---
title: Cloud Quickstart
weight: 4
partition: cloud
aliases:
- ../cloud-quick-start
- cloud-quick-start
- cloud-quickstart
- cloud/quickstart-cloud/
- /documentation/quickstart-cloud/
---
# Quick Start with Qdrant Cloud
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.
## 1. Create a Cloud Cluster
1. Register for a [Cloud account](https://cloud.qdrant.io/signup) with your email, Google or Github credentials.
2. Go to **Clusters** and click **Create First Cluster**.
3. Copy your **API key** when prompted - you'll need it to connect. Store it somewhere safe as it won't be displayed again.
For detailed cluster setup instructions, see the [Cloud documentation](/documentation/cloud-intro/).
## 2. Install the Qdrant Client
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
```
## 3. Connect to Qdrant Cloud
Import the qdrant client and create a connection to your Qdrant Cloud cluster using your cluster URL and API key.
```python
from qdrant_client import QdrantClient
# connect to Qdrant Cloud
client = QdrantClient(
url="https://xyz-example.eu-central.aws.cloud.qdrant.io",
api_key="your-api-key",
)
```
```rust
use qdrant_client::Qdrant;
// Connect to Qdrant Cloud
let client = Qdrant::from_url("https://xyz-example.eu-central.aws.cloud.qdrant.io:6334")
.api_key("your-api-key")
.build()?;
```
```typescript
import { QdrantClient } from "@qdrant/js-client-rest";
const client = new QdrantClient({
url: "https://xyz-example.eu-central.aws.cloud.qdrant.io",
apiKey: "your-api-key",
});
```
```bash
# test the connection
curl -X GET \
'http://:6333/collections' \
--header 'api-key: '
```
## 4. Create our collection
We will use some sample menu items to demonstrate how to create a collection and add data to it. Each menu item has a name, description, price, and category. First, we need to create a collection in Qdrant to store our menu items.
```python
from qdrant_client.models import Distance, VectorParams
# create collection
client.create_collection(
collection_name="items",
vectors_config=VectorParams(size=384, distance=Distance.COSINE),
)
```
```rust
use qdrant_client::qdrant::{CreateCollectionBuilder, Distance, VectorParamsBuilder};
// create collection
client.create_collection(
CreateCollectionBuilder::new("items")
.vectors_config(VectorParamsBuilder::new(384, Distance::Cosine)),
)
.await?;
```
```typescript
await client.createCollection("items", {
vectors: { size: 384, distance: "Cosine" },
});
```
```bash
curl -X PUT \
'http://:6333/collections/items' \
--header 'api-key: ' \
--header 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"size": 384,
"distance": "Cosine"
}
}'
```
## 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.
```python
from qdrant_client.models import PointStruct
from fastembed import TextEmbedding
# load the embedding model
model = TextEmbedding('BAAI/bge-small-en-v1.5')
menu_items = [
("Pad Thai with Tofu", "Stir-fried rice noodles with tofu bean sprouts scallions and crushed peanuts in traditional tamarind sauce", "$13.95", "Noodles"),
("Grilled Salmon Fillet", "Wild-caught Atlantic salmon grilled with lemon butter and fresh herbs served with seasonal vegetables", "$24.50", "Seafood Entrees"),
("Mushroom Risotto", "Creamy arborio rice with mixed mushrooms parmesan truffle oil and fresh thyme", "$16.75", "Vegetarian"),
("Bibimbap Bowl", "Korean rice bowl with seasoned vegetables fried egg gochujang sauce and choice of protein", "$14.50", "Korean Bowls"),
("Falafel Wrap", "Crispy chickpea fritters with hummus tahini cucumber tomato and pickled vegetables in warm pita", "$11.25", "Mediterranean"),
("Shrimp Tacos", "Three soft tacos with grilled shrimp cabbage slaw chipotle aioli and fresh lime", "$13.00", "Tacos"),
("Vegetable Curry", "Mixed vegetables in aromatic coconut curry sauce with jasmine rice and naan bread", "$12.95", "Indian Curries"),
("Tuna Poke Bowl", "Fresh ahi tuna with avocado edamame cucumber seaweed salad over sushi rice with spicy mayo", "$16.50", "Poke Bowls"),
