> For the complete documentation index, see [llms.txt](https://docs.wobby.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.wobby.ai/semantic-layer.md).

# Semantic Layer

- [Overview](https://docs.wobby.ai/semantic-layer/overview.md)
- [Models](https://docs.wobby.ai/semantic-layer/models.md): Models represent the core entities in your business—customers, orders, shipments, products. They map to tables or views in your data warehouse, but add crucial business context.
- [Creating Models](https://docs.wobby.ai/semantic-layer/models/creating-models.md)
- [Creating Models with Steward](https://docs.wobby.ai/semantic-layer/models/creating-models/creating-models-with-steward.md)
- [Creating Models from SQL](https://docs.wobby.ai/semantic-layer/models/creating-models/creating-models-from-sql.md)
- [Creating Models from Table](https://docs.wobby.ai/semantic-layer/models/creating-models/creating-models-from-table.md)
- [Dimensions](https://docs.wobby.ai/semantic-layer/models/dimensions.md): What you analyze by
- [Measures](https://docs.wobby.ai/semantic-layer/models/measures.md): The building blocks of business calculations
- [Pre-defined Filters](https://docs.wobby.ai/semantic-layer/models/pre-defined-filters.md): Filters define reusable conditions for subsetting data. They're like saved WHERE clauses.
- [Relationships](https://docs.wobby.ai/semantic-layer/models/relationships.md): Relationships define how models join to each other. They make cross-entity analysis possible.
- [Metrics](https://docs.wobby.ai/semantic-layer/metrics.md)
- [Glossary](https://docs.wobby.ai/semantic-layer/glossary.md): The glossary exists to solve a specific problem: people don't talk like database schemas.
- [Semantic SQL (SemQL)](https://docs.wobby.ai/semantic-layer/semql.md): The query language Actian AI Analyst agents use to analyze your data
- [Compilation & Dialects](https://docs.wobby.ai/semantic-layer/semql/compilation-and-dialects.md): How SemQL is compiled to SQL, which databases are supported, and practical rules of thumb
- [Core Syntax](https://docs.wobby.ai/semantic-layer/semql/core-syntax.md): A complete reference for SemQL syntax and clauses
- [Dimensions, Measures & Filters](https://docs.wobby.ai/semantic-layer/semql/dimensions-vs-measures.md): The most important concept in SemQL — and why it prevents the most common mistakes
- [Geo Functions](https://docs.wobby.ai/semantic-layer/semql/geo-functions.md): Built-in functions for location-based queries and distance calculations
- [Examples](https://docs.wobby.ai/semantic-layer/semql/examples.md): Real-world SemQL queries from simple to complex


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.wobby.ai/semantic-layer.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.