LazyLLM教程 | 第8讲：不止是cosine！匹配策略决定你召回的质量 - 紫竹林-程序员中文网-紫竹林-程序员中文网

在前面教程中，我们介绍了如何通过查询重写、各种优化检索策略和召回重排策略来提升检索模块的召回率。其中影响检索召回文档质量的一个关键组件为 similarity，它的作用是用来计算检索的文档和查询 query 之间的相似度。

LazyLLM 默认提供的相似度计算函数为 bm25(分为英文和中文) 相似度计算，余弦相似度计算方法。其中 bm25 算法主要针对文本进行计算，而余弦相似度算法主要针对 embedding 进行计算。如果 LazyLLM 提供的默认相似度计算方法不能满足自己的需求，可以自己来设计定义符合自己需求的相似度计算方法。与 Similarity 组件相似，本教程同时介绍 Transform的自定义方法。

本教程主要介绍如何使用自定义 Similarity 组件和 Transform的方法，读完本教程，您将学会 LazyLLM 中自定义 Similarity 和 Transform的方法，并基于 Similarity 和 Transform分别搭建一个简单的RAG应用。

为什么要自定义 Similarity

在 RAG服务中，检索模块的效果直接影响生成结果的相关性与准确性。不同应用场景对“相似度”的理解和需求各不相同。例如，在法律或医疗文档中，强调关键词级别的精确匹配；在通用问答场景中，更注重语义层面的理解；而在多模态场景中，则可能涉及文本与图像或结构化数据之间的对齐匹配。为适配多样化需求，LazyLLM 提供了两种通用的相似度计算方法：BM25/BM25_chinese和余弦相似度。尽管这两者在通用场景中具有良好表现，但在某些特定任务中仍存在局限性：

BM25 无法理解语义近似：它基于词频和词面匹配，无法处理近义词、同义术语或行业特定表述的归一化问题；
余弦相似度粒度较粗：在长文本或结构复杂的文档中，局部相关内容容易被整体平均，从而稀释匹配效果。

👇我们来看一个示例，关于BM25 无法理解领域语义（近义词、术语归一）的问题。

① 示例背景

用户查询：“高血糖的饮食干预方法”
查询说明：用户查询医学相关的资料，使用的是专业术语的同义表达。
候选文档：“糖尿病患者应控制碳水化合物摄入，以调节血糖水平。”

② 理想匹配预期

“高血糖”是“糖尿病”的核心表现之一。尽管查询和文档表述不同，但在医学语境中高度相关。理想的检索系统应能理解两者间的语义等价关系，从而高质量地命中该文档。

③ 问题分析

使用 BM25 或 BM25_chinese 时，由于它们仅关注词面重合，“高血糖”与“糖尿病”词面不同，因此得分较低，极有可能被错过。这类基于表层匹配的方式在专业领域语义检索中显得力不从心。因此，为提升检索质量，在实际系统中往往需要引入更语义敏感、领域定制化的相似度策略，以适配复杂多变的业务需求。

👇接着，看一下另一个示例，关于余弦相似度在长文档中被稀释的问题。

① 示例背景

用户查询：“谁具备优秀的项目管理能力”
查询说明：用户想找一段描述“张三的项目管理能力”的内容。
候选文档（长文档摘要）：“张三毕业于某重点高校，具有多年软件开发经验。他参与多个大型项目的研发与部署，包括某知名 Agent应用开发框架。他的主要职责包括代码实现、团队协作与任务分配。此外，他还在多个关键节点中承担项目负责人的角色，体现出良好的组织能力和推进力。”

② 理想匹配预期

用户查询和文档中 “项目负责人”，“组织能力”等部分高度相关，理想的系统应该能够识别出局部相关性，并提升该文档排名。

③ 问题分析

使用基于 TF-IDF 向量化的余弦相似度时，文档的所有词语都被纳入整体向量表示。在这种情况下，像“开发经验”、“代码实现”等内容虽然词频较高，却与查询目标无关，反而稀释了项目管理相关内容的权重，导致整体相似度偏低，文档难以被正确召回或排序靠前。因此，在处理包含多个主题的长文档时，单一的全文向量匹配显得力不从心，更需要引入局部语义感知机制（如片段级匹配、摘要引导、注意力机制等）来增强相关片段的识别与表达。

如何自定义 Similarity

1. 环境准备

如果您的电脑上安装了Python，请通过下方命令安装lazyllm及必要的依赖包。关于 LazyLLM 的环境更详细的准备可以参考第2讲：10分钟上手一个最小可用RAG系统中对应的内容。

<span><span style="color:#0e9ce5">pip</span> install lazyllm</span>

2. 实现函数

由于 LazyLLM 只提供了 BM25 和余弦相似度两种相似度计算方式，在实际应用中可能不能满足要求，这就需要自己根据实际需求来定制 similarity。这里我们以 TF-IDF 算法来实现一个 similarity 计算的例子来说明怎么定义及应用。

TF-IDF 原理与示例

TF-IDF 全称为 Term Frequency – Inverse Document Frequency，是一种在信息检索和文本挖掘中广泛使用的关键词提取算法。它的核心思想是：一个词如果在一篇文档中频繁出现，但在所有文档中不常见，那么它很可能是这篇文档的重要关键词。

1. 词频 TF（Term Frequency）

衡量词语在文档中的重要性：

其中， f(t,d) 表示词 t 在文档 d 中出现的次数，分母为该文档中所有词的出现次数之和。

2. 逆文档频率 IDF（Inverse Document Frequency）

衡量词语在语料库中的稀有程度：

其中， N 为总文档数， df(t) 为包含词 t 的文档数。

3. 最终计算公式：TF-IDF

4. 示例分析

文档集：

📄 文档1：「大模型正在改变世界」

📄 文档2：「大模型是人工智能的关键技术」

📄 文档3：「我喜欢编程」

查询：「大模型技术」

🔹 TF（以文档2为例）：共7词

“大模型”出现1次 → TF = 1 / 7 ≈ 0.143

“技术”出现1次 → TF = 1 / 7 ≈ 0.143

🔹 IDF（N = 3）

“大模型”：df = 2 → IDF = log(3 / (1+2)) = log(1) = 0

“技术”：df = 1 → IDF = log(3 / 2) ≈ 0.405

🔹 TF-IDF（以文档2为例）

TF-IDF(“大模型”) = 0.143 × 0 = 0

TF-IDF(“技术”) = 0.143 × 0.405 ≈ 0.058

🚨注意：为了避免IDF为负值，Sklearn中使用了平滑版，做了一些处理避免了除以 0，也确保了 IDF ≥ 1。

虽然“大模型”是 query 的关键词，但由于它在多个文档中频繁出现，IDF 为 0，无法帮助区分文档。而“技术”这个词仅在文档2中出现，它的 TF-IDF 值高，对区分文档有贡献。

代码示例：

（代码GitHub链接：

https://github.com/LazyAGI/Tutorial/blob/7abc91dbb82a007a78731845dd8c360ac0cc1e75/rag/codes/chapter8/rag_tfidf_similarity.py #L1 ）

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
from scipy.linalg import norm
import sys
import heapq
from typing import List, Tuple
from lazyllm.tools.rag import DocNode
def tfidf_similarity(query: str, nodes: List[DocNode], **kwargs) -> List[Tuple[DocNode, float]]:
    def add_space(s):
        return ' '.join(list(s))
    corpus = [add_space(node.get_text()) for node in nodes]
    query = add_space(query)
    topk = min(len(nodes), kwargs.get("topk", sys.maxsize))
    cv = TfidfVectorizer(tokenizer=lambda s: s.split())
    tfidf_matrix = cv.fit_transform(corpus+[query])
    query_vec = tfidf_matrix[-1]
    doc_vecs = tfidf_matrix[:-1]
    similairyties = cosine_similarity(query_vec, doc_vecs).flatten()
    indexes = heapq.nlargest(topk, range(len(similairyties)), similairyties.__getitem__)
    results = [(nodes[i], similairyties[i]) for i in indexes]
    return results

在上面的代码中，我们首先通过把 query 和 node 中的文本中插入空格，以用于后面的 tokenizer 处理。tfidf算法是调用 sklearn 中的 TfidfVectorizer 来实现的。计算完 query 和 node 的相似度之后，提取 topk 的结果作为输出。现在我们使用下面的例子进行测试。

query = "今天天气怎么样"
candidates = [
    DocNode(text="今天阳光明媚"),
    DocNode(text="衬衫的价格是100元"),
    DocNode(text="今天天气非常好"),
    DocNode(text="我喜欢吃苹果"),
    DocNode(text="今天天气真糟糕")
]
results = tfidf_similarity(query, candidates)

最后得到如下的输出结果：

3.注册函数

在上一节中我们定义了一个 tfidf 相似度计算方法，如果想在 LazyLLM 中使用该计算方法，则需要先把该方法注册进 LazyLLM 框架中。对相似度计算方法进行注册是通过装饰器来进行的，即通过 register_similarity 装饰器函数进行注册。

【装饰器（Decorator）是 Python 中的一种高级功能，它允许你在不修改原函数代码的情况下，给函数添加额外的功能（比如日志记录、性能测试、权限验证等）。其本质上是一个 “接受函数作为参数，并返回新函数” 的函数。你可以把它想象成 “函数的包装盒”——把函数放进去，它就会自动获得新能力。实现代码如下。】

代码如下:

<span><span style="color:#ca7d37">import</span> lazyllm</span>
<span><span style="color:#afafaf">@lazyllm.tools.rag.register_similarity(</span>
<span style="color:#afafaf"><span>mode=</span></span>
<span style="color:#afafaf"><span><span style="color:#dd1144">'text'</span></span></span>
<span style="color:#afafaf"><span>, batch=</span></span>
<span style="color:#afafaf"><span><span style="color:#0e9ce5">True</span></span></span>
<span style="color:#afafaf">)</span></span>
<span><span style="color:#ca7d37">def</span> <span style="color:#dd1144">tfidf_similarity</span>(<span>query: </span>
<span><span style="color:#ca7d37">str</span></span>
<span>, nodes: </span>
<span><span style="color:#0e9ce5">List</span></span>
<span>[DocNode], **kwargs</span>) -> <span style="color:#0e9ce5">List</span>[<span style="color:#0e9ce5">Tuple</span>[DocNode, <span style="color:#ca7d37">float</span>]]:</span>
<span>    ...</span>

register_similarity装饰器函数有四个参数，这里我们详细介绍一下。

func，它是一个可选的调用对象，表示要被装饰的函数，如果没有提供，则会返回一个装饰器函数。
mode，它是可选的字面类型，取值为 “text” 或 “embedding”，表示相似度计算的模式：

→”text” 表示该相似度计算方法主要是针对文本进行计算的；

→”embedding” 表示该相似度计算方法主要是针对嵌入向量进行计算的。
descend，它是一个布尔值，指示结果是否按降序排序，默认值为 True，需要注意这个参数不是在在计算相似度输出时生效，而是在索引的时候才会生效。
batch，它也是一个布尔值，指示是否支持批量处理，默认值为 False。

下面简单展示一下装饰器的三种使用方式：

<span><em># 使用方式1：作为函数使用</em></span>
<span>func1 = lazyllm.tools.rag.register_similarity(tfidf)</span>
<span><em># 使用方式2：作为无参数的装饰器使用</em></span>
<span><span style="color:#afafaf">@lazyllm.tools.rag.register_similarity</span></span>
<span><span style="color:#ca7d37">def</span> <span style="color:#dd1144">tfidf</span>(<span>query: </span>
<span><span style="color:#ca7d37">str</span></span>
<span>, nodes: </span>
<span><span style="color:#0e9ce5">List</span></span>
<span>[DocNode], </span></span>
<span>             **kwargs) -> <span style="color:#0e9ce5">List</span>[<span style="color:#0e9ce5">Tuple</span>[DocNode, <span style="color:#ca7d37">float</span>]]:</span>
<span>    ……</span>
<span><em># 使用方式3：作为有参数的装饰器使用</em></span>
<span><span style="color:#afafaf">@lazyllm.tools.rag.register_similarity(</span>
<span style="color:#afafaf"><span>mode=‘text’</span></span>
<span style="color:#afafaf">)</span></span>
<span><span style="color:#ca7d37">def</span> <span style="color:#dd1144">tfidf</span>(<span>query: </span>
<span><span style="color:#ca7d37">str</span></span>
<span>, nodes: </span>
<span><span style="color:#0e9ce5">List</span></span>
<span>[DocNode], </span></span>
<span>             **kwargs) -> <span style="color:#0e9ce5">List</span>[<span style="color:#0e9ce5">Tuple</span>[DocNode, <span style="color:#ca7d37">float</span>]]:</span>

4. 参数详解

下面我们通过一些小例子来比较每个参数的作用：

1. 参数 func

关于 register_similarity函数的 func 参数，有两种方式使用，一种是正常函数调用的方式传参进去，一种是通过装饰器的方式直接在函数定义时注册。下面为了说明这个参数传进去和没有传进去的区别，我们使用函数调用的方式来演示。

<span>func1 = lazyllm.tools.rag.register_similarity(tfidf_similarity)</span>
<span>func2 = lazyllm.tools.rag.register_similarity()</span>
<span><span style="color:#ca7d37">print</span>(<span style="color:#dd1144">f"func1: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{func1.__name__}</span></span>
<span style="color:#dd1144">, func2: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{func2.__name__}</span></span>
<span style="color:#dd1144">"</span>)</span>

输出结果为：

<span><span style="color:#0e9ce5">func1</span>
<span>: </span>tfidf_similarity, func2: decorator</span>

func1 对应的是注册函数传入了相似度计算函数，所以它的返回值是被装饰的相似度计算函数，而 func2 对应的是是没有传入相似度计算函数，所以它的返回值是装饰器函数。

2. 参数 mode

Mode，可选的字面类型，取值为 “text” 或 “embedding”，表示相似度计算的模式。

“text” 表示该相似度计算方法主要是针对文本进行计算的;
“embedding” 表示该相似度计算方法主要是针对嵌入向量进行计算的;

📌 从表中可以看出，我们的输入、mode和参数三者类型必须完全一致才可以使用。但实际使用时，只需要保证函数的node参数类型和注册时候的mode参数一致即可，无需关心query的类型。

这里我们先定义一个欧式距离的计算函数:

<span><span style="color:#ca7d37">import</span> lazyllm</span>
<span><span style="color:#ca7d37">from</span> typing <span style="color:#ca7d37">import</span> <span style="color:#0e9ce5">List</span></span>
<span><span style="color:#ca7d37">import</span> numpy <span style="color:#ca7d37">as</span> np</span>
<span><span style="color:#ca7d37">def</span> <span style="color:#dd1144">euclidean_distance</span>(<span>query: </span>
<span><span style="color:#0e9ce5">List</span></span>
<span>[</span>
<span><span style="color:#ca7d37">float</span></span>
<span>], node: </span>
<span><span style="color:#0e9ce5">List</span></span>
<span>[</span>
<span><span style="color:#ca7d37">float</span></span>
<span>], **kwargs</span>) -> <span style="color:#ca7d37">float</span>:</span>
<span>    point1 = np.array(query)</span>
<span>    point2 = np.array(node)</span>
<span>    <span style="color:#ca7d37">return</span> np.linalg.norm(point1 - point2)</span>

然后把这个相似度计算函数注册一下，这里使用不同的mode:

<span><span>func1</span> = lazyllm.tools.rag.register_similarity(euclidean_distance, mode=<span style="color:#dd1144">"text"</span>)</span>
<span><span>func2</span> = lazyllm.tools.rag.register_similarity(euclidean_distance, mode=<span style="color:#dd1144">"embedding"</span>)</span>

然后我们定义不同的 query 和 node 来用不同模式的相似度计算函数计算相似度，看看效果:

<span><span>query_t</span> = <span style="color:#dd1144">"hello world."</span></span>
<span><span>node_t</span> = [DocNode(text=<span style="color:#dd1144">"hello lazyllm."</span>)]</span>
<span><span>query_e</span> = {<span style="color:#dd1144">"key"</span>: [<span style="color:#0e9ce5">1.0</span>, <span style="color:#0e9ce5">0.4</span>, <span style="color:#0e9ce5">2.1</span>]}</span>
<span><span>node_e</span> = [DocNode(embedding={<span style="color:#dd1144">"key"</span>: [<span style="color:#0e9ce5">4.2</span>, <span style="color:#0e9ce5">2.1</span>, <span style="color:#0e9ce5">3.9</span>]})]</span>

