zengli
/
coding-agent


			
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  <h1>1.2 项目环境(window)</h1>

<h2 id="_1">前置说明<a class="headerlink" href="#_1" title="Permanent link">&para;</a></h2>
<p>本教程介绍如何在 Windows 11 和 M1 芯片的 macOS 系统上使用 Docker 同时部署和运行单机版 Milvus（向量数据库）和 Redis（键值存储数据库）。教程分为两部分，分别针对不同操作系统提供详细步骤。</p>
<ul>
<li>操作系统</li>
<li>Windows 11</li>
<li>硬件要求</li>
<li>至少 8GB 内存（建议 16GB 或更高）。</li>
<li>至少 20GB 可用磁盘空间（Milvus 和 Redis 数据存储）。</li>
<li>网络要求</li>
<li>稳定的互联网连接，用于拉取 Docker 镜像。</li>
<li>软件版本</li>
<li>Milvus：2.4.4（最新稳定版）。</li>
<li>Redis：7.2（最新稳定版）。</li>
<li>端口要求</li>
<li>Milvus：<code>19530</code>（gRPC）、<code>9091</code>（RESTful API）。</li>
<li>Redis：<code>6379</code>（默认端口）。</li>
<li>确保以上端口未被占用。</li>
</ul>
<h2 id="python">一、python虚拟环境<a class="headerlink" href="#python" title="Permanent link">&para;</a></h2>
<h3 id="1">1 创建虚拟环境<a class="headerlink" href="#1" title="Permanent link">&para;</a></h3>
<div class="highlight"><pre><span></span><code><span class="c1"># 创建虚拟环境, 安装3.12版本的python解析器</span>
conda<span class="w"> </span>create<span class="w"> </span>-n<span class="w"> </span>EnterpriseKB-RAG<span class="w"> </span><span class="nv">python</span><span class="o">==</span><span class="m">3</span>.12
<span class="c1"># 切换虚拟环境 </span>
conda<span class="w"> </span>activate<span class="w"> </span>EnterpriseKB-RAG
</code></pre></div>
<h3 id="2">2 找到依赖文件<a class="headerlink" href="#2" title="Permanent link">&para;</a></h3>
<blockquote>
<p>找到支持人员发的requirements.txt文件</p>
</blockquote>
<h3 id="3">3 安装项目依赖包<a class="headerlink" href="#3" title="Permanent link">&para;</a></h3>
<div class="highlight"><pre><span></span><code><span class="c1"># 安装依赖包, 在requirements.txt对应路径下执行以下命令 </span>
pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements.txt<span class="w"> </span>
</code></pre></div>
<p><img alt="image-20250814174558612" src="%E4%BC%81%E4%B8%9A%E7%9F%A5%E8%AF%86%E5%BA%93RAG%E9%A1%B9%E7%9B%AE%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85%E6%B5%81%E7%A8%8B.assets/image-20250814174558612.png" /></p>
<p>如果安装过程不顺利，可以按需逐个安装：</p>
<div class="highlight"><pre><span></span><code><span class="c1"># conda create -n enterprise_kb_rag python==3.12</span>
<span class="c1"># conda activate enterprise_kb_rag</span>

<span class="c1"># Python版本： 3.12</span>
<span class="c1"># 使用方法：先安装python 3.12或者创建一个conda的3.12的环境，并激活。然后运行以下命令</span>

