ovos-adapt-parser 1.3.2a1

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.

This repository contains a OVOS pipeline plugin and bundles a fork of the original adapt-parser from the defunct MycroftAI.

Pipeline Plugins

Three OPM pipeline entry points are exposed:

• ovos-adapt-pipeline-plugin (AdaptPipeline) — the flat pipeline, wrapping a single IntentDeterminationEngine. All skills share one trie.
• ovos-adapt-domain-pipeline-plugin (DomainAdaptPipeline) — a per-skill pipeline wrapping DomainIntentDeterminationEngine. Each skill_id gets its own sub-engine ("domain"); at match time every domain is scored in parallel and a global argmax wins. Configurable under intents.ovos_adapt_domain_pipeline.
• ovos-adapt-hierarchical-pipeline-plugin (HierarchicalAdaptPipeline) — the per-skill domain model with two-stage routing: a stage-1 classifier picks one domain, then only that domain's sub-engine is scored. Configurable under intents.ovos_adapt_hierarchical_pipeline.

See Pipeline variants for when to use each.

Documentation

A zero-to-hero guide lives in docs/:

• Concepts — entities, intents, cliques, and confidence
• Quickstart — install, enable, match your first utterance
• Writing intents — the IntentBuilder API with examples
• Configuration — confidence tiers and every config key
• Pipeline variants — the flat, domain, and hierarchical plugins
• Bus protocol — the messagebus API skills register over
• Internals — tagging, clique expansion, the confidence math
• Engine comparison reference — how the variants diverge

Examples

Executable examples can be found in the examples folder.

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:

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

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.

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.

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')