用 func1 计算文本的相似度

<span>ret = <span style="color:#ca7d37">func1</span>(<span style="color:#0e9ce5">query_t</span>, <span style="color:#0e9ce5">node_t</span>)</span>
<span><span style="color:#ca7d37">print</span>(f<span style="color:#dd1144">"ret: {ret}"</span>)</span>

输出结果报错了：

<span><span style="color:#dd1144">TypeError</span>: unsupported operand <span style="color:#dd1144">type</span>(s) <span style="color:#ca7d37">for</span> -: <span style="color:#dd1144">'str'</span> and <span style="color:#dd1144">'DocNode'</span></span>

用 func1 计算向量相似度

<span>ret = func1(query_e, node_e)</span>
<span><span style="color:#ca7d37">print</span>(<span style="color:#dd1144">f"ret: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{ret}</span></span>
<span style="color:#dd1144">"</span>)</span>

输出结果报错了：

<span><span style="color:#dd1144">TypeError</span>: unsupported operand <span style="color:#dd1144">type</span>(s) <span style="color:#ca7d37">for</span> -: <span style="color:#dd1144">'dict'</span> and <span style="color:#dd1144">'DocNode'</span></span>

用 func2 计算文本的相似度

<span>ret = <span style="color:#ca7d37">func2</span>(<span style="color:#0e9ce5">query_t</span>, <span style="color:#0e9ce5">node_t</span>)</span>
<span><span style="color:#ca7d37">print</span>(f<span style="color:#dd1144">"ret: {ret}"</span>)</span>

输出结果报错了：

<span>AssertionError: query must be of <span style="color:#ca7d37">dict</span> <span style="color:#ca7d37">type</span>, used <span style="color:#ca7d37">for</span> similarity calculation.</span>

用 func2 计算向量相似度

<span>ret_2e = func2(query_e, node_e)</span>
<span><span style="color:#ca7d37">print</span>(<span style="color:#dd1144">f"ret_2e: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{ret_2e}</span></span>
<span style="color:#dd1144">"</span>)</span>

输出结果为：

<span><span>ret</span>: {<span style="color:#dd1144">'key'</span>: [(<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">Node</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">id</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">2865d5c9-730b-4fda-8077-57b706944ad9</span></span>
<span style="color:#0e9ce5">></span>, 4.045985664828782)]}</span>

输出结果符合预期，因为 LazyLLM 是支持多 embedding 的，所以前面的 key 表示的是哪类 embedding，然后 tuple 里面的第一项 Node 表示的是哪个节点和 query 进行计算的，第二项表示的是相似度分数。

通过上面四种结果比较，就可以非常清楚 mode 参数的作用了。

3. 参数 descend

descend，布尔值，指示结果是否按降序排序，默认值为 True。需要注意这个参数不是在计算相似度输出时生效，而是在索引的时候才会生效。因为输出时会去重，所以只是返回了topK个node，并不一定以按序排列的。这里我们通过查看 Retriever 检索的结果是否和 query 相似就可以看出该参数的效果了。

我们还是继续使用上面定义的欧氏距离的计算函数，把这个相似度计算函数注册一下，我们先使用 descend 为默认值True的情况:

<span><span style="color:#ca7d37">import</span> lazyllm</span>
<span><span style="color:#ca7d37">from</span> typing <span style="color:#ca7d37">import</span> <span style="color:#0e9ce5">List</span></span>
<span><span style="color:#ca7d37">import</span> numpy <span style="color:#ca7d37">as</span> np</span>
<span><span style="color:#afafaf">@lazyllm.tools.rag.register_similarity(</span>
<span style="color:#afafaf"><span>mode=</span></span>
<span style="color:#afafaf"><span><span style="color:#dd1144">"embedding"</span></span></span>
<span style="color:#afafaf">)</span></span>
<span><span style="color:#ca7d37">def</span> <span style="color:#dd1144">euclidean_distance</span>(<span>query: </span>
<span><span style="color:#0e9ce5">List</span></span>
<span>[</span>
<span><span style="color:#ca7d37">float</span></span>
<span>], node: </span>
<span><span style="color:#0e9ce5">List</span></span>
<span>[</span>
<span><span style="color:#ca7d37">float</span></span>
<span>], **kwargs</span>) -> <span style="color:#ca7d37">float</span>:</span>
<span>    point1 = np.array(query)</span>
<span>    point2 = np.array(node)</span>
<span>    <span style="color:#ca7d37">return</span> np.linalg.norm(point1 - point2)</span>

然后我们使用前面的 html 文档进行测试，我们先定义一个 Retriever 对象：

<span><span style="color:#ca7d37">import</span> lazyllm</span>
<span><span style="color:#ca7d37">import</span> os</span>
<span><span style="color:#ca7d37">from</span> lazyllm <span style="color:#ca7d37">import</span> OnlineEmbeddingModule, SentenceSplitter, Retriever</span>
<span><span style="color:#ca7d37">from</span> lazyllm.tools.rag <span style="color:#ca7d37">import</span> Document</span>
<span>prompt = <span style="color:#dd1144">'You will play the role of an AI Q&A assistant and complete a dialogue task. In this task, you need to provide your answer based on the given context and question.'</span></span>
<span>documents = Document(dataset_path=os.path.join(os.getcwd(), <span style="color:#dd1144">"rag_data"</span>), embed=OnlineEmbeddingModule(source=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"embedding-2"</span>), manager=<span style="color:#0e9ce5">False</span>)</span>
<span>documents.create_node_group(name=<span style="color:#dd1144">"sentences"</span>, transform=SentenceSplitter, chunk_size=<span style="color:#0e9ce5">1024</span>, chunk_overlap=<span style="color:#0e9ce5">100</span>)</span>
<span>ppl = Retriever(documents, group_name=<span style="color:#dd1144">"CoarseChunk"</span>, similarity=<span style="color:#dd1144">"euclidean_distance"</span>, similarity_cut_off=<span style="color:#0e9ce5">0.003</span>, topk=<span style="color:#0e9ce5">3</span>)</span>
<span>nodes = ppl(<span style="color:#dd1144">"全国住房城乡建设工作会议的主要内容"</span>)</span>
<span><span style="color:#ca7d37">for</span> node <span style="color:#ca7d37">in</span> nodes:</span>
<span>    <span style="color:#ca7d37">print</span>(<span style="color:#dd1144">f"node: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{node.text}</span></span>
<span style="color:#dd1144">"</span>)</span>

这里我们在定义 Retriever 的时候把前面注册过的 similarity 函数名称传进去即可，然后我们选取 top 3的结果。

输出结果为：

<span>node: cn/release/blogv2/dist/pc/themesSkin/skin-1024/skin-1024-ecd36efea2.min.css"></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">src</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"https://g.csdnimg.cn/lib/jquery/1.12.4/jquery.min.js"</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">type</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"text/javascript"</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">src</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"https://g.csdnimg.cn/lib/jquery-migrate/1.4.1/jquery-migrate.js"</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">type</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"text/javascript"</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">type</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"text/javascript"</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        <span style="color:#ca7d37">var</span> isCorporate = <span style="color:#0e9ce5">false</span>;</span>
<span>        <span style="color:#ca7d37">var</span> username =  <span style="color:#dd1144">"star_nwe"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> skinImg = <span style="color:#dd1144">"white"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> blog_address = <span style="color:#dd1144">"https://blog.csdn.net/star_nwe"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> currentUserName = <span style="color:#dd1144">"wangjian052163"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> isOwner = <span style="color:#0e9ce5">false</span>;</span>
<span>        <span style="color:#ca7d37">var</span> loginUrl = <span style="color:#dd1144">"http://passport.csdn.net/account/login?from=https://blog.csdn.net/star_nwe/article/details/141174167"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> blogUrl = <span style="color:#dd1144">"https://blog.csdn.net/"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> avatar = <span style="color:#dd1144">"https://profile-avatar.csdnimg.cn/9d615c5dd45743bea262227ce1ce205e_star_nwe.jpg!1"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> articleTitle = <span style="color:#dd1144">"大模型入门到进阶：什么是 RAG？为什么需要 RAG？RAG 的流程"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> articleDesc = <span style="color:#dd1144">"文章浏览阅读5k次，点赞22次，收藏25次。学习AI大模型是一个系统的过程，需要从基础开始，逐步深入到更高级的技术。这里给大家精心整理了一份全面的AI大模型学习资源，包括：AI大模型全套学习路线图（从入门到实战）、精品AI大模型学习书籍手册、视频教程、实战学习等录播视频，免费分享！_大模型rag"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> articleTitles = <span style="color:#dd1144">"大模型入门到进阶：什么是 RAG？为什么需要 RAG？RAG 的流程_大模型rag-CSDN博客"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> nickName = <span style="color:#dd1144">"AI老猴子"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> articleDetailUrl = <span style="color:#dd1144">"https://blog.csdn.net/star_nwe/article/details/141174167"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> vipUrlV = <span style="color:#dd1144">"https://mall.csdn.net/vip?vipSource=learningVip"</span>;</span>
<span>        <span style="color:#ca7d37">if</span>(<span style="color:#0e9ce5">window</span>.<span>location</span>.<span>host</span>.<span style="color:#dd1144">split</span>(<span style="color:#dd1144">'.'</span>).<span>length</span> == <span style="color:#0e9ce5">3</span>) {</span>
<span>            blog_address = blogUrl + username;</span>
<span>        }</span>
<span>        <span style="color:#ca7d37">var</span> skinStatus = <span style="color:#dd1144">"White"</span>;</span>
<span>        <span style="color:#ca7d37">var</span> blogStaticHost = <span style="color:#dd1144">"https://csdnimg.cn/release/blogv2/"</span></span>
<span>          <span style="color:#ca7d37">var</span> payColumn = <span style="color:#0e9ce5">false</span></span>
<span>    <span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">meta</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">name</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"toolbar"</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">content</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">'{"type":"0","fixModel":"1"}'</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">src</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"https://g.csdnimg.cn/??fixed-sidebar/1.1.7/fixed-sidebar.js"</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">type</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"text/javascript"</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>      <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">src</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"https://cdn-static-devbit.csdn.net/ai100/chat/chat-search.js?v=2"</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">type</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"text/javascript"</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">src</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">'//g.csdnimg.cn/common/csdn-report/report.js'</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">type</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">'text/javascript'</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">link</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">rel</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"stylesheet"</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">type</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"text/css"</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">href</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"https://csdnimg.cn/public/sandalstrap/1.4/css/sandalstrap.min.css"</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">style</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        <span>.MathJax</span>, <span>.MathJax_Message</span>, <span>.MathJax_Preview</span>{</span>
<span>            <span style="color:#0e9ce5">display</span>: none</span>
<span>        }</span>
<span>    <span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">style</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">src</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"https://dup.baidustatic.com/js/ds.js"</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>      <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">type</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"text/javascript"</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        (<span style="color:#ca7d37">function</span>(<span>c,l,a,r,i,t,y</span>){</span>
<span>            c[a]=c[a]||<span style="color:#ca7d37">function</span>(){(c[a].<span>q</span>=c[a].<span>q</span>||[]).<span style="color:#dd1144">push</span>(<span style="color:#0e9ce5">arguments</span>)};</span>
<span>            t=l.<span style="color:#dd1144">createElement</span>(r);t.<span>async</span>=<span style="color:#0e9ce5">1</span>;t.<span>src</span>=<span style="color:#dd1144">"https://www.clarity.ms/tag/"</span>+i;</span>
<span>            y=l.<span style="color:#dd1144">getElementsByTagName</span>(r)[<span style="color:#0e9ce5">0</span>];y.<span>parentNode</span>.<span style="color:#dd1144">insertBefore</span>(t,y);</span>
<span>        })(<span style="color:#0e9ce5">window</span>, <span style="color:#0e9ce5">document</span>, <span style="color:#dd1144">"clarity"</span>, <span style="color:#dd1144">"script"</span>, <span style="color:#dd1144">"lgtpix6r85"</span>);</span>
<span>      <span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">src</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"/cdn_cgi_bs_captcha/static/js/waf_captcha_embedded_bs.</span></span></span>
<span>node: opener.location.href;</span>
<span>                    }</span>
<span>                } catch (e) {}</span>
<span>            }</span>
<span>            if (referrer) {</span>
<span>                $.post(web_root_url + "/<span>api</span>/<span>waplog</span>", {</span>
<span>                    <span>uuid:</span> $("#<span>uuid</span>")<span>.val</span>(),</span>
<span>                    <span>refer:</span> <span>referrer</span>,</span>
<span>                    <span>ua:</span> <span>navigator.userAgent</span></span>
<span>                }, <span>function</span>(<span>e</span>) {</span>
<span>                    //<span>console.log</span>('<span>waplog:</span>' + <span>e</span>);</span>
<span>                });</span>
<span>            } <span>else</span> {</span>
<span>                //$<span>.post</span>(<span>web_root_url</span> + "/<span>api</span>/<span>waplog</span>", {<span>uuid:</span>'<span>1234567890123</span>', <span>refer:</span>'<span>https:</span>//<span>www.baidu.com</span>', <span>ua:</span>'<span>user-agent</span>!'}, <span>function</span>(<span>e</span>){</span>
<span>                //<span>console.log</span>('<span>waplog:</span>' + <span>e</span>);</span>
<span>                //});</span>
<span>            }</span>
<span>            //<span>console.log</span>(<span>navigator.userAgent</span>);</span>
<span>            // <span>记录分享行为</span></span>
<span>            <span>var</span> <span>urlSearch</span> = <span style="color:#dd1144">window.location.href.substring(window.location.href.indexOf(</span>"?")+<span>1</span>) || "";</span>
<span>            <span>let</span> <span>shareuser</span> = <span style="color:#dd1144">""</span>;</span>
<span>            <span>let</span> <span>shareUrl</span> = <span style="color:#dd1144">""</span></span>
<span>            <span>let</span> <span>searchArr</span> = <span style="color:#dd1144">urlSearch.split(</span>"&")</span>
<span>            <span>searchArr.forEach</span>((<span>item</span>,<span>index</span>) => {</span>
<span>                <span style="color:#ca7d37">let</span> ab = item.<span style="color:#dd1144">split</span>(<span style="color:#dd1144">"="</span>);</span>
<span>                <span style="color:#ca7d37">if</span>(ab[<span style="color:#0e9ce5">0</span>] === <span style="color:#dd1144">'shareuser'</span>){</span>
<span>                    shareuser = ab[<span style="color:#0e9ce5">1</span>];</span>
<span>                    shareUrl = item;</span>
<span>                }</span>
<span>            })</span>
<span>            <em>// console.log('shareUrl1 ',shareUrl)</em></span>
<span>            <span style="color:#ca7d37">if</span>(shareuser){</span>
<span>                $.<span style="color:#dd1144">ajax</span>({</span>
<span>                    <span>type</span>: <span style="color:#dd1144">'GET'</span>,</span>
<span>                    <span>url</span>: web_root_url + <span style="color:#dd1144">'/japi/pv?article_uuid='</span> + uuid + <span style="color:#dd1144">'&user_uuid='</span> + shareuser,</span>
<span>                    <span>dataType</span>: <span style="color:#dd1144">'JSON'</span>,</span>
<span>                    <span>success</span>: <span style="color:#ca7d37">function</span>(<span>st</span>) {</span>
<span>                        <span style="color:#ca7d37">if</span> (st.<span>suc</span> == <span style="color:#0e9ce5">1</span> && st.<span>info</span> == <span style="color:#dd1144">'success'</span>) {</span>
<span>                        }<span style="color:#ca7d37">else</span>{</span>
<span>                        }</span>
<span>                    },</span>
<span>                    <span>error</span>: <span style="color:#ca7d37">function</span>() {}</span>
<span>                })</span>
<span>            }</span>
<span>        });</span>
<span>    <span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <em><!-- 与预览页公用 --></em></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">src</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"https://static.bjnews.com.cn/wap/js/article.js"</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">src</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"https://res.wx.qq.com/open/js/jweixin-1.6.0.js"</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <em><!--分享--></em></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">script</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">type</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"text/javascript"</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        $.ajax({</span>
<span>            type: "GET",</span>
<span>            url: "https://m.bjnews.com.cn/api/wxshare?app_id=wxbad48ed25d961bb9",</span>
<span>            // url: "https://m.bjnews.com.cn/api/wxshare?app_id=wxbad48ed25d961bb9",</span>
<span>            dataType: 'json',</span>
<span>            success: function(data) {</span>
<span>                var signpackage = data.signpackage;</span>
<span>                wx.config({</span>
<span>                    // debug: true,</span>
<span>                    appId: "wxbad48ed25d961bb9", // 必填，公众号的唯一标识</span>
<span>                    // appId: "wxbad48ed25d961bb9", // 必填，公众号的唯一标识</span>
<span>                    timestamp: signpackage.timestamp, // 必填，生成签名的时间戳</span>
<span>                    nonceStr: signpackage.nonceStr, // 必填，生成签名的随机串</span>
<span>                    signature: signpackage.signature, // 必填，签名，见附录1</span>
<span>                    jsApiList: [</span>
<span>                        'checkJsApi',</span>
<span>                        'onMenuShareTimeline',</span>
<span>                        'onMenuShareAppMessage'</span>
<span>                    ],</span>
<span>                    openTagList: ['wx-open-launch-app']</span>
<span>                });</span>
<span>            }</span>
<span>        });</span>
<span>        wx.ready(function() {</span>
<span>            var share_uuid = "1735095611129204";</span>
<span>            // 获取url分享后缀</span>
<span>            var wxurlSearch = window.location.href.substring(window.location.href.indexOf("?")+1) || "";</span>
<span>            let wxshareuser = "";//url 分享信息</span>
<span>            if(wxurlSearch){</span>
<span>                let wxsearchArr = wxurlSearch.split("&")</span>
<span>                wxsearchArr.forEach((item,index) => {</span>
<span>                    let ab = item.split("=");</span>
<span>                    if(ab[0] === 'shareuser'){</span>
<span>                        wxshareuser = item;</span>
<span>                    }</span>
<span>                })</span>
<span>            }</span>
<span>            // 处理分享链接</span>
<span>            var shareLinkUrl;</span>
<span>            shareLinkUrl = web_root_url + "/detail/" + share_uuid + ".html";</span>
<span>            if (wxshareuser) {</span>
<span>                shareLinkUrl = shareLinkUrl + "?"</span>
<span>node: <span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>llm<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>.<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>llm <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token keyword"</span></span>
<span style="color:#0e9ce5">></span>import<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> ChatSparkLLM<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>,<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> ChunkPrintHandler</span>
<span><span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token keyword"</span></span>
<span style="color:#0e9ce5">></span>from<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> sparkai<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>.<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>core<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>.<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>messages <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token keyword"</span></span>
<span style="color:#0e9ce5">></span>import<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> ChatMessage</span>
<span><span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token keyword"</span></span>
<span style="color:#0e9ce5">></span>from<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> dotenv <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token keyword"</span></span>
<span style="color:#0e9ce5">></span>import<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> load_dotenv</span>
<span>load_dotenv<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>(<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>)<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span><span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token keyword"</span></span>
<span style="color:#0e9ce5">></span>if<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> __name__ <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#39;</span>__main__<span style="color:#dd1144">&#39;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>:<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    spark <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> ChatSparkLLM<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>(<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        spark_api_url<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>os<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>.<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>environ<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>[<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#34;</span>SPARKAI_URL<span style="color:#dd1144">&#34;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>]<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>,<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        spark_app_id<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>os<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>.<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>environ<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>[<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#34;</span>SPARKAI_APP_ID<span style="color:#dd1144">&#34;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>]<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>,<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        spark_api_key<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>os<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>.<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>environ<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>[<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#34;</span>SPARKAI_API_KEY<span style="color:#dd1144">&#34;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>]<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>,<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        spark_api_secret<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>os<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>.<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>environ<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>[<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#34;</span>SPARKAI_API_SECRET<span style="color:#dd1144">&#34;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>]<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>,<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        spark_llm_domain<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>os<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>.<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>environ<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>[<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#34;</span>SPARKAI_DOMAIN<span style="color:#dd1144">&#34;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>]<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>,<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        streaming<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token boolean"</span></span>
<span style="color:#0e9ce5">></span>False<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>,<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>)<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    prompt <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string-interpolation"</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string"</span></span>
<span style="color:#0e9ce5">></span>f<span style="color:#dd1144">&#34;&#34;&#34;</span></span>
<span>              你是一个善于回答问题的助手。请使用以下提供的检索内容和自身知识来回答问题。如果你不知道答案<span style="color:#dd1144">&#xff0c;</span>请直接说不知道<span style="color:#dd1144">&#xff0c;</span>不要杜撰答案。请用三句话以内回答<span style="color:#dd1144">&#xff0c;</span>保持简洁。</span>
<span>              问题<span style="color:#dd1144">&#xff1a;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token interpolation"</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>{<em><!-- --></em>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>query<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>}<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string"</span></span>
<span style="color:#0e9ce5">></span></span>
<span>              检索内容<span style="color:#dd1144">&#xff1a;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token interpolation"</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>{<em><!-- --></em>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>retrieval_content<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>}<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string"</span></span>
<span style="color:#0e9ce5">></span></span>
<span>              <span style="color:#dd1144">&#34;&#34;&#34;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    messages <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>[<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span>ChatMessage<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>(<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        role <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token string"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#34;</span>user<span style="color:#dd1144">&#34;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>,<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>        content <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span>
<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span> prompt</span>
<span>    <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>)<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token punctuation"</span></span>
<span style="color:#0e9ce5">></span>]<span style="color:#0e9ce5"></</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5">></span></span>
<span>    handler <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">span</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">class</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">"token operator"</span></span>
<span style="color:#0e9ce5">></span>
<span style="color:#dd1144">&#61;</span></span>

