Azure AI Search .NET SDK 文档

azure-search-documents-dotnet
分类通用
作者Agentic Awesome Skills 社区
许可MIT
评分4.20/5
使用3.6K

Azure.Search.Documents (.NET)

构建具备全文搜索、向量搜索、语义搜索和混合搜索能力的搜索应用程序。

安装

bash
dotnet add package Azure.Search.Documents
dotnet add package Azure.Identity

当前版本：稳定版 v11.7.0，预览版 v11.8.0-beta.1

环境变量

bash
SEARCH_ENDPOINT=https://<search-service>.search.windows.net
SEARCH_INDEX_NAME=<index-name>

用于 API 密钥认证（生产环境不推荐）

SEARCH_API_KEY=<api-key>

身份验证

DefaultAzureCredential (推荐)：

csharp
using Azure.Identity;
using Azure.Search.Documents;


var credential = new DefaultAzureCredential();
var client = new SearchClient(
    new Uri(Environment.GetEnvironmentVariable("SEARCH_ENDPOINT")),
    Environment.GetEnvironmentVariable("SEARCH_INDEX_NAME"),
    credential);

API 密钥：

csharp
using Azure;
using Azure.Search.Documents;


var credential = new AzureKeyCredential(
    Environment.GetEnvironmentVariable("SEARCH_API_KEY"));
var client = new SearchClient(
    new Uri(Environment.GetEnvironmentVariable("SEARCH_ENDPOINT")),
    Environment.GetEnvironmentVariable("SEARCH_INDEX_NAME"),
    credential);

客户端选择

| 客户端 | 用途 |
|--------|---------|
| SearchClient | 查询索引，上传/更新/删除文档 |
| SearchIndexClient | 创建/管理索引、同义词映射 |
| SearchIndexerClient | 管理索引器、技能集、数据源 |

创建索引

使用 FieldBuilder (推荐)

csharp
using Azure.Search.Documents.Indexes;
using Azure.Search.Documents.Indexes.Models;

// 使用特性定义模型
public class Hotel
{
    [SimpleField(IsKey = true, IsFilterable = true)]
    public string HotelId { get; set; }


[SearchableField(IsSortable = true)]
    public string HotelName { get; set; }


[SearchableField(AnalyzerName = LexicalAnalyzerName.EnLucene)]
    public string Description { get; set; }


[SimpleField(IsFilterable = true, IsSortable = true, IsFacetable = true)]
    public double? Rating { get; set; }


[VectorSearchField(VectorSearchDimensions = 1536, VectorSearchProfileName = "vector-profile")]
    public ReadOnlyMemory<float>? DescriptionVector { get; set; }
}


// 创建索引
var indexClient = new SearchIndexClient(endpoint, credential);
var fieldBuilder = new FieldBuilder();
var fields = fieldBuilder.Build(typeof(Hotel));


var index = new SearchIndex("hotels")
{
    Fields = fields,
    VectorSearch = new VectorSearch
    {
        Profiles = { new VectorSearchProfile("vector-profile", "hnsw-algo") },
        Algorithms = { new HnswAlgorithmConfiguration("hnsw-algo") }
    }
};


await indexClient.CreateOrUpdateIndexAsync(index);

手动定义字段

csharp
var index = new SearchIndex("hotels")
{
    Fields =
    {
        new SimpleField("hotelId", SearchFieldDataType.String) { IsKey = true, IsFilterable = true },
        new SearchableField("hotelName") { IsSortable = true },
        new SearchableField("description") { AnalyzerName = LexicalAnalyzerName.EnLucene },
        new SimpleField("rating", SearchFieldDataType.Double) { IsFilterable = true, IsSortable = true },
        new SearchField("descriptionVector", Sear
chFieldDataType.Collection(SearchFieldDataType.Single)) { VectorSearchDimensions = 1536, VectorSearchProfileName = "vector-profile" } } };
code
## 文档操作
csharp var searchClient = new SearchClient(endpoint, indexName, credential);

// 上传（新增）
var hotels = new[] { new Hotel { HotelId = "1", HotelName = "Hotel A" } };
await searchClient.UploadDocumentsAsync(hotels);

// 合并（更新现有）
await searchClient.MergeDocumentsAsync(hotels);

// 合并或上传（upsert）
await searchClient.MergeOrUploadDocumentsAsync(hotels);

// 删除
await searchClient.DeleteDocumentsAsync("hotelId", new[] { "1", "2" });

// 批量操作
var batch = IndexDocumentsBatch.Create(
IndexDocumentsAction.Upload(hotel1),
IndexDocumentsAction.Merge(hotel2),
IndexDocumentsAction.Delete(hotel3));
await searchClient.IndexDocumentsAsync(batch);

code
## 搜索模式


基础搜索
csharp var options = new SearchOptions { Filter = "rating ge 4", OrderBy = { "rating desc" }, Select = { "hotelId", "hotelName", "rating" }, Size = 10, Skip = 0, IncludeTotalCount = true };

