Tutorial¶
In this section, we will build an NLU assistant for home automation tasks that will be able to understand queries about lights and thermostat. 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 two options to create your dataset. You can build it manually by respecting the format used in the sample or alternatively you can use the dataset creation CLI that is contained in the lib.
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:
turnLightOn.txt
turnLightOff.txt
setTemperature.txt
room.txt
The name of each file is important as the tool will map it to the intent or entity name.
Let’s add training examples for the first intent by inserting the following
lines in the first file, 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 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, 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]
,
which name is roomTemperature
and type is snips/temperature
. The slot
type that we used here is a builtin entity
that would help us resolve properly the temperature values.
Let’s move to the room.txt
entity file:
bedroom
living room,main room
garden,yard,"backyard,"
The entity file is a comma (,
) separated file. Each line correspond to a entity value followed by its potential synonyms.
If a value or a synonym has a comma in it, 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 correspond to the actual value A value with a "," in it
We are now ready to generate our dataset:
generate-dataset --language en --intent-files turnLightOn.txt turnLightOff.txt setTemperature.txt --entity-files 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.
Let’s have a look at what has been generated and more precisely the
"entities"
part of the json:
{
"entities": {
"room": {
"use_synonyms": true,
"automatically_extensible": true,
"data": [
{
"value": "bedroom",
"synonyms": []
},
{
"value": "living room",
"synonyms": ["main room"]
},
{
"value": "bathroom",
"synonyms": []
},
{
"value": "garden",
"synonyms": ["yard", "backyard,"]
}
]
},
"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. We also add some
synonyms, so at the end this is what we have:
{
"entities": {
"room": {
"use_synonyms": true,
"automatically_extensible": false,
"data": [
{
"value": "bedroom",
"synonyms": ["sleeping room"]
},
{
"value": "living room",
"synonyms": ["main room"]
},
{
"value": "bathroom",
"synonyms": []
},
{
"value": "garden",
"synonyms": ["yard", "backyard,"]
}
]
},
"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 a bit the engine 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 created a list of sample 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 import SnipsNLUEngine, load_resources
load_resources(u"en")
with io.open("config_en.json") as f:
config = json.load(f)
engine = SnipsNLUEngine(config=config)
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:
dataset = json.load(f)
engine.fit(dataset)
Parsing¶
We are now ready to parse:
parsing = engine.parse(u"Hey, lights on in the entrance !")
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.
Persisting¶
As a final step, we will persist the engine in a json. 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_json = json.dumps(engine.to_dict())
with io.open("trained_engine.json", mode="w") as f:
# f.write(engine_json.decode("utf8")) # Python 2
f.write(engine_json) # Python 3
And load it:
with io.open("trained_engine.json") as f:
engine_dict = json.load(f)
loaded_engine = SnipsNLUEngine.from_dict(engine_dict)
loaded_engine.parse(u"Turn lights on in the bathroom please")