APIs to create, read, update, and delete semantic layer configurations for your datamarts. The semantic layer enriches your datamart with business-friendly metadata — descriptions, synonyms, column types, and feedback — to power AI chat mode and improve data discoverability.Documentation Index
Fetch the complete documentation index at: https://docs.usedatabrain.com/llms.txt
Use this file to discover all available pages before exploring further.
The Semantic Layer API operates on existing datamarts. You must create a datamart first using the Datamart API before adding a semantic layer.
API Endpoints
Cloud Databrain Endpoint
Self-hosted Databrain Endpoint
/api/v2/dataApp/... (camelCase) if your client or proxy uses that prefix.
Authentication
All semantic layer endpoints require a service token (not a data app API token). Data app tokens will be rejected with a403 AUTHENTICATION_ERROR.
Bearer token for API authentication. Must be a service token.
Available Operations
Get Semantic Layer
Retrieve the semantic layer for a datamart including tables, columns, and completion score.
Create Semantic Layer
Add semantic metadata to a datamart that doesn’t have one yet.
Update Semantic Layer
Modify existing semantic layer metadata (descriptions, synonyms, feedback).
Delete Semantic Layer
Remove all semantic layer metadata from a datamart.
Key Concepts
Tables & Columns
Enrich your datamart tables and columns with:- Descriptions — natural-language explanations of what the data represents
- Synonyms — alternative names users might search for (up to 10 per entity)
- Column types — semantic classifications like
String,Number,ENUM,Range,Identifier, etc. - Column type config — type-specific shape (e.g. value-to-description maps for
ENUM/Stringtypes,{ lowerLimit, upperLimit }forRange, strings forExpression/JSON) - Miscellaneous info — extra context for the AI to improve query generation

