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Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

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Qdrant

Vector Search Engine for the next generation of AI applications

Tests status OpenAPI Docs Apache 2.0 License Discord Roadmap 2024 Qdrant Cloud

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!

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

Quick StartClient LibrariesDemo ProjectsIntegrationsContact

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

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 with ease:

Where do I go from here?

Demo Projects Run on Repl.it

Discover Semantic Text Search 🔍

Unlock the power of semantic embeddings with Qdrant, transcending keyword-based search to find meaningful connections in short texts. Deploy a neural search in minutes using a pre-trained neural network, and experience the future of text search. Try it online!

Explore Similar Image Search - Food Discovery 🍕

There's more to discovery than text search, especially when it comes to food. People often choose meals based on appearance rather than descriptions and ingredients. Let Qdrant help your users find their next delicious meal using visual search, even if they don't know the dish's name. Check it out!

Master Extreme Classification - E-commerce Product Categorization 📺

Enter the cutting-edge realm of extreme classification, an emerging machine learning field tackling multi-class and multi-label problems with millions of labels. Harness the potential of similarity learning models, and see how a pre-trained transformer model and Qdrant can revolutionize e-commerce product categorization. Play with it online!

More solutions
Semantic Text Search Similar Image Search Recommendations
Chat Bots Matching Engines Anomaly Detection

API

REST

Online OpenAPI 3.0 documentation is available here. OpenAPI makes it easy to generate a client for virtually any framework or programming language.

You can also download raw OpenAPI definitions.

gRPC

For faster production-tier searches, Qdrant also provides a gRPC interface. You can find gRPC documentation here.

Features

Filtering and Payload

Qdrant can attach any JSON payloads to vectors, allowing for both the storage and filtering of data based on the values in these payloads. Payload supports a wide range of data types and query conditions, including keyword matching, full-text filtering, numerical ranges, geo-locations, and more.

Filtering conditions can be combined in various ways, including should, must, and must_not clauses, ensuring that you can implement any desired business logic on top of similarity matching.

Hybrid Search with Sparse Vectors

To address the limitations of vector embeddings when searching for specific keywords, Qdrant introduces support for sparse vectors in addition to the regular dense ones.

Sparse vectors can be viewed as an generalisation of BM25 or TF-IDF ranking. They enable you to harness the capabilities of transformer-based neural networks to weigh individual tokens effectively.

Vector Quantization and On-Disk Storage

Qdrant provides multiple options to make vector search cheaper and more resource-efficient. Built-in vector quantization reduces RAM usage by up to 97% and dynamically manages the trade-off between search speed and precision.

Distributed Deployment

Qdrant offers comprehensive horizontal scaling support through two key mechanisms:

  1. Size expansion via sharding and throughput enhancement via replication
  2. Zero-downtime rolling updates and seamless dynamic scaling of the collections

Highlighted Features

  • Query Planning and Payload Indexes - leverages stored payload information to optimize query execution strategy.
  • SIMD Hardware Acceleration - utilizes modern CPU x86-x64 and Neon architectures to deliver better performance.
  • Async I/O - uses io_uring to maximize disk throughput utilization even on a network-attached storage.
  • Write-Ahead Logging - ensures data persistence with update confirmation, even during power outages.

Integrations

Examples and/or documentation of Qdrant integrations:

Contacts

Contributors ✨

Thanks to the people who contributed to Qdrant:

Andrey Vasnetsov
Andrey Vasnetsov

💻
Andre Zayarni
Andre Zayarni

📖
Joan Fontanals
Joan Fontanals

💻
trean
trean

💻
Konstantin
Konstantin

💻
Daniil Naumetc
Daniil Naumetc

💻
Viacheslav Poturaev
Viacheslav Poturaev

📖
Alexander Galibey
Alexander Galibey

💻
HaiCheViet
HaiCheViet

💻
Marcin Puc
Marcin Puc

💻
Anton V.
Anton V.

💻
Arnaud Gourlay
Arnaud Gourlay

💻
Egor Ivkov
Egor Ivkov

💻
Ivan Pleshkov
Ivan Pleshkov

💻
Daniil
Daniil

💻
Anton Kaliaev
Anton Kaliaev

💻
Andre Julius
Andre Julius

💻
Prokudin Alexander
Prokudin Alexander

💻
Tim Eggert
Tim Eggert

💻
Gabriel Velo
Gabriel Velo

💻
Boqin Qin(秦 伯钦)
Boqin Qin(秦 伯钦)

🐛
Russ Cam
Russ Cam

💻
erare-humanum
erare-humanum

💻
Roman Titov
Roman Titov

💻
Hozan
Hozan

💻
George
George

💻
Kornél Csernai
Kornél Csernai

💻
Luis Cossío
Luis Cossío

💻
Tim Visée
Tim Visée

💻
Timon Vonk
Timon Vonk

💻
Yiping Deng
Yiping Deng

💻
Alex Huang
Alex Huang

💻
Ibrahim M. Akrab
Ibrahim M. Akrab

💻
stencillogic
stencillogic

💻
Moaz bin Mokhtar
Moaz bin Mokhtar

📖

License

Qdrant is licensed under the Apache License, Version 2.0. View a copy of the License file.