zengli
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coding-agent


			
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    5.2 实现企业知识库RAG系统的评估
    
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    6.1 融合FQA和知识库查询
    
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  <h1>2.2 LangChain基础知识.md</h1>

<h2 id="_1">学习目标<a class="headerlink" href="#_1" title="Permanent link">¶</a></h2>
<ul>
<li>熟悉什么是LangChain及其作用</li>
<li>熟悉LangChain中基本组件及使用</li>
</ul>
<h2 id="_2">一、简述<a class="headerlink" href="#_2" title="Permanent link">¶</a></h2>
<h3 id="1-langchain">1 什么是LangChain<a class="headerlink" href="#1-langchain" title="Permanent link">¶</a></h3>
<p>LangChain由 Harrison Chase 创建于2022年10月，它是围绕LLMs（大语言模型）建立的一个框架，它的核心理念是为各种LLMs实现通用的接口，把LLMs相关的组件“链接”在一起，简化LLMs应用的开发难度，方便开发者快速地开发复杂的LLMs应用。</p>
<blockquote>
<p>LangChain 最初是于 2022 年 10 月开源的一个项目，在 GitHub 上获得大量关注之后迅速转变为一家初创公司。2017 年 Harrison Chase 还在哈佛上大学，如今已是硅谷的一家热门初创公司的 CEO，这对他来说是一次重大而迅速的跃迁。Insider 独家报道，人工智能初创公司 LangChain 在种子轮一周后，再次获得红杉领投的 2000 万至 2500 万美元融资，估值达到 2 亿美元。</p>
</blockquote>
<p><strong>LangChain官网</strong>：<a href="https://python.langchain.com/docs/introduction/">https://python.langchain.com/docs/introduction/</a></p>
<p><strong>LangChain官网中文版</strong>：<a href="https://www.langchain.com.cn/">https://www.langchain.com.cn/</a></p>
<p>LangChain 简化了 LLM 应用程序生命周期的每个阶段：</p>
<ul>
<li>开发：使用 LangChain 的开源组件和第三方集成构建应用程序。使用 LangGraph 构建具有一流流式和人机交互支持的状态智能体。</li>
<li>生产化：使用 LangSmith 检查、监控和评估应用程序，方便持续优化和部署。</li>
<li>部署：使用 LangGraph平台 将应用程序转变为可用于生产的 API 和助手。</li>
</ul>
<h3 id="2">2 主要组件<a class="headerlink" href="#2" title="Permanent link">¶</a></h3>
<p><img alt="image-20250703152303288" src="assets/image-20250703152303288.png"></p>
<ul>
<li>Models：模型，各种类型的模型和模型集成，比如GPT-4</li>
<li>Prompts：提示，包括提示管理、提示优化和提示序列化</li>
<li>Chains：链，一系列对各种组件的调用</li>
<li>Memory：记忆，用来保存和模型交互时的上下文状态</li>
<li>Indexes：索引，用来结构化文档，以便和模型交互</li>
<li>Agents：代理，决定模型采取哪些行动，执行并且观察流程，直到完成为止</li>
</ul>
<h3 id="3-langchain">3 LangChain核心包<a class="headerlink" href="#3-langchain" title="Permanent link">¶</a></h3>
<ul>
<li><strong>langchain-core</strong>：聊天模型和其他组件的基础抽象。</li>
<li>
<p><strong>集成包（例如 langchain-openai、langchain-anthropic 等）</strong>：重要的集成被拆分为轻量级的独立包，由 LangChain 团队和集成方共同维护。</p>
</li>
<li>
<p><strong>langchain</strong>：包含链（chains）、智能体（agents）和检索策略，这些构成了应用的认知架构。</p>
</li>
<li>
<p><strong>langchain-community</strong>：由社区维护的第三方集成。</p>
</li>
<li>
<p><strong>langgraph</strong>：一个编排框架，用于将 LangChain 组件组合成可用于生产的应用，支持持久化、流式处理及其他关键特性。</p>
</li>
</ul>
<h3 id="4">4 环境准备<a class="headerlink" href="#4" title="Permanent link">¶</a></h3>
<p>本知识文档以LangChain+Qwen进行学习，需要提前安装</p>
<blockquote>
<p>pip install openai</p>
<p>pip install langchain </p>
<p>pip install modelscope</p>
</blockquote>
<p>借助阿里云-百炼平台（需要申请API Key 以及Secret Key）：</p>
<blockquote>
<p><a href="https://bailian.console.aliyun.com/#/home">https://bailian.console.aliyun.com/#/home</a></p>
</blockquote>
<h2 id="models">二、Models<a class="headerlink" href="#models" title="Permanent link">¶</a></h2>
<p>现在市面上的模型多如牛毛，各种各样的模型不断出现，LangChain模型组件提供了与各种模型的集成，并为所有模型提供一个精简的统一接口。</p>
<p>LangChain目前支持三种类型的模型：LLMs、Chat Models(聊天模型)、Embeddings Models(嵌入模型）。</p>
<ul>
<li>LLMs：大语言模型接收文本字符作为输入，返回的也是文本字符。</li>
<li>聊天模型：基于LLMs, 不同的是它接收聊天消(一种特定格式的数据)作为输入，返回的也是聊天消息。</li>
<li>文本嵌入模型：文本嵌入模型接收文本作为输入, 返回的是浮点数列表。</li>
</ul>
<p><img alt="image-20250704171332127" src="assets/image-20250704171332127.png"></p>
<p>LangChain支持的三类模型，它们的使用场景不同，输入和输出不同，开发者需要根据项目需要选择相应的模型。</p>
<h3 id="1-llms">1 LLMs<a class="headerlink" href="#1-llms" title="Permanent link">¶</a></h3>
<p>LLMs（大语言模型）使用场景最多，常用大模型的下载库：</p>
<blockquote>
<p><a href="https://huggingface.co/models">https://huggingface.co/models</a></p>
<p><a href="https://modelscope.cn/models">https://modelscope.cn/models</a></p>
</blockquote>
<p>下面Qwen为例进行讲解。</p>
<hr>
<p>模型调用有2种方式：</p>
<ul>
<li>通过ChatOpenAI进行调用</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_openai</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatOpenAI</span>
<span class="kn">import</span><span class="w">  </span><span class="nn">os</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">ChatOpenAI</span><span class="p">(</span>
    <span class="n">base_url</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'base_url'</span><span class="p">,</span><span class="s1">'https://dashscope.aliyuncs.com/compatible-mode/v1'</span><span class="p">),</span>
    <span class="n">model</span><span class="o">=</span><span class="s1">'qwen-plus'</span><span class="p">,</span>
    <span class="n">openai_api_key</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'api_key'</span><span class="p">,</span><span class="s1">'sk-XXXXXX'</span><span class="p">),</span>
    <span class="n">max_tokens</span><span class="o">=</span><span class="mi">1000</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="c1"># 获取问答结果</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="s2">"帮我讲个笑话吧"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">content</span><span class="p">)</span>
</code></pre></div>
<ul>
<li>通过Ollama进行调用 <a href="https://ollama.com/">Ollama支持模型</a></li>
</ul>
<div class="highlight"><pre><span></span><code><span class="c1"># from langchain_community.llms import Ollama # Langchain 0.x版本使用</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 实例化模型</span>
<span class="c1"># model = Ollama(model="qwen2.5:7b") # Langchain 0.x版本使用</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="c1"># 获取问答结果</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="s2">"请给我讲个笑话吧"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
</code></pre></div>
<h3 id="2-chat-models">2 Chat Models<a class="headerlink" href="#2-chat-models" title="Permanent link">¶</a></h3>
<p>聊天模型，聊天消息包含下面几种类型，使用时需要按照约定传入合适的值：</p>
<ul>
<li>
<p><strong>AIMessage</strong>: 就是 AI 输出的消息，可以是针对问题的回答.</p>
</li>
<li>
<p><strong>HumanMessage</strong>: 人类消息就是用户信息，由人给出的信息发送给LLMs的提示信息，比如“实现一个快速排序方法”.</p>
</li>
<li>
<p><strong>SystemMessage</strong>: 可以用于指定模型具体所处的环境和背景，如角色扮演等。你可以在这里给出具体的指示，比如“作为一个代码专家”，或者“返回json格式”.</p>
</li>
<li>
<p><strong>ChatMessage</strong>: Chat 消息可以接受任意角色的参数，但是在大多数时间，我们应该使用上面的三种类型.</p>
</li>
</ul>
<p>LangChain支持大量的chat 模型,可以通过官网查询：</p>
<blockquote>
<p><a href="https://python.langchain.com/docs/integrations/chat/">https://python.langchain.com/docs/integrations/chat/</a></p>
</blockquote>
<p>也可以通过langchain源码查看</p>
<p><img alt="image-20250704174858064" src="assets/image-20250704174858064.png"></p>
<ul>
<li>SystemMessage+HumanMessage+AIMessage</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.messages</span><span class="w"> </span><span class="kn">import</span> <span class="n">HumanMessage</span><span class="p">,</span> <span class="n">SystemMessage</span><span class="p">,</span> <span class="n">AIMessage</span>
<span class="c1"># from langchain_community.chat_models import ChatOllama # Langchain 0.x版本使用</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatOllama</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">ChatOllama</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="c1"># 定义提示词</span>
<span class="n">messages</span> <span class="o">=</span> <span class="p">[</span>
        <span class="n">SystemMessage</span><span class="p">(</span><span class="n">content</span><span class="o">=</span><span class="s2">"现在你是一个著名的诗人"</span><span class="p">),</span>
        <span class="n">HumanMessage</span><span class="p">(</span><span class="n">content</span><span class="o">=</span><span class="s2">"给我写一首唐诗"</span><span class="p">)]</span>

<span class="c1"># 获取问答结果</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="n">messages</span><span class="p">)</span>
<span class="c1"># print(result)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">content</span><span class="p">)</span>
</code></pre></div>
<p><img src="assets/1762153476605.png" alt="image-20251004021532441" style="zoom:50%;"></p>
<h3 id="3-embeddings-models">3 Embeddings Models<a class="headerlink" href="#3-embeddings-models" title="Permanent link">¶</a></h3>
<p>Embeddings Models(嵌入模型）特点：将字符串作为输入，返回一个浮动数的列表。在NLP中，Embedding的作用就是将数据进行文本向量化。</p>
<div align="center"><img src="assets/6-4.png" style="zoom:40%"><img></div>

<p>Embeddings Models可以为文本创建向量映射，这样就能在向量空间里去考虑文本，执行诸如语义搜索之类的操作，比如说寻找相似的文本片段。</p>
<blockquote>
<p><a href="https://python.langchain.com/docs/integrations/text_embedding/">https://python.langchain.com/docs/integrations/text_embedding/</a></p>
</blockquote>
<hr>
<p>不同的Embedding模型对多语言支持和文本类型有不同的特点：</p>
<ul>
<li><strong>多语言支持</strong>：<ul>
<li><code>text-embedding-ada-002</code>：支持多种语言，但对中文等亚洲语言的支持相对较弱</li>
<li><code>bge-large-zh</code>：对中文有很好的支持</li>
<li><code>multilingual-e5-large</code>：对多语言都有较好的支持</li>
<li>mxbai-embed-large：</li>
</ul>
</li>
<li><strong>文本类型适用性</strong>：<ul>
<li>代码文本：建议使用专门的代码Embedding模型，如 <code>CodeBERT</code></li>
<li>通用文本：可以使用<code>text-embedding-ada-002</code>或 <code>bge-large-zh</code></li>
<li>专业领域文本：建议使用该领域的专门模型</li>
</ul>
</li>
</ul>
<p>可以参考MTEB（大规模文本嵌入基准）排行榜以获取最新模型效果：<a href="https://huggingface.co/spaces/mteb/leaderboard">https://huggingface.co/spaces/mteb/leaderboard</a></p>
<p>接下来以一个文本嵌入模型的例子进行说明：</p>
<div class="highlight"><pre><span></span><code><span class="c1"># from langchain_community.embeddings import OllamaEmbeddings # Langchain 0.x版本使用</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaEmbeddings</span>

<span class="c1"># 初始化Ollama嵌入模型，使用mxbai-embed-large模型，温度设置为0</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaEmbeddings</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"mxbai-embed-large"</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="c1"># 对单个查询文本进行嵌入编码</span>
<span class="n">res1</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">embed_query</span><span class="p">(</span><span class="s1">'这是第一个测试文档'</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'result1-&gt;</span><span class="si">{</span><span class="n">res1</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'result1的长度-&gt;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">res1</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>

