步骤 4 - 探索 .NET 搜索代码

Artículo
11/18/2024

在前面的课程中，你已将搜索添加到静态 Web 应用。本课重点介绍了建立集成的必要步骤。如果你正在寻找有关如何将搜索集成到 Web 应用的速查表，本文便介绍了你需要了解的内容。

Azure SDK Azure.Search.Documents

函数应用使用 Azure SDK 进行 Azure AI 搜索：

NuGet：Azure.Search.Documents
参考文档：客户端库

函数应用使用资源名称、资源键和索引名称通过 SDK 向基于云的 Azure AI 搜索 API 进行身份验证。密码存储在静态 Web 应用设置中，并作为环境变量拉取到函数中。

在 local.settings.json 文件中配置机密

在 ./api/ 创建一个名为 local.settings.json 的新文件，并将以下 JSON 对象复制到该文件中。

{
  "IsEncrypted": false,
  "Values": {
    "AzureWebJobsStorage": "",
    "FUNCTIONS_WORKER_RUNTIME": "dotnet",
    "SearchApiKey": "YOUR_SEARCH_QUERY_KEY",
    "SearchServiceName": "YOUR_SEARCH_RESOURCE_NAME",
    "SearchIndexName": "good-books"
  }
}

为下方内容更改为你自己的搜索资源值：
- YOUR_SEARCH_RESOURCE_NAME
- YOUR_SEARCH_QUERY_KEY

Azure Function：搜索目录

Search API 采用搜索词并在搜索索引中的文档之间搜索，并返回匹配项的列表。

Azure 函数拉取搜索配置信息并完成查询。

using System.IO;
using System.Threading.Tasks;
using Microsoft.AspNetCore.Http;
using Microsoft.AspNetCore.Mvc;
using Microsoft.Azure.WebJobs;
using Microsoft.Azure.WebJobs.Extensions.Http;
using Microsoft.Extensions.Logging;
using System.Text.Json;
using Azure.Search.Documents;
using Azure.Search.Documents.Models;
using System;
using Azure;
using System.Collections.Generic;
using System.Linq;



namespace FunctionApp_web_search
{
    public static class Search
    {
        private static string searchApiKey = Environment.GetEnvironmentVariable("SearchApiKey", EnvironmentVariableTarget.Process);
        private static string searchServiceName = Environment.GetEnvironmentVariable("SearchServiceName", EnvironmentVariableTarget.Process);
        private static string searchIndexName = Environment.GetEnvironmentVariable("SearchIndexName", EnvironmentVariableTarget.Process) ?? "good-books";


        [FunctionName("search")]
        public static async Task<IActionResult> Run(
            [HttpTrigger(AuthorizationLevel.Anonymous, "post", Route = null)] HttpRequest req,
            ILogger log)
        {

            string requestBody = await new StreamReader(req.Body).ReadToEndAsync();
            var data = JsonSerializer.Deserialize<RequestBodySearch>(requestBody);

            // Azure AI Search 
            Uri serviceEndpoint = new Uri($"https://{searchServiceName}.search.azure.cn/");

            SearchClient searchClient = new SearchClient(
                serviceEndpoint,
                searchIndexName,
                new AzureKeyCredential(searchApiKey)
            );

            SearchOptions options = new SearchOptions()
            {
                Size = data.Size,
                Skip = data.Skip,
                IncludeTotalCount = true,
                Filter= CreateFilterExpression(data.Filters)
            };
            options.Facets.Add("authors");
            options.Facets.Add("language_code");

            SearchResults<SearchDocument> response = searchClient.Search<SearchDocument>(data.SearchText, options);

            var facetOutput = new Dictionary<String, IList<FacetValue>>();
            foreach(var facetResult in response.Facets) {
                facetOutput[facetResult.Key] = facetResult.Value
                           .Select(x => new FacetValue() { value = x.Value.ToString(), count = x.Count })
                           .ToList();     
            }

            var output = new SearchOutput
            {
                Count = response.TotalCount,
                Results = response.GetResults().ToList(),
                Facets = facetOutput
            };

            return new OkObjectResult(output);
        }
        public static string CreateFilterExpression(List<SearchFilter> filters)
        {
            if (filters == null || filters.Count <= 0)
            {
                return null;
            }

