OVOS Intent Pipeline

The OpenVoiceOS (OVOS) Intent Pipeline is a modular and extensible system designed to interpret user utterances and map them to appropriate actions or responses.

It orchestrates various intent parsers and fallback mechanisms to ensure accurate and contextually relevant responses.

---

What is an Intent Pipeline?

An intent pipeline in OVOS is a sequence of processing stages that analyze user input to determine the user's intent. Each stage employs different strategies, ranging from high-confidence intent parsers to fallback mechanisms, to interpret the input.

This layered approach ensures that OVOS can handle a wide range of user queries with varying degrees of specificity and complexity.

---

Pipeline Structure

OVOS pipelines are structured to prioritize intent matching based on confidence levels:

• High Confidence: Primary intent parsers that provide precise matches.
• Medium Confidence: Secondary parsers that handle less specific queries.
• Low Confidence: Fallback mechanisms for ambiguous or unrecognized inputs.

Each component in the pipeline is a plugin that can be enabled, disabled, or reordered according to user preferences. This flexibility allows for customization based on specific use cases or device capabilities.

---

Available Pipeline Components

Below is a list of available pipeline components, categorized by their confidence levels and functionalities:

High Confidence Components

Pipeline	Description	Notes
stop_high	Exact match for stop commands	Replaces skill-ovos-stop
converse	Continuous conversation interception for skills
padatious_high	High-confidence matches using Padatious
adapt_high	High-confidence matches using Adapt
fallback_high	High-priority fallback skill matches
ocp_high	High-confidence media-related queries
ovos-persona-pipeline-plugin-high	Active persona conversation (e.g., LLM integration)
ovos-m2v-pipeline-high	Multilingual intent classifier capable of handling paraphrasing	Only supports default skills, not dynamic

Medium Confidence Components

Pipeline	Description	Notes
stop_medium	Medium-confidence stop command matches	Replaces skill-ovos-stop
padatious_medium	Medium-confidence matches using Padatious
adapt_medium	Medium-confidence matches using Adapt
ocp_medium	Medium-confidence media-related queries
fallback_medium	Medium-priority fallback skill matches
ovos-m2v-pipeline-medium	Multilingual intent classifier capable of handling paraphrasing	Only supports default skills, not dynamic

Low Confidence Components

Pipeline	Description	Notes
stop_low	Low-confidence stop command matches	Disabled by default
padatious_low	Low-confidence matches using Padatious	Often inaccurate; disabled by default
adapt_low	Low-confidence matches using Adapt
ocp_low	Low-confidence media-related queries
fallback_low	Low-priority fallback skill matches
common_query	Sends utterance to common_query skills	Selects the best match among available skills
ovos-persona-pipeline-plugin-low	Persona catch-all fallback (e.g., LLM integration)
ovos-m2v-pipeline-low	Multilingual intent classifier capable of handling paraphrasing	Only supports default skills, not dynamic

---

Customizing the Pipeline

OVOS allows users to customize the intent pipeline through configuration files. Users can enable or disable specific components, adjust their order, and set confidence thresholds to tailor the system's behavior to their needs. This customization ensures that OVOS can be optimized for various applications, from simple command recognition to complex conversational agents.

  "intents": {
    "adapt": {
      "conf_high": 0.5,
      "conf_med": 0.3,
      "conf_low": 0.2
    },
    "persona": {
      "handle_fallback": true,
      "default_persona": "Remote Llama"
    },
    "pipeline": [
      "ovos-m2v-pipeline-high",
      "ocp_high",
      "stop_high",
      "converse",
      "padatious_high",
      "adapt_high",
      "stop_medium",
      "adapt_medium",
      "common_qa",
      "fallback_medium",
      "fallback_low"
    ]
  },

---

By understanding and configuring the OVOS Intent Pipeline, developers and users can enhance the accuracy and responsiveness of their voice assistant applications.