<span class="c1"># 对多个文档进行批量嵌入编码</span>
<span class="n">res2</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">embed_documents</span><span class="p">([</span><span class="s1">'这是第一个测试文档'</span><span class="p">,</span> <span class="s1">'这是第二个测试文档'</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">res2</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="1762164216432" src="assets/1762164216432.png"></p>
<h2 id="prompts">三、Prompts<a class="headerlink" href="#prompts" title="Permanent link">¶</a></h2>
<h3 id="1-prompt">1 通用prompt<a class="headerlink" href="#1-prompt" title="Permanent link">¶</a></h3>
<p>Prompt是指当用户输入信息给模型时加入的提示，这个提示的形式可以是zero-shot或者few-shot等方式，目的是让模型理解更为复杂的业务场景以便更好的解决问题。</p>
<p>提示模板：如果你有了一个起作用的提示，你可能想把它作为一个模板用于解决其他问题，LangChain就提供了PromptTemplates组件，它可以帮助你更方便的构建提示。</p>
<ul>
<li><strong>zero-shot提示方式</strong></li>
</ul>
<div class="highlight"><pre><span></span><code><span class="c1"># from langchain import PromptTemplate # Langchain 0.x版本使用</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">PromptTemplate</span>
<span class="c1"># from langchain_community.llms import Ollama  # Langchain 0.x版本使用</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># model = Ollama(model="qwen2.5:7b") # Langchain 0.x版本使用</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>
<span class="c1"># 定义模板</span>
<span class="n">template</span> <span class="o">=</span> <span class="s2">"我的邻居姓</span><span class="si">{lastname}</span><span class="s2">，他生了个儿子，给他儿子起个名字"</span>

<span class="n">prompt</span> <span class="o">=</span> <span class="n">PromptTemplate</span><span class="p">(</span>
    <span class="n">input_variables</span><span class="o">=</span><span class="p">[</span><span class="s2">"lastname"</span><span class="p">],</span>
    <span class="n">template</span><span class="o">=</span><span class="n">template</span><span class="p">)</span>

<span class="n">prompt_text</span> <span class="o">=</span> <span class="n">prompt</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lastname</span><span class="o">=</span><span class="s2">"王"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">prompt_text</span><span class="p">)</span>
<span class="c1"># result: 我的邻居姓王，他生了个儿子，给他儿子起个名字</span>

<span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="n">prompt_text</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251002154154332" src="assets/image-20251002154154332.png"></p>
<ul>
<li><strong>few-shot提示方式</strong></li>
</ul>
<div class="highlight"><pre><span></span><code><span class="c1"># from langchain import PromptTemplate, FewShotPromptTemplate # Langchain 0.x版本使用</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">PromptTemplate</span><span class="p">,</span> <span class="n">FewShotPromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="n">examples</span> <span class="o">=</span> <span class="p">[</span>
    <span class="p">{</span><span class="s2">"word"</span><span class="p">:</span> <span class="s2">"开心"</span><span class="p">,</span> <span class="s2">"antonym"</span><span class="p">:</span> <span class="s2">"难过"</span><span class="p">},</span>
    <span class="p">{</span><span class="s2">"word"</span><span class="p">:</span> <span class="s2">"高"</span><span class="p">,</span> <span class="s2">"antonym"</span><span class="p">:</span> <span class="s2">"矮"</span><span class="p">}]</span>

<span class="n">example_template</span> <span class="o">=</span> <span class="s2">"""</span>
<span class="s2">单词: </span><span class="si">{word}</span>
<span class="s2">反义词: </span><span class="si">{antonym}</span><span class="se">\\</span><span class="s2">n</span>
<span class="s2">"""</span>

<span class="n">example_prompt</span> <span class="o">=</span> <span class="n">PromptTemplate</span><span class="p">(</span>
    <span class="n">input_variables</span><span class="o">=</span><span class="p">[</span><span class="s2">"word"</span><span class="p">,</span> <span class="s2">"antonym"</span><span class="p">],</span>
    <span class="n">template</span><span class="o">=</span><span class="n">example_template</span><span class="p">,</span>
<span class="p">)</span>

<span class="n">few_shot_prompt</span> <span class="o">=</span> <span class="n">FewShotPromptTemplate</span><span class="p">(</span>
    <span class="n">examples</span><span class="o">=</span><span class="n">examples</span><span class="p">,</span>
    <span class="n">example_prompt</span><span class="o">=</span><span class="n">example_prompt</span><span class="p">,</span>
    <span class="n">prefix</span><span class="o">=</span><span class="s2">"给出每个单词的反义词"</span><span class="p">,</span>
    <span class="n">suffix</span><span class="o">=</span><span class="s2">"单词: </span><span class="si">{input}</span><span class="se">\\</span><span class="s2">n反义词:"</span><span class="p">,</span>
    <span class="n">input_variables</span><span class="o">=</span><span class="p">[</span><span class="s2">"input"</span><span class="p">],</span>
    <span class="n">example_separator</span><span class="o">=</span><span class="s2">"</span><span class="se">\\</span><span class="s2">n"</span><span class="p">,</span>
<span class="p">)</span>

<span class="n">prompt_text</span> <span class="o">=</span> <span class="n">few_shot_prompt</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">input</span><span class="o">=</span><span class="s2">"粗"</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="n">prompt_text</span><span class="p">))</span>
<span class="c1"># 细</span>
</code></pre></div>
<p>运行结果：</p>
<p><img src="assets/image-20251002152859092.png" alt="image-20251002152859092" style="zoom:67%;"></p>
<h3 id="2-chatprompts">2 ChatPrompts<a class="headerlink" href="#2-chatprompts" title="Permanent link">¶</a></h3>
<p>适合交互式对话应用，如聊天机器人、智能客服等，这些应用需要处理用户和LLM之间的多轮对话。</p>
<blockquote>
<p>ChatPromptTemplate</p>
<p>SystemMessagePromptTemplate</p>
<p>HumanMessagePromptTemplate</p>
<p>history=[("system","......"),('human',"......"),("ai","......")]</p>
</blockquote>
<ul>
<li><strong>直接提问</strong></li>
</ul>
<p>提示模板就是把一些常见的提示整理成模板，用户只需要修改模板中特定的词语，就能快速准确地告诉模型自己的需求。我们看个例子：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatPromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="c1"># 定义提示词模版</span>
<span class="n">template_str</span> <span class="o">=</span> <span class="s2">"帮我讲个关于</span><span class="si">{name}</span><span class="s2">笑话吧"</span>
<span class="n">prompt_template</span> <span class="o">=</span> <span class="n">ChatPromptTemplate</span><span class="o">.</span><span class="n">from_template</span><span class="p">(</span><span class="n">template_str</span><span class="p">)</span>
<span class="n">prompt</span> <span class="o">=</span> <span class="n">prompt_template</span><span class="o">.</span><span class="n">format_messages</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"气球"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'prompt--&gt;</span><span class="si">{</span><span class="n">prompt</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>

<span class="c1"># 调用模型</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="n">prompt</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'result--&gt;</span><span class="si">{</span><span class="n">result</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img src="assets/image-20251002153543943.png" alt="image-20251002153543943" style="zoom:67%;"></p>
<ul>
<li><strong>zero-shot提示方式</strong></li>
</ul>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatPromptTemplate</span><span class="p">,</span> <span class="n">HumanMessagePromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.messages</span><span class="w"> </span><span class="kn">import</span> <span class="n">SystemMessage</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="c1"># 系统信息</span>
<span class="n">system_prompt</span> <span class="o">=</span> <span class="n">SystemMessage</span><span class="p">(</span><span class="s2">"你是取名专家。"</span><span class="p">)</span>
<span class="c1"># 用户信息模版</span>
<span class="n">human_str</span> <span class="o">=</span> <span class="s2">"我的邻居姓</span><span class="si">{lastname}</span><span class="s2">，他生了个儿子，给他儿子起个名字。"</span>
<span class="n">human_template</span> <span class="o">=</span> <span class="n">HumanMessagePromptTemplate</span><span class="o">.</span><span class="n">from_template</span><span class="p">(</span><span class="n">human_str</span><span class="p">)</span>
<span class="c1"># 组装</span>
<span class="n">chat_template</span> <span class="o">=</span> <span class="n">ChatPromptTemplate</span><span class="o">.</span><span class="n">from_messages</span><span class="p">([</span><span class="n">system_prompt</span><span class="p">,</span> <span class="n">human_template</span><span class="p">])</span>
<span class="c1"># 生成最终的提示词</span>
<span class="n">prompt</span> <span class="o">=</span> <span class="n">chat_template</span><span class="o">.</span><span class="n">format_messages</span><span class="p">(</span><span class="n">lastname</span><span class="o">=</span><span class="s2">"王"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'prompt--&gt;</span><span class="si">{</span><span class="n">prompt</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>

<span class="c1"># 调用模型</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="n">prompt</span><span class="p">)</span>  <span class="c1"># 返回结果出了content外，还有元数据信息</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'result--&gt;</span><span class="si">{</span><span class="n">result</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251002200159523" src="assets/image-20251002200159523.png"></p>
<ul>
<li><strong>few-shot提示方式</strong></li>
</ul>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatPromptTemplate</span><span class="p">,</span> <span class="n">MessagesPlaceholder</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="c1"># 创建 prompt 模版</span>
<span class="n">prompt_template</span> <span class="o">=</span> <span class="n">ChatPromptTemplate</span><span class="o">.</span><span class="n">from_messages</span><span class="p">(</span>
      <span class="p">[</span>
         <span class="p">(</span><span class="s2">"system"</span><span class="p">,</span> <span class="s2">"给出每个单词的反义词"</span><span class="p">),</span>
         <span class="n">MessagesPlaceholder</span><span class="p">(</span><span class="s2">"history"</span><span class="p">),</span>
         <span class="p">(</span><span class="s2">"human"</span><span class="p">,</span> <span class="s2">"</span><span class="si">{question}</span><span class="s2">"</span><span class="p">)</span>
      <span class="p">]</span>
<span class="p">)</span>
<span class="c1"># 创建 few-shot prompt</span>
<span class="c1"># history = [</span>
<span class="c1">#     HumanMessage(content="开心"),</span>
<span class="c1">#     AIMessage(content="难过"),</span>
<span class="c1">#     HumanMessage(content="高"),</span>
<span class="c1">#     AIMessage(content="矮")</span>
<span class="c1"># ]</span>
<span class="n">history</span> <span class="o">=</span> <span class="p">[(</span><span class="s2">"human"</span><span class="p">,</span> <span class="s2">"开心"</span><span class="p">),</span> <span class="p">(</span><span class="s2">"ai"</span><span class="p">,</span> <span class="s2">"难过"</span><span class="p">),(</span><span class="s2">"human"</span><span class="p">,</span> <span class="s2">"高"</span><span class="p">),</span> <span class="p">(</span><span class="s2">"ai"</span><span class="p">,</span> <span class="s2">"矮"</span><span class="p">)]</span>
<span class="n">prompt</span> <span class="o">=</span> <span class="n">prompt_template</span><span class="o">.</span><span class="n">format_messages</span><span class="p">(</span><span class="n">history</span><span class="o">=</span><span class="n">history</span><span class="p">,</span> <span class="n">question</span><span class="o">=</span><span class="s2">"富有"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"prompt--&gt;</span><span class="si">{</span><span class="n">prompt</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>

<span class="c1"># 调用模型</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="n">prompt</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'result--&gt;</span><span class="si">{</span><span class="n">result</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251002205814344" src="assets/image-20251002205814344.png"></p>
<h2 id="chains">四、Chains<a class="headerlink" href="#chains" title="Permanent link">¶</a></h2>
<p>在LangChain中，Chains描述了将LLM与其他组件结合起来完成一个应用程序的过程。
针对上一小节的提示模版例子，zero-shot里面，我们可以用链来连接提示模版组件和模型，进而可以实现代码的更改，主要使用LCEL方法。</p>
<p>LCEL（Lang Chain Expression Language） 是一种声明式的方法，用于轻松组合链条。</p>
<p>LCEL的基本语法规则是使用<code>|</code>符号将不同的组件连接起来，形成一个链式结构。<code>|</code>符号类似于Unix的管道操作符，它将一个组件的输出作为下一个组件的输入，从而实现数据的传递和处理。</p>
<p>上一个组件的输出作为下一个组件的输入，输出和输入的类型必须保持一致，否则不能连接。</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain</span><span class="w"> </span><span class="kn">import</span> <span class="n">PromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">PromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 定义模板</span>
<span class="n">template</span> <span class="o">=</span> <span class="s2">"我的邻居姓</span><span class="si">{lastname}</span><span class="s2">，他生了个儿子，给他儿子起个名字"</span>

