By name: qdrant description: "Qdrant — vector similarity search engine. Payload filtering, quantized indexing, multi-tenant, and horizontal scaling. REST and gRPC API. Docker-native deployment for production RAG and recommendation." tags: [qdrant, vector-database, similarity-search, embeddings, rag, infrastructure, zorai]
Overview
Qdrant is a high-performance vector similarity search engine supporting dense and sparse vectors, payload indexing and filtering, scalar/PQ quantization, multi-tenancy, and horizontal scaling via clustering. REST and gRPC APIs with async support.
Installation
docker run -p 6333:6333 qdrant/qdrant
Python Client
from qdrant_client import QdrantClient, models
import numpy as np
client = QdrantClient("localhost", port=6333)
client.create_collection("documents", vectors_config=models.VectorParams(
size=384, distance=models.Distance.COSINE))
client.upsert("documents", points=[
models.PointStruct(id=1, vector=np.random.rand(384).tolist(), payload={"text": "Paris is capital of France"}),
models.PointStruct(id=2, vector=np.random.rand(384).tolist(), payload={"text": "Berlin is capital of Germany"}),
])
results = client.search("documents", query_vector=np.random.rand(384).tolist(), limit=5)
for hit in results:
print(hit.payload["text"], hit.score)