add missing pages to sidebar

website/docs/best-practices/how-we-build-our-metrics/semantic-layer-2-setup.md

@@ -13,9 +13,23 @@ git clone [email protected]:dbt-labs/jaffle-sl-template.git
 cd path/to/project
 ```
 
-Next, before you start writing code, you need to install MetricFlow as an extension of a dbt adapter from PyPI  (dbt Core users only). The MetricFlow is compatible with Python versions 3.8 through 3.11.
+Next, before you start writing code, you need to install MetricFlow:
 
-We'll use pip to install MetricFlow and our dbt adapter:
+<Tabs>
+
+<TabItem value="cloud" label="dbt Cloud">
+
+- [dbt Cloud CLI](/docs/cloud/cloud-cli-installation) &mdash; MetricFlow commands are embedded in the dbt Cloud CLI. This means you can immediately run them once you install the dbt Cloud CLI. Using dbt Cloud means you won't need to manage versioning — your dbt Cloud account will automatically manage the versioning.
+
+- [dbt Cloud IDE](/docs/cloud/dbt-cloud-ide/develop-in-the-cloud) &mdash; You can create metrics using MetricFlow in the dbt Cloud IDE. However, support for running MetricFlow commands in the IDE will be available soon.
+
+</TabItem>
+
+<TabItem value="core" label="dbt Core">
+
+- Download MetricFlow as an extension of a dbt adapter from PyPI  (dbt Core users only). The MetricFlow is compatible with Python versions 3.8 through 3.11. 
+  - **Note**, you'll need to manage versioning between dbt Core, your adapter, and MetricFlow.
+- We'll use pip to install MetricFlow and our dbt adapter:
 
 ```shell
 # activate a virtual environment for your project,
@@ -27,13 +41,16 @@ python -m pip install "dbt-metricflow[adapter name]"
 # e.g. python -m pip install "dbt-metricflow[snowflake]"
 ```
 
-Lastly, to get to the pre-Semantic Layer starting state, checkout the `start-here` branch.
+</TabItem>
+</Tabs>
+
+- Now that you're ready to use MetricFlow, get to the pre-Semantic Layer starting state by checking out the `start-here` branch:
 
 ```shell
 git checkout start-here
 ```
 
-For more information, refer to the [MetricFlow commands](/docs/build/metricflow-commands) or a [quickstart](/guides) to get more familiar with setting up a dbt project.
+For more information, refer to the [MetricFlow commands](/docs/build/metricflow-commands) or a [quickstart guides](/guides) to get more familiar with setting up a dbt project.
 
 ## Basic commands
 

website/docs/docs/build/metricflow-commands.md

@@ -17,15 +17,16 @@ MetricFlow is compatible with Python versions 3.8, 3.9, 3.10, and 3.11.
 
 MetricFlow is a dbt package that allows you to define and query metrics in your dbt project. You can use MetricFlow to query metrics in your dbt project in the dbt Cloud CLI, dbt Cloud IDE, or dbt Core.
 
-**Note** — MetricFlow commands aren't supported in dbt Cloud jobs yet. However, you can add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`python -m pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs.
+Using MetricFlow with dbt Cloud means you won't need to manage versioning — your dbt Cloud account will automatically manage the versioning.
+
+**dbt Cloud jobs** — MetricFlow commands aren't supported in dbt Cloud jobs yet. However, you can add MetricFlow validations with your git provider (such as GitHub Actions) by installing MetricFlow (`python -m pip install metricflow`). This allows you to run MetricFlow commands as part of your continuous integration checks on PRs.
 
 <Tabs>
 
 <TabItem value="cloudcli" label="dbt Cloud CLI">
 
-MetricFlow commands are embedded in the dbt Cloud CLI, which means you can immediately run them once you install the dbt Cloud CLI. 
-
-A benefit to using the dbt Cloud is that you won't need to manage versioning &mdash; your dbt Cloud account will automatically manage the versioning.
+- MetricFlow commands are embedded in the dbt Cloud CLI. This means you can immediately run them once you install the dbt Cloud CLI and don't need to install MetricFlow separately.
+- You don't need to manage versioning &mdash; your dbt Cloud account will automatically manage the versioning for you.
 
 </TabItem>
 
@@ -35,7 +36,7 @@ A benefit to using the dbt Cloud is that you won't need to manage versioning &md
 You can create metrics using MetricFlow in the dbt Cloud IDE. However, support for running MetricFlow commands in the IDE will be available soon.
 :::
 
-A benefit to using the dbt Cloud is that you won't need to manage versioning &mdash; your dbt Cloud account will automatically manage the versioning.
+
 
 </TabItem>
 

website/sidebars.js

@@ -1027,6 +1027,8 @@ const sidebarSettings = {
             id: "best-practices/how-we-build-our-metrics/semantic-layer-1-intro",
           },
           items: [
+            "best-practices/how-we-build-our-metrics/semantic-layer-1-intro",
+            "best-practices/how-we-build-our-metrics/semantic-layer-2-setup",
             "best-practices/how-we-build-our-metrics/semantic-layer-3-build-semantic-models",
             "best-practices/how-we-build-our-metrics/semantic-layer-4-build-metrics",
             "best-practices/how-we-build-our-metrics/semantic-layer-5-refactor-a-mart",