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small inconsistencies fixes

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Nathan LeRoy
2026-01-08 12:36:46 -05:00
parent 623c857645
commit b82289b69f
1 changed files with 24 additions and 83 deletions
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qdrant-landing/content/documentation/cloud-quickstart.md
+24 -83
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@@ -14,21 +14,9 @@ aliases:
<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, create a collection, insert vectors, and perform your first semantic search in just a few minutes.
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 1,000 IMDB movies pre-embedded with the `jinaai/jina-embeddings-v2-base-en` model to get you started quickly.
## 1. Install the Qdrant Client
The fastest way to get started is to use the `qdrant-client` Python library which provides a convenient interface for working with Qdrant.
Install it using pip in your terminal or virtual environment:
```bash
pip install qdrant-client polars fastembed tqdm # for Python projects
# cargo add qdrant-client[fastembed] polars # for Rust projects
# npm install @qdrant/js-client-rest polars fastembed # for Node.js projects
```
## 2. Create a Cloud Cluster
## 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**.
@@ -36,9 +24,19 @@ pip install qdrant-client polars fastembed tqdm # for Python projects
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 `QdrantClient` and create a connection to your Qdrant Cloud cluster using your cluster URL and API key.
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
@@ -75,70 +73,13 @@ curl -X GET \
--header 'api-key: <api-key-value>'
```
## 4. Create a Collection
## 4. Load the Sample Dataset
Collections are the primary unit of data organization in Qdrant. Each collection stores vectors of the same dimensionality.
Specify the **vector size** (matching your embedding model) and the **distance metric** (Cosine, Euclidean, or Dot Product).
```python
from qdrant_client.models import Distance, VectorParams
# create a collection with 768-dimensional vectors
collection_name = "movies"
client.create_collection(
collection_name=collection_name,
vectors_config=VectorParams(
size=768,
distance=Distance.COSINE
)
)
```
```rust
use qdrant_client::qdrant::{CreateCollectionBuilder, Distance, VectorParamsBuilder, ScalarQuantizationBuilder};
// -- snip --
let collection_name = "movies";
client
.create_collection(
CreateCollectionBuilder::new(collection_name)
.vectors_config(VectorParamsBuilder::new(768, Distance::Cosine))
.quantization_config(ScalarQuantizationBuilder::default()),
)
.await?;
```
```typescript
const collectionName = "movies";
await client.collections.createCollection(collectionName, {
vectors: {
size: 768,
distance: "Cosine",
},
});
```
```bash
curl -X PUT \
'http://<your-qdrant-host>:6333/collections/movies' \
--header 'api-key: <api-key-value>' \
--header 'Content-Type: application/json' \
--data-raw '{
"vectors": {
"size": 768,
"distance": "Cosine"
}
}'
```
## 5. Load and Insert Vectors
We'll use a sample qdrant snapshot to quickly load data. It is a dataset of IMDB movies embedded with the `jinaai/jina-embeddings-v2-base-en` model.
We'll load a pre-embedded dataset of 1,000 IMDB movies using a [Qdrant snapshot](https://qdrant.tech/documentation/concepts/snapshots/). This snapshot contains vectors created with the `jinaai/jina-embeddings-v2-base-en` model (768 dimensions) and will automatically create the collection for you.
```python
client.recover_snapshot(
collection_name="products",
collection_name="movies",
snapshot_url="snapshots.qdrant.io/imdb-1000-jina.snapshot"
)
```
@@ -149,7 +90,7 @@ client.recover_snapshot(
```
```typescript
await client.collections.recoverSnapshot("products", {
await client.collections.recoverSnapshot("movies", {
snapshotUrl: "snapshots.qdrant.io/imdb-1000-jina.snapshot",
});
```
@@ -164,9 +105,9 @@ curl -X PUT \
}'
```
## 6. Search Vectors
## 5. Search the Movies
Perform semantic search by passing a query text. Qdrant returns the most similar vectors based on your chosen distance metric.
Now we can search the movie dataset! We'll use the same `jinaai/jina-embeddings-v2-base-en` model to embed our query text, then find similar movies in the collection.
```python
from fastembed import TextEmbedding
@@ -178,7 +119,7 @@ model = TextEmbedding('jinaai/jina-embeddings-v2-base-en')
query_text = "alien invasion movie"
query_vector = next(iter(model.embed(query_text)))
# search for similar products
# search for similar movies
results = client.query_points(
collection_name="movies",
query_vector=query_vector,
@@ -207,7 +148,7 @@ let query_text = "alien invasion movie";
let query_embeddings = model.embed(vec![query_text], None)?;
let query_vector = query_embeddings[0].clone();
// search for similar products
// search for similar movies
let results = client
.query(
QueryPointsBuilder::new("movies")
@@ -219,7 +160,7 @@ let results = client
// print results
for result in results.result {
let payload = result.payload;
println!("Product: {}", payload.get("prod_name")
println!("Movie: {}", payload.get("prod_name")
.and_then(|v| v.as_str()).unwrap_or("N/A"));
println!("Score: {}", result.score);
println!("Description: {}", payload.get("detail_desc")
@@ -241,7 +182,7 @@ const queryText = "alien invasion movie";
const queryEmbeddings = await model.embed([queryText]);
const queryVector = Array.from(queryEmbeddings[0]);
// search for similar products
// search for similar movies
const results = await client.query(collectionName, {
query: queryVector,
limit: 5,
@@ -249,7 +190,7 @@ const results = await client.query(collectionName, {
// print results
for (const result of results.points) {
console.log(`Product: ${result.payload?.prod_name || 'N/A'}`);
console.log(`Movie: ${result.payload?.prod_name || 'N/A'}`);
console.log(`Score: ${result.score}`);
console.log(`Description: ${result.payload?.detail_desc || 'N/A'}`);
console.log('---');