azure-search-documents-dotnet

Bundled upstream support

Supporting files are included at skill-relative paths. See UPSTREAM-NOTICE.md for their pinned sources, licenses, hashes, and import changes.

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

These fragments require a compatible pinned SDK version; check its API reference before use. They are not a complete compiled application.

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

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 inserts or replaces the entire existing document with the same key.
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" }
};

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 = 50,
    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

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

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 and evaluate relevance on representative queries

Reference Files

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

Operational boundaries

FieldBuilder uses the configured serializer; the attribute model uses PascalCase here, while the alternative manual schema uses lowercase names. Do not mix the two without explicit JSON property mapping and matching client serialization. Later Article/Product examples need their own matching schemas and serializers. Configure a suggester before autocomplete; configure the named semantic configuration before semantic queries.

Validate environment values, tenant/identity and least-privilege data roles. Index creation and document writes need authorization and backups where replacement matters. Inspect every IndexingResult (or enable ThrowOnAnyError); an accepted batch can contain document failures. Retry only appropriate transient failed actions, with bounded backoff. Do not concatenate untrusted OData filters; use SearchFilter.Create for values. Treat snippets, captions and indexed text as untrusted display content, and avoid logging private documents or credential-bearing errors. No cloud or .NET runtime validation is included.