semantic manifest artifact

website/docs/docs/dbt-cloud-apis/sl-api-overview.md

@@ -48,10 +48,4 @@ plan="dbt Cloud Team or Enterprise"
     link="/docs/dbt-cloud-apis/sl-python"
     icon="dbt-bit"/>
 
-<Card
-    title="Semantic manifest"
-    body="Learn about the semantic manifest.json file and how you can use artifacts to gain insights about your Semantic Layer."
-    link="/docs/dbt-cloud-apis/sl-manifest"
-    icon="dbt-bit"/>
-
 </div>

website/docs/reference/artifacts/dbt-artifacts.md

@@ -22,7 +22,7 @@ dbt has produced artifacts since the release of dbt-docs in v0.11.0. Starting in
 ### When are artifacts produced? <Lifecycle status="team,enterprise"/>
 
 Most dbt commands (and corresponding RPC methods) produce artifacts:
-- [semantic manifest](/docs/dbt-cloud-apis/sl-manifest): produced whenever your dbt project is parsed
+- [semantic manifest](/reference/artifacts/sl-manifest): produced whenever your dbt project is parsed
 - [manifest](/reference/artifacts/manifest-json): produced by commands that read and understand your project
 - [run results](/reference/artifacts/run-results-json): produced by commands that run, compile, or catalog nodes in your DAG
 - [catalog](catalog-json): produced by `docs generate`

website/docs/reference/artifacts/other-artifacts.md

@@ -39,7 +39,7 @@ Each of those points in time contains the `name` and `type` of each node and `su
 
 ### semantic_manifest.json
 
-The [`semantic_manifest.json`](/docs/dbt-cloud-apis/sl-manifest) file is useful as an internal interface between `dbt-core` and MetricFlow. As such, it functions as a behind-the-scenes bridge for interaction between the two systems. You can find all of the `semantic_manifest.json` information in the [`semantic_manifest.json`](/docs/dbt-cloud-apis/sl-manifest).
+The [`semantic_manifest.json`](/reference/artifacts/sl-manifest) file is useful as an internal interface between `dbt-core` and MetricFlow. As such, it functions as a behind-the-scenes bridge for interaction between the two systems. You can find all of the `semantic_manifest.json` information in the [`semantic_manifest.json`](/reference/artifacts/sl-manifest).
 
 There are two reasons why `semantic_manifest.json` exists alongside `manifest.json`:
 

website/docs/docs/dbt-cloud-apis/sl-manifest.md → website/docs/reference/artifacts/sl-manifest.md

@@ -7,26 +7,24 @@ sidebar_label: "Semantic manifest"
 pagination_next: null
 ---
 
+**Produced by:**  Any command that parses your project. This includes all commands _except_ [`deps`](/reference/commands/deps), [`clean`](/reference/commands/clean), [`debug`](/reference/commands/debug), and [`init`](/reference/commands/init).
+
 dbt creates an [artifact](/reference/artifacts/dbt-artifacts) file called the _Semantic Manifest_ (`semantic_manifest.json`), which MetricFlow requires to build and run metric queries properly for the dbt Semantic Layer. This artifact contains comprehensive information about your dbt Semantic Layer. It is an internal file that acts as the integration point with MetricFlow. 
 
 By using the semantic manifest produced by dbt Core, MetricFlow will instantiate a data flow plan and generate SQL from Semantic Layer query requests. It's a valuable reference that you can use to understand the structure and details of your data models.
 
 Similar to the [`manifest.json` file](/reference/artifacts/manifest-json), the `semantic_manifest.json` file also lives in the [target directory](/reference/global-configs/json-artifacts) of your dbt project where dbt stores various artifacts (such as compiled models and tests) generated during the execution of your project.
 
-## How it's produced 
-
-Just like `manifest.json`, the `semantic_manifest.json` is produced whenever your dbt project is parsed. All dbt commands will parse your project and create a `semantic_manifest.json` file, _except_ [`deps`](/reference/commands/deps), [`clean`](/reference/commands/clean), [`debug`](/reference/commands/debug), and [`init`](/reference/commands/init).
-
-
-## Top level keys
+## Top-level keys
 
 Top-level keys for the semantic manifest are:
 -  `semantic_models` — Starting points of data with entities, dimensions, and measures, and correspond to models in your dbt project. 
 -  `metrics` — Functions combining measures, constraints, and so on to define quantitative indicators.
 - `project_configuration` — Contains information around your project configurations 
 
-<details>
-<summary>Example <code>target/semantic_manifest.json</code> file </summary>
+### Example
+
+<File name="target/semantic_manifest.json"> 
 
 ```json
 {
@@ -112,7 +110,7 @@ Top-level keys for the semantic manifest are:
 }
 ```
 
-</details>
+</File>
 
 ## Related docs
 

website/sidebars.js

@@ -727,7 +727,6 @@ const sidebarSettings = {
             "docs/dbt-cloud-apis/sl-jdbc",
             "docs/dbt-cloud-apis/sl-graphql",
             "docs/dbt-cloud-apis/sl-python",
-            "docs/dbt-cloud-apis/sl-manifest",
           ],
         },
       ],
@@ -1174,6 +1173,7 @@ const sidebarSettings = {
         "reference/artifacts/run-results-json",
         "reference/artifacts/catalog-json",
         "reference/artifacts/sources-json",
+        "reference/artifacts/sl-manifest",
         "reference/artifacts/other-artifacts",
       ],
     },

website/snippets/_sl-run-prod-job.md

@@ -6,7 +6,7 @@ This section explains how you can perform a job run in your deployment environme
 3. To create a new environment, navigate to **Deploy** in the navigation menu, select **Environments**, and then select **Create new environment**.
 4. Fill in your deployment credentials with your Snowflake username and password. You can name the schema anything you want. Click **Save** to create your new production environment.
 5. [Create a new deploy job](/docs/deploy/deploy-jobs#create-and-schedule-jobs) that runs in the environment you just created. Go back to the **Deploy** menu, select **Jobs**, select **Create job**, and click **Deploy job**.
-6. Set the job to run a `dbt parse` job to parse your projects and generate a [`semantic_manifest.json` artifact](/docs/dbt-cloud-apis/sl-manifest) file. Although running `dbt build` isn't required, you can choose to do so if needed.
+6. Set the job to run a `dbt parse` job to parse your projects and generate a [`semantic_manifest.json` artifact](/reference/artifacts/sl-manifest) file. Although running `dbt build` isn't required, you can choose to do so if needed.
 7. Run the job by clicking the **Run now** button. Monitor the job's progress in real-time through the **Run summary** tab. 
 
     Once the job completes successfully, your dbt project, including the generated documentation, will be fully deployed and available for use in your production environment. If any issues arise, review the logs to diagnose and address any errors.

website/vercel.json

@@ -2,6 +2,11 @@
   "cleanUrls": true,
   "trailingSlash": false,
   "redirects": [
+    {
+      "source": "/docs/dbt-cloud-apis/sl-manifest",
+      "destination": "/reference/artifacts/sl-manifest",
+      "permanent": true
+    },
     {
       "source": "/docs/cloud/dbt-assist-data",
       "destination": "/docs/cloud/dbt-copilot-data",