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This release is a pre-release and may not be stable for production use.

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Padatious

Padatious is a neural network intent parser, implemented in pure numpy with a FANN-compatible model format. This repository packages it as an OpenVoiceOS (OVOS) pipeline plugin and bundles a maintained fork of the original padatious from Mycroft AI.

Features

  • Intents are easy to create from a handful of example sentences.
  • Each intent trains its own small network, independent of the others.
  • Fast training on a small amount of data.
  • Entity extraction from a matched sentence (for example, Find the nearest {place} matches "Find the nearest gas station" and extracts place: gas station).

Installing

Padatious is pure Python (numpy). It needs no native libraries or compilers.

Install from PyPI:

pip install ovos-padatious-pipeline-plugin

Direct Usage

from ovos_padatious import IntentContainer

container = IntentContainer('intent_cache')
container.add_intent('hello', ['Hi there!', 'Hello.'])
container.add_intent('goodbye', ['See you!', 'Goodbye!'])
container.add_intent('search', ['Search for {query} (using|on) {engine}.'])
container.train()

print(container.calc_intent('Hello there!'))
print(container.calc_intent('Search for cats on CatTube.'))

container.remove_intent('goodbye')

Inside OVOS, the plugin is discovered automatically through its opm.pipeline entry point. See docs/ for installation details, the intent file syntax, the full Python API, pipeline configuration, and the matching algorithm.

Inside OVOS, training and compiling always run on a background worker, never on the thread that registered or queried something (including the very first pass); a test or tool that registers an intent and needs to query it right away should call PadatiousPipeline.wait_until_trained() (see docs/ovos_pipeline.md) rather than polling or sleeping.

Related projects

License

Licensed under the Apache 2.0 license.

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