            List<string> filterExpressions = new List<string>();

            List<SearchFilter> authorFilters = filters.Where(f => f.field == "authors").ToList();
            List<SearchFilter> languageFilters = filters.Where(f => f.field == "language_code").ToList();

            List<string> authorFilterValues = authorFilters.Select(f => f.value).ToList();

            if (authorFilterValues.Count > 0)
            {
                string filterStr = string.Join(",", authorFilterValues);
                filterExpressions.Add($"{"authors"}/any(t: search.in(t, '{filterStr}', ','))");
            }

            List<string> languageFilterValues = languageFilters.Select(f => f.value).ToList();
            foreach (var value in languageFilterValues)
            {
                filterExpressions.Add($"language_code eq '{value}'");
            }

            return string.Join(" and ", filterExpressions);
        }

    }
}

客户端：从目录中搜索

通过以下代码在 React 客户端中调用 Azure Function。

import React, { useEffect, useState } from 'react';
import axios from 'axios';
import CircularProgress  from '@material-ui/core/CircularProgress';
import { useLocation, useHistory } from "react-router-dom";

import Results from '../../components/Results/Results';
import Pager from '../../components/Pager/Pager';
import Facets from '../../components/Facets/Facets';
import SearchBar from '../../components/SearchBar/SearchBar';

import "./Search.css";

export default function Search() {
  
  let location = useLocation();
  let history = useHistory();
  
  const [ results, setResults ] = useState([]);
  const [ resultCount, setResultCount ] = useState(0);
  const [ currentPage, setCurrentPage ] = useState(1);
  const [ q, setQ ] = useState(new URLSearchParams(location.search).get('q') ?? "*");
  const [ top ] = useState(new URLSearchParams(location.search).get('top') ?? 8);
  const [ skip, setSkip ] = useState(new URLSearchParams(location.search).get('skip') ?? 0);
  const [ filters, setFilters ] = useState([]);
  const [ facets, setFacets ] = useState({});
  const [ isLoading, setIsLoading ] = useState(true);

  let resultsPerPage = top;
  
  useEffect(() => {
    setIsLoading(true);
    setSkip((currentPage-1) * top);
    const body = {
      q: q,
      top: top,
      skip: skip,
      filters: filters
    };

    axios.post( '/api/search', body)
      .then(response => {
            //console.log(JSON.stringify(response.data))
            setResults(response.data.results);
            setFacets(response.data.facets);
            setResultCount(response.data.count);
            setIsLoading(false);
        } )
        .catch(error => {
            console.log(error);
            setIsLoading(false);
        });
    
  }, [q, top, skip, filters, currentPage]);

  // pushing the new search term to history when q is updated
  // allows the back button to work as expected when coming back from the details page
  useEffect(() => {
    history.push('/search?q=' + q);  
    setCurrentPage(1);
    setFilters([]);
    // eslint-disable-next-line react-hooks/exhaustive-deps
  }, [q]);


  let postSearchHandler = (searchTerm) => {
    //console.log(searchTerm);
    setQ(searchTerm);
  }

  var body;
  if (isLoading) {
    body = (
      <div className="col-md-9">
        <CircularProgress />
      </div>);
  } else {
    body = (
      <div className="col-md-9">
        <Results documents={results} top={top} skip={skip} count={resultCount}></Results>
        <Pager className="pager-style" currentPage={currentPage} resultCount={resultCount} resultsPerPage={resultsPerPage} setCurrentPage={setCurrentPage}></Pager>
      </div>
    )
  }

  return (
    <main className="main main--search container-fluid">
      
      <div className="row">
        <div className="col-md-3">
          <div className="search-bar">
            <SearchBar postSearchHandler={postSearchHandler} q={q}></SearchBar>
          </div>
          <Facets facets={facets} filters={filters} setFilters={setFilters}></Facets>
        </div>
        {body}
      </div>
    </main>
  );
}