<span class="n">prompt</span> <span class="o">=</span> <span class="n">PromptTemplate</span><span class="p">(</span>
    <span class="n">input_variables</span><span class="o">=</span><span class="p">[</span><span class="s2">"lastname"</span><span class="p">],</span>
    <span class="n">template</span><span class="o">=</span><span class="n">template</span><span class="p">)</span>

<span class="c1"># 实例化模型</span>
<span class="n">llm</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="n">chain</span> <span class="o">=</span> <span class="n">prompt</span> <span class="o">|</span> <span class="n">llm</span>
<span class="c1"># 执行链</span>
<span class="nb">print</span><span class="p">(</span><span class="n">chain</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="s2">"王"</span><span class="p">))</span>
</code></pre></div>
<p>如果你想将第一个模型输出的结果，直接作为第二个模型的输入，直接使用管道符, 代码如下：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">PromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 创建第一条链</span>
<span class="n">template</span> <span class="o">=</span> <span class="s2">"我的邻居姓</span><span class="si">{lastname}</span><span class="s2">，他生了个儿子，给他儿子起个名字"</span>

<span class="n">first_prompt</span> <span class="o">=</span> <span class="n">PromptTemplate</span><span class="p">(</span>
    <span class="n">input_variables</span><span class="o">=</span><span class="p">[</span><span class="s2">"lastname"</span><span class="p">],</span>
    <span class="n">template</span><span class="o">=</span><span class="n">template</span><span class="p">,)</span>

<span class="c1"># 实例化模型</span>
<span class="n">llm</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="n">first_chain</span> <span class="o">=</span> <span class="n">first_prompt</span> <span class="o">|</span> <span class="n">llm</span>

<span class="c1"># 创建第二条链</span>
<span class="n">second_prompt</span> <span class="o">=</span> <span class="n">PromptTemplate</span><span class="p">(</span>
    <span class="n">input_variables</span><span class="o">=</span><span class="p">[</span><span class="s2">"child_name"</span><span class="p">],</span>
    <span class="n">template</span><span class="o">=</span><span class="s2">"邻居的儿子名字叫</span><span class="si">{child_name}</span><span class="s2">，给他起一个小名"</span><span class="p">)</span>

<span class="n">second_chain</span> <span class="o">=</span> <span class="n">second_prompt</span> <span class="o">|</span> <span class="n">llm</span>

<span class="c1"># 链接两条链</span>
<span class="n">overall_chain</span> <span class="o">=</span> <span class="n">first_chain</span> <span class="o">|</span> <span class="n">second_chain</span>

<span class="nb">print</span><span class="p">(</span><span class="n">overall_chain</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'*'</span><span class="o">*</span><span class="mi">80</span><span class="p">)</span>
<span class="c1"># 执行链，只需要传入第一个参数</span>
<span class="n">catchphrase</span> <span class="o">=</span> <span class="n">overall_chain</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="s2">"王"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">catchphrase</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251002212834547" src="assets/image-20251002212834547.png"></p>
<p><img alt="image-20251002212930745" src="assets/image-20251002212930745.png"></p>
<h2 id="output_parsers">五、output_parsers<a class="headerlink" href="#output_parsers" title="Permanent link">¶</a></h2>
<p>LLM 的输出是自然语言文本，但在应用开发中，我们经常需要将这些文本转换为结构化的数据格式，如列表、字典或对象。LangChain 输出解析器负责获取 LLM 的输出并将其转换为更合适的格式。</p>
<p>部分解析器如下：</p>
<table>
<thead>
<tr>
<th>解析器名称</th>
<th>核心功能</th>
<th>输出的 Python 类型</th>
<th>工业级应用场景</th>
</tr>
</thead>
<tbody>
<tr>
<td>StrOutputParser</td>
<td>默认解析器。将 LLM 的输出直接解析为字符串。</td>
<td>str</td>
<td>只需要原始回答时（如问答任务、对话场景）</td>
</tr>
<tr>
<td>CommaSeparatedListParser</td>
<td>将 LLM 输出的、用逗号分隔的文本解析为列表。</td>
<td>list[str]</td>
<td>列表/枚举型输出</td>
</tr>
<tr>
<td>JsonOutputParser</td>
<td>极其常用。将 LLM 输出的 JSON 字符串解析为 Python 字典。</td>
<td>dict</td>
<td>结构化 JSON 输出</td>
</tr>
<tr>
<td>PydanticOutputParser</td>
<td>极其常用。将 LLM 输出解析为预先定义的 Pydantic 对象，提供类型安全和数据验证。</td>
<td>自定义的 pydantic.BaseModel 对象</td>
<td>输出需要严格结构化（JSON-like）数据时</td>
</tr>
<tr>
<td>DatetimeOutputParser</td>
<td>从文本中智能地解析出日期和时间信息。</td>
<td>datetime.datetime</td>
<td>需要时间格式时</td>
</tr>
</tbody>
</table>
<h3 id="1">1 字符串解析器<a class="headerlink" href="#1" title="Permanent link">¶</a></h3>
<p>StrOutputParser，最简单的解析器，用于提取模型返回的原始文本：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.output_parsers</span><span class="w"> </span><span class="kn">import</span> <span class="n">StrOutputParser</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatPromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="c1"># 创建简单链</span>
<span class="n">prompt</span> <span class="o">=</span> <span class="n">ChatPromptTemplate</span><span class="o">.</span><span class="n">from_template</span><span class="p">(</span><span class="s2">"解释</span><span class="si">{topic}</span><span class="s2">是什么？回答控制20字以内"</span><span class="p">)</span>
<span class="n">chain1</span> <span class="o">=</span> <span class="n">prompt</span> <span class="o">|</span> <span class="n">model</span>
<span class="n">result1</span> <span class="o">=</span> <span class="n">chain1</span><span class="o">.</span><span class="n">invoke</span><span class="p">({</span><span class="s2">"topic"</span><span class="p">:</span> <span class="s2">"ai"</span><span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"result1--&gt;</span><span class="si">{</span><span class="n">result1</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>

<span class="c1"># 添加字符串解析器</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">StrOutputParser</span><span class="p">()</span>
<span class="n">chain2</span> <span class="o">=</span> <span class="n">prompt</span> <span class="o">|</span> <span class="n">model</span> <span class="o">|</span> <span class="n">parser</span>
<span class="n">result2</span> <span class="o">=</span> <span class="n">chain2</span><span class="o">.</span><span class="n">invoke</span><span class="p">({</span><span class="s2">"topic"</span><span class="p">:</span> <span class="s2">"ai"</span><span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"result2--&gt;</span><span class="si">{</span><span class="n">result2</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251002230930266" src="assets/image-20251002230930266.png"></p>
<h3 id="2_1">2 列表解析器<a class="headerlink" href="#2_1" title="Permanent link">¶</a></h3>
<p>CommaSeparatedListOutputParser，将逗号分隔的文本转换为Python列表：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.output_parsers</span><span class="w"> </span><span class="kn">import</span> <span class="n">CommaSeparatedListOutputParser</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatPromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="c1"># 创建列表解析器</span>
<span class="n">parser</span> <span class="o">=</span> <span class="n">CommaSeparatedListOutputParser</span><span class="p">()</span>

<span class="c1"># 创建带格式说明的提示模板</span>
<span class="n">format_instructions</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">get_format_instructions</span><span class="p">()</span>
<span class="n">prompt</span> <span class="o">=</span> <span class="n">ChatPromptTemplate</span><span class="o">.</span><span class="n">from_template</span><span class="p">(</span>
    <span class="s2">"用中文列出</span><span class="si">{topic}</span><span class="s2">的五个最重要特点。</span><span class="se">\n</span><span class="si">{format_instructions}</span><span class="s2">"</span>
<span class="p">)</span>

<span class="c1"># 组合组件</span>
<span class="n">chain</span> <span class="o">=</span> <span class="n">prompt</span> <span class="o">|</span> <span class="n">model</span> <span class="o">|</span> <span class="n">parser</span>

<span class="c1"># 调用链</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">chain</span><span class="o">.</span><span class="n">invoke</span><span class="p">({</span>
    <span class="s2">"topic"</span><span class="p">:</span> <span class="s2">"大模型"</span><span class="p">,</span>
    <span class="s2">"format_instructions"</span><span class="p">:</span> <span class="n">format_instructions</span>
<span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img src="assets/image-20251002231200108.png" alt="image-20251002231200108" style="zoom: 67%;"></p>
<h3 id="3-json">3 JSON解析器<a class="headerlink" href="#3-json" title="Permanent link">¶</a></h3>
<p>JsonOutputParser，将JSON格式文本转换为Python字典或列表：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.output_parsers</span><span class="w"> </span><span class="kn">import</span> <span class="n">JsonOutputParser</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatPromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="c1"># 创建JSON解析器</span>
<span class="n">json_parser</span> <span class="o">=</span> <span class="n">JsonOutputParser</span><span class="p">()</span>

<span class="c1"># 创建带格式说明的提示模板</span>
<span class="n">json_format_instructions</span> <span class="o">=</span> <span class="n">json_parser</span><span class="o">.</span><span class="n">get_format_instructions</span><span class="p">()</span>
<span class="n">json_prompt</span> <span class="o">=</span> <span class="n">ChatPromptTemplate</span><span class="o">.</span><span class="n">from_template</span><span class="p">(</span>
    <span class="s2">"生成一个包含</span><span class="si">{person}</span><span class="s2">基本信息的JSON。应包括姓名、职业、年龄和技能列表, 不要包含任何注释或额外说明。</span><span class="se">\n</span><span class="si">{format_instructions}</span><span class="s2">"</span>
<span class="p">)</span>

<span class="c1"># 组合组件</span>
<span class="n">json_chain</span> <span class="o">=</span> <span class="n">json_prompt</span> <span class="o">|</span> <span class="n">model</span> <span class="o">|</span> <span class="n">json_parser</span>

<span class="c1"># 调用链</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">json_chain</span><span class="o">.</span><span class="n">invoke</span><span class="p">({</span>
    <span class="s2">"person"</span><span class="p">:</span> <span class="s2">"雷军"</span><span class="p">,</span>
    <span class="s2">"format_instructions"</span><span class="p">:</span> <span class="n">json_format_instructions</span>
<span class="p">})</span>

<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251003000254030" src="assets/image-20251003000254030.png"></p>
<h3 id="4-pydantic">4 Pydantic解析器<a class="headerlink" href="#4-pydantic" title="Permanent link">¶</a></h3>
<p>PydanticOutputParser，使用Pydantic模型定义输出结构：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatPromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.output_parsers</span><span class="w"> </span><span class="kn">import</span> <span class="n">PydanticOutputParser</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">pydantic</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseModel</span><span class="p">,</span> <span class="n">Field</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">List</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="c1"># 定义Pydantic模型</span>
<span class="k">class</span><span class="w"> </span><span class="nc">Movie</span><span class="p">(</span><span class="n">BaseModel</span><span class="p">):</span>
    <span class="n">title</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">Field</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">"电影标题"</span><span class="p">)</span>
    <span class="n">director</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">Field</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">"导演姓名"</span><span class="p">)</span>
    <span class="n">year</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">Field</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">"上映年份"</span><span class="p">)</span>
    <span class="n">genre</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">Field</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">"电影类型"</span><span class="p">)</span>
    <span class="n">rating</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">Field</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s2">"评分（1-10）"</span><span class="p">)</span>


<span class="c1"># 创建Pydantic解析器</span>
<span class="n">pydantic_parser</span> <span class="o">=</span> <span class="n">PydanticOutputParser</span><span class="p">(</span><span class="n">pydantic_object</span><span class="o">=</span><span class="n">Movie</span><span class="p">)</span>

