Using the Azure AI Search Vector Store connector (Preview)

Getting started

Add the Azure AI Search Vector Store connector NuGet package to your project.

dotnet add package Microsoft.SemanticKernel.Connectors.AzureAISearch --prerelease

You can add the vector store to the dependency injection container available on the KernelBuilder or to the IServiceCollection dependency injection container using extension methods provided by Semantic Kernel.

using Azure;
using Microsoft.SemanticKernel;

// Using Kernel Builder.
var kernelBuilder = Kernel
    .CreateBuilder()
    .AddAzureAISearchVectorStore(new Uri(azureAISearchUri), new AzureKeyCredential(secret));

using Azure;
using Microsoft.SemanticKernel;

// Using IServiceCollection with ASP.NET Core.
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddAzureAISearchVectorStore(new Uri(azureAISearchUri), new AzureKeyCredential(secret));

Extension methods that take no parameters are also provided. These require an instance of the Azure AI Search SearchIndexClient to be separately registered with the dependency injection container.

using Azure;
using Azure.Search.Documents.Indexes;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.SemanticKernel;

// Using Kernel Builder.
var kernelBuilder = Kernel.CreateBuilder();
kernelBuilder.Services.AddSingleton<SearchIndexClient>(
    sp => new SearchIndexClient(
        new Uri(azureAISearchUri),
        new AzureKeyCredential(secret)));
kernelBuilder.AddAzureAISearchVectorStore();

using Azure;
using Azure.Search.Documents.Indexes;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.SemanticKernel;

// Using IServiceCollection with ASP.NET Core.
var builder = WebApplication.CreateBuilder(args);
builder.Services.AddSingleton<SearchIndexClient>(
    sp => new SearchIndexClient(
        new Uri(azureAISearchUri),
        new AzureKeyCredential(secret)));
builder.Services.AddAzureAISearchVectorStore();

You can construct an Azure AI Search Vector Store instance directly.

using Azure;
using Azure.Search.Documents.Indexes;
using Microsoft.SemanticKernel.Connectors.AzureAISearch;

var vectorStore = new AzureAISearchVectorStore(
    new SearchIndexClient(
        new Uri(azureAISearchUri),
        new AzureKeyCredential(secret)));

It is possible to construct a direct reference to a named collection.

using Azure;
using Azure.Search.Documents.Indexes;
using Microsoft.SemanticKernel.Connectors.AzureAISearch;

var collection = new AzureAISearchVectorStoreRecordCollection<Hotel>(
    new SearchIndexClient(new Uri(azureAISearchUri), new AzureKeyCredential(secret)),
    "skhotels");

Getting started

Install semantic kernel with the azure extras, which includes the Azure AI Search SDK.

pip install semantic-kernel[azure]

You can then create a vector store instance using the AzureAISearchStore class, this will use the environment variables AZURE_AI_SEARCH_ENDPOINT and AZURE_AI_SEARCH_API_KEY to connect to the Azure AI Search instance, those values can also be supplied directly. You can also supply azure credentials or token credentials instead of a API key.

from semantic_kernel.connectors.memory.azure_ai_search import AzureAISearchStore

vector_store = AzureAISearchStore()

You can also create the vector store with your own instance of the azure search client.

from azure.search.documents.indexes import SearchIndexClient
from semantic_kernel.connectors.memory.azure_ai_search import AzureAISearchStore

search_client = SearchIndexClient(endpoint="https://<your-search-service-name>.search.windows.net", credential="<your-search-service-key>")
vector_store = AzureAISearchStore(search_index_client=search_client)

You can also create a collection directly.

from semantic_kernel.connectors.memory.azure_ai_search import AzureAISearchCollection

collection = AzureAISearchCollection(collection_name="skhotels", data_model_type=hotel)

Serialization

Since the Azure AI Search connector needs a simple dict with the fields corresponding to the index as the input, the serialization is quite easy, just return a dict with the values with the keys corresponding to the index fields, the built-in step from dict to the the store model is a straight passthrough of the created dict.

For more details on this concept see the serialization documentation.

Getting started

Include the latest version of the Semantic Kernel Azure AI Search data connector in your Maven project by adding the following dependency to your pom.xml:

<dependency>
    <groupId>com.microsoft.semantic-kernel</groupId>
    <artifactId>semantickernel-data-azureaisearch</artifactId>
    <version>[LATEST]</version>
</dependency>

You can then create a vector store instance using the AzureAISearchVectorStore class, having the AzureAISearch client as a parameter.

import com.azure.core.credential.AzureKeyCredential;
import com.azure.search.documents.indexes.SearchIndexClientBuilder;
import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStore;
import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStoreOptions;
import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStoreRecordCollection;
import com.microsoft.semantickernel.data.azureaisearch.AzureAISearchVectorStoreRecordCollectionOptions;

public class Main {
    public static void main(String[] args) {
        // Build the Azure AI Search client
        var searchClient = new SearchIndexClientBuilder()
                .endpoint("https://<your-search-service-name>.search.windows.net")
                .credential(new AzureKeyCredential("<your-search-service-key>"))
                .buildAsyncClient();

        // Build an Azure AI Search Vector Store
        var vectorStore = AzureAISearchVectorStore.builder()
                .withSearchIndexAsyncClient(searchClient)
                .withOptions(new AzureAISearchVectorStoreOptions())
                .build();
    }
}

You can also create a collection directly.

var collection = new AzureAISearchVectorStoreRecordCollection<>(searchClient, "skhotels",
        AzureAISearchVectorStoreRecordCollectionOptions.<Hotel>builder()
                .withRecordClass(Hotel.class)
                .build());

Data mapping

The default mapper used by the Azure AI Search connector when mapping data from the data model to storage is the one provided by the Azure AI Search SDK.

This mapper does a direct conversion of the list of properties on the data model to the fields in Azure AI Search and uses System.Text.Json.JsonSerializer to convert to the storage schema. This means that usage of the JsonPropertyNameAttribute is supported if a different storage name to the data model property name is required.

It is also possible to use a custom JsonSerializerOptions instance with a customized property naming policy. To enable this, the JsonSerializerOptions must be passed to both the SearchIndexClient and the AzureAISearchVectorStoreRecordCollection on construction.

var jsonSerializerOptions = new JsonSerializerOptions { PropertyNamingPolicy = JsonNamingPolicy.SnakeCaseUpper };
var collection = new AzureAISearchVectorStoreRecordCollection<Hotel>(
    new SearchIndexClient(
        new Uri(azureAISearchUri),
        new AzureKeyCredential(secret),
        new() { Serializer = new JsonObjectSerializer(jsonSerializerOptions) }),
    "skhotels",
    new() { JsonSerializerOptions = jsonSerializerOptions });