Create Skillset (Azure AI Search REST API)
Important
This API reference is for a legacy version. See Data plane REST operations for updated reference documentation. Use the filter on the top left to select a version.
A skillset is a collection of cognitive skills used for AI enrichment, with an optional specification for creating an external knowledge store in Azure Storage. Skills invoke natural language processing and other transformations, such as entity recognition, key phrase extraction, chunking text into logical pages, among others.
A skillset is attached to an indexer. To use the skillset, reference it in an indexer and then run the indexer to import data, invoke transformations and enrichment, and map the output fields to an index. A skillset is high-level resource, but it's operational only within indexer processing. As a high-level resource, you can design a skillset once, and then reference it in multiple indexers.
You can use either POST or PUT on the request. For either one, the JSON document in the request body provides the object definition.
PUT https://[servicename].search.windows.net/skillsets/[skillset name]?api-version=[api-version]
Content-Type: application/json
api-key: [admin key]
HTTPS is required for all service requests. If the skillset doesn't exist, it's created. If it already exists, it's updated to the new definition.
Note
Skillsets are the basis of AI enrichment. A free resource is available for limited processing, but for larger or more frequent workloads, a billable Cognitive Services resource is required.
URI Parameters
| Parameter | Description |
|---|---|
| service name | Required. Set this to the unique, user-defined name of your search service. |
| skillset name | Required on the URI if using PUT. The name must be lower case, start with a letter or number, have no slashes or dots, and be fewer than 128 characters. The name must start with a letter or number, but the rest of the name can include any letter, number and dashes, as long as the dashes aren't consecutive. |
| api-version | Required. See API versions for a list of supported versions. |
Request Headers
The following table describes the required and optional request headers.
| Fields | Description |
|---|---|
| Content-Type | Required. Set this to application/json |
| api-key | Optional if you're using Azure roles and a bearer token is provided on the request, otherwise a key is required. Create requests must include an api-key header set to your admin key (as opposed to a query key). See Connect to Azure AI Search using key authentication for details. |
Request Body
The body of the request contains the skillset definition. Skills are either standalone or chained together through input-output associations, where the output of one transform becomes input to another. A skillset must have at least one skill. There's no theoretical limit on maximum number of skills, but three to five is a common configuration.
The following JSON is a high-level representation of the main parts of the definition.
{
"name" : (optional on PUT; required on POST) "Name of the skillset",
"description" : (optional) "Anything you want, or nothing at all",
"skills" : (required) ["An array of skills. Each skill has an odata.type, name, input and output parameters"],
"cognitiveServices":
{
"@odata.type": "#Microsoft.Azure.Search.CognitiveServicesByKey",
"description": "Optional. Anything you want, or null",
"key": "<YOUR-COGNITIVE-SERVICES-ALL-IN-ONE-KEY>"
},
"knowledgeStore": (optional) { ... },
"encryptionKey": (optional) { }
}
Request contains the following properties:
| Property | Description |
|---|---|
| name | Required. The name of the skillset. The name must be lower case, start with a letter or number, have no slashes or dots, and be fewer than 128 characters. The name must start with a letter or number, but the rest of the name can include any letter, number and dashes, as long as the dashes aren't consecutive. |
| skills | An array of skills. Each skill has an odata.type, name, context, and input and output parameters. The array can include built-in skills and custom skills. At least one skill is required. If you're using a knowledge store, include a Shaper skill unless you're defining the data shape within the projection. |
| cognitiveServices | An all-in-one key is required for billable skills that call Cognitive Services APIs on more than 20 documents daily, per indexer. The key must be for a resource in the same region as the search service. For more information, see Attach a Cognitive Services resource. If you're using the Custom Entity Lookup skill, include this section and a key to enable transactions beyond 20 transactions daily per indexer. You don't need Cognitive Services Key and so can exclude cognitiveServices section if your skillset consists of only custom skills, utility skills (Conditional, Shaper, Text Merge, Text Split), or the Document Extraction skill.If you want to remove your attached cognitive service resource from a skillset (to revert to using the "default" limits) specify @odata.type as #Microsoft.Azure.Search.DefaultCognitiveServices, See this example for more information. |
| knowledgeStore | Optional. Destination for enrichment output to Azure Storage. Requires a connection string to an Azure Storage account and projections. storageConnectionString (required) A string in this format: "DefaultEndpointsProtocol=https;AccountName=<ACCOUNT-NAME>;AccountKey=<ACCOUNT-KEY>;EndpointSuffix=core.windows.net". projections (required) An array of projection objects consisting of tables, objects, files, which are either specified or null. tables Creates one or more tables in Azure Table Storage, projecting content from each document as rows in a table. Each table can have the following three properties:
objects Projects documents as blobs in Azure Blob Storage. Each object has two required properties:
files Each file entry defines storage of binary images in Blob Storage. File projections have two required properties:
|
| encryptionKey | Optional. Used to encrypt a skillset definition at rest with your own keys, managed in your Azure Key Vault. Available for billable search services created on or after 2019-01-01. An encryptionKey section contains a user-defined keyVaultKeyName (required), a system-generated keyVaultKeyVersion (required), and a keyVaultUri providing the key (required, also referred to as DNS name). An example URI might be "https://my-keyvault-name.vault.azure.net". Optionally, you can specify accessCredentials if you aren't using a managed system identity. Properties of accessCredentials include applicationId (Microsoft Entra ID application ID that was granted access permissions to your specified Azure Key Vault), and applicationSecret (authentication key of the registered application). An example in the next section illustrates the syntax. |
Response
For a successful request, you should see status code "201 Created".
