This sample will show you how to perform vector semantic hybrid search.
Let's consider the example of a Hotel. First, we need to create an index for storing hotel information. In this index, we will define vector fields called DescriptionVector and CategoryVector. To configure these vector fields, you need to specify the model dimensions, indicating the size of the embeddings generated for each field and the name of the vector search profile, which specifies the algorithm configuration and any optional search parameters for these vector fields. In addition to that, you also need to configure semantic settings for the index. You can find detailed instructions on how to create a vector index in the documentation.
We will create an instace of SearchIndex and define Hotel fields.
string vectorSearchProfileName = "my-vector-profile";
string vectorSearchHnswConfig = "my-hsnw-vector-config";
int modelDimensions = 1536;
string indexName = "hotel";
SearchIndex searchIndex = new(indexName)
{
Fields =
{
new SimpleField("HotelId", SearchFieldDataType.String) { IsKey = true, IsFilterable = true, IsSortable = true, IsFacetable = true },
new SearchableField("HotelName") { IsFilterable = true, IsSortable = true },
new SearchableField("Description") { IsFilterable = true },
new VectorSearchField("DescriptionVector", modelDimensions, vectorSearchProfileName),
new SearchableField("Category") { IsFilterable = true, IsSortable = true, IsFacetable = true },
new VectorSearchField("CategoryVector", modelDimensions, vectorSearchProfileName),
},
VectorSearch = new()
{
Profiles =
{
new VectorSearchProfile(vectorSearchProfileName, vectorSearchHnswConfig)
},
Algorithms =
{
new HnswAlgorithmConfiguration(vectorSearchHnswConfig)
}
},
SemanticSearch = new()
{
Configurations =
{
new SemanticConfiguration("my-semantic-config", new()
{
TitleField = new SemanticField("HotelName"),
ContentFields =
{
new SemanticField("Description")
},
KeywordsFields =
{
new SemanticField("Category")
}
})
}
}
};After creating an instance of the SearchIndex, we need to instantiate the SearchIndexClient and call the CreateIndex method to create the search index.
Uri endpoint = new(Environment.GetEnvironmentVariable("SEARCH_ENDPOINT"));
string key = Environment.GetEnvironmentVariable("SEARCH_API_KEY");
AzureKeyCredential credential = new(key);
SearchIndexClient indexClient = new(endpoint, credential);
await indexClient.CreateIndexAsync(searchIndex);Let's create a simple model type for Hotel:
public class Hotel
{
public string HotelId { get; set; }
public string HotelName { get; set; }
public string Description { get; set; }
public ReadOnlyMemory<float> DescriptionVector { get; set; }
public string Category { get; set; }
public ReadOnlyMemory<float> CategoryVector { get; set; }
}Next, we will create sample hotel documents. The vector field requires submitting text input to an embedding model that converts human-readable text into a vector representation. To convert a text query string provided by a user into a vector representation, your application should utilize an embedding library that offers this functionality. For more details about how to generate embeddings, refer to the documentation. Here's an example of how you can get embeddings using Azure.AI.OpenAI library.
public static ReadOnlyMemory<float> GetEmbeddings(string input)
{
Uri endpoint = new Uri(Environment.GetEnvironmentVariable("OpenAI_ENDPOINT"));
string key = Environment.GetEnvironmentVariable("OpenAI_API_KEY");
AzureKeyCredential credential = new AzureKeyCredential(key);
AzureOpenAIClient openAIClient = new AzureOpenAIClient(endpoint, credential);
EmbeddingClient embeddingClient = openAIClient.GetEmbeddingClient("text-embedding-ada-002");
OpenAIEmbedding embedding = embeddingClient.GenerateEmbedding(input);
return embedding.ToFloats();
}In the sample code below, we are using GetEmbeddings method mentioned above to get embeddings for the vector fields named DescriptionVector and CategoryVector:
public static Hotel[] GetHotelDocuments()
{
return new[]
{
new Hotel()
{
HotelId = "1",
HotelName = "Fancy Stay",
Description =
"Best hotel in town if you like luxury hotels. They have an amazing infinity pool, a spa, " +
"and a really helpful concierge. The location is perfect -- right downtown, close to all " +
"the tourist attractions. We highly recommend this hotel.",
DescriptionVector = GetEmbeddings(
"Best hotel in town if you like luxury hotels. They have an amazing infinity pool, a spa, " +
"and a really helpful concierge. The location is perfect -- right downtown, close to all " +
"the tourist attractions. We highly recommend this hotel."),
Category = "Luxury",
CategoryVector = GetEmbeddings("Luxury")
},
new Hotel()
{
HotelId = "2",
HotelName = "Roach Motel",
Description = "Cheapest hotel in town. Infact, a motel.",
DescriptionVector = GetEmbeddings("Cheapest hotel in town. Infact, a motel."),
Category = "Budget",
CategoryVector = GetEmbeddings("Budget")
},
// Add more hotel documents here...
