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pythonintermediate

Qdrant Vector Database Client

Index and search high-dimensional embeddings with the Qdrant Python client.

python
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct
import numpy as np

client = QdrantClient(':memory:')

client.recreate_collection(
    collection_name='docs',
    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
)

points = [
    PointStruct(id=i, vector=np.random.rand(384).tolist(), payload={'text': f'document {i}', 'source': 'wiki'})
    for i in range(100)
]
client.upsert(collection_name='docs', points=points)

query_vector = np.random.rand(384).tolist()
results = client.search(collection_name='docs', query_vector=query_vector, limit=5)
for r in results:
    print(f'ID={r.id}, score={r.score:.4f}, text={r.payload["text"]}')

Use Cases

  • vector similarity search
  • semantic retrieval
  • embedding storage

Tags

#qdrant#vector-db#embeddings#search

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