fix index

qdrant-landing/content/documentation/database-tutorials/_index.md

@@ -11,9 +11,9 @@ partition: qdrant
 
 # Database Tutorials 
 
-| Tutorial                      | Description                                                    | 
-|--------------------------------------------|----------------------------------------------------------------|
-| [Bulk Upload Vectors](/documentation/database-tutorials/bulk-upload/)        | Upload a large scale dataset.                                  | 
-| [Asynchronous API](/documentation/database-tutorials/async-api/)            | Communicate with Qdrant server asynchronously with Python SDK. | 
-| [Create Dataset Snapshots](/documentation/database-tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. |
-| [Load HuggingFace Dataset](/documentation/database-tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant                          | 
+|                 | 
+|--------------------------------------------|
+| [Bulk Upload Vectors to a Qdrant Collection](/documentation/database-tutorials/bulk-upload/)        | 
+| [Backup and Restore Qdrant Collections Using Snapshots](/documentation/database-tutorials/create-snapshot/) |
+| [Load and Search Hugging Face Datasets with Qdrant](/documentation/database-tutorials/huggingface-datasets/)                       | 
+| [Using Qdrant’s Async API for Efficient Python Applications](/documentation/database-tutorials/async-api/)            | 

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

@@ -1,11 +1,11 @@
 ---
-title: Using the Async API
+title: Build With Async API
 aliases:
   - /documentation/tutorials/async-api/
 weight: 4
 ---
 
-# Using Qdrant asynchronously
+# Using Qdrant’s Async API for Efficient Python Applications
 
 Asynchronous programming is being broadly adopted in the Python ecosystem. Tools such as FastAPI [have embraced this new 
 paradigm](https://fastapi.tiangolo.com/async/), but it is also becoming a standard for ML models served as SaaS. For example, the Cohere SDK

qdrant-landing/content/documentation/database-tutorials/bulk-upload.md

@@ -5,7 +5,7 @@ aliases:
 weight: 1
 ---
 
-# Bulk upload a large number of vectors
+# Bulk Upload Vectors to a Qdrant Collection
 
 Uploading a large-scale dataset fast might be a challenge, but Qdrant has a few tricks to help you with that.
 

qdrant-landing/content/documentation/database-tutorials/create-snapshot.md

@@ -5,7 +5,7 @@ aliases:
 weight: 2
 ---
 
-# Create and restore collections from snapshot
+# Backup and Restore Qdrant Collections Using Snapshots
 
 | Time: 20 min | Level: Beginner |  |    |
 |--------------|-----------------|--|----|

qdrant-landing/content/documentation/database-tutorials/huggingface-datasets.md

@@ -5,7 +5,7 @@ aliases:
 weight: 3
 ---
 
-# Loading a dataset from Hugging Face hub
+# Load and Search Hugging Face Datasets with Qdrant
 
 [Hugging Face](https://huggingface.co/) provides a platform for sharing and using ML models and
 datasets. [Qdrant](https://huggingface.co/Qdrant) also publishes datasets along with the