Azure Function：来自目录的建议

在用户键入内容时，Suggest API 将使用搜索词，并为搜索索引中的文档建议搜索词（如书籍标题和作者），并返回一个较小的匹配列表。

搜索建议器 sg 在大容量上传期间使用的架构文件中定义。

using System.IO;
using System.Threading.Tasks;
using Microsoft.AspNetCore.Http;
using Microsoft.AspNetCore.Mvc;
using Microsoft.Azure.WebJobs;
using Microsoft.Azure.WebJobs.Extensions.Http;
using Microsoft.Extensions.Logging;
using System.Text.Json;
using Azure.Search.Documents;
using Azure.Search.Documents.Models;
using System;
using Azure;
using System.Collections.Generic;
using System.Linq;

namespace FunctionApp_web_search
{
    public static class Suggest
    {

        private static string searchApiKey = Environment.GetEnvironmentVariable("SearchApiKey", EnvironmentVariableTarget.Process);
        private static string searchServiceName = Environment.GetEnvironmentVariable("SearchServiceName", EnvironmentVariableTarget.Process);
        private static string searchIndexName = Environment.GetEnvironmentVariable("SearchIndexName", EnvironmentVariableTarget.Process) ?? "good-books";


        [FunctionName("suggest")]
        public static async Task<IActionResult> Run(
            [HttpTrigger(AuthorizationLevel.Anonymous,"post", Route = null)] HttpRequest req,
            ILogger log)
        {

            // Get Document Id
            string requestBody = await new StreamReader(req.Body).ReadToEndAsync();
            var data = JsonSerializer.Deserialize<RequestBodySuggest>(requestBody);

            // Azure AI Search 
            Uri serviceEndpoint = new Uri($"https://{searchServiceName}.search.azure.cn/");

            SearchClient searchClient = new SearchClient(
                serviceEndpoint,
                searchIndexName,
                new AzureKeyCredential(searchApiKey)
            );

            SuggestOptions options = new SuggestOptions()
            {
                Size = data.Size
            };

            var suggesterResponse = await searchClient.SuggestAsync<BookModel>(data.SearchText, data.SuggesterName, options);
            var response = new Dictionary<string, List<SearchSuggestion<BookModel>>>();
            response["suggestions"] = suggesterResponse.Value.Results.ToList();

            return new OkObjectResult(response);
        }
    }
}

客户端：来自目录的建议

建议函数 API 在 \client\src\components\SearchBar\SearchBar.js 中作为组件初始化的一部分在 React 应用中调用：

import React, {useState, useEffect} from 'react';
import axios from 'axios';
import Suggestions from './Suggestions/Suggestions';

import "./SearchBar.css";

export default function SearchBar(props) {

    let [q, setQ] = useState("");
    let [suggestions, setSuggestions] = useState([]);
    let [showSuggestions, setShowSuggestions] = useState(false);

    const onSearchHandler = () => {
        props.postSearchHandler(q);
        setShowSuggestions(false);
    }

    const suggestionClickHandler = (s) => {
        document.getElementById("search-box").value = s;
        setShowSuggestions(false);
        props.postSearchHandler(s);    
    }

    const onEnterButton = (event) => {
        if (event.keyCode === 13) {
            onSearchHandler();
        }
    }

    const onChangeHandler = () => {
        var searchTerm = document.getElementById("search-box").value;
        setShowSuggestions(true);
        setQ(searchTerm);

        // use this prop if you want to make the search more reactive
        if (props.searchChangeHandler) {
            props.searchChangeHandler(searchTerm);
        }
    }

    useEffect(_ =>{
        const timer = setTimeout(() => {
            const body = {
                q: q,
                top: 5,
                suggester: 'sg'
            };

            if (q === '') {
                setSuggestions([]);
            } else {
                axios.post( '/api/suggest', body)
                .then(response => {
                    console.log(JSON.stringify(response.data))
                    setSuggestions(response.data.suggestions);
                } )
                .catch(error => {
                    console.log(error);
                    setSuggestions([]);
                });
            }
        }, 300);
        return () => clearTimeout(timer);
    }, [q, props]);

