remove mentions of the exact domain from the content (#902)

* remove mentions of the exact domain from the content * fix links * fix links
2026-09-26 22:48:30 +02:00 · 2024-05-15 18:01:28 +02:00
parent 72016345ca
commit 9894eac76e
23 changed files with 38 additions and 38 deletions
@@ -263,7 +263,7 @@ In the blog post file, you'll see:

 - Add tags. While they're not shown on the blog post page, they are used to display related posts.
 - If post has `featured: true` property in the front matter this post will appear in the "Features and News" blog section. Only the last 4 featured posts will be displayed in this section. Featured posts will not appear in the regular post list.
-  - If there are more than 4 `featured: true` posts (where `draft: false`), the oldest post disappears from https://qdrant.tech/blog.
+  - If there are more than 4 `featured: true` posts (where `draft: false`), the oldest post disappears from /blog.

 ## Marketing Landing Pages

@@ -54,7 +54,7 @@ As embeddings are vectors, one can apply a simple function to calculate the simi
 So with similarity learning, all we need to do is provide pairs of correct questions and answers.
 And then, the model will learn to distinguish proper answers by the similarity of embeddings.

->If you want to learn more about similarity learning and applications, check out this [article](https://qdrant.tech/documentation/tutorials/neural-search/) which might be an asset.
+>If you want to learn more about similarity learning and applications, check out this [article](/documentation/tutorials/neural-search/) which might be an asset.

 ## Let's build

@@ -71,7 +71,7 @@ by LangChain. If you want to get into the source code right away, all the proces

 ### Configuration

-A journey of a thousand miles begins with a single step, in our case with the configuration of all the services. We'll be using [Qdrant Cloud](https://qdrant.tech),
+A journey of a thousand miles begins with a single step, in our case with the configuration of all the services. We'll be using [Qdrant Cloud](https://cloud.qdrant.io),
 so we need an API key. The same is for OpenAI - the API key has to be obtained from their website.

 ![](/articles_data/langchain-integration/code-configuration.png)
@@ -27,7 +27,7 @@ set up your collections.

 Previously, you had to send multiple requests to the Qdrant API to perform multiple non-related tasks. However, this 
 can cause significant network overhead and slow down the process, especially if you have a poor connection speed. 
-Fortunately, the [new batch search feature](https://qdrant.tech/documentation/concepts/search/#batch-search-api) allows 
+Fortunately, the [new batch search feature](/documentation/concepts/search/#batch-search-api) allows 
 you to avoid this issue. With just one API call, Qdrant will handle multiple search requests in the most efficient way 
 possible. This means that you can perform multiple tasks simultaneously without having to worry about network overhead
 or slow performance.
@@ -44,6 +44,6 @@ both ARM and non-ARM architectures using similar setups to understand the potent

 Qdrant is a vector database that allows you to quickly search for the nearest neighbors. However, you may need to apply 
 additional filters on top of the semantic search. Up until version 0.10, Qdrant only supported keyword filters. With the 
-release of Qdrant 0.10, [you can now use full-text filters](https://qdrant.tech/documentation/concepts/filtering/#full-text-match) 
+release of Qdrant 0.10, [you can now use full-text filters](/documentation/concepts/filtering/#full-text-match) 
 as well. This new filter type can be used on its own or in combination with other filter types to provide even more 
 flexibility in your searches.
@@ -1,6 +1,6 @@
 ---
 title: "Qdrant x.y.0 - <include headline> #required; update version and headline"
-draft: true # Change to false to publish the article at https://qdrant.tech/articles/
+draft: true # Change to false to publish the article at /articles/
 slug: qdrant-x.y.z # required; subtitute version number
 short_description: "Headline-like description."
 description: "Headline with more detail. Suggested limit: 140 characters. " 
@@ -38,7 +38,7 @@ As your data grows, you’ll need efficient ways to identify the most relevant i

 **Vector databases** store information as **vector embeddings**. This format supports efficient similarity searches to retrieve relevant data for your query. For example, Qdrant is specifically designed to perform fast, even in scenarios dealing with billions of vectors.

-This article will focus on RAG systems and architecture. If you’re interested in learning more about vector search, we recommend the following articles: [What is a Vector Database?](https://qdrant.tech/articles/what-is-a-vector-database/) and [What are Vector Embeddings?](https://qdrant.tech/articles/what-are-embeddings/).
+This article will focus on RAG systems and architecture. If you’re interested in learning more about vector search, we recommend the following articles: [What is a Vector Database?](/articles/what-is-a-vector-database/) and [What are Vector Embeddings?](/articles/what-are-embeddings/).