可以从输出的结果中看到检索出来的文本和请求的query几乎完全没关系，因为我们使用的相似度计算方法是欧氏距离，距离越近说明文本越相似，由于是降序排列，所以 top3 对应的是距离最大的三段文本，那这三段文本和query不相似是符合预期的。

接下来我们在注册相似度计算函数是把 descend 设置为 False:

import lazyllm
from typing import List
import numpy as np
@lazyllm.tools.rag.register_similarity(mode="embedding", descend=False)
def euclidean_distance(query: List[float], node: List[float], **kwargs) -> float:
    point1 = np.array(query)
    point2 = np.array(node)
    return np.linalg.norm(point1 - point2)

然后我们再运行一次上面的检索代码，发现输出结果为：

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<span>    <<span style="color:#ca7d37">head</span>></span>
<span>    <title>全国住房城乡建设工作会议：大力推进商品住房销售制度改革 有力有序推行现房销售_发展</title></span>
<span><meta http-equiv=<span style="color:#dd1144">"Cache-Control"</span> content=<span style="color:#dd1144">"no-transform"</span> /></span>
<span><meta http-equiv=<span style="color:#dd1144">"Cache-Control"</span> content=<span style="color:#dd1144">"no-siteapp"</span> /></span>
<span><meta name=<span style="color:#dd1144">"copyright"</span> content=<span style="color:#dd1144">"Copyright © 2017 Sohu.com Inc.All Rights Reserved."</span>/></span>
<span><meta name=<span style="color:#dd1144">"mediaid"</span> content=<span style="color:#dd1144">"财联社"</span>/></span>
<span><meta property=<span style="color:#dd1144">"og:type"</span> content=<span style="color:#dd1144">"news"</span>/></span>
<span><meta property=<span style="color:#dd1144">"og:image"</span> content=<span style="color:#dd1144">""</span>/></span>
<span><meta property=<span style="color:#dd1144">"og:url"</span> content=<span style="color:#dd1144">"www.sohu.com/a/841580226_222256"</span>/></span>
<span><meta property=<span style="color:#dd1144">"og:release_date"</span> content=<span style="color:#dd1144">"2024-12-25 10:27"</span>/></span>
<span><meta itemprop=<span style="color:#dd1144">"dateUpdate"</span> content=<span style="color:#dd1144">"2024-12-25 10:27"</span> /></span>
<span><meta itemprop=<span style="color:#dd1144">"datePublished"</span> content=<span style="color:#dd1144">"2024-12-25 10:27"</span> /></span>
<span><<span style="color:#ca7d37">link</span> rel=<span style="color:#dd1144">"canonical"</span> href=<span style="color:#dd1144">"https://www.sohu.com/a/841580226_222256"</span>/></span>
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<span><meta name=<span style="color:#dd1144">"keywords"</span> content=<span style="color:#dd1144">"商品,现房,住房,制度,工作,改革,销售,会议,发展,大力,现房,住房,工作会议,房地产,财联社"</span> /></span>
<span><meta name=<span style="color:#dd1144">"description"</span> content=<span style="color:#dd1144">"财联社12月25日电，全国住房城乡建设工作会议24日至25日在北京召开。会议指出，2025年，推动构建房地产发展新模式。一是着力优化和完善住房供应体系，加快发展保障性住房，满足城镇住房困难工薪群体刚性住房需求…"</span> /></span>
<span><meta property=<span style="color:#dd1144">"og:description"</span> content=<span style="color:#dd1144">"财联社12月25日电，全国住房城乡建设工作会议24日至25日在北京召开。会议指出，2025年，推动构建房地产发展新模式。一是着力优化和完善住房供应体系，加快发展保障性住房，满足城镇住房困难工薪群体刚性住房需求…"</span>/></span>
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<span style="color:#dd1144"><span style="color:#0e9ce5">$mpNews</span></span>
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<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"http://sports.sohu.com/"</span>,</span>
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<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"http://business.sohu.com/"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"财经"</span>,</span>
<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"http://yule.sohu.com/"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"娱乐"</span>,</span>
<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"http://baobao.sohu.com/"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"母婴"</span>,</span>
<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"https://healthnews.sohu.com/"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"健康"</span>,</span>
<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"http://history.sohu.com/"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"历史"</span>,</span>
<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"http://mil.sohu.com/"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"军事"</span>,</span>
<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"http://chihe.sohu.com/"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"美食"</span>,</span>
<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"http://cul.sohu.com/"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"文化"</span>,</span>
<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"http://astro.sohu.com/"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"星座"</span>,</span>
<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"https://www.sohu.com/xchannel/TURBd01EQXhPVGt5"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"专题"</span>,</span>
<span>            }</span>
<span>                                    , {</span>
<span>                <span style="color:#dd1144">"url"</span>: <span style="color:#dd1144">"http://game.sohu.com/"</span>,</span>
<span>                <span style="color:#dd1144">"name"</span>: <span style="color:#dd1144">"游戏"</span>,</span>
<span>            }</span>
<span>                                    ,</span>
<span>node: 三是大力推进商品住房销售制度改革，有力有序推行现房销售，优化预售<span>资金监管</span>。四是加快建立房屋全生命周期安全管理制度，为房屋安全提供有力保障。五是完善房地产全过程监管，整治房地产市场秩序，切实维护群众合法权益。<a href=<span style="color:#dd1144">"//www.sohu.com/?strategyid=00001 "</span> target=<span style="color:#dd1144">"_blank"</span> title=<span style="color:#dd1144">"点击进入搜狐首页"</span> <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"backsohucom"</span> style=<span style="color:#dd1144">"white-space: nowrap;"</span>><span class=<span style="color:#dd1144">"backword"</span>><i class=<span style="color:#dd1144">"backsohu"</span>></i>返回搜狐，查看更多</span></a></p>                <!-- 政务账号添加来源标示处理 --></span>
<span>        <!-- 政务账号添加来源标示处理 --></span>
<span>        <p data-role=<span style="color:#dd1144">"editor-name"</span>>责任编辑：<span></span></p></span>
<span>    </article></span>
<span><div <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"articleTransfer"</span>><transfer/></div></span>
<span><!-- 评论禁言通知 --></span>
<span><div <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"bannedNotice"</span>><banned-notice/></div></span>
<span><div class=<span style="color:#dd1144">"statement"</span>>平台声明：该文观点仅代表作者本人，搜狐号系信息发布平台，搜狐仅提供信息存储空间服务。</div><div class=<span style="color:#dd1144">"bottom-relate-wrap clear type-3"</span>></span>
<span>            <div <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"article-like"</span> data-like-type=<span style="color:#dd1144">"type-3"</span>></span>
<span>            <article-like/></span>
<span>        </div></span>
<span>        <div class=<span style="color:#dd1144">"read-wrap"</span>></span>
<span>        <span class=<span style="color:#dd1144">"read-num"</span>>阅读 (<em data-role=<span style="color:#dd1144">"pv"</span> data-val=<span style="color:#dd1144">"</span>
<span style="color:#dd1144"><span style="color:#0e9ce5">$articleStat</span></span>
<span style="color:#dd1144">.pv"</span>></em>)</span></span>
<span>    </div></span>
<span>    <div <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"articleReport"</span>></span>
<span>        <report/></span>
<span>    </div></span>
<span></div></span>
<span>                            <div <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"sohu-play-content"</span>></div></span>
<span>                        </div></span>
<span>                    </div></span>
<span>                    <div data-spm=<span style="color:#dd1144">"middle-banner-ad"</span>></span>
<span>                                            </div></span>
<span>                                            <div <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"articleAllsee"</span> style=<span style="color:#dd1144">'height:629px'</span>><all-see-list/></div></span>
<span>                                        <div class=<span style="color:#dd1144">"_0u4o3bh76zbp"</span>></div></span>
<span>                    <div class=<span style="color:#dd1144">"god-article-bottom"</span> <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"god_bottom_banner"</span> data-spm=<span style="color:#dd1144">"ad-text-bottom"</span> style=<span style="color:#dd1144">"display:block"</span>></span>
<span></div></span>
<span><div class=<span style="color:#dd1144">"user-god clear"</span> <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"user-post"</span> style=<span style="color:#dd1144">"display:none"</span>></span>
<span></div>                    <!-- 评论 --></span>
<span>                    <div <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"meComment"</span> style=<span style="color:#dd1144">'min-height: 100px;'</span>></span>
<span>                        <me-comment/></span>
<span>                    </div></span>
<span>                    <div <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"commentList"</span>></span>
<span>                        <comment-list></comment-list></span>
<span>                    </div></span>
<span>                    <div <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"discuss"</span>></div></span>
<span>                    <!-- 推荐阅读 --></span>
<span>                                        <div style=<span style="color:#dd1144">'min-height:1500px'</span> <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"groomRead"</span>></span>
<span>                        <groom-read/></span>
<span>                    </div></span>
<span>                                    </div></span>
<span>                <!-- 右侧边栏 --></span>
<span>                <div class=<span style="color:#dd1144">"sidebar right"</span> <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"right-side-bar"</span> data-a=<span style="color:#dd1144">"</span>
<span style="color:#dd1144"><span style="color:#0e9ce5">${isBaiDuAd}</span></span>
<span style="color:#dd1144">"</span>></span>
<span>                    <right-side-bar/></span>
<span>                </div></span>
<span>             </div></span>
<span>            <div <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"float-btn"</span>> <float-btn/> </div></span>
<span>            <div class=<span style="color:#dd1144">"left-bottom-float-fullScreenSleepContainer"</span> style=<span style="color:#dd1144">"display:none;"</span>></span>
<span>    <div class=<span style="color:#dd1144">"left-bottom-float-fullScreenSleep"</span> style=<span style="color:#dd1144">"display:none;"</span> data-spm=<span style="color:#dd1144">"ad-fullScreenSleep"</span>></span>
<span>        <div class=<span style="color:#dd1144">"close-tag"</span>></div></span>
<span>    </div></span>
<span></div></span>
<span><div class=<span style="color:#dd1144">"left-bottom-float"</span> <span style="color:#ca7d37">id</span>=<span style="color:#dd1144">"left-bottom-god"</span> data-spm=<span style="color:#dd1144">"ad-ss"</span>></span>
<span></div>        </div></span>
<span>        <script <span style="color:#ca7d37">type</span>=<span style="color:#dd1144">"text/javascript"</span>></span>
<span>                        window.deployEnv = <span style="color:#dd1144">"prod"</span></span>
<span>                    </script></span>
<span>        <script src=<span style="color:#dd1144">"//js.sohu.com/pv.js"</span>></script><script src=<span style="color:#dd1144">"https://g1.itc.cn/msfe-pcarti-prod/300000000000/assets/js/vendors-d407b7.js"</span>></script><script src=<span style="color:#dd1144">"https://g1.itc.cn/msfe-pcarti-prod/300000000000/assets/js/main_article-64e941.js"</span>></script>        <script></span>
<span>try {</span>
<span>        var cfgs = {</span>
<span>        channel_id: <span style="color:#dd1144">"39"</span>,</span>
<span>        news_id: <span style="color:#dd1144">"841580226"</span>,</span>
<span>        cms_id: <span style="color:#dd1144">"</span>
<span style="color:#dd1144"><span style="color:#0e9ce5">$mpNews</span></span>
<span style="color:#dd1144">.cmsId"</span>,</span>
<span>        media_id: <span style="color:#dd1144">"222256"</span>,</span>
<span>        passport: <span style="color:#dd1144">"pj6PliNNPJ@virtual-author.com"</span>,</span>
<span>        weboUrl: <span style="color:#dd1144">"https://mp.sohu.</span></span>

