 到 CPU，关注生成速度，必要时降 quant 或换小模型",[48,1231,1232,1235],{},[51,1233,1234],{},"MLX 优先（Mac M 系列）","：能下 MLX 版就别下 GGUF，速度差距明显",[48,1237,1238,1241],{},[51,1239,1240],{},"Local Server 暴露要谨慎","：默认 0.0.0.0 + 无鉴权，对外开放前加反代 + Bearer",[48,1243,1244,1247],{},[51,1245,1246],{},"闭源合规要核","：企业内部使用前查 license；商用必须联系官方",[30,1249,384],{"id":383},[45,1251,1252,1255,1258,1261,1264,1267,1270],{},[48,1253,1254],{},"✅ 本地 LLM 入门 \u002F 评估",[48,1256,1257],{},"✅ Mac M 系列用户",[48,1259,1260],{},"✅ 想给 Cursor \u002F Cline 接本地 OpenAI 兼容 endpoint",[48,1262,1263],{},"✅ 需要 GUI 调参 \u002F 模型比较",[48,1265,1266],{},"❌ 多用户并发生产服务",[48,1268,1269],{},"❌ 嵌入式 \u002F 边缘设备",[48,1271,1272],{},"❌ 强合规 \u002F 必须开源审计",[30,1274,410],{"id":410},[45,1276,1277,1281,1286,1291],{},[48,1278,1279],{},[178,1280,427],{"href":21},[48,1282,1283],{},[178,1284,1285],{"href":24},"Open WebUI 评测",[48,1287,1288],{},[178,1289,1290],{"href":499},"Cherry Studio 评测",[48,1292,1293],{},[178,1294,1296],{"href":1295},"\u002Fplaybook\u002Fonboarding\u002Fclaude-code-getting-started","Claude Code 上手 Playbook",[30,1298,436],{"id":436},[166,1300,1301,1308,1315],{},[48,1302,1303,1304],{},"LM Studio 官网 ",[178,1305,1306],{"href":1306,"rel":1307},"https:\u002F\u002Flmstudio.ai\u002F",[182],[48,1309,1310,1311],{},"Codersera — LM Studio Complete Guide 2026 ",[178,1312,1313],{"href":1313,"rel":1314},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Flm-studio-complete-guide-2026\u002F",[182],[48,1316,1317,1318],{},"Codersera — Ollama vs LM Studio vs vLLM vs llama.cpp vs MLX 2026 ",[178,1319,1320],{"href":1320,"rel":1321},"https:\u002F\u002Fcodersera.com\u002Fblog\u002Follama-vs-lm-studio-vs-vllm-vs-llama-cpp-vs-mlx-2026\u002F",[182],{"title":461,"searchDepth":462,"depth":462,"links":1323},[1324,1325,1326,1327,1328,1329,1330,1331,1332,1333],{"id":32,"depth":465,"text":33},{"id":43,"depth":465,"text":43},{"id":93,"depth":465,"text":93},{"id":1001,"depth":465,"text":1002},{"id":164,"depth":465,"text":164},{"id":194,"depth":465,"text":194},{"id":348,"depth":465,"text":348},{"id":383,"depth":465,"text":384},{"id":410,"depth":465,"text":410},{"id":436,"depth":465,"text":436},"\u002Fimg\u002Ftools\u002Flm-studio.webp","LM Studio 真实评测：跨平台桌面应用，运行本地 GGUF \u002F MLX 大模型。50–90 tok\u002Fs 持续批处理、OpenAI 兼容本地 API（默认端口 1234）、Headless 模式、Mac \u002F Win 双端。对个人开发者免费，企业咨询。",[1337,1340,1343,1346],{"q":1338,"a":1339},"和 Ollama 怎么选？","LM Studio 是 GUI 优先（模型浏览器 + 参数面板 + 聊天界面），适合个人 \u002F 评估 \u002F 上手。Ollama 是 CLI \u002F Daemon 优先（后台跑 + REST API），适合应用嵌入 \u002F 脚本调用。两者都基于 llama.cpp，在 Mac M 系列上都已用 MLX。",{"q":1341,"a":1342},"支持 MLX 吗？","支持。Mac M1+ 上可加载 MLX 格式模型，速度比 GGUF + Metal 快 30–50%。模型搜索时筛选 MLX 即可。",{"q":1344,"a":1345},"OpenAI 兼容 API 怎么用？","开 Local Server → 默认端口 1234 → `http:\u002F\u002Flocalhost:1234\u002Fv1`。任何 OpenAI SDK 把 baseURL 改这个就能跑本地模型，零代码改动。",{"q":1347,"a":1348},"Headless 模式？","0.3+ 起支持 `lms server start` CLI 启动后台服务，无 GUI 即可跑 OpenAI 兼容 API，适合服务器 \u002F SSH 场景。",[496,495],{},[502,503,504],[1353,1356],{"plan":986,"price":508,"features":1354,"notes":1355},"全功能 GUI + Headless API + GGUF\u002FMLX","供个人 \u002F 评估使用",{"plan":992,"price":1357,"features":1358,"notes":1359},"联系咨询","团队部署 \u002F 商用 license","邮件 \u002F 官网联系","免费（个人 \u002F 评估） \u002F 企业 \u002F 商用咨询",[1362,1363],{"name":519,"url":432},{"name":1364,"url":1295},"Claude Code 上手",{"power":524,"ux":476,"price":476,"cn_support":462,"stability":524},{"title":215,"description":1335},"coding\u002Flocal\u002Flm-studio",[1369,1371,1373],{"name":1370,"url":1306,"accessed":530},"LM Studio 官网",{"name":1372,"url":1313,"accessed":530},"Codersera — LM Studio Complete Guide 2026",{"name":1374,"url":1320,"accessed":530},"Codersera — Ollama vs LM Studio vs vLLM 2026","tools\u002Fcoding\u002Flocal\u002Flm-studio","本地 LLM 的 GUI 首选——模型浏览器 + GGUF\u002FMLX 推理 + OpenAI 兼容 API + Mac 原生优化",[475,1378,1379,1380,1381,1382,1383],"gui","gguf","mlx","llama-cpp","mac","openai-compatible","Mac \u002F Windows 桌面本地 LLM 的 GUI 首选——上手最快、模型浏览最舒服、自带 OpenAI 兼容 API。