};
}Now, we can instantiate the SearchClient and upload the documents to the Hotel index we created earlier:
SearchClient searchClient = new(endpoint, indexName, credential);
Hotel[] hotelDocuments = GetHotelDocuments();
await searchClient.IndexDocumentsAsync(IndexDocumentsBatch.Upload(hotelDocuments));When using VectorizedQuery, the query for a vector field must also be a vector. To convert a text query string provided by a user into a vector representation, your application must call an embedding library that provides this capability. Use the same embedding library that you used to generate embeddings in the source documents. For more details on how to generate embeddings, please refer to the documentation. In the sample code below, we are using GetEmbeddings method mentioned above to get embeddings to query vector field.
Let's query the index and make sure everything works as implemented. You can also refer to the documentation for more information on querying vector data.
In the context of vector search, the Queries collection contains the vectors that represent the query input. The Fields property specifies which vector fields should be searched. The KNearestNeighborsCount property determines the number of nearest neighbors to retrieve as the top hits.
For semantic search, we will specify SemanticSearch.SemanticConfigurationName as SearchQueryType.Semantic in the SearchOptions. We will use the same SemanticConfigurationName that we defined when creating the index. Additionally, we have enabled SemanticSearch.QueryCaption and SemanticSearch.QueryAnswer in the SearchOptions to obtain the caption and answer in the response. With these configurations in place, we are prepared to execute a vector semantic hybrid query.
With these settings in place, we're ready to execute a vector semantic hybrid query:
ReadOnlyMemory<float> vectorizedResult = GetEmbeddings("Top hotels in town");
SearchResults<Hotel> response = await searchClient.SearchAsync<Hotel>(
"Is there any hotel located on the main commercial artery of the city in the heart of New York?",
new SearchOptions
{
VectorSearch = new()
{
Queries = { new VectorizedQuery(vectorizedResult) { KNearestNeighborsCount = 3, Fields = { "DescriptionVector" } } }
},
SemanticSearch = new()
{
SemanticConfigurationName = "my-semantic-config",
QueryCaption = new(QueryCaptionType.Extractive),
QueryAnswer = new(QueryAnswerType.Extractive)
},
QueryLanguage = QueryLanguage.EnUs,
QueryType = SearchQueryType.Semantic,
});
int count = 0;
Console.WriteLine($"Semantic Hybrid Search Results:");
Console.WriteLine($"\nQuery Answer:");
foreach (QueryAnswerResult result in response.SemanticSearch.Answers)
{
Console.WriteLine($"Answer Highlights: {result.Highlights}");
Console.WriteLine($"Answer Text: {result.Text}");
}
await foreach (SearchResult<Hotel> result in response.GetResultsAsync())
{
count++;
Hotel doc = result.Document;
Console.WriteLine($"{doc.HotelId}: {doc.HotelName}");
if (result.SemanticSearch.Captions != null)
{
var caption = result.SemanticSearch.Captions.FirstOrDefault();
if (caption.Highlights != null && caption.Highlights != "")
{
Console.WriteLine($"Caption Highlights: {caption.Highlights}");
}
else
{
Console.WriteLine($"Caption Text: {caption.Text}");
}
}
}
Console.WriteLine($"Total number of search results:{count}");