fix links?

docs/website/blog/2024-01-15-dlt-dbt-runner-on-cloud-functions.md

@@ -150,7 +150,7 @@ The second option is running dbt using data load tool(dlt).
 
 I work at dlthub and often create dlt pipelines. These often need dbt for modeling the data, making
 the dlt-dbt combination highly effective. For using this combination on cloud functions, we used
-[dlt-dbt runner](../../api_reference/helpers/dbt/runner#create_runner) developed
+[dlt-dbt runner](../docs/api_reference/helpers/dbt/runner.md#create_runner) developed
 at dlthub.
 
 The main reason I use this runner is because I load data with dlt and can re-use dlt’s connection to
@@ -331,7 +331,7 @@ event-driven pipelines with small to medium workloads. For larger data loads nea
 consider separating dlt and dbt into different cloud functions.
 
 > For more info on using `dlt-dbt runner` , please refer to the
-> [official documentation by clicking here.](../../api_reference/helpers/dbt/runner#dbtpackagerunner-objects)
+> [official documentation by clicking here.](../docs/api_reference/helpers/dbt/runner.md#dbtpackagerunner-objects)
 
 ### Deployment considerations: How does cloud functions compare to Git Actions?
 

docs/website/docs/dlt-ecosystem/destinations/bigquery.md

@@ -263,7 +263,7 @@ bigquery_adapter(my_resource, partition="partition_column_name")
 my_resource = bigquery_adapter(my_resource, partition="partition_column_name")
 ```
 
-Refer to the [full API specification](../../api_reference/destinations/impl/bigquery/bigquery_adapter) for more details.
+Refer to the [full API specification](../../api_reference/destinations/impl/bigquery/bigquery_adapter.md) for more details.
 
 <!--@@@DLT_TUBA bigquery-->