 ## RAG architecture
@@ -64,7 +64,7 @@ As shown in the image above, here’s the process:
 * Start with a _loader_ that gathers _documents_ containing your data. These documents could be anything from articles and books to web pages and social media posts. 
 * Next, a _splitter_ divides the documents into smaller chunks, typically sentences or paragraphs. 
 * This is because RAG models work better with smaller pieces of text. In the diagram, these are _document snippets_.
-* Each text chunk is then fed into an _embedding machine_. This machine uses complex algorithms to convert the text into [vector embeddings](https://qdrant.tech/articles/what-are-embeddings/).
+* Each text chunk is then fed into an _embedding machine_. This machine uses complex algorithms to convert the text into [vector embeddings](/articles/what-are-embeddings/).

 All the generated vector embeddings are stored in a knowledge base of indexed information. This supports efficient retrieval of similar pieces of information when needed.

@@ -94,14 +94,14 @@ The classic approach is **keyword search**, which scans documents for the exact

 [TF-IDF](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) (Term Frequency-Inverse Document Frequency) and [BM25](https://en.wikipedia.org/wiki/Okapi_BM25) are two classic related algorithms. They're simple and computationally efficient. However, they can struggle with synonyms and don't always capture semantic similarities.

-If you’re interested in going deeper, refer to our article on [Sparse Vectors](https://qdrant.tech/articles/sparse-vectors/).
+If you’re interested in going deeper, refer to our article on [Sparse Vectors](/articles/sparse-vectors/).


 ##### Dense vector embeddings

 This approach uses large language models like [BERT](https://en.wikipedia.org/wiki/BERT_(language_model)) to encode the query and passages into dense vector embeddings. These models are compact numerical representations that capture semantic meaning. Vector databases like Qdrant store these embeddings, allowing retrieval based on **semantic similarity** rather than just keywords using distance metrics like cosine similarity.

-This allows the retriever to match based on semantic understanding rather than just keywords. So if I ask about "compounds that cause BO," it can retrieve relevant info about "molecules that create body odor" even if those exact words weren't used. We explain more about it in our [What are Vector Embeddings](https://qdrant.tech/articles/what-are-embeddings/) article.
+This allows the retriever to match based on semantic understanding rather than just keywords. So if I ask about "compounds that cause BO," it can retrieve relevant info about "molecules that create body odor" even if those exact words weren't used. We explain more about it in our [What are Vector Embeddings](/articles/what-are-embeddings/) article.


 #### Hybrid search 
@@ -121,7 +121,7 @@ Some common hybrid approaches include:
 * Considering both semantic vector closeness and statistical keyword patterns/weights in a combined scoring model.
 * Having multiple stages were different techniques. One example: start with an initial keyword retrieval, followed by semantic re-ranking, then a final re-ranking using even more complex models.

-When you combine the powers of different search methods in a complementary way, you can provide higher quality, more comprehensive results. Check out our article on [Hybrid Search](https://qdrant.tech/articles/hybrid-search/) if you’d like to learn more.
+When you combine the powers of different search methods in a complementary way, you can provide higher quality, more comprehensive results. Check out our article on [Hybrid Search](/articles/hybrid-search/) if you’d like to learn more.


 ### The Generator
@@ -101,7 +101,7 @@ This technology integrates Kubernetes clusters from any setting - cloud, on-prem

 You can test out Qdrant Hybrid Cloud today. Sign up or log into your [Qdrant Cloud account](https://cloud.qdrant.io/login) and get started in the **Hybrid Cloud** section. 

-Also, to learn more about Qdrant Hybrid Cloud read our [Official Release Blog](https://qdrant.tech/blog/hybrid-cloud/) or our [Qdrant Hybrid Cloud website](https://hybrid-cloud.qdrant.tech/). For additional technical insights, please read our [documentation](https://qdrant.tech/documentation/hybrid-cloud/).
+Also, to learn more about Qdrant Hybrid Cloud read our [Official Release Blog](/blog/hybrid-cloud/) or our [Qdrant Hybrid Cloud website](https://hybrid-cloud.qdrant.tech/). For additional technical insights, please read our [documentation](/documentation/hybrid-cloud/).

