fix headings

qdrant-landing/content/documentation/tutorials/async-api.md

@@ -56,7 +56,7 @@ client = QdrantClient("localhost")
 The default client only exposes the synchronous methods for interacting with the server. To access the async client and it's underlying methods, you need to use autogenerated async clients.
 It is possible to use asynchronous HTTP API by using `qdrant_client.http.api_client.AsyncApis`. We recommend you use the asynchronous gRPC API, as it is more efficient than the HTTP API. 
 
-### gRPC API
+## Using the gRPC API 
 
 Every instance of `QdrantClient` has two properties: `async_grpc_collections` and `async_grpc_points`. They expose
 the autogenerated clients for [collections](/documentation/concepts/collections/) and [points](/documentation/concepts/points/) respectively. 
@@ -71,7 +71,7 @@ from qdrant_client import grpc
 If your IDE does not support autocompletion for the autogenerated clients, you can refer to the documentation and [see the available
 fields for each of them](https://github.com/qdrant/qdrant/blob/master/docs/grpc/docs.md).
 
-#### Creating a collection
+## Step 1: Create a collection
 
 Let's create a collection and add some points to it.
 
@@ -99,7 +99,7 @@ response = await client.async_grpc_collections.Create(
 Our collection was set up for 100-dimensional vectors, cosine distance and 8-bit quantization. The timeout is set to 10 seconds, so
 if the server does not respond within 10 seconds, the request will be aborted.
 
-#### Checking collection info
+### Verify collection info
 
 You can check the collection info to see if it was created and properly configured:
 
@@ -173,7 +173,7 @@ As you can see, the collection was created properly and is ready to be used. To
 [API specification](https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/get_collection) that describes the
 meaning of each parameter.
 
-##### Handling exceptions
+### Handling exceptions
 
 If the collection does not exist, the server will return an error. We can handle it by catching the exception. We may expect
 some specific exceptions, or simply catch all the errors that subclass `RpcError` from `grpc` package:
@@ -204,7 +204,7 @@ Our exception contain the details about the error, including the status code and
 We can use those details to properly handle some specific cases. For example, if we want to create a collection only if it does not
 exist, we can check the status code and create it if it is `StatusCode.NOT_FOUND`:
 
-#### Updating collection configuration
+## Step 2: Update collection configuration
 
 It's quite likely that we will want to update the collection configuration. For example, we may want to change the HNSW parameters to improve
 the search precision. Let's change the `ef_construct` parameter to 200:
@@ -220,7 +220,7 @@ await client.async_grpc_collections.Update(
 )
 ```
 
-#### Deleting a collection
+### Delete a collection
 
 There are plenty of methods available for collections, such as creating aliases or checking the status of the cluster. This tutorial won't use all of them, but you can always [check the list of available methods and their parameters in the 
 documentation](https://github.com/qdrant/qdrant/blob/master/docs/grpc/docs.md#collections_serviceproto).
@@ -236,7 +236,7 @@ await client.async_grpc_collections.Delete(
 ```
 That's it when it comes to collection management. Let's move on to points (individual vectors) allowing us to load data into our collection
 
-#### Adding points to the collection
+## Step 3: Add points to the collection
 
 Assuming your collection was created successfuly, you can now add some
 points to it. Let's add our first two points into the collection:
@@ -279,7 +279,7 @@ Those points are automatically indexed and available for search. In the future,
 
 The main operation you need from the vector database is semantic search. Let's see how to perform it.
 
-#### Asynchronous semantic search
+## Step 4: Run a query
 
 The search operation requires a query vector and a collection name. You can also specify the number of results you want to retrieve and some other parameters. A minimal example might look like this:
 
@@ -308,7 +308,7 @@ result {
 time: 0.000456134
 ```
 
-##### Returning vectors and payloads
+### Return vectors and payloads
 
 If you need the vectors and payloads of the points, you can specify it in the request:
 
@@ -326,7 +326,7 @@ response = await client.async_grpc_points.Search(
 
 That's all the code you need to bring back the vector and payload.
 
-##### Applying filters
+## Step 5: Add a filter
 
 If you ever used SQL, you are probably familiar with the `WHERE` clause. It lets you filter the results of the query. Qdrant has a similar functionality. Imagine you want to find all the points similar to given vector, but with `int_param` equal to 32. You can do it like this:
 
@@ -355,7 +355,7 @@ You can build even more sophisticated filters with the `must`, `should` and `mus
 
 Analogous to search, you can also [scroll our collection](https://qdrant.tech/documentation/concepts/points/#scroll-points) to retrieve all the points or use the [recommendation API](https://qdrant.tech/documentation/concepts/search/#recommendation-api) to find points similar to positive and dissimilar to negative examples.
 
-## Support of Qdrant async API in Python libraries
+## Supported Python libraries
 
 Qdrant integrates with numerous Python libraries. Until recently, only [Langchain]() provided async Python API support. 
 Qdrant is the only vector database with full coverage of async API in Langchain. Their documentation [describes how to use