azure-search-documents-dotnet
用 .NET 的 Azure AI Search SDK 做全文、向量、語意和混合搜尋
中文翻譯
---
name: azure-search-documents-dotnet
description: |
.NET 版的 Azure AI Search SDK（Azure.Search.Documents）。用來建構支援全文檢索、向量、語意和混合搜尋的搜尋應用。涵蓋 SearchClient（查詢、文件 CRUD）、SearchIndexClient（index 管理）和 SearchIndexerClient（indexer、skillset）。Triggers: "Azure Search .NET", "SearchClient", "SearchIndexClient", "vector search C#", "semantic search .NET", "hybrid search", "Azure.Search.Documents".
license: MIT
metadata:
author: Microsoft
version: "1.0.0"
package: Azure.Search.Documents
---
# Azure.Search.Documents (.NET)
建構具備全文檢索、向量、語意和混合搜尋能力的搜尋應用。
## 安裝
```bash
dotnet add package Azure.Search.Documents
dotnet add package Azure.Identity
```
**目前版本**：Stable v11.7.0、Preview v11.8.0-beta.1
## 環境變數
```bash
SEARCH_ENDPOINT=https://<search-service>.search.windows.net # 必填：search service endpoint
SEARCH_INDEX_NAME=<index-name> # 必填：search index 名稱
AZURE_TOKEN_CREDENTIALS=prod # 只有在 production 使用 DefaultAzureCredential 時才需要
SEARCH_API_KEY=<api-key> # 只有用 AzureKeyCredential 驗證時才需要
```
## 驗證
**Microsoft Entra Token Credential**：
```csharp
using Azure.Identity;
using Azure.Search.Documents;
// 本機開發：DefaultAzureCredential。Production：設定 AZURE_TOKEN_CREDENTIALS=prod 或 AZURE_TOKEN_CREDENTIALS=<specific_credential>
var credential = new DefaultAzureCredential(
DefaultAzureCredential.DefaultEnvironmentVariableName
);
// 或在 production 直接用特定的 credential：
// See https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
var client = new SearchClient(
new Uri(Environment.GetEnvironmentVariable("SEARCH_ENDPOINT")),
Environment.GetEnvironmentVariable("SEARCH_INDEX_NAME"),
credential);
```
**API Key**：
```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);
```
## 選擇 Client
| Client | 用途 |
|--------|---------|
| `SearchClient` | 查詢 index、上傳／更新／刪除文件 |
| `SearchIndexClient` | 建立／管理 index、synonym map |
| `SearchIndexerClient` | 管理 indexer、skillset、data source |
## 建立 Index
### 使用 FieldBuilder（建議）
```csharp
using Azure.Search.Documents.Indexes;
using Azure.Search.Documents.Indexes.Models;
// 用 attribute 定義 model
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; }
}
// 建立 index
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);
```
### 手動定義欄位
```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", SearchFieldDataType.Collection(SearchFieldDataType.Single))
{
VectorSearchDimensions = 1536,
VectorSearchProfileName = "vector-profile"
}
}
};
```
## 文件操作
```csharp
var searchClient = new SearchClient(endpoint, indexName, credential);
// Upload（新增）
var hotels = new[] { new Hotel { HotelId = "1", HotelName = "Hotel A" } };
await searchClient.UploadDocumentsAsync(hotels);
// Merge（更新既有文件）
await searchClient.MergeDocumentsAsync(hotels);
// Merge or Upload（upsert）
await searchClient.MergeOrUploadDocumentsAsync(hotels);
// Delete
await searchClient.DeleteDocumentsAsync("hotelId", new[] { "1", "2" });
// 批次操作
var batch = IndexDocumentsBatch.Create(
IndexDocumentsAction.Upload(hotel1),
IndexDocumentsAction.Merge(hotel2),
IndexDocumentsAction.Delete(hotel3));
await searchClient.IndexDocumentsAsync(batch);
```
## 搜尋模式
### 基本搜尋
```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})");
}
```
### Faceted 搜尋
```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($"Rating {facet.Value}: {facet.Count}");
}
```
### 自動完成與建議
```csharp
// Autocomplete
var autocompleteOptions = new AutocompleteOptions { Mode = AutocompleteMode.OneTermWithContext };
var autocomplete = await searchClient.AutocompleteAsync("lux", "suggester-name", autocompleteOptions);
// Suggestions
