Create a semantic query in Azure AI Search

In this article, learn how to invoke a semantic ranking over a result set, promoting the most semantically relevant results to the top of the stack. You can also get semantic captions, with highlights over the most relevant terms and phrases, and semantic answers.

Prerequisites

• A search service, Basic tier or higher, with semantic ranking.

• An existing search index with a semantic configuration and rich text content.

• Review semantic ranking if you need an introduction to the feature.

Note

Captions and answers are extracted verbatim from text in the search document. The semantic subsystem uses machine reading comprehension to recognize content having the characteristics of a caption or answer, but doesn't compose new sentences or phrases. For this reason, content that includes explanations or definitions work best for semantic ranking. If you want chat-style interaction with generated responses, see Retrieval Augmented Generation (RAG).

Choose a client

Choose a search client that supports semantic ranking. Here are some options:

• Azure portal, using the index designer to add a semantic configuration.
• Visual Studio Code with a REST client
• Azure SDK for .NET
• Azure SDK for Python
• Azure SDK for Java
• Azure SDK for JavaScript

Avoid features that bypass relevance scoring

Several query capabilities in Azure AI Search bypass relevance scoring or are otherwise incompatible with semantic ranking. If your query logic includes the following features, you can't semantically rank your results:

• A query with search=* or an empty search string, such as pure filter-only query, won't work because there's nothing to measure semantic relevance against. The query must provide terms or phrases that can be assessed during processing.

• A query composed in the full Lucene syntax (queryType=full) is incompatible with semantic ranking (queryType=semantic). The semantic model doesn't support the full Lucene syntax.

• Sorting (orderBy clauses) on specific fields overrides search scores and a semantic score. Given that the semantic score is supposed to provide the ranking, adding an orderby clause results in an HTTP 400 error if you apply semantic ranking over ordered results.

Set up the query

In this step, add parameters to the query request. To be successful, your query should be full text search (using the search parameter to pass in a string), and the index should contain text fields with rich semantic content and a semantic configuration.

Azure portal

Search explorer includes options for semantic ranking.

1. Sign in to the Azure portal.

2. Open a search index and select Search explorer.

3. Select Query options. If you already defined a semantic configuration, it's selected by default. If you don't have one, create a semantic configuration for your index.

   

4. Enter a query, such as "historic hotel with good food", and select Search.

5. Alternatively, select JSON view and paste definitions into the query editor:

   

   Here's some JSON text that you can paste into the view:

    {
        "queryType": "semantic",
        "search": "historic hotel with good food",
        "semanticConfiguration": "my-semantic-config",
        "answers": "extractive|count-3",
        "captions": "extractive|highlight-true",
        "highlightPreTag": "<strong>",
        "highlightPostTag": "</strong>",
        "select": "HotelId,HotelName,Description,Category",
        "count": true
    }
   

REST API

Use Search Documents to formulate the request.

A response includes an @search.rerankerScore automatically. If you want captions or answers in the response, add captions and answers to the request.

The following example in this section uses the hotels-sample-index to demonstrate semantic ranking with semantic answers and captions.

1. Paste the following request into a web client as a template. Replace the service name and index name with valid values.

   POST https://[service name].search.windows.net/indexes/hotels-sample-index/docs/search?api-version=2023-11-01      
   {
       "queryType": "semantic",
       "search": "newer hotel near the water with a great restaurant",
       "semanticConfiguration": "my-semantic-config",
       "answers": "extractive|count-3",
       "captions": "extractive|highlight-true",
       "highlightPreTag": "<strong>",
       "highlightPostTag": "</strong>",
       "select": "HotelId,HotelName,Description,Category",
       "count": true
   }
   

2. Set "queryType" to "semantic".

   In other queries, the "queryType" is used to specify the query parser. In semantic ranking, it's set to "semantic". For the "search" field, you can specify queries that conform to the simple syntax.

3. Set "search" to a full text search query based on the simple syntax. Semantic ranking is an extension of full text search, so while this parameter isn't required, you won't get an expected outcome if it's null.

4. Set "semanticConfiguration" to a predefined semantic configuration that's embedded in your index.

5. Set "answers" to specify whether semantic answers are included in the result. Currently, the only valid value for this parameter is extractive. Answers can be configured to return a maximum of 10. The default is one. This example shows a count of three answers: extractive|count-3.

   Answers aren't guaranteed on every request. To get an answer, the query must look like a question and the content must include text that looks like an answer.

6. Set "captions" to specify whether semantic captions are included in the result. Currently, the only valid value for this parameter is extractive. Captions can be configured to return results with or without highlights. The default is for highlights to be returned. This example returns captions without highlights: extractive|highlight-false.

   The basis for captions and answers are the fields referenced in the "semanticConfiguration". These fields are under a combined limit in the range of 2,000 tokens or approximately 20,000 characters. If you anticipate a token count exceeding this limit, consider a data chunking step using the Text split skill. This approach introduces a dependency on an AI enrichment pipeline and indexers.

7. Set "highlightPreTag" and "highlightPostTag" if you want to override the default highlight formatting that's applied to captions.

   Captions apply highlight formatting over key passages in the document that summarize the response. The default is <em>. If you want to specify the type of formatting (for example, yellow background), you can set the highlightPreTag and highlightPostTag.

8. Set "select" to specify which fields are returned in the response, and "count" to return the number of matches in the index. These parameters improve the quality of the request and readability of the response.

9. Send the request to execute the query and return results.

.NET SDK

Use QueryType or SemanticQuery to invoke semantic ranking on a semantic query. The following example is from the Azure SDK team.

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
    {
        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 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}");

Evaluate the response

Only the top 50 matches from the initial results can be semantically ranked. As with all queries, a response is composed of all fields marked as retrievable, or just those fields listed in the select parameter. A response includes the original relevance score, and might also include a count, or batched results, depending on how you formulated the request.

In semantic ranking, the response has more elements: a new semantically ranked relevance score, an optional caption in plain text and with highlights, and an optional answer. If your results don't include these extra elements, then your query might be misconfigured. As a first step towards troubleshooting the problem, check the semantic configuration to ensure it's specified in both the index definition and query.

In a client app, you can structure the search page to include a caption as the description of the match, rather than the entire contents of a specific field. This approach is useful when individual fields are too dense for the search results page.

The response for the above example query returns the following match as the top pick. Captions are returned because the "captions" property is set, with plain text and highlighted versions. Answers are omitted from the example because one couldn't be determined for this particular query and corpus.

"@odata.count": 35,
"@search.answers": [],
"value": [
    {
        "@search.score": 1.8810667,
        "@search.rerankerScore": 1.1446577133610845,
        "@search.captions": [
            {
                "text": "Oceanside Resort. Luxury. New Luxury Hotel. Be the first to stay. Bay views from every room, location near the pier, rooftop pool, waterfront dining & more.",
                "highlights": "<strong>Oceanside Resort.</strong> Luxury. New Luxury Hotel. Be the first to stay.<strong> Bay</strong> views from every room, location near the pier, rooftop pool, waterfront dining & more."
            }
        ],
        "HotelName": "Oceanside Resort",
        "Description": "New Luxury Hotel. Be the first to stay. Bay views from every room, location near the pier, rooftop pool, waterfront dining & more.",
        "Category": "Luxury"
    },
  ...
]

Next steps

Semantic ranking can be used in hybrid queries that combine keyword search and vector search into a single request and a unified response.

Hybrid query with semantic ranking