diff --git a/qdrant-landing/content/blog/hybrid-cloud-airbyte.md b/qdrant-landing/content/blog/hybrid-cloud-airbyte.md
index 13ee756da..0373d8ac8 100644
--- a/qdrant-landing/content/blog/hybrid-cloud-airbyte.md
+++ b/qdrant-landing/content/blog/hybrid-cloud-airbyte.md
@@ -12,15 +12,13 @@ tags:
- Vector Database
---
-**Elevate Your Data Performance With Airbyte and Qdrant Hybrid Cloud**
-
In their mission to support large-scale AI innovation, Airbyte and Qdrant are collaborating on the launch of Qdrant’s new offering - Qdrant Hybrid Cloud. This collaboration allows users to leverage the synergistic capabilities of both Airbyte and Qdrant within a private infrastructure. Qdrant’s new offering represents the first managed vector database that can be deployed in any environment. Businesses optimizing their data infrastructure with Airbyte are now able to host a vector database either on premise, or on a public cloud of their choice - while still reaping the benefits of a managed database product.
This is a major step forward in offering enterprise customers incredible synergy for maximizing the potential of their AI data. Qdrant's new Kubernetes-native design, coupled with Airbyte’s powerful data ingestion pipelines meet the needs of developers who are both prototyping and building production-level apps. Airbyte simplifies the process of data integration by providing a platform that connects to various sources and destinations effortlessly. Moreover, Qdrant Hybrid Cloud leverages advanced indexing and search capabilities to empower users to explore and analyze their data efficiently.
In a major benefit to Generative AI, businesses can leverage Airbyte's data replication capabilities to ensure that their data in Qdrant Hybrid Cloud is always up to date. This empowers all users of Retrieval Augmented Generation (RAG) applications with effective analysis and decision-making potential, all based on the latest information. Furthermore, by combining Airbyte's platform and Qdrant's hybrid cloud infrastructure, users can optimize their data operations while keeping costs under control via flexible pricing models tailored to individual usage requirements.
-**Optimizing Your GenAI Data Stack With Airbyte and Qdrant Hybrid Cloud**
+#### Optimizing Your GenAI Data Stack With Airbyte and Qdrant Hybrid Cloud
By integrating Airbyte with Qdrant Hybrid Cloud, you can achieve seamless data ingestion from diverse sources into Qdrant's powerful indexing system. This integration enables you to derive valuable insights from your data. Here are some key advantages:
@@ -32,22 +30,24 @@ By integrating Airbyte with Qdrant Hybrid Cloud, you can achieve seamless data i
**Open-Source Compatibility:** Airbyte and Qdrant pride themselves on maintaining a reliable and mature integration that brings peace of mind to those prototyping and deploying large-scale AI solutions. Extensive open-source documentation and code samples help users of all skill levels in leveraging highly advanced features of data ingestion and vector search.
-**Build a Modern GenAI Application With Qdrant Hybrid Cloud and Airbyte**
+#### Build a Modern GenAI Application With Qdrant Hybrid Cloud and Airbyte
We put together an end-to-end tutorial to show you how to build a GenAI application with Qdrant Hybrid Cloud and Airbyte’s advanced data pipelines.
-**Tutorial: Build a RAG System to Answer Customer Support Queries**
+#### Tutorial: Build a RAG System to Answer Customer Support Queries
Learn how to set up a private AI service that addresses customer support issues with high accuracy and effectiveness. By leveraging Airbyte’s data pipelines with Qdrant Hybrid Cloud, you will create a customer support system that is always synchronized with up-to-date knowledge.
[Try the Tutorial](/documentation/tutorials/rag-customer-support-cohere-airbyte-aws/)
-**Documentation: Deploy Qdrant in a few clicks**
+#### Documentation: Deploy Qdrant in a few clicks
Our simple Kubernetes-native design lets you deploy Qdrant Hybrid Cloud on your hosting platform of choice in just a few steps. Learn how in our documentation.
[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
-Read more about Qdrant Hybrid Cloud in our official release blog. To deploy your first cluster in a few clicks, begin by creating a Qdrant Cloud account. Our Hybrid Cloud docs will help you with the rest.
\ No newline at end of file
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-aleph-alpha.md b/qdrant-landing/content/blog/hybrid-cloud-aleph-alpha.md
new file mode 100644
index 000000000..43b4aa7f9
--- /dev/null
+++ b/qdrant-landing/content/blog/hybrid-cloud-aleph-alpha.md
@@ -0,0 +1,51 @@
+---
+draft: false
+title: "Enhance AI Data Sovereignty with Aleph Alpha and Qdrant Hybrid Cloud"
+short_description: "Empowering the world’s best companies in their AI journey."
+description: "Empowering the world’s best companies in their AI journey."
+preview_image: /blog/hybrid-cloud-aleph-alpha/hybrid-cloud-aleph-alpha.png
+date: 2024-04-11T00:01:00Z
+author: Qdrant
+featured: false
+tags:
+ - Qdrant
+ - Vector Database
+---
+
+Aleph Alpha and Qdrant are on a joint mission to empower the world’s best companies in their AI journey. The launch of Qdrant Hybrid Cloud furthers this effort by ensuring complete data sovereignty and hosting security. This latest collaboration is all about giving enterprise customers complete transparency and sovereignty to make use of AI in their own environment. By using a hybrid cloud vector database, those looking to leverage vector search for the AI applications can now ensure their proprietary and customer data is completely secure.
+
+Aleph Alpha’s state-of-the-art technology, offering unmatched quality and safety, cater perfectly to large-scale business applications and complex scenarios utilized by professionals across fields such as science, law, and security globally. Recognizing that these sophisticated use cases often demand comprehensive data processing capabilities beyond what standalone LLMs can provide, the collaboration between Aleph Alpha and Qdrant Hybrid Cloud introduces a robust platform. This platform empowers customers with full data sovereignty, enabling secure management of highly specific and sensitive information within their own infrastructure.
+
+Together with Aleph Alpha, Qdrant Hybrid Cloud offers an ecosystem where individual components seamlessly integrate with one another. Qdrant's new Kubernetes-native design coupled with Aleph Alpha's powerful technology meet the needs of developers who are both prototyping and building production-level apps.
+
+#### How Aleph Alpha and Qdrant Blend Data Control, Scalability, and European Standards
+
+Building apps with Qdrant Hybrid Cloud and Aleph Alpha’s models leverages some common value propositions:
+
+**Data Sovereignty:** Qdrant Hybrid Cloud is the first vector database that can be deployed anywhere, with complete database isolation, while still providing fully managed cluster management. Furthermore, as the best option for organizations that prioritize data sovereignty, Aleph Alpha offers foundation models which are aimed at serving regional use cases. Together, both products can be leveraged to keep highly specific data safe and isolated.
+
+**Scalable Vector Search:** Once deployed to a customer’s host of choice, Qdrant Hybrid Cloud provides a fully managed vector database that lets users effortlessly scale the setup through vertical or horizontal scaling. Deployed in highly secure environments, this is a robust setup that is designed to meet the needs of large enterprises, ensuring a full spectrum of solutions for various projects and workloads.
+
+**European Origins & Expertise**: With a strong presence in the European Union ecosystem, Aleph Alpha is ideally positioned to partner with European-based companies like Qdrant, providing local expertise and infrastructure that aligns with European regulatory standards.
+
+#### Build a Data-Sovereign AI System With Qdrant Hybrid Cloud and Aleph Alpha’s Models
+
+
+
+To get you started, we created a comprehensive tutorial that shows how to build next-gen AI applications with Qdrant Hybrid Cloud and Aleph Alpha’s advanced models.
+
+#### Tutorial: Build a Region-Specific Contract Management System
+
+Learn how to develop an AI system that reads lengthy contracts and gives complex answers based on stored content. This system is completely hosted inside of Germany for GDPR compliance purposes. The tutorial shows how enterprises with a vast number of stored contract documents can leverage AI in a closed environment that doesn’t leave the hosting region, thus ensuring data sovereignty and security.
+
+[Try the Tutorial](/documentation/examples/rag-contract-management-stackit-aleph-alpha/)
+
+#### Documentation: Deploy Qdrant in a few clicks
+
+Our simple Kubernetes-native design lets you deploy Qdrant Hybrid Cloud on your hosting platform of choice in just a few steps. Learn how in our documentation.
+
+[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
+
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-cohere.md b/qdrant-landing/content/blog/hybrid-cloud-cohere.md
index 01c679405..437ea1cda 100644
--- a/qdrant-landing/content/blog/hybrid-cloud-cohere.md
+++ b/qdrant-landing/content/blog/hybrid-cloud-cohere.md
@@ -12,15 +12,13 @@ tags:
- Vector Database
---
-**Qdrant Hybrid Cloud and Cohere Collaborate to Support Enterprise GenAI Solutions**
-
We’re excited to share that Qdrant and Cohere are partnering on the launch of Qdrant Hybrid Cloud to enable global audiences to build and scale their AI applications quickly and securely. With Cohere's world-class large language models (LLMs), getting the most out of vector search becomes incredibly easy. Qdrant's new Hybrid Cloud offering and its Kubernetes-native design can be coupled with Cohere's powerful models and APIs. This combination allows for simple setup when prototyping and deploying AI solutions.
It’s no secret that Retrieval Augmented Generation (RAG) has shown to be a powerful method of building conversational AI products, such as chatbots or customer support systems. With Cohere's managed LLM service, scientists and developers can tap into state-of-the-art text generation and understanding capabilities, all accessible via API. Qdrant Hybrid Cloud seamlessly integrates with Cohere’s foundation models, enabling convenient data vectorization and highly accurate semantic search.
With Qdrant Hybrid Cloud, users have the flexibility to deploy their vector database in an environment of their choice. By using container-based scalable deployments, global businesses can keep both products deployed in the same hosting architecture. By combining Cohere’s foundation models with Qdrant’s vector search capabilities, developers can create robust and scalable GenAI applications tailored to meet the demands of modern enterprises. This powerful combination empowers organizations to build strong and secure applications that search, understand meaning and converse in text.
-**Take Full Control of Your GenAI Application with Qdrant Hybrid Cloud and Cohere**
+#### Take Full Control of Your GenAI Application with Qdrant Hybrid Cloud and Cohere
Building apps with Qdrant Hybrid Cloud and Cohere’s models comes with several key advantages:
@@ -30,22 +28,24 @@ Building apps with Qdrant Hybrid Cloud and Cohere’s models comes with several
**Cost Efficiency:** By leveraging Qdrant's quantization for efficient data handling and pairing it with Cohere's scalable and affordable pricing structure, the price/performance ratio of this integration is next to none. Companies who are just getting started with both will have a minimal upfront investment and optimal cost management going forward.
-**Start Building Your New App With Cohere and Qdrant Hybrid Cloud**
+#### Start Building Your New App With Cohere and Qdrant Hybrid Cloud
We put together an end-to-end tutorial to show you how to build a GenAI application with Qdrant Hybrid Cloud and Cohere’s embeddings.
-**Tutorial: Build a RAG System to Answer Customer Support Queries**
+#### Tutorial: Build a RAG System to Answer Customer Support Queries
Learn how to set up a private AI service that addresses customer support issues with high accuracy and effectiveness. By leveraging Cohere’s models with Qdrant Hybrid Cloud, you will create a fully private customer support system.
[Try the Tutorial](/documentation/tutorials/rag-customer-support-cohere-airbyte-aws/)
-**Documentation: Deploy Qdrant in a few clicks**
+#### Documentation: Deploy Qdrant in a few clicks
Our simple Kubernetes-native design lets you deploy Qdrant Hybrid Cloud on your hosting platform of choice in just a few steps. Learn how in our documentation.
[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
-Read more about Qdrant Hybrid Cloud in our official release blog. To deploy your first cluster in a few clicks, begin by creating a Qdrant Cloud account. Our Hybrid Cloud docs will help you with the rest.
\ No newline at end of file
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-digitalocean.md b/qdrant-landing/content/blog/hybrid-cloud-digitalocean.md
new file mode 100644
index 000000000..0a344e7cc
--- /dev/null
+++ b/qdrant-landing/content/blog/hybrid-cloud-digitalocean.md
@@ -0,0 +1,45 @@
+---
+draft: false
+title: "DigitalOcean and Qdrant Hybrid Cloud for Scalable and Secure AI Solutions"
+short_description: "Enabling developers to deploy a managed vector database in their DigitalOcean Environment."
+description: "Enabling developers to deploy a managed vector database in their DigitalOcean Environment."
+preview_image: /blog/hybrid-cloud-digitalocean/hybrid-cloud-digitalocean.png
+date: 2024-04-11T00:02:00Z
+author: Qdrant
+featured: false
+tags:
+ - Qdrant
+ - Vector Database
+---
+
+In the realm of artificial intelligence (AI), developers are constantly seeking new ways to enhance their applications with new customer experiences. At the core of this are vector databases, as they enable the efficient handling of complex, unstructured data, making it possible to power applications with semantic search, personalized recommendation systems, and intelligent Q&A platforms. However, when deploying such new AI applications, especially those handling sensitive or personal user data, privacy becomes important.
+
+DigitalOcean and Qdrant are actively addressing this with an integration that let’s developers deploy a managed vector database directly in their existing DigitalOcean environments. With the launch of Qdrant Hybrid Cloud developers can seamlessly deploy Qdrant on DigitalOcean Kubernetes (DOKS) clusters, making it easier for developers to handle vector databases without getting bogged down in the complexity of managing the underlying infrastructure.
+
+#### Unlocking the Power of Generative AI for DigitalOcean Customers with Qdrant
+
+User data is a critical asset for a business, and user privacy should always be a top priority. This is why businesses require tools that enable them to leverage their user data as a valuable asset while respecting privacy. Qdrant Hybrid Cloud on DigitalOcean brings these capabilities directly into developers' hands, enhancing deployment flexibility and ensuring greater control over data.
+
+> *“Qdrant, with its seamless integration and robust performance, equips businesses to develop cutting-edge applications that truly resonate with their users. Through applications such as semantic search, Q&A systems, recommendation engines, image search, and RAG, DigitalOcean customers can leverage their data to the fullest, ensuring privacy and driving innovation.“* - Bikram Gupta, Lead Product Manager, Kubernetes & App Platform, DigitalOcean.
+
+#### Get Started with Qdrant on DigitalOcean
+
+DigitalOcean customers can easily deploy Qdrant on their DigitalOcean Kubernetes (DOKS) clusters through a simple Kubernetis-native “One-line” installment. This simplicity allows businesses to start small and scale efficiently.
+
+- **Simple Deployment**: Leveraging Kubernetes, deploying Qdrant Hybrid Cloud on DigitalOcean is streamlined, making the management of vector search workloads in the own environment more efficient.
+- **Own Infrastructure**: Hosting the vector database on your own DigitalOcean infrastructure in a managed way, provides flexibility and allows you to manage the full AI stack in one place.
+- **Data Control**: Deploying within the own DigitalOcean environment ensures data control, keeping sensitive information within the own security perimeter.
+
+To get Qdrant Hybrid Cloud setup on DigitalOcean, just follow these steps:
+
+- **Hybrid Cloud Setup**: Begin by logging into your [Qdrant Cloud account](https://cloud.qdrant.io/login) and enable the 'Hybrid Cloud' feature.
+- **Cluster Configuration**: Go to the Hybrid Cloud settings and integrate your DigitalOcean Kubernetes clusters as a private region.
+- **Simplified Deployment**: Use the Qdrant Management Console to effortlessly establish and oversee your Qdrant clusters on DigitalOcean.
+
+For a comprehensive guide, our documentation provides detailed instructions on setting up Qdrant on DigitalOcean.
+
+[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
+
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-haystack.md b/qdrant-landing/content/blog/hybrid-cloud-haystack.md
index 28f375930..3f596a1f8 100644
--- a/qdrant-landing/content/blog/hybrid-cloud-haystack.md
+++ b/qdrant-landing/content/blog/hybrid-cloud-haystack.md
@@ -12,17 +12,15 @@ tags:
- Vector Database
---
-**Qdrant Hybrid Cloud and Haystack by deepset: A Winning Combination for Enterprise-Scale RAG**
-
We’re excited to share that Qdrant and Haystack are continuing to expand their seamless integration to the new Qdrant Hybrid Cloud offering, allowing developers to deploy a managed vector database in their own environment of choice. Earlier this year, both Qdrant and Haystack, started to address their user’s growing need for production-ready retrieval-augmented-generation (RAG) deployments. The ability to build and deploy AI apps anywhere now allows for complete data sovereignty and control. This gives large enterprise customers the peace of mind they need before they expand AI functionalities throughout their operations.
With a highly customizable framework like Haystack, implementing vector search becomes incredibly simple. Qdrant's new Qdrant Hybrid Cloud offering and its Kubernetes-native design supports customers all the way from a simple prototype setup to a production scenario on any hosting platform. Users can attach AI functionalities to their existing in-house software by creating custom integration components. Don’t forget, both products are open-source and highly modular!
