fix remaining pages and structure

qdrant-landing/content/documentation/_index.md

@@ -9,9 +9,13 @@ Qdrant (read: quadrant ) is a vector similarity search engine. It provides a pro
 
 Qdrant is released under the open-source Apache License 2.0. Its source code is available on [GitHub](https://github.com/qdrant/qdrant).
 
-## Common uses
+## User manual
 
-Qdrant is ideal for deploying applications based on the matching of embeddings produced by neural network encoders.
+Read more about our [key concepts](../documentation/concepts/points/) and visit our [guides](../documentation/guides/installation/) to set up and further configure Qdrant for your own use.
+
+## Tutorials
+
+Check out the Tutorials section to learn more about common use cases. Qdrant is ideal for deploying applications based on the matching of embeddings produced by neural network encoders.
 
 These can be:
 
@@ -24,3 +28,14 @@ In addition to this documentation, you may be interested in looking at examples
 - [Semantic Search for startups](https://demo.qdrant.tech/) + [Source Code](https://github.com/qdrant/qdrant_demo)
 - [Visual Food Discovery](https://food-discovery.qdrant.tech/)
 - [Step-by-Step tutorial on building neural search](/articles/neural-search-tutorial/)
+
+
+## Integrations
+
+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
+higher-level libraries. Visit our Integrations section to learn more. 
+
+## Get started
+
+Go to the [Quickstart](../documentation/quick-start/) guide to get a production-ready vector search service up and running in minutes.

qdrant-landing/content/documentation/benchmarks.md

@@ -1,6 +1,5 @@
 ---
 title: Benchmarks
 weight: 33
+draft: true
 ---
-
-https://github.com/qdrant/ann-benchmarks

qdrant-landing/content/documentation/contribution-guidelines.md

@@ -1,6 +1,7 @@
 ---
 title: Contribution Guidelines
 weight: 35
+draft: true
 ---
 
 # How to contribute

qdrant-landing/content/documentation/guides/configuration.md

@@ -7,18 +7,36 @@ weight: 160
 
 To change or correct Qdrant's behavior, default collection settings, and network interface parameters, you can use the configuration file.
 
-Default configuration file is located in [config/config.yaml](https://github.com/qdrant/qdrant/blob/master/config/config.yaml).
+The configuration file is read when you start the service from the directory `./config/`.
 
-In the production environment, you can override any value of this file by providing new values in `/qdrant/config/production.yaml` inside the docker.
+The default values are stored in the file [./config/config.yaml](https://github.com/qdrant/qdrant/blob/master/config/config.yaml).
 
-Here is an example of how you can pass custom configuration inside the docker container:
+You can overwrite values by adding new records to the file `./config/production.yaml`. See an example [here](https://github.com/qdrant/qdrant/blob/master/config/production.yaml).
+
+If you are using Docker, then running the service with a custom configuration will be as follows:
 
 ```bash
 docker run -p 6333:6333 \
+    -v $(pwd)/path/to/data:/qdrant/storage \
     -v $(pwd)/path/to/custom_config.yaml:/qdrant/config/production.yaml \
     qdrant/qdrant
 ```
 