批量服务 \u002F 多用户场景用 vLLM；纯 CLI \u002F 嵌入应用走 Ollama。","https:\u002F\u002Flmstudio.ai","mFDr4hC-XSmdJmmz0qk02JMEKIemqp0HnyhGIxnoWo0",{"id":1388,"title":212,"alternatives":1389,"api_compatible":25,"body":1394,"category":475,"chinese_friendly":476,"cover":1830,"description":1831,"domestic":479,"extension":480,"faq":1832,"free":479,"github":25,"languages":1845,"meta":1846,"models":25,"navigation":498,"notSuitable":25,"opensource":498,"path":15,"pillar":500,"platforms":1847,"priceTable":1850,"pricing":1858,"published":516,"relatedPlaybooks":1859,"relatedReviews":25,"score":1862,"self_host":498,"seo":1863,"slug":1864,"sources":1865,"stem":1872,"suitable":25,"tagline":1873,"tags":1874,"updated":530,"verdict":1877,"website":1878,"__hash__":1879},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Flobe-chat.md",[1390,1391,1392,1393],{"name":902,"url":499},{"name":23,"url":24},{"name":20,"url":21},{"name":17,"url":18},{"type":27,"value":1395,"toc":1818},[1396,1398,1405,1408,1410,1472,1474,1487,1490,1494,1498,1518,1522,1539,1541,1567,1569,1705,1707,1745,1747,1773,1775,1793,1795],[30,1397,33],{"id":32},[35,1399,1400,1401,1404],{},"LobeChat 是 LobeHub 团队的开源 AI 聊天框架，2023 年发布、GitHub 72k+ stars、MIT 协议。",[51,1402,1403],{},"Web + 桌面 + Docker 自托管三形态","，把 OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek \u002F Qwen \u002F Kimi \u002F Ollama \u002F LM Studio 等 80+ 模型聚合到一个现代设计的客户端里。内置 RAG 知识库 + 插件市场 + 助手市场 + 多模型对比 + MCP，是当下综合最强的多模型 AI 客户端之一。",[35,1406,1407],{},"适合：需要 Web 端访问、Docker 自托管、多模型对比、丰富助手市场的用户；中文重度用户；想给团队 \u002F 家庭部署一个共享 AI 工作台。不适合：只用桌面 + 不需要 Web（Cherry Studio 同样优秀且更精细）、强企业 RBAC + 多租户（Open WebUI 多用户更完善）。",[30,1409,43],{"id":43},[45,1411,1412,1417,1423,1428,1434,1440,1445,1450,1456,1462],{},[48,1413,1414,1416],{},[51,1415,53],{},"：OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek \u002F Qwen \u002F Kimi \u002F 豆包 \u002F Groq \u002F Together \u002F OpenRouter \u002F Ollama \u002F LM Studio",[48,1418,1419,1422],{},[51,1420,1421],{},"多模型对比","：同 prompt 给多模型并排回答",[48,1424,1425,1427],{},[51,1426,59],{},"：上传 PDF \u002F Word \u002F 网页 → 向量化 → 检索引用",[48,1429,1430,1433],{},[51,1431,1432],{},"插件市场","：联网搜索 \u002F 代码执行 \u002F 图像生成 \u002F 翻译等几十款官方插件",[48,1435,1436,1439],{},[51,1437,1438],{},"助手市场","：几百个预设 AI 角色，一键导入",[48,1441,1442,1444],{},[51,1443,71],{},"：扩展任意工具能力",[48,1446,1447],{},[51,1448,1449],{},"代码解释器 \u002F 文件上传 \u002F TTS \u002F 多模态",[48,1451,1452,1455],{},[51,1453,1454],{},"Web + 桌面 + Docker","：三形态，数据可完全本地",[48,1457,1458,1461],{},[51,1459,1460],{},"LobeHub Cloud","：官方云托管，免部署",[48,1463,1464,1467,1468,1471],{},[51,1465,1466],{},"快捷指令 \u002F 工作流","：自定义 prompt 模板，",[912,1469,1470],{},"\u002Fpodcast-summary"," 类用法",[30,1473,93],{"id":93},[45,1475,1476,1482],{},[48,1477,1478,1481],{},[51,1479,1480],{},"自托管 \u002F 桌面","：完全免费、MIT 开源",[48,1483,1484,1486],{},[51,1485,1460],{},"：订阅制，云端托管 + 团队协作 + 同步",[35,1488,1489],{},"模型 API 费用按你自己的供应商付费；本地 Ollama \u002F LM Studio 零成本。",[30,1491,1493],{"id":1492},"实测m2-自托管-docker连-openai-deepseek-本地-ollama","实测（M2 + 自托管 Docker，连 OpenAI + DeepSeek + 本地 Ollama）",[35,1495,1496],{},[51,1497,122],{},[45,1499,1500,1503,1506,1509,1512,1515],{},[48,1501,1502],{},"界面颜值是这一类工具里第一档（深色 \u002F 透明 \u002F 现代感）",[48,1504,1505],{},"多模型并排对比对选型极其有用：写一道复杂题，Claude \u002F GPT \u002F DeepSeek 直接对比答案",[48,1507,1508],{},"知识库 RAG 上传 50+ PDF 后检索准确，引用片段可视化",[48,1510,1511],{},"助手市场拿来即用——「Code Reviewer」「Translation Polish」节省 prompt 编写",[48,1513,1514],{},"Docker 一键部署，团队 5 人共享流畅",[48,1516,1517],{},"多平台数据同步（Cloud \u002F WebDAV）",[35,1519,1520],{},[51,1521,144],{},[45,1523,1524,1527,1530,1533,1536],{},[48,1525,1526],{},"自托管要熟悉 Docker + 反代 + HTTPS",[48,1528,1529],{},"国内连 OpenAI \u002F Claude 需自带网络方案",[48,1531,1532],{},"Web 版数据存 LobeHub，隐私敏感场景走桌面 \u002F Docker",[48,1534,1535],{},"插件市场质量参差，要自筛",[48,1537,1538],{},"团队多人共享需配 LobeHub Cloud 或自建数据库（Postgres + S3）",[30,1540,164],{"id":164},[166,1542,1543,1546,1552,1555,1558,1561,1564],{},[48,1544,1545],{},"选形态：Web（chat.lobehub.com 注册即用） \u002F 桌面（GitHub Releases 下载） \u002F Docker",[48,1547,1548,1549],{},"Docker：",[912,1550,1551],{},"docker run -d -p 3210:3210 -e OPENAI_API_KEY=sk-xxx --name lobe-chat lobehub\u002Flobe-chat",[48,1553,1554],{},"设置 → AI 服务商 → 添加 OpenAI \u002F Claude \u002F DeepSeek \u002F Ollama",[48,1556,1557],{},"模型选择器测试对话",[48,1559,1560],{},"知识库：拖文件 → 等向量化 → 对话引用",[48,1562,1563],{},"助手市场拉「Code Reviewer」「论文翻译润色」试用",[48,1565,1566],{},"进阶：插件市场启用联网搜索 \u002F 代码执行；MCP 