With Haystack and Qdrant Hybrid Cloud, the path to production has never been clearer. The elaborate integration of Qdrant as a Document Store simplifies the deployment of Haystack-based AI applications in any production-grade environment. Coupled with Qdrant’s Hybrid Cloud offering, your application can be deployed anyplace, on your own terms.
-“We hope that with Haystack 2.0 and our growing partnerships such as what we have here with Qdrant Hybrid Cloud, engineers are able to build AI systems with full autonomy. Both in how their pipelines are designed, and how their data are managed.” Tuana Çelik, Developer Relations Lead, deepset.
+>*“We hope that with Haystack 2.0 and our growing partnerships such as what we have here with Qdrant Hybrid Cloud, engineers are able to build AI systems with full autonomy. Both in how their pipelines are designed, and how their data are managed.”* Tuana Çelik, Developer Relations Lead, deepset.
-**Simplifying RAG Deployment: Qdrant Hybrid Cloud and Haystack 2.0 Integration**
+#### Simplifying RAG Deployment: Qdrant Hybrid Cloud and Haystack 2.0 Integration
Building apps with Qdrant Hybrid Cloud and deepset’s framework has become even simpler with Haystack 2.0. Both products are completely optimized for RAG in production scenarios. Here are some key advantages:
@@ -32,22 +30,24 @@ Building apps with Qdrant Hybrid Cloud and deepset’s framework has become even
**Flexible & Customizable:** The open-source nature of Qdrant and Haystack’s 2.0 makes it easy to extend the capabilities of both products through customization. When tailoring vector RAG systems to their own needs, users can develop custom components and plug them into both Qdrant Hybrid Cloud and Haystack for maximum modularity. [Creating custom components](https://docs.haystack.deepset.ai/docs/custom-components) is a core functionality.
-**Learn How to Build a Production-Level RAG Service With Qdrant and deepset**
+#### Learn How to Build a Production-Level RAG Service With Qdrant and Haystack
To get you started, we created a comprehensive tutorial that shows how to build next-gen AI applications with Qdrant Hybrid Cloud using deepset’s Haystack framework.
-**Tutorial: Private Chatbot for Interactive Learning**
+#### Tutorial: Private Chatbot for Interactive Learning
Learn how to develop a tutor chatbot from online course materials. You will create a Retrieval Augmented Generation (RAG) pipeline with Haystack for enhanced generative AI capabilities and Qdrant Hybrid Cloud for vector search. By deploying every tool on RedHat OpenShift, you will ensure complete privacy and data sovereignty, whereby no course content leaves your cloud.
[Try the Tutorial](/documentation/tutorials/rag-chatbot-red-hat-openshift-haystack/)
-**Documentation: Deploy Qdrant in a few clicks**
+#### Documentation: Deploy Qdrant in a few clicks
Our simple Kubernetes-native design lets you deploy Qdrant Hybrid Cloud on your hosting platform of choice in just a few steps. Learn how in our documentation.
[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
-Read more about Qdrant Hybrid Cloud in our official release blog. To deploy your first cluster in a few clicks, begin by creating a Qdrant Cloud account. Our Hybrid Cloud docs will help you with the rest.
\ No newline at end of file
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-jinaai.md b/qdrant-landing/content/blog/hybrid-cloud-jinaai.md
index 5aead363d..8d0193614 100644
--- a/qdrant-landing/content/blog/hybrid-cloud-jinaai.md
+++ b/qdrant-landing/content/blog/hybrid-cloud-jinaai.md
@@ -12,15 +12,13 @@ tags:
- Vector Database
---
-**Develop Cutting-Edge GenAI Apps with Jina AI and Qdrant Hybrid Cloud**
-
We're thrilled to announce the collaboration between Qdrant and Jina AI for the launch of Qdrant Hybrid Cloud, empowering users worldwide to rapidly and securely develop and scale their AI applications. By leveraging Jina AI's top-tier large language models (LLMs), engineers and scientists can optimize their vector search efforts. Qdrant's latest Hybrid Cloud solution, designed natively with Kubernetes, seamlessly integrates with Jina AI's robust embedding models and APIs. This synergy streamlines both prototyping and deployment processes for AI solutions.
Retrieval Augmented Generation (RAG) is broadly adopted as the go-to Generative AI solution, as it enables powerful and cost-effective chatbots, customer support agents and other forms of semantic search applications. Through Jina AI's managed service, users gain access to cutting-edge text generation and comprehension capabilities, conveniently accessible through an API. Qdrant Hybrid Cloud effortlessly incorporates Jina AI's embedding models, facilitating smooth data vectorization and delivering exceptionally precise semantic search functionality.
With Qdrant Hybrid Cloud, users have the flexibility to deploy their vector database in an environment of their choice. By using container-based scalable deployments, global businesses can keep both products deployed in the same hosting architecture. By combining Jina AI’s models with Qdrant’s vector search capabilities, developers can create robust and scalable applications tailored to meet the demands of modern enterprises. This combination allows organizations to build strong and secure Generative AI solutions.
-**Benefits of Qdrant’s Vector Search With Jina AI Embeddings in Enterprise RAG Scenarios**
+#### Benefits of Qdrant’s Vector Search With Jina AI Embeddings in Enterprise RAG Scenarios
Building apps with Qdrant Hybrid Cloud and Jina AI’s embeddings comes with several key advantages:
@@ -30,22 +28,24 @@ Building apps with Qdrant Hybrid Cloud and Jina AI’s embeddings comes with sev
**Cost Efficiency:** By leveraging Jina AI's scalable and affordable pricing structure and pairing it with Qdrant's quantization for efficient data handling, this integration offers great value for its cost. Companies who are just getting started with both will have a minimal upfront investment and optimal cost management going forward.
-**Start Building Gen AI Apps With Jina AI and Qdrant Hybrid Cloud**
+#### Start Building Gen AI Apps With Jina AI and Qdrant Hybrid Cloud
To get you started, we created a comprehensive tutorial that shows how to build a modern GenAI application with Qdrant Hybrid Cloud and Jina AI embeddings.
-**Tutorial: Hybrid Search for Household Appliance Manuals**
+#### Tutorial: Hybrid Search for Household Appliance Manuals
Learn how to build an app that retrieves information from PDF user manuals to enhance user experience for companies that sell household appliances. The system will leverage Jina AI embeddings and Qdrant Hybrid Cloud for enhanced generative AI capabilities, while the RAG pipeline will be tied together using the LlamaIndex framework. This example demonstrates how complex tables in PDF documentation can be processed as high quality embeddings with no extra configuration. By introducing Hybrid Search from Qdrant, the RAG functionality is highly accurate.
-[Try the Tutorial](/documentation/tutorials/hybrid-search-llamaindex-jinaai/
+[Try the Tutorial](/documentation/tutorials/hybrid-search-llamaindex-jinaai/)
-**Documentation: Deploy Qdrant in a few clicks**
+#### Documentation: Deploy Qdrant in a few clicks
Our simple Kubernetes-native design lets you deploy Qdrant Hybrid Cloud on your hosting platform of choice in just a few steps. Learn how in our documentation.
[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
-Read more about Qdrant Hybrid Cloud in our official release blog. To deploy your first cluster in a few clicks, begin by creating a Qdrant Cloud account. Our Hybrid Cloud docs will help you with the rest.
\ No newline at end of file
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-launch-partners.md b/qdrant-landing/content/blog/hybrid-cloud-launch-partners.md
index 988f08459..7264e4065 100644
--- a/qdrant-landing/content/blog/hybrid-cloud-launch-partners.md
+++ b/qdrant-landing/content/blog/hybrid-cloud-launch-partners.md
@@ -2,11 +2,11 @@
draft: false
title: "Qdrant's Trusted Partners for Hybrid Cloud Deployment"
slug: hybrid-cloud-launch-partners
-short_description: "With the launch of Qdrant Hybrid Cloud we provide developers the ability to deploy Qdrant as a managed vector database in any desired environment, be it in the cloud, on premise, or on the edge."
-description: "With the launch of Qdrant Hybrid Cloud we provide developers the ability to deploy Qdrant as a managed vector database in any desired environment, be it in the cloud, on premise, or on the edge."
+short_description: "With the launch of Qdrant Hybrid Cloud we provide developers the ability to deploy Qdrant as a managed vector database in any desired environment."
+description: "With the launch of Qdrant Hybrid Cloud we provide developers the ability to deploy Qdrant as a managed vector database in any desired environment."
preview_image: /blog/hybrid-cloud-launch-partners/hybrid-cloud-launch-partners.png
social_preview_image: /blog/hybrid-cloud-launch-partners/hybrid-cloud-launch-partners.png
-date: 2024-04-10T00:09:00Z
+date: 2024-04-11T00:05:00Z
author: Manuel Meyer
featured: false
tags:
@@ -14,10 +14,90 @@ tags:
- launch partners
---
-**Qdrant Hybrid Cloud Vector Database, Together With Its Wide Range of Launch Partners, Offers Developers Unmatched Flexibility for Creating Production-Ready AI Applications**
+With the launch of Qdrant Hybrid Cloud we provide developers the ability to deploy Qdrant as a managed vector database in any desired environment, be it *in the cloud, on premise, or on the edge*.
-With the launch of Qdrant Hybrid Cloud we provide developers the ability to deploy Qdrant as a managed vector database in any desired environment, be it in the cloud, on premise, or on the edge.
+We are excited to have trusted industry players support the launch of Qdrant Hybrid Cloud, allowing developers to unlock best-in-class advantages for building production-ready AI applications:
-We are convinced that Qdrant Hybrid Cloud marks a significant advancement in vector databases, offering the most flexible way to implement vector search. We’re excited to learn more about what you will be building with it. We invite you to test out Qdrant Hybrid cloud today. Simply sign up for or log into your Qdrant Cloud account and enable the feature with a click on "request access to hybrid cloud.” Also, to learn more about Qdrant Hybrid Cloud you can take a look at our Official Release Blog or our Qdrant Hybrid Cloud website. For additional technical insights, please visit our documentation.
+- **Deploy In Your Own Environment:** Deploy the Qdrant vector database as a managed service on the infrastructure of choice, such as our launch partner solutions **Oracle Cloud Infrastructure (OCI), Red Hat OpenShift, Vultr, DigitalOcean, OVHcloud, Scaleway, STACKIT**, and **Civo**
-
\ No newline at end of file
+- **Seamlessly Integrate with Every Key Component of the Modern AI Stack:** Our new hybrid cloud offering also allows you to integrate with all of the relevant solutions for building AI applications. These include partner frameworks like **LlamaIndex**, **Haystack by deepset**, and **Airbyte**, as well as large language models (LLMs) like **Cohere**, **JinaAI**, and **AlephAlpha**.
+
+- **Ensure Full Data Sovereignty and Privacy Control:** Qdrant Hybrid Cloud offers unparalleled data isolation and the flexibility to process workloads either in the cloud or on-premise, ensuring data privacy and sovereignty requirements - all while being fully managed.
+
+#### Tutorials and Use Cases That Leverage Qdrant Hybrid Cloud and Trusted Partner Technologies
+
+
+
+Together with our launch partners, we created in-depth tutorials and use cases for production-ready vector search that explain how developers can leverage Qdrant Hybrid Cloud alongside the best-in-class solutions of our launch partners. This provies to have the most flexible foundation to build modern, customer-centric AI applications with endless deployment options and full data sovereignty. Let’s dive right in:
+
+**AI Customer Support Chatbot** with Qdrant Hybrid Cloud, Cohere, Airbyte, and AWS
+
+> This tutorial shows how to build a private AI customer support system using Cohere's AI models on AWS, Airbyte, and Qdrant Hybrid Cloud for efficient and secure query automation.
+
+[View Tutorial](/documentation/tutorials/rag-customer-support-cohere-airbyte-aws/)
+
+**RAG System for Employee Onboarding** with Qdrant Hybrid Cloud, Oracle Cloud Infrastructure (OCI), Cohere, and LangChain
+
+> This tutorial demonstrates how to use Oracle Cloud Infrastructure (OCI) for a secure setup that integrates Cohere's language models with Qdrant Hybrid Cloud, using LangChain to orchestrate natural language search for corporate documents, enhancing resource discovery and onboarding.
+
+[View Tutorial](/documentation/tutorials/natural-language-search-oracle-cloud-infrastructure-cohere-langchain/)
+
+**Hybrid Search for Product PDF Manuals** with Qdrant Hybrid Cloud, LlamaIndex, and JinaAI
+
+> Create a RAG-based chatbot that enhances customer support by parsing product PDF manuals using Qdrant Hybrid Cloud, LlamaIndex, and JinaAI. This tutorial will guide you through the setup and integration process, enabling your system to deliver precise, context-aware responses for household appliance inquiries.
+
+[View Tutorial](/documentation/tutorials/hybrid-search-llamaindex-jinaai/)
+
+**Region-Specific RAG System for Contract Management** with Qdrant Hybrid Cloud, Aleph Alpha, and STACKIT
+
+> Learn how to streamline contract management with a RAG-based system in this tutorial, which utilizes Aleph Alpha’s embeddings and a region-specific cloud setup. Hosted on STACKIT with Qdrant Hybrid Cloud, this solution ensures secure, GDPR-compliant storage and processing of data, ideal for businesses with intensive contractual needs.
+
+[View Tutorial](/documentation/tutorials/rag-contract-management-stackit-aleph-alpha/)
+
+**Movie Recommendation System** with Qdrant Hybrid Cloud and OVHcloud
+
+> Discover how to build a recommendation system with our guide on collaborative filtering, using sparse vectors and the Movielens dataset.
+
+[View Tutorial](/documentation/tutorials/recommendation-system-ovhcloud/)
+
+**Private RAG Information Extraction Engine** with Qdrant Hybrid Cloud and Vultr using DSPy and Ollama
+
+> This tutorial teaches you how to handle and structure private documents with large unstructured data. Learn to use DSPy for information extraction, run your LLM with Ollama on Vultr, and manage data with Qdrant Hybrid Cloud on Vultr, perfect for regulated environments needing data privacy.
+
+[View Tutorial](/documentation/tutorials/rag-chatbot-vultr-dspy-ollama/)
+
+**RAG System That Chats with Blog Contents** with Qdrant Hybrid Cloud and Scaleway using LangChain.
+
+> Build a RAG system that combines blog scanning with the capabilities of semantic search. RAG enhances the generation of answers by retrieving relevant documents to aid the question-answering process. This setup showcases the integration of advanced search and AI language processing to improve information retrieval and generation tasks.
+
+[View Tutorial](/documentation/tutorials/rag-chatbot-scaleway/)
+
+**Private Chatbot for Interactive Learning** with Qdrant Hybrid Cloud and Red Hat OpenShift using Haystack.
+
+> In this tutorial, you will combine open source tools inside of a closed infrastructure and tie them together with a reliable framework. This custom solution lets you run a chatbot without public internet access. You will be able to keep sensitive data secure without compromising privacy.
+
+[View Tutorial](/documentation/tutorials/rag-chatbot-red-hat-openshift-haystack/)
+
+#### Supporting Documentation
+
+Additionally, we built comprehensive documentation tutorials on how to successfully deploy Qdrant Hybrid Cloud on the right infrastructure of choice. For more information, please visit our documentation pages:
+
+- How to Deploy Qdrant Hybrid Cloud on AWS
+- How to Deploy Qdrant Hybrid Cloud on GCP
+- How to Deploy Qdrant Hybrid Cloud on Azure
+- How to Deploy Qdrant Hybrid Cloud on DigitalOcean
+- How to Deploy Qdrant on Oracle Cloud
+- How to Deploy Qdrant on Vultr
+- How to Deploy Qdrant on Scaleway
+- How to Deploy Qdrant on OVHcloud
+- How to Deploy Qdrant on STACKIT
+- How to Deploy Qdrant on Red Hat OpenShift
+- How to Deploy Qdrant on Linode
+
+#### Get started now!
+
+We are convinced that Qdrant Hybrid Cloud marks a significant advancement in vector databases, offering the most flexible way to implement vector search.
+
+You can test out Qdrant Hybrid Cloud today! Simply sign up for 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](/blog/hybrid-cloud/) or our Qdrant Hybrid Cloud website. For additional technical insights, please visit our documentation.
+
+[](https://cloud.qdrant.io/login)
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-llamaindex.md b/qdrant-landing/content/blog/hybrid-cloud-llamaindex.md
index ed5395ac9..080e79ba5 100644
--- a/qdrant-landing/content/blog/hybrid-cloud-llamaindex.md
+++ b/qdrant-landing/content/blog/hybrid-cloud-llamaindex.md
@@ -12,17 +12,13 @@ tags:
- Vector Database
---
-**Unlock New RAG Opportunities with Qdrant Hybrid Cloud and LlamaIndex**
-
We're happy to announce the collaboration between LlamaIndex and Qdrant’s new Hybrid Cloud launch, aimed at empowering engineers and scientists worldwide to swiftly and securely develop and scale their GenAI applications. By leveraging LlamaIndex's robust framework, users can maximize the potential of vector search and create stable and effective AI products. Qdrant Hybrid Cloud offers the same Qdrant functionality on a Kubernetes-based architecture, which further expands the ability of LlamaIndex to support any user on any environment.
