A text-to-intent parsing framework.
Adapt Intent Parser
The Adapt Intent Parser is a flexible and extensible intent definition and determination framework. It is intended to parse natural language text into a structured intent that can then be invoked programatically.
To take a dependency on Adapt, it's recommended to use virtualenv and pip to install source from github.
$ virtualenv myvirtualenv $ . myvirtualenv/bin/activate $ pip install -e git+https://github.com/mycroftai/adapt#egg=adapt-parser
Executable examples can be found in the examples folder.
In this context, an Intent is an action the system should perform. In the context of Pandora, we’ll define two actions: List Stations, and Select Station (aka start playback)
With the Adapt intent builder:
list_stations_intent = IntentBuilder('pandora:list_stations')\ .require('Browse Music Command')\ .build()
For the above, we are describing a “List Stations” intent, which has a single requirement of a “Browse Music Command” entity.
play_music_command = IntentBuilder('pandora:select_station')\ .require('Listen Command')\ .require('Pandora Station')\ .optionally('Music Keyword')\ .build()
For the above, we are describing a “Select Station” (aka start playback) intent, which requires a “Listen Command” entity, a “Pandora Station”, and optionally a “Music Keyword” entity.
Entities are a named value. Examples include:
Blink 182 is an
The Big Bang Theory is a
Play is a
Song(s) is a
For my Pandora implementation, there is a static set of vocabulary for the Browse Music Command, Listen Command, and Music Keyword (defined by me, a native english speaker and all-around good guy). Pandora Station entities are populated via a "List Stations" API call to Pandora. Here’s what the vocabulary registration looks like.
def register_vocab(entity_type, entity_value): pass # a tiny bit of code def register_pandora_vocab(emitter): for v in ["stations"]: register_vocab('Browse Music Command', v) for v in ["play", "listen", "hear"]: register_vocab('Listen Command', v) for v in ["music", "radio"]: register_vocab('Music Keyword', v) for v in ["Pandora"]: register_vocab('Plugin Name', v) station_name_regex = re.compile(r"(.*) Radio") p = get_pandora() for station in p.stations: m = station_name_regex.match(station.get('stationName')) if not m: continue for match in m.groups(): register_vocab('Pandora Station', match)
Further documentation can be found at https://mycroft-ai.gitbook.io/docs/mycroft-technologies/adapt
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