DeepSeek本地部署详细指南！从 Ollama 到个人知识库应用
作者：lushen一、系统介绍
mbp pro：
二、Ollama 安装与配置
1. 跨平台安装指南
Ollama 作为本地运行大模型的利器，支持三大主流操作系统：
# macOS一键安装# Windows用户访问官网 https://ollama.com/download 下载安装包# Linux安装（Ubuntu/Debian为例）curl -fsSL https://ollama.com/install.sh | sudo bashsudo usermod -aG ollama $USER# 添加用户权限sudo systemctl start ollama # 启动服务
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2. 服务验证
ollama -v# 输出ollama version is 0.5.7
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出现上述则表示安装成功，可浏览器访问http://localhost:11434/验证。
三、Deepseek 模型部署
1. 模型下载与加载
以 deepseek r1 模型为例：
(1) 访问https://ollama.com/library/deepseek-r1，默认为 7b 模型，如需其他模型，可以在当前页搜索所需模型
(2) 模型详情页复制安装命令ollama run deepseek-r1
(3) 安装完成后在终端执行：
ollama run deepseek-r1# 执行后pulling manifestpulling 96c415656d37... 100% ▕██████████████▏ 4.7 GBpulling 369ca498f347... 100% ▕██████████████▏ 387 Bpulling 6e4c38e1172f... 100% ▕██████████████▏ 1.1 KBpulling f4d24e9138dd... 100% ▕██████████████▏ 148 Bpulling 40fb844194b2... 100% ▕██████████████▏ 487 Bverifying sha256 digestwriting manifestsuccess> > > Send a message (/? for help)> > > `> > > 当看到上述提示，即可开始模型对话。
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[*]mac 后台标识
[*]win 后台标识见任务栏托盘区
2. 模型验证测试
运行交互式对话测试：
请用Python写一个快速排序算法
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当看到完整代码输出，说明模型已成功加载。
硬件要求建议：
[*]最低配置：16GB 内存 + 8GB 显存
[*]推荐配置：32GB 内存 + 16GB 显存（RTX 3060 级别）
四、安装交互 ui
1. chatbox
(1) 下载地址chatboxai.app
(2) 配置本地模型
[*]进入设置页面
[*]选择 ollama api (本地部署)
[*]配置本机地址，默认http://127.0.0.1:11434
至此即可开启问答模式。
2. Page Assist 浏览器插件
[*]安装地址Page Assist - 本地 AI 模型的 Web UI
[*]安装后简单配置即可开启问答模式，功能丰富，可以参考官方引导
[*]本插件支持本地知识库建设，因本次使用 Dify 建设，在此不赘述。
五、Dify 知识库搭建
参考文档地址Docker Compose 部署
1. 环境准备
(1) 拉取源代码，准备环境
# mac os# 克隆 Dify 源代码至本地环境。git clone https://github.com/langgenius/dify.git# 进入 Dify 源代码的 Docker 目录cd dify/docker# 复制环境配置文件cp .env.example .env
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(2) 启动 Docker 容器（需要先安装 D ocker）
docker compose up -d# 如果版本是 Docker Compose V1，使用以下命令：docker-compose up -d# 正常返回[+] Running 74/9 ✔ db Pulled 834.2s ✔ sandbox Pulled 1120.7s ✔ weaviate Pulled 526.5s ✔ web Pulled 174.0s ✔ redis Pulled 893.7s ✔ api Pulled 2919.8s ✔ worker Pulled 2919.8s ✔ ssrf_proxy Pulled 494.0s ✔ nginx Pulled 184.7s[+] Running 11/11 ✔ Network docker_default Created 0.0s ✔ Network docker_ssrf_proxy_networkCreated 0.0s ✔ Container docker-db-1 Started 1.1s ✔ Container docker-web-1 Started 1.1s ✔ Container docker-redis-1 Started 1.1s ✔ Container docker-sandbox-1 Started 1.1s ✔ Container docker-weaviate-1 Started 1.1s ✔ Container docker-ssrf_proxy-1 Started 1.1s ✔ Container docker-api-1 Started 0.7s ✔ Container docker-worker-1 Started 0.7s ✔ Container docker-nginx-1 Started 0.8s
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在此阶段可能会遇到下列失败的情况，可以尝试切换源解决我当时的条件。
[*]修改配置后重启 docker
[*]办公网环境下