自定义工具",[30,1568,194],{"id":194},[196,1570,1571,1585],{},[199,1572,1573],{},[202,1574,1575,1577,1579,1581,1583],{},[205,1576,207],{},[205,1578,212],{},[205,1580,11],{},[205,1582,218],{},[205,1584,215],{},[220,1586,1587,1599,1612,1625,1638,1654,1667,1681,1693],{},[202,1588,1589,1591,1593,1595,1597],{},[225,1590,227],{},[225,1592,1454],{},[225,1594,230],{},[225,1596,238],{},[225,1598,230],{},[202,1600,1601,1603,1606,1608,1610],{},[225,1602,53],{},[225,1604,1605],{},"✅ 80+",[225,1607,268],{},[225,1609,268],{},[225,1611,250],{},[202,1613,1614,1616,1619,1621,1623],{},[225,1615,1421],{},[225,1617,1618],{},"✅ 一等",[225,1620,268],{},[225,1622,265],{},[225,1624,265],{},[202,1626,1627,1629,1631,1633,1636],{},[225,1628,257],{},[225,1630,268],{},[225,1632,268],{},[225,1634,1635],{},"✅ + oikb",[225,1637,265],{},[202,1639,1640,1643,1646,1649,1652],{},[225,1641,1642],{},"插件 \u002F 助手市场",[225,1644,1645],{},"✅ 丰富",[225,1647,1648],{},"300+ 助手",[225,1650,1651],{},"Tools",[225,1653,265],{},[202,1655,1656,1658,1660,1662,1665],{},[225,1657,273],{},[225,1659,268],{},[225,1661,268],{},[225,1663,1664],{},"✅ mcpo",[225,1666,265],{},[202,1668,1669,1672,1675,1677,1679],{},[225,1670,1671],{},"多用户",[225,1673,1674],{},"配 Cloud \u002F 自建",[225,1676,295],{},[225,1678,1618],{},[225,1680,295],{},[202,1682,1683,1685,1687,1689,1691],{},[225,1684,333],{},[225,1686,339],{},[225,1688,336],{},[225,1690,345],{},[225,1692,342],{},[202,1694,1695,1697,1699,1701,1703],{},[225,1696,317],{},[225,1698,323],{},[225,1700,320],{},[225,1702,323],{},[225,1704,326],{},[30,1706,348],{"id":348},[45,1708,1709,1715,1721,1727,1733,1739],{},[48,1710,1711,1714],{},[51,1712,1713],{},"Web 版数据不本地","：隐私敏感选桌面或 Docker 自托管",[48,1716,1717,1720],{},[51,1718,1719],{},"国内连海外模型走中转","：直连 OpenAI \u002F Claude 不稳，配 OpenRouter \u002F Ofox \u002F 国内中转",[48,1722,1723,1726],{},[51,1724,1725],{},"Docker 自托管暴露公网","：上反代 + HTTPS + Auth + 备份数据库",[48,1728,1729,1732],{},[51,1730,1731],{},"嵌入模型中文优化","：默认嵌入对中文一般，配 bge-m3 \u002F 硅基流动 Pro 版",[48,1734,1735,1738],{},[51,1736,1737],{},"插件市场审一遍","：第三方插件可执行代码，团队部署谨慎启用",[48,1740,1741,1744],{},[51,1742,1743],{},"同步选 Cloud vs WebDAV","：团队多端走 LobeHub Cloud；个人多设备 WebDAV 即可",[30,1746,384],{"id":383},[45,1748,1749,1752,1755,1758,1761,1764,1767,1770],{},[48,1750,1751],{},"✅ Web + 桌面双形态需求",[48,1753,1754],{},"✅ Docker 自托管 \u002F 团队共享",[48,1756,1757],{},"✅ 多模型对比 \u002F 选型",[48,1759,1760],{},"✅ 中文重度用户",[48,1762,1763],{},"✅ 助手市场 \u002F 插件生态用户",[48,1765,1766],{},"❌ 强企业 RBAC + 多租户（Open WebUI 更完善）",[48,1768,1769],{},"❌ 只要桌面 + 数据完全本地（Cherry Studio 同样优秀）",[48,1771,1772],{},"❌ 完全不会碰 Docker",[30,1774,410],{"id":410},[45,1776,1777,1781,1785,1789],{},[48,1778,1779],{},[178,1780,1290],{"href":499},[48,1782,1783],{},[178,1784,1285],{"href":24},[48,1786,1787],{},[178,1788,427],{"href":21},[48,1790,1791],{},[178,1792,433],{"href":432},[30,1794,436],{"id":436},[166,1796,1797,1804,1811],{},[48,1798,1799,1800],{},"LobeChat GitHub 仓库（72k+ stars，MIT）",[178,1801,1802],{"href":1802,"rel":1803},"https:\u002F\u002Fgithub.com\u002Flobehub\u002Flobe-chat",[182],[48,1805,1806,1807],{},"腾讯云开发者社区 — Lobe Chat 本地化 AI 聊天终极桌面客户端（2026-01）",[178,1808,1809],{"href":1809,"rel":1810},"https:\u002F\u002Fcloud.tencent.com\u002Fdeveloper\u002Farticle\u002F2622150",[182],[48,1812,1813,1814],{},"Ofox.ai — LobeChat 完全配置指南 2026（2026-04-17）",[178,1815,1816],{"href":1816,"rel":1817},"https:\u002F\u002Fofox.ai\u002Fzh\u002Fblog\u002Flobechat-api-configuration-guide-2026",[182],{"title":461,"searchDepth":462,"depth":462,"links":1819},[1820,1821,1822,1823,1824,1825,1826,1827,1828,1829],{"id":32,"depth":465,"text":33},{"id":43,"depth":465,"text":43},{"id":93,"depth":465,"text":93},{"id":1492,"depth":465,"text":1493},{"id":164,"depth":465,"text":164},{"id":194,"depth":465,"text":194},{"id":348,"depth":465,"text":348},{"id":383,"depth":465,"text":384},{"id":410,"depth":465,"text":410},{"id":436,"depth":465,"text":436},"\u002Fimg\u002Ftools\u002Flobe-chat.webp","LobeChat 真实评测：LobeHub 团队开源 AI 聊天框架，GitHub 72k+ stars、MIT 协议。