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Building models with AI

The Model Assistant is a chat panel in the Model IDE that helps you build and maintain your semantic model. Use it to ask questions about your model, get suggestions for fields and relationships, and draft YAML edits — all without leaving the IDE.

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Optimizing models for AI

Providing context to Omni AI — such as field descriptions, possible values, and usage hints — improves the accuracy and relevance of AI-generated queries and suggestions. Refer to Optimize models for Omni AI to learn how to curate your models for better AI performance.

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Branch Mode

More structured than document drafts but less complex than the git integration, Branch Mode allows you to test changes to models and content before making them visible to your teammates. By creating a separate space to experiment with updates, you can develop confidently without disrupting production data. This approach supports scalable and reliable workflows, reducing the need for reactive fixes while improving traceability and accountability in the development process. Refer to the Using Branch Mode for collaboration guide for more information.

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Local development with the Omni model editor CLI

The Omni model editor CLI is currently in beta and under active development.
The @omni-co/model-local-editor CLI tool lets you edit Omni model YAML files locally in your preferred editor with real-time synchronization back to Omni. Changes you save locally are automatically pushed to your Omni branch, giving you full IDE support for syntax highlighting, autocomplete, and version control. To install the CLI, run the following: 
npm install -g @omni-co/model-local-editor
The tool requires an API key and works with branches for isolated development. Refer to the Local development guide for full setup and usage instructions.

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Shared model promotion

Shared model promotion pushes changes made in the workbook layer into the model layer so the changes are reusable and available in all workbooks. Refer to the Promoting workbook changes to the shared model to learn more.

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Incorporating database changes with schema refreshes

Keeping your BI tool in sync with your database when schema changes occur can be a challenge. As such, there may be times when you need to manually refresh the schema model - or make Omni aware of the changes - that your Omni model is built on. Refer to the Schema refresh guide for more information.

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Validating models against content

The Content Validator, accessible by clicking the (checked document icon) in the IDE’s left navigation, helps you keep your content accurate and up-to-date as your data model evolves. If a field is renamed, a table is swapped, or a topic is removed, the validator makes it easy to spot where things broke and quickly fix them - either one by one or in bulk. Additionally, the Content Validator is a powerful discovery tool: you can search for all the content that references a specific field, view, or topic to see how widely it’s used before making changes. This way, you can make updates with confidence and ensure nothing slips through the cracks. Refer to the Content Validator guide for more information.

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Shared extension models

Shared extension models let you create specialized versions of a parent model that inherit its core logic — joins, topics, metrics — while allowing their own customizations. Using shared extensions, you could create:
  • Multi-tenant environments - Dynamically route users to tenant-specific extensions based on user attribute mapping, giving each customer a tailored data experience from a single parent model.
  • Departmental models - Use an extension as the static base model for a workbook, giving teams like Finance or Marketing their own curated view of shared data without affecting other groups.
Extensions can also support isolated branches for independent development workflows, letting teams develop and test changes without affecting the parent model.

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Export to dbt

Omni aims to make model management easy both in Omni and below the BI layer. To this end, Omni can export a view file’s dbt SQL. This will allow users to build fact tables, or virtualized fields into their dbt models. Note: This will require some rework of your Omni model, as new dbt views will be populated into the model upon schema refresh. This means field references would need to be remapped to maintain continuity.