这次返回的结果中可以看到都是和请求query相似度很高的文本了，符合预期。

4. 参数 batch

batch，布尔值，指示是否将所有的文档（Node）一次性给到相似度计算公式，默认值为 False。Batch主要是在需要计算均值、方差等统计特征时需要，而不是为了并行计算，LazyLLM 在 similarity 外面已经考虑了并行计算。

欧式距离计算函数

这里我们先以上面定义的欧式距离计算函数为例，把这个相似度计算函数注册一下，这里使用不同的 batch:

<span><span>func1</span> = lazyllm.tools.rag.register_similarity(euclidean_distance, mode=<span style="color:#dd1144">"embedding"</span>, batch = <span style="color:#0e9ce5">True</span>)</span>
<span><span>func2</span> = lazyllm.tools.rag.register_similarity(euclidean_distance, mode=<span style="color:#dd1144">"embedding"</span>)</span>

我们先计算 batch 设置为 True 的相似度计算函数:

<span>ret = func1(query_e, node_e)</span>
<span><span style="color:#ca7d37">print</span>(<span style="color:#dd1144">f"ret: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{ret}</span></span>
<span style="color:#dd1144">"</span>)</span>

输出结果报错了：

<span><span style="color:#ca7d37">TypeError</span>: <span style="color:#dd1144">'numpy.float64'</span> <span style="color:#ca7d37">object</span> is not <span style="color:#ca7d37">iterable</span></span>

我们再计算 batch 设为 False 的相似度计算函数:

<span>ret = func2(query_e, node_e)</span>
<span><span style="color:#ca7d37">print</span>(<span style="color:#dd1144">f"ret: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{ret}</span></span>
<span style="color:#dd1144">"</span>)</span>

输出结果为:

<span><span>ret</span>: {<span style="color:#dd1144">'key'</span>: [(<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">Node</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">id</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">07cce4d6-6d4f-466b-9895-b0d77f607dbd</span></span>
<span style="color:#0e9ce5">></span>, 4.045985664828782)]}</span>

因为该函数支持非批量计算，所及结果符合预期。

tfidf 相似度计算函数

接下来我们使用前面自定义的 tfidf_similarity 来看一下效果，也是先注册一下，这里使用不同的 batch:

<span><span>func3</span> = lazyllm.tools.rag.register_similarity(tfidf_similarity, mode=<span style="color:#dd1144">"text"</span>, batch=<span style="color:#0e9ce5">True</span>)</span>
<span><span>func4</span> = lazyllm.tools.rag.register_similarity(tfidf_similarity, mode=<span style="color:#dd1144">"text"</span>)</span>

我们先计算 batch 设置为 True 的相似度计算函数:

<span>ret = <span style="color:#ca7d37">func3</span>(<span style="color:#0e9ce5">query_t</span>, <span style="color:#0e9ce5">node_t</span>)</span>
<span><span style="color:#ca7d37">print</span>(f<span style="color:#dd1144">"ret: {ret}"</span>)</span>

输出结果为:

<span><span>ret</span>: [(<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">Node</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">id</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">4a04a93d-28b4-4dbc-83d5-91150f60a648</span></span>
<span style="color:#0e9ce5">></span>, 0.7031197927433698)]</span>

因为前面定义的 tfidf 相似度计算函数是支持批量计算的，所以结果符合预期。

我们再计算 batch 为 False 的相似度计算函数:

<span>ret = <span style="color:#ca7d37">func4</span>(<span style="color:#0e9ce5">query_t</span>, <span style="color:#0e9ce5">node_t</span>)</span>
<span><span style="color:#ca7d37">print</span>(f<span style="color:#dd1144">"ret: {ret}"</span>)</span>

输出结果报错了：

<span><span style="color:#ca7d37">TypeError</span>: <span style="color:#dd1144">'DocNode'</span> <span style="color:#ca7d37">object</span> is not <span style="color:#ca7d37">iterable</span></span>

因为该函数不支持非批量计算，所及结果符合预期。

使用自定义 Similarity 搭建 RAG 应用

我们先使用 LazyLLM 默认的 similarity 函数搭建一个 RAG 应用。这里我们还是使用前面用过的 html 数据为例进行演示:

<span><span style="color:#ca7d37">import</span> os</span>
<span><span style="color:#ca7d37">import</span> lazyllm</span>
<span><span style="color:#ca7d37">from</span> lazyllm <span style="color:#ca7d37">import</span> pipeline, bind, OnlineEmbeddingModule, SentenceSplitter, Retriever, Reranker</span>
<span><span style="color:#ca7d37">from</span> lazyllm.tools.rag <span style="color:#ca7d37">import</span> Document</span>
<span>prompt = <span style="color:#dd1144">'You will play the role of an AI Q&A assistant and complete a dialogue task. In this task, you need to provide your answer based on the given context and question.'</span></span>
<span>documents = Document(dataset_path=os.path.join(os.getcwd(), <span style="color:#dd1144">"rag_data"</span>), embed=OnlineEmbeddingModule(source=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"embedding-2"</span>), manager=<span style="color:#0e9ce5">False</span>)</span>
<span>documents.create_node_group(name=<span style="color:#dd1144">"sentences"</span>, transform=SentenceSplitter, chunk_size=<span style="color:#0e9ce5">1024</span>, chunk_overlap=<span style="color:#0e9ce5">100</span>)</span>
<span><span style="color:#ca7d37">with</span> pipeline() <span style="color:#ca7d37">as</span> ppl:</span>
<span>    ppl.prl = Retriever(documents, group_name=<span style="color:#dd1144">"CoarseChunk"</span>, similarity=<span style="color:#dd1144">"bm25_chinese"</span>, similarity_cut_off=<span style="color:#0e9ce5">0.003</span>, topk=<span style="color:#0e9ce5">3</span>)</span>
<span>    ppl.reranker = Reranker(<span style="color:#dd1144">"ModuleReranker"</span>, model=OnlineEmbeddingModule(<span style="color:#ca7d37">type</span>=<span style="color:#dd1144">"rerank"</span>, source=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"rerank"</span>), topk=<span style="color:#0e9ce5">1</span>, output_format=<span style="color:#dd1144">'content'</span>, join=<span style="color:#0e9ce5">True</span>) | bind(query=ppl.<span style="color:#ca7d37">input</span>)</span>
<span>    ppl.formatter = (<span style="color:#ca7d37">lambda</span> nodes, query: <span style="color:#ca7d37">dict</span>(context_str=nodes, query=query)) | bind(query=ppl.<span style="color:#ca7d37">input</span>)</span>
<span>    ppl.llm = lazyllm.OnlineChatModule(source=<span style="color:#dd1144">"glm"</span>, model=<span style="color:#dd1144">"glm-4"</span>, stream=<span style="color:#0e9ce5">False</span>).prompt(lazyllm.ChatPrompter(prompt, extra_keys=[<span style="color:#dd1144">"context_str"</span>]))</span>
<span><span style="color:#ca7d37">print</span>(ppl(<span style="color:#dd1144">"为什么需要RAG"</span>))</span>

输出结果为:

<span>需要RAG（检索增强生成）的原因在于大语言模型存在一些固有的局限性。这些局限性主要包括：</span>
<span><span style="color:#dd1144">1.</span> <strong>**时效性**</strong>：大语言模型的训练数据集是截至某个时间点的，这意味着之后发生的事件、新发现、新趋势或数据更新都不会包含在模型的知识库中。由于模型的训练需要庞大的计算资源和时间，频繁更新模型以包含最新信息是不现实的。</span>
<span><span style="color:#dd1144">2.</span> <strong>**覆盖性**</strong>：尽管大模型的训练数据集非常庞大，但仍可能无法涵盖所有领域的知识或特定领域的深入信息。例如，一些专业的医学、法律或技术问题可能只在特定文献中被详细讨论，而这些文献并未包含在模型的训练数据中。</span>
<span>当大模型面对其训练数据集并未覆盖的问题时，可能会产生不准确或误导性的答案，也就是所谓的“幻觉”。</span>
<span>为了解决这些问题，RAG通过以下流程来增强大语言模型的性能：</span>
<span><span style="color:#dd1144">-</span> <strong>**检索**</strong>：根据用户的查询内容，从外部知识库中检索相关信息。</span>
<span><span style="color:#dd1144">-</span> <strong>**增强**</strong>：将用户的查询内容和检索到的相关知识整合到一个提示词模板中。</span>
<span><span style="color:#dd1144">-</span> <strong>**生成**</strong>：将这个经过增强的提示词输入到大语言模型中，生成更准确、更有针对性的答案。</span>
<span>通过这样的方式，RAG能够弥补大语言模型的不足，减少误导性信息的产生，提高答案的可信度和准确性。</span>

接下来我们在这个 RAG 应用中修改为使用前面自定义的 tfidf_similarity 方法，因为前面 tfidf_similarity 函数已经注册过了，所以这里使用很简单，只需要在 Retriever 初始化的时候指定参数 similarity 为我们自定义的相似度计算函数名即可。代码如下:

<span><span style="color:#ca7d37">import</span> lazyllm</span>
<span><span style="color:#ca7d37">import</span> os</span>
<span><span style="color:#ca7d37">from</span> lazyllm <span style="color:#ca7d37">import</span> pipeline, bind, OnlineEmbeddingModule, SentenceSplitter, Retriever, Reranker</span>
<span><span style="color:#ca7d37">from</span> lazyllm.tools.rag <span style="color:#ca7d37">import</span> Document</span>
<span>prompt = <span style="color:#dd1144">'You will play the role of an AI Q&A assistant and complete a dialogue task. In this task, you need to provide your answer based on the given context and question.'</span></span>
<span>documents = Document(dataset_path=os.path.join(os.getcwd(), <span style="color:#dd1144">"rag_data"</span>), embed=OnlineEmbeddingModule(source=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"embedding-2"</span>), manager=<span style="color:#0e9ce5">False</span>)</span>
<span>documents.create_node_group(name=<span style="color:#dd1144">"sentences"</span>, transform=SentenceSplitter, chunk_size=<span style="color:#0e9ce5">1024</span>, chunk_overlap=<span style="color:#0e9ce5">100</span>)</span>
<span><span style="color:#ca7d37">with</span> pipeline() <span style="color:#ca7d37">as</span> ppl:</span>
<span>    ppl.prl = Retriever(documents, group_name=<span style="color:#dd1144">"CoarseChunk"</span>, similarity=<span style="color:#dd1144">"tfidf_similarity"</span>, similarity_cut_off=<span style="color:#0e9ce5">0.003</span>, topk=<span style="color:#0e9ce5">3</span>)</span>
<span>    ppl.reranker = Reranker(<span style="color:#dd1144">"ModuleReranker"</span>, model=OnlineEmbeddingModule(<span style="color:#ca7d37">type</span>=<span style="color:#dd1144">"rerank"</span>, source=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"rerank"</span>), topk=<span style="color:#0e9ce5">1</span>, output_format=<span style="color:#dd1144">'content'</span>, join=<span style="color:#0e9ce5">True</span>) | bind(query=ppl.<span style="color:#ca7d37">input</span>)</span>
<span>    ppl.formatter = (<span style="color:#ca7d37">lambda</span> nodes, query: <span style="color:#ca7d37">dict</span>(context_str=nodes, query=query)) | bind(query=ppl.<span style="color:#ca7d37">input</span>)</span>
<span>    ppl.llm = lazyllm.OnlineChatModule(source=<span style="color:#dd1144">"glm"</span>, model=<span style="color:#dd1144">"glm-4"</span>, stream=<span style="color:#0e9ce5">False</span>).prompt(lazyllm.ChatPrompter(prompt, extra_keys=[<span style="color:#dd1144">"context_str"</span>]))</span>
<span><span style="color:#ca7d37">print</span>(ppl(<span style="color:#dd1144">"为什么需要RAG"</span>))</span>

输出内容为:

<span>需要RAG（检索增强生成）的原因在于，大规模语言模型虽然能力强大，但在生成回答或文本时仍存在一些局限性。以下是几个具体的原因：</span>
<span><span style="color:#dd1144">1.</span> <strong>**知识更新限制**</strong>：大语言模型的知识是基于训练数据集的，这意味着它的知识是有限的，并且可能不会包含最新的信息。RAG通过检索外部知识库，可以引入最新的信息来增强生成的内容。</span>
<span><span style="color:#dd1144">2.</span> <strong>**上下文敏感性**</strong>：在没有额外信息的情况下，大模型可能无法针对特定上下文生成最精确的回答。RAG能够检索与当前上下文相关的信息，使得生成的文本更加准确和贴合。</span>
<span><span style="color:#dd1144">3.</span> <strong>**减少误导性信息**</strong>：大模型有时可能会生成不准确或误导性的信息。RAG通过整合检索到的准确信息，有助于减少这种误导性信息的产生。</span>
<span><span style="color:#dd1144">4.</span> <strong>**提升生成质量**</strong>：通过结合检索到的外部信息，RAG能够为大模型提供更丰富的上下文，从而优化生成结果，提高文本的质量和相关性。</span>
<span><span style="color:#dd1144">5.</span> <strong>**应对未知或稀疏数据**</strong>：对于某些特定领域或稀疏数据集，大模型可能缺乏足够的训练数据来生成高质量的输出。RAG通过检索相关领域的知识，可以帮助模型更好地处理这些情况。</span>
<span>综上所述，RAG旨在利用检索技术来弥补大语言模型的不足，提高生成文本的准确性和相关性，同时降低误导性信息的风险。</span>

因为 tfidf 算法不如 BM25 算法的效果好，所以最后输出的结果也不如上面的好，符合预期。

1. 查看Similarity分数

（完整GitHub代码链接：

https://github.com/LazyAGI/Tutorial/blob/7abc91dbb82a007a78731845dd8c360ac0cc1e75/rag/codes/chapter8/view_similarity_score.py #L1 ）

这里再补充一点，如何查看检索出来的 node 的 similarity 分数？我们其实可以通过 node 的 similarity_score 属性即可查看。这里我们还是以 tfidf_similarity 为例，代码如下：

<span><span style="color:#ca7d37">import</span> lazyllm</span>
<span><span style="color:#ca7d37">import</span> os</span>
<span><span style="color:#ca7d37">from</span> lazyllm <span style="color:#ca7d37">import</span> OnlineEmbeddingModule, SentenceSplitter, Retriever</span>
<span><span style="color:#ca7d37">from</span> lazyllm.tools.rag <span style="color:#ca7d37">import</span> Document</span>
<span>prompt = <span style="color:#dd1144">'You will play the role of an AI Q&A assistant and complete a dialogue task. In this task, you need to provide your answer based on the given context and question.'</span></span>
<span>documents = Document(dataset_path=os.path.join(os.getcwd(), <span style="color:#dd1144">"rag_data"</span>), embed=OnlineEmbeddingModule(source=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"embedding-2"</span>), manager=<span style="color:#0e9ce5">False</span>)</span>
<span>documents.create_node_group(name=<span style="color:#dd1144">"sentences"</span>, transform=SentenceSplitter, chunk_size=<span style="color:#0e9ce5">1024</span>, chunk_overlap=<span style="color:#0e9ce5">100</span>)</span>
<span>ppl = Retriever(documents, group_name=<span style="color:#dd1144">"CoarseChunk"</span>, similarity=<span style="color:#dd1144">"tfidf_similarity"</span>, similarity_cut_off=<span style="color:#0e9ce5">0.003</span>, topk=<span style="color:#0e9ce5">3</span>)</span>
<span>nodes = ppl(<span style="color:#dd1144">"全国住房城乡建设工作会议的主要内容"</span>)</span>
<span><span style="color:#ca7d37">print</span>(<span style="color:#dd1144">f"nodes: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{nodes}</span></span>
<span style="color:#dd1144">"</span>)</span>
<span><span style="color:#ca7d37">for</span> node <span style="color:#ca7d37">in</span> nodes:</span>
<span>    <span style="color:#ca7d37">print</span>(<span style="color:#dd1144">f"node: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{node.similarity_score}</span></span>
<span style="color:#dd1144">"</span>)</span>

