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174 lines
6.5 KiB
Markdown
174 lines
6.5 KiB
Markdown
---
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title: "Qdrant Setup"
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description: Set up your Qdrant Cloud cluster in minutes. Learn to create collections, manage data, access the Web UI, and connect securely from Python.
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weight: 2
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isLesson: true
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---
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{{< date >}} Day 0 {{< /date >}}
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# Qdrant Setup
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<div class="video">
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<iframe
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src="https://www.youtube.com/embed/9JBlgNBQoOY?si=7t3LAvMsUUtlUMN7&rel=0"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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referrerpolicy="strict-origin-when-cross-origin"
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allowfullscreen>
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</iframe>
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</div>
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<br/>
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Spin up production-grade vector search in minutes. Qdrant Cloud gives you a managed endpoint with TLS, automatic backups, high-availability options, and a clean API.
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## Create your cluster
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1. Sign up at [cloud.qdrant.io](https://cloud.qdrant.io/signup) with email, Google, or GitHub.
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2. Open **Clusters** → **Create a Free Cluster**. The Free Tier is enough for this course.
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3. Pick a region close to your users or app.
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4. When the cluster is ready, copy the API key and store it securely. You can make new keys later from **API Keys** on the cluster page.
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## Access the Web UI
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1. Click **Cluster UI** in the top-right of the cluster page to open the dashboard.
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### What you can do in the Web UI
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Use the Web UI to manage collections, inspect data, and debug search performance.
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#### Main Navigation
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**Console**: Run REST calls in the browser. Test endpoints, inspect responses, and debug queries without writing code. Handy for exploring the full API.
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**Collections**: See and manage all collections. Create collections, upload snapshots, and track status, size, and configuration at a glance.
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**Tutorial**: Follow an interactive walkthrough with sample data. Create a collection, add vectors, and run semantic search with live results.
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**Datasets**: Bulk-load preconfigured public datasets into your cluster.
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#### Inside a Collection
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When you open a collection by clicking it's name,
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you’ll get a detailed view with these tabs:
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* **Points Tab**: Inspect, search, and manage individual points. Use the search bar to find by ID or filter by payload fields (e.g., `colony: "Mars"`). For each point, you can:
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* See its payload and vector(s).
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* Click **Find Similar** to run an ad-hoc similarity search.
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* Click **Open Graph** to jump to a graph view of its HNSW connections.
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* **Info Tab**: Get a full overview of collection health, config, and stats. Key fields:
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* `status`: `green` means healthy.
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* `points_count`: Number of active points.
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* `indexed_vectors_count`: Points currently in the HNSW index. If this lags behind `points_count`, background indexing is still running.
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* `config`: JSON view of all parameters, from vector settings to optimizer options.
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* **Cluster Tab**: See how shards are placed across nodes. Use it to monitor health, find hot spots, and verify shard placement in distributed setups.
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* **Search Quality Tab**: Evaluate and benchmark retrieval precision against ground truth. Tune parameters and measure the impact on accuracy.
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* **Snapshots Tab**: Manage backups for this collection. Create a [snapshot](/documentation/concepts/snapshots/), restore it later, or migrate it to another cluster.
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* **Visualize Tab**: Explore your vector space with an interactive 2D projection. See clusters, spot outliers, and build intuition about your embeddings.
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* **Graph Tab**: Explore the HNSW graph interactively. Start from any point, follow nearest neighbors, and see how the graph structure powers fast search.
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## Connect from Python
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Store credentials in an `.env` file at the root of your working directory or in colab:
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```env
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QDRANT_URL=https://YOUR-CLUSTER.cloud.qdrant.io:6333
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QDRANT_API_KEY=YOUR_API_KEY
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```
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Load the credentials from the environment and create a Qdrant client:
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```python
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from qdrant_client import QdrantClient, models
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import os
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client = QdrantClient(url=os.getenv("QDRANT_URL"), api_key=os.getenv("QDRANT_API_KEY"))
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# For Colab:
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# from google.colab import userdata
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# client = QdrantClient(url=userdata.get("QDRANT_URL"), api_key=userdata.get("QDRANT_API_KEY"))
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# Quick health check
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collections = client.get_collections()
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print(f"Connected to Qdrant Cloud: {len(collections.collections)} collections")
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```
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## Other ways to connect
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You can also send your key in the `Authorization` header:
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```bash
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# Using api-key header
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curl -X GET https://xyz-example.eu-central.aws.cloud.qdrant.io:6333/collections \
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--header 'api-key: <your-api-key>'
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# Using Authorization header
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curl -X GET https://xyz-example.eu-central.aws.cloud.qdrant.io:6333/collections \
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--header 'Authorization: Bearer <your-api-key>'
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```
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## Quick validation
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Check basic connectivity:
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```bash
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# Service health
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curl -s "$QDRANT_URL/healthz" -H "api-key: $QDRANT_API_KEY"
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# List collections
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curl -s "$QDRANT_URL/collections" -H "api-key: $QDRANT_API_KEY"
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```
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## Good practices
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* Keep secrets out of code; use environment variables or a secret manager.
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* Restrict access with IP allow-lists or private networking.
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* Rotate API keys regularly from the cluster **Access** tab.
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* Use HTTPS only; turn on RBAC and strict limits when exposing endpoints to untrusted clients.
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## Common issues
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* **Authentication error**: Recheck the API key and the `api-key` header.
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* **Connection error**: Confirm cluster status and region URL; some corporate proxies block outbound TLS.
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## Qdrant Cloud Inference
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Qdrant Cloud also offers **[Cloud Inference](/cloud-inference/)**—managed embedding generation for text and images. Skip running your own embedding models; create vectors in Qdrant Cloud and write them straight into your collections.
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<div class="video">
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<iframe
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src="https://www.youtube.com/embed/nJIX0zhrBL4?rel=0"
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frameborder="0"
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
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referrerpolicy="strict-origin-when-cross-origin"
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allowfullscreen>
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</iframe>
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</div>
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Cut steps from your pipeline: send raw text or images to Qdrant, get vectors and search results in one API call. This helps prototypes and production systems alike by ending the separate embedding-infrastructure layer.
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Learn more: [Qdrant Cloud Inference](/documentation/cloud/inference/) |