zengli
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coding-agent


			
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  <h1>2.1 Ollama大模型高效管理工具</h1>

<h2 id="_1">学习目标<a class="headerlink" href="#_1" title="Permanent link">¶</a></h2>
<ul>
<li>了解项目开发背景</li>
<li>掌握融合本地知识的RAG系统实现过程</li>
</ul>
<h2 id="ollama">一、Ollama概述<a class="headerlink" href="#ollama" title="Permanent link">¶</a></h2>
<h3 id="1-ollama">1 Ollama简介<a class="headerlink" href="#1-ollama" title="Permanent link">¶</a></h3>
<p>随着人工智能技术的快速发展，大型语言模型 LLM 已成为NLP领域的重要工具。然而，这些模型的运行通常需要大量的计算资源和复杂的部署流程。为了解决这个问题，Ollama应运而生，成为了一个高效的本地大语言模型LLM运行专家。</p>
<p>Ollama是一个开源的大型语言模型服务工具，它帮助用户快速在本地运行大模型。通过简单的安装指令，用户可以执行一条命令就在本地运行开源大型语言模型，如Llama 2。Ollama极大地简化了部署和管理LLM的过程，使得用户能够快速地在本地运行大型语言模型。</p>
<hr>
<h3 id="2-ollama">2 Ollama的安装<a class="headerlink" href="#2-ollama" title="Permanent link">¶</a></h3>
<h4 id="21-ollama">2.1 下载安装Ollama<a class="headerlink" href="#21-ollama" title="Permanent link">¶</a></h4>
<ul>
<li>
<p>Ollama主页：<a href="https://ollama.com/">https://ollama.com/</a> ，支持macOS, Linux 和 Windows 系统，如果是macOS、Linux系统，直接安装使用即可；如果是Windows，需要按照以下教程进行安装使用：</p>
</li>
<li>
<p>点击下载按钮，获取<code>OllamaSetup.exe</code>安装程序。</p>
</li>
</ul>
<div align="center"><img src="../img/9-2-1.png" style="zoom:40%"><img></div>

<div align="center"><img src="../img/9-2-2.png" style="zoom:40%"><img></div>

<ul>
<li>双击安装文件，点击「Install」开始安装。目前的Ollama会默认安装到C盘，路径如下：<code>C:\Users\%username%\AppData\Local\Programs\Ollama</code>，并不会让用户自定义选择安装路径。</li>
</ul>
<div align="center"><img src="../img/9-2-3.png" style="zoom:30%"><img></div>

<div align="center"><img src="../img/9-2-4.png" style="zoom:40%"><img></div>

<ul>
<li>安装完成后，会在电脑右下角出现Ollama图标，可以不用先启动Ollama，先退出做一些必要路径的配置！右键图标，会出现退出的按钮「Quit Ollama」，注意：一定要退出Ollama，否则下边的环境配置无法生效！</li>
</ul>
<div align="center"><img src="../img/9-2-5.png" style="zoom:60%"><img></div>

<hr>
<h4 id="22">2.2 环境配置<a class="headerlink" href="#22" title="Permanent link">¶</a></h4>
<ul>
<li>
<p>关闭开机自启动（可选）：</p>
</li>
<li>
<p>Ollama 默认会随 Windows 自动启动，可以在「文件资源管理器」的地址栏中访问以下路径，删除其中的Ollama.lnk快捷方式文件，阻止它自动启动。</p>
</li>
</ul>
<div class="highlight"><pre><span></span><code>%APPDATA%\Microsoft\Windows\Start Menu\Programs\Startup
</code></pre></div>
<div align="center"><img src="../img/9-2-6.png" style="zoom:60%"><img></div>

<ul>
<li>
<p>配置环境变量（必须）：</p>
</li>
<li>
<p>Ollama 的默认模型存储路径如下：C:\Users\%username%.ollama\models，无论 C 盘空间大小，需要安装多少模型，都建议换一个存放路径到其它盘，否则会影响电脑运行速度。</p>
</li>
<li>打开「系统环境变量」，新建一个系统变量OLLAMA_MODELS ，然后设置ollama模型的存储路径。</li>
<li>变量名：OLLAMA_MODELS</li>
<li>变量值（路径）：D:\Work\ollama\models</li>
</ul>
<div align="center"><img src="../img/9-2-7.png" style="zoom:60%"><img></div>

<hr>
<h4 id="23-ollama">2.3 运行Ollama<a class="headerlink" href="#23-ollama" title="Permanent link">¶</a></h4>
<ul>
<li>将上述更改的环境变量保存后，在「开始」菜单中找到并重新启动 Ollama。找到ollama.exe的所在位置，在该位置打开cmd</li>
</ul>
<div align="center"><img src="../img/9-2-8.png" style="zoom:60%"><img></div>

