Tutorial¶

In this section, we will build an NLU assistant for home automation tasks. It will be able to understand queries about lights and thermostats. More precisely our assistant will contain three intents:

• turnLightOn
• turnLightOff
• setTemperature

The first two intents will be about turning on and off the lights in a specific room. Thus, these intents will have one Slot which will be the room. The third intent will let you control the temperature of a specific room, thus it will have two slots: the roomTemperature and the room.

The first step is to create an appropriate dataset for this task.

Snips dataset format¶

The format used by Snips to describe the input data is designed to be simple to parse as well as easy to read.

We created a sample dataset that you can check to better understand the format.

You have three options to create your dataset. You can build it manually by respecting the format used in the sample, you can also use the dataset creation CLI included in the lib, or alternatively you can use chatito a DSL tool for dataset generation.

We will go for the second option here and start by creating three files corresponding to our three intents and one entity file corresponding to the room entity:

• intent_turnLightOn.txt
• intent_turnLightOff.txt
• intent_setTemperature.txt
• entity_room.txt

The name of each file is important as the tool will map it to the intent or entity name. In particular, the prefixes intent_ and entity_ are required in order to distinguish intents from entity files.

Let’s add training examples for the first intent by inserting the following lines in the first file, intent_turnLightOn.txt:

Turn on the lights in the [room:room](kitchen)
give me some light in the [room:room](bathroom) please
Can you light up the [room:room](living room) ?
switch the [room:room](bedroom)'s lights on please


We use a standard markdown-like annotation syntax to annotate slots within utterances. The [room:room] chunks describe the slot with its two components: the slot name and the entity. In our case we used the same value, room, to describe both. The parts with parenthesis, like (kitchen), correspond to the text value of the slot.

Let’s move on to the second intent, and insert this into intent_turnLightOff.txt:

Turn off the lights in the [room:room](entrance)
turn the [room:room](bathroom)'s light out please
switch off the light the [room:room](kitchen), will you?
Switch the [room:room](bedroom)'s lights off please


And now the last file, intent_setTemperature.txt:

Set the temperature to [roomTemperature:snips/temperature](19 degrees) in the [room:room](bedroom)
please set the [room:room](living room)'s temperature to [roomTemperature:snips/temperature](twenty two degrees celsius)
I want [roomTemperature:snips/temperature](75 degrees fahrenheit) in the [room:room](bathroom) please
Can you increase the temperature to [roomTemperature:snips/temperature](22 degrees) ?


As you can see here, we used a new slot, [room_temperature:snips/temperature], whose name is roomTemperature and whose type is snips/temperature. The slot type used here is a builtin entity. It allows you to resolve the temperature values properly.

Let’s move to the entity_room.txt entity file:

bedroom
living room,main room
garden,yard,"backyard,"


The entity file is a comma (,) separated file. Each line corresponds to an entity value followed by its potential synonyms.

If a value or a synonym contains a comma, the value must be put between double quotes ". If the value contains double quotes, it must be doubled to be escaped like this: "A value with a "","" in it" which corresponds to the actual value A value with a "," in it.

Note

By default entities are generated as automatically extensible, i.e. the recognition will accept additional values than the ones listed in the entity file. This behavior can be changed by adding at the beginning of the entity file the following:

# automatically_extensible=false


We are now ready to generate our dataset:

snips-nlu generate-dataset en intent_turnLightOn.txt intent_turnLightOff.txt intent_setTemperature.txt entity_room.txt > dataset.json


Note

We used en as the language here but other languages are supported, please check the Supported languages section to know more.

Now, the "entities" part of the generated json looks like that:

{
"entities": {
"room": {
"automatically_extensible": true,
"data": [
{
"synonyms": [],
"value": "bedroom"
},
{
"synonyms": [
"main room"
],
"value": "living room"
},
{
"synonyms": [
"yard",
"backyard,"
],
"value": "garden"
}
],
"use_synonyms": true
},
"snips/temperature": {}
}
}


You can see that both entities from the intent utterances and from the room entity file were added.

By default, the room entity is set to be automatically extensible but in our case we don’t want to handle any entity value that would not be part of the dataset, so we set this attribute to false. Moreover, we are going to add some rooms that were not in the previous sentences and that we want our assistant to cover. Additionally, we add some synonyms. Finally, the entities part looks like that:

{
"entities": {
"room": {
"automatically_extensible": false,
"data": [
{
"synonyms": [],
"value": "bathroom"
},
{
"synonyms": [
"sleeping room"
],
"value": "bedroom"
},
{
"synonyms": [
"main room",
"lounge"
],
"value": "living room"
},
{
"synonyms": [
"yard",
"backyard,"
],
"value": "garden"
}
],
"use_synonyms": true
},
"snips/temperature": {}
}
}


We don’t need to edit the snips/temperature entity as it is a builtin entity.

Now that we have our dataset ready, let’s move to the next step which is to create an NLU engine.

The Snips NLU Engine¶

The main API of Snips NLU is an object called a SnipsNLUEngine. This engine is the one you will train and use for parsing.

The simplest way to create an NLU engine is the following:

from snips_nlu import SnipsNLUEngine

default_engine = SnipsNLUEngine()


In this example the engine was created with default parameters which, in many cases, will be sufficient.

However, in some cases it may be required to tune the engine a bit and provide a customized configuration. Typically, different languages may require different sets of features. You can check the NLUEngineConfig to get more details about what can be configured.

We have built a list of default configurations, one per supported language, that have some language specific enhancements. In this tutorial we will use the english one.

Before training the engine, note that you need to load language specific resources used to improve performance with the load_resources() function.

import io
import json

from snips_nlu.default_configs import CONFIG_EN

engine = SnipsNLUEngine(config=CONFIG_EN)


At this point, we can try to parse something:

engine.parse(u"Please give me some lights in the entrance !")


That will raise a NotTrained error, as we did not train the engine with the dataset that we created.

Training the engine¶

In order to use the engine we created, we need to train it or fit it with the dataset we generated earlier:

with io.open("dataset.json") as f:

engine.fit(dataset)


Parsing¶

We are now ready to parse:

parsing = engine.parse(u"Hey, lights on in the lounge !")
print(json.dumps(parsing, indent=2))


You should get the following output (with a slightly different probability value):

{
"input": "Hey, lights on in the lounge !",
"intent": {
"intentName": "turnLightOn",
"probability": 0.4879843917522865
},
"slots": [
{
"range": {
"start": 22,
"end": 28
},
"rawValue": "lounge",
"value": {
"kind": "Custom",
"value": "living room"
},
"entity": "room",
"slotName": "room"
}
]
}


Notice that the lounge slot value points to living room as defined earlier in the entity synonyms of the dataset.

The None intent¶

On top of the intents that you have declared in your dataset, the NLU engine generates an implicit intent to cover utterances that does not correspond to any of your intents. We refer to it as the None intent.

The NLU engine is trained to recognize when the input corresponds to the None intent. Here is what you should get if you try parsing "foo bar" with the engine we previously created:

{
"input": "foo bar",
"intent": null,
"slots": null
}


Persisting¶

As a final step, we will persist the engine into a directory. That may be useful in various contexts, for instance if you want to train on a machine and parse on another one.

You can persist the engine with the following API:

engine.persist("path/to/directory")


loaded_engine = SnipsNLUEngine.from_path("path/to/directory")

Alternatively, you can persist/load the engine as a bytearray:
engine_bytes = engine.to_byte_array()