Merge branch 'current' into update-dot

website/docs/docs/build/incremental-models.md

@@ -156,15 +156,17 @@ Building this model incrementally without the `unique_key` parameter would resul
 ## How do I rebuild an incremental model?
 If your incremental model logic has changed, the transformations on your new rows of data may diverge from the historical transformations, which are stored in your target table. In this case, you should rebuild your incremental model.
 
-To force dbt to rebuild the entire incremental model from scratch, use the `--full-refresh` flag on the command line. This flag will cause dbt to drop the existing target table in the database before rebuilding it for all-time.
+To force dbt to rebuild the entire incremental model from scratch, use the `--full-refresh` flag on the command line. This flag will cause dbt to drop the existing target table in the database before rebuilding it for all-time. 
 
 ```bash
 $ dbt run --full-refresh --select my_incremental_model+
 ```
+
 It's also advisable to rebuild any downstream models, as indicated by the trailing `+`.
 
-For detailed usage instructions, check out the [dbt run](/reference/commands/run) documentation.
+You can optionally use the [`full_refresh config`](/reference/resource-configs/full_refresh) to set a resource to always or never full-refresh at the project or resource level. If specified as true or false, the `full_refresh` config will take precedence over the presence or absence of the `--full-refresh` flag.
 
+For detailed usage instructions, check out the [dbt run](/reference/commands/run) documentation. 
 
 ## What if the columns of my incremental model change?
 

website/docs/docs/build/python-models.md

@@ -641,7 +641,8 @@ In their initial launch, Python models are supported on three of the most popula
 **Installing packages:** Snowpark supports several popular packages via Anaconda. Refer to the [complete list](https://repo.anaconda.com/pkgs/snowflake/) for more details. Packages are installed when your model is run. Different models can have different package dependencies. If you use third-party packages, Snowflake recommends using a dedicated virtual warehouse for best performance rather than one with many concurrent users.
 
 **Python version:** To specify a different python version, use the following configuration:
-```
+
+```python
 def model(dbt, session):
     dbt.config(
         materialized = "table",
@@ -653,7 +654,7 @@ def model(dbt, session):
 
 **External access integrations and secrets**: To query external APIs within dbt Python models, use Snowflake’s [external access](https://docs.snowflake.com/en/developer-guide/external-network-access/external-network-access-overview) together with [secrets](https://docs.snowflake.com/en/developer-guide/external-network-access/secret-api-reference). Here are some additional configurations you can use:
 
-```
+```python
 import pandas
 import snowflake.snowpark as snowpark