AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

07-11 1090阅读

系列篇章💥

No.文章
1AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践
2AI大模型探索之路-实战篇：智能化IT领域搜索引擎之GLM-4大模型技术的实践探索
3AI大模型探索之路-实战篇：智能化IT领域搜索引擎之知乎网站数据获取（初步实践）
4AI大模型探索之路-实战篇：智能化IT领域搜索引擎之知乎网站数据获取（函数封装）
5AI大模型探索之路-实战篇：智能化IT领域搜索引擎之知乎网站数据获取（流程优化）
6AI大模型探索之路-实战篇：智能化IT领域搜索引擎之github网站在线搜索
7AI大模型探索之路-实战篇：智能化IT领域搜索引擎之HuggingFace网站在线搜索

目录

  • 系列篇章💥
  • 前言
  • 一、总体概览
    • 1、需求分析
    • 2、技术方案
    • 二、创建Google Cloud Search API（准备工作）
      • 1、Google Cloud 简介
      • 2、Custom Search API概述
      • 3、GoogleCloud平台注册
      • 4、创建项目
      • 5、启用API
      • 6、获取API密钥
      • 三、创建Google Custom Search Engine（准备工作）
        • 1、可编程的搜索引擎简介
        • 2、创建自己的搜索引擎
        • 3、获取cx、配置环境变量
        • 四、Google自定义搜索引擎使用测试
          • 1、页面搜索测试
          • 2、代码调用测试
          • 结语

            前言

            在信息技术飞速发展的当下，对于专业知识的检索需求正呈现出前所未有的增长态势。为了应对这一挑战，本文将深入探讨如何巧妙地结合大型语言模型的功能调用能力与谷歌搜索引擎API的强大功能，共同打造一个专门针对IT领域的智能在线搜索平台。这个平台不仅能够为用户提供更为精准、高效的搜索结果，还将引领信息技术检索的新潮流，为专业人士带来更加便捷、智能的搜索体验。

            一、总体概览

            1、需求分析

            在构建智能在线搜索平台的过程中，深入的需求分析是至关重要的一步。用户通过输入查询（Prompt）向大型语言模型提出知识性问题，这不仅是用户与系统交互的起点，也是整个搜索流程的核心。当用户提出问题时，我们首先会分析问题的性质和类型，以确定是否需要调用外部搜索引擎进行实时查询。

            接下来，如果大型语言模型内置的知识库能够提供答案，则直接回应用户，这样可以大大缩短响应时间，提高搜索效率。然而，若内置知识库无法提供满意的答案，系统将自动触发外部搜索引擎的调用机制，进行实时查询。这一过程中，我们需要确保系统的自动判断逻辑准确无误，以便在内置知识库无法满足需求时，及时且准确地获取外部信息资源。

            AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

            2、技术方案

            本项目的技术核心由以下三部分组成：

            1）利用GLM4大模型的函数调用（Funcation Calling）能力，实现智能调度和规划；

            2）使用谷歌搜索引擎提供的API对特定技术平台进行爬取和搜索。

            3）结合提示词Prompt的能力实现实现智能在线搜索。

            AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

            在技术方案的实施过程中，我们首先利用GLM4大模型的函数调用能力，对用户的查询请求进行智能解析和规划。通过大模型的强大处理能力，我们能够准确理解用户的意图，并快速制定出相应的搜索策略。

            随后，我们使用谷歌搜索引擎提供的API接口，对特定的技术平台进行深度爬取和搜索。谷歌搜索引擎作为全球领先的搜索引擎之一，其强大的搜索能力和海量的数据资源，为我们提供了丰富、准确的搜索结果。

            最后，我们结合提示词Prompt的能力，进一步提升了搜索的智能化水平。通过精心设计的提示词，我们能够引导大模型更加精准地理解用户的搜索需求，并返回更为符合期望的结果。

            二、创建Google Cloud Search API（准备工作）

            1、Google Cloud 简介

            Google Cloud，全称Google Cloud Platform（GCP），是Google提供的一系列云计算服务和API。它允许个人、企业和政府机构在Google的数据中心上运行其应用程序和存储数据。

