Machine-learning entity
Important
LUIS will be retired on October 1st 2025 and starting April 1st 2023 you will not be able to create new LUIS resources. We recommend migrating your LUIS applications to conversational language understanding to benefit from continued product support and multilingual capabilities.
The machine-learning entity is the preferred entity for building LUIS applications.
Example JSON
Suppose the app takes pizza orders, such as the decomposable entity tutorial. Each order can include several different pizzas, including different sizes.
Example utterances include:
| Example utterances for pizza app |
|---|
Can I get a pepperoni pizza and a can of coke please |
can I get a small pizza with onions peppers and olives |
pickup an extra large meat lovers pizza |
Because a machine-learning entity can have many subentities with required features, this is just an example only. It should be considered a guide for what your entity will return.
Consider the query:
deliver 1 large cheese pizza on thin crust and 2 medium pepperoni pizzas on deep dish crust
This is the JSON if verbose=false is set in the query string:
"entities": {
"Order": [
{
"FullPizzaWithModifiers": [
{
"PizzaType": [
"cheese pizza"
],
"Size": [
[
"Large"
]
],
"Quantity": [
1
]
},
{
"PizzaType": [
"pepperoni pizzas"
],
"Size": [
[
"Medium"
]
],
"Quantity": [
2
],
"Crust": [
[
"Deep Dish"
]
]
}
]
}
],
"ToppingList": [
[
"Cheese"
],
[
"Pepperoni"
]
],
"CrustList": [
[
"Thin"
]
]
}
This is the JSON if verbose=true is set in the query string:
"entities": {
"Order": [
{
"FullPizzaWithModifiers": [
{
"PizzaType": [
"cheese pizza"
],
"Size": [
[
"Large"
]
],
"Quantity": [
1
],
"$instance": {
"PizzaType": [
{
"type": "PizzaType",
"text": "cheese pizza",
"startIndex": 16,
"length": 12,
"score": 0.999998868,
"modelTypeId": 1,
"modelType": "Entity Extractor",
"recognitionSources": [
"model"
]
}
],
"Size": [
{
"type": "SizeList",
"text": "large",
"startIndex": 10,
"length": 5,
"score": 0.998720646,
"modelTypeId": 1,
"modelType": "Entity Extractor",
"recognitionSources": [
"model"
]
}
],
"Quantity": [
{
"type": "builtin.number",
"text": "1",
"startIndex": 8,
"length": 1,
"score": 0.999878645,
"modelTypeId": 1,
"modelType": "Entity Extractor",
"recognitionSources": [
"model"
]
}
]
}
},
{
"PizzaType": [
"pepperoni pizzas"
],
"Size": [
[
"Medium"
]
],
"Quantity": [
2
],
"Crust": [
[
"Deep Dish"
]
],
"$instance": {
"PizzaType": [
{
"type": "PizzaType",
"text": "pepperoni pizzas",
"startIndex": 56,
"length": 16,
"score": 0.999987066,
"modelTypeId": 1,
"modelType": "Entity Extractor",
"recognitionSources": [
"model"
]
}
],
"Size": [
{
"type": "SizeList",
"text": "medium",
"startIndex": 49,
"length": 6,
"score": 0.999841452,
"modelTypeId": 1,
"modelType": "Entity Extractor",
"recognitionSources": [
"model"
]
}
],
"Quantity": [
{
"type": "builtin.number",
"text": "2",
"startIndex": 47,
"length": 1,
"score": 0.9996054,
"modelTypeId": 1,
"modelType": "Entity Extractor",
"recognitionSources": [
"model"
]
}
],
"Crust": [
{
"type": "CrustList",
"text": "deep dish crust",
"startIndex": 76,
"length": 15,
"score": 0.761551,
"modelTypeId": 1,
"modelType": "Entity Extractor",
"recognitionSources": [
"model"
]
}
]
}
}
],
"$instance": {
"FullPizzaWithModifiers": [
{
"type": "FullPizzaWithModifiers",
"text": "1 large cheese pizza on thin crust",
"startIndex": 8,
"length": 34,
"score": 0.616001546,
"modelTypeId": 1,
"modelType": "Entity Extractor",
"recognitionSources": [
"model"
]
},
{
"type": "FullPizzaWithModifiers",
"text": "2 medium pepperoni pizzas on deep dish crust",
"startIndex": 47,
"length": 44,
"score": 0.7395033,
"modelTypeId": 1,
"modelType": "Entity Extractor",
"recognitionSources": [
"model"
]
}
]
}
}
],
"ToppingList": [
[
"Cheese"
],
[
"Pepperoni"
]
],
"CrustList": [
[
"Thin"
]
],
"$instance": {
"Order": [
{
"type": "Order",
"text": "1 large cheese pizza on thin crust and 2 medium pepperoni pizzas on deep dish crust",
"startIndex": 8,
"length": 83,
"score": 0.6881274,
"modelTypeId": 1,
"modelType": "Entity Extractor",
"recognitionSources": [
"model"
]
}
],
"ToppingList": [
{
"type": "ToppingList",
"text": "cheese",
"startIndex": 16,
"length": 6,
"modelTypeId": 5,
"modelType": "List Entity Extractor",
"recognitionSources": [
"model"
]
},
{
"type": "ToppingList",
"text": "pepperoni",
"startIndex": 56,
"length": 9,
"modelTypeId": 5,
"modelType": "List Entity Extractor",
"recognitionSources": [
"model"
]
}
],
"CrustList": [
{
"type": "CrustList",
"text": "thin crust",
"startIndex": 32,
"length": 10,
"modelTypeId": 5,
"modelType": "List Entity Extractor",
"recognitionSources": [
"model"
]
}
]
}
}
Next steps
Learn more about the machine-learning entity including a tutorial, concepts, and how-to guide.
Learn about the list entity and regular expression entity.