/qdrant

Qdrant - Vector Search Engine and Database for the next generation of AI applications. Also available in the cloud https://cloud.qdrant.io/

Primary LanguageRustApache License 2.0Apache-2.0

Qdrant

Vector Search Engine for the next generation of AI applications

Tests status OpenAPI Docs Apache 2.0 License Discord Roadmap 2023

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 πŸ¦€, which makes it fast and reliable even under high load.

With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!

Also available as managed solution in the Qdrant Cloud https://cloud.qdrant.io/ β›…

Demo Projects

Semantic Text Search πŸ”

The neural search uses semantic embeddings instead of keywords and works best with short texts. With Qdrant and a pre-trained neural network, you can build and deploy semantic neural search on your data in minutes. Try it online!

Similar Image Search - Food Discovery πŸ•

There are multiple ways to discover things, text search is not the only one. In the case of food, people rely more on appearance than description and ingredients. So why not let people choose their next lunch by its appearance, even if they don’t know the name of the dish? Check it out!

Extreme classification - E-commerce Product Categorization πŸ“Ί

Extreme classification is a rapidly growing research area within machine learning focusing on multi-class and multi-label problems involving an extremely large number of labels. Sometimes it is millions and tens of millions classes. The most promising way to solve this problem is to use similarity learning models. We put together a demo example of how you could approach the problem with a pre-trained transformer model and Qdrant. So you can 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 programing 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.

Clients

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

Features

Filtering and Payload

Qdrant supports any JSON payload associated with vectors. It does not only store payload but also allows filter results based on payload values. It allows any combinations of should, must, and must_not conditions, but unlike ElasticSearch post-filtering, Qdrant guarantees all relevant vectors are retrieved.

Rich Data Types

Vector payload supports a large variety of data types and query conditions, including string matching, numerical ranges, geo-locations, and more. Payload filtering conditions allow you to build almost any custom business logic that should work on top of similarity matching.

Query Planning and Payload Indexes

Using the information about the stored payload values, the query planner decides on the best way to execute the query. For example, if the search space limited by filters is small, it is more efficient to use a full brute force than an index.

SIMD Hardware Acceleration

Qdrant can take advantage of modern CPU x86-x64 architectures. It allows you to search even faster on modern hardware.

Write-Ahead Logging

Once the service confirmed an update - it won't lose data even in case of power shut down. All operations are stored in the update journal and the latest database state could be easily reconstructed at any moment.

Distributed Deployment

Since v0.8.0 Qdrant supports distributed deployment. In this mode, multiple Qdrant machines are joined into a cluster to provide horizontal scaling. Coordination with the distributed consensus is provided by the Raft protocol.

Stand-alone

Qdrant does not rely on any external database or orchestration controller, which makes it very easy to configure.

Usage

Docker 🐳

Build your own from source

docker build . --tag=qdrant/qdrant

Or use latest pre-built image from DockerHub

docker pull qdrant/qdrant

To run the container, use the command:

docker run -p 6333:6333 qdrant/qdrant

And once you need a fine-grained setup, you can also define a storage path and custom configuration:

docker run -p 6333:6333 \
    -v $(pwd)/path/to/data:/qdrant/storage \
    -v $(pwd)/path/to/custom_config.yaml:/qdrant/config/production.yaml \
    qdrant/qdrant
  • /qdrant/storage - is a place where Qdrant persists all your data. Make sure to mount it as a volume, otherwise docker will drop it with the container.
  • /qdrant/config/production.yaml - is the file with engine configuration. You can override any value from the reference config

Now Qdrant should be accessible at localhost:6333.

Docs πŸ““

Contacts

Building something special with Qdrant? We can help!

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“–

License

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