pip<span class="w"> </span>install<span class="w"> </span><span class="nv">ollama</span><span class="o">==</span><span class="m">0</span>.4.4<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">requests</span><span class="o">==</span><span class="m">2</span>.32.3
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">numexpr</span><span class="o">==</span><span class="m">2</span>.11.0<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">unstructured</span><span class="o">==</span><span class="m">0</span>.11.0<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span>langchain-core<span class="o">==</span><span class="m">1</span>.2.16<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">langchain_community</span><span class="o">==</span><span class="m">0</span>.4.1<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span>langchain-ollama<span class="o">==</span><span class="m">1</span>.0.1<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">langchain_unstructured</span><span class="o">==</span><span class="m">1</span>.0.1
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">langchain_experimental</span><span class="o">==</span><span class="m">0</span>.4.1<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">langchain_chroma</span><span class="o">==</span><span class="m">1</span>.1.0<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span>langchain-text-splitters<span class="o">==</span><span class="m">1</span>.1.1<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">langgraph</span><span class="o">==</span><span class="m">1</span>.0.8<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">nltk</span><span class="o">==</span><span class="m">3</span>.9.1
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">chromadb</span><span class="o">==</span><span class="m">1</span>.3.5
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">wikipedia</span><span class="o">==</span><span class="m">1</span>.4.0<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span>faiss-cpu<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">pymilvus</span><span class="o">==</span><span class="m">2</span>.5.4<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">pandas</span><span class="o">==</span><span class="m">2</span>.3.1
pip<span class="w"> </span>install<span class="w"> </span>jieba
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">rank_bm25</span><span class="o">==</span><span class="m">0</span>.2.2
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">redis</span><span class="o">==</span><span class="m">5</span>.3.1<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">pymysql</span><span class="o">==</span><span class="m">1</span>.1.1
pip<span class="w"> </span>install<span class="w"> </span>opencv-python<span class="o">==</span><span class="m">4</span>.10.0.84<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">PyMuPDF</span><span class="o">==</span><span class="m">1</span>.23.16<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span>python-docx<span class="o">==</span><span class="m">1</span>.1.2<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">pillow</span><span class="o">==</span><span class="m">10</span>.4.0
pip<span class="w"> </span>install<span class="w"> </span>rapidocr-onnxruntime<span class="o">==</span><span class="m">1</span>.3.8
pip<span class="w"> </span>install<span class="w"> </span>python-pptx<span class="o">==</span><span class="m">0</span>.6.23<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">transformers</span><span class="o">==</span><span class="m">4</span>.45.0<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">modelscope</span><span class="o">==</span><span class="m">1</span>.23.0<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">addict</span><span class="o">==</span><span class="m">2</span>.4.0
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">datasets</span><span class="o">==</span><span class="m">3</span>.3.1<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">simplejson</span><span class="o">==</span><span class="m">3</span>.19.2
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">sortedcontainers</span><span class="o">==</span><span class="m">2</span>.4.0
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">markdown</span><span class="o">==</span><span class="m">3</span>.6
