How does this skill handle table joins?

It uses Entities; by defining the same entity name as a 'primary' or 'foreign' type across different views, the system automatically maps relationships and generates joins.

How do I verify that my semantic layer is correct?

The skill provides access to 'oxy validate' to check YAML syntax and 'oxy build' to verify references across views and topics.

What is an Oxy semantic layer?

It is a metadata layer that sits between your database and business users, translating complex SQL schemas into understandable business terms for natural language querying.

What are synonyms used for in the view files?

Synonyms allow the natural language engine to map various user phrases (like 'turnover' or 'sales') to a single technical field (like 'total_revenue').

Can I use custom SQL expressions in views?

Yes, both dimensions and measures support the 'expr' field, allowing you to use standard SQL functions, CASE statements, and complex calculations.

Oxy Semantic Layer Builder

Name: Oxy Semantic Layer Builder
Author: oxy-hq

byoxy-hq

0•

Gestión de Bases de Datos

Builds and maintains structured Oxy semantic layer files to enable natural language data querying from database schemas.

The Oxy Semantic Layer skill empowers developers to bridge the gap between complex database schemas and business-friendly analytics. By analyzing existing table structures, it automates the creation of views and topics—defining entities, dimensions, and measures—that allow non-technical users to query data using natural language. It handles the intricacies of join logic through entity mapping, supports complex calculated dimensions, and ensures production-readiness with built-in validation and testing commands, making it an essential tool for data modeling and BI automation.

Características Principales

01Intelligent entity mapping for automated cross-table joins

02Integrated validation and testing using oxy build and semantic-engine

03Automated database schema analysis and view generation

04Natural language optimization via business-friendly synonyms and samples

05Support for complex SQL-based dimensions and filtered measures

060 GitHub stars

Casos de Uso

01Creating a semantic layer for a new data warehouse to enable AI-powered analytics

02Refactoring complex SQL joins into reusable, modular view and topic files

03Adding business-friendly synonyms to existing database models for improved NLQ performance

Características Principales

01Intelligent entity mapping for automated cross-table joins

02Integrated validation and testing using oxy build and semantic-engine

03Automated database schema analysis and view generation

04Natural language optimization via business-friendly synonyms and samples

05Support for complex SQL-based dimensions and filtered measures

060 GitHub stars

Casos de Uso

01Creating a semantic layer for a new data warehouse to enable AI-powered analytics

02Refactoring complex SQL joins into reusable, modular view and topic files

03Adding business-friendly synonyms to existing database models for improved NLQ performance