docs auto-sync

qdrant-landing/content/documentation/administration.md

@@ -1,6 +1,6 @@
 ---
 title: Administration
-weight: 21
+weight: 170
 ---
 
 Qdrant exposes administration tools which enable to modify at runtime the behavior of a qdrant instance without changing its configuration manually.

qdrant-landing/content/documentation/collections.md

@@ -1,6 +1,6 @@
 ---
 title: Collections
-weight: 21
+weight: 30
 ---
 
 ## Collections

qdrant-landing/content/documentation/configuration.md

@@ -1,6 +1,6 @@
 ---
 title: Configuration
-weight: 45
+weight: 160
 ---
 
 To change or correct Qdrant's behavior, default collection settings, and network interface parameters, you can use the configuration file.

qdrant-landing/content/documentation/distributed_deployment.md

@@ -1,6 +1,6 @@
 ---
 title: Distributed deployment
-weight: 50
+weight: 100
 ---
 
 Since version v0.8.0 Qdrant supports a distributed deployment mode.

qdrant-landing/content/documentation/filtering.md

@@ -1,6 +1,6 @@
 ---
 title: Filtering
-weight: 27
+weight: 60
 ---
 
 Qdrant allows you to set the conditions to be used when searching or retrieving points.

qdrant-landing/content/documentation/how_to.md

@@ -1,6 +1,6 @@
 ---
 title: How To
-weight: 15
+weight: 130
 ---
 
 

qdrant-landing/content/documentation/indexing.md

@@ -1,6 +1,6 @@
 ---
 title: Indexing
-weight: 30
+weight: 90
 ---
 
 A key feature of Qdrant is the effective combination of vector and traditional indices. It is essential to have this because for vector search to work effectively with filters, having vector index only is not enough.

qdrant-landing/content/documentation/install.md

@@ -1,6 +1,6 @@
 ---
 title: Install
-weight: 11
+weight: 20
 ---
 
 ## With Docker

qdrant-landing/content/documentation/integrations.md

@@ -1,6 +1,6 @@
 ---
 title: Integrations
-weight: 52
+weight: 140
 ---
 Qdrant is a vector database performing an approximate nearest neighbours search on neural embeddings. It can work perfectly fine
 as a standalone system, yet, in some cases, you may find it easier to implement your semantic search application using some

qdrant-landing/content/documentation/optimizer.md

@@ -1,6 +1,6 @@
 ---
 title: Optimizer
-weight: 28
+weight: 70
 ---
 
 It is much more efficient to apply changes in batches than perform each change individually, as many other databases do. Qdrant here is no exception. Since Qdrant operates with data structures that are not always easy to change, it is sometimes necessary to rebuild those structures completely.

qdrant-landing/content/documentation/payload.md

@@ -1,6 +1,6 @@
 ---
 title: Payload
-weight: 25
+weight: 40
 ---
 
 One of the significant features of Qdrant is the ability to store additional information along with vectors.

qdrant-landing/content/documentation/points.md

@@ -1,6 +1,6 @@
 ---
 title: Points
-weight: 24
+weight: 40
 ---
 
 The points are the central entity that Qdrant operates with.

qdrant-landing/content/documentation/quantization.md

@@ -1,6 +1,6 @@
 ---
 title: Quantization
-weight: 55
+weight: 120
 ---
 
 <!---What is quantization and what it used for.

qdrant-landing/content/documentation/search.md

@@ -1,6 +1,6 @@
 ---
 title: Search
-weight: 26
+weight: 50
 ---
 
 

qdrant-landing/content/documentation/snapshots.md

@@ -1,6 +1,6 @@
 ---
 title: Snapshots
-weight: 51
+weight: 110
 ---
 
 *Available since v0.8.4*

qdrant-landing/content/documentation/storage.md

@@ -1,6 +1,6 @@
 ---
 title: Storage
-weight: 29
+weight: 80
 ---
 
 All data within one collection is divided into segments.

qdrant-landing/content/documentation/telemetry.md

@@ -1,6 +1,6 @@
 ---
 title: Telemetry
-weight: 40
+weight: 150
 ---