With Qdrant Hybrid Cloud, users have the flexibility to deploy their vector database in an environment of their choice. By using container-based scalable deployments, companies can leverage a cutting-edge framework like LlamaIndex, while staying deployed in the same hosting architecture as data sources, embedding models and LLMs. This powerful combination empowers organizations to build strong and secure applications that search, understand meaning and converse in text.
While LLMs are trained on a great deal of data, they are not trained on user-specific data, which may be private or highly specific. LlamaIndex meets this challenge by adding context to LLM-based generation methods. In turn, Qdrant’s popular vector database sorts through semantically relevant information, which can further enrich the performance gains from LlamaIndex’s data connection features. With LlamaIndex, users can tap into state-of-the-art functions to query, chat, sort or parse data. Through the integration of Qdrant Hybrid Cloud and LlamaIndex developers can conveniently vectorize their data and perform highly accurate semantic search - all within their own environment.
-// Possibility to insert quote from LlamaIndex
-
-**Reap the Benefits of Advanced Integration Features With Qdrant and LlamaIndex**
+#### Reap the Benefits of Advanced Integration Features With Qdrant and LlamaIndex
Building apps with Qdrant Hybrid Cloud and LlamaIndex comes with several key advantages:
@@ -32,22 +28,24 @@ Building apps with Qdrant Hybrid Cloud and LlamaIndex comes with several key adv
**Advanced Search Features:** LlamaIndex comes with built-in Qdrant Hybrid Search functionality, which combines search results from sparse and dense vectors. As a highly sought-after use case, hybrid search is easily accessible from within the LlamaIndex ecosystem. Deploying this particular type vector search on Hybrid Cloud is a matter of a few lines of code.
-**Start Building With LlamaIndex and Qdrant Hybrid Cloud: Hybrid Search in Complex PDF Documentation Use Cases**
+#### Start Building With LlamaIndex and Qdrant Hybrid Cloud: Hybrid Search in Complex PDF Documentation Use Cases
To get you started, we created a comprehensive tutorial that shows how to build next-gen AI applications with Qdrant Hybrid Cloud using the LlamaIndex framework and the LlamaParse API.
-**Tutorial: Hybrid Search for Household Appliance Manuals**
+#### Tutorial: Hybrid Search for Household Appliance Manuals
Use this end-to-end tutorial to create a system that retrieves information from complex user manuals in PDF format to enhance user experience for companies that sell household appliances. You will build a RAG pipeline with LlamaIndex leveraging Qdrant Hybrid Cloud for enhanced generative AI capabilities. The LlamaIndex integration shows how complex tables inside of items’ PDF documents can be processed via hybrid vector search with no additional configuration.
-[Try the Tutorial](/documentation/tutorials/hybrid-search-llamaindex-jinaai/
+[Try the Tutorial](/documentation/tutorials/hybrid-search-llamaindex-jinaai/)
-**Documentation: Deploy Qdrant in a few clicks**
+#### Documentation: Deploy Qdrant in a few clicks
Our simple Kubernetes-native design lets you deploy Qdrant Hybrid Cloud on your hosting platform of choice in just a few steps. Learn how in our documentation.
[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
-Read more about Qdrant Hybrid Cloud in our official release blog. To deploy your first cluster in a few clicks, begin by creating a Qdrant Cloud account. Our Hybrid Cloud docs will help you with the rest.
\ No newline at end of file
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-oracle-cloud-infrastructure.md b/qdrant-landing/content/blog/hybrid-cloud-oracle-cloud-infrastructure.md
new file mode 100644
index 000000000..6d9611166
--- /dev/null
+++ b/qdrant-landing/content/blog/hybrid-cloud-oracle-cloud-infrastructure.md
@@ -0,0 +1,50 @@
+---
+draft: false
+title: "OCI and Qdrant Hybrid Cloud for Maximum Data Sovereignty"
+short_description: "Qdrant Hybrid Cloud is now available for OCI customers as a managed vector search engine for data-sensitive AI apps."
+description: "Qdrant Hybrid Cloud is now available for OCI customers as a managed vector search engine for data-sensitive AI apps."
+preview_image: /blog/hybrid-cloud-oracle-cloud-infrastructure/hybrid-cloud-oracle-cloud-infrastructure.png
+date: 2024-04-11T00:03:00Z
+author: Qdrant
+featured: false
+tags:
+ - Qdrant
+ - Vector Database
+---
+
+Qdrant and Oracle Cloud Infrastructure (OCI) Cloud Engineering are thrilled to announce the ability to deploy Qdrant Hybrid Cloud as a managed service on OCI. This marks the next step in the collaboration between Qdrant and Oracle Cloud Infrastructure, which will enable enterprises to realize the benefits of artificial intelligence powered through scalable vector search. In 2023, OCI added Qdrant to its [Oracle Cloud Infrastructure solution portfolio](https://blogs.oracle.com/cloud-infrastructure/post/vecto-database-qdrant-support-oci-kubernetes). Qdrant Hybrid Cloud is the managed service of the Qdrant vector search engine that can be deployed and run in any existing OCI environment, allowing enterprises to run fully managed vector search workloads in their existing infrastructure. This is a milestone for leveraging a managed vector search engine for data-sensitive AI applications.
+
+In the past years, enterprises have been actively engaged in exploring AI applications to enhance their products and services or unlock internal company knowledge to drive the productivity of teams. These applications range from generative AI use cases, for example, powered by retrieval augmented generation (RAG), recommendation systems, or advanced enterprise search through semantic, similarity, or neural search. As these vector search applications continue to evolve and grow with respect to dimensionality and complexity, it will be increasingly relevant to have a scalable, manageable vector search engine, also called out by Gartner’s 2024 Impact Radar. In addition to scalability, enterprises also require flexibility in deployment options to be able to maximize the use of these new AI tools within their existing environment, ensuring interoperability and full control over their data.
+
+"We are excited to partner with Qdrant to bring their powerful vector search capabilities to Oracle Cloud Infrastructure," said Dr. Sanjay Basu, Senior Director of Cloud Engineering, AI/GPU Infrastructure at Oracle. "By offering Qdrant Hybrid Cloud as a managed service on OCI, we are empowering enterprises to harness the full potential of AI-driven applications while maintaining complete control over their data. This collaboration represents a significant step forward in making scalable vector search accessible and manageable for businesses across various industries, enabling them to drive innovation, enhance productivity, and unlock valuable insights from their data."
+
+#### How Qdrant and OCI Support Enterprises in Unlocking Value through AI
+
+Deploying Qdrant Hybrid Cloud on OCI facilitates vector search in production environments without altering existing setups, ideal for enterprises and developers leveraging OCI's services. Key benefits include:
+
+- **Seamless Deployment:** Qdrant Hybrid Cloud’s Kubernetes-native architecture allows to simply connect your OCI cluster as a private region and deploy Qdrant with a one-step installation ensuring a smooth and scalable setup.
+- **Seamless Integration with OCI Services:** The integration facilitates efficient resource utilization and enhances security provisions by leveraging OCI's comprehensive suite of services.
+- **Simplified Cluster Management**: Qdrant’s central cluster management allows to scale your cluster on OCI (vertically and horizontally), and supports seamless zero-downtime upgrades and disaster recovery,
+- **Control and Data Privacy**: Deploying Qdrant on OCI ensures complete data isolation, while enjoying the benefits of a fully managed cluster management.
+
+#### Qdrant on OCI in Action: Building a RAG System for AI-enabled Support
+
+
+
+We created a comprehensive tutorial to show how to leverage the benefits of Qdrant Hybrid Cloud on OCI and build AI applications with a focus on data sovereignty. This use case is focused on building a RAG system for FAQ, leveraging the strengths of **Qdrant Hybrid Cloud**, [Oracle](https://www.linkedin.com/company/oracle/) Cloud Infrastructure (OCI), [Cohere](https://www.linkedin.com/company/cohere-ai/) models, and [Langchain](https://www.langchain.com/). This step-by-step guide illustrates how to route incoming customer questions efficiently - either trying to answer them directly or redirecting them for human intervention while keeping the sensitive data within your premises.
+
+[Try the Tutorial](/documentation/tutorials/natural-language-search-oracle-cloud-infrastructure-cohere-langchain/)
+
+Deploying Qdrant Hybrid Cloud on Oracle Cloud Infrastructure only takes a few minutes due to the seamless Kubernetes-native integration. You can get started by following these three steps:
+
+1. **Hybrid Cloud Activation**: Start by signing into your [Qdrant Cloud account](https://qdrant.to/cloud) and activate ‘Hybrid Cloud’.
+2. **Cluster Integration**: In the Hybrid Cloud section, add your OCI Kubernetes clusters as a private region.
+3. **Effortless Deployment**: Utilize the Qdrant Management Console to seamlessly create and manage your Qdrant clusters on OCI.
+
+You can find a detailed description in our documentation focused on deploying Qdrant on OCI.
+
+[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
+
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-ovhcloud.md b/qdrant-landing/content/blog/hybrid-cloud-ovhcloud.md
index 1bf26ee5d..493dd8f63 100644
--- a/qdrant-landing/content/blog/hybrid-cloud-ovhcloud.md
+++ b/qdrant-landing/content/blog/hybrid-cloud-ovhcloud.md
@@ -12,13 +12,11 @@ tags:
- Vector Database
---
-**Qdrant and OVHcloud Collaborate To Bring Vector Search to Startups and Enterprises in Europe With A Strong Focus on Data Control and Privacy**
-
With the official release of Qdrant Hybrid Cloud, businesses running their data infrastructure on OVHcloud are now able to deploy a fully managed vector database in their existing OVHcloud environment. We are excited about this partnership, which has been established through the [OVHcloud Open Trusted Cloud](https://opentrustedcloud.ovhcloud.com/en/) program, as it is based on our shared understanding of the importance of trust, control, and data privacy in the context of the emerging landscape of enterprise-grade AI applications. As part of this collaboration, we are also providing a detailed use case tutorial on building a recommendation system with ‘collaborative filtering using sparse vectors’ that demonstrates the benefits of running Qdrant Hybrid Cloud on OVHcloud.
Deploying Qdrant Hybrid Cloud on OVHcloud's infrastructure represents a significant leap for European businesses invested in AI-driven projects, as this collaboration underscores the commitment to meeting the rigorous requirements for data privacy and control of European startups and enterprises building AI solutions. As businesses are progressing on their AI journey, they require dedicated solutions that allow them to make their data accessible for machine learning and AI projects, without having it leave the company's security perimeter. Prioritizing data sovereignty, a crucial aspect in today's digital landscape, will help startups and enterprises accelerate their AI agenda’s and build even more differentiating AI-enabled applications. The ability of running Qdrant Hybrid Cloud on OVHcloud not only underscores the commitment to innovative, secure AI solutions but also ensures that companies can navigate the complexities of AI and machine learning workloads with the flexibility and security required.
-**Qdrant & OVHcloud: High Performance Vector Search With Full Data Control**
+#### Qdrant & OVHcloud: High Performance Vector Search With Full Data Control
Through the seamless integration between Qdrant Hybrid Cloud and OVHcloud, developers and businesses are able to deploy the fully managed vector database within their existing OVHcloud setups in minutes, enabling faster, more accurate AI-driven insights.
@@ -27,15 +25,15 @@ Through the seamless integration between Qdrant Hybrid Cloud and OVHcloud, devel
- **Open standards and open ecosystem**: OVHcloud’s commitment to open standards and an open ecosystem not only facilitates the easy integration of Qdrant Hybrid Cloud with OVHcloud’s AI services and GPU-powered instances but also ensures compatibility with a wide range of external services and applications, enabling seamless data workflows across the modern AI stack.
- **Cost efficient sector search:** By leveraging Qdrant's quantization for efficient data handling and pairing it with OVHcloud's eco-friendly, water-cooled infrastructure, known for its superior price/performance ratio, this collaboration provides a strong foundation for cost efficient vector search.
-**Build a Recommendation System with Collaborative Filtering Using Sparse Vectors with Qdrant Hybrid Cloud and OVHcloud**
+#### Build a Recommendation System with Collaborative Filtering Using Sparse Vectors with Qdrant Hybrid Cloud and OVHcloud
To show how Qdrant Hybrid Cloud deployed on OVHcloud allows developers to leverage the benefits of an AI use case that is completely run within the existing infrastructure, we put together a comprehensive use case tutorial. This tutorial guides you through creating a recommendation system using collaborative filtering and sparse vectors with Qdrant Hybrid Cloud on OVHcloud. It employs the Movielens dataset for practical application, providing insights into building efficient, scalable recommendation engines suitable for developers and data scientists looking to leverage advanced vector search technologies within a secure, GDPR-compliant European cloud infrastructure.
-[Try the Tutorial](/documentation/tutorials/recommendation-system-ovhcloud/
+[Try the Tutorial](/documentation/tutorials/recommendation-system-ovhcloud/)
-**Get started today and leverage the benefits of Qdrant Hybrid Cloud**
+#### Get started today and leverage the benefits of Qdrant Hybrid Cloud
Setting up Qdrant Hybrid Cloud on OVHcloud is straightforward and quick, thanks to the intuitive integration with Kubernetes. Here's how:
@@ -45,4 +43,6 @@ Setting up Qdrant Hybrid Cloud on OVHcloud is straightforward and quick, thanks
[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
-Learn more about Qdrant Hybrid Cloud in the official release blog. Ready to get started? Create your Qdrant Hybrid Cloud cluster in a few minutes by creating your Qdrant Cloud account.
\ No newline at end of file
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-red-hat-openshift.md b/qdrant-landing/content/blog/hybrid-cloud-red-hat-openshift.md
new file mode 100644
index 000000000..ee14f9ddf
--- /dev/null
+++ b/qdrant-landing/content/blog/hybrid-cloud-red-hat-openshift.md
@@ -0,0 +1,58 @@
+---
+draft: false
+title: "Red Hat OpenShift and Qdrant Hybrid Cloud Offer Seamless and Scalable AI"
+short_description: "Qdrant brings managed vector databases to Red Hat OpenShift for large-scale GenAI."
+description: "Qdrant brings managed vector databases to Red Hat OpenShift for large-scale GenAI."
+preview_image: /blog/hybrid-cloud-red-hat-openshift/hybrid-cloud-red-hat-openshift.png
+date: 2024-04-11T00:04:00Z
+author: Qdrant
+featured: false
+tags:
+ - Qdrant
+ - Vector Database
+---
+
+We’re excited about our collaboration with Red Hat to bring the Qdrant vector database to Red Hat OpenShift customers! With the release of Qdrant Hybrid Cloud, developers can now deploy and run the Qdrant vector database directly in their Red Hat OpenShift environment. This collaboration enables developers to scale more seamlessly, operate more consistently across hybrid cloud environments, and maintain complete control over their vector data. This is a big step forward in simplifying AI infrastructure and empowering data-driven projects, like retrieval augmented generation (RAG) use cases, advanced search scenarios, or recommendations systems.
+
+In the rapidly evolving field of Artificial Intelligence and Machine Learning, the demand for being able to manage the modern AI stack within the existing infrastructure becomes increasingly relevant for businesses. As enterprises are launching new AI applications and use cases into production, they require the ability to maintain complete control over their data, since these new apps often work with sensitive internal and customer-centric data that needs to remain within the owned premises. This is why enterprises are increasingly looking for maximum deployment flexibility for their AI workloads.
+
+#### The Synergy of Qdrant Hybrid Cloud and Red Hat OpenShift
+
+Qdrant Hybrid Cloud is the first vector database that can be deployed anywhere, with complete database isolation, while still providing a fully managed cluster management. Running Qdrant Hybrid Cloud on Red Hat OpenShift allows enterprises to deploy and run a fully managed vector database in their own environment, ultimately allowing businesses to run managed vector search on their existing cloud and infrastructure environments, with full data sovereignty.
+
+Red Hat OpenShift, the industry’s leading hybrid cloud application platform powered by Kubernetes, helps streamline the deployment of Qdrant Hybrid Cloud within an enterprise's secure premises. Red Hat OpenShift provides features like auto-scaling, load balancing, and advanced security controls that can help you manage and maintain your vector database deployments more effectively. In addition, Red Hat OpenShift supports deployment across multiple environments, including on-premises, public, private and hybrid cloud landscapes. This flexibility, coupled with Qdrant Hybrid Cloud, allows organizations to choose the deployment model that best suits their needs.
+
+#### Why run Qdrant Hybrid Cloud on Red Hat OpenShift?
+
+- **Scalability**: Red Hat OpenShift's container orchestration effortlessly scales Qdrant Hybrid Cloud components, accommodating fluctuating workload demands with ease.
+- **Portability**: The consistency across hybrid cloud environments provided by Red Hat OpenShift allows for smoother operation of Qdrant Hybrid Cloud across various infrastructures.
+- **Automation**: Deployment, scaling, and management tasks are automated, reducing operational overhead and simplifying the management of Qdrant Hybrid Cloud.
