Adapt Pipeline Plugin

The Adapt Pipeline Plugin brings rule-based intent parsing to the OVOS intent pipeline using the Adapt parser. It supports high, medium, and low confidence intent detection and integrates seamlessly with OVOS’s multi-stage pipeline.

While Adapt is powerful for explicit, deterministic matching, it has notable limitations in multilingual environments and complex skill ecosystems. In general, Adapt is not recommended for broad deployments—it is best suited for personal skills where you control the full context and can craft precise intent rules.

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Pipeline Stages

This plugin registers three pipelines:

Pipeline ID	Description	Recommended Use
adapt_high	High-confidence Adapt intent matches	✅ Personal skills only
adapt_medium	Medium-confidence Adapt matches	⚠️ Use with caution
adapt_low	Low-confidence Adapt matches	🚫 Not recommended

Each pipeline is scored by Adapt and routed according to configured confidence thresholds.

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Limitations

Adapt requires hand-crafted rules for every intent:

• ❌ Poor scalability — hard to manage with many skills
• ❌ Difficult to localize — rules rely on exact words and phrases
• ❌ Prone to conflicts — multiple skills defining overlapping rules can cause collisions or missed matches

As your skill library grows or if you operate in a multilingual setup, these problems increase.

Recommendation:

🟢 Use Adapt only in personal projects or controlled environments where you can fully define and test every possible phrase.

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Configuration

Adapt confidence thresholds can be set in ovos.conf:

"intents": {
  "adapt": {
    "conf_high": 0.65,
    "conf_med": 0.45,
    "conf_low": 0.25
  }
}

• These thresholds control routing into adapt_high, adapt_medium, and adapt_low.
• The plugin is included by default in OVOS.

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When to Use Adapt in OVOS

Use this plugin only when:

• You are building a personal or private skill.
• You need strict, predictable matching (e.g., command-and-control).
• You are working in a single language and control all skill interactions.

Avoid using Adapt for public-facing or general-purpose assistant skills. Modern alternatives like Padatious, LLM-based parsers, or neural fallback models are more scalable and adaptable.