docker compose up -d[+] Running 9/9 ✘ web Error context canceled 14.9s ✘ redis Error context canceled 14.9s ✘ db Error context canceled 14.9s ✘ nginx Error context canceled 14.9s ✘ ssrf_proxy Error context canceled 14.9s ✘ sandbox Error Head "https://registry-1.do... 14.9s ✘ api Error context canceled 14.9s ✘ worker Error context canceled 14.9s ✘ weaviate Error context canceled 14.9sError response from daemon: Head "https://registry-1.docker.io/v2/langgenius/dify-sandbox/manifests/0.2.10": Get "https://auth.docker.io/token?scope=repository%3Alanggenius%2Fdify-sandbox%3Apull&service=registry.docker.io": EOF
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解决方法：
[*]右上角齿轮图标进入设置 -> Docker engine，在配置中添加
[*]写入以下内容 ocker）
{// ..."registry-mirrors": [ "https://docker.hpcloud.cloud", "https://docker.m.daocloud.io", "https://docker.unsee.tech", "https://docker.1panel.live", "http://mirrors.ustc.edu.cn", "https://docker.chenby.cn", "http://mirror.azure.cn", "https://dockerpull.org", "https://dockerhub.icu", "https://hub.rat.dev"]}
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2. Dify 创建聊天
(1) 访问http://localhost/（默认 80 端口） 进入 dify
(2) 首次进入初始化设置账号密码
(3) 点击 Dify 平台右上角头像 → 设置 → 模型供应商，选择 Ollama，轻点“添加模型”。
在配置 url 时，因为是 docker 服务，http://localhost:11434 存在无法访问的情况，可以尝试http://host.docker.internal:11434。
(4) 至此，可以开始创建应用，在主页选择 全部 -> 创建空白应用 -> 填入应用信息即可
3. Dify 知识库创建
主页选择 知识库 -> 创建知识库 -> 上传知识 -> 等待处理完成
进入聊天应用，选择刚才创建的知识库，即可开始带有私域知识的沟通。
六、应用测试
1. 翻译场景
(1) 本地客户端具有部分国际化测试文件需要执行翻译，格式示例如下，多层嵌套的 json 格式，value 为string类型。需要利用大模型对整个 json 文件进行翻译，将中文翻译为英文后按原格式返回
// zh.json{"window": { "willUnload": { "title": "确认刷新当前页面吗?", "message": "系统可能不会保存您做的更改", "unload_bt": "重新加载", "cancel_bt": "取消" }}}ocker）
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(2) 实际应用测试，以deepseek-r1:7b/14b模型做测试。得到结果如下
(3) 执行脚本trans.js
const fs = require("fs");const axios = require("axios");// 1. 读取本地JSON文件const readJsonFile = (filePath) => {return new Promise((resolve, reject) => { fs.readFile(filePath, "utf8", (err, data) => { if (err) { reject(err); } else { resolve(JSON.parse(data)); } });});};const MODEL = "deepseek-r1:14b";// 2. 调用本地大模型接口进行翻译const translateText = async (text, key) => {let response;try { console.time(`run worker ${key}`); response = await axios.post("http://localhost:11434/api/generate", { // model: 'deepseek-r1:7b', model: MODEL, prompt: `有部分客户端国际化的配置文件，内容为json格式，需要翻译，要求按步骤进行翻译： 1. 将中文翻译为英文 2. 保持原有json格式不变，将value替换成翻译后的文本 3. 你始终以合法的JSON格式响应，返回结果格式如: {"key1":"翻译后的文本1","key2":"翻译后的文本2"}，直接返回结果，不需要符号包裹 配置文件 """${JSON.stringify(text)}"""`, stream: false, }); console.timeEnd(`run worker ${key}`); const splitText = ""; const startIndex = response.data.response.indexOf(splitText); const result = response.data.response .slice(startIndex + splitText.length) .trim() .replace(/+/g, ""); // console.log('response.data.response:', response.data.response, JSON.parse(result), result) return JSON.parse(result); // 假设接口返回的翻译结果在response.data.translatedText中} catch (error) { console.error("翻译出错:", key); return translateText(text, key); // 如果翻译失败，返回原文}};// 3. 