pip<span class="w"> </span>install<span class="w"> </span>sentence-transformers<span class="o">==</span><span class="m">3</span>.0.1
pip<span class="w"> </span>install<span class="w"> </span>milvus-model<span class="o">==</span><span class="m">0</span>.2.5<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">tiktoken</span><span class="o">==</span><span class="m">0</span>.7.0<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">sentencepiece</span><span class="o">==</span><span class="m">0</span>.2.0<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">ragas</span><span class="o">==</span><span class="m">0</span>.2.6<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">starlette</span><span class="o">==</span><span class="m">0</span>.46.2<span class="w"> </span>
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">fastapi</span><span class="o">==</span><span class="m">0</span>.115.12
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">FlagEmbedding</span><span class="o">==</span><span class="m">1</span>.3.5
pip<span class="w"> </span>install<span class="w"> </span><span class="nv">peft</span><span class="o">==</span><span class="m">0</span>.18.1
</code></pre></div>
<h2 id="dockerdesktop">二、安装DockerDesktop<a class="headerlink" href="#dockerdesktop" title="Permanent link">&para;</a></h2>
<h3 id="1-wsl">1 安装WSL<a class="headerlink" href="#1-wsl" title="Permanent link">&para;</a></h3>
<ul>
<li>
<p><strong>启用WSL</strong>打开具有管理员权限的PowerShell，运行以下命令以安装WSL</p>
<div class="highlight"><pre><span></span><code>wsl<span class="w"> </span>--install<span class="w"> </span>
</code></pre></div>
<p><img alt="1753623042372" src="assets/1753623042372.png" /></p>
<p><img alt="1753623117795" src="assets/1753623117795.png" /></p>
</li>
</ul>
<p>输入命令后效果如下<img alt="image-20250814174828917" src="%E4%BC%81%E4%B8%9A%E7%9F%A5%E8%AF%86%E5%BA%93RAG%E9%A1%B9%E7%9B%AE%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85%E6%B5%81%E7%A8%8B.assets/image-20250814174828917.png" /></p>
<p>安装完后自动跳转页面,关闭即可</p>
<p><img alt="image-20250814174921643" src="%E4%BC%81%E4%B8%9A%E7%9F%A5%E8%AF%86%E5%BA%93RAG%E9%A1%B9%E7%9B%AE%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85%E6%B5%81%E7%A8%8B.assets/image-20250814174921643.png" /></p>
<p>安装完成后，重启电脑</p>
<h3 id="2-docker">2 安装docker<a class="headerlink" href="#2-docker" title="Permanent link">&para;</a></h3>
<ul>
<li>
<p>下载并安装Docker Desktop</p>
<ul>
<li>访问<a href="https://www.docker.com/products/docker-desktop/">Docker官方网站</a>，下载适用于Windows/Mac的Docker Desktop安装包。(支持人员已经提前下载了,大家直接用即可)</li>
</ul>
<p><img alt="image-20250822095538468" src="%E4%BC%81%E4%B8%9A%E7%9F%A5%E8%AF%86%E5%BA%93RAG%E9%A1%B9%E7%9B%AE%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85%E6%B5%81%E7%A8%8B.assets/image-20250822095538468.png" /></p>
</li>
<li>
<p>安装Docker Desktop</p>
<ul>
<li>双击下载的安装文件(支持人员发的资料中有)，按照提示一路下一步完成安装</li>
<li>安装完成后，点击<strong>close and restart</strong>重启电脑以完成配置</li>
</ul>
<p><img alt="image-20250814175817544" src="%E4%BC%81%E4%B8%9A%E7%9F%A5%E8%AF%86%E5%BA%93RAG%E9%A1%B9%E7%9B%AE%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85%E6%B5%81%E7%A8%8B.assets/image-20250814175817544.png" /></p>
</li>
<li>
<p>验证Docker安装</p>
<ul>
<li>
<p>打开PowerShell或命令提示符，运行以下命令,验证是否成功安装</p>
<div class="highlight"><pre><span></span><code>docker --version
docker compose version
</code></pre></div>
<p><img alt="1753623823676" src="assets/1753623823676.png" /></p>
</li>
</ul>
</li>
</ul>
<h2 id="dockercompose">三、配置DockerCompose文件<a class="headerlink" href="#dockercompose" title="Permanent link">&para;</a></h2>
<h3 id="1_1">1 创建项目目录<a class="headerlink" href="#1_1" title="Permanent link">&para;</a></h3>
<ul>
<li>
<p>在本地磁盘（例如:C盘）创建文件夹milvus_redis，用于存放Milvus和Redis的配置文件和数据：   </p>
<p><img alt="1753623917487" src="assets/1753623917487.png" /></p>
</li>
</ul>
<h3 id="2-docker-compose">2 创建docker-compose文件<a class="headerlink" href="#2-docker-compose" title="Permanent link">&para;</a></h3>
<p>先创建docker-compose.txt格式文件, 然后修改为docker-compose.yml格式</p>
<p><img alt="1753623986186" src="assets/1753623986186.png" /></p>
<h3 id="3_1">3 添加内容<a class="headerlink" href="#3_1" title="Permanent link">&para;</a></h3>
<p>把以下内容复制粘贴到你创建的docker-compose.yml文件:</p>
<div class="highlight"><pre><span></span><code><span class="c1"># version: &#39;3.5&#39;</span>