("Margherita Pizza", "Fresh mozzarella san marzano tomatoes basil and extra virgin olive oil on wood-fired crust", "$14.00", "Pizza"),
("Chicken Tikka Masala", "Tandoori chicken in creamy tomato sauce with aromatic spices served with basmati rice", "$15.95", "Indian Entrees"),
("Greek Salad", "Romaine lettuce tomatoes cucumbers kalamata olives feta cheese red onion with lemon oregano dressing", "$10.50", "Salads"),
("Lobster Roll", "Fresh Maine lobster meat with light mayo on toasted buttery roll served with chips", "$22.00", "Seafood Sandwiches"),
("Quinoa Buddha Bowl", "Organic quinoa with roasted chickpeas kale sweet potato tahini dressing and hemp seeds", "$13.50", "Healthy Bowls"),
("Beef Pho", "Traditional Vietnamese beef noodle soup with rice noodles fresh herbs bean sprouts and lime", "$12.75", "Noodle Soups"),
("Eggplant Parmesan", "Breaded eggplant layered with marinara mozzarella and parmesan served with pasta", "$15.25", "Italian Entrees"),
("Crab Cakes", "Maryland-style lump crab cakes with remoulade sauce and mixed greens", "$18.50", "Seafood Appetizers"),
("Tofu Stir Fry", "Crispy tofu with broccoli bell peppers snap peas in garlic ginger sauce over steamed rice", "$12.50", "Vegetarian Entrees"),
("Salmon Sushi Platter", "12 pieces of fresh salmon nigiri and sashimi with wasabi pickled ginger and soy sauce", "$19.95", "Sushi"),
("Caprese Sandwich", "Fresh mozzarella tomatoes basil pesto balsamic glaze on ciabatta bread", "$11.75", "Sandwiches"),
("Tom Yum Soup", "Spicy and sour Thai soup with shrimp lemongrass galangal mushrooms and kaffir lime leaves", "$11.50", "Soups"),
("Lentil Dal", "Red lentils simmered with turmeric cumin coriander served with rice and naan", "$11.95", "Vegan Entrees"),
("Fish and Chips", "Beer-battered cod with crispy fries malt vinegar and tartar sauce", "$16.00", "British Classics"),
("Veggie Burger", "House-made black bean and quinoa patty with avocado sprouts tomato on brioche bun", "$13.25", "Burgers"),
("Miso Ramen", "Rich miso broth with ramen noodles soft-boiled egg bamboo shoots nori and scallions", "$14.50", "Ramen"),
("Stuffed Bell Peppers", "Roasted bell peppers filled with rice vegetables herbs and melted cheese", "$13.75", "Vegetarian Entrees"),
("Scallop Risotto", "Pan-seared sea scallops over creamy parmesan risotto with white wine and lemon", "$26.50", "Seafood Specials"),
("Spring Rolls", "Fresh rice paper rolls with vegetables tofu rice noodles herbs and peanut dipping sauce", "$8.95", "Appetizers"),
("Oyster Po Boy", "Fried oysters with lettuce tomato pickles and remoulade on french bread", "$15.50", "Sandwiches"),
("Portobello Mushroom Steak", "Grilled portobello cap marinated in balsamic with roasted vegetables and quinoa", "$14.95", "Vegan Entrees"),
("Coconut Shrimp", "Jumbo shrimp breaded in shredded coconut served with sweet chili sauce", "$14.25", "Seafood Appetizers")
]
# embedding generator
points = []
embeddings = model.embed([f"{item[0]} {item[1]}" for item in menu_items])
for i, embedding in enumerate(embeddings):
vector = embedding.tolist()
point = PointStruct(
id=i,
vector=vector,
payload={
"item_name": menu_items[i][0],
"description": menu_items[i][1],
"price": menu_items[i][2],
"category": menu_items[i][3],
}
)
points.append(point)
# upsert points to collection
client.upsert(
collection_name="items",
points=points,
)
```
```rust
use fastembed::{EmbeddingModel, InitOptions, TextEmbedding};
use qdrant_client::qdrant::{PointStruct, UpsertPointsBuilder};
use qdrant_client::{Qdrant, Payload};
use serde_json::json;
// load the embedding model
let mut model = TextEmbedding::try_new(
InitOptions::new(EmbeddingModel::BGESmallENV15).with_show_download_progress(true),
)
.expect("Failed to load embedding model");
// generate embeddings and prepare points
let menu_items = vec![
(
"Pad Thai with Tofu",
"Stir-fried rice noodles with tofu bean sprouts scallions and crushed peanuts in traditional tamarind sauce",
"$13.95",
"Noodles",
),