<span class="c1"># 创建带格式说明的提示模板</span>
<span class="n">format_instructions</span> <span class="o">=</span> <span class="n">pydantic_parser</span><span class="o">.</span><span class="n">get_format_instructions</span><span class="p">()</span>
<span class="n">pydantic_prompt</span> <span class="o">=</span> <span class="n">ChatPromptTemplate</span><span class="o">.</span><span class="n">from_template</span><span class="p">(</span>
    <span class="s2">"生成一部</span><span class="si">{genre}</span><span class="s2">电影的信息。</span><span class="se">\n</span><span class="si">{format_instructions}</span><span class="s2">"</span>
<span class="p">)</span>

<span class="c1"># 组合组件</span>
<span class="n">pydantic_chain</span> <span class="o">=</span> <span class="n">pydantic_prompt</span> <span class="o">|</span> <span class="n">model</span> <span class="o">|</span> <span class="n">pydantic_parser</span>

<span class="c1"># 调用链</span>
<span class="n">movie_data</span> <span class="o">=</span> <span class="n">pydantic_chain</span><span class="o">.</span><span class="n">invoke</span><span class="p">({</span>
    <span class="s2">"genre"</span><span class="p">:</span> <span class="s2">"科幻"</span><span class="p">,</span>
    <span class="s2">"format_instructions"</span><span class="p">:</span> <span class="n">format_instructions</span>
<span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="n">movie_data</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251003000720225" src="assets/image-20251003000720225.png"></p>
<h3 id="5">5 自定义解析器<a class="headerlink" href="#5" title="Permanent link">¶</a></h3>
<p>创建自定义输出解析器</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.output_parsers</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseOutputParser</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.prompts</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatPromptTemplate</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="k">class</span><span class="w"> </span><span class="nc">CustomKeyValueParser</span><span class="p">(</span><span class="n">BaseOutputParser</span><span class="p">[</span><span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]):</span>
<span class="w">    </span><span class="sd">"""解析形如'key: value'的文本"""</span>
    <span class="k">def</span><span class="w"> </span><span class="nf">parse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">text</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
<span class="w">        </span><span class="sd">"""从文本中解析键值对"""</span>
        <span class="n">result</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="n">lines</span> <span class="o">=</span> <span class="n">text</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">lines</span><span class="p">:</span>
            <span class="k">if</span> <span class="s1">':'</span> <span class="ow">in</span> <span class="n">line</span><span class="p">:</span>
                <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">':'</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
                <span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="o">.</span><span class="n">strip</span><span class="p">()]</span> <span class="o">=</span> <span class="n">value</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>

        <span class="k">return</span> <span class="n">result</span>

    <span class="k">def</span><span class="w"> </span><span class="nf">get_format_instructions</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
<span class="w">        </span><span class="sd">"""提供格式指导给模型"""</span>
        <span class="k">return</span> <span class="s2">"""请以'键: 值'的格式返回信息，每行一个键值对。</span>
<span class="s2">                例如：</span>
<span class="s2">                名称: 爱因斯坦</span>
<span class="s2">                职业: 物理学家</span>
<span class="s2">                贡献: 相对论"""</span>


<span class="c1"># 使用自定义解析器</span>
<span class="n">custom_parser</span> <span class="o">=</span> <span class="n">CustomKeyValueParser</span><span class="p">()</span>
<span class="n">custom_prompt</span> <span class="o">=</span> <span class="n">ChatPromptTemplate</span><span class="o">.</span><span class="n">from_template</span><span class="p">(</span>
    <span class="s2">"提供关于</span><span class="si">{person}</span><span class="s2">的基本信息。</span><span class="se">\n</span><span class="si">{format_instructions}</span><span class="s2">"</span>
<span class="p">)</span>

<span class="c1"># 组合组件</span>
<span class="n">custom_chain</span> <span class="o">=</span> <span class="n">custom_prompt</span> <span class="o">|</span> <span class="n">model</span> <span class="o">|</span> <span class="n">custom_parser</span>

<span class="c1"># 调用模型</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">custom_chain</span><span class="o">.</span><span class="n">invoke</span><span class="p">({</span>
    <span class="s2">"person"</span><span class="p">:</span> <span class="s2">"屠呦呦"</span><span class="p">,</span>
    <span class="s2">"format_instructions"</span><span class="p">:</span> <span class="n">custom_parser</span><span class="o">.</span><span class="n">get_format_instructions</span><span class="p">()</span>
<span class="p">})</span>
<span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251003001007923" src="assets/image-20251003001007923.png"></p>
<h2 id="memory">六、Memory<a class="headerlink" href="#memory" title="Permanent link">¶</a></h2>
<p>大模型本身不具备上下文的概念，它并不保存上次交互的内容，ChatGPT之所以能够和人正常沟通对话，因为它进行了一层封装，将历史记录回传给了模型。</p>
<p>因此 LangChain 也提供了Memory组件, Memory分为两种类型： <strong>短期记忆和长期记忆</strong> 。短期记忆一般指单一会话时传递数据，长期记忆则是处理多个会话时获取和更新信息。</p>
<ul>
<li>使用 <strong>ChatMessageHistory</strong> 手动添加上下文</li>
</ul>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_community.chat_message_histories</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatMessageHistory</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.messages</span><span class="w"> </span><span class="kn">import</span> <span class="n">messages_to_dict</span><span class="p">,</span> <span class="n">messages_from_dict</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">json</span>

<span class="c1"># 1.创建一个ChatMessageHistory对象，用来存储对话信息</span>
<span class="n">history</span> <span class="o">=</span> <span class="n">ChatMessageHistory</span><span class="p">()</span>
<span class="c1"># 添加用户消息</span>
<span class="n">history</span><span class="o">.</span><span class="n">add_user_message</span><span class="p">(</span><span class="s2">"在吗？"</span><span class="p">)</span>
<span class="c1"># 添加大模型消息</span>
<span class="n">history</span><span class="o">.</span><span class="n">add_ai_message</span><span class="p">(</span><span class="s2">"在"</span><span class="p">)</span>
<span class="c1"># 打印所有消息</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'history.messages--&gt;</span><span class="si">{</span><span class="n">history</span><span class="o">.</span><span class="n">messages</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>

<span class="c1"># 2.可以将history.messages中的信息保存到字典中，然后保存到数据库或者文件中，方便后续读取</span>
<span class="c1"># 2.1 messages_to_dict()方法将history.messages中的信息转换成字典</span>
<span class="n">dicts</span> <span class="o">=</span> <span class="n">messages_to_dict</span><span class="p">(</span><span class="n">history</span><span class="o">.</span><span class="n">messages</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'dicts--&gt;</span><span class="si">{</span><span class="n">dicts</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="c1"># 2.2 这里将dicts保存到文件中</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'history.json'</span><span class="p">,</span> <span class="s1">'w'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">dicts</span><span class="p">,</span> <span class="n">indent</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ensure_ascii</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>

<span class="c1"># 3.从文件中读取出字典，然后将字典转换成消息</span>
<span class="c1"># 3.1 读取文件</span>
<span class="n">messages</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">'history.json'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">))</span>
<span class="c1"># 3.2 然后将字典转换成消息</span>
<span class="n">chat_messages</span> <span class="o">=</span> <span class="n">messages_from_dict</span><span class="p">(</span><span class="n">messages</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'chat_messages--&gt;</span><span class="si">{</span><span class="n">chat_messages</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251003001546687" src="assets/image-20251003001546687.png"></p>
<p>LangChain v1.0 版本对 Memory（记忆） 组件进行了深度优化与工程化重构，使其不仅支持多轮对话上下文管理，更成为构建生产级智能体（Agent）不可或缺的核心能力之一。在 LangChain 从“实验性框架”迈向“企业级平台”的关键转型中，Memory 模块被赋予了更清晰的职责边界、更灵活的策略机制以及更强的可扩展性，从而支撑复杂任务场景下的长期记忆、状态追踪与个性化交互。</p>
<h3 id="1-short-term-memory">1 短期记忆（Short-term Memory）<a class="headerlink" href="#1-short-term-memory" title="Permanent link">¶</a></h3>
<p>短期记忆的本质是线程级状态管理]。在 LangGraph 中：</p>
<blockquote>
<p>短期记忆 = Agent 状态（State） + 检查点持久化（Checkpointer） + 线程标识（thread_id）。</p>
</blockquote>
<p><strong>（1）状态（State）</strong></p>
<p>通常是一个包含 messages 字段的字典或 Pydantic 模型（如 MessagesState 或自定义 CustomState），用于存储当前会话的所有消息、中间变量、工具调用结果等。</p>
<p><strong>（2）检查点器（Checkpointer）</strong></p>
<p>负责将状态序列化并持久化到内存、SQLite、PostgreSQL 等后端。每次状态变更（如新增一条消息）都会触发一次检查点保存。</p>
<p><strong>（3）会话ID（thread_id）</strong></p>
<p>作为会话的唯一标识符，确保不同用户或不同对话之间的状态完全隔离。即使多个用户并发交互，也不会发生记忆混淆。</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain.agents</span><span class="w"> </span><span class="kn">import</span> <span class="n">create_agent</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langgraph.checkpoint.memory</span><span class="w"> </span><span class="kn">import</span> <span class="n">InMemorySaver</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain.messages</span><span class="w"> </span><span class="kn">import</span> <span class="n">HumanMessage</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaLLM</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">time</span>

<span class="c1"># 初始化大模型，这里使用的是 Qwen 的聊天模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">OllamaLLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"qwen2.5:7b"</span><span class="p">)</span>

<span class="c1"># 创建内存保存器 (Checkpointer)</span>
<span class="c1"># 这是 LangGraph 的核心概念之一，用于在对话过程中保存和恢复状态（记忆）</span>
<span class="n">checkpointer</span> <span class="o">=</span> <span class="n">InMemorySaver</span><span class="p">()</span>

<span class="c1"># 定义 Agent 的配置</span>
<span class="c1"># "thread_id" (线程ID) 非常关键：它相当于对话的 ID，用于区分不同的会话记忆</span>
<span class="c1"># 使用时间戳作为 ID，确保每次运行脚本时的线程 ID 不同（但在同一次运行中复用）</span>
<span class="n">timestamp</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">config</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">"configurable"</span><span class="p">:</span> <span class="p">{</span><span class="s2">"thread_id"</span><span class="p">:</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">timestamp</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">}</span>
<span class="p">}</span>

<span class="c1"># 创建 Agent 实例</span>
<span class="c1"># model: 指定使用的底层大模型</span>
<span class="c1"># tools: 工具列表，这里为空，表示 Agent 只能进行对话，不能调用外部工具（如搜索、计算器等）</span>
<span class="c1"># checkpointer: 传入上面的内存保存器，赋予 Agent 记忆能力</span>
<span class="c1"># system_prompt: 设定 Agent 的角色和行为模式</span>
<span class="n">agent</span> <span class="o">=</span> <span class="n">create_agent</span><span class="p">(</span>
    <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span>
    <span class="n">tools</span><span class="o">=</span><span class="p">[],</span>
    <span class="n">checkpointer</span><span class="o">=</span><span class="n">checkpointer</span><span class="p">,</span>
    <span class="n">system_prompt</span><span class="o">=</span><span class="s2">"你是一个翻译官，擅长中英互译。"</span>
<span class="p">)</span>

<span class="c1"># 第一轮对话输入</span>
<span class="n">input_1</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">"messages"</span><span class="p">:</span> <span class="p">[</span><span class="n">HumanMessage</span><span class="p">(</span><span class="s2">"你好，我叫小呆。"</span><span class="p">)]</span>
<span class="p">}</span>

<span class="c1"># 调用 Agent (Response A)</span>
<span class="c1"># 使用 config，这意味着这次对话会记录在 thread_id 对应的内存中</span>
<span class="c1"># Agent 会记住 "我叫小呆" 这个信息</span>
<span class="n">response_a</span> <span class="o">=</span> <span class="n">agent</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span>
    <span class="nb">input</span><span class="o">=</span><span class="n">input_1</span><span class="p">,</span>
    <span class="n">config</span><span class="o">=</span><span class="n">config</span>
<span class="p">)</span>

<span class="c1"># 第二轮对话输入（测试是否记得之前的信息）</span>
<span class="n">input_2</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">"messages"</span><span class="p">:</span> <span class="p">[</span><span class="n">HumanMessage</span><span class="p">(</span><span class="s2">"你好，我叫什么？"</span><span class="p">)]</span>
<span class="p">}</span>

<span class="c1"># 定义一个新的配置 (config_2)，使用完全不同的 thread_id</span>
<span class="c1"># 这代表开启了一个全新的、独立的对话会话</span>
<span class="n">config_2</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s2">"configurable"</span><span class="p">:</span> <span class="p">{</span><span class="s2">"thread_id"</span><span class="p">:</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">())</span><span class="si">}</span><span class="s2">"</span><span class="p">}</span>  <span class="c1"># 这里生成了一个新的时间戳 ID</span>
<span class="p">}</span>