+- **Security**: Red Hat OpenShift provides built-in security features, including container isolation, network policies, and role-based access control (RBAC), enhancing the security posture of Qdrant Hybrid Cloud deployments.
+- **Flexibility:** Red Hat OpenShift supports a wide range of programming languages, frameworks, and tools, providing flexibility in developing and deploying Qdrant Hybrid Cloud applications.
+- **Integration:** Red Hat OpenShift can be integrated with various Red Hat and third-party tools, facilitating seamless integration of Qdrant Hybrid Cloud with other enterprise systems and services.
+
+
+#### Get Started with Qdrant Hybrid Cloud on Red Hat OpenShift
+
+We're thrilled about our collaboration with Red Hat to help simplify AI infrastructure for developers and enterprises alike. By deploying Qdrant Hybrid Cloud on Red Hat OpenShift, developers can gain the ability to more easily scale and maintain greater operational consistency across hybrid cloud environments.
+
+To get started, we created a comprehensive tutorial that shows how to build next-gen AI applications with Qdrant Hybrid Cloud on Red Hat OpenShift. Additionally, you can find more details on the seamless deployment process in our documentation:
+
+#### Tutorial: Private Chatbot for Interactive Learning
+
+Learn how to develop a Retrieval Augmented Generation (RAG) pipeline with Qdrant Hybrid Cloud on Red Hat OpenShift, leveraging Haystack for enhanced generative AI capabilities. This tutorial especially explores how this setup ensures that not a single data point leaves the environment.
+
+[Try the Tutorial](/documentation/tutorials/rag-chatbot-red-hat-openshift-haystack/)
+
+#### Documentation: Deploy Qdrant in a few clicks
+
+> Our simple Kubernetes-native design allows you to deploy Qdrant Hybrid Cloud on your Red Hat OpenShift instance in just a few steps. Learn how in our documentation.
+
+[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
+
+This collaboration marks an important milestone in the quest for simplified AI infrastructure, offering a robust, scalable, and security-optimized solution for managing vector databases in a hybrid cloud environment. The combination of Qdrant's performance and Red Hat OpenShift's operational excellence opens new avenues for enterprises looking to leverage the power of AI and ML.
+
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
+
diff --git a/qdrant-landing/content/blog/hybrid-cloud-scaleway.md b/qdrant-landing/content/blog/hybrid-cloud-scaleway.md
index 4970e06d7..4e59533c0 100644
--- a/qdrant-landing/content/blog/hybrid-cloud-scaleway.md
+++ b/qdrant-landing/content/blog/hybrid-cloud-scaleway.md
@@ -12,15 +12,13 @@ tags:
- Vector Database
---
-**Qdrant and Scaleway Empower Innovation in AI with Launch of Hybrid Cloud Vector Search for Startups and Developers**
-
In a move to empower the next wave of AI innovation, Qdrant and Scaleway collaborate to introduce Qdrant Hybrid Cloud, a fully managed vector database that can be deployed on existing Scaleway environments. This collaboration is set to democratize access to advanced AI capabilities, enabling developers to easily deploy and scale vector search technologies within Scaleway's robust and developer-friendly cloud infrastructure. By focusing on the unique needs of startups and the developer community, Qdrant and Scaleway are providing access to intuitive and easy to use tools, making cutting-edge AI more accessible than ever before.
Building on this vision, the integration between Scaleway and Qdrant Hybrid Cloud leverages the strengths of both Qdrant, with its leading open-source vector database, and Scaleway, known for its innovative and scalable cloud solutions. This integration means startups and developers can now harness the power of vector search - essential for AI applications like recommendation systems, image recognition, and natural language processing - within their existing environment without the complexity of maintaining such advanced setups.
*"With our partnership with Qdrant, Scaleway reinforces its status as Europe's leading cloud provider for AI innovation. The integration of Qdrant's fast and accurate vector database enriches our expanding suite of AI solutions. This means you can build smarter, faster AI projects with us, worry-free about performance and security." Frédéric BARDOLLE, Lead PM AI @ Scaleway*
-**Developing a Retrieval Augmented Generation (RAG) Application with Qdrant Hybrid Cloud, Scaleway, and LangChain**
+#### Developing a Retrieval Augmented Generation (RAG) Application with Qdrant Hybrid Cloud, Scaleway, and LangChain
Retrieval Augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating vector search to provide precise, context-rich responses. This combination allows LLMs to access and incorporate specific data in real-time, vastly improving the quality of AI-generated content.
@@ -30,9 +28,9 @@ RAG applications often rely on sensitive or proprietary internal data, emphasizi
We created a tutorial that guides you through setting up and leveraging Qdrant Hybrid Cloud on Scaleway for a RAG application, providing insights into efficiently managing data within a secure, sovereign framework. It highlights practical steps to integrate vector search with LLMs, optimizing the generation of high-quality, relevant AI content, while ensuring data sovereignty is maintained throughout.
-[Try the Tutorial](/documentation/tutorials/rag-chabot-scaleway/
+[Try the Tutorial](/documentation/tutorials/rag-chatbot-scaleway/)
-**The Benefits of Running Qdrant Hybrid Cloud on Scaleway**
+#### The Benefits of Running Qdrant Hybrid Cloud on Scaleway
Choosing Qdrant Hybrid Cloud and Scaleway for AI applications offers several key advantages:
@@ -41,11 +39,11 @@ Choosing Qdrant Hybrid Cloud and Scaleway for AI applications offers several key
- **European Roots and Focus**: With a strong presence in Europe and a commitment to supporting the European tech ecosystem, Scaleway is ideally positioned to partner with European-based companies like Qdrant, providing local expertise and infrastructure that aligns with European regulatory standards.
- **Sustainability Commitment**: Scaleway leads with an eco-conscious approach, featuring adiabatic data centers that significantly reduce cooling costs and environmental impact. Scaleway prioritizes extending hardware lifecycle beyond industry norms to lessen our ecological footprint.
-**Get Started In a Few Seconds**
+#### Get Started In a Few Seconds
Setting up Qdrant Hybrid Cloud on Scaleway is streamlined and quick, thanks to its Kubernetes-native architecture. Follow these simple three steps to launch:
-1. **Activate Hybrid Cloud**: First, log into your Qdrant account and select ‘Hybrid Cloud’ to activate.
+1. **Activate Hybrid Cloud**: First, log into your [Qdrant Cloud account](https://cloud.qdrant.io/login) and select ‘Hybrid Cloud’ to activate.
2. **Integrate Your Clusters**: Navigate to the Hybrid Cloud settings and add your Scaleway Kubernetes clusters as a private region.
3. **Simplified Management**: Use the Qdrant Management Console for easy creation and oversight of your Qdrant clusters on Scaleway.
@@ -53,4 +51,6 @@ For more comprehensive guidance, our documentation provides step-by-step instruc
[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
-Read more about Qdrant Hybrid Cloud in our official release blog. To deploy your first cluster in a few clicks, begin by creating a Qdrant Cloud account. Our Hybrid Cloud docs will help you with the rest.
\ No newline at end of file
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-stackit.md b/qdrant-landing/content/blog/hybrid-cloud-stackit.md
index 72c6f4666..0d29cfba6 100644
--- a/qdrant-landing/content/blog/hybrid-cloud-stackit.md
+++ b/qdrant-landing/content/blog/hybrid-cloud-stackit.md
@@ -12,21 +12,19 @@ tags:
- Vector Database
---
-**Qdrant Hybrid Cloud and STACKIT: Empowering German AI Development with a Data Privacy-First Platform**
-
Qdrant and STACKIT are thrilled to announce that developers are now able to deploy a fully managed vector database to their STACKIT environment with the introduction of Qdrant Hybrid Cloud. This is a great step forward for the German AI ecosystem as it enables developers and businesses to build cutting edge AI applications that run on German data centers with full control over their data.
Vector databases are an essential component of the modern AI stack. They enable rapid and accurate retrieval of high-dimensional data, crucial for powering search, recommendation systems, and augmenting machine learning models. In the rising field of GenAI, vector databases power retrieval-augmented-generation (RAG) scenarios as they are able to enhance the output of large language models (LLMs) by injecting relevant contextual information. However, this contextual information is often rooted in confidential internal or customer-related information, which is why enterprises are in pursuit of solutions that allow them to make this data available for their AI applications without compromising data privacy, losing data control, or letting data exit the company's secure environment.
Qdrant Hybrid Cloud is the first managed vector database that can be deployed in an existing STACKIT environment. The Kubernetes-native setup allows businesses to operate a fully managed vector database, while maintaining control over their data through complete data isolation. Qdrant Hybrid Cloud's managed service seamlessly integrates into STACKIT's cloud environment, allowing businesses to deploy fully managed vector search workloads, secure in the knowledge that their operations are backed by the stringent data protection standards of Germany's data centers and in full compliance with GDPR. This setup not only ensures that data remains under the businesses control but also paves the way for secure, AI-driven application development.
-**Key Features and Benefits of Qdrant on STACKIT:**
+#### Key Features and Benefits of Qdrant on STACKIT:
- **Seamless Integration and Deployment**: With Qdrant’s Kubernetes-native design, businesses can effortlessly connect their STACKIT cloud as a private region, enabling a one-step, scalable Qdrant deployment.
- **Enhanced Data Privacy**: Leveraging STACKIT's German data centers ensures that all data processing complies with GDPR and other relevant European data protection standards, providing businesses with unparalleled control over their data.
- **Scalable and Managed AI Solutions**: Deploying Qdrant on STACKIT provides a fully managed vector search engine with the ability to scale vertically and horizontally, with robust support for zero-downtime upgrades and disaster recovery, all within STACKIT's secure infrastructure.
-**Use Case: AI-enabled Contract Management built with Qdrant Hybrid Cloud, STACKIT, and Aleph Alpha**
+#### Use Case: AI-enabled Contract Management built with Qdrant Hybrid Cloud, STACKIT, and Aleph Alpha
@@ -34,11 +32,11 @@ To demonstrate the power of Qdrant Hybrid Cloud on STACKIT, we’ve developed a
[Try the Tutorial](/documentation/tutorials/rag-contract-management-stackit-aleph-alpha/)
-**Start Using Qdrant with STACKIT**
+#### Start Using Qdrant with STACKIT
Deploying Qdrant Hybrid Cloud on STACKIT is straightforward, thanks to the seamless integration facilitated by Kubernetes. Here are the steps to kickstart your journey:
-1. **Qdrant Hybrid Cloud Activation**: Start by activating ‘Hybrid Cloud’ in your Qdrant Cloud account.
+1. **Qdrant Hybrid Cloud Activation**: Start by activating ‘Hybrid Cloud’ in your [Qdrant Cloud account](https://cloud.qdrant.io/login).
2. **Cluster Integration**: Add your STACKIT Kubernetes clusters as a private region in the Hybrid Cloud section.
3. **Effortless Deployment**: Use the Qdrant Management Console to effortlessly create and manage your Qdrant clusters on STACKIT.
@@ -46,4 +44,6 @@ We invite you to explore the detailed documentation on deploying Qdrant on STACK
[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
-Learn more about Qdrant Hybrid Cloud in the official release blog. Ready to get started? Create your Qdrant Hybrid Cloud cluster in a few minutes by creating your Qdrant Cloud account.
\ No newline at end of file
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud-vultr.md b/qdrant-landing/content/blog/hybrid-cloud-vultr.md
index 2adcbafd6..0d90b8ad1 100644
--- a/qdrant-landing/content/blog/hybrid-cloud-vultr.md
+++ b/qdrant-landing/content/blog/hybrid-cloud-vultr.md
@@ -12,19 +12,17 @@ tags:
- Vector Database
---
-**Qdrant Hybrid Cloud and Vultr Provide A Flexible Platform For High-Performance Vector Search For Next-Gen AI Workloads**
-
We’re excited to share that Qdrant and Vultr are partnering to provide seamless scalability and performance for vector search workloads. With Vultr's global footprint and customizable platform, deploying vector search workloads becomes incredibly flexible. Qdrant's new Qdrant Hybrid Cloud offering and its Kubernetes-native design, coupled with Vultr's straightforward virtual machine provisioning, allows for simple setup when prototyping and building next-gen AI apps.
-**Adapting to Diverse AI Development Needs with Customization and Deployment Flexibility**
+#### Adapting to Diverse AI Development Needs with Customization and Deployment Flexibility
In the fast-paced world of AI and ML, businesses are eagerly integrating AI and generative AI to enhance their products with new features like AI assistants, develop new innovative solutions, and streamline internal workflows with AI-driven processes. Given the diverse needs of these applications, it's clear that a one-size-fits-all approach doesn't apply to AI development. This variability in requirements underscores the need for adaptable and customizable development environments.
Recognizing this, Qdrant and Vultr have teamed up to offer developers unprecedented flexibility and control. The collaboration enables the deployment of a fully managed vector database on Vultr’s adaptable platform, catering to the specific needs of diverse AI projects. This unique setup offers developers the ideal Vultr environment for their vector search workloads. It ensures seamless adaptability and data privacy with all data residing in their environment. For the first time, Qdrant Hybrid Cloud allows for fully managing a vector database on Vultr, promoting rapid development cycles without the hassle of modifying existing setups and ensuring that data remains secure within the organization. Moreover, this partnership empowers developers with centralized management over their vector database clusters via Qdrant’s control plane, enabling precise size adjustments based on workload demands. This joint setup marks a significant step in providing the AI and ML field with flexible, secure, and efficient application development tools.
-"Our collaboration with Qdrant empowers developers to unlock the potential of vector search applications, such as RAG, by deploying Qdrant Hybrid Cloud with its high-performance search capabilities directly on Vultr's global, automated cloud infrastructure. This partnership creates a highly scalable and customizable platform, uniquely designed for deploying and managing AI workloads with unparalleled efficiency." Kevin Cochrane, Vultr CMO.
+> *"Our collaboration with Qdrant empowers developers to unlock the potential of vector search applications, such as RAG, by deploying Qdrant Hybrid Cloud with its high-performance search capabilities directly on Vultr's global, automated cloud infrastructure. This partnership creates a highly scalable and customizable platform, uniquely designed for deploying and managing AI workloads with unparalleled efficiency."* Kevin Cochrane, Vultr CMO.
-**The Benefits of Deploying Qdrant Hybrid Cloud on Vultr**
+#### The Benefits of Deploying Qdrant Hybrid Cloud on Vultr
Together, Qdrant Hybrid Cloud and Vultr offer enhanced AI and ML development with streamlined benefits:
@@ -33,22 +31,24 @@ Together, Qdrant Hybrid Cloud and Vultr offer enhanced AI and ML development wit
- **Unified AI Stack Management:** Seamlessly manage the entire lifecycle of AI applications, from vector search with Qdrant Hybrid Cloud to deployment and scaling with the Vultr platform and its AI and ML solutions, all within a single, integrated environment. This setup simplifies workflows, reduces complexity, accelerates development cycles, and simplifies the integration with other elements of the AI stack like model development, finetuning, or inference and training.
- **Global Reach, Local Execution**: With Vultr's worldwide infrastructure and Qdrant's fast vector search, deploy AI solutions globally while ensuring low latency and compliance with local data regulations, enhancing user satisfaction.
-**Getting Started with Qdrant Hybrid Cloud and Vultr**
+#### Getting Started with Qdrant Hybrid Cloud and Vultr
We've compiled an in-depth guide for leveraging Qdrant Hybrid Cloud on Vultr to kick off your journey into building cutting-edge AI solutions. For further insights into the deployment process, refer to our comprehensive documentation.
-**Tutorial Crafting a Personalized AI Assistant with RAG**
+#### Tutorial Crafting a Personalized AI Assistant with RAG
This tutorial outlines creating a personalized AI assistant using Qdrant Hybrid Cloud on Vultr, incorporating advanced vector search to power dynamic, interactive experiences. We will develop a RAG pipeline powered by DSPy and detail how to maintain data privacy within your Vultr environment.
[Try the Tutorial](/documentation/tutorials/rag-chatbot-vultr-dspy-ollama/)
-**Documentation: Effortless Deployment with Qdrant**
+#### Documentation: Effortless Deployment with Qdrant
Our Kubernetes-native framework simplifies the deployment of Qdrant Hybrid Cloud on Vultr, enabling you to get started in just a few straightforward steps. Dive into our documentation to learn more.
[Read Hybrid Cloud Documentation](/documentation/hybrid-cloud/)
-Learn more about Qdrant Hybrid Cloud in the official release blog. Ready to get started? Create your Qdrant Hybrid Cloud cluster in a few minutes by creating your Qdrant Cloud account.
\ No newline at end of file
+#### Ready to get started?
+
+Create a [Qdrant Cloud account](https://cloud.qdrant.io/login) and deploy your first **Qdrant Hybrid Cloud** cluster in a few minutes. You can always learn more in the [official release blog](/blog/hybrid-cloud/).