            2、Custom Search API概述

            Google Cloud上的Custom Search API是Google提供的一项服务，允许开发者将Google的搜索功能集成到自己的应用程序中。这项服务特别适用于需要在特定数据集或网站上提供搜索功能的情况。以下是Custom Search API的一些关键特性：

            • 自定义搜索引擎：用户可以创建一个搜索引擎，只搜索他们选择的网站或网页。
            • 编程访问：通过API，开发者可以以编程方式访问搜索结果，并将它们集成到自己的应用程序中。
            • 搜索结果定制：可以定制搜索结果的显示方式，包括排序、过滤和样式。
            • 搜索范围控制：可以指定搜索的范围，例如只搜索特定的网站或排除某些网站。
            • 安全性选项：包括成人内容过滤等安全搜索选项。
            • 使用限制和计费：Custom Search API可能有使用限制和计费要求，具体取决于使用量和功能。

              3、GoogleCloud平台注册

              登录GoogleCloud平台注册一个账号，有一定的免费额度可以使用；选择一个项目，开始新建项目

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              4、创建项目

              新增一个项目名称“MyProject001”,地点可以选择无组织

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              5、启用API

              创建完项目后，启用API和服务（必须启用Custom Search API才能取得凭证）

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              输入“google search”搜索，找到自定义搜索的API服务

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              启用自定义搜索的API服务

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              6、获取API密钥

              创建凭证

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              创建一个API密钥

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              配置环境变量

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              三、创建Google Custom Search Engine（准备工作）

              1、可编程的搜索引擎简介

              地址：https://programmablesearchengine.google.com/

              创建自己专属的搜索引擎。

              Google可编程的搜索引擎通常指的是Google Custom Search Engine（CSE），这是Google提供的一项服务，允许用户创建定制化的搜索引擎。用户可以利用CSE来搜索指定的网站、网页或整个网络。以下是一些关于Google CSE的主要特点：

              1. 定制化搜索：用户可以自定义搜索结果的显示方式，包括搜索框的样式、搜索结果的布局和排序方式。
              2. 控制搜索范围：用户可以指定搜索引擎搜索的特定网站或网页，或者排除某些网站。
              3. 编程接口：Google CSE提供了编程接口（API），允许开发者通过编程方式集成和控制搜索引擎的行为。
              4. 搜索结果过滤：用户可以设置过滤条件，比如只显示某个时间段内的内容，或者排除某些关键词。
              5. 广告选项：Google CSE允许用户在搜索结果中显示广告，并可能从中获得收入。
              6. 易于集成：用户可以将CSE集成到自己的网站或应用程序中，提供无缝的搜索体验。
              7. 搜索结果优化：用户可以对搜索结果进行优化，以提高搜索的相关性和准确性。
              8. 安全搜索：CSE提供了安全搜索选项，可以过滤掉成人内容或其他不适宜的内容。
              9. 使用限制：Google CSE的使用可能受到一定的限制，比如搜索次数限制或特定的使用条款。
              10. 易于管理：用户可以通过Google的控制面板轻松管理自己的CSE。

                Google Custom Search Engine是一个很好的工具，适用于需要在特定内容上提供搜索功能的网站管理员或开发者。通过定制化的搜索引擎，可以提高用户体验并满足特定的搜索需求。

              2、创建自己的搜索引擎

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              设置搜索引擎信息

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              3、获取cx、配置环境变量

              取出搜索引擎的信息，主要是里面的cx，也需要配置到本地的环境变量中

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              四、Google自定义搜索引擎使用测试

              1、页面搜索测试

              通过浏览器页面专用的搜索引擎地址，进行搜索查询测试

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              2、代码调用测试

              使用Python代码调用Google自定义搜索引擎进行测试：

              import os
import requests
from dotenv import load_dotenv
# 加载环境变量
load_dotenv()
# 获取API密钥
API_KEY = os.getenv("GOOGLE_SEARCH_API_KEY")
# 获取搜索引擎ID
CSE_ID = os.getenv("CSE_ID")
# 定义搜索API的URL
url = "https://www.googleapis.com/customsearch/v1"
# 设置查询参数
query = "AIGC"  # 替换为你的查询词
params = {
    "key": API_KEY,
    "cx": CSE_ID,  # 替换为你的搜索引擎ID
    "q": query,
    # 添加其他需要的参数
}
# 发送HTTP请求
response = requests.get(url, params=params)
# 检查请求是否成功
if response.status_code == 200:
    print("请求成功")
    print(response.json())
else:
    print("请求失败")
    print(response.status_code)
#打印结果如下

