Qdrant is a cutting-edge vector similarity search engine and vector database designed for the next generation of AI applications. It provides a robust and production-ready service with an intuitive API for storing, searching, and managing points—vectors with additional payload. Qdrant is built with Rust 🦀, ensuring high performance and reliability even under heavy load. For benchmarks, visit here.

Key Features

Vector Similarity Search

Qdrant excels at vector similarity search, making it ideal for use cases involving neural-network or semantic-based matching, faceted search, and more. It supports extended filtering, allowing for complex query capabilities.

High Performance

Built in Rust, Qdrant leverages the language's efficiency and safety features, providing exceptional performance and reliability. It uses modern CPU architectures and asynchronous I/O to maximize throughput.

Filtering and Payload Support

Qdrant can attach JSON payloads to vectors, enabling both storage and filtering based on payload values. It supports various data types and query conditions, including keyword matching, full-text filtering, numerical ranges, and geo-locations.

Hybrid Search with Sparse Vectors

Qdrant introduces support for sparse vectors, allowing for efficient keyword-specific searches. Sparse vectors extend traditional BM25 or TF-IDF ranking, enhancing transformer-based neural network capabilities.

Vector Quantization and On-Disk Storage

Qdrant offers built-in vector quantization to reduce RAM usage by up to 97%. It dynamically balances search speed and precision, making vector search more resource-efficient.

Distributed Deployment

Qdrant supports horizontal scaling through sharding and replication. It ensures zero-downtime rolling updates and seamless dynamic scaling, providing robustness for production environments.

Managed Cloud Service

Qdrant is available as a fully managed cloud service, including a free tier, offering convenience and scalability for users without the need for infrastructure management.

Getting Started

Python Client

To start using Qdrant with Python, install the client:

pip install qdrant-client

Create an in-memory or persistent Qdrant instance:

from qdrant_client import QdrantClient
qdrant = QdrantClient(":memory:")  # For testing
client = QdrantClient(path="path/to/db")  # For persistent storage

Docker Deployment

Run Qdrant locally using Docker:

docker run -p 6333:6333 qdrant/qdrant

Connect to the Qdrant instance:

qdrant = QdrantClient("http://localhost:6333")

For production deployment, refer to the installation and security guides.

Client Libraries

Qdrant offers official and community-maintained client libraries for various languages:

Demo Projects

Explore the capabilities of Qdrant through various demo projects:

For more solutions, visit the Demo Projects section.

API Documentation

Qdrant provides REST and gRPC APIs for integration:


Qdrant integrates seamlessly with various tools and platforms:

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