 #### Try it out!

@@ -41,8 +41,8 @@ Ready to experience the benefits of Qdrant on Azure Marketplace? Getting started

 1. **Visit the Azure Marketplace**: Navigate to [Qdrant's Marketplace listing](https://azuremarketplace.microsoft.com/en-en/marketplace/apps/qdrantsolutionsgmbh1698769709989.qdrant-db).
 2. **Deploy Qdrant**: Follow the simple deployment instructions to set up your instance.
-3. **Start Using Qdrant**: Once deployed, start exploring the [features and capabilities of Qdrant](https://qdrant.tech/documentation/concepts/) on Azure.
-4. **Read Documentation**: Read Qdrant's [Documentation](https://qdrant.tech/documentation/) and build demo apps using [Tutorials](https://qdrant.tech/documentation/tutorials/). 
+3. **Start Using Qdrant**: Once deployed, start exploring the [features and capabilities of Qdrant](/documentation/concepts/) on Azure.
+4. **Read Documentation**: Read Qdrant's [Documentation](/documentation/) and build demo apps using [Tutorials](/documentation/tutorials/). 

 ## Join Us on this Exciting Journey:

@@ -38,7 +38,7 @@ more time shipping features and fixing bugs.
 bloop’s mission is to make software engineers autonomous and semantic code search is the cornerstone 
 of that vision. The project is maintained by a group of Rust and Typescript engineers and ML researchers. 
 It leverages many prominent nascent technologies, such as [Tauri](http://tauri.app), [tantivy](https://docs.rs/tantivy), 
-[Qdrant](https://qdrant.tech) and [Anthropic](https://www.anthropic.com/).
+[Qdrant](https://github.com/qdrant/qdrant) and [Anthropic](https://www.anthropic.com/).

 ## About Qdrant

@@ -41,17 +41,17 @@ is not at least v1.8.3.

 To confirm the version of your Qdrant deployment in the cloud or on your local
 or cloud system, run an API GET call, as described in the [Qdrant Quickstart
-guide](https://qdrant.tech/documentation/cloud/quickstart-cloud/#step-2-test-cluster-access).
+guide](/documentation/cloud/quickstart-cloud/#step-2-test-cluster-access).
 If your Qdrant deployment is local, you do not need an API key.

 Your next step depends on how you installed Qdrant. For details, read the
-[Qdrant Installation](https://qdrant.tech/documentation/guides/installation/)
+[Qdrant Installation](/documentation/guides/installation/)
 guide.

 #### If you use the Qdrant container or binary

 Upgrade your deployment. Run the commands in the applicable section of the
-[Qdrant Installation](https://qdrant.tech/documentation/guides/installation/)
+[Qdrant Installation](/documentation/guides/installation/)
 guide. The default commands automatically pull the latest version of Qdrant.

 #### If you use the Qdrant helm chart
@@ -63,5 +63,5 @@ Thanks to the [DataTalks.Club](https://datatalks.club) for organizing [this podc
 If you're interested in a similar discussion, watch for the recording from the [following event](https://www.eventbrite.co.uk/e/the-evolution-of-genai-exploring-practical-applications-tickets-778359172237?aff=oddtdtcreator), organized by [DeepRec.ai](https://deeprec.ai).

 ### Further reading
- https://qdrant.tech/blog
+- [Qdrant Blog](/blog/)
 - https://hub.superlinked.com/blog
@@ -69,4 +69,4 @@ As large companies continue to integrate sophisticated AI and machine learning t

 Qdrant is open source and offers a complete SaaS solution, hosted on AWS, GCP, and Azure.

-Getting started is easy, either spin up a [container image](https://hub.docker.com/r/qdrant/qdrant) or start a [free Cloud instance](https://cloud.qdrant.io/login). The documentation covers [adding the data](https://qdrant.tech/documentation/tutorials/bulk-upload/) to your Qdrant instance as well as [creating your indices](https://qdrant.tech/documentation/tutorials/optimize/). We would love to hear about what you are building and please connect with our engineering team on [Github](https://github.com/qdrant/qdrant), [Discord](https://discord.com/invite/tdtYvXjC4h), or [LinkedIn](https://www.linkedin.com/company/qdrant).
+Getting started is easy, either spin up a [container image](https://hub.docker.com/r/qdrant/qdrant) or start a [free Cloud instance](https://cloud.qdrant.io/login). The documentation covers [adding the data](/documentation/tutorials/bulk-upload/) to your Qdrant instance as well as [creating your indices](/documentation/tutorials/optimize/). We would love to hear about what you are building and please connect with our engineering team on [Github](https://github.com/qdrant/qdrant), [Discord](https://discord.com/invite/tdtYvXjC4h), or [LinkedIn](https://www.linkedin.com/company/qdrant).
@@ -77,7 +77,7 @@ The first part of this video explains how caching works. In the second part, you

