qdrant

Vector Search Engine for the next generation of AI applications

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 for extended filtering support, making 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!

Qdrant is also available as a fully managed Qdrant Cloud ⛅ including a free tier.

Quick Start • Agent Skills • Client Libraries • Demo Projects • Integrations • Contact

Getting Started

Agent Skills

Qdrant provides a collection of ready-to-use agent skills that bring Qdrant's vector search capabilities directly into your AI coding assistant. Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.

Client-Server

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

docker run -p 6333:6333 qdrant/qdrant

Note that this starts an insecure deployment without authentication, open to all network interfaces. Please refer to secure your instance.

Now you can connect to the server with any client. For example, using Python:

from qdrant_client import QdrantClient

client = QdrantClient(url="http://localhost:6333")

Before deploying Qdrant to production, be sure to read our installation and security guides.

Clients

Qdrant offers the following client libraries to help you integrate it into your application stack:

• Official:
  • [Go client](https://github.com/qdrant/go-c