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davidmyriel
2024-04-11 18:10:42 -07:00
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qdrant-landing/content/blog/hybrid-cloud-launch-partners.md
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draft: false draft: false
title: "Qdrant's Trusted Partners for Hybrid Cloud Deployment" title: "Qdrant's Trusted Partners for Hybrid Cloud Deployment"
slug: hybrid-cloud-launch-partners 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." 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, 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."
preview_image: /blog/hybrid-cloud-launch-partners/hybrid-cloud-launch-partners.png 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 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 author: Manuel Meyer
featured: false featured: false
tags: tags:
qdrant-landing/content/blog/hybrid-cloud-oracle-cloud-infrastructure.md
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--- ---
draft: true draft: false
title: "Oracle Cloud Infrastructure and Qdrant Hybrid Cloud" title: "OCI and Qdrant Hybrid Cloud for Maximum Data Sovereignty"
short_description: "A winning combination for enterprise-scale RAG consists of a strong framework and a scalable database." short_description: "Qdrant Hybrid Cloud is now available for OCI customers as a managed vector search engine for data-sensitive AI apps."
description: "A winning combination for enterprise-scale RAG consists of a strong framework and a scalable database." 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 preview_image: /blog/hybrid-cloud-oracle-cloud-infrastructure/hybrid-cloud-oracle-cloud-infrastructure.png
date: 2024-04-11T00:03:00Z date: 2024-04-11T00:03:00Z
author: Qdrant author: Qdrant
@@ -12,7 +12,7 @@ tags:
- Vector Database - Vector Database
--- ---
**Qdrant Hybrid Cloud is Now Available for OCI Customers: Managed Vector Search Engine for Data-Sensitive AI Applications** ****
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. 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.
qdrant-landing/content/blog/hybrid-cloud-red-hat-openshift.md
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draft: true draft: false
title: "Red Hat OpenShift and Qdrant Hybrid Cloud" title: "Red Hat OpenShift and Qdrant Hybrid Cloud Offer Seamless and Scalable AI"
short_description: "A winning combination for enterprise-scale RAG consists of a strong framework and a scalable database." short_description: "Qdrant brings managed vector databases to Red Hat OpenShift for large-scale GenAI."
description: "A winning combination for enterprise-scale RAG consists of a strong framework and a scalable database." 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 preview_image: /blog/hybrid-cloud-red-hat-openshift/hybrid-cloud-red-hat-openshift.png
date: 2024-04-11T00:04:00Z date: 2024-04-11T00:04:00Z
author: Qdrant author: Qdrant
@@ -12,8 +12,6 @@ tags:
- Vector Database - Vector Database
--- ---
**Qdrant Brings Managed Vector Database to Red Hat OpenShift Environments: A More Seamless Integration for Scalable AI Applications**
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. 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. 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.
qdrant-landing/content/documentation/examples/rag-chatbot-scaleway.md
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--- ---
title: Blog-Reading Web Scraper Chatbot on Scaleway title: Blog-Reading RAG Chatbot on Scaleway
weight: 35 weight: 35
aliases: aliases:
- /documentation/tutorials/rag-chatbot-scaleway/ - /documentation/tutorials/rag-chatbot-scaleway/
--- ---
# Blog-Reading Web Scraper Chatbot on Scaleway # Blog-Reading RAG Chatbot on Scaleway
| Time: 90 min | Level: Advanced |[GitHub](https://github.com/qdrant/examples/blob/master/langchain-lcel-rag/Langchain-LCEL-RAG-Demo.ipynb)| | | Time: 90 min | Level: Advanced |[GitHub](https://github.com/qdrant/examples/blob/master/langchain-lcel-rag/Langchain-LCEL-RAG-Demo.ipynb)| |
|--------------|-----------------|--|----| |--------------|-----------------|--|----|
qdrant-landing/content/documentation/examples/recommendation-system-ovhcloud.md
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Let's try to recommend something for ourselves: Let's try to recommend something for ourselves:
```
1 = Like 1 = Like
-1 = dislike -1 = dislike
```
```python ```python
# Search with movies[movies.title.str.contains("Matrix", case=False)]. # Search with movies[movies.title.str.contains("Matrix", case=False)].