Qdrant : A High-Performance Vector Similarity Search Engine

Qdrant (read: quadrant) is a vector similarity search engine and vector database.

It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload Qdrant is tailored to extended filtering support.

It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.

Qdrant is written in Rust, which makes it fast and reliable even under high load. See benchmarks.

With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!

Getting Started

Python

pip install qdrant-client

The python client offers a convenient way to start with Qdrant locally:

from qdrant_client import QdrantClient
qdrant = QdrantClient(":memory:") # Create in-memory Qdrant instance, for testing, CI/CD
# OR
client = QdrantClient(path="path/to/db")  # Persists changes to disk, fast prototyping

Client-Server

To experience the full power of Qdrant locally, run the container with this command:

docker run -p 6333:6333 qdrant/qdrant

Now you can connect to this with any client, including Python:

qdrant = QdrantClient("http://localhost:6333") # Connect to existing Qdrant instance

For more information click here.