输出结果为:

<span><span style="color:#0e9ce5">nodes</span>:<span style="color:#afafaf"> [<Node id=8076e2ef-61c5-46b4-b622-91e9fddf1294>, <Node id=c68319a3-8f34-4cd4-948b-2cb4de22cf0c>, <Node id=7e6c3032-c9b8-4929-bce4-8fbc42417df2>]</span></span>
<span><span style="color:#0e9ce5">node</span>: <span style="color:#0e9ce5">0</span>.<span style="color:#0e9ce5">24727356514169233</span></span>
<span><span style="color:#0e9ce5">node</span>: <span style="color:#0e9ce5">0</span>.<span style="color:#0e9ce5">20530975832300122</span></span>
<span><span style="color:#0e9ce5">node</span>: <span style="color:#0e9ce5">0</span>.<span style="color:#0e9ce5">0970966448783235</span></span>

可以看到检索到的 node 有三个，最高分是0.247，另两个是0.205和0.097。注意：Retriever 返回的节点是经过去重的，所以节点的相似度分数不一定是有序的。我们怎么能把分数比较低的节点过滤掉呢？这就需要设置 Retriever 中的 similarity_cut_off 参数，这个参数的作用就是过滤掉相似度分数低于此阈值的节点。这次我们把 Retriever 中的 similarity_cut_off 设为0.21，再看一下效果:

<span><span style="color:#0e9ce5">nodes</span>:<span style="color:#afafaf"> [<Node id=f2c8f5eb-4250-4aa1-ba32-81f0fa4c1017>]</span></span>
<span><span style="color:#0e9ce5">node</span>: <span style="color:#0e9ce5">0</span>.<span style="color:#0e9ce5">24727356514169233</span></span>

可以看到这次输出只有一个节点了，说明 similarity_cut_off 参数配置生效了。

【补充】在上面 RAG 的代码中，我们可以看见 Reranker 实例化时传入了 output_format 和 join 这两个参数，它们是做什么用的呢？其实它们不光是可以在 Reranker里面设置，还可以在 Retriever里设置。它们是用来做后处理的。

2. Retriever参数详解

（完整GitHub代码链接：

https://github.com/LazyAGI/Tutorial/blob/7abc91dbb82a007a78731845dd8c360ac0cc1e75/rag/codes/chapter8/reranker_parameters_explanation.py#L1）

我们先来看 Retriever 里面的这两个参数的取值。output_format 的有效取值是 content 、dict 和 None，content 表示 Retriever 检索输出的是 Node 的内容，即 str 类型。dict 表示 Retriever 检索输出的是字典类型，即把 Node 中的内容转换成字典进行输出。None 表示不做任何后处理，直接以 Node 类型输出。join 的有效取值是布尔值、字符串值。当 join 为 False 时，只会对 output_format 为 content 有影响，即不对输出内容进行拼接，是以 List[str] 格式进行输出。当 join 为 True 时，会给 join 赋值空字符串。当 join 为字符串时同时 output_format 为 content，则使用 join 对 nodes中的文本进行拼接输出，即以 str 形式输出。

下面我们用代码演示一下。我们以上面的代码为例进行说明，并使用 LazyLLM 提供的 bm25_chinese相似度计算方法，然后使用国学的数据集 （数据集下载方式：https://huggingface.co/datasets/LazyAGI/Chinese_Classics_Articles/tree/main） 。

<span><span style="color:#ca7d37">from</span> lazyllm <span style="color:#ca7d37">import</span> OnlineEmbeddingModule, SentenceSplitter, Retriever</span>
<span><span style="color:#ca7d37">from</span> lazyllm.tools.rag <span style="color:#ca7d37">import</span> Document</span>
<span>prompt = <span style="color:#dd1144">'You will play the role of an AI Q&A assistant and complete a dialogue task. In this task, you need to provide your answer based on the given context and question.'</span></span>
<span>documents = Document(dataset_path=<span style="color:#dd1144">"rag_master"</span>, embed=OnlineEmbeddingModule(source=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"embedding-2"</span>), manager=<span style="color:#0e9ce5">False</span>)</span>
<span>documents.create_node_group(name=<span style="color:#dd1144">"sentences"</span>, transform=SentenceSplitter, chunk_size=<span style="color:#0e9ce5">1024</span>, chunk_overlap=<span style="color:#0e9ce5">100</span>)</span>
<span>ppl = Retriever(documents, group_name=<span style="color:#dd1144">"sentences"</span>, similarity=<span style="color:#dd1144">"cosine"</span>, similarity_cut_off=<span style="color:#0e9ce5">0.003</span>, topk=<span style="color:#0e9ce5">3</span>)</span>
<span>nodes = ppl(<span style="color:#dd1144">"何为天道"</span>)</span>
<span><span style="color:#ca7d37">print</span>(<span style="color:#dd1144">f"nodes: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{nodes}</span></span>
<span style="color:#dd1144">"</span>)</span>

结果输出为:

<span>nodes: [<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">Node</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">id</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">e25dbd6c-fb37-4181-88e5-4dc734afe2bf</span></span>
<span style="color:#0e9ce5">></span>, <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">Node</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">id</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">3c92362f-f553-4325-9eb9-080d4ceb8757</span></span>
<span style="color:#0e9ce5">></span>, <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">Node</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">id</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">e863a9b9-9962-4592-9c31-62bbab55052f</span></span>
<span style="color:#0e9ce5">></span>]</span>

可以看到什么都不设置的话，检索出来的结果是以 Node 形式输出的。

下面我们把 output_format 设置为 dict:

<span><span style="color:#0e9ce5">ppl</span> = Retriever(documents, group_name=<span style="color:#dd1144">"sentences"</span>, similarity=<span style="color:#dd1144">"cosine"</span>, similarity_cut_off=<span style="color:#0e9ce5">0</span>.<span style="color:#0e9ce5">003</span>, topk=<span style="color:#0e9ce5">3</span>, output_format=<span style="color:#dd1144">"dict"</span>)</span>

运行上面的代码，结果输出为：

<span><span>nodes</span>: [{<span style="color:#dd1144">'content'</span>: <span style="color:#dd1144">'第一章\n道可道，非常道。\n名可名，非常名。\n无，名天地之始；有，名万物之母。\n故常无，欲以观其妙；常有，欲以观其徼。\n此两者同出而异名，同谓之玄，玄之又玄，众妙之门。\n第二章\n天下皆知美之为美，斯恶已；\n皆知善之为善，斯不善已。\n故有无相生，难易相成，长短相形，高下相盈，音声相和，前后相随，恒也。\n是以圣人处无为之事，行不言之教。\n万物作而弗始，生而不有，为而不恃，功成而弗居。\n夫唯弗居，是以不去。\n第三章\n不尚贤，使民不争。\n不贵难得之货，使民不为盗。\n不见可欲，使民心不乱。\n是以圣人之治：\n虚其心，实其腹；弱其志，强其骨。\n常使民无知、无欲，使夫知者不敢为也。\n为无为，则无不治。\n第四章\n道冲而用之或不盈。\n渊兮似万物之宗，湛兮似若存。\n吾不知谁之子，象帝之先。\n第五章\n天地不仁，以万物为刍狗。\n圣人不仁，以百姓为刍狗。\n天地之间，其犹橐龠乎？\n虚而不屈，动而愈出。\n多言数穷，不如守中。\n第六章\n谷神不死，是谓玄牝。\n玄牝之门，是谓天地根。\n绵绵若存，用之不勤。\n第七章\n天长地久。\n天地所以能长且久者，以其不自生，故能长生。\n是以圣人后其身而身先，外其身而身存。\n非以其无私邪！\n故能成其私。\n第八章\n上善若水。\n水善利万物而不争，\n处众人之所恶，\n故几于道。\n居善地，心善渊，与善仁，言善信，政善治，事善能，动善时。\n夫唯不争，故无尤。\n第九章\n持而盈之，不如其已；揣而锐之，不可长保。\n金玉满堂，莫之能守；富贵而骄，自遗其咎。\n功遂身退，天之道也。\n第十章\n载营魄抱一，能无离乎。\n专气致柔，能如婴儿乎。\n涤除玄览，能无疵乎。\n爱民治国，能无为乎。\n天门开阖，能为雌乎。\n明白四达，能无知乎。\n第十一章\n三十辐共一毂，当其无，有车之用。\n埏埴以为器，当其无，有器之用。\n凿户牖以为室，当其无，有室之用。\n故有之以为利，无之以为用。'</span>, <span style="color:#dd1144">'embedding'</span>: {<span style="color:#dd1144">'__default__'</span>: [<span style="color:#0e9ce5">0.02805577</span>, <span style="color:#0e9ce5">0.023282783</span>, -<span style="color:#0e9ce5">0.010409373</span>, <span style="color:#0e9ce5">0.053345673</span>, <span style="color:#0e9ce5">0.054548774</span>, -<span style="color:#0e9ce5">0.021444315</span>, -<span style="color:#0e9ce5">0.03477331</span>, <span style="color:#0e9ce5">0.009788121</span>, -<span style="color:#0e9ce5">0.00026711644</span>, -<span style="color:#0e9ce5">0.019736782</span>, <span style="color:#0e9ce5">0.011505052</span>, -<span style="color:#0e9ce5">0.0037637316</span>, -<span style="color:#0e9ce5">0.005963491</span>, <span style="color:#0e9ce5">0.041383855</span>, <span style="color:#0e9ce5">0.040610127</span>, <span style="color:#0e9ce5">0.000985921</span>, -<span style="color:#0e9ce5">0.0166217</span>, <span style="color:#0e9ce5">0.025156088</span>, <span style="color:#0e9ce5">0.0017116133</span>, -<span style="color:#0e9ce5">0.004511433</span>, -<span style="color:#0e9ce5">0.026657365</span>, <span style="color:#0e9ce5">0.027576007</span>, <span style="color:#0e9ce5">0.01825779</span>, -<span style="color:#0e9ce5">0.019136382</span>, <span style="color:#0e9ce5">0.011662057</span>, <span style="color:#0e9ce5">0.009423636</span>, <span style="color:#0e9ce5">0.0024921207</span>, <span style="color:#0e9ce5">0.0025204297</span>, <span style="color:#0e9ce5">0.0037775566</span>, -<span style="color:#0e9ce5">0.022618348</span>, <span style="color:#0e9ce5">0.0013726661</span>, -<span style="color:#0e9ce5">0.035238307</span>, -<span style="color:#0e9ce5">0.013865939</span>, <span style="color:#0e9ce5">0.001558638</span>, <span style="color:#0e9ce5">0.022386922</span>, <span style="color:#0e9ce5">0.008083175</span>, <span style="color:#0e9ce5">0.07320647</span>, <span style="color:#0e9ce5">0.08896838</span>, -<span style="color:#0e9ce5">0.0036581368</span>, -<span 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style="color:#0e9ce5">0.012868148</span>, <span style="color:#0e9ce5">0.052692164</span>, <span style="color:#0e9ce5">0.005785138</span>, -<span style="color:#0e9ce5">0.005816314</span>, -<span style="color:#0e9ce5">0.017770205</span>, <span style="color:#0e9ce5">0.052668773</span>, -<span style="color:#0e9ce5">0.011497479</span>, -<span style="color:#0e9ce5">0.0042478456</span>, -<span style="color:#0e9ce5">0.041163344</span>, <span style="color:#0e9ce5">0.017727418</span>, <span style="color:#0e9ce5">0.019524269</span>, <span style="color:#0e9ce5">0.0028672623</span>, <span style="color:#0e9ce5">0.027206969</span>, <span style="color:#0e9ce5">0.0098760445</span>, -<span style="color:#0e9ce5">0.01839258</span>, <span style="color:#0e9ce5">0.064081036</span>, -<span style="color:#0e9ce5">0.018610574</span>, -<span style="color:#0e9ce5">0.03167949</span>, <span style="color:#0e9ce5">0.028112203</span>, <span style="color:#0e9ce5">0.038009446</span>, <span 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style="color:#0e9ce5">0.04184604</span>, -<span style="color:#0e9ce5">0.0027126037</span>, <span style="color:#0e9ce5">0.022172572</span>, -<span style="color:#0e9ce5">0.0019665314</span>, -<span style="color:#0e9ce5">0.020220164</span>, -<span style="color:#0e9ce5">0.0055029565</span>, <span style="color:#0e9ce5">0.059257552</span>, -<span style="color:#0e9ce5">0.038126208</span>, -<span style="color:#0e9ce5">0.03557876</span>, <span style="color:#0e9ce5">0.01125294</span>, <span style="color:#0e9ce5">0.024858668</span>, -<span style="color:#0e9ce5">0.0056422777</span>, <span style="color:#0e9ce5">0.0049045635</span>, <span style="color:#0e9ce5">0.011553989</span>, <span style="color:#0e9ce5">0.026200697</span>, -<span style="color:#0e9ce5">1.432319e-27</span>, -<span style="color:#0e9ce5">0.0010860946</span>, <span style="color:#0e9ce5">0.09268514</span>, <span style="color:#0e9ce5">0.06090386</span>, -<span style="color:#0e9ce5">0.005796361</span>, <span style="color:#0e9ce5">0.025179828</span>, <span style="color:#0e9ce5">0.06804877</span>, -<span style="color:#0e9ce5">0.050707188</span>, <span style="color:#0e9ce5">0.02777679</span>, -<span style="color:#0e9ce5">0.031054588</span>, -<span style="color:#0e9ce5">0.019260377</span>, -<span style="color:#0e9ce5">0.04869623</span>, <span style="color:#0e9ce5">0.04939678</span>, -<span style="color:#0e9ce5">0.024753766</span>, -<span style="color:#0e9ce5">0.0072507886</span>, <span style="color:#0e9ce5">0.06201284</span>, <span style="color:#0e9ce5">0.038341094</span>, -<span style="color:#0e9ce5">0.023940781</span>, -<span style="color:#0e9ce5">0.0044804853</span>, <span style="color:#0e9ce5">0.0602865</span>, <span style="color:#0e9ce5">0.012955638</span>, <span style="color:#0e9ce5">0.041328467</span>, <span style="color:#0e9ce5">0.012215266</span>, <span style="color:#0e9ce5">0.041857783</span>, <span style="color:#0e9ce5">0.05578706</span>, -<span style="color:#0e9ce5">0.04423121</span>, -<span style="color:#0e9ce5">0.030089064</span>, <span style="color:#0e9ce5">0.039828192</span>, -<span style="color:#0e9ce5">0.025488378</span>, <span style="color:#0e9ce5">0.044566587</span>, <span style="color:#0e9ce5">0.01636707</span>, <span style="color:#0e9ce5">0.05779351</span>, -<span style="color:#0e9ce5">0.029481513</span>, <span style="color:#0e9ce5">0.0053500645</span>, <span style="color:#0e9ce5">0.051323455</span>, <span style="color:#0e9ce5">0.03458597</span>, <span style="color:#0e9ce5">0.013696377</span>, -<span style="color:#0e9ce5">0.035192043</span>, <span style="color:#0e9ce5">0.04330355</span>, <span style="color:#0e9ce5">0.058219656</span>, <span style="color:#0e9ce5">0.049582005</span>, -<span style="color:#0e9ce5">0.02016202</span>, -<span style="color:#0e9ce5">0.036638554</span>, <span style="color:#0e9ce5">0.020224864</span>, -<span style="color:#0e9ce5">0.039496087</span>, -<span style="color:#0e9ce5">0.015062504</span>, <span style="color:#0e9ce5">0.0016461068</span>, <span style="color:#0e9ce5">0.015035624</span>, -<span style="color:#0e9ce5">0.03048053</span>, -<span style="color:#0e9ce5">0.027442321</span>, -<span style="color:#0e9ce5">0.019795708</span>, <span style="color:#0e9ce5">0.061282612</span>, -<span style="color:#0e9ce5">0.0035766829</span>, <span style="color:#0e9ce5">0.049931448</span>, -<span style="color:#0e9ce5">0.044324502</span>, -<span style="color:#0e9ce5">0.034937955</span>, -<span style="color:#0e9ce5">0.055052124</span>, -<span style="color:#0e9ce5">0.002334567</span>, <span style="color:#0e9ce5">0.0011809984</span>, -<span style="color:#0e9ce5">0.0005358032</span>, <span style="color:#0e9ce5">0.014579384</span>, -<span style="color:#0e9ce5">0.006727375</span>, <span style="color:#0e9ce5">0.01961919</span>, -<span style="color:#0e9ce5">0.024230124</span>, <span style="color:#0e9ce5">0.022260979</span>, <span style="color:#0e9ce5">0.041638635</span>, <span style="color:#0e9ce5">0.015936956</span>, -<span style="color:#0e9ce5">0.0129846865</span>, -<span style="color:#0e9ce5">0.028698431</span>, -<span style="color:#0e9ce5">0.023311313</span>, <span style="color:#0e9ce5">0.020681119</span>, -<span style="color:#0e9ce5">0.022004813</span>, <span style="color:#0e9ce5">0.0045342017</span>, <span style="color:#0e9ce5">0.10454673</span>, -<span style="color:#0e9ce5">0.00907232</span>, <span style="color:#0e9ce5">0.018019272</span>, <span style="color:#0e9ce5">0.027693322</span>, -<span style="color:#0e9ce5">0.045678418</span>, <span style="color:#0e9ce5">0.008820441</span>, -<span style="color:#0e9ce5">0.018247915</span>, <span style="color:#0e9ce5">0.0022897322</span>, -<span style="color:#0e9ce5">0.0045168954</span>, <span style="color:#0e9ce5">0.0330645</span>, -<span style="color:#0e9ce5">0.046881303</span>, -<span style="color:#0e9ce5">0.02397729</span>, -<span style="color:#0e9ce5">0.03653085</span>, <span style="color:#0e9ce5">0.03674319</span>, -<span style="color:#0e9ce5">0.027837344</span>, -<span style="color:#0e9ce5">0.02493074</span>, -<span style="color:#0e9ce5">0.025111457</span>, -<span style="color:#0e9ce5">0.02385346</span>, -<span style="color:#0e9ce5">0.026238898</span>, <span style="color:#0e9ce5">0.019991593</span>, -<span style="color:#0e9ce5">0.00067811715</span>, -<span style="color:#0e9ce5">0.014781632</span>, <span style="color:#0e9ce5">0.024634153</span>, -<span style="color:#0e9ce5">0.016004443</span>, -<span style="color:#0e9ce5">0.0052194847</span>, <span style="color:#0e9ce5">0.024627017</span>, <span style="color:#0e9ce5">0.0032142564</span>, <span style="color:#0e9ce5">0.013624659</span>, -<span style="color:#0e9ce5">5.867911e-24</span>, -<span style="color:#0e9ce5">9.8801356e-05</span>, <span style="color:#0e9ce5">0.07649344</span>, <span style="color:#0e9ce5">0.02002069</span>, <span style="color:#0e9ce5">0.029181154</span>, <span style="color:#0e9ce5">0.03094678</span>, <span style="color:#0e9ce5">0.021331448</span>, <span style="color:#0e9ce5">0.029769164</span>, -<span style="color:#0e9ce5">0.01106459</span>, <span style="color:#0e9ce5">0.035867818</span>, <span style="color:#0e9ce5">0.0059047374</span>, <span style="color:#0e9ce5">0.044848107</span>, -<span style="color:#0e9ce5">0.034437712</span>, <span style="color:#0e9ce5">0.056731243</span>, <span style="color:#0e9ce5">0.0032158946</span>, <span style="color:#0e9ce5">0.018998314</span>, -<span style="color:#0e9ce5">0.070217706</span>, -<span style="color:#0e9ce5">0.029742291</span>, <span style="color:#0e9ce5">0.034644052</span>, <span style="color:#0e9ce5">0.024905458</span>]}, <span style="color:#dd1144">'metadata'</span>: {}}]</span>