var suggestOptions = new SuggestOptions { UseFuzzyMatching = true };
var suggestions = await searchClient.SuggestAsync<Hotel>("lux", "suggester-name", suggestOptions);
```
## 向量搜尋
詳細模式請看 [references/vector-search.md](references/vector-search.md)。
```csharp
using Azure.Search.Documents.Models;
// 純向量搜尋
var vectorQuery = new VectorizedQuery(embedding)
{
KNearestNeighborsCount = 5,
Fields = { "descriptionVector" }
};
var options = new SearchOptions
{
VectorSearch = new VectorSearchOptions
{
Queries = { vectorQuery }
}
};
var results = await searchClient.SearchAsync<Hotel>(null, options);
```
## 語意搜尋
詳細模式請看 [references/semantic-search.md](references/semantic-search.md)。
```csharp
var options = new SearchOptions
{
QueryType = SearchQueryType.Semantic,
SemanticSearch = new SemanticSearchOptions
{
SemanticConfigurationName = "my-semantic-config",
QueryCaption = new QueryCaption(QueryCaptionType.Extractive),
QueryAnswer = new QueryAnswer(QueryAnswerType.Extractive)
}
};
var results = await searchClient.SearchAsync<Hotel>("best hotel for families", options);
// 取得語意 answer
foreach (var answer in results.Value.SemanticSearch.Answers)
{
Console.WriteLine($"Answer: {answer.Text} (Score: {answer.Score})");
}
// 取得 caption
await foreach (var result in results.Value.GetResultsAsync())
{
var caption = result.SemanticSearch?.Captions?.FirstOrDefault();
Console.WriteLine($"Caption: {caption?.Text}");
}
```
## 混合搜尋（向量 + 關鍵字 + 語意）
```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 }
}
};
// 結合關鍵字搜尋、向量搜尋和語意排序
var results = await searchClient.SearchAsync<Hotel>("luxury beachfront", options);
```
## 欄位 Attribute 參考
| Attribute | 用途 |
|-----------|---------|
| `SimpleField` | 不可搜尋的欄位（用於 filter、排序、facet） |
| `SearchableField` | 可全文檢索的欄位 |
| `VectorSearchField` | 向量 embedding 欄位 |
| `IsKey = true` | 文件 key（必填，每個 index 一個） |
| `IsFilterable = true` | 啟用 $filter 運算式 |
| `IsSortable = true` | 啟用 $orderby |
| `IsFacetable = true` | 啟用 faceted 導覽 |
| `IsHidden = true` | 從結果中排除 |
| `AnalyzerName` | 指定文字 analyzer |
## 錯誤處理
```csharp
using Azure;
try
{
var results = await searchClient.SearchAsync<Hotel>("query");
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
Console.WriteLine("Index not found");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"Search error: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}
```
## 最佳實務
1. production 用 **`DefaultAzureCredential`**，不要用 API key
2. 用 **`FieldBuilder`** 搭配 model attribute，讓 index 定義有型別安全
3. 用 **`CreateOrUpdateIndexAsync`** 建立 index，這樣重跑也不會出問題（idempotent）
4. 文件操作用**批次**，吞吐量比較好
5. 用 **`Select`** 只回傳需要的欄位
6. 自然語言查詢要**設定語意搜尋**
7. **向量 + 關鍵字 + 語意搭配使用**，相關性最好
## 參考檔案
| 檔案 | 內容 |
|------|----------|
| [references/vector-search.md](references/vector-search.md) | 向量搜尋、混合搜尋、vectorizer |
| [references/semantic-search.md](references/semantic-search.md) | 語意排序、caption、answer |英文原文
---
name: azure-search-documents-dotnet
description: |
Azure AI Search SDK for .NET (Azure.Search.Documents). Use for building search applications with full-text, vector, semantic, and hybrid search. Covers SearchClient (queries, document CRUD), SearchIndexClient (index management), and SearchIndexerClient (indexers, skillsets). Triggers: "Azure Search .NET", "SearchClient", "SearchIndexClient", "vector search C#", "semantic search .NET", "hybrid search", "Azure.Search.Documents".
license: MIT
metadata:
author: Microsoft
version: "1.0.0"
package: Azure.Search.Documents
---
# Azure.Search.Documents (.NET)
Build search applications with full-text, vector, semantic, and hybrid search capabilities.
