diff --git a/resources/support-center/knowledge-base/general-faqs/vector-search.mdx b/resources/support-center/knowledge-base/general-faqs/vector-search.mdx
index edf0628cd..078d4fd5d 100644
--- a/resources/support-center/knowledge-base/general-faqs/vector-search.mdx
+++ b/resources/support-center/knowledge-base/general-faqs/vector-search.mdx
@@ -21,44 +21,53 @@ The main advantages of using ClickHouse for vector search compared to using more
 
 Here is a quick tutorial on how to use ClickHouse for vector search.
 
-## 1. Create embeddings {#1-create-embeddings}
-Your data (documents, images, or structured data) must be converted to _embeddings_. We recommend creating embeddings using the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings) or using the open-source Python library [SentenceTransformers](https://www.sbert.net/).
+<Steps>
+  <Step>
+    ## Create embeddings {#1-create-embeddings}
 
-You can think of an embedding as a large array of floating-point numbers that represent your data. [Check out this guide from OpenAI to learn more about embeddings](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
+    Your data (documents, images, or structured data) must be converted to _embeddings_. We recommend creating embeddings using the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings) or using the open-source Python library [SentenceTransformers](https://www.sbert.net/).
 
-## 2. Store the embeddings {#2-store-the-embeddings}
-Once you have generated embeddings, you need to store them in ClickHouse. Each embedding should be stored in a separate row and can include metadata for filtering, aggregations, or analytics. Here's an example of a table that can store images with captions:
+    You can think of an embedding as a large array of floating-point numbers that represent your data. [Check out this guide from OpenAI to learn more about embeddings](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings).
+  </Step>
+  <Step>
+    ## Store the embeddings {#2-store-the-embeddings}
 
-```sql
-CREATE TABLE images
-(
-	`_file` LowCardinality(String),
-	`caption` String,
-	`image_embedding` Array(Float32)
-)
-ENGINE = MergeTree;
-```
+    Once you have generated embeddings, you need to store them in ClickHouse. Each embedding should be stored in a separate row and can include metadata for filtering, aggregations, or analytics. Here's an example of a table that can store images with captions:
 
-## 3. Search for related embeddings {#3-search-for-related-embeddings}
-Let's say you want to search for pictures of dogs in your dataset. You can use a distance function like `cosineDistance` to take an embedding of a dog image and search for related images:
+    ```sql
+    CREATE TABLE images
+    (
+    	`_file` LowCardinality(String),
+    	`caption` String,
+    	`image_embedding` Array(Float32)
+    )
+    ENGINE = MergeTree;
+    ```
+  </Step>
+  <Step>
+    ## Search for related embeddings {#3-search-for-related-embeddings}
 
-```sql
-SELECT
-    _file,
-	caption,
-	cosineDistance(
-        -- An embedding of your "input" dog picture
-        [0.5736801028251648, 0.2516217529773712, ...,  -0.6825592517852783],
-        image_embedding
-    ) AS score
-FROM images
-ORDER BY score ASC
-LIMIT 10
-```
+    Let's say you want to search for pictures of dogs in your dataset. You can use a distance function like `cosineDistance` to take an embedding of a dog image and search for related images:
 
-This query returns the `_file` names and `caption` of the top 10 images most likely to be related to your provided dog image.
+    ```sql
+    SELECT
+        _file,
+    	caption,
+    	cosineDistance(
+            -- An embedding of your "input" dog picture
+            [0.5736801028251648, 0.2516217529773712, ...,  -0.6825592517852783],
+            image_embedding
+        ) AS score
+    FROM images
+    ORDER BY score ASC
+    LIMIT 10
+    ```
 
-## Further Reading {#further-reading}
+    This query returns the `_file` names and `caption` of the top 10 images most likely to be related to your provided dog image.
+  </Step>
+</Steps>
+
+## Further reading {#further-reading}
 To follow a more in-depth tutorial on vector search using ClickHouse, please see:
 
 - [Exact and Approximate Vector Search](/reference/engines/table-engines/mergetree-family/annindexes)