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Nathan LeRoy
2026-03-17 13:49:21 -04:00
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qdrant-landing/content/documentation/cloud-quickstart.md
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<p align="center"><iframe width="560" height="315" src="https://www.youtube.com/embed/xvWIssi_cjQ?si=CLhFrUDpQlNog9mz&rel=0" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe></p>
Learn how to set up Qdrant Cloud and perform your first semantic search in just a few minutes. We'll use a sample dataset of menu items pre-embedded with the `BAAI/bge-small-en-v1.5` model.
Learn how to set up Qdrant Cloud and perform your first semantic search in just a few minutes. We'll use a sample dataset of menu items embedded with the `sentence-transformers/all-MiniLM-L6-v2` model via [Cloud Inference](/documentation/concepts/inference/).
## 1. Create a Cloud Cluster
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Once you have a cluster, the fastest way to get started is to use our official SDKs which provide a convenient interface for working with Qdrant in your preferred programming language.
```bash
pip install qdrant-client fastembed # for Python projects
# cargo add qdrant-client fastembed # for Rust projects
# npm install @qdrant/js-client-rest fastembed # for Node.js projects
#
pip install qdrant-client # for Python projects
# cargo add qdrant-client # for Rust projects
# npm install @qdrant/js-client-rest # for Node.js projects
# dotnet add package Qdrant.Client # for .NET projects
# go get github.com/qdrant/go-client # for Go projects
```
## 3. Connect to Qdrant Cloud
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```
## 5. Populate the collection
Next, we will populate the collection with menu items. Each item will be represented as a point in the collection, with its vector embedding and associated metadata.
Next, we will populate the collection with menu items. Each item will be represented as a point in the collection with its associated metadata. Instead of generating embeddings locally, we pass a `Document` object with the text and model name — Qdrant Cloud Inference handles the embedding automatically.
```python
menu_items = [