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Qdrant

High-performance vector similarity search engine built in Rust. Provides fast and scalable vector storage with rich filtering, payload indexing, and a gRPC/REST API.
Category vector-database
Official Docs Qdrant Documentation
CLI Install ./cli vector-database qdrant install
CLI Uninstall ./cli vector-database qdrant uninstall
Namespace qdrant

Overview

Qdrant is the default vector database in the demo stack: - High Performance: Written in Rust for maximum throughput and low latency - Rich Filtering: Combine vector search with payload-based filters - Scalar & Binary Quantization: Reduce memory usage while maintaining accuracy - Distributed Mode: Horizontal scaling with sharding and replication - Snapshot & Backup: Point-in-time snapshots for data protection - REST & gRPC APIs: Full-featured APIs for all operations

Installation

Prerequisites

Configure in .env:

QDRANT_USERNAME=admin
QDRANT_PASSWORD=your-password

Install

./cli vector-database qdrant install

The installer: 1. Adds the Qdrant Helm chart repository 2. Renders Helm values and deploys via Helm 3. Creates Nginx basic auth secret for ingress 4. Configures ingress with domain (if set)

Verification

# Check pods
kubectl get pods -n qdrant

# Check service
kubectl get svc -n qdrant

# Check ingress
kubectl get ingress -n qdrant

# Port-forward for local access
kubectl port-forward svc/qdrant 6333:6333 -n qdrant --address 0.0.0.0 &

# Check cluster info
curl http://localhost:6333/cluster

# List collections
curl http://localhost:6333/collections

Configuration

Environment Variables

Variable Description
QDRANT_USERNAME Username for ingress basic auth
QDRANT_PASSWORD Password for ingress basic auth
DOMAIN Domain for ingress (e.g., qdrant.example.com)

Usage

Create a Collection

curl -X PUT http://localhost:6333/collections/my-collection \
  -H "Content-Type: application/json" \
  -d '{
    "vectors": {
      "size": 768,
      "distance": "Cosine"
    }
  }'

Insert Vectors

curl -X PUT http://localhost:6333/collections/my-collection/points \
  -H "Content-Type: application/json" \
  -d '{
    "points": [
      {
        "id": 1,
        "vector": [0.1, 0.2, ...],
        "payload": {"text": "Hello world"}
      }
    ]
  }'

curl -X POST http://localhost:6333/collections/my-collection/points/search \
  -H "Content-Type: application/json" \
  -d '{
    "vector": [0.1, 0.2, ...],
    "limit": 5,
    "with_payload": true
  }'

Python Client

from qdrant_client import QdrantClient

client = QdrantClient(url="http://qdrant.qdrant:6333")

# Create collection
client.create_collection(
    collection_name="my-collection",
    vectors_config={"size": 768, "distance": "Cosine"}
)

# Search
results = client.search(
    collection_name="my-collection",
    query_vector=[0.1, 0.2, ...],
    limit=5
)

Integration with TEI

Use TEI to generate embeddings and store them in Qdrant:

import requests
from qdrant_client import QdrantClient

# Generate embedding
response = requests.post(
    "http://tei.tei:8080/v1/embeddings",
    json={"model": "qwen3-embedding-06b-bf16-cpu", "input": "Hello world"}
)
embedding = response.json()["data"][0]["embedding"]

# Store in Qdrant
client = QdrantClient(url="http://qdrant.qdrant:6333")
client.upsert(
    collection_name="my-collection",
    points=[{"id": 1, "vector": embedding, "payload": {"text": "Hello world"}}]
)

Troubleshooting

Pod not starting

# Check pod events
kubectl describe pod -n qdrant -l app=qdrant

# Check PVC
kubectl get pvc -n qdrant

# Check logs
kubectl logs -n qdrant -l app=qdrant

Ingress authentication issues

# Check auth secret exists
kubectl get secret -n qdrant

# Regenerate auth
htpasswd -nb <username> <password>

Learn More