<ul>
<li>执行指令<code>ollama run &lt;模型名称&gt;</code>，首次执行会从模型库中下载模型，所需时间取决于你的网速和模型大小。模型库地址：<a href="https://ollama.org.cn/library">https://ollama.org.cn/library</a></li>
</ul>
<div align="center"><img src="../img/9-2-10.png" style="zoom:60%"><img></div>

<ul>
<li>在Ollama模型库中，比较常用的LLM模型有：</li>
</ul>
<div align="center"><img src="../img/9-2-9.png" style="zoom:60%"><img></div>

<blockquote>
<p>运行7B至少需要8GB显存，运行13B至少需要16G显存，如果没有GPU默认加载CPU；如果有默认加载GPU</p>
</blockquote>
<ul>
<li>等待下载模型完成后，就可以使用了：</li>
</ul>
<div align="center"><img src="../img/9-2-11.png" style="zoom:60%"><img></div>

<h2 id="ollama_1">二、Ollama应用<a class="headerlink" href="#ollama_1" title="Permanent link">¶</a></h2>
<h3 id="1-python-ollama-api">1 在 Python 中使用 Ollama API<a class="headerlink" href="#1-python-ollama-api" title="Permanent link">¶</a></h3>
<ul>
<li>环境准备</li>
<li>在开始使用 Python 与 Ollama API 交互之前，请确保您的开发环境满足以下条件：</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="na">安装ollama库</span><span class="o">:</span><span class="w"> </span><span class="s">pip install ollama</span>
</code></pre></div>
<ul>
<li>
<p>代码实现：</p>
</li>
<li>
<p>方式1</p>
</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">ollama</span>
<span class="c1"># 聊天式</span>
<span class="n">response</span> <span class="o">=</span> <span class="n">ollama</span><span class="o">.</span><span class="n">chat</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s1">'qwen2:1.5b'</span><span class="p">,</span>
                       <span class="n">messages</span><span class="o">=</span><span class="p">[{</span><span class="s1">'role'</span><span class="p">:</span> <span class="s1">'user'</span><span class="p">,</span> <span class="s1">'content'</span><span class="p">:</span> <span class="s1">'为什么天空是蓝色的？'</span><span class="p">,</span> <span class="p">}])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">response</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">response</span><span class="p">[</span><span class="s1">'message'</span><span class="p">][</span><span class="s1">'content'</span><span class="p">])</span>
<span class="c1"># 生成式</span>
<span class="c1">## response = ollama.generate(model='qwen2:1.5b', prompt='为什么天空是蓝色的？')</span>
</code></pre></div>
<ul>
<li>方式2</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="c1"># 可以远程调用ollama</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">Client</span>
<span class="c1"># client = Client(host='http://192.168.1.100:11434')</span>
<span class="n">client</span> <span class="o">=</span> <span class="n">Client</span><span class="p">(</span><span class="n">host</span><span class="o">=</span><span class="s1">'http://127.0.0.1:11434'</span><span class="p">)</span>

<span class="n">response</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">chat</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s1">'qwen2:1.5b'</span><span class="p">,</span> <span class="n">messages</span><span class="o">=</span><span class="p">[</span>
  <span class="p">{</span>
    <span class="s1">'role'</span><span class="p">:</span> <span class="s1">'user'</span><span class="p">,</span>
    <span class="s1">'content'</span><span class="p">:</span> <span class="s1">'为什么天空是蓝色的？'</span><span class="p">,</span>
  <span class="p">},</span>
<span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">response</span><span class="p">[</span><span class="s1">'message'</span><span class="p">][</span><span class="s1">'content'</span><span class="p">])</span>
</code></pre></div>
<ul>
<li>方式3（了解）<ul>
<li>可以通过设置 <code>stream=True</code> 启用响应流，使函数调用返回一个 Python 生成器，其中每个部分都是流中的一个对象。</li>
</ul>
</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="kn">import</span><span class="w"> </span><span class="nn">ollama</span>

<span class="n">stream</span> <span class="o">=</span> <span class="n">ollama</span><span class="o">.</span><span class="n">chat</span><span class="p">(</span>
    <span class="n">model</span><span class="o">=</span><span class="s1">'qwen2:1.5b'</span><span class="p">,</span>
    <span class="n">messages</span><span class="o">=</span><span class="p">[{</span><span class="s1">'role'</span><span class="p">:</span> <span class="s1">'user'</span><span class="p">,</span> <span class="s1">'content'</span><span class="p">:</span> <span class="s1">'为什么天空是蓝色的？'</span><span class="p">}],</span>
    <span class="n">stream</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="p">)</span>