\ No newline at end of file
diff --git a/qdrant-landing/content/blog/hybrid-cloud.md b/qdrant-landing/content/blog/hybrid-cloud.md
index 238505cf1..e7446fec2 100644
--- a/qdrant-landing/content/blog/hybrid-cloud.md
+++ b/qdrant-landing/content/blog/hybrid-cloud.md
@@ -13,9 +13,7 @@ tags:
- Hybrid Cloud
---
-**Qdrant Hybrid Cloud: Introducing the First Managed Vector Database You Can Run Anywhere with Unmatched Flexibility and Control**
-
-- *André Zayarni, CEO & Co-Founder, Qdrant*
+Qdrant Hybrid Cloud: Introducing the First Managed Vector Database You Can Run Anywhere with Unmatched Flexibility and Control
We are excited to announce the official launch of Qdrant Hybrid Cloud today, a significant leap forward in the field of vector search and enterprise AI. Rooted in our open-source origin, we’re committed to offering our users and customers unparalleled control and sovereignty over their data and vector search workloads. Qdrant Hybrid Cloud stands as the industry's first managed vector database that can be deployed in any environment - be it cloud, on-premise, or the edge.
@@ -30,7 +28,7 @@ Qdrant Hybrid Cloud provides developers a vector database that can be deployed i
Let’s explore these aspects in more detail:
-**1. Maximizing Deployment Flexibility: Enabling Applications to Run Across Any Environment**
+#### Maximizing Deployment Flexibility: Enabling Applications to Run Across Any Environment
@@ -44,13 +42,13 @@ In addition to our partnerships with key cloud providers, we are also launching
Together with our launch partners we have created detailed tutorials that show how to build cutting-edge AI applications with Qdrant Hybrid Cloud on the infrastructure of your choice. These tutorials are available in our launch partner blog. Additionally, you can find expansive documentation and tutorials on how to deploy Qdrant Hybrid Cloud.
-**2. Powering Vector Search & AI with Unmatched Data Sovereignty**
+#### Powering Vector Search & AI with Unmatched Data Sovereignty
Proprietary data, the lifeblood of AI-driven innovation, fuels personalized experiences, accurate recommendations, and timely anomaly detection. This data, unique to each organization, encompasses customer behaviors, internal processes, and market insights - crucial for tailoring AI applications to specific business needs and competitive differentiation. However, leveraging such data effectively while ensuring its security, privacy, and control requires diligence.
Our Qdrant Hybrid Cloud offering is tailored to meet these multifaceted requirements in a data-sensitive world. The innovative architecture of Qdrant Hybrid Cloud ensures complete database isolation, empowering developers with the autonomy to decide where to process their vector search workloads, thus maintaining total data sovereignty. This strategic approach, rooted deeply in our commitment to open-source principles, is aimed at fostering a new level of trust and reliability by providing the essential tools to navigate the evolving landscape of enterprise AI.
-**3. How We Designed the Qdrant Hybrid Cloud Architecture**
+#### How We Designed the Qdrant Hybrid Cloud Architecture
We designed the architecture of Qdrant Hybrid Cloud to meet the evolving needs of businesses seeking unparalleled flexibility, control, and privacy.
@@ -61,7 +59,7 @@ We designed the architecture of Qdrant Hybrid Cloud to meet the evolving needs o
-**4. Quick Start: Effortless Setup with our One-Step Installation**
+#### Quickstart: Effortless Setup with our One-Step Installation
We’ve made getting started with Qdrant Hybrid Cloud as simple as possible. The Kubernetes “One-Step” installation will allow you to connect with the infrastructure of your choice. This is how you can get started:
@@ -71,11 +69,20 @@ We’ve made getting started with Qdrant Hybrid Cloud as simple as possible. The
Explore our detailed documentation and tutorials to seamlessly deploy Qdrant Hybrid Cloud in your preferred environment, and don't miss our launch partner blog post for practical insights. Start leveraging the full potential of Qdrant Hybrid Cloud and create your first Qdrant cluster today, unlocking the flexibility and control essential for your AI and vector search workloads.
-
+[](https://cloud.qdrant.io/login)
+
+## Launch Partners
Thank you to our launch partners - learn what they have to say about Qdrant Hybrid Cloud:
-- **Oracle**: "We are excited to partner with Qdrant to bring their powerful vector search capabilities to Oracle Cloud Infrastructure. By offering Qdrant Hybrid Cloud as a managed service on OCI, we are empowering enterprises to harness the full potential of AI-driven applications while maintaining complete control over their data. This collaboration represents a significant step forward in making scalable vector search accessible and manageable for businesses across various industries, enabling them to drive innovation, enhance productivity, and unlock valuable insights from their data." Dr. Sanjay Basu, Senior Director of Cloud Engineering, AI/GPU Infrastructure at Oracle
-- **Vultr**: "Our collaboration with Qdrant empowers developers to unlock the potential of vector search applications, such as RAG, by deploying Qdrant Hybrid Cloud with its high-performance search capabilities directly on Vultr's global, automated cloud infrastructure. This partnership creates a highly scalable and customizable platform, uniquely designed for deploying and managing AI workloads with unparalleled efficiency." Kevin Cochrane, Vultr CMO.
-- **Scaleway**: "With our partnership with Qdrant, Scaleway reinforces its status as Europe's leading cloud provider for AI innovation. The integration of Qdrant's fast and accurate vector database enriches our expanding suite of AI solutions. This means you can build smarter, faster AI projects with us, worry-free about performance and security." Frédéric Bardolle, Lead PM AI, Scaleway
-- **Deepset**: “We hope that with Haystack 2.0 and our growing partnerships such as what we have here with Qdrant Hybrid Cloud, engineers are able to build AI systems with full autonomy. Both in how their pipelines are designed, and how their data are managed.” Tuana Çelik, Developer Relations Lead, deepset.
\ No newline at end of file
+#### Oracle:
+> *"We are excited to partner with Qdrant to bring their powerful vector search capabilities to Oracle Cloud Infrastructure. By offering Qdrant Hybrid Cloud as a managed service on OCI, we are empowering enterprises to harness the full potential of AI-driven applications while maintaining complete control over their data. This collaboration represents a significant step forward in making scalable vector search accessible and manageable for businesses across various industries, enabling them to drive innovation, enhance productivity, and unlock valuable insights from their data."* Dr. Sanjay Basu, Senior Director of Cloud Engineering, AI/GPU Infrastructure at Oracle
+
+#### Vultr:
+> *"Our collaboration with Qdrant empowers developers to unlock the potential of vector search applications, such as RAG, by deploying Qdrant Hybrid Cloud with its high-performance search capabilities directly on Vultr's global, automated cloud infrastructure. This partnership creates a highly scalable and customizable platform, uniquely designed for deploying and managing AI workloads with unparalleled efficiency."* Kevin Cochrane, Vultr CMO.
+
+#### Scaleway:
+> *"With our partnership with Qdrant, Scaleway reinforces its status as Europe's leading cloud provider for AI innovation. The integration of Qdrant's fast and accurate vector database enriches our expanding suite of AI solutions. This means you can build smarter, faster AI projects with us, worry-free about performance and security."* Frédéric Bardolle, Lead PM AI, Scaleway
+
+#### Deepset:
+> *“We hope that with Haystack 2.0 and our growing partnerships such as what we have here with Qdrant Hybrid Cloud, engineers are able to build AI systems with full autonomy. Both in how their pipelines are designed, and how their data are managed.”* Tuana Çelik, Developer Relations Lead, deepset.
\ No newline at end of file
diff --git a/qdrant-landing/content/documentation/4-dl.md b/qdrant-landing/content/documentation/4-dl.md
new file mode 100644
index 000000000..281f55a0f
--- /dev/null
+++ b/qdrant-landing/content/documentation/4-dl.md
@@ -0,0 +1,7 @@
+---
+#Delimiter files are used to separate the list of documentation pages into sections.
+title: "Managed Services"
+type: delimiter
+weight: 13 # Change this weight to change order of sections
+sitemapExclude: True
+---
\ No newline at end of file
diff --git a/qdrant-landing/content/documentation/_index.md b/qdrant-landing/content/documentation/_index.md
index bd17e1680..f650f57ee 100644
--- a/qdrant-landing/content/documentation/_index.md
+++ b/qdrant-landing/content/documentation/_index.md
@@ -9,7 +9,7 @@ weight: 10
## Product Release: Announcing Qdrant Hybrid Cloud!
***
Now you can attach your own infrastructure to [Qdrant Cloud](/documentation/cloud/)!
***
-[](https://qdrant.to/cloud)
+[](https://qdrant.to/cloud)
Build the best private environment that suits your needs. Use our Cloud to manage your clusters, but continue to run them within your own private infrastructure. **Get the most out of Qdrant: scalability, flexibility and data sovereignty!**
## First-Time Users:
diff --git a/qdrant-landing/content/documentation/api-reference.md b/qdrant-landing/content/documentation/api-reference.md
index 3eba7446b..489a3a3e9 100644
--- a/qdrant-landing/content/documentation/api-reference.md
+++ b/qdrant-landing/content/documentation/api-reference.md
@@ -1,6 +1,6 @@
---
title: API Reference
-weight: 20
+weight: 12
type: external-link
external_url: https://qdrant.github.io/qdrant/redoc/index.html
sitemapExclude: True
diff --git a/qdrant-landing/content/documentation/cloud/_index.md b/qdrant-landing/content/documentation/cloud/_index.md
index 2eac8961f..266ab4566 100644
--- a/qdrant-landing/content/documentation/cloud/_index.md
+++ b/qdrant-landing/content/documentation/cloud/_index.md
@@ -1,47 +1,68 @@
---
title: Qdrant Cloud
-weight: 20
+weight: 14
aliases:
- /documentation/overview/qdrant-alternatives/documentation/cloud/
---
# About Qdrant Cloud
-Qdrant Cloud is our SaaS (software-as-a-service) solution, providing managed Qdrant instances on the cloud.
-We provide you with the same fast and reliable similarity search engine, but without the need to maintain your own infrastructure.
+Qdrant Cloud is our SaaS (software-as-a-service) solution, providing managed
+Qdrant instances on the cloud. We provide you the same fast and reliable
+similarity search engine, but without the need to maintain your own infrastructure.
-Transitioning from on-premise to the cloud version of Qdrant does not require changing anything in the way you interact with the service. All you have to do is [create a Qdrant Cloud account](https://qdrant.to/cloud/) and [provide a new API key](/documentation/cloud/authentication/) to each request.
+Transitioning from on-premise to the cloud version of Qdrant does not change
+how you interact with the service. All you need is a [Qdrant Cloud account](https://qdrant.to/cloud/)
+and an [API key](/documentation/cloud/authentication/) for each request.
-The transition is even easier if you use the official client libraries. For example, the [Python Client](https://github.com/qdrant/qdrant-client/) has the support of the API key already built-in, so you only need to provide it once, when the QdrantClient instance is created.
+Our official [client libraries](/documentation/interfaces/#client-libraries/)
+can help. For example, if you use the [Python Client](https://github.com/qdrant/qdrant-client/)
+you can take advantage of the built-in API key. With that client, you provide
+the API key only once, when the QdrantClient instance is created.
-### Cluster configuration
+*Available as of v1.8.2*
-Each instance comes pre-configured with the following tools, features and support services:
+You can also attach your own infrastructure as a private region on the Hybrid
+Cloud. Once attached, you can control this cloud using the same tools and UI
+that you use for other cloud providers. For details, see our
+[Hybrid Cloud](/documentation/hybrid-cloud/) documentation.
-- Automatically created with the latest available version of Qdrant.
-- Upgradeable to later versions of Qdrant as they are released.
-- Equipped with monitoring and logging to observe the health of each cluster.
-- Accessible through the Qdrant Cloud Console.
+## Cluster configuration
+
+Each instance comes pre-configured with the following tools, features, and
+support services:
+
+- Uses the latest available version of Qdrant.
+- Supports upgrades to later versions of Qdrant as they are released.
+- Includes monitoring and logging to observe the health of each cluster.
+- Configurable through the Qdrant Cloud Console.
- Vertically scalable.
-- Offered on AWS and GCP, with Azure currently in development.
+- Available natively on AWS and GCP, and Azure.
+- Available on other providers if you use the Hybrid Cloud.
-### Getting started with Qdrant Cloud
+## Getting started with Qdrant Cloud
-To use Qdrant Cloud, you will need to create at least one cluster. There are two ways to start:
+To use Qdrant Cloud, you need at least one cluster. You can create one in the
+following ways:
1. [**Create a Free Tier cluster**](/documentation/cloud/quickstart-cloud/) with
- 1 node and a default configuration (1 GB RAM, 0.5 CPU and 4 GB Disk). This
+ one node and a default configuration (1 GB RAM, 0.5 CPU and 4 GB Disk). This
option is perfect for prototyping. You don't need a credit card to join.
-2. [**Configure a custom cluster**](/documentation/cloud/create-cluster/) with additional nodes and more resources. For this option, you will have to provide billing information.
+2. [**Configure a custom cluster**](/documentation/cloud/create-cluster/) with
+ additional nodes and resources. For this option, you need billing information.
+
+If you're testing Qdrant, We recommend the Free Tier cluster. The capacity
+should be enough to serve up to 1 M vectors of 768 dimensions. To calculate
+your needs, refer to our documentation on [Capacity and sizing](/documentation/cloud/capacity-sizing/).
We recommend that you use the Free Tier cluster for testing purposes. The
capacity should be enough to serve up to 1 M vectors of 768 dimensions. To
calculate your needs, refer to our documentation on [Capacity and sizing](/documentation/cloud/capacity-sizing/).
-### Support & Troubleshooting
+## Support & Troubleshooting
All Qdrant Cloud users are welcome to join our [Discord community](https://qdrant.to/discord/).
Our Support Engineers are available to help you anytime.
-Additionally, paid customers can also contact support through channels provided during cluster
+Paid customers can also contact support through channels provided during cluster
creation and/or on-boarding.
diff --git a/qdrant-landing/content/documentation/examples.md b/qdrant-landing/content/documentation/examples/_index.md
similarity index 54%
rename from qdrant-landing/content/documentation/examples.md
rename to qdrant-landing/content/documentation/examples/_index.md
index 46e7919c0..11562fa72 100644
--- a/qdrant-landing/content/documentation/examples.md
+++ b/qdrant-landing/content/documentation/examples/_index.md
@@ -1,11 +1,28 @@
---
title: Examples
-weight: 25
+weight: 34
# If the index.md file is empty, the link to the section will be hidden from the sidebar
is_empty: false
---
+# Examples
-# Sample Use Cases
+| End-to-End Code Samples | Description | Stack |
+|---------------------------------------------------------------------------------|-------------------------------------------------------------------|---------------------------------------------|
+| [Aleph Alpha Search](../examples/aleph-alpha-search/) | Build a multimodal search that combines text and image data. | Qdrant, Aleph Alpha |
+| [Mighty Semantic Search](../examples/mighty/) | Build a simple semantic search with an on-demand NLP service. | Qdrant, Mighty |
+| [Multitenancy with LlamaIndex](../examples/llama-index-multitenancy/) | Handle data coming from multiple users in LlamaIndex. | Qdrant, Python, LlamaIndex |
+| [Implement custom connector for Cohere RAG](../examples/cohere-rag-connector/) | Bring data stored in Qdrant to Cohere RAG | Qdrant, Cohere, FastAPI |
+| [Chatbot for Interactive Learning](../examples/rag-chatbot-red-hat-openshift-haystack/) | Build a Private RAG Chatbot for Interactive Learning | Qdrant, Haystack, OpenShift |
+| [Information Extraction Engine](../examples/rag-chatbot-vultr-dspy-ollama/) | Build a Private RAG Information Extraction Engine | Qdrant, Vultr, DSPy, Ollama |
+| [System for Employee Onboarding](../examples/natural-language-search-oracle-cloud-infrastructure-cohere-langchain/) | Build a RAG System for Employee Onboarding | Qdrant, Cohere, LangChain |
+| [System for Contract Management](../examples/rag-contract-management-stackit-aleph-alpha/) | Build a Region-Specific RAG System for Contract Management | Qdrant, Aleph Alpha, STACKIT |
+| [Question-Answering System for Customer Support](../examples/rag-customer-support-cohere-airbyte-aws/) | Build a RAG System for AI Customer Support | Qdrant, Cohere, Airbyte, AWS |
+| [Hybrid Search on PDF Documents](../examples/hybrid-search-llamaindex-jinaai/) | Develop a Hybrid Search System for Product PDF Manuals | Qdrant, LlamaIndex, Jina AI
+| [Build a RAG-based Chatbot](../examples/rag-chatbot-scaleway) | Develop Build a RAG-based Chatbot on Scaleway and with LangChain | Qdrant, LlamaIndex, Jina AI
+| [Movie Recommendation System](../examples/recommendation-system-ovhcloud/) | Build a Movie Recommendation System with LlamaIndex and With JinaAI | Qdrant, LlamaIndex, Jina AI
+
+
+## Notebooks
Our Notebooks offer complex instructions that are supported with a throrough explanation. Follow along by trying out the code and get the most out of each example.