Web + 桌面（Win\u002FMac\u002FLinux\u002FDocker）双形态，支持 OpenAI \u002F Claude \u002F Gemini \u002F DeepSeek \u002F Qwen \u002F Ollama 等 80+ 模型，内置 RAG 知识库 + 插件市场 + 助手市场 + 多模型对比。",[1833,1836,1839,1842],{"q":1834,"a":1835},"Web 版 vs 桌面版 vs Docker 自托管，怎么选？","Web 版（chat.lobehub.com）最快上手但数据存 LobeHub 服务器；桌面版数据本地存、隐私好；Docker 自托管对团队 \u002F 公司部署最优，完全掌控数据。",{"q":1837,"a":1838},"支持哪些模型？","80+ 模型：OpenAI 全系列、Anthropic Claude、Google Gemini、DeepSeek、Qwen、Kimi、Moonshot、字节豆包、Groq、Together、OpenRouter、Ollama \u002F LM Studio 本地模型，以及任何 OpenAI 兼容 API。",{"q":1840,"a":1841},"多模型对比怎么用？","同一对话窗口里把消息广播给多个模型并排回答，选型 \u002F 评估特别有用——直接看 Claude 和 GPT 在同一 prompt 下的回答差异。",{"q":1843,"a":1844},"助手市场是什么？","LobeHub 维护的预设 AI 角色市场（代码审查 \u002F 翻译 \u002F 写作 \u002F 角色扮演等几百个），一键拉到本地用，省去自己写 System Prompt。",[495,496],{},[1848,502,503,504,1849],"web","docker",[1851,1854],{"plan":1480,"price":508,"features":1852,"notes":1853},"全功能 \u002F 80+ 模型 \u002F 知识库 \u002F 插件 \u002F 助手市场","MIT 协议",{"plan":1460,"price":1855,"features":1856,"notes":1857},"订阅制","云端托管 \u002F 免部署 \u002F 团队协作 \u002F 同步","chat.lobehub.com 注册即用","完全免费（MIT 开源） \u002F LobeHub Cloud 订阅",[1860,1861],{"name":519,"url":432},{"name":1364,"url":1295},{"power":476,"ux":476,"price":476,"cn_support":476,"stability":524},{"title":212,"description":1831},"coding\u002Flocal\u002Flobe-chat",[1866,1868,1870],{"name":1867,"url":1802,"accessed":530},"LobeChat GitHub",{"name":1869,"url":1809,"accessed":530},"腾讯云开发者社区 — Lobe Chat 终极桌面客户端",{"name":1871,"url":1816,"accessed":530},"Ofox.ai — LobeChat 完全配置指南 2026","tools\u002Fcoding\u002Flocal\u002Flobe-chat","现代设计的开源 AI 聊天框架——Web + 桌面双形态、72k+ stars、多模型 + 知识库 + 插件市场",[475,1848,538,539,541,1875,1876,542],"plugin","mcp","颜值与功能双优的多模型 AI 聊天客户端。要 Web + 桌面双形态、自托管 Docker、多模型对比、丰富助手市场——LobeChat 是综合最强；纯桌面体验 Cherry Studio 同样优秀。","https:\u002F\u002Flobehub.com","qiaN3oNudbNSX66toalN5tVx-_meCcKL6o6rq4RdAdI",{"id":1881,"title":1087,"alternatives":1882,"api_compatible":25,"body":1887,"category":475,"chinese_friendly":462,"cover":2306,"description":2307,"domestic":479,"extension":480,"faq":2308,"free":479,"github":25,"languages":2321,"meta":2322,"models":25,"navigation":498,"notSuitable":25,"opensource":498,"path":21,"pillar":500,"platforms":2323,"priceTable":2324,"pricing":2328,"published":516,"relatedPlaybooks":2329,"relatedReviews":25,"score":2332,"self_host":498,"seo":2333,"slug":2334,"sources":2335,"stem":2341,"suitable":25,"tagline":2342,"tags":2343,"updated":530,"verdict":2348,"website":2349,"__hash__":2350},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Follama.md",[1883,1884,1885,1886],{"name":17,"url":18},{"name":23,"url":24},{"name":902,"url":499},{"name":14,"url":15},{"type":27,"value":1888,"toc":2294},[1889,1891,1898,1901,1903,1971,1973,1976,1980,1984,2004,2008,2039,2041,2075,2077,2192,2194,2226,2228,2251,2253,2271,2273],[30,1890,33],{"id":32},[35,1892,1893,1894,1897],{},"Ollama 是本地 LLM 的 Daemon 事实标准——后台跑、暴露 REST API（11434）+ CLI、Modelfile 配置、GGUF 一站式。MIT 开源，跨 Win \u002F Mac \u002F Linux。0.19+ 起 Mac M 系列底层切 MLX 推理。模型库覆盖 Llama \u002F Qwen \u002F DeepSeek \u002F Gemma \u002F Mistral 等主流开源模型，",[912,1895,1896],{},"ollama pull"," 一键拉。",[35,1899,1900],{},"适合：给 Cursor \u002F Cline \u002F Continue \u002F Open WebUI 接本地 OpenAI 兼容 endpoint、个人 \u002F 评估 \u002F 原型、嵌入应用、自动化脚本。不适合：GUI 偏好用户（用 LM Studio）、多用户并发生产服务（用 vLLM）、模型浏览 \u002F 调参界面（用 LM Studio）。",[30,1902,43],{"id":43},[45,1904,1905,1911,1919,1925,1932,1947,1953,1959,1965],{},[48,1906,1907,1910],{},[51,1908,1909],{},"后台 Daemon","：开机自启，应用调用零延迟",[48,1912,1913,946,1916],{},[51,1914,1915],{},"CLI",[912,1917,1918],{},"ollama pull \u002F run \u002F list \u002F show \u002F create \u002F serve",[48,1920,1921,1924],{},[51,1922,1923],{},"Modelfile","：类 Dockerfile 注册任意 GGUF，配 SYSTEM \u002F PARAMETER \u002F TEMPLATE",[48,1926,1927,946,1929],{},[51,1928,1146],{},[912,1930,1931],{},"http:\u002F\u002Flocalhost:11434\u002Fv1\u002Fchat\u002Fcompletions",[48,1933,1934,946,1937,1940,1941,1940,1944],{},[51,1935,1936],{},"原生 API",[912,1938,1939],{},"\u002Fapi\u002Fchat","、",[912,1942,1943],{},"\u002Fapi\u002Fgenerate",[912,1945,1946],{},"\u002Fapi\u002Fembeddings",[48,1948,1949,1952],{},[51,1950,1951],{},"模型库","：官方注册表内置 Llama \u002F Qwen \u002F DeepSeek \u002F Gemma \u002F Mistral \u002F GPT-OSS 等",[48,1954,1955,1958],{},[51,1956,1957],{},"MLX 加速（Mac）","：0.19+ 起 M 系列自动用 MLX",[48,1960,1961,1964],{},[51,1962,1963],{},"量化","：默认 Q4_K_M、支持 Q5 \u002F Q8 \u002F FP16",[48,1966,1967,1970],{},[51,1968,1969],{},"跨平台","：Win \u002F Mac \u002F Linux 安装包，Docker 官方镜像",[30,1972,93],{"id":93},[35,1974,1975],{},"完全免费、MIT 开源、商用免费。",[30,1977,1979],{"id":1978},"实测m2-pro-qwen3-coder-7b-q4","实测（M2 Pro + Qwen3-Coder-7B