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

* remove mentions of the exact domain from the content

* fix links

* fix links

qdrant-landing/content/documentation/examples/databricks.md

@@ -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`.

qdrant-landing/content/documentation/examples/rag-chatbot-red-hat-openshift-haystack.md

@@ -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/).

qdrant-landing/content/documentation/examples/rag-customer-support-cohere-airbyte-aws.md

@@ -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

qdrant-landing/content/documentation/examples/recommendation-system-ovhcloud.md

@@ -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.