并行翻译逻辑（手动控制并发）const translateJson = async (jsonData, concurrency = 5) => {const entries = Object.entries(jsonData);const translatedData = {};let currentIndex = 0; // 当前处理的任务索引// 定义工作线程：每个线程不断处理下一个任务const worker = async () => { while (currentIndex </span entriesspan./spanlengthspan)/span span{/span spanconst/span index span=/span currentIndexspan++/spanspan;/span spanif/span span(/spanindex span>= entries.length) break; // 所有任务已完成 const = entries; try { translatedData = await translateText(value, key); } catch (error) { translatedData = value; // 保留原文 } }};// 启动指定数量的工作线程const workers = Array(concurrency).fill(null).map(worker);await Promise.all(workers); // 等待所有线程完成const result = {};// 保持原有顺序entries.forEach(() => { result = translatedData || value;});return result;};// 4. 将翻译后的内容生成新的文件const writeTranslatedJson = (filePath, data) => {return new Promise((resolve, reject) => { fs.writeFile(filePath, JSON.stringify(data, null, 2), "utf8", (err) => { if (err) { reject(err); } else { resolve(); } });});};function compareObjectsWithPath(obj1, obj2, path = "") {// 类型不同时直接返回路径if (typeof obj1 !== typeof obj2) { return { success: false, path: path || "root" };}// 处理可遍历对象（对象或数组）if (typeof obj1 === "object" && obj1 !== null && obj2 !== null) { const isArr1 = Array.isArray(obj1); const isArr2 = Array.isArray(obj2); // 数组类型不一致 if (isArr1 !== isArr2) { return { success: false, path: path || "root" }; } if (isArr1) { // 数组长度不同 if (obj1.length !== obj2.length) { return { success: false, path: path || "root" }; } // 递归检查数组元素 for (let i = 0; i </span obj1span./spanlengthspan;/span ispan++/spanspan)/span span{/span spanconst/span currentPath span=/span spanspan`/spanspanspan${/spanpathspan}/span/spanspan/spanspan`/span/spanspan;/span spanconst/span result span=/span spancompareObjectsWithPath/spanspan(/spanobj1span/spanspan,/span obj2span/spanspan,/span currentPathspan)/spanspan;/span spanif/span span(/spanspan!/spanresultspan./spansuccessspan)/span spanreturn/span resultspan;/span span}/span spanreturn/span span{/span spansuccess/spanspan:/span spantrue/span span}/spanspan;/span span}/span spanelse/span span{/span span// 检查是否为纯对象（字面量对象）/span spanconst/span isPlainObj1 span=/span spanisPlainObject/spanspan(/spanobj1span)/spanspan;/span spanconst/span isPlainObj2 span=/span spanisPlainObject/spanspan(/spanobj2span)/spanspan;/span spanif/span span(/spanisPlainObj1 span!==/span isPlainObj2span)/span span{/span spanreturn/span span{/span spansuccess/spanspan:/span spanfalse/spanspan,/span spanpath/spanspan:/span path span||/span span"root"/span span}/spanspan;/span span}/span span// 非纯对象（如 Date、RegExp）需检查是否均为字符串/span spanif/span span(/spanspan!/spanisPlainObj1span)/span span{/span spanreturn/span spantypeof/span obj1 span===/span span"string"/span span&&/span spantypeof/span obj2 span===/span span"string"/span span?/span span{/span spansuccess/spanspan:/span spantrue/span span}/span span:/span span{/span spansuccess/spanspan:/span spanfalse/spanspan,/span spanpath/spanspan:/span path span||/span span"root"/span span}/spanspan;/span span}/span span// 合并所有 key 并检查数量/span spanconst/span keys1 span=/span Objectspan./spanspankeys/spanspan(/spanobj1span)/spanspan;/span spanconst/span keys2 span=/span Objectspan./spanspankeys/spanspan(/spanobj2span)/spanspan;/span spanconst/span allKeys span=/span spannew/span spanSet/spanspan(/spanspan/spanspan)/spanspan;/span spanif/span span(/spanallKeysspan./spansize span!