Agent skill
azure-search-documents-dotnet
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".
Install this agent skill to your Project
npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/sickn33/azure-search-documents-dotnet
SKILL.md
Azure.Search.Documents (.NET)
Build search applications with full-text, vector, semantic, and hybrid search capabilities.
Installation
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
SEARCH_ENDPOINT=https://<search-service>.search.windows.net
SEARCH_INDEX_NAME=<index-name>
# For API key auth (not recommended for production)
SEARCH_API_KEY=<api-key>
Authentication
DefaultAzureCredential (preferred):
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 Key:
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)
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
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
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
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
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($"Rating {facet.Value}: {facet.Count}");
}
Autocomplete and Suggestions
// Autocomplete
var autocompleteOptions = new AutocompleteOptions { Mode = AutocompleteMode.OneTermWithContext };
var autocomplete = await searchClient.AutocompleteAsync("lux", "suggester-name", autocompleteOptions);
// Suggestions
var suggestOptions = new SuggestOptions { UseFuzzyMatching = true };
var suggestions = await searchClient.SuggestAsync<Hotel>("lux", "suggester-name", suggestOptions);
Vector Search
See references/vector-search.md for detailed patterns.
using Azure.Search.Documents.Models;
// Pure vector search
var vectorQuery = new VectorizedQuery(embedding)
{
KNearestNeighborsCount = 5,
Fields = { "descriptionVector" }
};
var options = new SearchOptions
{
VectorSearch = new VectorSearchOptions
{
Queries = { vectorQuery }
}
};
var results = await searchClient.SearchAsync<Hotel>(null, options);
Semantic Search
See references/semantic-search.md for detailed patterns.
var options = new SearchOptions
{
QueryType = SearchQueryType.Semantic,
SemanticSearch = new SemanticSearchOptions
{
SemanticConfigurationName = "my-semantic-config",
QueryCaption = new QueryCaption(QueryCaptionType.Extractive),
QueryAnswer = new QueryAnswer(QueryAnswerType.Extractive)
}
};
var results = await searchClient.SearchAsync<Hotel>("best hotel for families", options);
// Access semantic answers
foreach (var answer in results.Value.SemanticSearch.Answers)
{
Console.WriteLine($"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)
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 $orderby |
IsFacetable = true |
Enable faceted navigation |
IsHidden = true |
Exclude from results |
AnalyzerName |
Specify text analyzer |
Error Handling
using Azure;
try
{
var results = await searchClient.SearchAsync<Hotel>("query");
}
catch (RequestFailedException ex) when (ex.Status == 404)
{
Console.WriteLine("Index not found");
}
catch (RequestFailedException ex)
{
Console.WriteLine($"Search error: {ex.Status} - {ex.ErrorCode}: {ex.Message}");
}
Best Practices
- Use
DefaultAzureCredentialover API keys for production - Use
FieldBuilderwith model attributes for type-safe index definitions - Use
CreateOrUpdateIndexAsyncfor idempotent index creation - Batch document operations for better throughput
- Use
Selectto return only needed fields - Configure semantic search for natural language queries
- Combine vector + keyword + semantic for best relevance
Reference Files
| File | Contents |
|---|---|
| references/vector-search.md | Vector search, hybrid search, vectorizers |
| references/semantic-search.md | Semantic ranking, captions, answers |
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