Update links to macro docs (#923)

Author: treysp

docs/concepts/architecture/serialization.md

@@ -1,6 +1,6 @@
 # Serialization
 
-SQLMesh executes Python code through [macros](../macros.md) and [Python models](../../concepts/models/python_models.md). Each Python model is stored as a standalone [snapshot](../architecture/snapshots.md), which includes all of the Python code necessary to generate it.
+SQLMesh executes Python code through [macros](../macros/overview.md) and [Python models](../../concepts/models/python_models.md). Each Python model is stored as a standalone [snapshot](../architecture/snapshots.md), which includes all of the Python code necessary to generate it.
 
 ## Serialization format
 

docs/concepts/audits.md

@@ -42,7 +42,7 @@ AUDIT (
 SELECT * FROM @this_model
 WHERE @column >= @threshold;
 ```
-In the example above we utilized [macros](macros.md) to parameterize the audit implementation. `@this_model` is a special macro which refers to a model that is being audited. For incremental models, this macro also ensures that only relevant data intervals are affected. `@column` and `@threshold` are generic parameters, values for which are set in the model definition.
+In the example above we utilized [macros](./macros/overview.md) to parameterize the audit implementation. `@this_model` is a special macro which refers to a model that is being audited. For incremental models, this macro also ensures that only relevant data intervals are affected. `@column` and `@threshold` are generic parameters, values for which are set in the model definition.
 
 The generic audit can now be applied to a model by being referenced in its definition:
 ```sql linenums="1"

docs/concepts/models/model_kinds.md

@@ -10,7 +10,7 @@ Only missing time intervals are processed during each execution for `INCREMENTAL
 
 An `INCREMENTAL_BY_TIME_RANGE` model query must contain an expression in its SQL `WHERE` clause that filters the upstream records by time range. SQLMesh provides special macros that represent the start and end of the time range being processed: `@start_date` / `@end_date` and `@start_ds` / `@end_ds`.
 
-Refer to [Macros](../macros.md#predefined-variables) for more information.
+Refer to [Macros](../macros/macro_variables.md) for more information.
 
 This example implements an `INCREMENTAL_BY_TIME_RANGE` model by specifying the `kind` in the `MODEL` ddl and including a SQL `WHERE` clause to filter records by time range:
 ```sql linenums="1" hl_lines="3-5 12-13"

docs/concepts/models/overview.md

@@ -112,7 +112,7 @@ For models that are incremental, the following parameters can be specified in th
 - Batch size is used to optimize backfilling incremental data. It determines the maximum number of intervals to run in a single job. For example, if a model specifies a cron of `@hourly` and a batch_size of `12`, when backfilling 3 days of data, the scheduler will spawn 6 jobs. (3 days * 24 hours/day = 72 hour intervals to fill. 72 intervals / 12 intervals per job = 6 jobs.)
 
 ## Macros
-Macros can be used for passing in parameterized arguments such as dates, as well as for making SQL less repetitive. By default, SQLMesh provides several predefined macro variables that can be used. Macros are used by prefixing with the `@` symbol. For more information, refer to [macros](../macros.md).
+Macros can be used for passing in parameterized arguments such as dates, as well as for making SQL less repetitive. By default, SQLMesh provides several predefined macro variables that can be used. Macros are used by prefixing with the `@` symbol. For more information, refer to [macros](../macros/overview.md).
 
 ## Statements
 Models can have additional statements that run before the main query. This can be useful for loading things such as [UDFs](../glossary.md#user-defined-function-udf).

docs/concepts/models/sql_models.md

@@ -92,6 +92,6 @@ You can also use advanced syntax that may not be available in your engine of cho
 Additionally, you won't have to worry about minor formatting differences such as trailing commas, as SQLGlot will remove them at parse time.
 
 ## Macros
-Although standard SQL is very powerful, complex data systems often require running SQL queries with dynamic components such as date filters. For example, you may want to change the date ranges in a `between` statement so that you can get the latest batch of data. SQLMesh provides these dates automatically through [macro variables](../macros.md#variables).
+Although standard SQL is very powerful, complex data systems often require running SQL queries with dynamic components such as date filters. For example, you may want to change the date ranges in a `between` statement so that you can get the latest batch of data. SQLMesh provides these dates automatically through [macro variables](../macros/macro_variables.md).
 
-Additionally, large queries can be difficult to read and maintain. In order to make queries more compact, SQLMesh supports a powerful [macro syntax](../macros.md) as well as [Jinja](https://jinja.palletsprojects.com/en/3.1.x/), allowing you to write macros that make your SQL queries easier to manage.
+Additionally, large queries can be difficult to read and maintain. In order to make queries more compact, SQLMesh supports a powerful [macro syntax](../macros/overview.md) as well as [Jinja](https://jinja.palletsprojects.com/en/3.1.x/), allowing you to write macros that make your SQL queries easier to manage.

docs/concepts/overview.md

@@ -59,7 +59,7 @@ In contrast to tests, SQLMesh "audits" validate the results of model code applie
 
 You create audits by writing SQL queries that should return 0 rows. For example, an audit query to ensure `your_field` has no `NULL` values would include `WHERE your_field IS NULL`. If any NULLs are detected, the query will return at least one row and the audit will fail.
 
-Audits are flexible — they can be tied to a specific model's contents, or you can use [macros](macros.md) to create audits that are usable by multiple models. SQLMesh also includes pre-made audits for common use cases, such as detecting NULL or duplicated values.
+Audits are flexible — they can be tied to a specific model's contents, or you can use [macros](./macros/overview.md) to create audits that are usable by multiple models. SQLMesh also includes pre-made audits for common use cases, such as detecting NULL or duplicated values.
 
 You specify which audits should run for a model by including them in the model's metadata properties. 
 

docs/integrations/dbt.md

@@ -88,7 +88,7 @@ For example, the SQL `WHERE` clause with the "ds" column goes in a new jinja blo
 > {% endif %}
 ```
 
-`{{ start_ds }}` and `{{ end_ds }}` are the jinja equivalents of SQLMesh's `@start_ds` and `@end_ds` predefined time macro variables. See all [predefined time variables](../concepts/macros.md#predefined-variables) available in jinja.
+`{{ start_ds }}` and `{{ end_ds }}` are the jinja equivalents of SQLMesh's `@start_ds` and `@end_ds` predefined time macro variables. See all [predefined time variables](../concepts/macros/macro_variables.md) available in jinja.
 
 ### Incremental model configuration
 

docs/quick_start.md

@@ -34,7 +34,7 @@ This will create the directories and files that you can use to organize your SQL
 - ./tests
     - Unit test files. Refer to [testing](concepts/tests.md).
 - ./macros
-    - Macro files. Refer to [macros](concepts/macros.md).
+    - Macro files. Refer to [macros](concepts/macros/overview.md).
 
 ## 2. Plan and apply environments
 ### 2.1 Create a prod environment