<span class="c1"># Windows/x86_64 Version</span>
<span class="c1"># Redis Password set to: 1234</span>

<span class="na">services</span><span class="o">:</span>
<span class="w">  </span><span class="na">etcd</span><span class="o">:</span>
<span class="w">    </span><span class="na">container_name</span><span class="o">:</span><span class="w"> </span><span class="s">milvus-etcd</span>
<span class="w">    </span><span class="na">image</span><span class="o">:</span><span class="w"> </span><span class="s">quay.io/coreos/etcd:v3.5.5</span>
<span class="w">    </span><span class="na">environment</span><span class="o">:</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">ETCD_AUTO_COMPACTION_MODE=revision</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">ETCD_AUTO_COMPACTION_RETENTION=1000</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">ETCD_QUOTA_BACKEND_BYTES=4294967296</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">ETCD_ENABLE_V2=true</span>
<span class="w">    </span><span class="na">volumes</span><span class="o">:</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">./volumes/etcd:/etcd/data</span>
<span class="w">    </span><span class="na">command</span><span class="o">:</span><span class="w"> </span><span class="s">etcd -advertise-client-urls=http://127.0.0.1:2379 -listen-client-urls http://0.0.0.0:2379 --data-dir /etcd/data</span>

<span class="w">  </span><span class="na">minio</span><span class="o">:</span>
<span class="w">    </span><span class="na">container_name</span><span class="o">:</span><span class="w"> </span><span class="s">milvus-minio</span>
<span class="w">    </span><span class="na">image</span><span class="o">:</span><span class="w"> </span><span class="s">minio/minio:RELEASE.2023-03-20T20-16-18Z</span>
<span class="w">    </span><span class="na">environment</span><span class="o">:</span>
<span class="w">      </span><span class="na">MINIO_ACCESS_KEY</span><span class="o">:</span><span class="w"> </span><span class="s">minioadmin</span>
<span class="w">      </span><span class="na">MINIO_SECRET_KEY</span><span class="o">:</span><span class="w"> </span><span class="s">minioadmin</span>
<span class="w">    </span><span class="na">volumes</span><span class="o">:</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">./volumes/minio:/minio/data</span>
<span class="w">    </span><span class="na">command</span><span class="o">:</span><span class="w"> </span><span class="s">minio server /minio/data</span>
<span class="w">    </span><span class="na">healthcheck</span><span class="o">:</span>
<span class="w">      </span><span class="na">test</span><span class="o">:</span><span class="w"> </span><span class="s">[&quot;CMD&quot;, &quot;curl&quot;, &quot;-f&quot;, &quot;http://localhost:9000/minio/health/live&quot;]</span>
<span class="w">      </span><span class="na">interval</span><span class="o">:</span><span class="w"> </span><span class="s">30s</span>
<span class="w">      </span><span class="na">timeout</span><span class="o">:</span><span class="w"> </span><span class="s">20s</span>
<span class="w">      </span><span class="na">retries</span><span class="o">:</span><span class="w"> </span><span class="s">3</span>

<span class="w">  </span><span class="na">standalone</span><span class="o">:</span>
<span class="w">    </span><span class="na">container_name</span><span class="o">:</span><span class="w"> </span><span class="s">milvus-standalone</span>
<span class="w">    </span><span class="c1"># 使用多架构镜像，Docker on Windows 会自动拉取 x86_64 版本</span>
<span class="w">    </span><span class="na">image</span><span class="o">:</span><span class="w"> </span><span class="s">milvusdb/milvus:v2.4.4</span>
<span class="w">    </span><span class="na">command</span><span class="o">:</span><span class="w"> </span><span class="s">[&quot;milvus&quot;, &quot;run&quot;, &quot;standalone&quot;]</span>
<span class="w">    </span><span class="na">environment</span><span class="o">:</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">ETCD_ENDPOINTS=etcd:2379</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">MINIO_ADDRESS=minio:9000</span>
<span class="w">    </span><span class="na">volumes</span><span class="o">:</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">./volumes/milvus:/var/lib/milvus</span>
<span class="w">    </span><span class="na">ports</span><span class="o">:</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">&quot;19530:19530&quot;</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">&quot;9091:9091&quot;</span>
<span class="w">    </span><span class="na">depends_on</span><span class="o">:</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">&quot;etcd&quot;</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">&quot;minio&quot;</span>

<span class="w">  </span><span class="na">redis</span><span class="o">:</span>
<span class="w">    </span><span class="na">container_name</span><span class="o">:</span><span class="w"> </span><span class="s">milvus-redis</span>
<span class="w">    </span><span class="na">image</span><span class="o">:</span><span class="w"> </span><span class="s">redis:latest</span>
<span class="w">    </span><span class="na">restart</span><span class="o">:</span><span class="w"> </span><span class="s">always</span>
<span class="w">    </span><span class="na">ports</span><span class="o">:</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">&quot;6379:6379&quot;</span>
<span class="w">    </span><span class="na">volumes</span><span class="o">:</span>
<span class="w">      </span><span class="na">-</span><span class="w"> </span><span class="s">./volumes/redis:/data</span>
<span class="w">    </span><span class="c1"># 设置 Redis 密码为 1234</span>
<span class="w">    </span><span class="na">command</span><span class="o">:</span><span class="w"> </span><span class="s">redis-server --requirepass 1234</span>