<span class="c1"># 调用 Agent (Response B)</span>
<span class="c1"># 使用新的 config_2，Agent 看不到 config_1 中的历史记录</span>
<span class="c1"># 因此，Agent 应该不知道用户叫 "小呆"，因为它处于一个新的 "线程" 中</span>
<span class="n">response_b</span> <span class="o">=</span> <span class="n">agent</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span>
    <span class="nb">input</span><span class="o">=</span><span class="n">input_2</span><span class="p">,</span>
    <span class="n">config</span><span class="o">=</span><span class="n">config_2</span>
<span class="p">)</span>

<span class="c1"># 调用 Agent (Response C)</span>
<span class="c1"># 再次使用最初的 config</span>
<span class="c1"># 这会恢复到第一个会话的记忆中</span>
<span class="c1"># Agent 应该能回想起用户叫 "小呆"，因为它共享同一个 thread_id</span>
<span class="n">response_c</span> <span class="o">=</span> <span class="n">agent</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span>
    <span class="nb">input</span><span class="o">=</span><span class="n">input_2</span><span class="p">,</span>
    <span class="n">config</span><span class="o">=</span><span class="n">config</span>
<span class="p">)</span>

<span class="c1"># 输出结果进行对比</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Response A (第一次对话):"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">response_a</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Response B (新线程，无记忆):"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">response_b</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Response C (回到原线程，有记忆):"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">response_c</span><span class="p">)</span>
</code></pre></div>
<p><strong>代码运行逻辑：</strong></p>
<ul>
<li><strong>checkpointer (记忆核心)</strong>：这是 LangGraph 区别于传统简单 API 调用的关键。它允许 Agent 在多次 invoke 调用之间保存状态。</li>
<li><strong>thread_id (记忆索引)</strong>：你可以把 thread_id 想象成数据库中的主键。</li>
<li>Response A：向 ID 为 100 的记录里写入了名字。</li>
<li>Response B：向 ID 为 200 的新记录提问，因为 200 是空的，所以 Agent 不知道名字。</li>
<li>Response C：又回到 ID 为 100 的记录提问，Agent 读取了历史记录，所以知道名字。</li>
<li><strong>System Prompt</strong>：虽然输入是在对话，但因为设定了 “你是一个翻译官”，Agent 可能会试图在回答的同时进行翻译，或者在回复格式上符合翻译官的身份。</li>
</ul>
<h3 id="2_2">2 长期记忆<a class="headerlink" href="#2_2" title="Permanent link">¶</a></h3>
<p>对于需要长期运行和可靠记忆的应用，推荐使用数据库进行持久化。详见后面阶段LangGraph部分。</p>
<h2 id="indexes">七、Indexes<a class="headerlink" href="#indexes" title="Permanent link">¶</a></h2>
<p>Indexes组件的目的是让LangChain具备处理文档处理的能力，包括：文档加载、检索等。注意，这里的文档不局限于txt、pdf等文本类内容，还涵盖email、区块链、视频等内容。</p>
<p>Indexes组件主要包含类型：</p>
<ul>
<li>文档加载器</li>
<li>文本分割器</li>
<li>VectorStores</li>
<li>检索器</li>
</ul>
<h3 id="1_1">1 文档加载器<a class="headerlink" href="#1_1" title="Permanent link">¶</a></h3>
<blockquote>
<p><a href="https://python.langchain.com/v0.2/docs/introduction/">https://python.langchain.com/v0.2/docs/introduction/</a></p>
</blockquote>
<p>文档加载器主要基于<code>Unstructured</code> 包，<code>Unstructured</code> 是一个python包，可以把各种类型的文件转换成文本。文档加载器使用起来很简单，只需要引入相应的loader工具。</p>
<p>LangChain支持的文档加载器 (部分)：</p>
<p><img alt="image-20250705100227413" src="assets/image-20250705100227413.png"></p>
<p>示例代码：</p>
<blockquote>
<p>需要安装的包：</p>
<p>pip install unstructured</p>
<p>pip install langchain-unstructured</p>
</blockquote>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_unstructured</span><span class="w"> </span><span class="kn">import</span> <span class="n">UnstructuredLoader</span>

<span class="c1"># 创建 UnstructuredLoader 对象</span>
<span class="n">loader</span> <span class="o">=</span> <span class="n">UnstructuredLoader</span><span class="p">(</span><span class="s1">'./data/衣服属性.txt'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf8'</span><span class="p">)</span>
<span class="n">docs</span> <span class="o">=</span> <span class="n">loader</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'docs--&gt;</span><span class="si">{</span><span class="n">docs</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'len--&gt;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'第一行数据--&gt;</span><span class="si">{</span><span class="n">docs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">page_content</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'*'</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>

<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_community.document_loaders</span><span class="w"> </span><span class="kn">import</span> <span class="n">TextLoader</span>

<span class="c1"># 创建 TextLoader 对象</span>
<span class="n">loader</span> <span class="o">=</span> <span class="n">TextLoader</span><span class="p">(</span><span class="s1">'./data/衣服属性.txt'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf8'</span><span class="p">)</span>
<span class="n">docs</span> <span class="o">=</span> <span class="n">loader</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'docs--&gt;</span><span class="si">{</span><span class="n">docs</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'len--&gt;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'第一行数据--&gt;</span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">docs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">page_content</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">'</span><span class="p">)[</span><span class="mi">0</span><span class="p">]))</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251003010445727" src="assets/image-20251003010445727.png"></p>
<h3 id="2_3">2 文档分割器<a class="headerlink" href="#2_3" title="Permanent link">¶</a></h3>
<p>由于模型对输入的字符长度有限制，我们在碰到很长的文本时，需要把文本分割成多个小的文本片段。</p>
<p>文本分割最简单的方式是按照字符长度进行分割，但是这会带来很多问题，比如说如果文本是一段代码，一个函数被分割到两段之后就成了没有意义的字符，所以整体的原则是把语义相关的文本片段放在一起。</p>
<p>LangChain中最基本的文本分割器是<code>CharacterTextSplitter</code> ，它按照指定的分隔符（默认“\n\n”）进行分割，并且考虑文本片段的最大长度。我们看个例子：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.documents</span><span class="w"> </span><span class="kn">import</span> <span class="n">Document</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_text_splitters</span><span class="w"> </span><span class="kn">import</span> <span class="n">CharacterTextSplitter</span>

<span class="c1"># 创建分词器 separator参数指的是分割的分隔符，chunk_size指的是分割出来的每个块的大小，chunk_overlap指的是每个块之间重叠的大小</span>
<span class="n">text_splitter</span> <span class="o">=</span> <span class="n">CharacterTextSplitter</span><span class="p">(</span><span class="n">separator</span><span class="o">=</span><span class="s2">" "</span><span class="p">,</span> <span class="n">chunk_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">chunk_overlap</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># 一句话分割</span>
<span class="n">result1</span> <span class="o">=</span> <span class="n">text_splitter</span><span class="o">.</span><span class="n">split_text</span><span class="p">(</span><span class="s2">"a b c d e f"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'result1---&gt;</span><span class="si">{</span><span class="n">result1</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>

<span class="c1"># 多句话分割</span>
<span class="n">result2</span> <span class="o">=</span> <span class="n">text_splitter</span><span class="o">.</span><span class="n">create_documents</span><span class="p">([</span><span class="s2">"a b c d e f"</span><span class="p">,</span> <span class="s2">"e f g h"</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'result2---&gt;</span><span class="si">{</span><span class="n">result2</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>

<span class="c1"># 多句话分割</span>
<span class="n">result3</span> <span class="o">=</span> <span class="n">text_splitter</span><span class="o">.</span><span class="n">split_documents</span><span class="p">([</span><span class="n">Document</span><span class="p">(</span><span class="n">page_content</span><span class="o">=</span><span class="s2">"a b c d e f"</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">"id"</span><span class="p">:</span> <span class="s2">"1"</span><span class="p">})])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'result3---&gt;</span><span class="si">{</span><span class="n">result3</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251003011030583" src="assets/image-20251003011030583.png"></p>
<hr>
<p>除了CharacterTextSplitter分割器，LangChain还支持其他文档分割器 (部分)：</p>
<table>
<thead>
<tr>
<th>分割器名称</th>
<th>功能描述</th>
<th>类型</th>
<th>工业场景应用</th>
</tr>
</thead>
<tbody>
<tr>
<td>CharacterTextSplitter</td>
<td>简单按指定分隔符（如换行、逗号）直接分割。</td>
<td>基础字符解析</td>
<td>简单字符串或 CSV 数据处理，如传感器数据日志。</td>
</tr>
<tr>
<td>RecursiveCharacterTextSplitter</td>
<td>递归按字符分割，先尝试自然边界（如段落、句子），太大则继续细分。</td>
<td>通用字符解析</td>
<td>通用文本处理，如日志、报告、PDF 文档分割，便于 RAG 检索。</td>
</tr>
<tr>
<td>TokenTextSplitter</td>
<td>按 token（词元）分割，支持 LLM token 计数。</td>
<td>Token 基于解析</td>
<td>LLM 输入优化，如处理 API 响应或长查询，控制 token 限制。</td>
</tr>
<tr>
<td>SentenceTextSplitter</td>
<td>按句子边界分割，使用 NLP 识别句子（包括标点）。</td>
<td>语义解析</td>
<td>自然语言文本，如文章或对话分析，保持句子完整。</td>
</tr>
<tr>
<td>SpacyTextSplitter</td>
<td>使用 SpaCy NLP 库按句子或实体分割（需安装 SpaCy）。</td>
<td>语义解析</td>
<td>高级 NLP 场景，如实体提取或生物医学文本。</td>
</tr>
<tr>
<td>NLTKTextSplitter</td>
<td>使用 NLTK 库按句子或词分割（需安装 NLTK）。</td>
<td>语义解析</td>
<td>文本研究或分析，如时间序列数据描述。</td>
</tr>
<tr>
<td>MarkdownHeaderTextSplitter</td>
<td>按 Markdown 结构（如标题、列表）智能分割。</td>
<td>结构化解析</td>
<td>Markdown 文档分割，保留语义结构，用于知识库构建。</td>
</tr>
<tr>
<td>HTMLSplitter</td>
<td>按 HTML 标签（如 、）分割网页内容。</td>
<td>结构化解析</td>
<td>网页数据爬取，如在线技术文档或新闻提取。</td>
</tr>
<tr>
<td>LatexTextSplitter</td>
<td>按 LaTeX 结构（如章节、公式）分割。</td>
<td>结构化解析</td>
<td>学术论文或数学文档处理。</td>
</tr>
<tr>
<td>PythonCodeTextSplitter</td>
<td>按 Python 代码结构（如函数、类）分割。</td>
<td>代码解析</td>
<td>源代码文件分析，如脚本调试或代码库管理。</td>
</tr>
</tbody>
</table>
<p>下面就几个重要的分割器进行讲解。</p>
<ul>
<li>递归字符文本分割器（RecursiveCharacterTextSplitter）</li>
</ul>
<blockquote>
<p>递归字符文本分割器是一种更智能的分割方法，它尝试在特定分隔符处分割文本，以保持更好的语义完整性。 特点：</p>
<ul>
<li>尝试在自然断点处分割文本</li>
<li>比简单的字符分割更能保持语义完整性</li>
<li>适用于结构化程度较高的文本，如 Markdown、HTML 等</li>
</ul>
<p>运行流程：</p>
<ul>
<li>首先尝试使用第一个分隔符（如 "\n\n"）分割文本</li>
<li>如果分割后的块仍然过大，则使用下一个分隔符继续分割</li>
<li>重复此过程，直到达到指定的 chunk_size 或用完所有分隔符</li>
</ul>
<p>示例代码</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_text_splitters</span><span class="w"> </span><span class="kn">import</span> <span class="n">RecursiveCharacterTextSplitter</span>

