README

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:

bash
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:

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
  - Rust client
  - JavaScript/TypeScript client
  - Python client
  - .NET/C client
  - Java client
- Community:
  - Kotlin
  - PHP

 Qdrant Edge

Qdrant Edge is a lightweight version of Qdrant designed for edge devices and resource-constrained environments. Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Data is stored and queried locally and can be synchronized with a Qdrant server. It offers the same powerful vector search capabilities as the client-server version but with a smaller footprint, making it ideal for applications that require low latency and offline functionality.

To get started with Qdrant Edge from Python or Rust, initialize an instance of EdgeShard, which exposes methods to manage data, query it, and restore snapshots. For example:

python
from qdrant_edge import Distance, EdgeConfig, EdgeVectorParams, EdgeShard, Point, UpdateOperation

shard = EdgeShard.create("./shard", EdgeConfig(
    vectors={"my

Top issues

Discussions

Latest release

Changelog

 Features 🎶

- 10750 - BM25 scoring directly on the full-text payload index [docs]
- Milestone 57 - TurboQuant: new turbo8 and turbo16 vector datatypes [docs]
- 11125 - Per-element payload indexes: new nested index parameter, faster nested condition filtering.  [docs]
- 11180, 11263 - New code tokenizer for full-text index [docs]
- 10466 - Configurable audit log output: audit.output writes audit events to file (default), stdout or stderr  [docs]
- 10992 - Collection creation time: created_at is recorded and returned in collection info [docs]
- 10711 - Substring filter cond