              请求成功

              {'kind': 'customsearch#search', 'url': {'type': 'application/json', 'template': 'https://www.googleapis.com/customsearch/v1?q={searchTerms}&num={count?}&start={startIndex?}&lr={language?}&safe={safe?}&cx={cx?}&sort={sort?}&filter={filter?}&gl={gl?}&cr={cr?}&googlehost={googleHost?}&c2coff={disableCnTwTranslation?}&hq={hq?}&hl={hl?}&siteSearch={siteSearch?}&siteSearchFilter={siteSearchFilter?}&exactTerms={exactTerms?}&excludeTerms={excludeTerms?}&linkSite={linkSite?}&orTerms={orTerms?}&dateRestrict={dateRestrict?}&lowRange={lowRange?}&highRange={highRange?}&searchType={searchType}&fileType={fileType?}&rights={rights?}&imgSize={imgSize?}&imgType={imgType?}&imgColorType={imgColorType?}&imgDominantColor={imgDominantColor?}&alt=json'}, 'queries': {'request': [{'title': 'Google Custom Search - AIGC', 'totalResults': '43100000', 'searchTerms': 'AIGC', 'count': 10, 'startIndex': 1, 'inputEncoding': 'utf8', 'outputEncoding': 'utf8', 'safe': 'off', 'cx': '03f7e673e33a64855'}], 'nextPage': [{'title': 'Google Custom Search - AIGC', 'totalResults': '43100000', 'searchTerms': 'AIGC', 'count': 10, 'startIndex': 11, 'inputEncoding': 'utf8', 'outputEncoding': 'utf8', 'safe': 'off', 'cx': '03f7e673e33a64855'}]}, 'context': {'title': 'my-google-search'}, 'searchInformation': {'searchTime': 0.418583, 'formattedSearchTime': '0.42', 'totalResults': '43100000', 'formattedTotalResults': '43,100,000'}, 'items': [{'kind': 'customsearch#result', 'title': 'AI-Generated Content (AIGC): A Survey - NASA/ADS', 'htmlTitle': 'AI-Generated Content (AIGC): A Survey - NASA/ADS', 'link': 'http://ui.adsabs.harvard.edu/abs/2023arXiv230406632W/abstract', 'displayLink': 'ui.adsabs.harvard.edu', 'snippet': 'To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial\xa0...', 'htmlSnippet': 'To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial ...', 'formattedUrl': 'http://ui.adsabs.harvard.edu/abs/2023arXiv230406632W/abstract', 'htmlFormattedUrl': 'http://ui.adsabs.harvard.edu/abs/2023arXiv230406632W/abstract', 'pagemap': {'cse_thumbnail': [{'src': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcQzRxmtHKy5uDa2ltbasgB8sar1Xvpi5uSfmcoID2Rw2NkEdxpWqF8mf9Xt&s', 'width': '240', 'height': '210'}], 'metatags': [{'citation_keywords': 'Computer Science - Artificial Intelligence', 'og:image': 'https://ui.adsabs.harvard.edu/styles/img/transparent_logo.svg', 'citation_publication_date': '03/2023', 'prism.startingpage': 'arXiv:2304.06632', 'theme-color': '#ffffff', 'article:published_time': '03/2023', 'citation_title': 'AI-Generated Content (AIGC): A Survey', 'twitter:card': 'summary_large_image', 'og:site_name': 'NASA/ADS', 'apple-mobile-web-app-title': 'NASA ADS', 'citation_journal_title': 'arXiv e-prints', 'citation_date': '03/2023', 'prism.publicationdate': '03/2023', 'og:description': "To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinc", 'twitter:creator': '@adsabs', 'citation_arxiv_id': 'arXiv:2304.06632', 'twitter:site': '@adsabs', 'dc.source': 'arXiv', 'citation_language': 'en', 'dc.creator': 'Wu, Jiayang', 'application-name': 'NASA ADS', 'msapplication-tilecolor': '#ffc40d', 'prism.publicationname': 'arXiv', 'og:type': 'eprint', 'twitter:title': 'AI-Generated Content (AIGC): A Survey', 'twitter:domain': 'NASA/ADS', 'og:title': 'AI-Generated Content (AIGC): A