 ## Embrace the Future of AI Data Retrieval

-[Qdrant](https://qdrant.tech/) offers the most flexible way to implement vector search for your RAG and AI applications. You can test out semantic cache on your free Qdrant Cloud instance today! Simply sign up for or log into your [Qdrant Cloud account](https://cloud.qdrant.io/login) and follow our [documentation](/documentation/cloud/).
+[Qdrant](https://github.com/qdrant/qdrant) offers the most flexible way to implement vector search for your RAG and AI applications. You can test out semantic cache on your free Qdrant Cloud instance today! Simply sign up for or log into your [Qdrant Cloud account](https://cloud.qdrant.io/login) and follow our [documentation](/documentation/cloud/).

 You can also deploy Qdrant locally and manage via our UI. To do this, check our [Hybrid Cloud](/blog/hybrid-cloud/)!

@@ -23,7 +23,7 @@ tags:

 Qdrant's vector database quickly grew due to its ability to make Generative AI more effective. On its own, an LLM can be used to build a process-altering invention. With Qdrant, you can turn this invention into a production-level app that brings real business value.

-The use of vector search in GenAI now has a name: **Retrieval Augmented Generation (RAG)**. [In our previous article](https://qdrant.tech/articles/rag-is-dead/), we argued why RAG is an essential component of AI setups, and why large-scale AI can't operate without it. Numerous case studies explain that AI applications are simply too costly and resource-intensive to run using only LLMs. 
+The use of vector search in GenAI now has a name: **Retrieval Augmented Generation (RAG)**. [In our previous article](/articles/rag-is-dead/), we argued why RAG is an essential component of AI setups, and why large-scale AI can't operate without it. Numerous case studies explain that AI applications are simply too costly and resource-intensive to run using only LLMs. 

 > Going forward, the solution is to leverage composite systems that use models and vector databases.

@@ -52,7 +52,7 @@ When supported by LangChain, Qdrant can help you set up effective question-answe
 Integrating Qdrant and LangChain can revolutionize your AI applications. Let's take a look at what this integration can do for you: 

 *Enhance Natural Language Processing (NLP):* 
-LangChain is great for developing question-answering **chatbots**, where Qdrant is used to contextualize and retrieve results for the LLM. We cover this in [our article](https://qdrant.tech/articles/langchain-integration/), and in OpenAI's [cookbook examples](https://cookbook.openai.com/examples/vector_databases/qdrant/qa_with_langchain_qdrant_and_openai) that use LangChain and GPT to process natural language.
+LangChain is great for developing question-answering **chatbots**, where Qdrant is used to contextualize and retrieve results for the LLM. We cover this in [our article](/articles/langchain-integration/), and in OpenAI's [cookbook examples](https://cookbook.openai.com/examples/vector_databases/qdrant/qa_with_langchain_qdrant_and_openai) that use LangChain and GPT to process natural language.

 *Improve Recommendation Systems:*
 Food delivery services thrive on indecisive customers. Businesses need to accomodate a multi-aim search process, where customers seek recommendations though semantic search. With LangChain you can build systems for **e-commerce, content sharing, or even dating apps**.
@@ -88,19 +88,19 @@ If you are looking to scale up and keep the same level of performance, Qdrant an

 Whether you are building a bank fraud-detection system, RAG for e-commerce, or services for the federal government - you will need to leverage a scalable architecture for your product. Qdrant offers different features to help you considerably increase your application’s performance and lower your hosting costs.

-> Read more about out how we foster [best practices for large-scale deployments](https://qdrant.tech/articles/multitenancy/). 
+> Read more about out how we foster [best practices for large-scale deployments](/articles/multitenancy/). 

 ## Next Steps

 Now that you know how Qdrant and LangChain can elevate your setup - it's time to try us out. 

- Qdrant is open source and you can [quickstart locally](https://qdrant.tech/documentation/quick-start/), [install it via Docker](https://qdrant.tech/documentation/quick-start/), [or to Kubernetes](https://github.com/qdrant/qdrant-helm/). 
+- Qdrant is open source and you can [quickstart locally](/documentation/quick-start/), [install it via Docker](/documentation/quick-start/), [or to Kubernetes](https://github.com/qdrant/qdrant-helm/). 