==/span keys1span./spanlength span||/span allKeysspan./spansize span!==/span keys2span./spanlengthspan)/span span{/span spanreturn/span span{/span spansuccess/spanspan:/span spanfalse/spanspan,/span spanpath/spanspan:/span path span||/span span"root"/span span}/spanspan;/span span}/span span// 递归检查每个属性/span spanfor/span span(/spanspanconst/span key spanof/span allKeysspan)/span span{/span spanconst/span currentPath span=/span path span?/span spanspan`/spanspanspan${/spanpathspan}/span/spanspan./spanspanspan${/spankeyspan}/span/spanspan`/span/span span:/span keyspan;/span spanif/span span(/spanspan!/spankeys1span./spanspanincludes/spanspan(/spankeyspan)/span span||/span span!/spankeys2span./spanspanincludes/spanspan(/spankeyspan)/spanspan)/span span{/span spanreturn/span span{/span spansuccess/spanspan:/span spanfalse/spanspan,/span spanpath/spanspan:/span currentPath span}/spanspan;/span span}/span spanconst/span result span=/span spancompareObjectsWithPath/spanspan(/span obj1span/spanspan,/span obj2span/spanspan,/span currentPath span)/spanspan;/span spanif/span span(/spanspan!/spanresultspan./spansuccessspan)/span spanreturn/span resultspan;/span span}/span spanreturn/span span{/span spansuccess/spanspan:/span spantrue/span span}/spanspan;/span span}/spanspan}/span spanelse/span span{/span span// 基本类型：检查是否均为字符串/span spanreturn/span spantypeof/span obj1 span===/span span"string"/span span&&/span spantypeof/span obj2 span===/span span"string"/span span?/span span{/span spansuccess/spanspan:/span spantrue/span span}/span span:/span span{/span spansuccess/spanspan:/span spanfalse/spanspan,/span spanpath/spanspan:/span path span||/span span"root"/span span}/spanspan;/spanspan}/spanspan}/spanspan// 判断是否为纯对象（字面量对象）/spanspanfunction/span spanisPlainObject/spanspan(/spanspanvalue/spanspan)/span span{/spanspanreturn/span spanObject/spanspan./spanprototypespan./spanspantoString/spanspan./spanspancall/spanspan(/spanvaluespan)/span span===/span span""/spanspan;/spanspan}/spanspan// 主函数/spanspanconst/span spanmain/span span=/span spanasync/span span(/spanspan)/span span=> {console.time("run main");const inputFilePath = "./locales/zh.json"; // 输入的JSON文件路径const outputFilePath = `output_${MODEL}.json`; // 输出的JSON文件路径try { // 读取JSON文件 const jsonData = await readJsonFile(inputFilePath); // 翻译JSON内容 const translatedData = await translateJson(jsonData); // 将翻译后的内容写入新文件 await writeTranslatedJson(outputFilePath, translatedData); console.log( "翻译完成，结果是否存在遗漏项：", compareObjectsWithPath(jsonData, translatedData) ); console.log("翻译完成，结果已写入:", outputFilePath);} catch (error) { console.error("处理过程中出错:", error);}console.timeEnd("run main");};// 执行主函数main();
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7b：