Survey', 'citation_abstract_html_url': 'https://ui.adsabs.harvard.edu/abs/2023arXiv230406632W/abstract', 'dc.date': '03/2023', 'article:author': 'Wu, Jiayang', 'citation_authors': 'Wu, Jiayang;Gan, Wensheng;Chen, Zefeng;Wan, Shicheng;Lin, Hong', 'twitter:image:src': 'https://ui.adsabs.harvard.edu/styles/img/transparent_logo.svg', 'citation_firstpage': 'arXiv:2304.06632', 'viewport': 'width=device-width, initial-scale=1, shrink-to-fit=no', 'citation_doi': '10.48550/arXiv.2304.06632', 'twitter:description': "To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinc", 'dc.title': 'AI-Generated Content (AIGC): A Survey', 'dc.identifier': 'doi:10.48550/arXiv.2304.06632', 'og:url': 'https://ui.adsabs.harvard.edu/abs/2023arXiv230406632W/abstract'}], 'cse_image': [{'src': 'https://ui.adsabs.harvard.edu/styles/img/transparent_logo.svg'}]}}, {'kind': 'customsearch#result', 'title': 'AI-Generated Content (AIGC): A Survey', 'htmlTitle': 'AI-Generated Content (AIGC): A Survey', 'link': 'https://arxiv.org/pdf/2304.06632', 'displayLink': 'arxiv.org', 'snippet': 'Relevant technologies of. AIGC will boost the development of artificial intelligence, better serve human society, and achieve sustainable development. Index\xa0...', 'htmlSnippet': 'Relevant technologies of. AIGC will boost the development of artificial intelligence, better serve human society, and achieve sustainable development. Index ...', 'formattedUrl': 'https://arxiv.org/pdf/2304.06632', 'htmlFormattedUrl': 'https://arxiv.org/pdf/2304.06632', 'pagemap': {'cse_thumbnail': [{'src': 'https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSYSWDRFJ7CyOEV2u-ZXpE-TpGMIVcjT8wxLlqPFZnZIETekuZBf1bXW4M&s', 'width': '282', 'height': '178'}], 'metatags': [{'moddate': 'D:20230414003603Z', 'creationdate': 'D:20230414003603Z', 'creator': 'LaTeX with hyperref', 'fullbanner': 'This is pdfTeX, Version 3.14159265-2.6-1.40.21 (TeX Live 2020) kpathsea version 6.3.2', 'producer': 'pdfTeX-1.40.21'}], 'cse_image': [{'src': 'x-raw-image:///452d4486d6714c8a17e2815bbdee652289264ec2b4e26ca167124c0dafbbf471'}]}, 'mime': 'application/pdf', 'fileFormat': 'PDF/Adobe Acrobat'}, {'kind': 'customsearch#result', 'title': 'Partnering with our industry to advance AI transparency and literacy ...', 'htmlTitle': 'Partnering with our industry to advance AI transparency and literacy ...', 'link': 'https://newsroom.tiktok.com/en-us/partnering-with-our-industry-to-advance-ai-transparency-and-literacy', 'displayLink': 'newsroom.tiktok.com', 'snippet': "May 9, 2024 ... TikTok is starting to automatically label AI-generated content (AIGC) when it's uploaded from certain other platforms. To do this, we're\xa0...", 'htmlSnippet': 'May 9, 2024 ... TikTok is starting to automatically label AI-generated content (AIGC) when it's uploaded from certain other platforms. 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              结语

              通过上述步骤，我们完成了Google自定义搜索引擎相关准备工作；同时成功自定义了一个专属自己的搜索引擎，并实践体验了网页搜索和代码API搜索的两种方式。

              AI大模型探索之路-实战篇：智能化IT领域搜索引擎的构建与初步实践

              🎯🔖更多专栏系列文章：AIGC-AI大模型探索之路

              😎 作者介绍：我是寻道AI小兵，资深程序老猿，从业10年+、互联网系统架构师，目前专注于AIGC的探索。

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              如果文章内容对您有所触动，别忘了点赞、⭐关注，收藏！加入我，让我们携手同行AI的探索之旅，一起开启智能时代的大门！

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