 - We also offer [a free-tier of Qdrant Cloud](https://cloud.qdrant.io/) for prototyping and testing.

 - For best integration with LangChain, read the [official LangChain documentation](https://python.langchain.com/docs/integrations/vectorstores/qdrant/). 

- For all other cases, [Qdrant documentation](https://qdrant.tech/documentation/integrations/langchain/) is the best place to get there.
+- For all other cases, [Qdrant documentation](/documentation/integrations/langchain/) is the best place to get there.

 > We offer additional support tailored to your business needs. [Contact us](https://qdrant.to/contact-us) to learn more about implementation strategies and integrations that suit your company.

@@ -12,13 +12,13 @@ weight: 36

 Apache Spark is designed to scale horizontally, meaning it can handle expensive operations like generating vector embeddings by distributing computation across a cluster of machines. This scalability is crucial when dealing with large datasets.

-In this example, we will demonstrate how to vectorize a dataset with dense and sparse embeddings using Qdrant's [FastEmbed](https://qdrant.github.io/fastembed/) library. We will then load this vectorized data into a Qdrant cluster using the [Qdrant Spark connector](https://qdrant.tech/documentation/frameworks/spark/) on Databricks.
+In this example, we will demonstrate how to vectorize a dataset with dense and sparse embeddings using Qdrant's [FastEmbed](https://qdrant.github.io/fastembed/) library. We will then load this vectorized data into a Qdrant cluster using the [Qdrant Spark connector](/documentation/frameworks/spark/) on Databricks.

 ### Setting up a Databricks project

 - Set up a **[Databricks cluster](https://docs.databricks.com/en/compute/configure.html)** following the official documentation guidelines.

- Install the **[Qdrant Spark connector](https://qdrant.tech/documentation/frameworks/spark/)** as a library:
+- Install the **[Qdrant Spark connector](/documentation/frameworks/spark/)** as a library:
  - Navigate to the `Libraries` section in your cluster dashboard.
  - Click on `Install New` at the top-right to open the library installation modal.
  - Search for `io.qdrant:spark:VERSION` in the Maven packages and click on `Install`.
@@ -25,7 +25,7 @@ To maintain complete data isolation, we need to limit ourselves to open-source t
 - **LLM:** `mistralai/Mistral-7B-Instruct-v0.1`, deployed as a standalone service on OpenShift.
 - **Embedding Model:** `BAAI/bge-base-en-v1.5`, lightweight embedding model deployed from within the Haystack pipeline
  with [FastEmbed](https://github.com/qdrant/fastembed)
- **Vector DB:** [Qdrant Hybrid Cloud](https://qdrant.tech) running on OpenShift.
+- **Vector DB:** [Qdrant Hybrid Cloud](https://hybrid-cloud.qdrant.tech) running on OpenShift.
 - **Framework:** [Haystack 2.x](https://haystack.deepset.ai/) to connect all and [Hayhooks](https://docs.haystack.deepset.ai/docs/hayhooks) to serve the app through HTTP endpoints.

 ### Procedure
@@ -458,4 +458,4 @@ The response should be similar to the one we got in the Python before:
 - [Haystack's documentation](https://docs.haystack.deepset.ai/docs/kubernetes) describes [how to deploy the Hayhooks service in a Kubernetes 
 environment](https://docs.haystack.deepset.ai/docs/kubernetes), so you can easily move it to your own OpenShift infrastructure.

- If you are just getting started and need more guidance on Qdrant, read the [quickstart](https://qdrant.tech/documentation/quick-start/) or try out our [beginner tutorial](https://qdrant.tech/documentation/tutorials/neural-search/).
+- If you are just getting started and need more guidance on Qdrant, read the [quickstart](/documentation/quick-start/) or try out our [beginner tutorial](/documentation/tutorials/neural-search/).
@@ -27,7 +27,7 @@ to the selected Large Language Model, and have an established way to do it in a
 an ingestion pipeline and then a Retrieval Augmented Generation application that will use the data.

