OVOS Model2Vec Intent Pipeline

An intent matching pipeline for OpenVoiceOS (OVOS). It uses the Model2Vec model for intent classification.

This plugin uses a pretrained Model2Vec model to classify natural language utterances into intent labels registered with the system (Adapt, Padatious, and plugin-specific labels). It only considers intents from loaded skills and ignores labels from unregistered intents. Use this pipeline when deterministic engines fail to give a high-confidence match.

Features

Model2Vec drives intent classification.
The plugin integrates directly with OVOS pipelines.
The Model2Vec models train on GitLocalize exports.
English models come in several sizes, distilled from Potion.
The multilingual model is distilled from LaBSE.
The plugin syncs Adapt and Padatious intents dynamically at runtime.
The plugin considers only intents from loaded skills and ignores unregistered labels.

English models range from 8 MB to 150 MB. The multilingual model (the default) is over 500 MB.

Installation

Install the plugin with pip:

pip install ovos-m2v-pipeline

Configuration

In your mycroft.conf:

{
  "intents": {
    "ovos-m2v-pipeline": {
      "model": "Jarbas/ovos-model2vec-intents-LaBSE",
      "conf_high": 0.7,
      "conf_medium": 0.5,
      "conf_low": 0.15,
      "ignore_intents": []
    }
  }
}

model: Path to your pretrained Model2Vec model or huggingface repo.
conf_xxx: Minimum confidence threshold for intent matching.
ignore_intents: List of canonical labels to exclude from matching (deny-list, applied after label_map).
valid_labels: List of canonical labels eligible to match (allow-list, applied after label_map). When unset, every mapped label is eligible.
label_map: Maps a raw model label to its canonical skill_id:intent label. Merges over (and can override) the built-in OCP/common-query/stop remaps and any labels the model itself declares in labels.json; see Trained models document their labels.
prototype_strategy: Scoring strategy for prototype mode (default "max_over_all", back-compatible). See docs/strategies.md.
prototype_top_k: Top-k cosines averaged by the top_k_mean strategy (default 3).
prototype_tau: Softmax temperature for the softmax_weighted strategy (default 0.1).

The Model2Vec model is pretrained on GitLocalize exports. It cannot learn new skills dynamically.

Which entrypoint do I want?

This plugin ships two opm.pipeline entrypoints. Both use the same Model2VecIntentPipeline class, running in different modes:

ovos-m2v-pipeline (Model2VecIntentPipeline, mode: "classifier", the default) loads a pretrained, frozen classification head with a fixed label set baked in at training time. It is fast and needs no runtime fitting, but it can only ever return the labels it was trained on. It still tracks OVOS-INTENT-4 ovos.intent.register.template registrations from skills so it can gate/allowlist a trained label, but registering a new intent that was not part of training does not teach it to that skill. That intent will never be matched.
ovos-m2v-prototype-pipeline (Model2VecPrototypePipeline, mode: "prototype") loads a bare embedding model with no classification head and builds its label set entirely at runtime, from the example utterances supplied by Adapt/Padatious registrations and OVOS-INTENT-4 template registrations. Use this entrypoint whenever skills need to register new intents (including custom/dynamically-created skills) that must actually be matched.

You can enable both entrypoints together. Configure each independently under its own intents.<entrypoint-name> key (see the Model2VecPrototypePipeline docstring for an example), so a deployment can keep the fast frozen classifier for its core trained intents while the prototype matcher picks up everything else.

Trained models document their labels

In classifier mode the label head is frozen at training time, so which bus intent each label denotes (ocp:play -> ovos.common_play:ovos.common_play.play_search, for example) is a property of that particular trained model, not of the plugin code. A model can ship this mapping alongside its weights as a labels.json file in its repo/directory:

{
  "my_domain:book_flight": "travel_skill:book.flight.intent",
  "my_domain:cancel_flight": "travel_skill:cancel.flight.intent",
  "valid_labels": ["travel_skill:book.flight.intent", "travel_skill:cancel.flight.intent"]
}

labels.json has the same shape as the label_map config option, plus an optional valid_labels list. When present, list the labels a model was trained on on its model card too, so users know what to expect without downloading it first.

Three layers combine, each overriding the previous on a per-key basis:

Built-in defaults (the OCP / common-query / stop remaps that predate this mechanism).
The loaded model's own labels.json, if it ships one.
The deployment's label_map / valid_labels config.

For a Hugging Face hub model id, labels.json rides the same local cache as the model's own weights: the plugin never fetches it over the network at construction time, and only ever consults the cache entry already populated by whatever downloaded the model. If the model has not been cached yet, or the cached copy has no labels.json, the manifest layer is treated as empty

it is never a reason for plugin construction to touch the network or block on one.

A missing or corrupt labels.json is logged and ignored; the plugin falls back to the layers below it rather than failing to load. A label_map target that is not a skill_id:intent string (no colon) is logged as a warning (once per label) and used as-is - the plugin never invents a bus topic from it.

Usage

The Model2VecIntentPipeline class integrates with the OVOS intent system. It:

Receives an utterance (text).
Predicts intent labels using the pretrained Model2Vec model.
Filters out intents that are not part of the loaded skills.
Returns a match for the highest-confidence intent from the list of valid intents.

Tips

Tune min_conf to control the confidence threshold for intent matching.
Use the ignore_intents list to filter out specific problematic intents from predictions.
The plugin syncs Adapt and Padatious intents automatically at runtime, over the OVOS message bus.

Pre-trained models are available in the ovos-model2vec-intents Hugging Face collection.

Related projects

OpenVoiceOS: the OVOS org, and the intent-pipeline system this plugin extends.
ovos-plugin-manager: the base pipeline class and plugin infrastructure.
ovos-workshop: the skill framework this plugin integrates with.

License

This project is licensed under the Apache 2.0 License.

Credits

The model2vec intent pipeline was first prototyped by TigreGótico under the ILENIA project for OpenVoiceOS and later extended with an embeddings-only mode and new models, through the NGI0 Commons Fund.

This project was funded by the Ministerio para la Transformación Digital y de la Función Pública and Plan de Recuperación, Transformación y Resiliencia - Funded by EU, NextGenerationEU within the framework of the project ILENIA with reference 2022/TL22/00215337

This project was funded through the NGI0 Commons Fund, a fund established by NLnet with financial support from the European Commission's Next Generation Internet programme, under the aegis of DG Communications Networks, Content and Technology under grant agreement No 101135429.

Release files for ovos-m2v-pipeline 0.7.0a1

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Source distribution for ovos-m2v-pipeline 0.7.0a1
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