+Where `./path/to/custom_config.yaml` is your custom configuration file with values to override.
+
+Among other things, the configuration file allows you to specify the following settings:
+
+- Optimizer parameters
+- Network settings
+- Default vector index parameters
+- Storage settings
+- Security settings
+
+See the comments in the [configuration file itself](https://github.com/qdrant/qdrant/blob/master/config/config.yaml) for details.
+
+<aside role="status">Qdrant has no encryption or authentication by default and new instances are open to everyone. Please read <a href="https://qdrant.tech/documentation/security/">Security</a> carefully for details on how to secure your instance.</aside>
+
+
 ## Configuration file example
 
 ```yaml

qdrant-landing/content/documentation/guides/installation.md

@@ -1,9 +1,9 @@
 ---
 title: Installation
-weight: 13
+weight: 10
 ---
 
-# Installation
+# Installation options
 
 ## Docker
 
@@ -54,49 +54,6 @@ cargo build --release --bin qdrant
 
 After a successful build, the binary is available at `./target/release/qdrant`.
 
-## With Kubernetes
-
-You can use a ready-made [Helm Chart](https://helm.sh/docs/) to run Qdrant in your Kubeternetes cluster.
-
-```bash
-helm repo add qdrant https://qdrant.to/helm
-helm install qdrant-release qdrant/qdrant
-```
-
-Read further instructions in [qdrant-helm](https://github.com/qdrant/qdrant-helm) repository.
-
-## Configuration
-
-Qdrant gets its operating parameters from the configuration file.
-The configuration file is read when you start the service from the directory `./config/`.
-
-The default values are stored in the file [./config/config.yaml](https://github.com/qdrant/qdrant/blob/master/config/config.yaml).
-
-You can overwrite values by adding new records to the file `./config/production.yaml`. See an example [here](https://github.com/qdrant/qdrant/blob/master/config/production.yaml).
-
-If you are using Docker, then running the service with a custom configuration will be as follows:
-
-```bash
-docker run -p 6333:6333 \
-    -v $(pwd)/path/to/data:/qdrant/storage \
-    -v $(pwd)/path/to/custom_config.yaml:/qdrant/config/production.yaml \
-    qdrant/qdrant
-```
-
-Where `./path/to/custom_config.yaml` is your custom configuration file with values to override.
-
-Among other things, the configuration file allows you to specify the following settings:
-
-- Optimizer parameters
-- Network settings
-- Default vector index parameters
-- Storage settings
-- Security settings
-
-See the comments in the [configuration file itself](https://github.com/qdrant/qdrant/blob/master/config/config.yaml) for details.
-
-<aside role="status">Qdrant has no encryption or authentication by default and new instances are open to everyone. Please read <a href="https://qdrant.tech/documentation/security/">Security</a> carefully for details on how to secure your instance.</aside>
-
 ## Python client
 
 In addition to the service itself, Qdrant has a distinct python client, which has some additional features compared to [clients](https://qdrant.tech/documentation/quick_start/#clients) generated from OpenAPI directly.
@@ -107,8 +64,13 @@ To install this client, just run the following command:
 pip install qdrant-client
 ```
 
+## Kubernetes
 
-### Integrations
+You can use a ready-made [Helm Chart](https://helm.sh/docs/) to run Qdrant in your Kubeternetes cluster.
 
-Qdrant may be also used as an efficient vector search backend in some other tools. Please check out the [Integrations](../integrations/) section
-for some more details.
+```bash
+helm repo add qdrant https://qdrant.to/helm
+helm install qdrant-release qdrant/qdrant
+```
+
+Read further instructions in [qdrant-helm](https://github.com/qdrant/qdrant-helm) repository.

qdrant-landing/content/documentation/integrations/_index.md

@@ -2,11 +2,6 @@
 title: Integrations
 weight: 24
 # If the index.md file is empty, the link to the section will be hidden from the sidebar
-is_empty: false
+is_empty: true
 ---
 
-# Integrations
-
-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
-higher-level libraries. Some of such projects provide ready-to-go integrations and here is a curated list of them.

qdrant-landing/content/documentation/python-client.md

@@ -1,4 +1,7 @@
 ---
 title: Python Client
 weight: 14
+type: external-link
+external_url: https://github.com/qdrant/qdrant-client
+sitemapExclude: True
 ---

qdrant-landing/content/documentation/qdrant-101.md

@@ -1,36 +1,22 @@
 ---
-title: Complete Setup
-weight: 10
+title: Qdrant 101
+weight: 12
 ---
 
-# Getting Started with Qdrant
+# Tutorial: Introduction to Qdrant
 
 Vector databases shine in many applications like [semantic search](https://en.wikipedia.org/wiki/Semantic_search) and 
-[recommendation systems](https://en.wikipedia.org/wiki/Recommender_system), and in this tutorial, we'll learn about 
+[recommendation systems](https://en.wikipedia.org/wiki/Recommender_system). In this tutorial, we'll learn about 
 how to get started building these systems with one of the most popular and fastest growing vector databases in the 
 market, [Qdrant](qdrant.tech).
 
-## Table of Contents
-
-1. [Learning Outcomes](##-1.-Learning-Outcomes)
-2. [Installation](##-2.-Installation)
-3. [Getting Started](##-3.-Getting-Started)
-    - [Adding Points](###-3.1-Adding-Points)
-    - [Payload](###-3.2-Payloads)
-    - [Search](###-3.3-Search)
-4. [Recommendations](##-4.-Recommendations)
-5. [Conclusion](##-5.-Conclusion)
-6. [Resources](##-6.-Resources)
-
-## 1. Learning Outcomes
-
 By the end of this tutorial, you will be able to
 - Create, update, and query collections of vectors using Qdrant.
 - Conduct semantic search based on new data.
 - Develop an intuition for the mechanics behind the recommendation API of Qdrant.
 - Understand, and get creative with, the kind of data you can add to your payload.
 
-## 2. Installation
+## Installation
 
 The open source version of Qdrant is available as a docker image and it can be pulled and run from any machine 
 with docker in it. If you don't have Docker installed in your PC you can follow the instructions in the 
@@ -78,7 +64,7 @@ After your have your environment ready, let's get started using Qdrant.
 **Note:** At the time of writing, Qdrant supports Rust, GO, Python and TypeScript. We expect other 
 programming languages to be added in the future.
 