 - **Dataset:** a [set of Frequently Asked Questions from Qdrant 
-  users](https://qdrant.tech/documentation/faq/qdrant-fundamentals/) as an incrementally updated Excel sheet
+  users](/documentation/faq/qdrant-fundamentals/) as an incrementally updated Excel sheet
 - **Embedding model:** Cohere `embed-multilingual-v3.0`, to support different languages with the same pipeline
 - **Knowledge base:** Qdrant, running in Hybrid Cloud mode
 - **Ingestion pipeline:** [Airbyte](https://airbyte.com/), loading the data into Qdrant
@@ -21,7 +21,7 @@ In this tutorial, you will build a mechanism that recommends movies based on def

 - **Dataset:** The [MovieLens dataset](https://grouplens.org/datasets/movielens/) contains a list of movies and ratings given by users.
 - **Cloud:** [OVHcloud](https://ovhcloud.com/), with managed Kubernetes.
- **Vector DB:** [Qdrant Hybrid Cloud](https://qdrant.tech) running on [OVHcloud](https://ovhcloud.com/).
+- **Vector DB:** [Qdrant Hybrid Cloud](https://hybrid-cloud.qdrant.tech) running on [OVHcloud](https://ovhcloud.com/).

 **Methodology:** We're adopting a collaborative filtering approach to construct a recommendation system from the dataset provided. Collaborative filtering works on the premise that if two users share similar tastes, they're likely to enjoy similar movies. Leveraging this concept, we'll identify users whose ratings align closely with ours, and explore the movies they liked but we haven't seen yet. To do this, we'll represent each user's ratings as a vector in a high-dimensional, sparse space. Using Qdrant, we'll index these vectors and search for users whose ratings vectors closely match ours. Ultimately, we will see which movies were enjoyed by users similar to us.

@@ -58,7 +58,7 @@ By default, the `LIMIT` is set to 10 and the `OFFSET` is set to 0.

 #### Perform a similarity search using your embeddings

-<aside role="status">Qdrant supports <a href="https://qdrant.tech/documentation/concepts/indexing/#payload-index">payload indexing</a> that vastly improves retrieval efficiency with filters and is highly recommended. Please note that this feature currently cannot be configured via MindsDB and must be set up separately if needed.</aside>
+<aside role="status">Qdrant supports <a href="/documentation/concepts/indexing/#payload-index">payload indexing</a> that vastly improves retrieval efficiency with filters and is highly recommended. Please note that this feature currently cannot be configured via MindsDB and must be set up separately if needed.</aside>

 ```sql
 SELECT * FROM qdrant_test.test_table
@@ -76,7 +76,7 @@ public class QdrantSparkJavaExample {

 ### Loading data into Qdrant

-<aside role="status">Before loading the data using this connector, a collection has to be <a href="https://qdrant.tech/documentation/concepts/collections/#create-a-collection">created</a> in advance with the appropriate vector dimensions and configurations.</aside>
+<aside role="status">Before loading the data using this connector, a collection has to be <a href="/documentation/concepts/collections/#create-a-collection">created</a> in advance with the appropriate vector dimensions and configurations.</aside>

 The connector supports ingesting multiple named/unnamed, dense/sparse vectors.

@@ -17,11 +17,11 @@ speech recognition, object detection, and many others.

 These new 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 here, we'll 
-learn about one of the most popular and fastest growing vector databases in the market, [Qdrant](https://qdrant.tech).
+learn about one of the most popular and fastest growing vector databases in the market, [Qdrant](https://github.com/qdrant/qdrant).

 ## What is Qdrant?

-[Qdrant](https://qdrant.tech) "is a vector similarity search engine that provides a production-ready 
+[Qdrant](https://github.com/qdrant/qdrant) "is a vector similarity search engine that provides a production-ready 
 service with a convenient API to store, search, and manage points (i.e. vectors) with an additional 
 payload." You can think of the payloads as additional pieces of information that can help you 
 hone in on your search and also receive useful information that you can give to your users.
@@ -71,7 +71,7 @@ using Qdrant.Client;
 var client = new QdrantClient("localhost", 6334);
 ```

-<aside role="status">By default, Qdrant starts with no encryption or authentication . This means anyone with network access to your machine can access your Qdrant container instance. Please read <a href="https://qdrant.tech/documentation/security/">Security</a> carefully for details on how to secure your instance.</aside>
+<aside role="status">By default, Qdrant starts with no encryption or authentication . This means anyone with network access to your machine can access your Qdrant container instance. Please read <a href="/documentation/security/">Security</a> carefully for details on how to secure your instance.</aside>

 ## Create a collection

@@ -3,6 +3,6 @@ icon: <svg width="16" height="16" viewBox="0 0 16 16" fill="none" xmlns="http://
 text: Introducing Qdrant Hybrid Cloud
 link:
  text: Learn More
-  url: https://qdrant.tech/blog/hybrid-cloud/
+  url: /blog/hybrid-cloud/
 sitemapExclude: true
 ---