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Passaggio 4: Esplorare il codice di ricerca .NET

Nelle lezioni precedenti è stata aggiunta la ricerca a un'app Web statica. Questa lezione illustra i passaggi essenziali per stabilire l'integrazione. Se stai cercando una guida rapida su come integrare la ricerca nella tua applicazione web, questo articolo spiega tutto quello che devi sapere.

Azure SDK Azure.Search.Documents

L'app per le funzioni usa Azure SDK per Azure AI Search:

L'app per le funzioni esegue l'autenticazione tramite l'SDK all'API di Azure AI Search basata sul cloud, usando il nome della risorsa, la chiave di risorsa e il nome dell'indice. I segreti vengono archiviati nelle impostazioni della web app statica e importati nella funzione come variabili di ambiente.

Configurare secrets nel file local.settings.json

{
  "IsEncrypted": false,
  "Values": {
    "AzureWebJobsStorage": "",
    "FUNCTIONS_WORKER_RUNTIME": "dotnet-isolated",
    "SearchApiKey": "",
    "SearchServiceName": "",
    "SearchIndexName": "good-books"
  },
  "Host": {
    "CORS": "*"
  }
}

Funzione di Azure: eseguire ricerche nel catalogo

L'API di ricerca accetta un termine di ricerca e analizza i documenti dell'indice di ricerca, restituendo un elenco di corrispondenze. Tramite l'API Suggerisci, le stringhe parziali vengono inviate al motore di ricerca come tipi di utente, suggerendo termini di ricerca come i titoli dei libri e gli autori nei documenti nell'indice di ricerca e restituendo un piccolo elenco di corrispondenze.

La funzione di Azure raccoglie le informazioni di configurazione della ricerca ed esegue la query.

Lo strumento suggerimenti di ricerca, sg, viene definito nel file dello schema usato durante il caricamento in blocco.

using Azure;
using Azure.Core.Serialization;
using Azure.Search.Documents;
using Azure.Search.Documents.Models;
using Microsoft.Azure.Functions.Worker;
using Microsoft.Azure.Functions.Worker.Http;
using Microsoft.Extensions.Logging;
using System.Net;
using System.Text.Json;
using System.Text.Json.Serialization;
using WebSearch.Models;
using SearchFilter = WebSearch.Models.SearchFilter;

namespace WebSearch.Function
{
    public 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";

        private readonly ILogger<Lookup> _logger;

        public Search(ILogger<Lookup> logger)
        {
            _logger = logger;
        }

        [Function("search")]
        public async Task<HttpResponseData> RunAsync(
            [HttpTrigger(AuthorizationLevel.Anonymous, "post")] HttpRequestData req, 
            FunctionContext executionContext)
        {
            string requestBody = await new StreamReader(req.Body).ReadToEndAsync();
            var data = JsonSerializer.Deserialize<RequestBodySearch>(requestBody);

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

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

            SearchOptions options = new()

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

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

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

                           .ToList();
            }

            // Data to return 
            var output = new SearchOutput
            {
                Count = searchResults.TotalCount,
                Results = searchResults.GetResults().ToList(),
                Facets = facetOutput
            };
            
            var response = req.CreateResponse(HttpStatusCode.Found);

            // Serialize data
            var serializer = new JsonObjectSerializer(
                new JsonSerializerOptions(JsonSerializerDefaults.Web));
            await response.WriteAsJsonAsync(output, serializer);

            return response;
        }

        public static string CreateFilterExpression(List<SearchFilter> filters)
        {
            if (filters is null or { Count: <= 0 })
            {
                return null;
            }

            List<string> filterExpressions = new();


            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);
        }
    }
}

Cliente: Cerca nel catalogo

Chiamare la funzione di Azure nel client React utilizzando il seguente codice \client\src\pages\Search\Search.jsx per cercare libri.

import React, { useEffect, useState, Suspense } from 'react';
import fetchInstance from '../../url-fetch';
import CircularProgress from '@mui/material/CircularProgress';
import { useLocation, useNavigate } 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();
  const navigate = useNavigate();

  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;

  // Handle page changes in a controlled manner
  function handlePageChange(newPage) {
    setCurrentPage(newPage);
  }

  // Calculate skip value and fetch results when relevant parameters change
  useEffect(() => {
    // Calculate skip based on current page
    const calculatedSkip = (currentPage - 1) * top;
    