Q4）",[35,1981,1982],{},[51,1983,122],{},[45,1985,1986,1992,1995,1998,2001],{},[48,1987,1988,1991],{},[912,1989,1990],{},"ollama run qwen3-coder:7b"," 一行起飞，3 秒进交互",[48,1993,1994],{},"REST API 配 Cursor \u002F Cline \u002F Continue 几乎全工具开箱即用",[48,1996,1997],{},"Modelfile 写自定义编码助手（low temperature + system prompt + 16K context）几分钟搞定",[48,1999,2000],{},"多模型并存，按需切换，内存占用合理",[48,2002,2003],{},"Mac M 系列 MLX 后比旧 GGUF 模式快显著",[35,2005,2006],{},[51,2007,144],{},[45,2009,2010,2020,2026,2033,2036],{},[48,2011,2012,2013,2016,2017],{},"默认 ",[912,2014,2015],{},"num_ctx"," 偏小（2048），跑长上下文要在 Modelfile 加 ",[912,2018,2019],{},"PARAMETER num_ctx 16384",[48,2021,2022,2023],{},"模型默认走 0.0.0.0:11434 ↔ Docker 容器互访要 ",[912,2024,2025],{},"--add-host=host.docker.internal:host-gateway",[48,2027,2028,2029,2032],{},"国内 ",[912,2030,2031],{},"ollama.com\u002Flibrary"," 下载偶有慢，可手动 HF 下 GGUF + Modelfile 自建",[48,2034,2035],{},"多用户并发吞吐显著低于 vLLM",[48,2037,2038],{},"没有 GUI，模型浏览 \u002F 参数面板要走 LM Studio \u002F Open WebUI 配合",[30,2040,164],{"id":164},[166,2042,2043,2049,2055,2060,2066,2072],{},[48,2044,2045,2048],{},[912,2046,2047],{},"curl -fsSL https:\u002F\u002Follama.ai\u002Finstall.sh | sh","（Mac \u002F Linux）；Windows winget",[48,2050,2051,2054],{},[912,2052,2053],{},"ollama pull qwen3-coder:7b","（按需换模型）",[48,2056,2057,2059],{},[912,2058,1990],{}," 直接聊",[48,2061,2062,2063],{},"应用接入：baseURL = ",[912,2064,2065],{},"http:\u002F\u002Flocalhost:11434\u002Fv1",[48,2067,2068,2069],{},"自定义：写 Modelfile → ",[912,2070,2071],{},"ollama create my-coder -f Modelfile",[48,2073,2074],{},"进阶：装 Open WebUI 做前端 \u002F 多人共享",[30,2076,194],{"id":194},[196,2078,2079,2094],{},[199,2080,2081],{},[202,2082,2083,2085,2087,2089,2092],{},[205,2084,207],{},[205,2086,1087],{},[205,2088,215],{},[205,2090,2091],{},"vLLM",[205,2093,1092],{},[220,2095,2096,2112,2124,2137,2150,2166,2178],{},[202,2097,2098,2100,2103,2106,2109],{},[225,2099,227],{},[225,2101,2102],{},"CLI + Daemon",[225,2104,2105],{},"GUI + Headless",[225,2107,2108],{},"Python Server",[225,2110,2111],{},"C++ 二进制",[202,2113,2114,2116,2118,2120,2122],{},[225,2115,164],{},[225,2117,1205],{},[225,2119,1205],{},[225,2121,1184],{},[225,2123,1213],{},[202,2125,2126,2128,2130,2133,2135],{},[225,2127,1115],{},[225,2129,1915],{},[225,2131,2132],{},"✅ GUI",[225,2134,295],{},[225,2136,295],{},[202,2138,2139,2142,2144,2146,2148],{},[225,2140,2141],{},"OpenAI 兼容",[225,2143,1152],{},[225,2145,1149],{},[225,2147,268],{},[225,2149,268],{},[202,2151,2152,2155,2158,2161,2164],{},[225,2153,2154],{},"多用户吞吐",[225,2156,2157],{},"弱（~40 tok\u002Fs）",[225,2159,2160],{},"中（50–90）",[225,2162,2163],{},"强（800–12500）",[225,2165,1184],{},[202,2167,2168,2170,2172,2174,2176],{},[225,2169,1161],{},[225,2171,1166],{},[225,2173,268],{},[225,2175,1139],{},[225,2177,342],{},[202,2179,2180,2182,2184,2187,2190],{},[225,2181,1189],{},[225,2183,323],{},[225,2185,2186],{},"闭源",[225,2188,2189],{},"Apache 2.0",[225,2191,323],{},[30,2193,348],{"id":348},[45,2195,2196,2202,2208,2214,2220],{},[48,2197,2198,2201],{},[51,2199,2200],{},"num_ctx 一定要设","：默认 2K 太小，跑代码 \u002F 长文档要 16K+",[48,2203,2204,2207],{},[51,2205,2206],{},"Modelfile 模板别漏 TEMPLATE","：错的 chat template 会让模型输出乱码 \u002F 不停",[48,2209,2210,2213],{},[51,2211,2212],{},"KV cache 爆表 = 速度悬崖","：32B 模型 32K 上下文，KV cache 可能 12+ GB，超显存自动 offload 慢 10×",[48,2215,2216,2219],{},[51,2217,2218],{},"不要 0.0.0.0 直接对公网","：默认无鉴权，对外暴露走反代 + Bearer \u002F mTLS",[48,2221,2222,2225],{},[51,2223,2224],{},"Mac 让它自动用 MLX","：升 0.19+；不要手动强制 GGUF + Metal",[30,2227,384],{"id":383},[45,2229,2230,2233,2236,2239,2242,2245,2248],{},[48,2231,2232],{},"✅ 应用 \u002F IDE 接本地模型（Cursor \u002F Cline \u002F Continue）",[48,2234,2235],{},"✅ 个人 \u002F 评估 \u002F 脚本自动化",[48,2237,2238],{},"✅ Modelfile 自定义系统 prompt + 参数",[48,2240,2241],{},"✅ Mac M 系列 MLX 用户",[48,2243,2244],{},"❌ 多用户并发生产服务（用 vLLM）",[48,2246,2247],{},"❌ GUI 调参 \u002F 模型浏览（配 LM Studio \u002F Open WebUI）",[48,2249,2250],{},"❌ 极致单卡吞吐研究（直接 llama.cpp \u002F vLLM）",[30,2252,410],{"id":410},[45,2254,2255,2259,2263,2267],{},[48,2256,2257],{},[178,2258,422],{"href":18},[48,2260,2261],{},[178,2262,1285],{"href":24},[48,2264,2265],{},[178,2266,1290],{"href":499},[48,2268,2269],{},[178,2270,433],{"href":432},[30,2272,436],{"id":436},[166,2274,2275,2282,2289],{},[48,2276,2277,2278],{},"Markaicode — Import GGUF Models