Open Semantic Interchange Format

Wobby supports the Open Semantic Interchange (OSI) format for exporting and importing semantic layers across tools.

What is OSI?

OSI is a vendor-agnostic, open-source specification for semantic model exchange across AI agents, BI platforms, and analytics tools. It was initiated by Snowflake together with Salesforce, dbt Labs, and other players, and is governed as an open standard under the Apache 2.0 license. Participating organizations include Wobby, Tableau, Qlik, ThoughtSpot, Omni, Sigma, Alation, Select Star, and others.

Wobby to OSI Mapping

Wobby Entity	OSI Entity	Notes
Environment	semantic_model	Container for the export
Model	datasets	Direct mapping
Model.sourcePath	datasets.source	Formatted as db.schema.table
Model.grain	datasets.primary_key	Array of column names
Model.agentGuidance	datasets.ai_context	Combined with description
ModelDimension	datasets.fields	With expression dialect
ModelDimension (temporal)	fields.dimension.is_time	Boolean flag
ModelMeasure	datasets.fields	ai_context: "Measure. Unit: X..."
ModelRelationship	relationships	from_columns/to_columns as arrays
Metric	metrics	Expression with dialect
GlossaryTerm	ai_context	"Also known as: term, synonym1..."

What's NOT Exported in OSI

OSI is a lightweight interchange format. The following Wobby-specific features are not included:

• Filters - OSI has no filters concept

• Agents - No OSI equivalent

• Glossary definitions - Only terms/synonyms are preserved as ai_context

• Enum value descriptions - Only values are listed

• Sample data - Not part of OSI spec

For complete backups that preserve all details, use the Wobby Lossless format instead.

OSI Output Example

# OSI (Open Semantic Interface) Export
# Generated by Wobby
# Environment: My Analytics
# Exported at: 2026-01-28T10:30:00.000Z
---
semantic_model:
  - name: "My Analytics"
    description: "Exported from Wobby"
    ai_context: "Source: wobby. Environment ID: env_123"

    datasets:
      - name: "orders"
        source: "mydb.analytics.orders"
        primary_key: ["order_id"]
        description: "Order transactions"
        fields:
          - name: "order_date"
            expression:
              dialects:
                - dialect: "ANSI_SQL"
                  expression: "order_date"
            dimension:
              is_time: true
            ai_context: "Time grains: DAY, MONTH, YEAR"
          - name: "total_amount"
            expression:
              dialects:
                - dialect: "ANSI_SQL"
                  expression: "SUM(amount)"
            ai_context: "Measure. Unit: USD. Precision: 2."

    relationships:
      - name: "orders_to_customers"
        from: "orders"
        to: "customers"
        from_columns: ["customer_id"]
        to_columns: ["id"]

    metrics:
      - name: "total_revenue"
        expression:
          dialects:
            - dialect: "ANSI_SQL"
              expression: "SUM(orders.amount)"
        description: "Total revenue"
        ai_context: "Anchor model: orders. Unit: USD."