omni-model-builder

Omni Model Builder

Create and modify Omni's semantic model through the YAML API — views, topics, dimensions, measures, relationships, and query views.

Tip: Always use omni-model-explorer first to understand the existing model.

Prerequisites

export OMNI_BASE_URL="https://yourorg.omniapp.co"
export OMNI_API_KEY="your-api-key"

You need Modeler or Connection Admin permissions.

Omni's Layered Modeling Architecture

Omni uses a layered approach where each layer builds on top of the previous:

1. Schema Layer — Auto-generated from your database. Reflects tables, views, columns, and their types. Kept in sync via schema refresh.

2. Shared Model Layer — Your governed semantic model. Where you define dimensions, measures, joins, and topics that are reusable across the organization.

3. Workbook Model Layer — Ad hoc extensions within individual workbooks. Used for experimental fields before promotion to shared model.

4. Branch Layer — Intermediate development layer. Used when working in branches before merging changes to shared model.

Key concept: The schema layer is the foundation and source of truth for table/column structure. When your database schema changes (new tables, deleted columns, type changes), you refresh the schema to keep Omni in sync. All user-created content (dimensions, measures, relationships, topics) flows through the shared model layer.

Development workflow: When building or modifying the model, you work in branches (see "Safe Development Workflow" below). Branches are isolated copies where you can safely experiment before merging changes back to shared model. This skill covers creating and editing model definitions in both branches and shared models.

Determine SQL Dialect

Before writing any SQL expressions, confirm the dialect from the connection — don't guess from the connection name:

# 1. Get the model's connectionId
curl -L "$OMNI_BASE_URL/api/v1/models/{modelId}" \
  -H "Authorization: Bearer $OMNI_API_KEY"

Use dialect-appropriate functions in your SQL (e.g. SAFE_DIVIDE for BigQuery, NULLIF(a/b) for Postgres/Snowflake).

Schema Refresh: Syncing with Database Changes

The schema layer is auto-generated from your database. When your database schema changes (new/deleted/renamed columns, type changes), refresh Omni's schema layer to stay in sync.

When to trigger:

• New tables added to your database
• Column added/renamed/deleted in existing tables
• Creating a new view from scratch and want auto-generated base dimensions
• Model is out of sync with database

What it does:

• Introspects your data warehouse
• Auto-generates base dimensions for all columns with correct types and timeframes
• Detects deletions and broken references
• Runs as a background job (can take several minutes)

Side effect: May auto-generate dimensions for columns you don't need. Suppress with hidden: true in your extension layer.

Trigger via API:

Requires Connection Admin permissions.

API Discovery

When unsure whether an endpoint or parameter exists, fetch the OpenAPI spec:

Use this to verify endpoints, available parameters, and request/response schemas before making calls.

Safe Development Workflow

Always work in a branch. Never write directly to production.

Step 0: Create a Branch

Omni branches are models with modelKind: "BRANCH". There is no dedicated branch-creation endpoint — create one via POST /api/v1/models:

The response model.id is your branchId — a UUID you'll pass to all subsequent API calls. To list existing branches at any time:

Git-connected models: If your model is connected to a git repo (GET /api/v1/models/{modelId}/git returns an sshUrl), merging an Omni branch will automatically commit the changes back to your git baseBranch. Choose one workflow and stick to it — either edit via the Omni branch API (then git pull to sync local files), or edit local files and push via git. Mixing both leads to conflicts.

Step 1: Write YAML to a Branch

Note: The branchId parameter must be a UUID from the server (Step 0). Passing a string name instead will return 400 Bad Request: Unrecognized key: "branchName".

Step 2: Validate

Returns validation errors and warnings with message, yaml_path, and sometimes auto_fix.

Step 3: Merge the Branch

Important: Always ask the user for confirmation before merging. Merging applies changes to the production model and cannot be easily undone.

If git with required PRs is configured, merge through your git workflow instead.

YAML File Types

Write with mode: "extension" (shared model layer). To delete a file, send empty yaml.

Writing Views

Every view MUST have a primary_key: true dimension. Without a primary key, queries that join to this view will produce fanout errors or incorrect aggregations. Use the table's natural unique identifier (e.g., id, order_id, user_id). If the table has no single unique column, create a composite key with a SQL expression: sql: ${schema_name} || '-' || ${table_name}.

Basic View

Understanding Schema Layer vs Extension Layer

When you create a view, Omni separates schema (database structure) from model (your business logic):

• Schema layer: Auto-generated base dimensions, one per column. Types come from the database. Read-only, synced via schema refresh.
• Extension layer: Your custom YAML. Can override base dimensions, add new dimensions/measures, hide columns, add business logic.

When both layers exist for a field with the same name, your extension definition wins but type information comes from the schema layer.

Example: Table has columns created_at (DATE) and revenue (NUMERIC).

Result: created_at inherits its type from schema layer (DATE with automatic week/month/year granularities) but gets your label. The raw revenue column is hidden, only exposed through the total_revenue measure.

Key insight: If your extension layer defines a dimension but there's no schema layer base dimension to provide type information, Omni can't infer granularities or types. Solution: trigger schema refresh to auto-generate the schema layer (see "Schema Refresh" section above).

Dimension Parameters

See references/modelParameters.md for the complete list of 35+ dimension parameters, format values, and timeframes.

Most common parameters:

• sql — SQL expression using ${field_name} references
• label — display name · description — help text (also used by Blobby)
• primary_key: true — unique key (critical for aggregations)
• hidden: true — hides from picker, still usable in SQL
• format — number_2, currency_2, percent_2, id
• group_label — groups fields in the picker
• synonyms — alternative names for AI matching (e.g., [client, account, buyer])

Measure Parameters

See references/modelParameters.md for the complete list of 24+ measure parameters and all 13 aggregate types.

Measure filters restrict rows before aggregation:

Filter conditions: is, is_not, greater_than, less_than, contains, starts_with, ends_with

Writing Topics

See Topics setup for complete YAML examples with joins, fields, and ai_context, and Topic parameters for all available options.

Key topic elements:

• base_view — the primary view for this topic
• joins — nested structure for join chains (e.g., users: {} or inventory_items: { products: {} })
• ai_context — guides Blobby's field mapping (e.g., "Map 'revenue' → total_revenue")
• default_filters — applied to all queries unless removed
• always_where_sql — non-removable filters
• fields — field curation: [order_items.*, users.name, -users.internal_id]

Writing Relationships

Getting relationship_type right prevents fanout and symmetric aggregate errors.

Query Views

Virtual tables defined by a saved query. Like regular views, query views must include a primary_key: true dimension to be joinable:

Or with raw SQL:

Common Validation Errors

Troubleshooting: Model Out of Sync with Database

If your model doesn't reflect the database (missing columns, broken references, wrong types), trigger a schema refresh (see "Schema Refresh" section above). Then validate:

Common issues and fixes:

Docs Reference

• Model YAML API · Views · Topics · Dimensions · Measures · Relationships · Query Views · Branch Mode

Related Skills

• omni-model-explorer — understand the model before modifying
• omni-ai-optimizer — add AI context after building topics
• omni-query — test new fields