By default, the response body will contain the JSON for the skillset definition that was created. However, if the Prefer request header is set to return=minimal, the response body is empty, and the success status code is "204 No Content" instead of "201 Created". This is true regardless of whether PUT or POST is used to create the skillset.
Examples
Example: Skillset that recognizes business entities and sentiment in customer reviews
This skillset uses two skills asynchronously, independently processing /document/content as two different transformations. The skills are Entity Recognition and Sentiment. In the enrichment tree, /document/content provides the content (or customer reviews) from the external data source.
{
"name": "reviews-ss",
"description":
"Extract company names from customer reviews, and detect positive or negative sentiment from the same reviews.",
"skills":
[
{
"@odata.type": "#Microsoft.Skills.Text.V3.EntityRecognitionSkill",
"context": "/document/content",
"categories": [ "Organization" ],
"defaultLanguageCode": "en",
"inputs": [
{
"name": "text",
"source": "/document/content"
}
],
"outputs": [
{
"name": "organizations",
"targetName": "companyName"
}
]
},
{
"@odata.type": "#Microsoft.Skills.Text.V3.SentimentSkill",
"inputs": [
{
"name": "text",
"source": "/document/content"
},
{
"name": "languageCode",
"source": "/document/languageCode"
}
],
"outputs": [
{
"name": "sentiment",
"targetName": "reviewSentiment"
},
{
"name": "confidenceScores",
"targetName": "sentimentScore"
}
]
}
],
"cognitiveServices":
{
"@odata.type": "#Microsoft.Azure.Search.CognitiveServicesByKey",
"description": "mycogsvcs resource in West US 2",
"key": "<your cognitive services all-in-one key goes here>"
},
"knowledgeStore": { },
"encryptionKey": { }
}
Example: Knowledge store
A skillset can optionally send output to knowledge store in Azure Storage. It requires a connection string to an Azure Storage account and projections that determine whether enriched content lands in table or blob storage (as objects or files). Projections usually require an upstream Shaper skill that collects nodes from an enrichment tree as input, outputting a single shape that can be passed to projection. A shaper is typically the last skill to be processed.
{
"name": "reviews-ss",
"description":
"Extract company names from customer reviews, and detect positive or negative sentiment from the same reviews.",
"skills":
[
{ ... },
{ ... },
{
"@odata.type": "#Microsoft.Skills.Util.ShaperSkill",
"context": "/document/content",
"inputs": [
{
"name": "Company",
"source": "/document/content/companyName"
},
{
"name": "Sentiment_Score",
"source": "/document/content/sentimentScore"
},
{
"name": "Sentiment_Label",
"source": "/document/content/reviewSentiment"
}
],
"outputs": [
{
"name": "output",
"targetName": "shapeCustomerReviews"
}
]
}
],
"cognitiveServices":
{
"@odata.type": "#Microsoft.Azure.Search.CognitiveServicesByKey",
"description": "mycogsvcs resource in West US 2",
"key": "<your cognitive services all-in-one key goes here>"
},
"knowledgeStore": {
"storageConnectionString": "<your storage account connection string>",
"projections": [
{
"tables": [
{ "tableName": "CustomerReviews", "generatedKeyName": "DocID", "source": "/document/shapeCustomerReviews" }
. . .
],
"objects": [ ],
"files": [ ]
}
]
}
"encryptionKey": { }
}
Example: Encryption keys
Encryption keys are customer-managed keys used for additional encryption of sensitive content. This example shows you how to specify customer-managed encryption on a skillset.
{
"name": "reviews-ss",
"description": "A brief description of the skillset",
"skills": [ omitted for brevity ],
"cognitiveServices": { omitted for brevity },
"knowledgeStore": { omitted for brevity },
"encryptionKey": (optional) {
"keyVaultKeyName": "Name of the Azure Key Vault key used for encryption",
"keyVaultKeyVersion": "Version of the Azure Key Vault key",
"keyVaultUri": "URI of Azure Key Vault, also referred to as DNS name, that provides the key. An example URI might be https://my-keyvault-name.vault.azure.net",
"accessCredentials": (optional, only if not using managed system identity) {
"applicationId": "Azure Active Directory Application ID that was granted access permissions to your specified Azure Key Vault",
"applicationSecret": "Authentication key of the specified Azure AD application)"}
}
}