into Ollama 2026（2026-05-15）",[178,2279,2280],{"href":2280,"rel":2281},"https:\u002F\u002Fmarkaicode.com\u002Fimport-gguf-models-ollama-guide",[182],[48,2283,2284,2285],{},"ComputingForGeeks — Ollama Models Cheat Sheet 2026 ",[178,2286,2287],{"href":2287,"rel":2288},"https:\u002F\u002Fcomputingforgeeks.com\u002Follama-models-cheat-sheet",[182],[48,2290,1317,2291],{},[178,2292,1320],{"href":1320,"rel":2293},[182],{"title":461,"searchDepth":462,"depth":462,"links":2295},[2296,2297,2298,2299,2300,2301,2302,2303,2304,2305],{"id":32,"depth":465,"text":33},{"id":43,"depth":465,"text":43},{"id":93,"depth":465,"text":93},{"id":1978,"depth":465,"text":1979},{"id":164,"depth":465,"text":164},{"id":194,"depth":465,"text":194},{"id":348,"depth":465,"text":348},{"id":383,"depth":465,"text":384},{"id":410,"depth":465,"text":410},{"id":436,"depth":465,"text":436},"\u002Fimg\u002Ftools\u002Follama.webp","Ollama 真实评测：本地 LLM 的事实标准 Daemon，CLI + REST API，模型库 + Modelfile + GGUF 一站式。0.19+ 在 Mac M 系列用 MLX 加速；OpenAI 兼容端点 11434；MIT 开源 + 跨平台。",[2309,2312,2315,2318],{"q":2310,"a":2311},"和 LM Studio 怎么选？","Ollama = Daemon + CLI，开机自启在 11434 端口跑，应用 \u002F IDE 调它最方便。LM Studio = GUI，模型浏览 \u002F 调参 \u002F 聊天体验更好。两者底层都基于 llama.cpp，Mac M 系列上都已切 MLX。",{"q":2313,"a":2314},"Modelfile 是什么？","类 Dockerfile 的模型配置：`FROM .\u002Fxxx.gguf` + PARAMETER \u002F TEMPLATE \u002F SYSTEM。把任意 GGUF 注册成本地模型供调用。`ollama create my-model -f Modelfile`。",{"q":2316,"a":2317},"OpenAI 兼容端点？","`http:\u002F\u002Flocalhost:11434\u002Fv1`。任何 OpenAI SDK 改 baseURL 即用。也可走原生 `\u002Fapi\u002Fchat`、`\u002Fapi\u002Fgenerate`。",{"q":2319,"a":2320},"并发能力？","单用户原型场景顺滑（~40 tok\u002Fs peak），多用户并发明显不如 vLLM（vLLM 的 PagedAttention + 连续批处理高 16–20×）。生产并发选 vLLM。",[496],{},[502,503,504,1849],[2325],{"plan":100,"price":508,"features":2326,"notes":2327},"完整 CLI + REST API + Modelfile + 模型库 + MIT 协议","全平台、商用免费","完全免费 + 开源（MIT）",[2330,2331],{"name":519,"url":432},{"name":1364,"url":1295},{"power":524,"ux":524,"price":476,"cn_support":462,"stability":476},{"title":1087,"description":2307},"coding\u002Flocal\u002Follama",[2336,2338,2340],{"name":2337,"url":2280,"accessed":530},"Markaicode — Import GGUF 2026",{"name":2339,"url":2287,"accessed":530},"ComputingForGeeks — Ollama Cheat Sheet 2026",{"name":1374,"url":1320,"accessed":530},"tools\u002Fcoding\u002Flocal\u002Follama","本地 LLM 的 Daemon——CLI + REST API 后台跑，给 Cursor \u002F Cline \u002F Open WebUI 接本地模型最低门槛",[475,2344,2345,2346,2347,1379,1380,1383,542],"daemon","cli","rest-api","modelfile","本地 LLM 的 Daemon 事实标准，CLI \u002F Modelfile \u002F REST API 三件套配合最广泛。GUI 偏好用户走 LM Studio；多用户并发生产用 vLLM；其他场景几乎默认 Ollama。","https:\u002F\u002Follama.com","V4PvNLB8lbjAzlhHpWFHSyKzvb328rvWx3nckggFlD8",{"id":2352,"title":218,"alternatives":2353,"api_compatible":25,"body":2358,"category":475,"chinese_friendly":524,"cover":2773,"description":2774,"domestic":479,"extension":480,"faq":2775,"free":479,"github":25,"languages":2788,"meta":2789,"models":25,"navigation":498,"notSuitable":25,"opensource":498,"path":24,"pillar":500,"platforms":2790,"priceTable":2792,"pricing":2799,"published":516,"relatedPlaybooks":2800,"relatedReviews":25,"score":2803,"self_host":498,"seo":2804,"slug":2805,"sources":2806,"stem":2813,"suitable":25,"tagline":2814,"tags":2815,"updated":530,"verdict":2818,"website":2819,"__hash__":2820},"tools\u002Ftools\u002Fcoding\u002Flocal\u002Fopen-webui.md",[2354,2355,2356,2357],{"name":14,"url":15},{"name":902,"url":499},{"name":20,"url":21},{"name":17,"url":18},{"type":27,"value":2359,"toc":2761},[2360,2362,2365,2368,2370,2432,2434,2437,2441,2445,2473,2477,2501,2503,2537,2539,2646,2648,2691,2693,2716,2718,2736,2738],[30,2361,33],{"id":32},[35,2363,2364],{},"Open WebUI（原 Ollama WebUI）是 MIT 开源、自托管 AI 平台，最常见用法是 Docker 跑起来给 Ollama 套一个 ChatGPT 风格前端。GitHub 126k+ stars、282M+ Docker pulls，事实上的本地 AI 前端首选。支持任意 OpenAI 兼容后端 + RAG 知识库 + 多用户账号 + 工具调用 + MCP-OpenAPI 代理 + 联网搜索 + 语音 + 图像生成。",[35,2366,2367],{},"适合：团队 \u002F 家庭 \u002F 公司部署一份共享、要 Web 端访问、多用户分账号、SearXNG 联网搜索、Confluence \u002F S3 \u002F GitHub 数据源同步。