<span class="n">text_splitter</span> <span class="o">=</span> <span class="n">RecursiveCharacterTextSplitter</span><span class="p">(</span>
    <span class="n">chunk_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>  <span class="c1"># 每个块最多 100 个字符</span>
    <span class="n">chunk_overlap</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>  <span class="c1"># 相邻分块会共享 20 个字符</span>
    <span class="n">length_function</span><span class="o">=</span><span class="nb">len</span><span class="p">,</span>  <span class="c1"># 用字符数来衡量长度</span>
    <span class="n">separators</span><span class="o">=</span><span class="p">[</span><span class="s2">"</span><span class="se">\n\n</span><span class="s2">"</span><span class="p">,</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">"</span><span class="p">,</span> <span class="s2">" "</span><span class="p">,</span> <span class="s2">""</span><span class="p">]</span>  <span class="c1"># 会优先尝试按 \n\n 分段，如果太长，再按 \n → 空格 → 逐字符切分。</span>
<span class="p">)</span>

<span class="n">text</span> <span class="o">=</span> <span class="s2">"""</span>
<span class="s2">人工智能正在快速发展，尤其是大语言模型的应用，正在改变人类的工作方式。</span>
<span class="s2">它们可以帮助人们进行写作、代码生成、甚至是科研探索。</span>
<span class="s2">相比之下，新能源的发展同样重要。</span>
<span class="s2">电动车和太阳能正在逐渐替代传统能源，减少碳排放，对全球环境保护至关重要。</span>
<span class="s2">"""</span>
<span class="n">docs</span> <span class="o">=</span> <span class="n">text_splitter</span><span class="o">.</span><span class="n">split_text</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span>
</code></pre></div>
<p>运行结果</p>
<p><img alt="1768393012466" src="assets/1768393012466.png"></p>
</blockquote>
<ul>
<li>语义文档分割器（SemanticChunker）</li>
</ul>
<blockquote>
<p>语义文档分割器使用语义理解来分割文本，这是一种更高级的分割方法。 特点：</p>
<ul>
<li>基于语义相似性分割文本</li>
<li>能够更好地保持语义完整性</li>
<li>计算成本较高，处理大量文本时可能效率较低</li>
<li>适用于需要高度语义理解的场景</li>
</ul>
<p>示例代码</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_experimental.text_splitter</span><span class="w"> </span><span class="kn">import</span> <span class="n">SemanticChunker</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaEmbeddings</span>

<span class="n">embed</span> <span class="o">=</span> <span class="n">OllamaEmbeddings</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"mxbai-embed-large"</span><span class="p">)</span>

<span class="n">text_splitter</span> <span class="o">=</span> <span class="n">SemanticChunker</span><span class="p">(</span>
     <span class="n">embeddings</span><span class="o">=</span><span class="n">embed</span><span class="p">,</span>
     <span class="n">breakpoint_threshold_type</span><span class="o">=</span><span class="s1">'percentile'</span><span class="p">,</span>
     <span class="c1"># 更低的百分位会更“积极”地切分（数值越小 =&gt; 切得越多）</span>
     <span class="n">breakpoint_threshold_amount</span><span class="o">=</span><span class="mf">70.0</span><span class="p">,</span>
     <span class="c1"># 句子拆分正则为同时识别中/英文终结符</span>
     <span class="n">sentence_split_regex</span><span class="o">=</span><span class="sa">r</span><span class="s1">'(?&lt;=[。！？.!?])\s*'</span><span class="p">,</span>
     <span class="c1"># 每个切分后片段（chunk）至少要有多少个字符</span>
     <span class="n">min_chunk_size</span><span class="o">=</span><span class="mi">10</span>
    <span class="p">)</span>

<span class="n">text</span> <span class="o">=</span> <span class="s2">"""</span>
<span class="s2">人工智能正在快速发展，尤其是大语言模型的应用，正在改变人类的工作方式。</span>
<span class="s2">它们可以帮助人们进行写作、代码生成、甚至是科研探索。</span>
<span class="s2">相比之下，新能源的发展同样重要。</span>
<span class="s2">电动车和太阳能正在逐渐替代传统能源，减少碳排放，对全球环境保护至关重要。</span>
<span class="s2">"""</span>

<span class="n">docs</span> <span class="o">=</span> <span class="n">text_splitter</span><span class="o">.</span><span class="n">split_text</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">docs</span><span class="p">):</span>
     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"------ Chunk </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2"> ------"</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">strip</span><span class="p">())</span>
     <span class="nb">print</span><span class="p">()</span>
</code></pre></div>
<p>运行结果</p>
<p><img alt="image-20251003095433705" src="assets/image-20251003095433705.png"></p>
</blockquote>
<ul>
<li>MarkdownHeaderTextSplitter（Markdown文档切割器）</li>
</ul>
<blockquote>
<p>适用于Markdown文档，按照标题进行拆分</p>
<p>示例代码</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_text_splitters</span><span class="w"> </span><span class="kn">import</span> <span class="n">MarkdownHeaderTextSplitter</span>

<span class="n">headers_to_split_on</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">(</span><span class="s2">"#"</span><span class="p">,</span> <span class="s2">"Header 1"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"##"</span><span class="p">,</span> <span class="s2">"Header 2"</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"###"</span><span class="p">,</span> <span class="s2">"Header 3"</span><span class="p">),</span>
<span class="p">]</span>

<span class="n">markdown_splitter</span> <span class="o">=</span> <span class="n">MarkdownHeaderTextSplitter</span><span class="p">(</span><span class="n">headers_to_split_on</span><span class="o">=</span><span class="n">headers_to_split_on</span><span class="p">)</span>

<span class="n">markdown_text</span> <span class="o">=</span> <span class="s2">"# Header 1</span><span class="se">\n</span><span class="s2">Some text</span><span class="se">\n</span><span class="s2">## Header 2</span><span class="se">\n</span><span class="s2">More text</span><span class="se">\n</span><span class="s2">### Header 3</span><span class="se">\n</span><span class="s2">Even more text"</span>
<span class="n">docs</span> <span class="o">=</span> <span class="n">markdown_splitter</span><span class="o">.</span><span class="n">split_text</span><span class="p">(</span><span class="n">markdown_text</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span>
</code></pre></div>
<p>运行结果</p>
<p><img alt="image-20251003100510115" src="assets/image-20251003100510115.png"></p>
</blockquote>
<p>其他拓展知识可以参考：<a href="https://blog.csdn.net/qq_28540861/article/details/149161419">https://blog.csdn.net/qq_28540861/article/details/149161419</a></p>
<h3 id="3-vectorstores">3 VectorStores<a class="headerlink" href="#3-vectorstores" title="Permanent link">¶</a></h3>
<p>VectorStores是一种特殊类型的数据库，它的作用是存储由嵌入创建的向量，提供相似查询等功能。</p>
<p>LangChain支持的VectorStore有<a href="https://python.langchain.com/docs/integrations/vectorstores/">https://python.langchain.com/docs/integrations/vectorstores/</a>，常见的如下：</p>
<p><img alt="image-20250705114738060" src="assets/image-20250705114738060.png"></p>
<blockquote>
<p>我们使用其中一个<code>Chroma</code> 组件作为例子：</p>
<p>pip install chromadb</p>
<p>pip install langchain-chroma</p>
</blockquote>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_text_splitters</span><span class="w"> </span><span class="kn">import</span> <span class="n">CharacterTextSplitter</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_chroma</span><span class="w"> </span><span class="kn">import</span> <span class="n">Chroma</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_community.document_loaders</span><span class="w"> </span><span class="kn">import</span> <span class="n">TextLoader</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaEmbeddings</span>

<span class="c1"># 1.加载文档</span>
<span class="c1"># 创建 TextLoader 对象</span>
<span class="n">loader</span> <span class="o">=</span> <span class="n">TextLoader</span><span class="p">(</span><span class="s1">'./data/pku.txt'</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'utf-8'</span><span class="p">)</span>
<span class="c1"># 加载文档</span>
<span class="n">docs</span> <span class="o">=</span> <span class="n">loader</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
<span class="c1"># print(f'docs--&gt;{docs}')</span>

<span class="c1"># 2.将文档进行分块</span>
<span class="c1"># 创建 CharacterTextSplitter 对象</span>
<span class="n">text_splitter</span> <span class="o">=</span> <span class="n">CharacterTextSplitter</span><span class="p">(</span><span class="n">separator</span><span class="o">=</span><span class="s2">"</span><span class="se">\n\n</span><span class="s2">"</span><span class="p">,</span> <span class="n">chunk_size</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">chunk_overlap</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
<span class="c1"># 分块</span>
<span class="n">split_docs</span> <span class="o">=</span> <span class="n">text_splitter</span><span class="o">.</span><span class="n">split_documents</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'split_docs--&gt;</span><span class="si">{</span><span class="n">split_docs</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>

<span class="c1"># 3.将分割后的文档存储到向量数据库中</span>
<span class="c1"># 加载embedding模型</span>
<span class="n">embedding</span> <span class="o">=</span> <span class="n">OllamaEmbeddings</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"mxbai-embed-large"</span><span class="p">)</span>
<span class="c1"># 创建向量数据库，需要指定 存储的文档和向量模型名称以及持久化目录</span>
<span class="n">chromadaDB</span> <span class="o">=</span> <span class="n">Chroma</span><span class="o">.</span><span class="n">from_documents</span><span class="p">(</span><span class="n">documents</span><span class="o">=</span><span class="n">split_docs</span><span class="p">,</span>
                                   <span class="n">embedding</span><span class="o">=</span><span class="n">embedding</span><span class="p">,</span>
                                   <span class="n">persist_directory</span><span class="o">=</span><span class="s1">'./chroma_db'</span><span class="p">)</span>

<span class="c1"># 假如你的向量数据库已经存在，那么可以直接加载</span>
<span class="c1"># chromadaDB = Chroma(persist_directory='./chroma_db', embedding_function=embedding)</span>

<span class="c1"># 4.使用向量数据库进行查询</span>
<span class="n">query</span> <span class="o">=</span> <span class="s2">"1937年北京大学发生了什么？"</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">chromadaDB</span><span class="o">.</span><span class="n">similarity_search</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'result--&gt;</span><span class="si">{</span><span class="n">result</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251003101141993" src="assets/image-20251003101141993.png"></p>
<h3 id="4_1">4 检索器<a class="headerlink" href="#4_1" title="Permanent link">¶</a></h3>
<h4 id="41-langchain">4.1 LangChain的检索器定义<a class="headerlink" href="#41-langchain" title="Permanent link">¶</a></h4>
<p>检索器是 LangChain 中负责信息检索的模块，通常与 索引（Indexes） 模块（如向量存储、嵌入模型）结合使用。它的核心功能是：</p>
<ul>
<li>输入：接收用户查询（通常是文本）。</li>
<li>处理：根据查询从数据源中检索相关内容。</li>
<li>输出：返回一组相关文档或文本片段（通常是 Document 对象列表）。</li>
</ul>
<p>检索器在以下场景中扮演关键角色：</p>
<ul>
<li>问答系统：从文档或知识库中检索答案的上下文。</li>
<li>语义搜索：根据查询的语义返回相关结果。</li>
<li>上下文增强：为语言模型提供外部知识，解决其知识局限。</li>
</ul>
<h4 id="42">4.2 检索器的工作原理<a class="headerlink" href="#42" title="Permanent link">¶</a></h4>
<p>检索器通常与 向量存储（Vector Stores） 配合，通过嵌入模型（Embedding Models）将查询和文档转为向量，基于相似性进行检索。工作流程可以分为以下步骤：</p>
<ul>
<li>查询嵌入：将用户查询通过嵌入模型（如 OpenAIEmbeddings）转为向量表示</li>
<li>相似性搜索：在向量存储中查找与查询向量最相似的文档向量。</li>
<li>文档返回：返回匹配的文档（包含内容、元数据等）。</li>
<li>后处理（可选）：对检索结果进行排序、过滤或重新排名。</li>
</ul>
<p>检索器的核心依赖：</p>
<ul>
<li>嵌入模型：将文本转为向量（如 OpenAIEmbeddings, HuggingFaceEmbeddings）。</li>
<li>向量存储：存储文档向量（如 Chroma、FAISS、Pinecone）。</li>
<li>相似性度量：如余弦相似度、欧几里得距离</li>
</ul>
<h4 id="43">4.3 检索器类型<a class="headerlink" href="#43" title="Permanent link">¶</a></h4>
<p>langchain支持很多检索器<a href="https://python.langchain.com/docs/integrations/retrievers/">https://python.langchain.com/docs/integrations/retrievers/</a>，部分如下：</p>
<p><img alt="image-20250705114709874" src="assets/image-20250705114709874-17594578225791.png"></p>
<p>此处我们讲解VectorStoreRetriever。</p>
<p>在 LangChain 中，as_retriever() 方法的 search_type 参数决定了向量检索的具体算法和行为。</p>
<div class="highlight"><pre><span></span><code><span class="n">retriever</span> <span class="o">=</span> <span class="n">vector_store</span><span class="o">.</span><span class="n">as_retriever</span><span class="p">(</span>
    <span class="n">search_type</span><span class="o">=</span><span class="s2">"similarity"</span><span class="p">,</span>  <span class="c1"># 可选 "similarity"|"mmr"|"similarity_score_threshold"</span>
    <span class="n">search_kwargs</span><span class="o">=</span><span class="p">{</span>
        <span class="s2">"k"</span><span class="p">:</span> <span class="mi">5</span><span class="p">,</span>  <span class="c1"># 返回结果数量</span>
        <span class="s2">"score_threshold"</span><span class="p">:</span> <span class="mf">0.7</span><span class="p">,</span>  <span class="c1"># 仅当search_type="similarity_score_threshold"时有效,低于阈值的都丢弃。</span>
        <span class="s2">"filter"</span><span class="p">:</span> <span class="p">{</span><span class="s2">"source"</span><span class="p">:</span> <span class="s2">"重要文档.pdf"</span><span class="p">},</span>  <span class="c1"># 元数据过滤，只会检索满足条件的文档。</span>
        <span class="s2">"lambda_mult"</span><span class="p">:</span> <span class="mf">0.25</span>  <span class="c1"># 仅MMR搜索有效(控制多样性)：接近 0 则更强调和查询的相关性；接近 1 则更强调结果之间的差异性</span>
    <span class="p">}</span>
<span class="p">)</span>
</code></pre></div>
<p>以下是三种搜索类型的对比：</p>
<p><img alt="image-20250714160215042" src="assets/image-20250714160215042-17594581318012.png"></p>
<p>示例代码：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_chroma</span><span class="w"> </span><span class="kn">import</span> <span class="n">Chroma</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_ollama</span><span class="w"> </span><span class="kn">import</span> <span class="n">OllamaEmbeddings</span>