结果是以 dict 的形式输出的，之前没配置时是以 Node 的形式输出的。这个时候对 join 要么不设置，要么设置为 False对结果没有影响，不能设置为 True 或字符串否则会报错。

<span><span>ppl</span> = Retriever(documents, group_name=<span style="color:#dd1144">"sentences"</span>, similarity=<span style="color:#dd1144">"cosine"</span>, similarity_cut_<span style="color:#0e9ce5">off</span>=<span style="color:#0e9ce5">0.003</span>, topk=<span style="color:#0e9ce5">3</span>, output_format=<span style="color:#dd1144">"dict"</span>, join=<span style="color:#0e9ce5">True</span>)</span>

结果如下所示:

<span><span style="color:#0e9ce5">AssertionError</span>
<span>: </span>Only content output can be joined</span>

然后当我们把 output_format 设置为 content:

<span><span style="color:#0e9ce5">ppl</span> = Retriever(documents, group_name=<span style="color:#dd1144">"sentences"</span>, similarity=<span style="color:#dd1144">"cosine"</span>, similarity_cut_off=<span style="color:#0e9ce5">0</span>.<span style="color:#0e9ce5">003</span>, topk=<span style="color:#0e9ce5">3</span>, output_format=<span style="color:#dd1144">"content"</span>)</span>

运行上面的代码，结果输出如下:

<span><span>nodes</span>: [<span style="color:#dd1144">'观天之道，执天之行，尽矣。\n故天有五贼，见之者昌。\n五贼在心，施行于天。\n宇宙在乎手，万化生乎身。\n天性人也，人心机也。立天之道，以定人也。\n天发杀机，移星易宿；地发杀机，龙蛇起陆；人发杀机，天地反覆；天人合发，万化定基。\n性有巧拙，可以伏藏。九窍之邪，在乎三要，可以动静。\n火生于木，祸发必克；奸生于国，时动必溃。知之修炼，谓之圣人。\n天生天杀，道之理也。天地万物之盗，万物人之盗，人万物之盗。三盗既宜，三才既安。\n故曰食其时，百骸理；动其机，万化安。人知其神之神，不知不神之所以神也。\n日月有数，大小有定，圣功生焉，神明出焉。\n其盗机也，天下莫能见，莫能知。君子得之固躬，小人得之轻命。\n瞽者善听，聋者善视。绝利一源，用师十倍。三返昼夜，用师万倍。\n心生于物，死于物，机在目。\n天之无恩而大恩生。迅雷烈风莫不蠢然。\n至乐性余，至静性廉。天之至私，用之至公。\n禽之制在气。生者死之根，死者生之根。恩生于害，害生于恩。\n愚人以天地文理圣，我以时物文理哲。'</span>, <span style="color:#dd1144">'天命之谓性，率性之谓道，修道之谓教。\n道也者，不可须臾（yú）离也；可离，非道也。是故君子戒慎乎其所不睹，恐惧乎其所不闻。莫见（xian）乎隐，莫显乎微，故君子慎其独也。\n喜怒哀乐之未发，谓之中；发而皆中节，谓之和。中也者，天下之大本也；和也者，天下之达道也。致中和，天地位焉，万物育焉。\n\n仲尼曰：“君子中庸，小人反中庸。君子之中庸也，君子而时中；小人之反中庸也，小人而无忌惮也。”\n\n子曰：“中庸其至矣乎！民鲜能久矣！”\n\n子曰：“道之不行也，我知之矣：知者过之，愚者不及也。道之不明也，我知之矣：贤者过之，不肖者不及也。人莫不饮食也，鲜能知味也。”'</span>, <span style="color:#dd1144">'第一章\n道可道，非常道。\n名可名，非常名。\n无，名天地之始；有，名万物之母。\n故常无，欲以观其妙；常有，欲以观其徼。\n此两者同出而异名，同谓之玄，玄之又玄，众妙之门。\n第二章\n天下皆知美之为美，斯恶已；\n皆知善之为善，斯不善已。\n故有无相生，难易相成，长短相形，高下相盈，音声相和，前后相随，恒也。\n是以圣人处无为之事，行不言之教。\n万物作而弗始，生而不有，为而不恃，功成而弗居。\n夫唯弗居，是以不去。\n第三章\n不尚贤，使民不争。\n不贵难得之货，使民不为盗。\n不见可欲，使民心不乱。\n是以圣人之治：\n虚其心，实其腹；弱其志，强其骨。\n常使民无知、无欲，使夫知者不敢为也。\n为无为，则无不治。\n第四章\n道冲而用之或不盈。\n渊兮似万物之宗，湛兮似若存。\n吾不知谁之子，象帝之先。\n第五章\n天地不仁，以万物为刍狗。\n圣人不仁，以百姓为刍狗。\n天地之间，其犹橐龠乎？\n虚而不屈，动而愈出。\n多言数穷，不如守中。\n第六章\n谷神不死，是谓玄牝。\n玄牝之门，是谓天地根。\n绵绵若存，用之不勤。\n第七章\n天长地久。\n天地所以能长且久者，以其不自生，故能长生。\n是以圣人后其身而身先，外其身而身存。\n非以其无私邪！\n故能成其私。\n第八章\n上善若水。\n水善利万物而不争，\n处众人之所恶，\n故几于道。\n居善地，心善渊，与善仁，言善信，政善治，事善能，动善时。\n夫唯不争，故无尤。\n第九章\n持而盈之，不如其已；揣而锐之，不可长保。\n金玉满堂，莫之能守；富贵而骄，自遗其咎。\n功遂身退，天之道也。\n第十章\n载营魄抱一，能无离乎。\n专气致柔，能如婴儿乎。\n涤除玄览，能无疵乎。\n爱民治国，能无为乎。\n天门开阖，能为雌乎。\n明白四达，能无知乎。\n第十一章\n三十辐共一毂，当其无，有车之用。\n埏埴以为器，当其无，有器之用。\n凿户牖以为室，当其无，有室之用。\n故有之以为利，无之以为用。'</span>]</span>

因为没有配置 join，它的默认值是 False，所以是以 List[str]格式输出的。

这时候如果把 join设置为 True呢？

<span><span>ppl</span> = Retriever(documents, group_name=<span style="color:#dd1144">"sentences"</span>, similarity=<span style="color:#dd1144">"cosine"</span>, similarity_cut_<span style="color:#0e9ce5">off</span>=<span style="color:#0e9ce5">0.003</span>, topk=<span style="color:#0e9ce5">3</span>, output_format=<span style="color:#dd1144">"content"</span>, join=<span style="color:#0e9ce5">True</span>)</span>

结果输出如下：

<span><span style="color:#dd1144">nodes: 天命之谓性，率性之谓道，修道之谓教。</span></span>
<span>道也者，不可须臾（yú）离也；可离，非道也。是故君子戒慎乎其所不睹，恐惧乎其所不闻。莫见（xian）乎隐，莫显乎微，故君子慎其独也。</span>
<span>喜怒哀乐之未发，谓之中；发而皆中节，谓之和。中也者，天下之大本也；和也者，天下之达道也。致中和，天地位焉，万物育焉。</span>
<span>仲尼曰：“君子中庸，小人反中庸。君子之中庸也，君子而时中；小人之反中庸也，小人而无忌惮也。”</span>
<span>子曰：“中庸其至矣乎！民鲜能久矣！”</span>
<span>子曰：“道之不行也，我知之矣：知者过之，愚者不及也。道之不明也，我知之矣：贤者过之，不肖者不及也。人莫不饮食也，鲜能知味也。”第一章</span>
<span>道可道，非常道。</span>
<span>名可名，非常名。</span>
<span>无，名天地之始；有，名万物之母。</span>
<span>故常无，欲以观其妙；常有，欲以观其徼。</span>
<span>此两者同出而异名，同谓之玄，玄之又玄，众妙之门。</span>
<span>第二章</span>
<span>天下皆知美之为美，斯恶已；</span>
<span>皆知善之为善，斯不善已。</span>
<span>故有无相生，难易相成，长短相形，高下相盈，音声相和，前后相随，恒也。</span>
<span>是以圣人处无为之事，行不言之教。</span>
<span>万物作而弗始，生而不有，为而不恃，功成而弗居。</span>
<span>夫唯弗居，是以不去。</span>
<span>第三章</span>
<span>不尚贤，使民不争。</span>
<span>不贵难得之货，使民不为盗。</span>
<span>不见可欲，使民心不乱。</span>
<span>是以圣人之治：</span>
<span>虚其心，实其腹；弱其志，强其骨。</span>
<span>常使民无知、无欲，使夫知者不敢为也。</span>
<span>为无为，则无不治。</span>
<span>第四章</span>
<span>道冲而用之或不盈。</span>
<span>渊兮似万物之宗，湛兮似若存。</span>
<span>吾不知谁之子，象帝之先。</span>
<span>第五章</span>
<span>天地不仁，以万物为刍狗。</span>
<span>圣人不仁，以百姓为刍狗。</span>
<span>天地之间，其犹橐龠乎？</span>
<span>虚而不屈，动而愈出。</span>
<span>多言数穷，不如守中。</span>
<span>第六章</span>
<span>谷神不死，是谓玄牝。</span>
<span>玄牝之门，是谓天地根。</span>
<span>绵绵若存，用之不勤。</span>
<span>第七章</span>
<span>天长地久。</span>
<span>天地所以能长且久者，以其不自生，故能长生。</span>
<span>是以圣人后其身而身先，外其身而身存。</span>
<span>非以其无私邪！</span>
<span>故能成其私。</span>
<span>第八章</span>
<span>上善若水。</span>
<span>水善利万物而不争，</span>
<span>处众人之所恶，</span>
<span>故几于道。</span>
<span>居善地，心善渊，与善仁，言善信，政善治，事善能，动善时。</span>
<span>夫唯不争，故无尤。</span>
<span>第九章</span>
<span>持而盈之，不如其已；揣而锐之，不可长保。</span>
<span>金玉满堂，莫之能守；富贵而骄，自遗其咎。</span>
<span>功遂身退，天之道也。</span>
<span>第十章</span>
<span>载营魄抱一，能无离乎。</span>
<span>专气致柔，能如婴儿乎。</span>
<span>涤除玄览，能无疵乎。</span>
<span>爱民治国，能无为乎。</span>
<span>天门开阖，能为雌乎。</span>
<span>明白四达，能无知乎。</span>
<span>第十一章</span>
<span>三十辐共一毂，当其无，有车之用。</span>
<span>埏埴以为器，当其无，有器之用。</span>
<span>凿户牖以为室，当其无，有室之用。</span>
<span>故有之以为利，无之以为用。观天之道，执天之行，尽矣。</span>
<span>故天有五贼，见之者昌。</span>
<span>五贼在心，施行于天。</span>
<span>宇宙在乎手，万化生乎身。</span>
<span>天性人也，人心机也。立天之道，以定人也。</span>
<span>天发杀机，移星易宿；地发杀机，龙蛇起陆；人发杀机，天地反覆；天人合发，万化定基。</span>
<span>性有巧拙，可以伏藏。九窍之邪，在乎三要，可以动静。</span>
<span>火生于木，祸发必克；奸生于国，时动必溃。知之修炼，谓之圣人。</span>
<span>天生天杀，道之理也。天地万物之盗，万物人之盗，人万物之盗。三盗既宜，三才既安。</span>
<span>故曰食其时，百骸理；动其机，万化安。人知其神之神，不知不神之所以神也。</span>
<span>日月有数，大小有定，圣功生焉，神明出焉。</span>
<span>其盗机也，天下莫能见，莫能知。君子得之固躬，小人得之轻命。</span>
<span>瞽者善听，聋者善视。绝利一源，用师十倍。三返昼夜，用师万倍。</span>
<span>心生于物，死于物，机在目。</span>
<span>天之无恩而大恩生。迅雷烈风莫不蠢然。</span>
<span>至乐性余，至静性廉。天之至私，用之至公。</span>
<span>禽之制在气。生者死之根，死者生之根。恩生于害，害生于恩。</span>
<span>愚人以天地文理圣，我以时物文理哲。</span>