不适合：单人桌面体验（用 Cherry Studio）、零运维 \u002F 不愿碰 Docker。",[30,2369,43],{"id":43},[45,2371,2372,2378,2384,2390,2396,2402,2408,2414,2420,2426],{},[48,2373,2374,2377],{},[51,2375,2376],{},"多模型后端","：Ollama \u002F OpenAI \u002F vLLM \u002F Anthropic \u002F Groq \u002F LocalAI \u002F 任意 OpenAI 兼容",[48,2379,2380,2383],{},[51,2381,2382],{},"多用户 + RBAC","：注册 \u002F 邀请 \u002F 角色权限 \u002F 工作区隔离",[48,2385,2386,2389],{},[51,2387,2388],{},"RAG 知识库","：上传文档 \u002F 网址 \u002F SearXNG 联网搜索 → 向量化 → 对话引用",[48,2391,2392,2395],{},[51,2393,2394],{},"Tools \u002F Functions","：Python 写函数即扩展（联网 \u002F 计算器 \u002F 自定义 API）",[48,2397,2398,2401],{},[51,2399,2400],{},"mcpo","：MCP-to-OpenAPI 代理，任意 MCP 服务器接进来",[48,2403,2404,2407],{},[51,2405,2406],{},"oikb","：知识库同步本地文件夹 \u002F GitHub \u002F S3 \u002F Confluence 等 40+ 源",[48,2409,2410,2413],{},[51,2411,2412],{},"open-terminal \u002F cptr","：给 AI 真实终端 + 文件 + 沙箱执行",[48,2415,2416,2419],{},[51,2417,2418],{},"图像生成","：Stable Diffusion \u002F DALL·E \u002F 自托管接入",[48,2421,2422,2425],{},[51,2423,2424],{},"语音输入 \u002F TTS","：内置",[48,2427,2428,2431],{},[51,2429,2430],{},"企业 LTS","：custom branding + SLA + 长期支持版本（联系销售）",[30,2433,93],{"id":93},[35,2435,2436],{},"完全免费、MIT 开源、商用免费。Enterprise 提供品牌定制 + SLA + LTS。",[30,2438,2440],{"id":2439},"实测ubuntu-2404-ollama-后端-5-人小团队","实测（Ubuntu 24.04 + Ollama 后端 + 5 人小团队）",[35,2442,2443],{},[51,2444,122],{},[45,2446,2447,2454,2457,2464,2467,2470],{},[48,2448,2449,2450,2453],{},"单条 ",[912,2451,2452],{},"docker run"," 五分钟上线",[48,2455,2456],{},"自带的多用户 + 角色权限省去重新搭 Auth",[48,2458,2459,2460,2463],{},"RAG 直传 30 个 PDF 后向量化顺利，对话中 ",[912,2461,2462],{},"#知识库"," 引用准确",[48,2465,2466],{},"mcpo 把 GitHub MCP 服务器接进来，团队对话里直接 issue \u002F PR 操作",[48,2468,2469],{},"模型切换流畅，OpenAI + Ollama 并存",[48,2471,2472],{},"SearXNG 联网搜索给模型实时信息，过时知识截止问题缓解",[35,2474,2475],{},[51,2476,144],{},[45,2478,2479,2482,2488,2495,2498],{},[48,2480,2481],{},"Docker 镜像 ~1.5GB，首次拉取偏慢",[48,2483,2012,2484,2487],{},[912,2485,2486],{},"0.0.0.0"," 公网暴露要加 HTTPS + 反代",[48,2489,2490,2491,2494],{},"嵌入模型 ",[912,2492,2493],{},"sentence-transformers"," 中文效果一般，建议换 bge-m3",[48,2496,2497],{},"多用户共享 Ollama 时并发吞吐瓶颈在 Ollama，不在 Open WebUI（生产用 vLLM 后端）",[48,2499,2500],{},"版本升级要看 changelog，部分 minor 含 breaking 改动",[30,2502,164],{"id":164},[166,2504,2505,2511,2518,2521,2524,2527,2530],{},[48,2506,2507,2508],{},"装 Docker → ",[912,2509,2510],{},"docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:\u002Fapp\u002Fbackend\u002Fdata --name open-webui --restart always ghcr.io\u002Fopen-webui\u002Fopen-webui:main",[48,2512,2513,2514,2517],{},"浏览器开 ",[912,2515,2516],{},"http:\u002F\u002Flocalhost:3000"," → 注册第一个账号（管理员）",[48,2519,2520],{},"设置 → Connections → 连接 Ollama \u002F 加 OpenAI Key",[48,2522,2523],{},"Models → Pull \u002F Discover 模型",[48,2525,2526],{},"Workspaces → 建知识库 → 上传文档",[48,2528,2529],{},"Tools → 启用 \u002F 写自定义函数",[48,2531,2532,2533,2536],{},"生产部署：Nginx 反代 + Let's Encrypt + 备份 ",[912,2534,2535],{},"\u002Fapp\u002Fbackend\u002Fdata"," volume",[30,2538,194],{"id":194},[196,2540,2541,2555],{},[199,2542,2543],{},[202,2544,2545,2547,2549,2551,2553],{},[205,2546,207],{},[205,2548,218],{},[205,2550,212],{},[205,2552,11],{},[205,2554,215],{},[220,2556,2557,2569,2581,2595,2608,2622,2634],{},[202,2558,2559,2561,2563,2565,2567],{},[225,2560,227],{},[225,2562,238],{},[225,2564,233],{},[225,2566,230],{},[225,2568,230],{},[202,2570,2571,2573,2575,2577,2579],{},[225,2572,1671],{},[225,2574,1618],{},[225,2576,268],{},[225,2578,295],{},[225,2580,295],{},[202,2582,2583,2586,2589,2591,2593],{},[225,2584,2585],{},"RAG",[225,2587,2588],{},"✅ 强 + oikb",[225,2590,268],{},[225,2592,268],{},[225,2594,265],{},[202,2596,2597,2600,2602,2604,2606],{},[225,2598,2599],{},"工具 \u002F MCP",[225,2601,1664],{},[225,2603,268],{},[225,2605,268],{},[225,2607,265],{},[202,2609,2610,2613,2616,2618,2620],{},[225,2611,2612],{},"自托管",[225,2614,2615],{},"✅ Docker \u002F K8s",[225,2617,292],{},[225,2619,295],{},[225,2621,295],{},[202,2623,2624,2626,2628,2630,2632],{},[225,2625,333],{},[225,2627,345],{},[225,2629,339],{},[225,2631,336],{},[225,2633,342],{},[202,2635,2636,2638,2640,2642,2644],{},[225,2637,317],{},[225,2639,323],{},[225,2641,323],{},[225,2643,320],{},[225,2645,326],{},[30,2647,348],{"id":348},[45,2649,2650,2656,2664,2673,2679,2685],{},[48,2651,2652,2655],{},[51,2653,2654],{},"不要裸 0.0.0.0 + HTTP 暴露公网","：默认无 HTTPS，必上反代 + 强密码 + 速率限制",[48,2657,2658,2663],{},[51,2659,2660,2661,2536],{},"备份 ",[912,2662,2535],{},"：知识库 \u002F 用户 \u002F 