diff --git a/qdrant-landing/content/documentation/tutorials/aleph-alpha-search.md b/qdrant-landing/content/documentation/examples/aleph-alpha-search.md
similarity index 99%
rename from qdrant-landing/content/documentation/tutorials/aleph-alpha-search.md
rename to qdrant-landing/content/documentation/examples/aleph-alpha-search.md
index f12e2558a..df0c076cf 100644
--- a/qdrant-landing/content/documentation/tutorials/aleph-alpha-search.md
+++ b/qdrant-landing/content/documentation/examples/aleph-alpha-search.md
@@ -1,6 +1,8 @@
---
title: Aleph Alpha Search
weight: 16
+aliases:
+ - /documentation/tutorials/aleph-alpha-search/
---
# Multimodal Semantic Search with Aleph Alpha
diff --git a/qdrant-landing/content/documentation/tutorials/cohere-rag-connector.md b/qdrant-landing/content/documentation/examples/cohere-rag-connector.md
similarity index 99%
rename from qdrant-landing/content/documentation/tutorials/cohere-rag-connector.md
rename to qdrant-landing/content/documentation/examples/cohere-rag-connector.md
index 4a7332ba7..2fbc49b1a 100644
--- a/qdrant-landing/content/documentation/tutorials/cohere-rag-connector.md
+++ b/qdrant-landing/content/documentation/examples/cohere-rag-connector.md
@@ -1,6 +1,8 @@
---
title: Implement Cohere RAG connector
weight: 24
+aliases:
+ - /documentation/tutorials/cohere-rag-connector/
---
# Implement custom connector for Cohere RAG
diff --git a/qdrant-landing/content/documentation/tutorials/hybrid-search-llamaindex-jinaai.md b/qdrant-landing/content/documentation/examples/hybrid-search-llamaindex-jinaai.md
similarity index 99%
rename from qdrant-landing/content/documentation/tutorials/hybrid-search-llamaindex-jinaai.md
rename to qdrant-landing/content/documentation/examples/hybrid-search-llamaindex-jinaai.md
index e57d66577..5580a0a3c 100644
--- a/qdrant-landing/content/documentation/tutorials/hybrid-search-llamaindex-jinaai.md
+++ b/qdrant-landing/content/documentation/examples/hybrid-search-llamaindex-jinaai.md
@@ -1,6 +1,8 @@
---
title: Chat With Product PDF Manuals Using Hybrid Search
weight: 27
+aliases:
+ - /documentation/tutorials/hybrid-search-llamaindex-jinaai/
---
# Chat With Product PDF Manuals Using Hybrid Search
diff --git a/qdrant-landing/content/documentation/tutorials/llama-index-multitenancy.md b/qdrant-landing/content/documentation/examples/llama-index-multitenancy.md
similarity index 99%
rename from qdrant-landing/content/documentation/tutorials/llama-index-multitenancy.md
rename to qdrant-landing/content/documentation/examples/llama-index-multitenancy.md
index 5cfba5374..0e65e82a3 100644
--- a/qdrant-landing/content/documentation/tutorials/llama-index-multitenancy.md
+++ b/qdrant-landing/content/documentation/examples/llama-index-multitenancy.md
@@ -1,6 +1,8 @@
---
title: Multitenancy with LlamaIndex
weight: 18
+aliases:
+ - /documentation/tutorials/llama-index-multitenancy/
---
# Multitenancy with LlamaIndex
diff --git a/qdrant-landing/content/documentation/tutorials/mighty.md b/qdrant-landing/content/documentation/examples/mighty.md
similarity index 98%
rename from qdrant-landing/content/documentation/tutorials/mighty.md
rename to qdrant-landing/content/documentation/examples/mighty.md
index a83382353..fc97f6414 100644
--- a/qdrant-landing/content/documentation/tutorials/mighty.md
+++ b/qdrant-landing/content/documentation/examples/mighty.md
@@ -6,6 +6,8 @@ weight: 17
author: Andre Bogus
author_link: https://llogiq.github.io
date: 2023-06-01T11:24:20+01:00
+aliases:
+ - /documentation/tutorials/mighty.md/
keywords:
- vector search
- embeddings
diff --git a/qdrant-landing/content/documentation/tutorials/natural-language-search-oracle-cloud-infrastructure-cohere-langchain.md b/qdrant-landing/content/documentation/examples/natural-language-search-oracle-cloud-infrastructure-cohere-langchain.md
similarity index 99%
rename from qdrant-landing/content/documentation/tutorials/natural-language-search-oracle-cloud-infrastructure-cohere-langchain.md
rename to qdrant-landing/content/documentation/examples/natural-language-search-oracle-cloud-infrastructure-cohere-langchain.md
index 7bb56bb56..6c60b0dec 100644
--- a/qdrant-landing/content/documentation/tutorials/natural-language-search-oracle-cloud-infrastructure-cohere-langchain.md
+++ b/qdrant-landing/content/documentation/examples/natural-language-search-oracle-cloud-infrastructure-cohere-langchain.md
@@ -1,6 +1,8 @@
---
title: RAG System for Employee Onboarding
weight: 30
+aliases:
+ - /documentation/tutorials/natural-language-search-oracle-cloud-infrastructure-cohere-langchain/
---
# RAG System for Employee Onboarding
diff --git a/qdrant-landing/content/documentation/tutorials/rag-chatbot-red-hat-openshift-haystack.md b/qdrant-landing/content/documentation/examples/rag-chatbot-red-hat-openshift-haystack.md
similarity index 99%
rename from qdrant-landing/content/documentation/tutorials/rag-chatbot-red-hat-openshift-haystack.md
rename to qdrant-landing/content/documentation/examples/rag-chatbot-red-hat-openshift-haystack.md
index ab511eb68..b97c75cc9 100644
--- a/qdrant-landing/content/documentation/tutorials/rag-chatbot-red-hat-openshift-haystack.md
+++ b/qdrant-landing/content/documentation/examples/rag-chatbot-red-hat-openshift-haystack.md
@@ -1,6 +1,8 @@
---
title: Private Chatbot for Interactive Learning
weight: 23
+aliases:
+ - /documentation/tutorials/rag-chatbot-red-hat-openshift-haystack/
---
# Private Chatbot for Interactive Learning
diff --git a/qdrant-landing/content/documentation/examples/rag-chatbot-scaleway.md b/qdrant-landing/content/documentation/examples/rag-chatbot-scaleway.md
new file mode 100644
index 000000000..fa74a7657
--- /dev/null
+++ b/qdrant-landing/content/documentation/examples/rag-chatbot-scaleway.md
@@ -0,0 +1,137 @@
+---
+title: Blog-Reading RAG Chatbot
+weight: 35
+aliases:
+ - /documentation/tutorials/rag-chatbot-scaleway/
+---
+
+# Blog-Reading RAG Chatbot
+
+| Time: 90 min | Level: Advanced |[GitHub](https://github.com/qdrant/examples/blob/master/langchain-lcel-rag/Langchain-LCEL-RAG-Demo.ipynb)| |
+|--------------|-----------------|--|----|
+
+In this tutorial, you will build a RAG system that combines blog content ingestion with the capabilities of semantic search. RAG enhances the generation of answers by retrieving relevant documents to aid the question-answering process. This setup showcases the integration of advanced search and AI language processing to improve information retrieval and generation tasks.
+
+A notebook for this tutorial is available on [GitHub](https://github.com/qdrant/examples/blob/master/langchain-lcel-rag/Langchain-LCEL-RAG-Demo.ipynb).
+
+**Data Privacy and Sovereignty:** RAG applications often rely on sensitive or proprietary internal data. Running the entire stack within your own environment becomes crucial for maintaining control over this data. Qdrant Hybrid Cloud deployed on [Scaleway](https://www.scaleway.com/) addresses this need perfectly, offering a secure, scalable platform that still leverages the full potential of RAG. Scaleway offers serverless [Functions](https://www.scaleway.com/en/serverless-functions/) and serverless [Jobs](https://www.scaleway.com/en/serverless-jobs/), both of which are ideal for embedding creation in large-scale RAG cases.
+
+## Components
+
+- **Cloud Host:** [Scaleway on managed Kubernetes](https://www.scaleway.com/en/kubernetes-kapsule/) for compatibility with Qdrant Hybrid Cloud.
+- **Vector Database:** Qdrant Hybrid Cloud as the vector search engine for retrieval.
+- **LLM:** GPT-3.5, developed by OpenAI is utilized as the generator for producing answers.
+- **Framework:** [LangChain](https://www.langchain.com/) for extensive RAG capabilities.
+
+## Prerequisites
+
+To prepare the environment for working with Qdrant and related libraries, it's necessary to install all required Python packages. This can be done using Poetry, a tool for dependency management and packaging in Python. The code snippet imports various libraries essential for the tasks ahead, including `bs4` for parsing HTML and XML documents, `langchain` and its community extensions for working with language models and document loaders, and `Qdrant` for vector storage and retrieval. These imports lay the groundwork for utilizing Qdrant alongside other tools for natural language processing and machine learning tasks.
+
+```python
+import getpass
+import os
+
+import bs4
+from langchain import hub
+from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.vectorstores import Qdrant
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+```
+
+Set up the OpenAI API key:
+
+```python
+os.environ["OPENAI_API_KEY"] = getpass.getpass()
+```
+
+Initialize the language model:
+
+```python
+llm = ChatOpenAI(model="gpt-3.5-turbo-0125")
+```
+
+It is here that we configure both the Embeddings and LLM. You can replace this with your own models using Ollama or other services. Scaleway has some great [GPU Instances](https://www.scaleway.com/en/gpu-instances/) too - including H100 on the higher end, and soon L4 for everything small.
+
+## Download and parse data
+
+To begin working with blog post contents, the process involves loading and parsing the HTML content. This is achieved using `urllib` and `BeautifulSoup`, which are tools designed for such tasks. After the content is loaded and parsed, it is indexed using Qdrant, a powerful tool for managing and querying vector data. The code snippet demonstrates how to load, chunk, and index the contents of a blog post by specifying the URL of the blog and the specific HTML elements to parse. This step is crucial for preparing the data for further processing and analysis with Qdrant.
+
+```python
+# Load, chunk and index the contents of the blog.
+loader = WebBaseLoader(
+ web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
+ bs_kwargs=dict(
+ parse_only=bs4.SoupStrainer(
+ class_=("post-content", "post-title", "post-header")
+ )
+ ),
+)
+docs = loader.load()
+
+```
+
+### Chunking data
+
+When dealing with large documents, such as a blog post exceeding 42,000 characters, it's crucial to manage the data efficiently for processing. Many models have a limited context window and struggle with long inputs, making it difficult to extract or find relevant information. To overcome this, the document is divided into smaller chunks. This approach enhances the model's ability to process and retrieve the most pertinent sections of the document effectively.
+
+In this scenario, the document is split into chunks using the `RecursiveCharacterTextSplitter` with a specified chunk size and overlap. This method ensures that no critical information is lost between chunks. Following the splitting, these chunks are then indexed into Qdrant—a vector database for efficient similarity search and storage of embeddings. The `Qdrant.from_documents` function is utilized for indexing, with documents being the split chunks and embeddings generated through `OpenAIEmbeddings`. The entire process is facilitated within an in-memory database, signifying that the operations are performed without the need for persistent storage, and the collection is named "lilianweng" for reference.
+
+This chunking and indexing strategy significantly improves the management and retrieval of information from large documents, making it a practical solution for handling extensive texts in data processing workflows.
+
+```python
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+splits = text_splitter.split_documents(docs)
+
+vectorstore = Qdrant.from_documents(
+ documents=splits, embedding=OpenAIEmbeddings(), location=":memory:", collection_name="lilianweng"
+)
+```
+
+## Retrieve and generate content
+
+The `vectorstore` is used as a retriever to fetch relevant documents based on vector similarity. The `hub.pull("rlm/rag-prompt")` function is used to pull a specific prompt from a repository, which is designed to work with retrieved documents and a question to generate a response.
+
+The `format_docs` function formats the retrieved documents into a single string, preparing them for further processing. This formatted string, along with a question, is passed through a chain of operations. Firstly, the context (formatted documents) and the question are processed by the retriever and the prompt. Then, the result is fed into a large language model (`llm`) for content generation. Finally, the output is parsed into a string format using `StrOutputParser()`.
+
+This chain of operations demonstrates a sophisticated approach to information retrieval and content generation, leveraging both the semantic understanding capabilities of vector search and the generative prowess of large language models.
+
+Now, retrieve and generate data using relevant snippets from the blogL
+
+```python
+retriever = vectorstore.as_retriever()
+prompt = hub.pull("rlm/rag-prompt")
+
+
+def format_docs(docs):
+ return "\n\n".join(doc.page_content for doc in docs)
+
+
+rag_chain = (
+ {"context": retriever | format_docs, "question": RunnablePassthrough()}
+ | prompt
+ | llm
+ | StrOutputParser()
+)
+```
+
+### Invoking the RAG Chain
+
+```python
+rag_chain.invoke("What is Task Decomposition?")
+```
+
+## Next steps:
+We built a solid foundation for a simple chatbot, but there is still a lot to do. If you want to make the
+system production-ready, you should consider implementing the mechanism into your existing stack. We recommend
+
+Our vector database can easily be hosted on [Scaleway](https://www.scaleway.com/), our trusted [Qdrant Hybrid Cloud](/documentation/hybrid-cloud/) partner. This means that Qdrant can be run from your Scaleway region, but the database itself can still be managed from within Qdrant Cloud's interface. Both products have been tested for compatibility and scalability, and we recommend their [managed Kubernetes](https://www.scaleway.com/en/kubernetes-kapsule/) service.
+Their French deployment regions e.g. France are excellent for network latency and data sovereignty. For hosted GPUs, try [rendering with P100](https://www.scaleway.com/en/gpu-render-instances/).
+
+If you have any questions, feel free to ask on our [Discord community](https://qdrant.to/discord).
+
+
+
+
diff --git a/qdrant-landing/content/documentation/tutorials/rag-chatbot-vultr-dspy-ollama.md b/qdrant-landing/content/documentation/examples/rag-chatbot-vultr-dspy-ollama.md
similarity index 99%
rename from qdrant-landing/content/documentation/tutorials/rag-chatbot-vultr-dspy-ollama.md
rename to qdrant-landing/content/documentation/examples/rag-chatbot-vultr-dspy-ollama.md
index c2621d5f6..0a49bfbe5 100644
--- a/qdrant-landing/content/documentation/tutorials/rag-chatbot-vultr-dspy-ollama.md
+++ b/qdrant-landing/content/documentation/examples/rag-chatbot-vultr-dspy-ollama.md
@@ -1,6 +1,8 @@
---
title: Private RAG Information Extraction Engine
weight: 32
+aliases:
+ - /documentation/tutorials/rag-chatbot-vultr-dspy-ollama/
---
# Private RAG Information Extraction Engine
diff --git a/qdrant-landing/content/documentation/tutorials/rag-contract-management-stackit-aleph-alpha.md b/qdrant-landing/content/documentation/examples/rag-contract-management-stackit-aleph-alpha.md
similarity index 99%
rename from qdrant-landing/content/documentation/tutorials/rag-contract-management-stackit-aleph-alpha.md
rename to qdrant-landing/content/documentation/examples/rag-contract-management-stackit-aleph-alpha.md
index 1f61877cb..c3dd910a0 100644
--- a/qdrant-landing/content/documentation/tutorials/rag-contract-management-stackit-aleph-alpha.md
+++ b/qdrant-landing/content/documentation/examples/rag-contract-management-stackit-aleph-alpha.md
@@ -1,6 +1,8 @@
---
title: Region-Specific Contract Management System
weight: 28
+aliases:
+ - /documentation/tutorials/rag-contract-management-stackit-aleph-alpha/
---
# Region-Specific Contract Management System
diff --git a/qdrant-landing/content/documentation/tutorials/rag-customer-support-cohere-airbyte-aws.md b/qdrant-landing/content/documentation/examples/rag-customer-support-cohere-airbyte-aws.md
similarity index 99%
rename from qdrant-landing/content/documentation/tutorials/rag-customer-support-cohere-airbyte-aws.md
rename to qdrant-landing/content/documentation/examples/rag-customer-support-cohere-airbyte-aws.md
index 17c14101c..ecd2bafb4 100644
--- a/qdrant-landing/content/documentation/tutorials/rag-customer-support-cohere-airbyte-aws.md
+++ b/qdrant-landing/content/documentation/examples/rag-customer-support-cohere-airbyte-aws.md
@@ -1,6 +1,8 @@
---
title: Question-Answering System for AI Customer Support
weight: 26
+aliases:
+ - /documentation/tutorials/rag-customer-support-cohere-airbyte-aws/
---
# Question-Answering System for AI Customer Support
diff --git a/qdrant-landing/content/documentation/examples/recommendation-system-ovhcloud.md b/qdrant-landing/content/documentation/examples/recommendation-system-ovhcloud.md
new file mode 100644
index 000000000..aa1de2bc3
--- /dev/null
+++ b/qdrant-landing/content/documentation/examples/recommendation-system-ovhcloud.md
@@ -0,0 +1,239 @@
+---
+title: Movie Recommendation System
+weight: 34
+aliases:
+ - /documentation/tutorials/recommendation-system-ovhcloud/
+---
+
+# Movie Recommendation System
+
+| Time: 120 min | Level: Advanced | Output: [GitHub](https://github.com/infoslack/qdrant-example/blob/main/HC-demo/HC-OVH.ipynb) |
+| --- | ----------- | ----------- |----------- |
+
+In this tutorial, you will build a mechanism that recommends movies based on defined preferences. Vector databases like Qdrant are good for storing high-dimensional data, such as user and item embeddings. They can enable personalized recommendations by quickly retrieving similar entries based on advanced indexing techniques. In this specific case, we will use [sparse vectors](/articles/sparse-vectors/) to create an efficient and accurate recommendation system.
