margin after summary tags, removed outdated br tags (#988)

qdrant-landing/content/articles/memory-consumption.md

@@ -208,8 +208,6 @@ Now the out-of-memory happens when we allow using **600mb** RAM only
 
 </details>
 
-<br/>
-
 At this point we have to switch from network-mounted storage to a faster disk, as the network-based storage is too slow to handle the amount of sequential reads that our system needs to serve the queries.
 
 But let's first see how much RAM we need to serve 1 million vectors and then we will discuss the speed optimization as well.
@@ -251,8 +249,6 @@ With this configuration we are able to serve 1 million vectors with **only 135mb
 
 </details>
 
-<br/>
-
 At this point the importance of the disk speed becomes critical.
 We can serve the search requests with 135mb of RAM, but the speed of the requests makes it impossible to use the system in production.
 

qdrant-landing/content/benchmarks/benchmarks-intro.md

@@ -26,7 +26,5 @@ Here are the principles we followed while designing these benchmarks:
 
 </details>
 
-</br>
-
 Some of our experiment design decisions are described in the [F.A.Q Section](/benchmarks/#benchmarks-faq).
 Reach out to us on our [Discord channel](https://qdrant.to/discord) if you want to discuss anything related Qdrant or these benchmarks.

qdrant-landing/content/documentation/concepts/collections.md

@@ -1262,7 +1262,6 @@ await client.GetCollectionInfoAsync("{collection_name}");
 ```
 
 </details>
-<br/>
 
 If you insert the vectors into the collection, the `status` field may become
 `yellow` whilst it is optimizing. It will become `green` once all the points are

qdrant-landing/themes/qdrant-2024/assets/css/partials/_documentation-article.scss

@@ -23,7 +23,7 @@
     padding-top: $spacer * 0.5;
   }
 
-  p {
+  p, summary {
     margin-bottom: $spacer;
   }
 

qdrant-landing/themes/qdrant-2024/assets/css/partials/_qdrant-post.scss

@@ -46,7 +46,8 @@
 
   &__content {
     p,
-    li {
+    li,
+    summary {
       margin-bottom: $spacer * 2;
     }
     p,