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2024-04-14 20:44:58 -07:00
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qdrant-landing/content/documentation/examples/rag-chatbot-scaleway.md
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@@ -24,6 +24,13 @@ A notebook for this tutorial is available on [GitHub](https://github.com/qdrant/
- **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.
## Deploying Qdrant Hybrid Cloud on Scaleway
[Scaleway Kapsule](https://www.scaleway.com/en/kubernetes-kapsule/) and [Kosmos](https://www.scaleway.com/en/kubernetes-kosmos/) are managed Kubernetes services from [Scaleway](https://www.scaleway.com/en/). They abstract away the complexities of managing and operating a Kubernetes cluster. The primary difference being, Kapsule clusters are composed solely of Scaleway Instances. Whereas, a Kosmos cluster is a managed multi-cloud Kubernetes engine that allows you to connect instances from any cloud provider to a single managed Control-Plane.
1. To start using managed Kubernetes on Scaleway, follow the [platform-specific documentation](/documentation/hybrid-cloud/platform-deployment-options/#scaleway).
2. Once your Kubernetes clusters are up, [you can begin deploying Qdrant Hybrid Cloud](/documentation/hybrid-cloud).
## 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.