run worker window: 1:16.909 (m:ss.mmm)翻译出错: windowrun worker contextMenu: 1:19.915 (m:ss.mmm)翻译出错: contextMenurun worker autoUpdater: 1:24.182 (m:ss.mmm)run worker menu: 1:54.272 (m:ss.mmm)run worker openWindowWarn: 2:08.219 (m:ss.mmm)翻译出错: openWindowWarnrun worker contextMenu: 54.257s翻译出错: contextMenurun worker createPreloadFileWarn: 1:05.595 (m:ss.mmm)翻译出错: createPreloadFileWarnrun worker window: 1:13.320 (m:ss.mmm)翻译出错: windowrun worker openWindowWarn: 42.933srun worker renderer: 1:06.620 (m:ss.mmm)run worker contextMenu: 58.129srun worker createPreloadFileWarn: 51.205srun worker window: 1:10.067 (m:ss.mmm)翻译出错: windowrun worker window: 17.583s翻译出错: windowrun worker window: 16.479s翻译出错: windowrun worker window: 53.783s翻译完成，结果是否存在遗漏项： { success: false, path: 'menu' }翻译完成，结果已写入: output_deepseek-r1:7b.jsonrun main: 5:08.166 (m:ss.mmm)!(img_1.png)----------------run worker openWindowWarn: 27.835s翻译出错: openWindowWarnrun worker window: 47.317s翻译出错: windowrun worker contextMenu: 1:00.365 (m:ss.mmm)翻译出错: contextMenurun worker openWindowWarn: 42.320srun worker window: 1:00.580 (m:ss.mmm)翻译出错: windowrun worker menu: 2:01.575 (m:ss.mmm)翻译出错: menurun worker contextMenu: 1:05.158 (m:ss.mmm)run worker autoUpdater: 2:08.553 (m:ss.mmm)run worker createPreloadFileWarn: 1:41.123 (m:ss.mmm)run worker window: 1:28.518 (m:ss.mmm)翻译出错: windowrun worker renderer: 1:46.725 (m:ss.mmm)run worker menu: 1:54.031 (m:ss.mmm)翻译出错: menurun worker window: 57.867srun worker menu: 1:16.267 (m:ss.mmm)翻译完成，结果是否存在遗漏项： { success: false, path: 'menu' }翻译完成，结果已写入: output_deepseek-r1:7b.jsonrun main: 5:11.880 (m:ss.mmm)!(img_2.png)
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翻译结果：
"window": { "willUnload": { "title": "What should you confirm before refreshing the current page?", "message": "the system might not save your changes", "unload_bt": "Reload", "cancel_bt": "Cancel" } },
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14b：
run worker window: 2:15.983 (m:ss.mmm)run worker contextMenu: 2:17.554 (m:ss.mmm)run worker autoUpdater: 3:02.960 (m:ss.mmm)run worker menu: 4:06.753 (m:ss.mmm)run worker openWindowWarn: 4:14.074 (m:ss.mmm)run worker createPreloadFileWarn: 2:04.443 (m:ss.mmm)run worker renderer: 2:21.099 (m:ss.mmm)翻译完成，结果是否存在遗漏项： { success: true }翻译完成，结果已写入: output_deepseek-r1:14b.jsonrun main: 4:38.673 (m:ss.mmm)------------------------run worker autoUpdater: 1:34.068 (m:ss.mmm)run worker openWindowWarn: 1:57.715 (m:ss.mmm)run worker window: 2:09.907 (m:ss.mmm)run worker contextMenu: 2:14.214 (m:ss.mmm)run worker renderer: 1:38.631 (m:ss.mmm)run worker createPreloadFileWarn: 2:24.484 (m:ss.mmm)run worker menu: 4:16.409 (m:ss.mmm)翻译出错: menurun worker menu: 2:00.482 (m:ss.mmm)翻译完成，结果是否存在遗漏项： { success: true }翻译完成，结果已写入: output_deepseek-r1:14b.jsonrun main: 6:16.900 (m:ss.mmm)
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翻译结果：
"window": { "willUnload": { "title": "Confirm to refresh the current page?", "message": "The system may not save your changes.", "unload_bt": "Reload", "cancel_bt": "Cancel" }},
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(4) 整体体验下来，14b 模型在翻译工作上比 7b 模型更为准确，一次性翻译成功率高。7B 模型翻译结果噪声多，返回结果可序列化效果差。翻译结果远远不如 14b。
结论
14b 在 macos 执行效率能满足特定业务场景要求。
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