diff --git a/qdrant-landing/content/blog/batch-vector-search-with-qdrant.md b/qdrant-landing/content/blog/batch-vector-search-with-qdrant.md
index e0775812b..abe736b36 100644
--- a/qdrant-landing/content/blog/batch-vector-search-with-qdrant.md
+++ b/qdrant-landing/content/blog/batch-vector-search-with-qdrant.md
@@ -22,11 +22,11 @@ There are a lot of scenarios in which you may need to perform multiple non-relat
 
 Now, thanks to the new batch search, you don’t need to worry about that. Qdrant will handle multiple search requests in just one API call and will perform those requests in the most optimal way.
 
-# An example of using the batch search
+## An example of using the batch search
 
 We’ve used the official Python client to show how the batch search might be integrated with your application. Since there have been some changes in the interfaces of Qdrant 0.10.0, we’ll go step by step.
 
-# Creating the collection
+## Creating the collection
 
 The first step is to create a collection with a specified configuration — at least vector size and the distance function used to measure the similarity between vectors.
 
@@ -41,7 +41,7 @@ client.recreate_collection(
 )
 ```
 
-# Loading the vectors
+## Loading the vectors
 
 With the collection created, we can put some vectors into it. We’re going to have just a few examples.
 
@@ -64,7 +64,7 @@ client.upload_collection(
 )
 ```
 
-# Batch search in a single request
+## Batch search in a single request
 
 Now we’re ready to start looking for similar vectors, as our collection has some entries. Let’s say we want to find the distance between the selected vector and the most similar database entry and at the same time find the two most similar objects for a different vector query. Up till 0.9, we would need to call the API twice. Now, we can send both requests together:
 
@@ -99,7 +99,7 @@ Each instance of the SearchRequest class may provide its own search parameters,
 
 And that’s it! You no longer have to handle the multiple requests on your own. Qdrant will do it under the hood.
 
-# Benchmark
+## Benchmark
 
 The batch search is fairly easy to be integrated into your application, but if you prefer to see some numbers before deciding to switch, then it’s worth comparing four different options:
 
@@ -110,7 +110,7 @@ The batch search is fairly easy to be integrated into your application, but if y
 
 In order to do that, we’ll create a richer collection of points, with vectors from the *glove-25-angular* dataset, quite a common choice for ANN comparison. If you’re interested in seeing some more details of how we benchmarked Qdrant, let’s take a [look at the Gist](https://gist.github.com/kacperlukawski/2d12faa49e06a5080f4c35ebcb89a2a3).
 
-# The results
+## The results
 
 We launched the benchmark 5 times on 10000 test vectors and averaged the results. Presented numbers are the mean values of all the attempts:
 
@@ -123,6 +123,6 @@ The results you may achieve on a specific setup may vary depending on the hardwa
 
 Additional improvements could be achieved in the case of distributed deployment, as Qdrant won’t need to make extensive inter-cluster requests. Moreover, if your requests share the same filtering condition, the query optimizer would be able to reuse it among batch requests.
 
-# Summary
+## Summary
 
 Batch search allows packing different queries into a single API call and retrieving the results in a single response. If you ever struggled with sending several consecutive queries into Qdrant, then you can easily switch to the new batch search method and simplify your application code. As shown in the benchmarks, that may almost effortlessly speed up your interactions with Qdrant even by over 30%, even not considering the spare network overhead and possible reuse of filters!
\ No newline at end of file