SearchResults<Hotel> results = await searchClient.SearchAsync<Hotel>("luxury", options);

Console.WriteLine($"Total: {results.TotalCount}");
await foreach (SearchResult<Hotel> result in results.GetResultsAsync())
{
Console.WriteLine($"{result.Document.HotelName} (Score: {result.Score})");
}

code
### 分面搜索 (Faceted Search)
csharp
var options = new SearchOptions
{
Facets = { "rating,count:5", "category" }
};

var results = await searchClient.SearchAsync<Hotel>("*", options);

foreach (var facet in results.Value.Facets["rating"])
{
Console.WriteLine($"Rating {facet.Value}: {facet.Count}");
}

code
### 自动完成与建议
csharp
// 自动完成
var autocompleteOptions = new AutocompleteOptions { Mode = AutocompleteMode.OneTermWithContext };
var autocomplete = await searchClient.AutocompleteAsync("lux", "suggester-name", autocompleteOptions);

// 建议
var suggestOptions = new SuggestOptions { UseFuzzyMatching = true };
var suggestions = await searchClient.SuggestAsync<Hotel>("lux", "suggester-name", suggestOptions);

code
## 向量搜索


详细模式请参阅 references/vector-search.md。
csharp
using Azure.Search.Documents.Models;

// 纯向量搜索
var vectorQuery = new VectorizedQuery(embedding)
{
KNearestNeighborsCount = 5,
Fields = { "descriptionVector" }
};

var options = new SearchOptions
{
VectorSearch = new VectorSearchOptions
{
Queries = { vectorQuery }
}
};

var results = await searchClient.SearchAsync<Hotel>(null, options);

code
## 语义搜索


详细模式请参阅 references/semantic-search.md。
csharp
var options = new SearchOptions
{
QueryType = SearchQueryType.Semantic,
SemanticSearch = new SemanticSearchOptions
{
SemanticConfigurationName = "my-semantic-config",
QueryCaption = new QueryCaption(QueryCaptionType.Extractive),
QueryAnswer = new QueryAnswer(QueryAnswerType.Extractive)
}
};

var results = await searchClient.SearchAsync<Hotel>("best hotel for families", options);

// 获取语义答案
foreach (var answer in results.Value.SemanticSearch.Answers)
{
Console.WriteLine($"Answer: {answer.Text} (Score: {answer.Score})");
}

// 获取摘要 (Captions)
await foreach (var result in results.Value.GetResultsAsync())
{
var caption = result.SemanticSearch?
.Captions?.FirstOrDefault();
Console.WriteLine($"Caption: {caption?.Text}");
}

code
## 混合搜索 (向量 + 关键字 + 语义)
csharp
var vectorQuery = new VectorizedQuery(embedding)
{
KNearestNeighborsCount = 5,
Fields = { "descriptionVector" }
};

var options = new SearchOptions
{
QueryType = SearchQueryType.Semantic,
SemanticSearch = new SemanticSearchOptions
{
SemanticConfigurationName = "my-semantic-config"
},
VectorSearch = new VectorSearchOptions
{
Queries = { vectorQuery }
}
};

// 结合关键字搜索、向量搜索和语义排序
var results = await searchClient.SearchAsync<Hotel>("luxury beachfront", options);

code
## 字段属性参考


| 属性 | 用途 |
|-----------|---------|
| SimpleField | 非可搜索字段（用于筛选、排序、分面） |
| SearchableField | 全文可搜索字段 |
| VectorSearchField | 向量嵌入字段 |
| IsKey = true | 文档键（必填，每个索引仅限一个） |
| IsFilterable = true | 启用 $filter 表达式 |
| IsSortable = true | 启用 $orderby |
| IsFacetable = true | 启用分面导航 |
| IsHidden = true | 从结果中排除 |
| AnalyzerName | 指定文本分析器 |


错误处理
csharp using Azure;

try
{
var results = await searchClient.SearchAsync<Hotel>("query");
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
Console.WriteLine("Index not found");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"Search error: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}
``

最佳实践

1. 生产环境建议使用 DefaultAzureCredential 而非 API 密钥
2. 使用 FieldBuilder 配合模型属性以实现类型安全的索引定义
3. 使用 CreateOrUpdateIndexAsync 实现幂等索引创建
4. 对文档操作进行分批处理以提高吞吐量
5. 使用 Select` 仅返回必要的字段
6. 为自然语言查询配置语义搜索
7. 结合向量 + 关键字 + 语义搜索以获得最佳相关性

参考文件

| 文件 | 内容 |
|------|----------|
| references/vector-search.md | 向量搜索、混合搜索、向量化器 |
| references/semantic-search.md | 语义排序、摘要 (captions)、答案 |

适用场景

本技能适用于执行概览中所描述的工作流或操作。

局限性

  • 仅在任务明确符合上述范围时使用此技能。
  • 不要将输出视为环境特定验证、测试或专家评审的替代方案。
  • 如果缺少必要的输入、权限、安全边界或成功标准，请停止并请求澄清。