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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 pointsβ€”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 πŸ¦€, 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 β›… including a free tier.

Quick Start β€’ Client Libraries β€’ Demo Projects β€’ Integrations β€’ Contact

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

This is the recommended method for production usage. To run the container, use the 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 production

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 πŸ”

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 πŸ•

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 πŸ“Ί

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

πŸ’»
Andre Zayarni
Andre Zayarni

πŸ“–
Joan Fontanals
Joan Fontanals

πŸ’»
trean
trean

πŸ’»
Konstantin
Konstantin

πŸ’»
Daniil Naumetc
Daniil Naumetc

πŸ’»
Viacheslav Poturaev
Viacheslav Poturaev

πŸ“–
Alexander Galibey
Alexander Galibey

πŸ’»
HaiCheViet
HaiCheViet

πŸ’»
Marcin Puc
Marcin Puc

πŸ’»
Anton V.
Anton V.

πŸ’»
Arnaud Gourlay
Arnaud Gourlay

πŸ’»
Egor Ivkov
Egor Ivkov

πŸ’»
Ivan Pleshkov
Ivan Pleshkov

πŸ’»
Daniil
Daniil

πŸ’»
Anton Kaliaev
Anton Kaliaev

πŸ’»
Andre Julius
Andre Julius

πŸ’»
Prokudin Alexander
Prokudin Alexander

πŸ’»
Tim Eggert
Tim Eggert

πŸ’»
Gabriel Velo
Gabriel Velo

πŸ’»
Boqin Qin(秦 δΌ―ι’¦)
Boqin Qin(秦 δΌ―ι’¦)

πŸ›
Russ Cam
Russ Cam

πŸ’»
erare-humanum
erare-humanum

πŸ’»
Roman Titov
Roman Titov

πŸ’»
Hozan
Hozan

πŸ’»
George
George

πŸ’»
KornΓ©l Csernai
KornΓ©l Csernai

πŸ’»
Luis CossΓ­o
Luis CossΓ­o

πŸ’»
Tim VisΓ©e
Tim VisΓ©e

πŸ’»
Timon Vonk
Timon Vonk

πŸ’»
Yiping Deng
Yiping Deng

πŸ’»
Alex Huang
Alex Huang

πŸ’»
Ibrahim M. Akrab
Ibrahim M. Akrab

πŸ’»
stencillogic
stencillogic

πŸ’»
Moaz bin Mokhtar
Moaz bin Mokhtar

πŸ“–

License

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

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