<span class="n">embedding</span> <span class="o">=</span> <span class="n">OllamaEmbeddings</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">"mxbai-embed-large"</span><span class="p">)</span>

<span class="c1"># 向量数据库已经存在，那么可以直接加载</span>
<span class="n">chromadaDB</span> <span class="o">=</span> <span class="n">Chroma</span><span class="p">(</span><span class="n">persist_directory</span><span class="o">=</span><span class="s1">'./chroma_db'</span><span class="p">,</span> <span class="n">embedding_function</span><span class="o">=</span><span class="n">embedding</span><span class="p">)</span>

<span class="c1"># 使用向量数据库进行查询</span>
<span class="n">query</span> <span class="o">=</span> <span class="s2">"1937年北京大学发生了什么？"</span>
<span class="c1"># result = chromadaDB.similarity_search(query, k=2)</span>

<span class="c1"># 使用 as_retriever 方法返回 Retriever 对象，然后调用 invoke 方法进行查询</span>
<span class="n">retriever</span> <span class="o">=</span> <span class="n">chromadaDB</span><span class="o">.</span><span class="n">as_retriever</span><span class="p">(</span><span class="n">search_kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">"k"</span><span class="p">:</span><span class="mi">2</span><span class="p">})</span>
<span class="n">result</span> <span class="o">=</span> <span class="n">retriever</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="n">query</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'result--&gt;</span><span class="si">{</span><span class="n">result</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="image-20251003102843558" src="assets/image-20251003102843558.png"></p>
<p><strong>拓展：</strong></p>
<p><code>vectordb.as_retriever()</code> 和 <code>vectordb.similarity_search()</code> 都是用于从向量数据库中检索相关文档的方法，它们有什么异同：</p>
<ul>
<li>
<p>相同</p>
<ul>
<li>核心功能：两者都基于向量相似度（如余弦相似度）从向量数据库中检索与查询最相关的文档。</li>
<li>底层技术：通常使用相同的嵌入模型和相似度计算方式（如 FAISS、Chroma、Pinecone 等）。</li>
</ul>
</li>
<li>
<p>不同</p>
<p><img alt="image-20250705114109276" src="assets/image-20250705114109276.png"></p>
</li>
</ul>
<h4 id="44">4.4 拓展<a class="headerlink" href="#44" title="Permanent link">¶</a></h4>
<p>其他几种常用的检索器介绍如下。</p>
<ul>
<li>TFIDFRetriever</li>
</ul>
<blockquote>
<p>功能：基于 TF-IDF（词频-逆文档频率）的检索器。 </p>
<p>特点：</p>
<ul>
<li>使用 TF-IDF 向量表示文档和查询。</li>
<li>适合快速构建原型。</li>
<li>不支持语义搜索。</li>
</ul>
<p>适用场景：</p>
<ul>
<li>文本搜索</li>
<li>关键词提取</li>
</ul>
<p><strong>补充：理解 TF 和 IDF</strong></p>
<p>为了理解后面的 BM25，可以先把 TF-IDF 拆成两个部分来看：</p>
<ul>
<li><strong>TF（Term Frequency，词频）</strong>：回答“这个词在当前文档中出现得多不多？”</li>
<li><strong>IDF（Inverse Document Frequency，逆文档频率）</strong>：回答“这个词在整个知识库中是否稀有？”</li>
</ul>
<p>常见公式如下：</p>
<div class="highlight"><pre><span></span><code>TF(t,d) = 词 t 在文档 d 中出现次数 / 文档 d 总词数
IDF(t) = log(N / df(t))
</code></pre></div>
<ul>
<li><code>N</code>：知识库文档总数。</li>
<li><code>df(t)</code>：包含词 <code>t</code> 的文档数量。</li>
</ul>
<p>一个词越普遍，<code>df(t)</code> 越大，IDF 越小；一个词越稀有，<code>df(t)</code> 越小，IDF 越大。</p>
<p>例如，知识库有 10 篇文档。当前 100 词的文章中，“asyncio”出现 4 次，整个知识库只有 2 篇包含它：</p>
<div class="highlight"><pre><span></span><code>TF = 4 / 100 = 0.04
IDF = ln(10 / 2) = ln(5) ≈ 1.609
TF-IDF = 0.04 × 1.609 ≈ 0.064
</code></pre></div>
<p>如果“使用”出现在全部 10 篇文档中，则 <code>IDF = ln(10 / 10) = 0</code>。即使它在某篇文章里出现很多次，最终 TF-IDF 权重也会很低，因为它缺乏区分主题的能力。</p>
<p>所以，TF-IDF 的核心思想是：<strong>一个词在当前文档中越常见，并且在整个知识库中越稀有，它越能代表当前文档的主题。</strong> BM25 正是在这个思想上继续加入了长度归一化和词频饱和控制。</p>
<p>示例代码：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_community.retrievers</span><span class="w"> </span><span class="kn">import</span> <span class="n">TFIDFRetriever</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.documents</span><span class="w"> </span><span class="kn">import</span> <span class="n">Document</span>

<span class="n">docs</span> <span class="o">=</span> <span class="p">[</span>
    <span class="n">Document</span><span class="p">(</span><span class="n">page_content</span><span class="o">=</span><span class="s2">"量子计算是一种基于量子力学的计算范式。"</span><span class="p">),</span>
    <span class="n">Document</span><span class="p">(</span><span class="n">page_content</span><span class="o">=</span><span class="s2">"人工智能是模拟人类智能的技术。"</span><span class="p">)</span>
<span class="p">]</span>

<span class="n">retriever</span> <span class="o">=</span> <span class="n">TFIDFRetriever</span><span class="o">.</span><span class="n">from_documents</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span>
<span class="n">retriever</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># 返回 1 个结果</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">retriever</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="s2">"量子计算"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img src="assets/image-20251003104151768.png" alt="image-20251003104151768" style="zoom:80%;"></p>
</blockquote>
<ul>
<li>BM25Retriever</li>
</ul>
<blockquote>
<p>BM25算法：BM25 是对 TF-IDF 的改进版本，在 TF-IDF 基础上做了归一化（防止长文档优势）与饱和控制（防止词频无限增长）。</p>
<p><img src="assets/image-20251003105157106.png" alt="image-20251003105157106" style="zoom: 60%;"></p>
<p><img src="assets/image-20251006090343544.png" alt="image-20251006090343544" style="zoom:67%;"></p>
<p>功能：基于 BM25 算法的关键词检索器，适合基于词频的搜索。</p>
<p>特点：</p>
<ul>
<li>不依赖嵌入模型，使用词频和逆文档频率（TF-IDF）计算相关性。</li>
<li>适合关键词匹配场景，计算成本低。</li>
<li>不支持语义搜索，效果依赖文本的字面匹配。</li>
</ul>
<p>适用场景：</p>
<ul>
<li>传统搜索场景。</li>
<li>关键词驱动的问答。</li>
</ul>
<p>环境依赖：</p>
<p>pip install rank_bm25</p>
<p>示例代码：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_community.retrievers</span><span class="w"> </span><span class="kn">import</span> <span class="n">BM25Retriever</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.documents</span><span class="w"> </span><span class="kn">import</span> <span class="n">Document</span>

<span class="n">docs</span> <span class="o">=</span> <span class="p">[</span>
    <span class="n">Document</span><span class="p">(</span><span class="n">page_content</span><span class="o">=</span><span class="s2">"量子计算是一种基于量子力学的计算范式。"</span><span class="p">),</span>
    <span class="n">Document</span><span class="p">(</span><span class="n">page_content</span><span class="o">=</span><span class="s2">"人工智能是模拟人类智能的技术。"</span><span class="p">)</span>
<span class="p">]</span>