<span class="k">for</span> <span class="n">chunk</span> <span class="ow">in</span> <span class="n">stream</span><span class="p">:</span>
  <span class="nb">print</span><span class="p">(</span><span class="n">chunk</span><span class="p">[</span><span class="s1">'message'</span><span class="p">][</span><span class="s1">'content'</span><span class="p">],</span> <span class="n">end</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">flush</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</code></pre></div>
<h3 id="2-langchain">2 LangChain调用<a class="headerlink" href="#2-langchain" title="Permanent link">¶</a></h3>
<ul>
<li>
<p>环境准备
  </p><div class="highlight"><pre><span></span><code><span class="na">安装langchain库</span><span class="o">:</span><span class="w"> </span><span class="s">pip install langchain</span>
<span class="na">安装langchain_community库</span><span class="o">:</span><span class="w"> </span><span class="s">pip install langchain_community</span>
</code></pre></div>
</li>
<li>
<p>代码实现</p>
</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_community.llms</span><span class="w"> </span><span class="kn">import</span> <span class="n">Ollama</span>
<span class="n">host</span><span class="o">=</span><span class="s2">"xxx"</span>
<span class="n">port</span><span class="o">=</span><span class="s2">"xxx"</span> <span class="c1">#默认的端口号为11434</span>
<span class="c1"># 如果自己本地系统有ollama服务，可以省略base_url</span>
<span class="n">llm</span><span class="o">=</span><span class="n">Ollama</span><span class="p">(</span><span class="n">base_url</span><span class="o">=</span><span class="sa">f</span><span class="s2">"http://</span><span class="si">{</span><span class="n">host</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">port</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="s2">"qwen2:1.5b"</span><span class="p">,</span><span class="n">temperature</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">res</span><span class="o">=</span><span class="n">llm</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="s2">"你是谁"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
</code></pre></div>
<hr>
<h3 id="3-requests">3 requests调用<a class="headerlink" href="#3-requests" title="Permanent link">¶</a></h3>
<ul>
<li>
<p>环境准备
  </p><div class="highlight"><pre><span></span><code><span class="na">安装requests库</span><span class="o">:</span><span class="w"> </span><span class="s">pip install requests</span>
</code></pre></div>
</li>
<li>
<p>代码实现</p>
</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="n">host</span><span class="o">=</span><span class="s2">"xxx"</span>
<span class="n">port</span><span class="o">=</span><span class="s2">"xxx"</span>
<span class="n">url</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">"http://</span><span class="si">{</span><span class="n">host</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">port</span><span class="si">}</span><span class="s2">/api/chat"</span>
<span class="n">model</span> <span class="o">=</span> <span class="s2">"qwen2:1.5b"</span>
<span class="n">headers</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"Content-Type"</span><span class="p">:</span> <span class="s2">"application/json"</span><span class="p">}</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">{</span>
        <span class="s2">"model"</span><span class="p">:</span> <span class="n">model</span><span class="p">,</span> <span class="c1">#模型选择</span>
        <span class="s2">"options"</span><span class="p">:</span> <span class="p">{</span>
            <span class="s2">"temperature"</span><span class="p">:</span> <span class="mf">0.</span>  <span class="c1">#为0表示不让模型自由发挥，输出结果相对较固定，&gt;0的话，输出的结果会比较放飞自我</span>
         <span class="p">},</span>
        <span class="s2">"stream"</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span> <span class="c1">#流式输出</span>
        <span class="s2">"messages"</span><span class="p">:</span> <span class="p">[{</span>
            <span class="s2">"role"</span><span class="p">:</span> <span class="s2">"system"</span><span class="p">,</span>
            <span class="s2">"content"</span><span class="p">:</span><span class="s2">"你是谁？"</span>
        <span class="p">}]</span> <span class="c1">#对话列表</span>
    <span class="p">}</span>
<span class="n">response</span><span class="o">=</span><span class="n">requests</span><span class="o">.</span><span class="n">post</span><span class="p">(</span><span class="n">url</span><span class="p">,</span><span class="n">json</span><span class="o">=</span><span class="n">data</span><span class="p">,</span><span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">,</span><span class="n">timeout</span><span class="o">=</span><span class="mi">60</span><span class="p">)</span>
<span class="n">res</span><span class="o">=</span><span class="n">response</span><span class="o">.</span><span class="n">json</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
</code></pre></div>
<h2 id="_2">本节小结<a class="headerlink" href="#_2" title="Permanent link">¶</a></h2>
<p>本章节主要介绍了利用Ollama工具实现本地大模型的基本应用。</p>


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