[](LICENSE.md)
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.
This repository contains a OVOS pipeline plugin and bundles a fork of the original [adapt-parser](https://github.com/MycroftAI/adapt) from the defunct MycroftAI
Examples
========
Executable examples can be found in the [examples folder](https://github.com/MycroftAI/adapt/tree/master/examples).
Intent Modelling
================
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:
```Python
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.
```Python
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
========
Entities are a named value. Examples include:
`Blink 182` is an `Artist`
`The Big Bang Theory` is a `Television Show`
`Play` is a `Listen Command`
`Song(s)` is a `Music Keyword`
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.
```Python
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)
```
Reporting Issues
================
It's often difficult to debug issues with adapt without a complete context. To facilitate simpler debugging,
please include a serialized copy of the intent determination engine using the debug dump
utilities.
```python
from ovos_adapt.engine import IntentDeterminationEngine
engine = IntentDeterminationEngine()
# Load engine with vocabulary and parsers
import ovos_adapt.tools.debug as atd
atd.dump(engine, 'debug.ovos_adapt')
```
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"description": "[](LICENSE.md) \n\nAdapt Intent Parser\n==================\nThe 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.\n\nThis repository contains a OVOS pipeline plugin and bundles a fork of the original [adapt-parser](https://github.com/MycroftAI/adapt) from the defunct MycroftAI\n\nExamples\n========\nExecutable examples can be found in the [examples folder](https://github.com/MycroftAI/adapt/tree/master/examples).\n\nIntent Modelling\n================\nIn this context, an Intent is an action the system should perform. In the context of Pandora, we\u2019ll define two actions: List Stations, and Select Station (aka start playback)\n\nWith the Adapt intent builder:\n```Python\nlist_stations_intent = IntentBuilder('pandora:list_stations')\\\n .require('Browse Music Command')\\\n .build()\n```\n\nFor the above, we are describing a \u201cList Stations\u201d intent, which has a single requirement of a \u201cBrowse Music Command\u201d entity.\n\n```Python\nplay_music_command = IntentBuilder('pandora:select_station')\\\n .require('Listen Command')\\\n .require('Pandora Station')\\\n .optionally('Music Keyword')\\\n .build()\n```\n\n\nFor the above, we are describing a \u201cSelect Station\u201d (aka start playback) intent, which requires a \u201cListen Command\u201d entity, a \u201cPandora Station\u201d, and optionally a \u201cMusic Keyword\u201d entity.\n\nEntities\n========\n\nEntities are a named value. Examples include:\n`Blink 182` is an `Artist`\n`The Big Bang Theory` is a `Television Show`\n`Play` is a `Listen Command`\n`Song(s)` is a `Music Keyword`\n\nFor 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\u2019s what the vocabulary registration looks like.\n\n```Python\ndef register_vocab(entity_type, entity_value):\n pass\n # a tiny bit of code \n\ndef register_pandora_vocab(emitter):\n for v in [\"stations\"]:\n register_vocab('Browse Music Command', v)\n\n for v in [\"play\", \"listen\", \"hear\"]:\n register_vocab('Listen Command', v)\n\n for v in [\"music\", \"radio\"]:\n register_vocab('Music Keyword', v)\n\n for v in [\"Pandora\"]:\n register_vocab('Plugin Name', v)\n\n station_name_regex = re.compile(r\"(.*) Radio\")\n p = get_pandora()\n for station in p.stations:\n m = station_name_regex.match(station.get('stationName'))\n if not m:\n continue\n for match in m.groups():\n register_vocab('Pandora Station', match)\n```\n\n\nReporting Issues\n================\nIt's often difficult to debug issues with adapt without a complete context. To facilitate simpler debugging,\nplease include a serialized copy of the intent determination engine using the debug dump\nutilities.\n\n```python\nfrom ovos_adapt.engine import IntentDeterminationEngine\n\nengine = IntentDeterminationEngine()\n# Load engine with vocabulary and parsers\n\nimport ovos_adapt.tools.debug as atd\n\natd.dump(engine, 'debug.ovos_adapt')\n```\n\n\n\n",
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