Shaper cognitive skill
The Shaper skill is used to reshape or modify the structure of the in-memory enrichment tree created by a skillset. If skill outputs can't be mapped directly to search fields, you can add a Shaper skill to create the data shape you need for your search index or knowledge store.
Primary use-cases for this skill include:
You're populating a knowledge store. The physical structure of the tables and objects of a knowledge store are defined through projections. A Shaper skill adds granularity by creating data shapes that can be pushed to the projections.
You want to map multiple skill outputs into a single structure in your search index, usually a complex type, as described in scenario 1.
Skills produce multiple outputs, but you want to combine into a single field (it doesn't have to be a complex type), as described in scenario 2. For example, combining titles and authors into a single field.
Skills produce multiple outputs with child elements, and you want to combine them. This use-case is illustrated in scenario 3.
The output name of a Shaper skill is always "output". Internally, the pipeline can map a different name, such as "analyzedText" as shown in the examples below, but the Shaper skill itself returns "output" in the response. This might be important if you are debugging enriched documents and notice the naming discrepancy, or if you build a custom skill and are structuring the response yourself.
Note
This skill isn't bound to Azure AI services. It is non-billable and has no Azure AI services key requirement.
@odata.type
Microsoft.Skills.Util.ShaperSkill
Scenario 1: complex types
Consider a scenario where you want to create a structure called analyzedText that has two members: text and sentiment, respectively. In an index, a multi-part searchable field is called a complex type and it's often created when source data has a corresponding complex structure that maps to it.
However, another approach for creating complex types is through the Shaper skill. By including this skill in a skillset, the in-memory operations during skillset processing can output data shapes with nested structures, which can then be mapped to a complex type in your index.
The following example skill definition provides the member names as the input.
{
"@odata.type": "#Microsoft.Skills.Util.ShaperSkill",
"context": "/document/content/phrases/*",
"inputs": [
{
"name": "text",
"source": "/document/content/phrases/*"
},
{
"name": "sentiment",
"source": "/document/content/phrases/*/sentiment"
}
],
"outputs": [
{
"name": "output",
"targetName": "analyzedText"
}
]
}
Sample index
A skillset is invoked by an indexer, and an indexer requires an index. A complex field representation in your index might look like the following example.
"name":"my-index",
"fields":[
{ "name":"myId", "type":"Edm.String", "key":true, "filterable":true },
{ "name":"analyzedText", "type":"Edm.ComplexType",
"fields":[
{
"name":"text",
"type":"Edm.String",
"facetable":false,
"filterable":false,
"searchable":true,
"sortable":false },
{
"name":"sentiment",
"type":"Edm.Double",
"facetable":true,
"filterable":true,
"searchable":true,
"sortable":true }
}
Skill input
An incoming JSON document providing usable input for this Shaper skill could be:
{
"values": [
{
"recordId": "1",
"data": {
"text": "this movie is awesome",
"sentiment": 0.9
}
}
]
}
Skill output
The Shaper skill generates a new element called analyzedText with the combined elements of text and sentiment. This output conforms to the index schema. It will be imported and indexed in an Azure AI Search index.
{
"values": [
{
"recordId": "1",
"data":
{
"analyzedText":
{
"text": "this movie is awesome" ,
"sentiment": 0.9
}
}
}
]
}
Scenario 2: input consolidation
In another example, imagine that at different stages of pipeline processing, you have extracted the title of a book, and chapter titles on different pages of the book. You could now create a single structure composed of these various outputs.
The Shaper skill definition for this scenario might look like the following example:
{
"@odata.type": "#Microsoft.Skills.Util.ShaperSkill",
"context": "/document",
"inputs": [
{
"name": "title",
"source": "/document/content/title"
},
{
"name": "chapterTitles",
"source": "/document/content/pages/*/chapterTitles/*/title"
}
],
"outputs": [
{
"name": "output",
"targetName": "titlesAndChapters"
}
]
}
Skill output
In this case, the Shaper flattens all chapter titles to create a single array.
{
"values": [
{
"recordId": "1",
"data": {
"titlesAndChapters": {
"title": "How to be happy",
"chapterTitles": [
"Start young",
"Laugh often",
"Eat, sleep and exercise"
]
}
}
}
]
}
Scenario 3: input consolidation from nested contexts
Imagine you have chapter titles and chapter numbers of a book and have run entity recognition and key phrases on the contents and now need to aggregate results from the different skills into a single shape with the chapter name, entities, and key phrases.
This example adds an optional sourceContext property to the "chapterTitles" input. The source and sourceContext properties are mutually exclusive. If the input is at the context of the skill, you can use source. If the input is at a different context than the skill context, use sourceContext. The sourceContext requires you to define a nested input, where each input has a source that identifies the specific element used to populate the named node.
The Shaper skill definition for this scenario might look like the following example:
{
"@odata.type": "#Microsoft.Skills.Util.ShaperSkill",
"context": "/document",
"inputs": [
{
"name": "title",
"source": "/document/content/title"
},
{
"name": "chapterTitles",
"sourceContext": "/document/content/pages/*/chapterTitles/*",
"inputs": [
{
"name": "title",
"source": "/document/content/pages/*/chapterTitles/*/title"
},
{
"name": "number",
"source": "/document/content/pages/*/chapterTitles/*/number"
}
]
}
],
"outputs": [
{
"name": "output",
"targetName": "titlesAndChapters"
}
]
}
Skill output
In this case, the Shaper creates a complex type. This structure exists in-memory. If you want to save it to a knowledge store, you should create a projection in your skillset that defines storage characteristics.
{
"values": [
{
"recordId": "1",
"data": {
"titlesAndChapters": {
"title": "How to be happy",
"chapterTitles": [
{ "title": "Start young", "number": 1},
{ "title": "Laugh often", "number": 2},
{ "title": "Eat, sleep and exercise", "number: 3}
]
}
}
}
]
}