    var suggestionDiv;
    if (showSuggestions) {
        suggestionDiv = (<Suggestions suggestions={suggestions} suggestionClickHandler={(s) => suggestionClickHandler(s)}></Suggestions>);
    } else {
        suggestionDiv = (<div></div>);
    }

    return (
        <div >
            <div className="input-group" onKeyDown={e => onEnterButton(e)}>
                <div className="suggestions" >
                    <input 
                        autoComplete="off" // setting for browsers; not the app
                        type="text" 
                        id="search-box" 
                        className="form-control rounded-0" 
                        placeholder="What are you looking for?" 
                        onChange={onChangeHandler} 
                        defaultValue={props.q}
                        onBlur={() => setShowSuggestions(false)}
                        onClick={() => setShowSuggestions(true)}>
                    </input>
                    {suggestionDiv}
                </div>
                <div className="input-group-btn">
                    <button className="btn btn-primary rounded-0" type="submit" onClick={onSearchHandler}>
                        Search
                    </button>
                </div>
            </div>
        </div>
    );
};

Azure Function：获取特定文档

Lookup API 接受 ID 并从搜索索引中返回文档对象。

using System.IO;
using System.Threading.Tasks;
using Microsoft.AspNetCore.Http;
using Microsoft.AspNetCore.Mvc;
using Microsoft.Azure.WebJobs;
using Microsoft.Azure.WebJobs.Extensions.Http;
using Microsoft.Extensions.Logging;
using System.Text.Json;
using Azure.Search.Documents;
using Azure.Search.Documents.Models;
using System;
using Azure;

namespace FunctionApp_web_search
{
    public static class Lookup
    {

        private static string searchApiKey = Environment.GetEnvironmentVariable("SearchApiKey", EnvironmentVariableTarget.Process);
        private static string searchServiceName = Environment.GetEnvironmentVariable("SearchServiceName", EnvironmentVariableTarget.Process);
        private static string searchIndexName = Environment.GetEnvironmentVariable("SearchIndexName", EnvironmentVariableTarget.Process) ?? "good-books";


        [FunctionName("lookup")]
        public static async Task<IActionResult> Run(
            [HttpTrigger(AuthorizationLevel.Anonymous, "get", "post", Route = null)] HttpRequest req,
            ILogger log)
        {

            // Get Document Id
            string documentId = req.Query["id"]; ;

            // Azure AI Search 
            Uri serviceEndpoint = new Uri($"https://{searchServiceName}.search.azure.cn/");

            SearchClient searchClient = new SearchClient(
                serviceEndpoint,
                searchIndexName,
                new AzureKeyCredential(searchApiKey)
            );

            var response = await searchClient.GetDocumentAsync<SearchDocument>(documentId);

            var output = new LookupOutput
            {
                Document = response.Value
            };

            return new OkObjectResult(output);
        }
    }
}

客户端：获取特定文档

此函数 API 在 \client\src\pages\Details\Detail.js 作为组件初始化的一部分在 React 应用程序中调用：

import React, { useState, useEffect } from "react";
import { useParams } from 'react-router-dom';
import Rating from '@material-ui/lab/Rating';
import CircularProgress from '@material-ui/core/CircularProgress';
import axios from 'axios';

import "./Details.css";

export default function Details() {

  let { id } = useParams();
  const [document, setDocument] = useState({});
  const [selectedTab, setTab] = useState(0);
  const [isLoading, setIsLoading] = useState(true);

  useEffect(() => {
    setIsLoading(true);
    // console.log(id);
    axios.get('/api/lookup?id=' + id)
      .then(response => {
        //console.log(JSON.stringify(response.data))
        const doc = response.data.document;
        setDocument(doc);
        setIsLoading(false);
      })
      .catch(error => {
        console.log(error);
        setIsLoading(false);
      });

  }, [id]);