+
+**Privacy and Sovereignty:** Since preference data is proprietary, it should be stored in a secure and controlled environment. Our vector database can easily be hosted on [OVHcloud](https://ovhcloud.com/), our trusted [Qdrant Hybrid Cloud](/documentation/hybrid-cloud/) partner. This means that Qdrant can be run from your OVHcloud region, but the database itself can still be managed from within Qdrant Cloud's interface. Both products have been tested for compatibility and scalability, and we recommend their [managed Kubernetes](https://www.ovhcloud.com/en/public-cloud/kubernetes/) service.
+
+> To see the entire output, use our [notebook with complete instructions](https://github.com/infoslack/qdrant-example/blob/main/HC-demo/HC-OVH.ipynb).
+
+## Components
+
+- **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/).
+
+**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.
+
+## Prerequisites
+
+Download and unzip the MovieLens dataset:
+
+```shell
+mkdir -p data
+wget https://files.grouplens.org/datasets/movielens/ml-1m.zip
+unzip ml-1m.zip -d data
+```
+
+The necessary * libraries are installed using `pip`, including `pandas` for data manipulation, `qdrant-client` for interfacing with Qdrant, and `*-dotenv` for managing environment variables.
+
+```python
+!pip install -U \
+ pandas \
+ qdrant-client \
+ *-dotenv
+```
+
+The `.env` file is used to store sensitive information like the Qdrant host URL and API key securely.
+
+```shell
+QDRANT_HOST
+QDRANT_API_KEY
+```
+Load all environment variables into the setup:
+
+```python
+import os
+from dotenv import load_dotenv
+load_dotenv('./.env')
+```
+
+## Implementation
+
+Load the data from the MovieLens dataset into pandas DataFrames to facilitate data manipulation and analysis.
+
+```python
+from qdrant_client import QdrantClient, models
+import pandas as pd
+```
+Load user data:
+```python
+users = pd.read_csv('data/ml-1m/users.dat', sep='::', names=['user_id', 'gender', 'age', 'occupation', 'zip'], engine='*')
+users.head()
+```
+Add movies:
+```python
+movies = pd.read_csv('data/ml-1m/movies.dat', sep='::', names=['movie_id', 'title', 'genres'], engine='*', encoding='latin-1')
+movies.head()
+```
+Finally, add the ratings:
+```python
+ratings = pd.read_csv( 'data/ml-1m/ratings.dat', sep='::', names=['user_id', 'movie_id', 'rating', 'timestamp'], engine='*')
+ratings.head()
+```
+
+### Normalize the ratings
+
+Sparse vectors can use advantage of negative values, so we can normalize ratings to have a mean of 0 and a standard deviation of 1. This normalization ensures that ratings are consistent and centered around zero, enabling accurate similarity calculations. In this scenario we can take into account movies that we don't like.
+
+```python
+ratings.rating = (ratings.rating - ratings.rating.mean()) / ratings.rating.std()
+```
+To get the results:
+
+```python
+ratings.head()
+```
+
+### Data preparation
+
+Now you will transform user ratings into sparse vectors, where each vector represents ratings for different movies. This step prepares the data for indexing in Qdrant.
+
+First, create a collection with configured sparse vectors. For sparse vectors, you don't need to specify the dimension, because it's extracted from the data automatically.
+
+```python
+from collections import defaultdict
+
+user_sparse_vectors = defaultdict(lambda: {"values": [], "indices": []})
+
+for row in ratings.itertuples():
+ user_sparse_vectors[row.user_id]["values"].append(row.rating)
+ user_sparse_vectors[row.user_id]["indices"].append(row.movie_id)
+```
+Connect to Qdrant and create a collection called **movielens**:
+
+```python
+client = QdrantClient(
+ url = os.getenv("QDRANT_HOST"),
+ api_key = os.getenv("QDRANT_API_KEY")
+)
+
+client.create_collection(
+ "movielens",
+ vectors_config={},
+ sparse_vectors_config={
+ "ratings": models.SparseVectorParams()
+ }
+)
+```
+
+Upload user ratings to the **movielens** collection in Qdrant as sparse vectors, along with user metadata. This step populates the database with the necessary data for recommendation generation.
+
+```python
+def data_generator():
+ for user in users.itertuples():
+ yield models.PointStruct(
+ id=user.user_id,
+ vector={
+ "ratings": user_sparse_vectors[user.user_id]
+ },
+ payload=user._asdict()
+ )
+
+client.upload_points(
+ "movielens",
+ data_generator()
+)
+```
+
+## Recommendations
+
+Personal movie ratings are specified, where positive ratings indicate likes and negative ratings indicate dislikes. These ratings serve as the basis for finding similar users with comparable tastes.
+
+Personal ratings are converted into a sparse vector representation suitable for querying Qdrant. This vector represents the user's preferences across different movies.
+
+Let's try to recommend something for ourselves:
+
+```
+1 = Like
+-1 = dislike
+```
+
+```python
+# Search with movies[movies.title.str.contains("Matrix", case=False)].
+
+my_ratings = {
+ 2571: 1, # Matrix
+ 329: 1, # Star Trek
+ 260: 1, # Star Wars
+ 2288: -1, # The Thing
+ 1: 1, # Toy Story
+ 1721: -1, # Titanic
+ 296: -1, # Pulp Fiction
+ 356: 1, # Forrest Gump
+ 2116: 1, # Lord of the Rings
+ 1291: -1, # Indiana Jones
+ 1036: -1 # Die Hard
+}
+
+inverse_ratings = {k: -v for k, v in my_ratings.items()}
+
+def to_vector(ratings):
+ vector = models.SparseVector(
+ values=[],
+ indices=[]
+ )
+ for movie_id, rating in ratings.items():
+ vector.values.append(rating)
+ vector.indices.append(movie_id)
+ return vector
+```
+
+Query Qdrant to find users with similar tastes based on the provided personal ratings. The search returns a list of similar users along with their ratings, facilitating collaborative filtering.
+
+```python
+results = client.search(
+ "movielens",
+ query_vector=models.NamedSparseVector(
+ name="ratings",
+ vector=to_vector(my_ratings)
+ ),
+ with_vectors=True, # We will use those to find new movies
+ limit=20
+)
+```
+
+Movie scores are computed based on how frequently each movie appears in the ratings of similar users, weighted by their ratings. This step identifies popular movies among users with similar tastes. Calculate how frequently each movie is found in similar users' ratings
+
+```python
+def results_to_scores(results):
+ movie_scores = defaultdict(lambda: 0)
+
+ for user in results:
+ user_scores = user.vector['ratings']
+ for idx, rating in zip(user_scores.indices, user_scores.values):
+ if idx in my_ratings:
+ continue
+ movie_scores[idx] += rating
+
+ return movie_scores
+```
+
+The top-rated movies are sorted based on their scores and printed as recommendations for the user. These recommendations are tailored to the user's preferences and aligned with their tastes. Sort movies by score and print top five:
+
+```python
+movie_scores = results_to_scores(results)
+top_movies = sorted(movie_scores.items(), key=lambda x: x[1], reverse=True)
+
+for movie_id, score in top_movies[:5]:
+ print(movies[movies.movie_id == movie_id].title.values[0], score)
+```
+
+## Result
+
+```shell
+Star Wars: Episode V - The Empire Strikes Back (1980) 20.02387858
+Star Wars: Episode VI - Return of the Jedi (1983) 16.443184379999998
+Princess Bride, The (1987) 15.840068229999996
+Raiders of the Lost Ark (1981) 14.94489462
+Sixth Sense, The (1999) 14.570322149999999
+```
\ No newline at end of file
diff --git a/qdrant-landing/content/documentation/guides/installation.md b/qdrant-landing/content/documentation/guides/installation.md
index 69a7c8fc6..85d441d32 100644
--- a/qdrant-landing/content/documentation/guides/installation.md
+++ b/qdrant-landing/content/documentation/guides/installation.md
@@ -47,7 +47,7 @@ All Qdrant instances in a cluster must be able to:
Qdrant can be installed in different ways depending on your needs:
-For production, you can use our Qdrant Cloud to run Qdrant either fully managed in our infrastructure or with Hybrid SaaS in yours.
+For production, you can use our Qdrant Cloud to run Qdrant either fully managed in our infrastructure or with Hybrid Cloud in yours.
For testing or development setups, you can run the Qdrant container or as a binary executable.
diff --git a/qdrant-landing/content/documentation/hybrid-cloud/_index.md b/qdrant-landing/content/documentation/hybrid-cloud/_index.md
index cdad4d417..9abf40776 100644
--- a/qdrant-landing/content/documentation/hybrid-cloud/_index.md
+++ b/qdrant-landing/content/documentation/hybrid-cloud/_index.md
@@ -1,6 +1,30 @@
---
title: Hybrid Cloud
-weight: 20
-# If the index.md file is empty, the link to the section will be hidden from the sidebar
-is_empty: true
----
\ No newline at end of file
+weight: 15
+---
+
+# Qdrant Hybrid Cloud
+
+With Qdrant Hybrid Cloud you can bring your own cloud and manage it via Qdrant Cloud. Specifically, you can attach your own infrastructure as a private environment to our managed service. This lets you use Qdrant Cloud's UI to oversee your clusters, but they still remain within your infrastructure. **All Qdrant databases will operate solely within your network, using your storage and compute resources.**
+
+**How it works:** When you onboard a Kubernetes cluster to a Hybrid Cloud Environment, you can deploy the Qdrant Kubernetes Operator into this cluster. This operator will manage Qdrant databases within your Kubernetes cluster. It will create an outgoing connection to the Qdrant Cloud at `cloud.qdrant.io` on port `443`. You can then benefit from the same cloud management features and transport telemetry as is available with any managed Qdrant Cloud cluster.
+
+
+
+**Accessing Hybrid Cloud:** First, you will need to sign up for Hybrid Cloud. To do so, create a [Qdrant Cloud account](https://cloud.qdrant.io/login). You should get access to the Qdrant Cloud console. To activate Hybrid Cloud, go to the **Hybrid Cloud** tab. You will need to enter your **Company** and **Billing Information**. Then, you can request access.
+
+**Setup instructions:** To begin using Qdrant Hybrid Cloud, [read our installation guide](/documentation/hybrid-cloud/hybrid-cloud-setup/).
+
+## Upcoming roadmap items
+
+We plan to introduce the following configuration options directly in the Qdrant Cloud Console in the future. If you need any of them beforehand, please contact our Support team.
+
+* Self-service environment deletion
+* Node selectors
+* Tolerations
+* Affinities and anti-affinities
+* Service types and annotations
+* Ingresses
+* Network policies
+* Storage classes
+* Volume snapshot classes
diff --git a/qdrant-landing/content/documentation/hybrid-cloud/hybrid-cloud-setup.md b/qdrant-landing/content/documentation/hybrid-cloud/hybrid-cloud-setup.md
new file mode 100644
index 000000000..281ad4e67
--- /dev/null
+++ b/qdrant-landing/content/documentation/hybrid-cloud/hybrid-cloud-setup.md
@@ -0,0 +1,160 @@
+---
+title: Hybrid Cloud setup
+weight: 1
+---
+
+# Creating a Hybrid Cloud Environment
+
+This section list all the requirements needed to setup a Qdrant cluster in your Hybrid Cloud Environment.
+
+## Prerequisites
+
+- **Kubernetes cluster:** To create a Hybrid Cloud Environment, you need a [standard compliant](https://www.cncf.io/training/certification/software-conformance/) Kubernetes cluster. You can run this cluster in any cloud, on-premise or edge environment, with distributions that range from AWS EKS to VMWare vSphere.
+- **Storage:** For storage, you need to set up the Kubernetes cluster with a Container Storage Interface (CSI) driver that provides block storage. For vertical scaling, the CSI driver needs to support volume expansion. For backups and restores, the driver needs to support CSI snapshots and restores.
+
+
+- **Permissions:** To install the Qdrant Kubernetes Operator you need to have `cluster-admin` access in your Kubernetes cluster.
+- **Connection:** The Qdrant Kubernetes Operator in your cluster needs to be able to connect to Qdrant Cloud. It will create an outgoing connection to `cloud.qdrant.io` on port `443`.
+- **Locations:** By default, the Qdrant Cloud Agent and Operator pulls Helm charts and container images from `registry.cloud.qdrant.io`. The Qdrant database container image is pulled from `docker.io`.
+
+> **Note:** You can also mirror these images and charts into your own registry and pull them from there.
+
+### Required artifacts
+
+Container images:
+- `docker.io/qdrant/qdrant`
+- `registry.cloud.qdrant.io/qdrant/qdrant-cloud-agent`
+- `registry.cloud.qdrant.io/qdrant/qdrant-operator`
+- `registry.cloud.qdrant.io/qdrant/qdrant-cloud-cluster-manager`
+- `registry.cloud.qdrant.io/qdrant/prometheus`
+- `registry.cloud.qdrant.io/qdrant/prometheus-config-reloader`
+- `registry.cloud.qdrant.io/qdrant/kube-state-metrics`
+
+Open Containers Initiative (OCI) Helm charts:
+- `registry.cloud.qdrant.io/qdrant-charts/qdrant-cloud-agent`
+- `registry.cloud.qdrant.io/qdrant-charts/qdrant-operator`
+- `registry.cloud.qdrant.io/qdrant-charts/prometheus`
+
+## Installation
+
+1. To set up Hybrid Cloud, open the Qdrant Cloud Console at [cloud.qdrant.io](https://cloud.qdrant.io). On the dashboard, select **Hybrid Cloud**.
+
+2. Before creating your first Hybrid Cloud Environment, you have to provide billing information and accept the Hybrid Cloud license agreement. The installation wizard will guide you through the process.
+
+> **Note:** You will only be charged for the Qdrant cluster you create in a Hybrid Cloud Environment, but not for the environment itself.
+
+3. Now you can specify the following:
+
+- **Name:** A name for the Hybrid Cloud Environment
+- **Kubernetes Namespace:** The Kubernetes namespace for the operator and agent. Once you select a namespace, you can't change it.
+
+4. You can then enter the YAML configuration for your Kubernetes operator. Qdrant supports a specific list of configuration options, as described in the [Qdrant Operator configuration](/documentation/hybrid-cloud/operator-configuration/) section.
+
+5. (Optional) If you have special requirements for any of the following, activate the **Show advanced configuration** option:
+
+- Proxy server
+- Container registry URL for Qdrant Operator and Agent images. The default is .
+- Helm chart repository URL for the Qdrant Operator and Agent. The default is .
+- CA certificate
+- Log level for the operator and agent
+
+6. Once complete, click **Create**.
+
+> **Note:** All settings but the Kubernetes namespace can be changed later.
+
+### Generate Installation Command
+
+After creating your Hybrid Cloud, select **Generate Installation Command** to generate a script that you can run in your Kubernetes cluster which will perform the initial installation of the Kubernetes operator and agent. It will:
+
+- Create the Kubernetes namespace
+- Set up the necessary secrets with credentials to access the Qdrant container registry and the Qdrant Cloud API.
+- Sign in to the Helm registry at `registry.cloud.qdrant.io`
+- Install the Qdrant cloud agent and Kubernetes operator chart
+
+You need this command only for the initial installation. After that, you can update the agent and operator using the Qdrant Cloud Console.
+
+## Creating a Qdrant cluster
+
+Once you have created a Hybrid Cloud Environment, you can create a Qdrant cluster in that enviroment. Use the same process to [Create a cluster](/documentation/cloud/create-cluster/). Make sure to select your Hybrid Cloud Environment as the target.
+
+### Authentication at your Qdrant clusters
+
+In Hybrid Cloud the authentication information is provided with Kubernetes secrets.
+
+You can configure authentication for your Qdrant clusters in the "Configuration" section of the Qdrant Cluster detail page. There you can configure the Kubernetes secret name and key to be used as an API key and/or read-only API key.
+
+One way to create a secret is with kubectl:
+
+```shell
+kubectl create secret generic qdrant-api-key --from-literal=api-key=your-secret-api-key
+```
+
+With this command the secret name would be `qdrant-api-key` and the key would be `api-key`.
+
+### Exposing Qdrant clusters to your client applications
+
+You can expose your Qdrant clusters to your client applications using Kubernetes services and ingresses. By default, a `ClusterIP` service is created for each Qdrant cluster.