(
"Grilled Salmon Fillet",
"Wild-caught Atlantic salmon grilled with lemon butter and fresh herbs served with seasonal vegetables",
"$24.50",
"Seafood Entrees",
),
(
"Mushroom Risotto",
"Creamy arborio rice with mixed mushrooms parmesan truffle oil and fresh thyme",
"$16.75",
"Vegetarian",
),
(
"Bibimbap Bowl",
"Korean rice bowl with seasoned vegetables fried egg gochujang sauce and choice of protein",
"$14.50",
"Korean Bowls",
),
(
"Falafel Wrap",
"Crispy chickpea fritters with hummus tahini cucumber tomato and pickled vegetables in warm pita",
"$11.25",
"Mediterranean",
),
(
"Shrimp Tacos",
"Three soft tacos with grilled shrimp cabbage slaw chipotle aioli and fresh lime",
"$13.00",
"Tacos",
),
(
"Vegetable Curry",
"Mixed vegetables in aromatic coconut curry sauce with jasmine rice and naan bread",
"$12.95",
"Indian Curries",
),
(
"Tuna Poke Bowl",
"Fresh ahi tuna with avocado edamame cucumber seaweed salad over sushi rice with spicy mayo",
"$16.50",
"Poke Bowls",
),
(
"Margherita Pizza",
"Fresh mozzarella san marzano tomatoes basil and extra virgin olive oil on wood-fired crust",
"$14.00",
"Pizza",
),
(
"Chicken Tikka Masala",
"Tandoori chicken in creamy tomato sauce with aromatic spices served with basmati rice",
"$15.95",
"Indian Entrees",
),
(
"Greek Salad",
"Romaine lettuce tomatoes cucumbers kalamata olives feta cheese red onion with lemon oregano dressing",
"$10.50",
"Salads",
),
(
"Lobster Roll",
"Fresh Maine lobster meat with light mayo on toasted buttery roll served with chips",
"$22.00",
"Seafood Sandwiches",
),
(
"Quinoa Buddha Bowl",
"Organic quinoa with roasted chickpeas kale sweet potato tahini dressing and hemp seeds",
"$13.50",
"Healthy Bowls",
),
(
"Beef Pho",
"Traditional Vietnamese beef noodle soup with rice noodles fresh herbs bean sprouts and lime",
"$12.75",
"Noodle Soups",
),
(
"Eggplant Parmesan",
"Breaded eggplant layered with marinara mozzarella and parmesan served with pasta",
"$15.25",
"Italian Entrees",
),
(
"Crab Cakes",
"Maryland-style lump crab cakes with remoulade sauce and mixed greens",
"$18.50",
"Seafood Appetizers",
),
(
"Tofu Stir Fry",
"Crispy tofu with broccoli bell peppers snap peas in garlic ginger sauce over steamed rice",
"$12.50",
"Vegetarian Entrees",
),
(
"Salmon Sushi Platter",
"12 pieces of fresh salmon nigiri and sashimi with wasabi pickled ginger and soy sauce",
"$19.95",
"Sushi",
),
(
"Caprese Sandwich",
"Fresh mozzarella tomatoes basil pesto balsamic glaze on ciabatta bread",
"$11.75",
"Sandwiches",
),
(
"Tom Yum Soup",
"Spicy and sour Thai soup with shrimp lemongrass galangal mushrooms and kaffir lime leaves",
"$11.50",
"Soups",
),
(
"Lentil Dal",
"Red lentils simmered with turmeric cumin coriander served with rice and naan",
"$11.95",
"Vegan Entrees",
),
(
"Fish and Chips",
"Beer-battered cod with crispy fries malt vinegar and tartar sauce",
"$16.00",
"British Classics",
),
(
"Veggie Burger",
"House-made black bean and quinoa patty with avocado sprouts tomato on brioche bun",
"$13.25",
"Burgers",
),
(
"Miso Ramen",
"Rich miso broth with ramen noodles soft-boiled egg bamboo shoots nori and scallions",
"$14.50",
"Ramen",
),
(
"Stuffed Bell Peppers",
"Roasted bell peppers filled with rice vegetables herbs and melted cheese",
"$13.75",
"Vegetarian Entrees",
),
(
"Scallop Risotto",
"Pan-seared sea scallops over creamy parmesan risotto with white wine and lemon",
"$26.50",
"Seafood Specials",
),
(
"Spring Rolls",
"Fresh rice paper rolls with vegetables tofu rice noodles herbs and peanut dipping sauce",
"$8.95",
"Appetizers",
),
(
"Oyster Po Boy",
"Fried oysters with lettuce tomato pickles and remoulade on french bread",
"$15.50",
"Sandwiches",
),
(
"Portobello Mushroom Steak",
"Grilled portobello cap marinated in balsamic with roasted vegetables and quinoa",
"$14.95",
"Vegan Entrees",
),
(
"Coconut Shrimp",
"Jumbo shrimp breaded in shredded coconut served with sweet chili sauce",
"$14.25",
"Seafood Appetizers",
),
];
let embeddings = model
.embed(