这时候结果是以空字符串拼接成的文本，如果把 join 设置为 ‘11111111111111111111111111111’ 呢？

结果输出如下：

<span><span style="color:#dd1144">nodes: 第一章</span></span>
<span>道可道，非常道。</span>
<span>名可名，非常名。</span>
<span>无，名天地之始；有，名万物之母。</span>
<span>故常无，欲以观其妙；常有，欲以观其徼。</span>
<span>此两者同出而异名，同谓之玄，玄之又玄，众妙之门。</span>
<span>第二章</span>
<span>天下皆知美之为美，斯恶已；</span>
<span>皆知善之为善，斯不善已。</span>
<span>故有无相生，难易相成，长短相形，高下相盈，音声相和，前后相随，恒也。</span>
<span>是以圣人处无为之事，行不言之教。</span>
<span>万物作而弗始，生而不有，为而不恃，功成而弗居。</span>
<span>夫唯弗居，是以不去。</span>
<span>第三章</span>
<span>不尚贤，使民不争。</span>
<span>不贵难得之货，使民不为盗。</span>
<span>不见可欲，使民心不乱。</span>
<span>是以圣人之治：</span>
<span>虚其心，实其腹；弱其志，强其骨。</span>
<span>常使民无知、无欲，使夫知者不敢为也。</span>
<span>为无为，则无不治。</span>
<span>第四章</span>
<span>道冲而用之或不盈。</span>
<span>渊兮似万物之宗，湛兮似若存。</span>
<span>吾不知谁之子，象帝之先。</span>
<span>第五章</span>
<span>天地不仁，以万物为刍狗。</span>
<span>圣人不仁，以百姓为刍狗。</span>
<span>天地之间，其犹橐龠乎？</span>
<span>虚而不屈，动而愈出。</span>
<span>多言数穷，不如守中。</span>
<span>第六章</span>
<span>谷神不死，是谓玄牝。</span>
<span>玄牝之门，是谓天地根。</span>
<span>绵绵若存，用之不勤。</span>
<span>第七章</span>
<span>天长地久。</span>
<span>天地所以能长且久者，以其不自生，故能长生。</span>
<span>是以圣人后其身而身先，外其身而身存。</span>
<span>非以其无私邪！</span>
<span>故能成其私。</span>
<span>第八章</span>
<span>上善若水。</span>
<span>水善利万物而不争，</span>
<span>处众人之所恶，</span>
<span>故几于道。</span>
<span>居善地，心善渊，与善仁，言善信，政善治，事善能，动善时。</span>
<span>夫唯不争，故无尤。</span>
<span>第九章</span>
<span>持而盈之，不如其已；揣而锐之，不可长保。</span>
<span>金玉满堂，莫之能守；富贵而骄，自遗其咎。</span>
<span>功遂身退，天之道也。</span>
<span>第十章</span>
<span>载营魄抱一，能无离乎。</span>
<span>专气致柔，能如婴儿乎。</span>
<span>涤除玄览，能无疵乎。</span>
<span>爱民治国，能无为乎。</span>
<span>天门开阖，能为雌乎。</span>
<span>明白四达，能无知乎。</span>
<span>第十一章</span>
<span>三十辐共一毂，当其无，有车之用。</span>
<span>埏埴以为器，当其无，有器之用。</span>
<span>凿户牖以为室，当其无，有室之用。</span>
<span>故有之以为利，无之以为用。11111111111111111111111111111观天之道，执天之行，尽矣。</span>
<span>故天有五贼，见之者昌。</span>
<span>五贼在心，施行于天。</span>
<span>宇宙在乎手，万化生乎身。</span>
<span>天性人也，人心机也。立天之道，以定人也。</span>
<span>天发杀机，移星易宿；地发杀机，龙蛇起陆；人发杀机，天地反覆；天人合发，万化定基。</span>
<span>性有巧拙，可以伏藏。九窍之邪，在乎三要，可以动静。</span>
<span>火生于木，祸发必克；奸生于国，时动必溃。知之修炼，谓之圣人。</span>
<span>天生天杀，道之理也。天地万物之盗，万物人之盗，人万物之盗。三盗既宜，三才既安。</span>
<span>故曰食其时，百骸理；动其机，万化安。人知其神之神，不知不神之所以神也。</span>
<span>日月有数，大小有定，圣功生焉，神明出焉。</span>
<span>其盗机也，天下莫能见，莫能知。君子得之固躬，小人得之轻命。</span>
<span>瞽者善听，聋者善视。绝利一源，用师十倍。三返昼夜，用师万倍。</span>
<span>心生于物，死于物，机在目。</span>
<span>天之无恩而大恩生。迅雷烈风莫不蠢然。</span>
<span>至乐性余，至静性廉。天之至私，用之至公。</span>
<span>禽之制在气。生者死之根，死者生之根。恩生于害，害生于恩。</span>
<span>愚人以天地文理圣，我以时物文理哲。11111111111111111111111111111天命之谓性，率性之谓道，修道之谓教。</span>
<span>道也者，不可须臾（yú）离也；可离，非道也。是故君子戒慎乎其所不睹，恐惧乎其所不闻。莫见（xian）乎隐，莫显乎微，故君子慎其独也。</span>
<span>喜怒哀乐之未发，谓之中；发而皆中节，谓之和。中也者，天下之大本也；和也者，天下之达道也。致中和，天地位焉，万物育焉。</span>
<span>仲尼曰：“君子中庸，小人反中庸。君子之中庸也，君子而时中；小人之反中庸也，小人而无忌惮也。”</span>
<span>子曰：“中庸其至矣乎！民鲜能久矣！”</span>
<span>子曰：“道之不行也，我知之矣：知者过之，愚者不及也。道之不明也，我知之矣：贤者过之，不肖者不及也。人莫不饮食也，鲜能知味也。”</span>

可以看出结果是由三段文本拼接而成的字符串。

3. Reranker参数详解

（完整GitHub代码链接：

https://github.com/LazyAGI/Tutorial/blob/7abc91dbb82a007a78731845dd8c360ac0cc1e75/rag/codes/chapter8/reranker_parameters_explanation.py#L1）

Reranker 中的这两个参数值和上面是一样的，这里我们就不多说了，直接看代码。我们让 Reranker输出分数最高的两个结果。

<span><span style="color:#ca7d37">import</span> lazyllm</span>
<span><span style="color:#ca7d37">from</span> lazyllm <span style="color:#ca7d37">import</span> pipeline, bind, OnlineEmbeddingModule, SentenceSplitter, Retriever, Reranker</span>
<span><span style="color:#ca7d37">from</span> lazyllm.tools.rag <span style="color:#ca7d37">import</span> Document</span>
<span>prompt = <span style="color:#dd1144">'You will play the role of an AI Q&A assistant and complete a dialogue task. In this task, you need to provide your answer based on the given context and question.'</span></span>
<span>documents = Document(dataset_path=<span style="color:#dd1144">"rag_master"</span>, embed=OnlineEmbeddingModule(source=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"embedding-2"</span>), manager=<span style="color:#0e9ce5">False</span>)</span>
<span>documents.create_node_group(name=<span style="color:#dd1144">"sentences"</span>, transform=SentenceSplitter, chunk_size=<span style="color:#0e9ce5">1024</span>, chunk_overlap=<span style="color:#0e9ce5">100</span>)</span>
<span><span style="color:#ca7d37">with</span> pipeline() <span style="color:#ca7d37">as</span> ppl:</span>
<span>    ppl.retriever = Retriever(documents, group_name=<span style="color:#dd1144">"sentences"</span>, similarity=<span style="color:#dd1144">"cosine"</span>, similarity_cut_off=<span style="color:#0e9ce5">0.003</span>, topk=<span style="color:#0e9ce5">3</span>)</span>
<span>    ppl.reranker = Reranker(name=<span style="color:#dd1144">"ModuleReranker"</span>, model=lazyllm.OnlineEmbeddingModule(<span style="color:#ca7d37">type</span>=<span style="color:#dd1144">"rerank"</span>, source=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"rerank"</span>), topk=<span style="color:#0e9ce5">2</span>) | bind(query=ppl.<span style="color:#ca7d37">input</span>)</span>
<span>nodes = ppl(<span style="color:#dd1144">"何为天道"</span>)</span>
<span><span style="color:#ca7d37">print</span>(<span style="color:#dd1144">f"nodes: </span>
<span style="color:#dd1144"><span style="color:#ca7d37">{nodes}</span></span>
<span style="color:#dd1144">"</span>)</span>

结果输出如下：

<span>nodes: [<span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">Node</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">id</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">a8713a16-fc5b-4904-a06f-9e8c55d11988</span></span>
<span style="color:#0e9ce5">></span>, <span style="color:#0e9ce5"><</span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">Node</span></span>
<span style="color:#0e9ce5"> </span>
<span style="color:#0e9ce5"><span style="color:#0e9ce5">id</span></span>
<span style="color:#0e9ce5">=</span>
<span style="color:#0e9ce5"><span style="color:#dd1144">058767c8-de0e-4d17-addc-23816ae8e2d2</span></span>
<span style="color:#0e9ce5">></span>]</span>

可以看到什么都不设置的话，结果是以 Node 的形式输出的。

接下来把 output_format 设置为 dict:

<span>ppl.reranker = Reranker(name=<span style="color:#dd1144">"ModuleReranker"</span>, model=lazyllm.OnlineEmbeddingModule(<span style="color:#ca7d37">type</span>=<span style="color:#dd1144">"rerank"</span>, <span style="color:#ca7d37">source</span>=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"rerank"</span>), topk=2, output_format=<span style="color:#dd1144">"dict"</span>) | <span style="color:#ca7d37">bind</span>(query=ppl.input)</span>

结果输出如下：

<span><span>nodes</span>: [{<span style="color:#dd1144">'content'</span>: <span style="color:#dd1144">'观天之道，执天之行，尽矣。\n故天有五贼，见之者昌。\n五贼在心，施行于天。\n宇宙在乎手，万化生乎身。\n天性人也，人心机也。立天之道，以定人也。\n天发杀机，移星易宿；地发杀机，龙蛇起陆；人发杀机，天地反覆；天人合发，万化定基。\n性有巧拙，可以伏藏。九窍之邪，在乎三要，可以动静。\n火生于木，祸发必克；奸生于国，时动必溃。知之修炼，谓之圣人。\n天生天杀，道之理也。天地万物之盗，万物人之盗，人万物之盗。三盗既宜，三才既安。\n故曰食其时，百骸理；动其机，万化安。人知其神之神，不知不神之所以神也。\n日月有数，大小有定，圣功生焉，神明出焉。\n其盗机也，天下莫能见，莫能知。君子得之固躬，小人得之轻命。\n瞽者善听，聋者善视。绝利一源，用师十倍。三返昼夜，用师万倍。\n心生于物，死于物，机在目。\n天之无恩而大恩生。迅雷烈风莫不蠢然。\n至乐性余，至静性廉。天之至私，用之至公。\n禽之制在气。生者死之根，死者生之根。恩生于害，害生于恩。\n愚人以天地文理圣，我以时物文理哲。'</span>, <span style="color:#dd1144">'embedding'</span>: {<span style="color:#dd1144">'__default__'</span>: [<span style="color:#0e9ce5">0.0041554114</span>, <span style="color:#0e9ce5">0.0075854566</span>, -<span style="color:#0e9ce5">0.007057654</span>, <span style="color:#0e9ce5">0.045291275</span>, <span style="color:#0e9ce5">0.04896368</span>, <span style="color:#0e9ce5">0.028331915</span>, -<span style="color:#0e9ce5">0.05941855</span>, <span style="color:#0e9ce5">0.023905145</span>, <span style="color:#0e9ce5">0.041470468</span>, -<span style="color:#0e9ce5">0.048525453</span>, <span style="color:#0e9ce5">0.041954085</span>, -<span style="color:#0e9ce5">0.009402919</span>, -<span style="color:#0e9ce5">0.026280737</span>, -<span style="color:#0e9ce5">0.0059798467</span>, <span style="color:#0e9ce5">0.03535848</span>, -<span style="color:#0e9ce5">0.01118226</span>, -<span style="color:#0e9ce5">0.034457922</span>, <span style="color:#0e9ce5">0.05115644</span>, -<span style="color:#0e9ce5">0.0008446922</span>, -<span style="color:#0e9ce5">0.026550785</span>, -<span style="color:#0e9ce5">0.007863325</span>, <span style="color:#0e9ce5">0.027618073</span>, -<span style="color:#0e9ce5">0.011360486</span>, -<span style="color:#0e9ce5">0.039742425</span>, -<span style="color:#0e9ce5">0.004911823</span>, -<span style="color:#0e9ce5">0.04183471</span>, -<span style="color:#0e9ce5">0.005898696</span>, <span 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style="color:#dd1144">'天命之谓性，率性之谓道，修道之谓教。\n道也者，不可须臾（yú）离也；可离，非道也。是故君子戒慎乎其所不睹，恐惧乎其所不闻。莫见（xian）乎隐，莫显乎微，故君子慎其独也。\n喜怒哀乐之未发，谓之中；发而皆中节，谓之和。中也者，天下之大本也；和也者，天下之达道也。致中和，天地位焉，万物育焉。\n\n仲尼曰：“君子中庸，小人反中庸。君子之中庸也，君子而时中；小人之反中庸也，小人而无忌惮也。”\n\n子曰：“中庸其至矣乎！民鲜能久矣！”\n\n子曰：“道之不行也，我知之矣：知者过之，愚者不及也。道之不明也，我知之矣：贤者过之，不肖者不及也。人莫不饮食也，鲜能知味也。”'</span>, <span style="color:#dd1144">'embedding'</span>: {<span style="color:#dd1144">'__default__'</span>: [<span style="color:#0e9ce5">0.025426213</span>, <span style="color:#0e9ce5">0.034025285</span>, -<span style="color:#0e9ce5">0.031320818</span>, <span style="color:#0e9ce5">0.016555943</span>, <span style="color:#0e9ce5">0.06301127</span>, <span style="color:#0e9ce5">0.0053874906</span>, -<span style="color:#0e9ce5">0.03851282</span>, <span style="color:#0e9ce5">0.05617112</span>, -<span style="color:#0e9ce5">0.026647208</span>, -<span style="color:#0e9ce5">0.04409193</span>, <span style="color:#0e9ce5">0.07942905</span>, -<span style="color:#0e9ce5">0.016034704</span>, <span 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style="color:#0e9ce5">0.020220164</span>, -<span style="color:#0e9ce5">0.0055029565</span>, <span style="color:#0e9ce5">0.059257552</span>, -<span style="color:#0e9ce5">0.038126208</span>, -<span style="color:#0e9ce5">0.03557876</span>, <span style="color:#0e9ce5">0.01125294</span>, <span style="color:#0e9ce5">0.024858668</span>, -<span style="color:#0e9ce5">0.0056422777</span>, <span style="color:#0e9ce5">0.0049045635</span>, <span style="color:#0e9ce5">0.011553989</span>, <span style="color:#0e9ce5">0.026200697</span>, -<span style="color:#0e9ce5">1.432319e-27</span>, -<span style="color:#0e9ce5">0.0010860946</span>, <span style="color:#0e9ce5">0.09268514</span>, <span style="color:#0e9ce5">0.06090386</span>, -<span style="color:#0e9ce5">0.005796361</span>, <span style="color:#0e9ce5">0.025179828</span>, <span style="color:#0e9ce5">0.06804877</span>, -<span style="color:#0e9ce5">0.050707188</span>, <span style="color:#0e9ce5">0.02777679</span>, -<span style="color:#0e9ce5">0.031054588</span>, -<span style="color:#0e9ce5">0.019260377</span>, -<span style="color:#0e9ce5">0.04869623</span>, <span style="color:#0e9ce5">0.04939678</span>, -<span style="color:#0e9ce5">0.024753766</span>, -<span style="color:#0e9ce5">0.0072507886</span>, <span style="color:#0e9ce5">0.06201284</span>, <span style="color:#0e9ce5">0.038341094</span>, -<span style="color:#0e9ce5">0.023940781</span>, -<span style="color:#0e9ce5">0.0044804853</span>, <span style="color:#0e9ce5">0.0602865</span>, <span style="color:#0e9ce5">0.012955638</span>, <span style="color:#0e9ce5">0.041328467</span>, <span style="color:#0e9ce5">0.012215266</span>, <span style="color:#0e9ce5">0.041857783</span>, <span style="color:#0e9ce5">0.05578706</span>, -<span style="color:#0e9ce5">0.04423121</span>, -<span style="color:#0e9ce5">0.030089064</span>, <span style="color:#0e9ce5">0.039828192</span>, -<span style="color:#0e9ce5">0.025488378</span>, <span style="color:#0e9ce5">0.044566587</span>, <span style="color:#0e9ce5">0.01636707</span>, <span style="color:#0e9ce5">0.05779351</span>, -<span style="color:#0e9ce5">0.029481513</span>, <span style="color:#0e9ce5">0.0053500645</span>, <span style="color:#0e9ce5">0.051323455</span>, <span style="color:#0e9ce5">0.03458597</span>, <span style="color:#0e9ce5">0.013696377</span>, -<span style="color:#0e9ce5">0.035192043</span>, <span style="color:#0e9ce5">0.04330355</span>, <span style="color:#0e9ce5">0.058219656</span>, <span style="color:#0e9ce5">0.049582005</span>, -<span style="color:#0e9ce5">0.02016202</span>, -<span style="color:#0e9ce5">0.036638554</span>, <span style="color:#0e9ce5">0.020224864</span>, -<span style="color:#0e9ce5">0.039496087</span>, -<span style="color:#0e9ce5">0.015062504</span>, <span style="color:#0e9ce5">0.0016461068</span>, <span style="color:#0e9ce5">0.015035624</span>, -<span style="color:#0e9ce5">0.03048053</span>, -<span style="color:#0e9ce5">0.027442321</span>, -<span style="color:#0e9ce5">0.019795708</span>, <span style="color:#0e9ce5">0.061282612</span>, -<span style="color:#0e9ce5">0.0035766829</span>, <span style="color:#0e9ce5">0.049931448</span>, -<span style="color:#0e9ce5">0.044324502</span>, -<span style="color:#0e9ce5">0.034937955</span>, -<span style="color:#0e9ce5">0.055052124</span>, -<span style="color:#0e9ce5">0.002334567</span>, <span style="color:#0e9ce5">0.0011809984</span>, -<span style="color:#0e9ce5">0.0005358032</span>, <span style="color:#0e9ce5">0.014579384</span>, -<span style="color:#0e9ce5">0.006727375</span>, <span style="color:#0e9ce5">0.01961919</span>, -<span style="color:#0e9ce5">0.024230124</span>, <span style="color:#0e9ce5">0.022260979</span>, <span style="color:#0e9ce5">0.041638635</span>, <span style="color:#0e9ce5">0.015936956</span>, -<span style="color:#0e9ce5">0.0129846865</span>, -<span style="color:#0e9ce5">0.028698431</span>, -<span style="color:#0e9ce5">0.023311313</span>, <span style="color:#0e9ce5">0.020681119</span>, -<span style="color:#0e9ce5">0.022004813</span>, <span style="color:#0e9ce5">0.0045342017</span>, <span style="color:#0e9ce5">0.10454673</span>, -<span style="color:#0e9ce5">0.00907232</span>, <span style="color:#0e9ce5">0.018019272</span>, <span style="color:#0e9ce5">0.027693322</span>, -<span style="color:#0e9ce5">0.045678418</span>, <span style="color:#0e9ce5">0.008820441</span>, -<span style="color:#0e9ce5">0.018247915</span>, <span style="color:#0e9ce5">0.0022897322</span>, -<span style="color:#0e9ce5">0.0045168954</span>, <span style="color:#0e9ce5">0.0330645</span>, -<span style="color:#0e9ce5">0.046881303</span>, -<span style="color:#0e9ce5">0.02397729</span>, -<span style="color:#0e9ce5">0.03653085</span>, <span style="color:#0e9ce5">0.03674319</span>, -<span style="color:#0e9ce5">0.027837344</span>, -<span style="color:#0e9ce5">0.02493074</span>, -<span style="color:#0e9ce5">0.025111457</span>, -<span style="color:#0e9ce5">0.02385346</span>, -<span style="color:#0e9ce5">0.026238898</span>, <span style="color:#0e9ce5">0.019991593</span>, -<span style="color:#0e9ce5">0.00067811715</span>, -<span style="color:#0e9ce5">0.014781632</span>, <span style="color:#0e9ce5">0.024634153</span>, -<span style="color:#0e9ce5">0.016004443</span>, -<span style="color:#0e9ce5">0.0052194847</span>, <span style="color:#0e9ce5">0.024627017</span>, <span style="color:#0e9ce5">0.0032142564</span>, <span style="color:#0e9ce5">0.013624659</span>, -<span style="color:#0e9ce5">5.867911e-24</span>, -<span style="color:#0e9ce5">9.8801356e-05</span>, <span style="color:#0e9ce5">0.07649344</span>, <span style="color:#0e9ce5">0.02002069</span>, <span style="color:#0e9ce5">0.029181154</span>, <span style="color:#0e9ce5">0.03094678</span>, <span style="color:#0e9ce5">0.021331448</span>, <span style="color:#0e9ce5">0.029769164</span>, -<span style="color:#0e9ce5">0.01106459</span>, <span style="color:#0e9ce5">0.035867818</span>, <span style="color:#0e9ce5">0.0059047374</span>, <span style="color:#0e9ce5">0.044848107</span>, -<span style="color:#0e9ce5">0.034437712</span>, <span style="color:#0e9ce5">0.056731243</span>, <span style="color:#0e9ce5">0.0032158946</span>, <span style="color:#0e9ce5">0.018998314</span>, -<span style="color:#0e9ce5">0.070217706</span>, -<span style="color:#0e9ce5">0.029742291</span>, <span style="color:#0e9ce5">0.034644052</span>, <span style="color:#0e9ce5">0.024905458</span>]}, <span style="color:#dd1144">'metadata'</span>: {}}]</span>