    // Only update if skip has actually changed
    if (calculatedSkip !== skip) {
      setSkip(calculatedSkip);
      return; // Skip the fetch since skip will change and trigger another useEffect
    }
    
    // Proceed with fetch
    setIsLoading(true);
    
    const body = {
      q: q,
      top: top,
      skip: skip,
      filters: filters
    };

    
    fetchInstance('/api/search', { body, method: 'POST' })
      .then(response => {
        setResults(response.results);
        setFacets(response.facets);
        setResultCount(response.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(() => {
    navigate('/search?q=' + q);
    setCurrentPage(1);
    setFilters([]);
    // eslint-disable-next-line react-hooks/exhaustive-deps
  }, [q]);


  let postSearchHandler = (searchTerm) => {
    setQ(searchTerm);
  }


  // filters should be applied across entire result set, 
  // not just within the current page
  const updateFilterHandler = (newFilters) => {

    // Reset paging
    setSkip(0);
    setCurrentPage(1);

    // Set filters
    setFilters(newFilters);
  };

  return (
    <main className="main main--search container-fluid">
      <div className="row">
        <div className="search-bar-column col-md-3">
          <div className="search-bar-column-container">
            <SearchBar postSearchHandler={postSearchHandler} query={q} width={false}></SearchBar>
          </div>
          <Facets facets={facets} filters={filters} setFilters={updateFilterHandler}></Facets>
        </div>
        <div className="search-bar-results">
          {isLoading ? (
            <div className="col-md-9">
              <CircularProgress />
            </div>
          ) : (
            <div className="search-results-container">
              <Results documents={results} top={top} skip={skip} count={resultCount} query={q}></Results>
              <Pager className="pager-style" currentPage={currentPage} resultCount={resultCount} resultsPerPage={resultsPerPage} onPageChange={handlePageChange}></Pager>
            </div>
          )}
        </div>
      </div>
    </main>
  );
}

Client: suggerimenti dal catalogo

L'API della funzione Suggest viene chiamata nell'app React al \client\src\components\SearchBar\SearchBar.jsx come parte del componente Autocomplete di Material UI. Questo componente usa il testo di input per cercare autori e libri che corrispondono al testo di input e quindi visualizza le possibili corrispondenze in corrispondenza di elementi selezionabili nell'elenco a discesa.

import React, { useState, useEffect } from 'react';
import { TextField, Autocomplete, Button, Box } from '@mui/material';
import fetchInstance from '../../url-fetch';
import './SearchBar.css';

export default function SearchBar({ postSearchHandler, query, width }) {
  const [q, setQ] = useState(() => query || '');
  const [suggestions, setSuggestions] = useState([]);

  const search = (value) => {
    postSearchHandler(value);
  };

  useEffect(() => {
    if (q) {

      const body = { q, top: 5, suggester: 'sg' };

      fetchInstance('/api/suggest', { body, method: 'POST' })
      .then(response => {
        setSuggestions(response.suggestions.map(s => s.text));
      })
      .catch(error => {
        console.log(error);
        setSuggestions([]);
      });
    }
  }, [q]);


  const onInputChangeHandler = (event, value) => {
    setQ(value);
  };


  const onChangeHandler = (event, value) => {

    setQ(value);
    search(value);
  };

  const onEnterButton = (event) => {
    // if enter key is pressed
    if (event.key === 'Enter') {
      search(q);
    }
  };

  return (
    <div
      className={width ? "search-bar search-bar-wide" : "search-bar search-bar-narrow"}
    >
      <Box className="search-bar-box">
        <Autocomplete
          className="autocomplete"
          freeSolo
          value={q}
          options={suggestions}
          onInputChange={onInputChangeHandler}
          onChange={onChangeHandler}
          disableClearable
          renderInput={(params) => (
            <TextField
              {...params}
              id="search-box"
              className="form-control rounded-0"
              placeholder="What are you looking for?"
              onBlur={() => setSuggestions([])}
              onClick={() => setSuggestions([])}
            />
          )}
        />
        <div className="search-button" >
          <Button variant="contained" color="primary" onClick={() => {
            search(q)
          }
          }>
            Search
          </Button>
        </div>
      </Box>
    </div>
  );
}

Funzione di Azure: ottenere un documento specifico

L'API di ricerca documento accetta un ID e restituisce l'oggetto documento dall'indice di ricerca.