## Installation
```bash
dotnet add package Azure.Search.Documents
dotnet add package Azure.Identity
```
**Current Versions**: Stable v11.7.0, Preview v11.8.0-beta.1
## Environment Variables
```bash
SEARCH_ENDPOINT=https://<search-service>.search.windows.net # Required: search service endpoint
SEARCH_INDEX_NAME=<index-name> # Required: search index name
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
SEARCH_API_KEY=<api-key> # Only required for AzureKeyCredential auth
```
## Authentication
**Microsoft Entra Token Credential**:
```csharp
using Azure.Identity;
using Azure.Search.Documents;
// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
var credential = new DefaultAzureCredential(
DefaultAzureCredential.DefaultEnvironmentVariableName
);
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/dotnet/api/overview/azure/identity-readme?view=azure-dotnet#credential-classes
// var credential = new ManagedIdentityCredential();
var client = new SearchClient(
new Uri(Environment.GetEnvironmentVariable("SEARCH_ENDPOINT")),
Environment.GetEnvironmentVariable("SEARCH_INDEX_NAME"),
credential);
```
**API Key**:
```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);
```
## Client Selection
| Client | Purpose |
|--------|---------|
| `SearchClient` | Query indexes, upload/update/delete documents |
| `SearchIndexClient` | Create/manage indexes, synonym maps |
| `SearchIndexerClient` | Manage indexers, skillsets, data sources |
## Index Creation
### Using FieldBuilder (Recommended)
```csharp
using Azure.Search.Documents.Indexes;
using Azure.Search.Documents.Indexes.Models;
// Define model with attributes
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; }
}
// Create index
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);
```
### Manual Field Definition
```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", SearchFieldDataType.Collection(SearchFieldDataType.Single))
{
VectorSearchDimensions = 1536,
VectorSearchProfileName = "vector-profile"
}
}
};
```
## Document Operations
```csharp
var searchClient = new SearchClient(endpoint, indexName, credential);
// Upload (add new)
var hotels = new[] { new Hotel { HotelId = "1", HotelName = "Hotel A" } };
await searchClient.UploadDocumentsAsync(hotels);
// Merge (update existing)
await searchClient.MergeDocumentsAsync(hotels);
// Merge or Upload (upsert)
await searchClient.MergeOrUploadDocumentsAsync(hotels);
// Delete
await searchClient.DeleteDocumentsAsync("hotelId", new[] { "1", "2" });
// Batch operations
var batch = IndexDocumentsBatch.Create(
IndexDocumentsAction.Upload(hotel1),
IndexDocumentsAction.Merge(hotel2),
IndexDocumentsAction.Delete(hotel3));
await searchClient.IndexDocumentsAsync(batch);
```
## Search Patterns
### Basic Search
```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})");
}
```
### 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($"Rating {facet.Value}: {facet.Count}");
}
```
### Autocomplete and Suggestions
```csharp
// Autocomplete
var autocompleteOptions = new AutocompleteOptions { Mode = AutocompleteMode.OneTermWithContext };
var autocomplete = await searchClient.AutocompleteAsync("lux", "suggester-name", autocompleteOptions);
// Suggestions
var suggestOptions = new SuggestOptions { UseFuzzyMatching = true };
var suggestions = await searchClient.SuggestAsync<Hotel>("lux", "suggester-name", suggestOptions);
```
## Vector Search
See [references/vector-search.md](references/vector-search.md) for detailed patterns.
```csharp
using Azure.Search.Documents.Models;
// Pure vector search
var vectorQuery = new VectorizedQuery(embedding)
{
KNearestNeighborsCount = 5,
Fields = { "descriptionVector" }
};
var options = new SearchOptions
{
VectorSearch = new VectorSearchOptions
{
Queries = { vectorQuery }
}
};
var results = await searchClient.SearchAsync<Hotel>(null, options);
```
## Semantic Search
See [references/semantic-search.md](references/semantic-search.md) for detailed patterns.
```csharp
var options = new SearchOptions
{
QueryType = SearchQueryType.Semantic,
SemanticSearch = new SemanticSearchOptions
{
SemanticConfigurationName = "my-semantic-config",
QueryCaption = new QueryCaption(QueryCaptionType.Extractive),
QueryAnswer = new QueryAnswer(QueryAnswerType.Extractive)
}
};
var results = await searchClient.SearchAsync<Hotel>("best hotel for families", options);
// Access semantic answers
foreach (var answer in results.Value.SemanticSearch.Answers)
{
Console.WriteLine($"Answer: {answer.Text} (Score: {answer.Score})");
}
// Access captions
await foreach (var result in results.Value.GetResultsAsync())
{
var caption = result.SemanticSearch?.Captions?.FirstOrDefault();
Console.WriteLine($"Caption: {caption?.Text}");
}
```
## Hybrid Search (Vector + Keyword + Semantic)
```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 }
}
};
// Combines keyword search, vector search, and semantic ranking
var results = await searchClient.SearchAsync<Hotel>("luxury beachfront", options);
```
## Field Attributes Reference
| Attribute | Purpose |
|-----------|---------|
| `SimpleField` | Non-searchable field (filters, sorting, facets) |
| `SearchableField` | Full-text searchable field |
| `VectorSearchField` | Vector embedding field |
| `IsKey = true` | Document key (required, one per index) |
| `IsFilterable = true` | Enable $filter expressions |
| `IsSortable = true` | Enable $orderby |
| `IsFacetable = true` | Enable faceted navigation |
| `IsHidden = true` | Exclude from results |
| `AnalyzerName` | Specify text analyzer |
## Error Handling
```csharp
using Azure;
try
{
var results = await searchClient.SearchAsync<Hotel>("query");
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
Console.WriteLine("Index not found");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"Search error: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}
```
## Best Practices
1. **Use `DefaultAzureCredential`** over API keys for production
2. **Use `FieldBuilder`** with model attributes for type-safe index definitions
3. **Use `CreateOrUpdateIndexAsync`** for idempotent index creation
4. **Batch document operations** for better throughput
5. **Use `Select`** to return only needed fields
6. **Configure semantic search** for natural language queries
7. **Combine vector + keyword + semantic** for best relevance
## Reference Files
| File | Contents |
|------|----------|
| [references/vector-search.md](references/vector-search.md) | Vector search, hybrid search, vectorizers |
| [references/semantic-search.md](references/semantic-search.md) | Semantic ranking, captions, answers |