-## 3. Getting Started
+## Getting Started
 
 The two modules we'll use the most are the `QdrantClient` and the `models` one. The former allows us 
 to connect to Qdrant or it allows us to run an in-memory database by switching the parameter `location=` 
@@ -184,7 +170,7 @@ be recreated again, we would use `client.create_collection()` method instead.
 
 Now that we know how to create collections, let's create a bit of fake data and add some vectors to it.
 
-### 3.1 Adding Points
+### Adding Points
 
 The [points](https://qdrant.tech/documentation/points/) are the central entity Qdrant operates with, and 
 these contain records consisting of a vector, an optional `id` and an optional `payload` (which we'll talk 
@@ -330,7 +316,7 @@ client.count(
 
 
 
-### 3.2 Payloads
+### Payloads
 
 Qdrant has incredible features on top of speed and reliability, and one of its most useful ones is without 
 a doubt the ability to store additional information alongside the vectors. In Qdrant's terminology, this 
@@ -459,7 +445,7 @@ resutls[0].id
 
 Next, we'll use our payload it to search.
 
-### 3.3 Search
+### Search
 
 Now that we have our vectors with an ID and a payload, we can explore a few of ways in which we can search 
 for content when, in our use case, new music gets selected. Let's check it out.
@@ -550,7 +536,7 @@ client.clear_payload(
 )
 ```
 
-## 4. Recommendations
+## Recommendations
 
 A recommendation system is a technology that suggests items or content to users based on their preferences, 
 interests, or past behavior. It's like having a knowledgeable friend who can recommend movies, books, music, 
@@ -674,7 +660,7 @@ client.recommend(
 
 That's it! You have now gone over a whirlwind tour of vector databases and are ready to tackle new challenges. 😎
 
-## 5. Conclusion
+## Conclusion
 
 To wrap up, we have explored a bit of the fascinating world of vector databases, and we learned that these 
 databases provide efficient storage and retrieval of high-dimensional vectors, making them ideal for 
@@ -688,7 +674,7 @@ We can't wait to see what cool applications you build with Qdrant.
 
 If you liked this introductory tutorial, make sure you keep an eye out for new ones on our website.
 
-## 6. Resources
+## Resources
 
 Here is a list with some resources that we found useful, and that helped with the development of this tutorial.
 

qdrant-landing/content/documentation/quick-start.md

@@ -1,9 +1,9 @@
 ---
 title: Quickstart
-weight: 12
+weight: 11
 ---
 
-# Getting started with Qdrant
+# Quickstart
 
 ## Installation
 

qdrant-landing/content/documentation/roadmap.md

@@ -1,16 +1,11 @@
 ---
 title: Roadmap
 weight: 32
+draft: true
 ---
 
 # Qdrant 2023 Roadmap
 
-Hi!
-This document is our plan for Qdrant development in 2023.
-Previous year roadmap is available here:
-
-* [Roadmap 2022](roadmap-2022.md)
-
 Goals of the release:
 
 * **Maintain easy upgrades** - we plan to keep backward compatibility for at least one major version back. 

qdrant-landing/content/documentation/technical-support.md

@@ -1,4 +1,5 @@
 ---
 title: Technical Support
 weight: 34
+draft: true
 ---

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

@@ -5,6 +5,3 @@ weight: 23
 is_empty: true
 ---
 
-# Tutorials
-
-This section contains a collection of how-to guides and tutorials for different use cases of Qdrant.

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

@@ -1,5 +1,5 @@
 ---
-title: Bulk upload
+title: Bulk upload vectors
 weight: 13
 ---
 

qdrant-landing/content/documentation/tutorials/common-errors.md

@@ -1,6 +1,6 @@
 ---
 title: Troubleshooting
-weight: 14
+weight: 100
 ---
 
 # Solving common errors

qdrant-landing/content/documentation/tutorials/optimize.md

@@ -1,5 +1,5 @@
 ---
-title: Optimal configuration
+title: Configure for optimal use
 weight: 11
 ---
 

qdrant-landing/content/documentation/tutorials/qa-engine.md

@@ -0,0 +1,7 @@
+---
+title: Build a Q&A engine 
+weight: 13
+type: external-link
+external_url: https://github.com/qdrant/examples/blob/61749ebfdef5d5779dee343ee9fb4f9a304a6b55/llama_index_recency/Qdrant%20and%20LlamaIndex%20%E2%80%94%20A%20new%20way%20to%20keep%20your%20Q%26A%20systems%20up-to-date.ipynb
+sitemapExclude: True
+---