对话全在里面",[48,2665,2666,2669,2670,2672],{},[51,2667,2668],{},"中文 RAG 换嵌入模型","：默认 ",[912,2671,2493],{}," 中文一般，配 bge-m3 或硅基流动嵌入 API",[48,2674,2675,2678],{},[51,2676,2677],{},"mcpo 工具范围谨慎","：MCP 给 AI 真实能力，第三方服务器审一遍",[48,2680,2681,2684],{},[51,2682,2683],{},"后端吞吐看 Ollama","：5+ 并发上 vLLM 后端，Ollama 单 worker 会排队",[48,2686,2687,2690],{},[51,2688,2689],{},"升级前看 changelog","：weekly 更新，偶有 breaking",[30,2692,384],{"id":383},[45,2694,2695,2698,2701,2704,2707,2710,2713],{},[48,2696,2697],{},"✅ 团队 \u002F 家庭 \u002F 公司多人共享 AI 平台",[48,2699,2700],{},"✅ 要 Web 端访问 \u002F 移动端兼容",[48,2702,2703],{},"✅ 自托管 \u002F 完全控制数据",[48,2705,2706],{},"✅ MCP \u002F 工具调用刚需",[48,2708,2709],{},"❌ 单人桌面体验（用 Cherry Studio）",[48,2711,2712],{},"❌ 零运维 \u002F 不愿碰 Docker",[48,2714,2715],{},"❌ iOS 原生 App 主力",[30,2717,410],{"id":410},[45,2719,2720,2724,2728,2732],{},[48,2721,2722],{},[178,2723,417],{"href":15},[48,2725,2726],{},[178,2727,1290],{"href":499},[48,2729,2730],{},[178,2731,427],{"href":21},[48,2733,2734],{},[178,2735,433],{"href":432},[30,2737,436],{"id":436},[166,2739,2740,2747,2754],{},[48,2741,2742,2743],{},"Open WebUI 官方文档 ",[178,2744,2745],{"href":2745,"rel":2746},"https:\u002F\u002Fdocs.openwebui.com\u002F",[182],[48,2748,2749,2750],{},"Local AI Master — Open WebUI Setup Guide 2026 ",[178,2751,2752],{"href":2752,"rel":2753},"https:\u002F\u002Flocalaimaster.com\u002Fblog\u002Fopen-webui-setup-guide",[182],[48,2755,2756,2757],{},"AIToolDiscovery — Set Up Open-WebUI with Ollama 2026 ",[178,2758,2759],{"href":2759,"rel":2760},"https:\u002F\u002Fwww.aitooldiscovery.com\u002Fhow-to\u002Fsetup-open-webui-ollama",[182],{"title":461,"searchDepth":462,"depth":462,"links":2762},[2763,2764,2765,2766,2767,2768,2769,2770,2771,2772],{"id":32,"depth":465,"text":33},{"id":43,"depth":465,"text":43},{"id":93,"depth":465,"text":93},{"id":2439,"depth":465,"text":2440},{"id":164,"depth":465,"text":164},{"id":194,"depth":465,"text":194},{"id":348,"depth":465,"text":348},{"id":383,"depth":465,"text":384},{"id":410,"depth":465,"text":410},{"id":436,"depth":465,"text":436},"\u002Fimg\u002Ftools\u002Fopen-webui.webp","Open WebUI 真实评测：MIT 开源、自托管 AI 平台，离线优先。Docker 一行起飞、支持 Ollama \u002F OpenAI \u002F vLLM \u002F Anthropic \u002F Groq 等后端，内置 RAG 知识库 + 多用户 + 联网搜索 + 工具调用。GitHub 126k+ stars，事实标准本地 AI 前端。",[2776,2779,2782,2785],{"q":2777,"a":2778},"Docker 一行命令真的够用吗？","够。`docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:\u002Fapp\u002Fbackend\u002Fdata --name open-webui --restart always ghcr.io\u002Fopen-webui\u002Fopen-webui:main`，5 分钟可上线、能多人注册、能接 Ollama \u002F OpenAI。生产再加反代 + HTTPS + 备份。",{"q":2780,"a":2781},"支持哪些模型后端？","Ollama（首选）+ 任何 OpenAI 兼容 endpoint：OpenAI 官方 \u002F Anthropic（OpenAI 兼容代理）\u002F vLLM \u002F Groq \u002F LocalAI \u002F 自建 baseURL。可同时配多个，对话中切换。",{"q":2783,"a":2784},"RAG \u002F 知识库怎么做？","内置：上传 PDF \u002F DOCX \u002F TXT、网址抓取、SearXNG 联网搜索 → 自动向量化 → 在对话中 `#` 引用知识库。配套 oikb 项目可同步本地文件夹 \u002F GitHub \u002F S3 \u002F Confluence 等 40+ 数据源。",{"q":2786,"a":2787},"MCP 怎么接？","通过 mcpo（官方的 MCP-to-OpenAPI 代理）把任意 MCP 服务器暴露成 OpenAPI 工具，再在 Open WebUI 注册即可。无需写 glue code。",[496,495],{},[1849,504,503,502,2791],"kubernetes",[2793,2795],{"plan":100,"price":508,"features":2794,"notes":1853},"全功能 \u002F 多用户 \u002F RAG \u002F Tools \u002F 联网搜索 \u002F MCP-OpenAPI 代理 \u002F Docker \u002F K8s",{"plan":106,"price":2796,"features":2797,"notes":2798},"咨询","Custom branding \u002F SLA \u002F LTS 长期支持版本","邮件官方","完全免费（MIT 开源） \u002F Enterprise SLA 联系",[2801,2802],{"name":519,"url":432},{"name":1364,"url":1295},{"power":476,"ux":524,"price":476,"cn_support":524,"stability":476},{"title":218,"description":2774},"coding\u002Flocal\u002Fopen-webui",[2807,2809,2811],{"name":2808,"url":2745,"accessed":530},"Open WebUI 官方文档",{"name":2810,"url":2752,"accessed":530},"Local AI Master — Open WebUI Setup Guide 2026",{"name":2812,"url":2759,"accessed":530},"AIToolDiscovery — Open-WebUI with Ollama 2026","tools\u002Fcoding\u002Flocal\u002Fopen-webui","自托管的 ChatGPT 替代——Ollama \u002F OpenAI 兼容、多用户、RAG、126k+ GitHub stars",[475,2816,1849,541,2817,20,542],"self-host","multi-user","自托管多用户 AI 前端的事实标准。团队 \u002F 家庭 \u002F 公司部署一份共享，多模型聚合 + RAG + 工具调用全有。单机 \u002F 桌面体验首选 Cherry Studio \u002F LobeChat。","https:\u002F\u002Fdocs.openwebui.com","8iXO-BuvdCKJCMIXxc6Jlp_MhDAMVBwYJJnhXo6dErw",[],1782316490675]