using Azure;
using Azure.Core.Serialization;
using Azure.Search.Documents;
using Azure.Search.Documents.Models;
using Microsoft.Azure.Functions.Worker;
using Microsoft.Azure.Functions.Worker.Http;
using Microsoft.Extensions.Logging;
using System.Net;
using System.Text.Json;
using WebSearch.Models;

namespace WebSearch.Function
{
    public 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";

        private readonly ILogger<Lookup> _logger;

        public Lookup(ILogger<Lookup> logger)
        {
            _logger = logger;
        }


        [Function("lookup")]
        public async Task<HttpResponseData> RunAsync(
            [HttpTrigger(AuthorizationLevel.Anonymous, "get", "post")] HttpRequestData req, 
            FunctionContext executionContext)
        {

            // Get Document Id
            var query = System.Web.HttpUtility.ParseQueryString(req.Url.Query);
            string documentId = query["id"].ToString();

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

            SearchClient searchClient = new(

                serviceEndpoint,
                searchIndexName,
                new AzureKeyCredential(searchApiKey)
            );

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

            // Data to return 
            var output = new LookupOutput
            {
                Document = getDocumentResponse.Value
            };

            var response = req.CreateResponse(HttpStatusCode.Found);

            // Serialize data
            var serializer = new JsonObjectSerializer(
                new JsonSerializerOptions(JsonSerializerDefaults.Web));
            await response.WriteAsJsonAsync(output, serializer);

            return response;
        }
    }
}

Client: ottenere un documento specifico

Questa API di funzione viene chiamata nell'applicazione React in \client\src\pages\Details\Details.jsx come parte dell'inizializzazione del componente:

import React, { useState, useEffect } from "react";
import { useParams } from 'react-router-dom';
import Rating from '@mui/material/Rating';
import CircularProgress from '@mui/material/CircularProgress';
import Tabs from '@mui/material/Tabs';
import Tab from '@mui/material/Tab';
import Box from '@mui/material/Box';

import fetchInstance from '../../url-fetch';

import "./Details.css";


function CustomTabPanel(props) {
  const { children, value, index, ...other } = props;

  return (
    <div
      className="tab-panel"
      role="tabpanel"
      hidden={value !== index}
      id={`simple-tabpanel-${index}`}
      aria-labelledby={`simple-tab-${index}`}
      {...other}
       // Ensure it takes full width
    >
      {value === index && <Box className="tab-panel-value">{children}</Box>}
    </div>
  );
}

export default function BasicTabs() {
  const { id } = useParams();
  const [document, setDocument] = useState({});
  const [value, setValue] = React.useState(0);
  const [isLoading, setIsLoading] = useState(true);

  useEffect(() => {
    setIsLoading(true);
    fetchInstance('/api/lookup', { query: { id } })
      .then(response => {
        console.log(JSON.stringify(response))
        const doc = response.document;
        setDocument(doc);
        setIsLoading(false);
      })
      .catch(error => {
        console.log(error);
        setIsLoading(false);
      });

  }, [id]);

  const handleChange = (event, newValue) => {
    setValue(newValue);
  };


  if (isLoading || !id || Object.keys(document).length === 0) {
    return (
      <div className="loading-container">
        <CircularProgress />
        <p>Loading...</p>
      </div>
    );
  }

  return (
    <Box className="details-box-parent">
      <Box className="details-tab-box-header">
        <Tabs value={value} onChange={handleChange} aria-label="book-details-tabs">
          <Tab label="Result" />
          <Tab label="Raw Data" />
        </Tabs>
      </Box>
      <CustomTabPanel value={value} index={0} className="tab-panel box-content">
        <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>
      </CustomTabPanel>
      <CustomTabPanel value={value} index={1} className="tab-panel">
        <div className="card-body text-left card-text details-custom-tab-panel-json-div" >
          <pre><code>
            {JSON.stringify(document, null, 2)}
          </code></pre>
        </div>
      </CustomTabPanel>
    </Box>
  );
}

Modelli C# per supportare l'applicazione di funzioni

I modelli seguenti vengono usati per supportare le funzioni in questa app.

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

namespace WebSearch.Models
{
    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; }
    }
}

Passaggi successivi

Per altre informazioni sullo sviluppo di Azure AI Search, provare l’esercitazione successiva sull'indicizzazione:

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