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2024-04-11 21:00:03 -07:00
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qdrant-landing/content/blog/hybrid-cloud-launch-partners.md
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- launch partners
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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 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.
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:
![hybrid-cloud-get-started](/blog/hybrid-cloud-launch-partners/hybrid-cloud-get-started.png)
- **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**
- **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
// Visual for Tutorials Partner Tutorials (Qdrant Hybrid Cloud )
---
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 on Digital Ocean
- How to Deploy Qdrant on Oracle Cloud
- How to Deploy Qdrant on Vultr
- How to Deploy Qdrant on Scaleway
- How to Deploy Qdrant on OVH Cloud
- 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.
[![hybrid-cloud-get-started](/blog/hybrid-cloud-launch-partners/hybrid-cloud-get-started.png)](https://cloud.qdrant.io/login)
qdrant-landing/content/documentation/examples/rag-chatbot-scaleway.md
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| 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 web scraping 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.
In this tutorial, you will build a RAG system that combines blog content ingestino 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).