menu_items
.iter()
.map(|item| format!("{} {}", item.0, item.1))
.collect::>(),
None,
)
.expect("Failed to generate embeddings");
let points = embeddings
.into_iter()
.enumerate()
.map(|(idx, embedding)| {
PointStruct::new(
idx as u64,
embedding,
Payload::try_from(json!({
"item_name": menu_items[idx].0,
"description": menu_items[idx].1,
"price": menu_items[idx].2,
"category": menu_items[idx].3,
}))
.unwrap(),
)
})
.collect::>();
let _ = client
.upsert_points(UpsertPointsBuilder::new("items", points).wait(true))
.await;
```
```typescript
import { TextEmbedding, EmbeddingModel } from 'fastembed';
// load the embedding model
const model = await FlagEmbedding.init({
model: EmbeddingModel.BGESmallENV15,
});
let menuItems = [
[
"Pad Thai with Tofu",
"Stir-fried rice noodles with tofu bean sprouts scallions and crushed peanuts in traditional tamarind sauce",
"$13.95",
"Noodles",
],
[
"Grilled Salmon Fillet",
"Wild-caught Atlantic salmon grilled with lemon butter and fresh herbs served with seasonal vegetables",
"$24.50",
"Seafood Entrees",
],
[
"Mushroom Risotto",
"Creamy arborio rice with mixed mushrooms parmesan truffle oil and fresh thyme",
"$16.75",
"Vegetarian",
],
[
"Bibimbap Bowl",
"Korean rice bowl with seasoned vegetables fried egg gochujang sauce and choice of protein",
"$14.50",
"Korean Bowls",
],
[
"Falafel Wrap",
"Crispy chickpea fritters with hummus tahini cucumber tomato and pickled vegetables in warm pita",
"$11.25",
"Mediterranean",
],
[
"Shrimp Tacos",
"Three soft tacos with grilled shrimp cabbage slaw chipotle aioli and fresh lime",
"$13.00",
"Tacos",
],
[
"Vegetable Curry",
"Mixed vegetables in aromatic coconut curry sauce with jasmine rice and naan bread",
"$12.95",
"Indian Curries",
],
[
"Tuna Poke Bowl",
"Fresh ahi tuna with avocado edamame cucumber seaweed salad over sushi rice with spicy mayo",
"$16.50",
"Poke Bowls",
],
[
"Margherita Pizza",
"Fresh mozzarella san marzano tomatoes basil and extra virgin olive oil on wood-fired crust",
"$14.00",
"Pizza",
],
[
"Chicken Tikka Masala",
"Tandoori chicken in creamy tomato sauce with aromatic spices served with basmati rice",
"$15.95",
"Indian Entrees",
],
[
"Greek Salad",
"Romaine lettuce tomatoes cucumbers kalamata olives feta cheese red onion with lemon oregano dressing",
"$10.50",
"Salads",
],
[
"Lobster Roll",
"Fresh Maine lobster meat with light mayo on toasted buttery roll served with chips",
"$22.00",
"Seafood Sandwiches",
],
[
"Quinoa Buddha Bowl",
"Organic quinoa with roasted chickpeas kale sweet potato tahini dressing and hemp seeds",
"$13.50",
"Healthy Bowls",
],
[
"Beef Pho",
"Traditional Vietnamese beef noodle soup with rice noodles fresh herbs bean sprouts and lime",
"$12.75",
"Noodle Soups",
],
[
"Eggplant Parmesan",
"Breaded eggplant layered with marinara mozzarella and parmesan served with pasta",
"$15.25",
"Italian Entrees",
],
[
"Crab Cakes",
"Maryland-style lump crab cakes with remoulade sauce and mixed greens",
"$18.50",
"Seafood Appetizers",
],
[
"Tofu Stir Fry",
"Crispy tofu with broccoli bell peppers snap peas in garlic ginger sauce over steamed rice",
"$12.50",
"Vegetarian Entrees",
],
[
"Salmon Sushi Platter",
"12 pieces of fresh salmon nigiri and sashimi with wasabi pickled ginger and soy sauce",
"$19.95",
"Sushi",
],
[
"Caprese Sandwich",
"Fresh mozzarella tomatoes basil pesto balsamic glaze on ciabatta bread",
"$11.75",
"Sandwiches",
],
[
"Tom Yum Soup",
"Spicy and sour Thai soup with shrimp lemongrass galangal mushrooms and kaffir lime leaves",
"$11.50",
"Soups",
],
[
"Lentil Dal",
"Red lentils simmered with turmeric cumin coriander served with rice and naan",
"$11.95",
"Vegan Entrees",
],
[
"Fish and Chips",
"Beer-battered cod with crispy fries malt vinegar and tartar sauce",
"$16.00",
"British Classics",
],
[
"Veggie Burger",
"House-made black bean and quinoa patty with avocado sprouts tomato on brioche bun",
"$13.25",
"Burgers",
],
[
"Miso Ramen",
"Rich miso broth with ramen noodles soft-boiled egg bamboo shoots nori and scallions",