<span class="na">networks</span><span class="o">:</span>
<span class="w">  </span><span class="na">default</span><span class="o">:</span>
<span class="w">    </span><span class="na">name</span><span class="o">:</span><span class="w"> </span><span class="s">milvus-network</span>
</code></pre></div>
<p>说明</p>
<ul>
<li>
<p>Milvus 服务</p>
</li>
<li>
<p><code>image</code>: 使用 Milvus 官方镜像 <code>milvusdb/milvus:v2.4.10</code>。</p>
</li>
<li><code>ports</code>: 映射 gRPC（19530）和 RESTful API（9091）端口。</li>
<li><code>volumes</code>: 数据持久化存储到本地 <code>./volumes/milvus</code> 目录。</li>
<li><code>environment</code>: 配置日志级别和 Etcd 参数。</li>
<li>
<p><code>command</code>: 启动 Milvus 单机模式。</p>
</li>
<li>
<p>Redis 服务</p>
</li>
<li>
<p><code>image</code>: 使用 Redis 官方镜像 <code>redis:7.2</code>。</p>
</li>
<li><code>ports</code>: 映射默认端口 <code>6379</code>。</li>
<li><code>volumes</code>: 数据持久化存储到本地 <code>./volumes/redis</code> 目录。</li>
<li>
<p><code>command</code>: 启用 AOF（Append Only File）持久化模式。</p>
</li>
<li>
<p><code>volumes</code>: 定义持久化存储卷。</p>
</li>
</ul>
<h2 id="milvusredis">四、启动Milvus和Redis<a class="headerlink" href="#milvusredis" title="Permanent link">&para;</a></h2>
<h3 id="1_2">1 拉取镜像并启动容器<a class="headerlink" href="#1_2" title="Permanent link">&para;</a></h3>
<ul>
<li>
<p>进入上述自己创建的milvus_redis目录下，打开具有管理员权限的PowerShell终端，运行：</p>
<div class="highlight"><pre><span></span><code><span class="c1"># -d 表示后台运行容器</span>
docker<span class="w"> </span>compose<span class="w"> </span>up<span class="w"> </span>-d
</code></pre></div>
<p><img alt="1753625803111" src="assets/1753625803111.png" /></p>
<p><img alt="image-20250814180816031" src="%E4%BC%81%E4%B8%9A%E7%9F%A5%E8%AF%86%E5%BA%93RAG%E9%A1%B9%E7%9B%AE%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85%E6%B5%81%E7%A8%8B.assets/image-20250814180816031.png" /></p>
</li>
<li>
<p>注意: 拉取镜像时报错: Get "https://registry-1.docker.io/v2/": net/http: request canceled while waiting for connection (Client.Timeout exceeded while awaiting headers)</p>
</li>
</ul>
<p><strong>以下为解决方法, 添加镜像源:</strong></p>
<ul>
<li>
<p>linux方案: CSDN链接:https://blog.csdn.net/jhgj56/article/details/142209517?fromshare=blogdetail&amp;sharetype=blogdetail&amp;sharerId=142209517&amp;sharerefer=PC&amp;sharesource=weixin_45056110&amp;sharefrom=from_link</p>
</li>
<li>
<p>window方案: 咱们当前是window环境,所以直接复制以下内容去docker软件中粘贴即可:</p>
</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="nt">&quot;registry-mirrors&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
<span class="w">    </span><span class="s2">&quot;https://docker.211678.top&quot;</span><span class="p">,</span>
<span class="w">    </span><span class="s2">&quot;https://docker.1panel.live&quot;</span><span class="p">,</span>
<span class="w">    </span><span class="s2">&quot;https://hub.rat.dev&quot;</span><span class="p">,</span>
<span class="w">    </span><span class="s2">&quot;https://docker.m.daocloud.io&quot;</span><span class="p">,</span>
<span class="s2">&quot;https://do.nark.eu.org&quot;</span><span class="p">,</span>
<span class="w">    </span><span class="s2">&quot;https://dockerpull.com&quot;</span><span class="p">,</span>
<span class="s2">&quot;https://dockerproxy.cn&quot;</span><span class="p">,</span>
<span class="w">    </span><span class="s2">&quot;https://docker.awsl9527.cn&quot;</span>
<span class="p">]</span>
</code></pre></div>
<p><img alt="1753625438227" src="assets/1753625438227.png" /></p>
<blockquote>
<p><mark>重启docker生效</mark></p>
<p>然后再次去milvus_redis 目录下，打开 PowerShell，运行：</p>
</blockquote>
<p><img alt="image-20250816223314138" src="%E4%BC%81%E4%B8%9A%E7%9F%A5%E8%AF%86%E5%BA%93RAG%E9%A1%B9%E7%9B%AE%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85%E6%B5%81%E7%A8%8B.assets/image-20250816223314138.png" /></p>
<p><img alt="image-20250816233644355" src="%E4%BC%81%E4%B8%9A%E7%9F%A5%E8%AF%86%E5%BA%93RAG%E9%A1%B9%E7%9B%AE%E7%8E%AF%E5%A2%83%E5%AE%89%E8%A3%85%E6%B5%81%E7%A8%8B.assets/image-20250816233644355.png" /></p>
<h3 id="2-docker_1">2 docker查看是否成功<a class="headerlink" href="#2-docker_1" title="Permanent link">&para;</a></h3>
<p>启动成功后去docker看看是否正常启动</p>
<p><img alt="1753625673169" src="assets/1753625673169.png" /></p>
<h2 id="milvusredis_1">五、验证Milvus和Redis<a class="headerlink" href="#milvusredis_1" title="Permanent link">&para;</a></h2>
<h3 id="1-milvus">1 测试Milvus连接<a class="headerlink" href="#1-milvus" title="Permanent link">&para;</a></h3>
<ul>
<li>
<p>创建Python脚本（例如 test_milvus.py ）</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">pymilvus</span><span class="w"> </span><span class="kn">import</span> <span class="n">connections</span><span class="p">,</span> <span class="n">utility</span>