  // View default is loading with no active tab
  let detailsBody = (<CircularProgress />),
      resultStyle = "nav-link",
      rawStyle    = "nav-link";

  if (!isLoading && document) {
    // View result
    if (selectedTab === 0) {
      resultStyle += " active";
      detailsBody = (
        <div className="card-body">
          <h5 className="card-title">{document.original_title}</h5>
          <img className="image" src={document.image_url} alt="Book cover"></img>
          <p className="card-text">{document.authors?.join('; ')} - {document.original_publication_year}</p>
          <p className="card-text">ISBN {document.isbn}</p>
          <Rating name="half-rating-read" value={parseInt(document.average_rating)} precision={0.1} readOnly></Rating>
          <p className="card-text">{document.ratings_count} Ratings</p>
        </div>
      );
    }

    // View raw data
    else {
      rawStyle += " active";
      detailsBody = (
        <div className="card-body text-left">
          <pre><code>
            {JSON.stringify(document, null, 2)}
          </code></pre>
        </div>
      );
    }
  }

  return (
    <main className="main main--details container fluid">
      <div className="card text-center result-container">
        <div className="card-header">
          <ul className="nav nav-tabs card-header-tabs">
              <li className="nav-item"><button className={resultStyle} onClick={() => setTab(0)}>Result</button></li>
              <li className="nav-item"><button className={rawStyle} onClick={() => setTab(1)}>Raw Data</button></li>
          </ul>
        </div>
        {detailsBody}
      </div>
    </main>
  );
}

支持函数应用的 C# 模型

以下模型用于支持此应用中的函数。

using Azure.Search.Documents.Models;
using System;
using System.Collections.Generic;
using System.Text;
using System.Text.Json.Serialization;

namespace FunctionApp_web_search
{
    public class RequestBodyLookUp
    {
        [JsonPropertyName("id")]
        public string Id { get; set; }
    }

    public class RequestBodySuggest
    {
        [JsonPropertyName("q")]
        public string SearchText { get; set; }

        [JsonPropertyName("top")]
        public int Size { get; set; }

        [JsonPropertyName("suggester")]
        public string SuggesterName { get; set; }
    }

    public class RequestBodySearch
    {
        [JsonPropertyName("q")]
        public string SearchText { get; set; }

        [JsonPropertyName("skip")]
        public int Skip { get; set; }

        [JsonPropertyName("top")]
        public int Size { get; set; }

        [JsonPropertyName("filters")]
        public List<SearchFilter> Filters { get; set; }

    }


    public class SearchFilter
    {
        public string field { get; set; }
        public string value { get; set; }
    }

    public class FacetValue
    {
        public string value { get; set; }
        public long? count { get; set; }
    }
    class SearchOutput
    {
        [JsonPropertyName("count")]
        public long? Count { get; set; }
        [JsonPropertyName("results")]
        public List<SearchResult<SearchDocument>> Results { get; set; }
        [JsonPropertyName("facets")]
        public Dictionary<String, IList<FacetValue>> Facets { get; set; }
    }
    class LookupOutput
    {
        [JsonPropertyName("document")]
        public SearchDocument Document { get; set; }
    }
    public class BookModel
    {
        public string id { get; set; }
        public decimal? goodreads_book_id { get; set; }
        public decimal? best_book_id { get; set; }
        public decimal? work_id { get; set; }
        public decimal? books_count { get; set; }
        public string isbn { get; set; }
        public string isbn13 { get; set; }
        public string[] authors { get; set; }
        public decimal? original_publication_year { get; set; }
        public string original_title { get; set; }
        public string title { get; set; }
        public string language_code { get; set; }
        public double? average_rating { get; set; }
        public decimal? ratings_count { get; set; }
        public decimal? work_ratings_count { get; set; }
        public decimal? work_text_reviews_count { get; set; }
        public decimal? ratings_1 { get; set; }
        public decimal? ratings_2 { get; set; }
        public decimal? ratings_3 { get; set; }
        public decimal? ratings_4 { get; set; }
        public decimal? ratings_5 { get; set; }
        public string image_url { get; set; }
        public string small_image_url { get; set; }
    }
}

后续步骤

若要继续了解有关 Azure AI 搜索开发的详细信息，请尝试下一个有关索引的教程：

编制 Azure SQL 数据索引

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