<span class="n">retriever</span> <span class="o">=</span> <span class="n">BM25Retriever</span><span class="o">.</span><span class="n">from_documents</span><span class="p">(</span><span class="n">docs</span><span class="p">)</span>
<span class="n">retriever</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="mi">1</span>  <span class="c1"># 返回 1 个结果</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">retriever</span><span class="o">.</span><span class="n">invoke</span><span class="p">(</span><span class="s2">"量子计算"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<p><img src="assets/image-20251003104151768.png" alt="image-20251003104151768" style="zoom:80%;"></p>
</blockquote>
<ul>
<li>MultiQueryRetriever</li>
</ul>
<blockquote>
<p>功能：它借助 LLM 自动生成多个语义等价的改写查询，把用户的问题扩展成多个角度，然后对每个改写进行检索，最后合并结果，以提高召回率。 </p>
<p>特点：</p>
<ul>
<li>使用语言模型生成查询的多种表达方式</li>
<li>从向量存储中检索所有变体的结果并合并</li>
<li>提高召回率，适合复杂查询</li>
</ul>
<p>适用场景：</p>
<ul>
<li>查询表达不明确或需要覆盖多种语义</li>
<li>提高检索的全面性
</li>
</ul>
</blockquote>
<ul>
<li>EnsembleRetriever</li>
</ul>
<blockquote>
<p>功能：结合多种检索器（如 BM25 和向量存储），融合结果。</p>
<p>特点：</p>
<ul>
<li>结合关键词搜索和语义搜索的优点</li>
<li>支持加权融合，调整不同检索器的权重</li>
<li>提高召回率和精准度</li>
</ul>
<p>适用场景：</p>
<ul>
<li>需要综合关键词和语义的搜索</li>
<li>复杂查询场景
</li>
</ul>
</blockquote>
<ul>
<li>ContextualCompressionRetriever</li>
</ul>
<blockquote>
<p>功能：对检索结果进行压缩，提取最相关的内容 </p>
<p>特点：</p>
<ul>
<li>使用语言模型对检索到的文档进行重新排序或精炼</li>
<li>减少无关内容，提高结果质量</li>
<li>增加计算开销，但提升精准度</li>
</ul>
<p>适用场景：</p>
<ul>
<li>文档内容冗长，需要提取关键信息</li>
<li>提高问答系统的答案质量
</li>
</ul>
</blockquote>
<ul>
<li>Custom Retriever</li>
</ul>
<blockquote>
<p>功能：开发者可以自定义检索逻辑，适配特定数据源或算法 </p>
<p>特点：</p>
<ul>
<li>继承 BaseRetriever 类，实现 get_relevant_documents 方法</li>
<li>支持任意数据源（如数据库、API）</li>
</ul>
<p>适用场景：</p>
<ul>
<li>特定领域的数据源（如内部数据库）</li>
<li>自定义检索算法
</li>
</ul>
</blockquote>
<h2 id="agents">八、Agents<a class="headerlink" href="#agents" title="Permanent link">¶</a></h2>
<h3 id="1-agent">1 Agent 概念<a class="headerlink" href="#1-agent" title="Permanent link">¶</a></h3>
<p>Agent（智能体）是一种能够感知环境、进行决策和执行动作的智能实体。不同于传统的人工智能，Agent 具备通过主动思考、调用工具去逐步完成给定目标的能力。</p>
<p>为什么要调用工具？</p>
<ul>
<li>因为大模型虽然非常强大，但是也具备一定的局限性，比如不能回答实时信息、处理数学逻辑问题仍然非常的初级等等。因此，可以借助第三方工具来辅助大模型的应用。</li>
</ul>
<p>从大模型的角度来看，<strong>Agent其实就是基于大模型的语义理解和推理能力，让大模型拥有解决复杂问题时的任务规划能力，并调用外部工具来执行各种任务，利用向量数据库保留“记忆”的一个智能体</strong>。</p>
<blockquote>
<p>Agent = 大模型 + 任务规划（Planning） + 使用外部工具执行任务（Tools&amp;Action） + 记忆（Memory）</p>
<p>把Agent比作一个人的话，<strong>他应该有大脑（语义理解、存储记忆、推理规划、专业知识）、五官（接收文本、视觉输入、听觉输入等）、四肢（使用工具完成各种具体任务）等主要部件</strong></p>
</blockquote>
<hr>
<p><strong>工作流程概述：</strong></p>
<ul>
<li>用户提出任务。</li>
<li>Agent 启动： 将用户输入与预设的“提示词模板”结合，并结合当前的“上下文”和“变量”，形成一个完整的输入发送给大模型。</li>
<li>大模型思考与决策 (循环)：<ul>
<li>大模型接收输入后，根据其内置的逻辑和提示词的指导，进行“思考”。</li>
<li>它会判断完成当前任务是需要继续使用工具来获取更多信息/执行操作，还是已经可以直接生成最终答案。</li>
<li>如果需要工具： 大模型会根据任务需求和对工具的描述，选择合适的工具，并生成执行该工具所需的输入参数。这些参数通常会从上下文或变量中提取。</li>
<li>工具执行： 选定的工具被调用，执行其功能。</li>
<li>结果反馈： 工具执行的结果会返回给 Agent，并被用来更新“上下文”和“变量”。</li>
<li>循环： Agent 将更新后的“上下文”、“变量”以及工具执行结果再次反馈给大模型，大模型继续进行新一轮的“思考-行动”循环，直到任务完成。</li>
<li>如果直接回答： 当大模型判断任务已完成，或无需额外工具即可回答时，它会生成最终的答案。</li>
</ul>
</li>
<li>用户输出： 最终答案被呈现给用户。</li>
</ul>
<h3 id="2-agent">2 Agent关键组成部分<a class="headerlink" href="#2-agent" title="Permanent link">¶</a></h3>
<p><img alt="image-20250705115745718" src="assets/image-20250705115745718.png"></p>
<ul>
<li>
<p>任务规划（Planning）</p>
<p>Agent需要提前将一项复杂任务拆解为多个更小、更易于处理的子任务，从而实现对复杂任务的高效处理</p>
</li>
<li>
<p>长短期记忆（Memory）</p>
<p>聊天上下文，长期保留和回忆信息</p>
</li>
<li>
<p>工具&amp;执行（Tools&amp;Action）</p>
<p>根据拆分好的子任务，调用外部提供好的专业API解决专业问题，完成一个个具体的子任务，并把处理结果返回给大模型</p>
</li>
</ul>
<h3 id="3-langchainagent">3 langchain实现Agent<a class="headerlink" href="#3-langchainagent" title="Permanent link">¶</a></h3>
<p>LangChain 提供了不同类型的代理（主要罗列一下三种）:</p>
<ul>
<li>
<p><strong>Zero-shot ReAct Description</strong></p>
<ul>
<li>
<p>基于 <strong>ReAct 框架</strong>（推理 + 行动），仅依赖工具的 <strong>描述</strong> 来决定调用哪个工具。</p>
</li>
<li>
<p>特点：无需额外示例（zero-shot），但 <strong>不具备记忆能力</strong>，每次推理都独立进行。</p>
</li>
<li>
<p>使用场景：简单任务，工具选择完全靠工具描述即可。</p>
</li>
</ul>
</li>
<li>
<p><strong>Structured Chat Zero-shot ReAct Description</strong></p>
<ul>
<li>
<p>同样基于 <strong>ReAct 框架</strong>，但可以处理 <strong>结构化输入</strong>，即支持带多个参数的工具（类似函数调用）。</p>
</li>
<li>
<p>特点：不仅能像第一种代理那样根据描述选择工具，还能正确组织并传递复杂参数。</p>
</li>
<li>
<p>使用场景：调用接口类工具、需要多参数输入的任务。</p>
</li>
</ul>
</li>
<li>
<p><strong>Conversational ReAct Description</strong></p>
<ul>
<li>
<p>在 ReAct 框架基础上，增强了 <strong>对话记忆能力</strong>。</p>
</li>
<li>
<p>特点：能根据上下文对话历史来做出工具选择和回应，更适合持续性对话场景。</p>
</li>
<li>
<p>使用场景：多轮对话，用户可能引用之前的内容或需要长期上下文跟踪。</p>
</li>
</ul>
</li>
</ul>
<hr>
<p>LangChain 中集成了很多工具，可以通过下面的方式进行查询：</p>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_community.agent_toolkits.load_tools</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_all_tool_names</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">get_all_tool_names</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
</code></pre></div>
<p>运行结果：</p>
<div class="highlight"><pre><span></span><code>['sleep', 'wolfram-alpha', 'google-search', 'google-search-results-json', 'searx-search-results-json', 'bing-search', 'metaphor-search', 'ddg-search', 'google-books', 'google-lens', 'google-serper', 'google-scholar', 'google-finance', 'google-trends', 'google-jobs', 'google-serper-results-json', 'searchapi', 'searchapi-results-json', 'serpapi', 'dalle-image-generator', 'twilio', 'searx-search', 'merriam-webster', 'wikipedia', 'arxiv', 'golden-query', 'pubmed', 'human', 'awslambda', 'stackexchange', 'sceneXplain', 'graphql', 'openweathermap-api', 'dataforseo-api-search', 'dataforseo-api-search-json', 'eleven_labs_text2speech', 'google_cloud_texttospeech', 'read_file', 'reddit_search', 'news-api', 'tmdb-api', 'podcast-api', 'memorize', 'llm-math', 'open-meteo-api', 'requests', 'requests_get', 'requests_post', 'requests_patch', 'requests_put', 'requests_delete', 'terminal']
</code></pre></div>
<hr>
<p>接下来，通过一个示例来学习Agent的基本使用。</p>
<ul>
<li>问题1：计算一下300的25%是多少？</li>
<li>问题2：请帮我介绍一下故宫</li>
</ul>
<p>示例代码：</p>
<blockquote>
<p>需要提前安装 wikipedia</p>
<p>pip install wikipedia</p>
</blockquote>
<div class="highlight"><pre><span></span><code><span class="kn">from</span><span class="w"> </span><span class="nn">langchain_core.messages</span><span class="w"> </span><span class="kn">import</span> <span class="n">HumanMessage</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langgraph.checkpoint.memory</span><span class="w"> </span><span class="kn">import</span> <span class="n">MemorySaver</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain.agents</span><span class="w"> </span><span class="kn">import</span> <span class="n">create_agent</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_community.agent_toolkits.load_tools</span><span class="w"> </span><span class="kn">import</span> <span class="n">load_tools</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">langchain_openai</span><span class="w"> </span><span class="kn">import</span> <span class="n">ChatOpenAI</span>
<span class="kn">import</span><span class="w">  </span><span class="nn">os</span>

<span class="c1"># 实例化模型</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">ChatOpenAI</span><span class="p">(</span>
    <span class="n">base_url</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'base_url'</span><span class="p">,</span><span class="s1">'https://dashscope.aliyuncs.com/compatible-mode/v1'</span><span class="p">),</span>
    <span class="n">model</span><span class="o">=</span><span class="s1">'qwen-plus'</span><span class="p">,</span>
    <span class="n">openai_api_key</span><span class="o">=</span><span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'api_key'</span><span class="p">,</span><span class="s1">'sk-XXXXXXXX'</span><span class="p">),</span>
    <span class="n">max_tokens</span><span class="o">=</span><span class="mi">1000</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="c1"># Create the agent</span>
<span class="n">memory</span> <span class="o">=</span> <span class="n">MemorySaver</span><span class="p">()</span>
<span class="n">tools</span> <span class="o">=</span> <span class="n">load_tools</span><span class="p">([</span><span class="s1">'wikipedia'</span><span class="p">,</span> <span class="s1">'llm-math'</span><span class="p">],</span> <span class="n">llm</span><span class="o">=</span><span class="n">model</span><span class="p">)</span>
<span class="n">agent_executor</span> <span class="o">=</span> <span class="n">create_agent</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">tools</span><span class="p">,</span> <span class="n">checkpointer</span><span class="o">=</span><span class="n">memory</span><span class="p">)</span>

<span class="c1"># Use the agent</span>
<span class="n">config</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"configurable"</span><span class="p">:</span> <span class="p">{</span><span class="s2">"thread_id"</span><span class="p">:</span> <span class="s2">"abc123"</span><span class="p">}}</span>

<span class="k">for</span> <span class="n">chunk</span> <span class="ow">in</span> <span class="n">agent_executor</span><span class="o">.</span><span class="n">stream</span><span class="p">(</span>
    <span class="p">{</span><span class="s2">"messages"</span><span class="p">:</span> <span class="p">[</span><span class="n">HumanMessage</span><span class="p">(</span><span class="n">content</span><span class="o">=</span><span class="s2">"计算一下300的25%是多少？"</span><span class="p">)]},</span> <span class="n">config</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="nb">print</span><span class="p">(</span><span class="s2">"----"</span><span class="p">)</span>

<span class="c1"># for chunk in agent_executor.stream(</span>
<span class="c1">#     {"messages": [HumanMessage(content="大熊猫是哪个国家的国宝？")]}, config</span>
<span class="c1"># ):</span>
<span class="c1">#     print(chunk)</span>
<span class="c1">#     print("----")</span>
</code></pre></div>
<p>运行结果：</p>
<p><img alt="1768397211211" src="assets/1768397211211.png"></p>
<blockquote>
<p>参考官网 <a href="https://python.langchain.com/docs/how_to/custom_tools/">https://python.langchain.com/docs/how_to/custom_tools/</a></p>
</blockquote>
<h2 id="langchain">九、LangChain使用场景<a class="headerlink" href="#langchain" title="Permanent link">¶</a></h2>
<ul>
<li>个人助手</li>
<li>基于文档的问答系统</li>
<li>聊天机器人</li>
<li>Tabular数据查询</li>
<li>API交互</li>
<li>信息提取</li>
<li>文档总结</li>
</ul>
<h2 id="_3">十、本节小结<a class="headerlink" href="#_3" title="Permanent link">¶</a></h2>
<p>本章主要介绍了LangChain的基本组件及使用方式。</p>
<h2 id="_4">十一、本节作业<a class="headerlink" href="#_4" title="Permanent link">¶</a></h2>
<h3 id="1-1">1 作业1<a class="headerlink" href="#1-1" title="Permanent link">¶</a></h3>
<ul>
<li>题目1：什么是LangChain？请解释其核心组件和设计思想。</li>
<li>题目2：解释LangChain中的链（Chain）概念，并介绍主要的链类型。</li>
<li>题目3：解释LangChain中的Prompts概念，并介绍主要类型。</li>
<li>题目4：实现LangChain中的output_parsers的具体使用方式</li>
</ul>
<h3 id="2-2">2 作业2<a class="headerlink" href="#2-2" title="Permanent link">¶</a></h3>
<ul>
<li>题目1：什么是Memory组件？请解释LangChain中不同类型的记忆机制。</li>
<li>题目2：解释LangChain中的提示工程及其最佳实践。</li>
<li>题目3：LangChain的Document Loaders？请介绍其功能和主要类型。</li>
<li>题目4 ：什么是LangChain的代理（Agent）？请说明其工作原理和应用。</li>
</ul>


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