Quickstart¶

In this section, we assume that you have installed snips-nlu and loaded resources for English:

pip install snips-nlu


The Snips NLU engine, in its default configuration, needs to be trained on some data before it can start extracting information. Thus, the first thing to do is to build a dataset that can be fed into Snips NLU. For now, we will use this sample dataset which contains data for two intents:

• sampleGetWeather -> "What will be the weather in Tokyo tomorrow?"
• sampleTurnOnLight -> "Turn on the light in the kitchen"

The format used here is json, so let’s load it into a python dict:

import io
import json

with io.open("path/to/sample_dataset.json") as f:


Now that we have our dataset, we can move forward to the next step which is building a SnipsNLUEngine. This is the main object of this lib. Before training the engine, note that you need to load language-specific resources used to improve performance with the load_resources() function.

from snips_nlu import load_resources, SnipsNLUEngine

nlu_engine = SnipsNLUEngine()


Now that we have our engine object created, we need to feed it with our sample dataset. In general, this action will require some machine learning hence we will actually fit the engine:

nlu_engine.fit(sample_dataset)


Our NLU engine is now trained to recognize new utterances that extend beyond what is strictly contained in the dataset, it is able to generalize.

Let’s try to parse something now!

import json

parsing = nlu_engine.parse(u"What will be the weather in San Francisco next week?")
print(json.dumps(parsing, indent=2))


You should get something that looks like this:

{
"input": "What will be the weather in San Francisco next week?",
"intent": {
"intentName": "sampleGetWeather",
"probability": 0.641227710154331
},
"slots": [
{
"range": {
"start": 28,
"end": 41
},
"rawValue": "San Francisco",
"value": {
"kind": "Custom",
"value": "San Francisco"
},
"entity": "location",
"slotName": "weatherLocation"
},
{
"range": {
"start": 42,
"end": 51
},
"rawValue": "next week",
"value": {
"type": "value",
"grain": "week",
"precision": "exact",
"latent": false,
"value": "2018-02-12 00:00:00 +01:00"
},
"entity": "snips/datetime",
"slotName": "weatherDate"
}
]
}


Congrats, you parsed your first intent!