> For the complete documentation index, see [llms.txt](https://docs.whaly.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.whaly.io/models/models-sync/dbt-cloud/exposing-models-into-whaly.md).

# Exposing models into Whaly

By default, when synchronising your dbt Cloud project with Whaly, you won't see any models appearing in the workbench.

This is because most of your models and sources managed in dbt should not be exposed in your Business Intelligence as those are really the fundamental building blocks of your modelling data structure. You want to expose in your consumption layer only the most prepared and enriched models.

In order to expose a model or a source into Whaly, you'll need to flag them in your dbt project directly by adding a specific meta.

## Exposing a model

In the YAML definition of the model, please add the following meta to make it available in Whaly:

```yaml
meta:
    expose_in_whaly: true
```

Here is an example:

```yaml
version: 2

models:
    - name: jaffle_customers
      meta:
        expose_in_whaly: true # <===
      description: "A transformed version of customers through dbt"
      columns:
          - name: customer_id
            description: "The primary key for this table"
            tests:
                - unique
                - not_null
```

## Exposing a source

In the YAML definition of the source, please add the following meta to the source **and to all tables** that you want to expose to make it available in Whaly:

```yaml
meta:
    expose_in_whaly: true
```

Here is an example:

```yaml
version: 2 
sources: 
  - name: "jaffle_shop" 
    schema: "jaffle_shop"
    meta:
      expose_in_whaly: true # <===
    freshness: 
      warn_after: 
        count: 12 
        period: "hour" 
      error_after: 
        count: 24 
        period: "hour" 
    loaded_at_field: "_whaly_synced" 
    tables: 
      - name: "customer" 
        identifier: "customer"
        description: "A person who buys goods or services from a Jaffle shop."
        meta:
          expose_in_whaly: true # <===
        columns:
        - name: customer_id
          description: This is a unique identifier for a customer
          tests:
            - unique
            - not_null
        - name: first_name
          description: Customer's first name. PII.
        - name: last_name
          description: Customer's last name. PII.
```


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.whaly.io/models/models-sync/dbt-cloud/exposing-models-into-whaly.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.