<span class="c1"># 连接到 Milvus</span>
<span class="n">connections</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">&quot;localhost&quot;</span><span class="p">,</span> <span class="n">port</span><span class="o">=</span><span class="s2">&quot;19530&quot;</span><span class="p">)</span>

<span class="c1"># 检查版本</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Milvus version: </span><span class="si">{</span><span class="n">utility</span><span class="o">.</span><span class="n">get_server_version</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</code></pre></div>
</li>
<li>
<p>在EnterpriseKB-RAG虚拟环境下运行脚本</p>
<div class="highlight"><pre><span></span><code><span class="c1"># cmd终端执行命令</span>
<span class="c1"># 输出类似Milvus version: 2.4.10 表示Milvus部署成功</span>
python<span class="w"> </span>test_milvus.py
</code></pre></div>
</li>
</ul>
<h3 id="2-redis">2 测试Redis连接<a class="headerlink" href="#2-redis" title="Permanent link">&para;</a></h3>
<ul>
<li>
<p>创建Python脚本（例如 test_redis.py ）</p>
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">redis</span>

<span class="c1"># 连接到 Redis</span>
<span class="n">client</span> <span class="o">=</span> <span class="n">redis</span><span class="o">.</span><span class="n">Redis</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s2">&quot;localhost&quot;</span><span class="p">,</span> <span class="n">port</span><span class="o">=</span><span class="mi">6379</span><span class="p">,</span> <span class="n">password</span><span class="o">=</span><span class="mi">1234</span><span class="p">,</span> <span class="n">decode_responses</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># 测试读写</span>
<span class="n">client</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="s2">&quot;test_key&quot;</span><span class="p">,</span> <span class="s2">&quot;Hello, Redis!&quot;</span><span class="p">)</span>
<span class="n">value</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;test_key&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Redis value: </span><span class="si">{</span><span class="n">value</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</code></pre></div>
</li>
<li>
<p>在EnterpriseKB-RAG虚拟环境下运行脚本</p>
<div class="highlight"><pre><span></span><code><span class="c1"># cmd终端执行命令</span>
<span class="c1"># 输出类似Redis value: Hello, Redis! 表示Redis部署成功</span>
python<span class="w"> </span>test_redis.py
</code></pre></div>
</li>
</ul>
<h2 id="milvusattu">六、安装Milvus可视化工具Attu<a class="headerlink" href="#milvusattu" title="Permanent link">&para;</a></h2>
<h3 id="1_3">1 安装<a class="headerlink" href="#1_3" title="Permanent link">&para;</a></h3>
<p>拉取可视化组件attu</p>
<div class="highlight"><pre><span></span><code>docker<span class="w"> </span>pull<span class="w"> </span>zilliz/attu
</code></pre></div>
<p>运行可视化组件</p>
<div class="highlight"><pre><span></span><code><span class="c1"># 下面的30000就是访问的端口，可以自行修改，确保不要被占用即可</span>
<span class="n">docker</span> <span class="n">run</span> <span class="o">-</span><span class="n">d</span> <span class="o">-</span><span class="n">p</span> <span class="mi">30000</span><span class="p">:</span><span class="mi">3000</span> <span class="n">zilliz</span><span class="o">/</span><span class="n">attu</span>
</code></pre></div>
<p>打开127.0.0.1:30000</p>
<h3 id="2_1">2 连接<a class="headerlink" href="#2_1" title="Permanent link">&para;</a></h3>
<p><img alt="1753668545474" src="assets/1753668545474.png" /></p>
<p><img alt="1753668563908" src="assets/1753668563908.png" /></p>


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