"$14.50",
"Ramen",
],
[
"Stuffed Bell Peppers",
"Roasted bell peppers filled with rice vegetables herbs and melted cheese",
"$13.75",
"Vegetarian Entrees",
],
[
"Scallop Risotto",
"Pan-seared sea scallops over creamy parmesan risotto with white wine and lemon",
"$26.50",
"Seafood Specials",
],
[
"Spring Rolls",
"Fresh rice paper rolls with vegetables tofu rice noodles herbs and peanut dipping sauce",
"$8.95",
"Appetizers",
],
[
"Oyster Po Boy",
"Fried oysters with lettuce tomato pickles and remoulade on french bread",
"$15.50",
"Sandwiches",
],
[
"Portobello Mushroom Steak",
"Grilled portobello cap marinated in balsamic with roasted vegetables and quinoa",
"$14.95",
"Vegan Entrees",
],
[
"Coconut Shrimp",
"Jumbo shrimp breaded in shredded coconut served with sweet chili sauce",
"$14.25",
"Seafood Appetizers",
],
] as const;
// generate embeddings and prepare points
const points: any[] = [];
let idx = 0;
const embeddings = model.embed(menuItems.map(item => `${item[0]} ${item[1]}`));
for await (const embedding of embeddings) {
points.push({
id: idx,
vector: Array.from(embedding[0]),
payload: {
item_name: menuItems[idx][0],
description: menuItems[idx][1],
price: menuItems[idx][2],
category: menuItems[idx][3],
},
});
idx++;
}
// upsert points to collection
await client.upsert("items", { points });
```
## 6. Search the Menu Items
Now we can search the menu item dataset! We'll use the same `BAAI/bge-small-en-v1.5` model to embed our query text, then find the best dishes matching that embedding.
```python
# generate query embedding
query_text = "vegetarian dishes"
query_vector = next(iter(model.embed(query_text)))
# search for similar menu items
results = client.query_points(
collection_name="items",
query=query_vector,
with_payload=True,
limit=5
)
# print results
for result in results.points:
print(f"Item: {result.payload.get('item_name', 'N/A')}")
print(f"Score: {result.score}")
print(f"Description: {result.payload['description'][:150]}...")
print(f"Price: {result.payload.get('price', 'N/A')}")
print("---")
```
```rust
// generate query embedding
let query_text = "vegetarian dishes";
let query_embeddings = model
.embed(vec![query_text], None)
.expect("Failed to generate embeddings");
let query_vector = query_embeddings[0].clone();
let results = client
.query(
QueryPointsBuilder::new("items")
.query(query_vector)
.with_payload(true)
.limit(5),
)
.await
.expect("Query failed");
let na_str = "N/A".to_string();
for result in results.result {
let payload = result.payload;
println!("Item: {}", payload.get("item_name")
.and_then(|v| v.as_str()).unwrap_or(&na_str));
println!("Score: {}", result.score);
println!("Description: {}", payload.get("description")
.and_then(|v| v.as_str()).unwrap_or(&na_str));
println!("Price: {}", payload.get("price")
.and_then(|v| v.as_str()).unwrap_or(&na_str));
println!("---");
}
```
```typescript
// generate query embedding
const queryText = "vegetarian dishes";
const queryEmbedding = (await model.embed([queryText]).next()).value!
// search for similar items
const results = await client.query("items", {
query: Array.from(queryEmbedding[0]),
with_payload: true,
limit: 5,
});
// print results
for (const result of results.points) {
console.log(`Item: ${result.payload?.item_name || 'N/A'}`);
console.log(`Score: ${result.score}`);
console.log(`Description: ${result.payload?.description || 'N/A'}`);
console.log(`Price: ${result.payload?.price || 'N/A'}`);
console.log('---');
}
```
## That's Vector Search!
You've just performed semantic search on real menu item data. The query "vegetarian dishes" returned similar menu items based on meaning, not just keyword matching.
## What's Next?
- Explore [filtering](/documentation/concepts/filtering/) to combine semantic search with structured queries
- Learn about [collections](/documentation/concepts/collections/) and advanced configuration options
- Check out more [examples and tutorials](/documentation/tutorials-overview/)