+
+Within your Kubernetes cluster, you can access the Qdrant cluster using the service name and port:
+
+```
+http://qdrant-9a9f48c7-bb90-4fb2-816f-418a46a74b24.qdrant-namespace.svc:6333
+```
+
+This endpoint is also visible on the cluster detail page.
+
+If you want to access the database from your local developer machine, you can use `kubectl port-forward` to forward the service port to your local machine:
+
+```
+kubectl -n qdrant-namespace port-forward service/qdrant-9a9f48c7-bb90-4fb2-816f-418a46a74b24 6333:6333
+```
+
+You can also expose the database outside the Kubernetes cluster with a `LoadBalancer` (if supported in your Kubernetes environment) or `NodePort` service or an ingress.
+
+A simple Loadbalancer service could look like this:
+
+```yaml
+apiVersion: v1
+kind: Service
+metadata:
+ name: qdrant-9a9f48c7-bb90-4fb2-816f-418a46a74b24-lb
+ namespace: qdrant-namespace
+spec:
+ type: LoadBalancer
+ ports:
+ - name: http
+ port: 6333
+ - name: grpc
+ port: 6334
+ selector:
+ app: qdrant
+ cluster-id: 9a9f48c7-bb90-4fb2-816f-418a46a74b24
+```
+
+An ingress could look like this:
+
+```yaml
+apiVersion: networking.k8s.io/v1
+kind: Ingress
+metadata:
+ name: qdrant-9a9f48c7-bb90-4fb2-816f-418a46a74b24
+ namespace: qdrant-namespace
+spec:
+ rules:
+ - host: qdrant-9a9f48c7-bb90-4fb2-816f-418a46a74b24.your-domain.com
+ http:
+ paths:
+ - path: /
+ pathType: Prefix
+ backend:
+ service:
+ name: qdrant-9a9f48c7-bb90-4fb2-816f-418a46a74b24
+ port:
+ number: 6333
+```
+
+Please refer to the Kubernetes, ingress controller and cloud provider documention for more details.
+
+## Deleting a Hybrid Cloud Environment
+
+To delete a Hybrid Cloud Environment, first delete all Qdrant database clusters in it. Then please open a support ticket, referencing the `id` of the environment you wish to delete.
\ No newline at end of file
diff --git a/qdrant-landing/content/documentation/hybrid-cloud/networking-logging-monitoring.md b/qdrant-landing/content/documentation/hybrid-cloud/networking-logging-monitoring.md
new file mode 100644
index 000000000..bd44c5f9c
--- /dev/null
+++ b/qdrant-landing/content/documentation/hybrid-cloud/networking-logging-monitoring.md
@@ -0,0 +1,88 @@
+---
+title: Networking, logging & monitoring
+weight: 3
+---
+
+## Network policies
+
+For security reasons, each database cluster is secured with tight network policies. By default, database pods only allow egress traffic between each and only allow ingress traffic from the operator for monitoring.
+
+To allow additional ingress or egress traffic, you can either deploy additional network policies on your own.
+
+```yaml
+apiVersion: networking.k8s.io/v1
+kind: NetworkPolicy
+metadata:
+ name: qdrant-9a9f48c7-bb90-4fb2-816f-418a46a74b24
+ namespace: qdrant-namespace
+spec:
+ podSelector:
+ matchLabels:
+ app: qdrant
+ cluster-id: 9a9f48c7-bb90-4fb2-816f-418a46a74b24
+ policyTypes:
+ - Ingress
+ ingress:
+ - from:
+ - ipBlock:
+ cidr: 192.168.0.0/22
+ - podSelector:
+ matchLabels:
+ app: client-app
+ namespaceSelector:
+ matchLabels:
+ kubernetes.io/metadata.name: client-namespace
+ - podSelector:
+ matchLabels:
+ app: traefik
+ namespaceSelector:
+ matchLabels:
+ kubernetes.io/metadata.name: kube-system
+ ports:
+ - protocol: TCP
+ port: 6333
+ - protocol: TCP
+ port: 6334
+```
+
+Or you can modify the default network policies in the Hybrid Cloud environment configuration:
+
+```yaml
+qdrant:
+ networkPolicies:
+ ingress:
+ - from:
+ - ipBlock:
+ cidr: 192.168.0.0/22
+ - podSelector:
+ matchLabels:
+ app: client-app
+ namespaceSelector:
+ matchLabels:
+ kubernetes.io/metadata.name: client-namespace
+ - podSelector:
+ matchLabels:
+ app: traefik
+ namespaceSelector:
+ matchLabels:
+ kubernetes.io/metadata.name: kube-system
+ ports:
+ - port: 6333
+ protocol: TCP
+ - port: 6334
+ protocol: TCP
+```
+
+## Logging
+
+You can access the logs with kubectl or the Kubernetes log management tool of your choice. For example:
+
+```bash
+kubectl -n qdrant-namespace logs -l app=qdrant,cluster-id=9a9f48c7-bb90-4fb2-816f-418a46a74b24
+```
+
+**Configuring log levels:** You can configure log levels for the databases individually in the configuration section of the Qdrant Cluster detail page. The log level for the **Qdrant Cloud Agent** and **Operator** can be set in the [Hybrid Cloud Environment configuration](/documentation/hybrid-cloud/operator-configuration/).
+
+## Monitoring
+
+The Qdrant Cloud console gives you access to basic metrics about CPU, memory and disk usage of your Qdrant clusters. You can also access Prometheus metrics endpoint of yourQdrant databases. Finally, you can use a Kubernetes workload monitoring tool of your choice to monitor your Qdrant clusters.
diff --git a/qdrant-landing/content/documentation/hybrid-cloud/operator-configuration.md b/qdrant-landing/content/documentation/hybrid-cloud/operator-configuration.md
new file mode 100644
index 000000000..4915d2a31
--- /dev/null
+++ b/qdrant-landing/content/documentation/hybrid-cloud/operator-configuration.md
@@ -0,0 +1,97 @@
+---
+title: Qdrant Operator configuration
+weight: 2
+---
+
+# Qdrant Operator configuration
+
+You should configure the Qdrant Operator with the configuration for Hybrid Cloud.
+Use the following options, in YAML format:
+
+```yaml
+# Configuration for the Qdrant operator
+settings:
+ # Does the operator run inside a Kubernetes cluster (kubernetes) or outside (local)
+ app_environment: kubernetes
+ # Retention for the backup history of Qdrant clusters
+ backupHistoryRetentionDays: 2
+ # Timeout configuration for the Qdrant operator operations
+ operationTimeout: 7200 # 2 hours
+ handlerTimeout: 21600 # 6 hours
+ backupTimeout: 12600 # 3.5 hours
+ # Incremental backoff configuration for the Qdrant operator operations
+ backOff:
+ minDelay: 5
+ maxDelay: 300
+ increment: 5
+ # Cluster-manager configuration for a Qdrant cluster (experimental)
+ clusterManager:
+ image:
+ repository: qdrant/qdrant-cloud-cluster-manager
+ tag: 0.1.2
+ pullInterval: 10
+ logSize: 10
+ debug: false
+ # node_selector: {}
+ # tolerations: []
+ # Default ingress configuration for a Qdrant cluster
+ ingress:
+ enabled: false
+ provider: KubernetesIngress # or NginxIngress
+# kubernetesIngress:
+# ingressClassName: ""
+ # Default storage configuration for a Qdrant cluster
+# storage:
+# # Default VolumeSnapshotClass for a Qdrant cluster
+# snapshot_class: "csi-snapclass"
+# # Default StorageClass for a Qdrant cluster, uses cluster default StorageClass if not set
+# default_storage_class_names:
+# # StorageClass for DB volumes
+# db: ""
+# # StorageClass for snapshot volumes
+# snapshot: ""
+ # Default scheduling configuration for a Qdrant cluster
+# scheduling:
+# default_topology_spread_constraints: []
+# default_pod_disruption_budget: {}
+ qdrant:
+ # Default security context for Qdrant cluster
+# securityContext:
+# enabled: false
+# user: ""
+# fsGroup: ""
+# group: ""
+ # Default Qdrant image configuration
+# image:
+# pull_secret: ""
+# pull_policy: IfNotPresent
+# repository: qdrant/qdrant
+ # Default Qdrant log_level
+# log_level: INFO
+ # Default network policies to create for a qdrant cluster
+ networkPolicies:
+# ingress:
+# - from:
+# - podSelector:
+# matchLabels:
+# app.kubernetes.io/name: traefik
+# namespaceSelector:
+# matchLabels:
+# kubernetes.io/metadata.name: kube-system
+# ports:
+# - protocol: TCP
+# port: 6333
+# - protocol: TCP
+# port: 6334
+# - protocol: TCP
+# port: 6335
+ # Allow DNS resolution from qdrant pods at Kubernetes internal DNS server
+ egress:
+ - to:
+ - namespaceSelector:
+ matchLabels:
+ kubernetes.io/metadata.name: kube-system
+ ports:
+ - protocol: UDP
+ port: 53
+```
diff --git a/qdrant-landing/content/documentation/hybrid-cloud/platform-deployment-options.md b/qdrant-landing/content/documentation/hybrid-cloud/platform-deployment-options.md
new file mode 100644
index 000000000..e4213e5b8
--- /dev/null
+++ b/qdrant-landing/content/documentation/hybrid-cloud/platform-deployment-options.md
@@ -0,0 +1,38 @@
+---
+title: Deployment platforms
+weight: 4
+---
+
+# Platform Deployment Options
+
+## Amazon Web Services
+
+
+## Google Cloud Platform
+
+
+## Microsoft Azure
+
+
+## DigitalOcean
+
+
+## Oracle Cloud Infrastructure
+
+
+## Vultr
+
+
+## Scaleway
+
+
+## OVHcloud
+
+
+## STACKIT
+
+
+## Red Hat OpenShift
+
+
+## Linode
\ No newline at end of file
diff --git a/qdrant-landing/content/documentation/interfaces.md b/qdrant-landing/content/documentation/interfaces/_index.md
similarity index 99%
rename from qdrant-landing/content/documentation/interfaces.md
rename to qdrant-landing/content/documentation/interfaces/_index.md
index 1a5da3f66..0fb0e2541 100644
--- a/qdrant-landing/content/documentation/interfaces.md
+++ b/qdrant-landing/content/documentation/interfaces/_index.md
@@ -1,6 +1,6 @@
---
title: Interfaces
-weight: 14
+weight: 11
aliases:
- /documentation/interfaces/
---
diff --git a/qdrant-landing/content/documentation/web-ui.md b/qdrant-landing/content/documentation/interfaces/web-ui.md
similarity index 96%
rename from qdrant-landing/content/documentation/web-ui.md
rename to qdrant-landing/content/documentation/interfaces/web-ui.md
index 75c6b238d..32149889b 100644
--- a/qdrant-landing/content/documentation/web-ui.md
+++ b/qdrant-landing/content/documentation/interfaces/web-ui.md
@@ -1,6 +1,8 @@
---
title: Qdrant Web UI
-weight: 20
+weight: 1
+aliases:
+ - /documentation/web-ui/
---
# Qdrant Web UI
diff --git a/qdrant-landing/content/documentation/tutorials/_index.md b/qdrant-landing/content/documentation/tutorials/_index.md
index ffb241326..d7bba91ba 100644
--- a/qdrant-landing/content/documentation/tutorials/_index.md
+++ b/qdrant-landing/content/documentation/tutorials/_index.md
@@ -14,32 +14,13 @@ These tutorials demonstrate different ways you can build vector search into your
| Essential How-Tos | Description | Stack |
|---------------------------------------------------------------------------------|-------------------------------------------------------------------|---------------------------------------------|
-| [Configure Optimal Use](../tutorials/optimize/) | Configure Qdrant collections for best resource use. | Qdrant |
-| [Separate Partitions](../tutorials/multiple-partitions/) | Serve vectors for many independent users. | Qdrant |
-| [Bulk Upload Vectors](../tutorials/bulk-upload/) | Upload a large scale dataset. | Qdrant |
-| [Create Dataset Snapshots](../tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. | Qdrant |
-| [Asynchronous API](../tutorials/async-api/) | Communicate with Qdrant server asynchronously with Python SDK. | Qdrant, Python |
-| [Load HuggingFace Dataset](../tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant | Qdrant, Python, datasets |
-| [Troubleshooting](../tutorials/common-errors/) | Solutions to common errors and fixes | Qdrant |
-
-| Beginner Tutorials | Description | Stack |
-|---------------------------------------------------------------------------------|-------------------------------------------------------------------|---------------------------------------------|
| [Semantic Search for Beginners](../tutorials/search-beginners/) | Create a simple search engine locally in minutes. | Qdrant |
| [Simple Neural Search](../tutorials/neural-search/) | Build and deploy a neural search that browses startup data. | Qdrant, BERT, FastAPI |
-| [Aleph Alpha Search](../tutorials/aleph-alpha-search/) | Build a multimodal search that combines text and image data. | Qdrant, Aleph Alpha |
-| [Mighty Semantic Search](../tutorials/mighty/) | Build a simple semantic search with an on-demand NLP service. | Qdrant, Mighty |
-| [Multitenancy with LlamaIndex](../tutorials/llama-index-multitenancy/) | Handle data coming from multiple users in LlamaIndex. | Qdrant, Python, LlamaIndex |
+| [Neural Search with FastEmbed](../tutorials/neural-search-fastembed/) | Build and deploy a neural search with our FastEmbed library. | Qdrant |
+| [Bulk Upload Vectors](../tutorials/bulk-upload/) | Upload a large scale dataset. | Qdrant |
+| [Asynchronous API](../tutorials/async-api/) | Communicate with Qdrant server asynchronously with Python SDK. | Qdrant, Python |
+| [Create Dataset Snapshots](../tutorials/create-snapshot/) | Turn a dataset into a snapshot by exporting it from a collection. | Qdrant |
+| [Load HuggingFace Dataset](../tutorials/huggingface-datasets/) | Load a Hugging Face dataset to Qdrant | Qdrant, Python, datasets |
| [Measure retrieval quality](../tutorials/retrieval-quality/) | Measure and fine-tune the retrieval quality | Qdrant, Python, datasets |
| [Use semantic search to navigate your codebase](../tutorials/code-search/) | Implement semantic search application for code search task | Qdrant, Python, sentence-transformers, Jina |
-| [Implement custom connector for Cohere RAG](../tutorials/cohere-rag-connector/) | Bring data stored in Qdrant to Cohere RAG | Qdrant, Cohere, FastAPI |
-
-
-| Advanced Tutorials | Description | Stack |
-|---------------------------------------------------------------------------------|-------------------------------------------------------------------|---------------------------------------------|
-| [Chatbot for Interactive Learning](../tutorials/rag-chatbot-red-hat-openshift-haystack/) | Build a Private RAG Chatbot for Interactive Learning | Qdrant, Haystack, OpenShift |
-| [Information Extraction Engine](../tutorials/rag-chatbot-vultr-dspy-ollama/) | Build a Private RAG Information Extraction Engine | Qdrant, Vultr, DSPy, Ollama |
-| [System for Employee Onboarding](../tutorials/natural-language-search-oracle-cloud-infrastructure-cohere-langchain/) | Build a RAG System for Employee Onboarding | Qdrant, Cohere, LangChain |
-| [System for Contract Management](../tutorials/rag-contract-management-stackit-aleph-alpha/) | Build a Region-Specific RAG System for Contract Management | Qdrant, Aleph Alpha, STACKIT |
-| [Question-Answering System for Customer Support](../tutorials/rag-customer-support-cohere-airbyte-aws/) | Build a RAG System for AI Customer Support | Qdrant, Cohere, Airbyte, AWS |
-| [Hybrid Search on PDF Documents](../tutorials/hybrid-search-llamaindex-jinaai/) | Develop a Hybrid Search System for Product PDF Manuals | Qdrant, LlamaIndex, Jina AI
diff --git a/qdrant-landing/content/documentation/tutorials/how-to.md b/qdrant-landing/content/documentation/tutorials/how-to.md
deleted file mode 100644
index 14299c5e2..000000000
--- a/qdrant-landing/content/documentation/tutorials/how-to.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: HowTos
-weight: 100
-draft: true
----
-
-
-
-
diff --git a/qdrant-landing/content/surveys/hybrid-saas.md b/qdrant-landing/content/surveys/hybrid-saas.md
index 8351f4a21..83101dd41 100644
--- a/qdrant-landing/content/surveys/hybrid-saas.md
+++ b/qdrant-landing/content/surveys/hybrid-saas.md
@@ -1,9 +1,9 @@
---
-title: Join the waiting list for the Qdrant Hybrid SaaS solution.
-section_title: Request early access to the Qdrant Hybrid SaaS
+title: Join the waiting list for the Qdrant Hybrid Cloud solution.
+section_title: Request early access to the Qdrant Hybrid Cloud
form:
- id: 0
- header: "Get Early Access to Qdrant Hybrid SaaS"
+ header: "Get Early Access to Qdrant Hybrid Cloud"
label: All right! 😊 What is your e-mail? *
placeholder: name@example.com
type: email
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