可以看到结果是以 dict的形式输出的。同样的，这个时候对 join 要么不设置，要么设置为 False对结果没有影响，不能设置为 True 或字符串否则会报错。

<span>ppl.reranker = Reranker(name=<span style="color:#dd1144">"ModuleReranker"</span>, model=lazyllm.OnlineEmbeddingModule(<span style="color:#ca7d37">type</span>=<span style="color:#dd1144">"rerank"</span>, <span style="color:#ca7d37">source</span>=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"rerank"</span>), topk=2, output_format=<span style="color:#dd1144">"dict"</span>, <span style="color:#ca7d37">join</span>=True) | <span style="color:#ca7d37">bind</span>(query=ppl.input)</span>

结果报错如下：

<span><span style="color:#dd1144">AssertionError: Only content output can be joined</span></span>

接下来我们把 output_format 设置为 content:

<span>ppl.reranker = Reranker(name=<span style="color:#dd1144">"ModuleReranker"</span>, model=lazyllm.OnlineEmbeddingModule(<span style="color:#ca7d37">type</span>=<span style="color:#dd1144">"rerank"</span>, <span style="color:#ca7d37">source</span>=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"rerank"</span>), topk=2, output_format=<span style="color:#dd1144">"content"</span> ) | <span style="color:#ca7d37">bind</span>(query=ppl.input)</span>

结果如下：

<span><span>nodes</span>: [<span style="color:#dd1144">'观天之道，执天之行，尽矣。\n故天有五贼，见之者昌。\n五贼在心，施行于天。\n宇宙在乎手，万化生乎身。\n天性人也，人心机也。立天之道，以定人也。\n天发杀机，移星易宿；地发杀机，龙蛇起陆；人发杀机，天地反覆；天人合发，万化定基。\n性有巧拙，可以伏藏。九窍之邪，在乎三要，可以动静。\n火生于木，祸发必克；奸生于国，时动必溃。知之修炼，谓之圣人。\n天生天杀，道之理也。天地万物之盗，万物人之盗，人万物之盗。三盗既宜，三才既安。\n故曰食其时，百骸理；动其机，万化安。人知其神之神，不知不神之所以神也。\n日月有数，大小有定，圣功生焉，神明出焉。\n其盗机也，天下莫能见，莫能知。君子得之固躬，小人得之轻命。\n瞽者善听，聋者善视。绝利一源，用师十倍。三返昼夜，用师万倍。\n心生于物，死于物，机在目。\n天之无恩而大恩生。迅雷烈风莫不蠢然。\n至乐性余，至静性廉。天之至私，用之至公。\n禽之制在气。生者死之根，死者生之根。恩生于害，害生于恩。\n愚人以天地文理圣，我以时物文理哲。'</span>, <span style="color:#dd1144">'天命之谓性，率性之谓道，修道之谓教。\n道也者，不可须臾（yú）离也；可离，非道也。是故君子戒慎乎其所不睹，恐惧乎其所不闻。莫见（xian）乎隐，莫显乎微，故君子慎其独也。\n喜怒哀乐之未发，谓之中；发而皆中节，谓之和。中也者，天下之大本也；和也者，天下之达道也。致中和，天地位焉，万物育焉。\n\n仲尼曰：“君子中庸，小人反中庸。君子之中庸也，君子而时中；小人之反中庸也，小人而无忌惮也。”\n\n子曰：“中庸其至矣乎！民鲜能久矣！”\n\n子曰：“道之不行也，我知之矣：知者过之，愚者不及也。道之不明也，我知之矣：贤者过之，不肖者不及也。人莫不饮食也，鲜能知味也。”'</span>]</span>

结果是以 List[str]的形式输出的。

如果把 join设置为True呢？

<span>ppl.reranker = Reranker(name=<span style="color:#dd1144">"ModuleReranker"</span>, model=lazyllm.OnlineEmbeddingModule(<span style="color:#ca7d37">type</span>=<span style="color:#dd1144">"rerank"</span>, <span style="color:#ca7d37">source</span>=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"rerank"</span>), topk=2, output_format=<span style="color:#dd1144">"content"</span>, <span style="color:#ca7d37">join</span>=True) | <span style="color:#ca7d37">bind</span>(query=ppl.input)</span>

结果如下：

<span><span style="color:#dd1144">nodes: 观天之道，执天之行，尽矣。</span></span>
<span>故天有五贼，见之者昌。</span>
<span>五贼在心，施行于天。</span>
<span>宇宙在乎手，万化生乎身。</span>
<span>天性人也，人心机也。立天之道，以定人也。</span>
<span>天发杀机，移星易宿；地发杀机，龙蛇起陆；人发杀机，天地反覆；天人合发，万化定基。</span>
<span>性有巧拙，可以伏藏。九窍之邪，在乎三要，可以动静。</span>
<span>火生于木，祸发必克；奸生于国，时动必溃。知之修炼，谓之圣人。</span>
<span>天生天杀，道之理也。天地万物之盗，万物人之盗，人万物之盗。三盗既宜，三才既安。</span>
<span>故曰食其时，百骸理；动其机，万化安。人知其神之神，不知不神之所以神也。</span>
<span>日月有数，大小有定，圣功生焉，神明出焉。</span>
<span>其盗机也，天下莫能见，莫能知。君子得之固躬，小人得之轻命。</span>
<span>瞽者善听，聋者善视。绝利一源，用师十倍。三返昼夜，用师万倍。</span>
<span>心生于物，死于物，机在目。</span>
<span>天之无恩而大恩生。迅雷烈风莫不蠢然。</span>
<span>至乐性余，至静性廉。天之至私，用之至公。</span>
<span>禽之制在气。生者死之根，死者生之根。恩生于害，害生于恩。</span>
<span>愚人以天地文理圣，我以时物文理哲。天命之谓性，率性之谓道，修道之谓教。</span>
<span>道也者，不可须臾（yú）离也；可离，非道也。是故君子戒慎乎其所不睹，恐惧乎其所不闻。莫见（xian）乎隐，莫显乎微，故君子慎其独也。</span>
<span>喜怒哀乐之未发，谓之中；发而皆中节，谓之和。中也者，天下之大本也；和也者，天下之达道也。致中和，天地位焉，万物育焉。</span>
<span>仲尼曰：“君子中庸，小人反中庸。君子之中庸也，君子而时中；小人之反中庸也，小人而无忌惮也。”</span>
<span>子曰：“中庸其至矣乎！民鲜能久矣！”</span>
<span>子曰：“道之不行也，我知之矣：知者过之，愚者不及也。道之不明也，我知之矣：贤者过之，不肖者不及也。人莫不饮食也，鲜能知味也。”</span>

可以看到结果是以字符串的形式输出的，如果把 join 设置为 ‘11111111111111111111111111111’ 呢？

<span>ppl.reranker = Reranker(name=<span style="color:#dd1144">"ModuleReranker"</span>, model=lazyllm.OnlineEmbeddingModule(<span style="color:#ca7d37">type</span>=<span style="color:#dd1144">"rerank"</span>, <span style="color:#ca7d37">source</span>=<span style="color:#dd1144">"glm"</span>, embed_model_name=<span style="color:#dd1144">"rerank"</span>), topk=2, output_format=<span style="color:#dd1144">"content"</span>, <span style="color:#ca7d37">join</span>=<span style="color:#dd1144">'11111111111111111111111111111'</span>) | <span style="color:#ca7d37">bind</span>(query=ppl.input)</span>

结果如下所示:

<span><span style="color:#dd1144">nodes: 观天之道，执天之行，尽矣。</span></span>
<span>故天有五贼，见之者昌。</span>
<span>五贼在心，施行于天。</span>
<span>宇宙在乎手，万化生乎身。</span>
<span>天性人也，人心机也。立天之道，以定人也。</span>
<span>天发杀机，移星易宿；地发杀机，龙蛇起陆；人发杀机，天地反覆；天人合发，万化定基。</span>
<span>性有巧拙，可以伏藏。九窍之邪，在乎三要，可以动静。</span>
<span>火生于木，祸发必克；奸生于国，时动必溃。知之修炼，谓之圣人。</span>
<span>天生天杀，道之理也。天地万物之盗，万物人之盗，人万物之盗。三盗既宜，三才既安。</span>
<span>故曰食其时，百骸理；动其机，万化安。人知其神之神，不知不神之所以神也。</span>
<span>日月有数，大小有定，圣功生焉，神明出焉。</span>
<span>其盗机也，天下莫能见，莫能知。君子得之固躬，小人得之轻命。</span>
<span>瞽者善听，聋者善视。绝利一源，用师十倍。三返昼夜，用师万倍。</span>
<span>心生于物，死于物，机在目。</span>
<span>天之无恩而大恩生。迅雷烈风莫不蠢然。</span>
<span>至乐性余，至静性廉。天之至私，用之至公。</span>
<span>禽之制在气。生者死之根，死者生之根。恩生于害，害生于恩。</span>
<span>愚人以天地文理圣，我以时物文理哲。11111111111111111111111111111天命之谓性，率性之谓道，修道之谓教。</span>
<span>道也者，不可须臾（yú）离也；可离，非道也。是故君子戒慎乎其所不睹，恐惧乎其所不闻。莫见（xian）乎隐，莫显乎微，故君子慎其独也。</span>
<span>喜怒哀乐之未发，谓之中；发而皆中节，谓之和。中也者，天下之大本也；和也者，天下之达道也。致中和，天地位焉，万物育焉。</span>
<span>仲尼曰：“君子中庸，小人反中庸。君子之中庸也，君子而时中；小人之反中庸也，小人而无忌惮也。”</span>
<span>子曰：“中庸其至矣乎！民鲜能久矣！”</span>
<span>子曰：“道之不行也，我知之矣：知者过之，愚者不及也。道之不明也，我知之矣：贤者过之，不肖者不及也。人莫不饮食也，鲜能知味也。”</span>

可以看出结果是由两个文本段拼接而成。可以看出 Retriever和Reranker的后处理是一样的。

（数据集的下载方式：

https://huggingface.co/datasets/LazyAGI/Chinese_Classics_Articles/tree/main）

更多技术细节，欢迎移步“LazyLLM”gzh~

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LazyLLM教程 | 第8讲：不止是cosine！匹配策略决定你召回的质量

为什么要自定义 Similarity

如何自定义 Similarity

1. 环境准备

2. 实现函数

3.注册函数

4. 参数详解

使用自定义 Similarity 搭建 RAG 应用

1. 查看Similarity分数

2. Retriever参数详解

3. Reranker参数详解

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近期文章

为什么要自定义 Similarity

如何自定义 Similarity

1. 环境准备

2. 实现函数

3.注册函数

4. 参数详解

使用自定义 Similarity 搭建 RAG 应用

1. 查看Similarity分数

2. Retriever参数详解

3. Reranker参数详解

相关推荐

评论 抢沙发

近期文章

评论抢沙发