Qdrant

Qdrant

Qdrant is an open-source vector database and similarity search engine written in Rust. It stores embedding vectors alongside JSON payloads, enabling filtered nearest-neighbor search at scale. It is a popular choice as the retrieval backend for RAG pipelines and agent memory systems.

Key Features

• Filtered search — combine ANN search with arbitrary JSON payload filters in a single query
• Named vectors — store multiple embedding spaces per record (dense + sparse + ColBERT)
• Quantization — scalar and product quantization with on-the-fly rescoring
• Snapshots — point-in-time collection snapshots for backup and migration
• Distributed — horizontal sharding and replication built-in
• REST + gRPC + Web UI — full API surface with an interactive dashboard
• MCP server — official qdrant-mcp allows agents to store and retrieve memories as vectors

Quick Start

docker run -p 6333:6333 qdrant/qdrant

from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, PointStruct

client = QdrantClient(":memory:")
client.create_collection("docs", vectors_config=VectorParams(size=384, distance=Distance.COSINE))
client.upsert("docs", points=[PointStruct(id=1, vector=[0.1]*384, payload={"text": "hello"})])
result = client.search("docs", query_vector=[0.1]*384, limit=3)
print(result)

Install via ai-supply

npx ai-supply add qdrant-vector-store

Curated mirror of the open-source Qdrant project (Apache-2.0). Install upstream from the repository.