Wildcards with snips-nlu

main.py

from __future__ import unicode_literals, print_function
import io
import json

from snips_nlu import SnipsNLUEngine
from snips_nlu.default_configs import CONFIG_EN
from snips_nlu.dataset import Dataset

with io.open("dataset/remind.yaml") as f:
    dataset = Dataset.from_yaml_files("en", [f])

nlu_engine = SnipsNLUEngine(config=CONFIG_EN)
nlu_engine = nlu_engine.fit(dataset.json)

text = "Remind me tomorrow find a flight to london"
parsing = nlu_engine.parse(text)

print(json.dumps(parsing, indent= 4, sort_keys=True))

remind.yaml

---
type: intent
name: Reminder
utterances:
- Remind me [snips/datetime](tomorrow) to [action](go to the gym)
- remind me to [action](take an umbrella) [snips/datetime](in 5 days)
- remind me to [action](walk the dog tonight)
- could you remind me to [action](call my mom)
- please remind me to [action](go to my appointment)
# Required to make this work
# - Remind me [snips/datetime](tomorrow) to [action](find a flight to london)

---
type: entity
name: action
automatically_extensible: yes


# searchFlight Intent
---
type: intent
name: searchFlight
slots:
  - name: origin
    entity: city
  - name: destination
    entity: city
  - name: date
    entity: snips/datetime
utterances:
  - find me a flight from [origin](Paris) to [destination](New York)
  - I need a flight leaving [date](this weekend) to [destination](Berlin)
  - show me flights to go to [destination](new york) leaving [date](this evening)

# City Entity
---
type: entity
name: city
values:
  - london
  - [new york, big apple]
  - [paris, city of lights]

shell output

{
    "input": "Remind me tomorrow to find a flight to london",
    "intent": {
        "intentName": "searchFlight",
        "probability": 0.6030061357370643
    },
    "slots": [
        {
            "entity": "city",
            "range": {
                "end": 45,
                "start": 39
            },
            "rawValue": "london",
            "slotName": "destination",
            "value": {
                "kind": "Custom",
                "value": "london"
            }
        }
    ]
}