Vearch is a scalable distributed system for efficient similarity search of deep learning vectors.
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Quickly compile the source codes to build a distributed vector search system with RESTful API, please see docs/SourceCompileDeployment.md.
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Vearch can be leveraged to build a complete visual search system to index billions of images. The image retrieval plugin for object detection and feature extraction is also required. For more information, please refer to docs/Quickstart.md.
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Vearch Python SDK enables vearch to use locally. Vearch python sdk can be installed easily by pip install vearch. For more information, please refer to docs/APIPythonSDK.md.
Vearch Architecture
Master
Responsible for schema mananagement, cluster-level metadata, and resource coordination.Router
Provides RESTful API: `create` , `delete` `search` and `update` ; request routing, and result merging.PartitionServer (PS)
Hosts document partitions with raft-based replication.Gamma is the core vector search engine implemented based on faiss. It provides the ability of storing, indexing and retrieving the vectors and scalars.
Reference to cite when you use Vearch in a research paper:
@misc{li2019design,
title={The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform},
author={Jie Li and Haifeng Liu and Chuanghua Gui and Jianyu Chen and Zhenyun Ni and Ning Wang},
year={2019},
eprint={1908.07389},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
You can report bugs or ask questions in the issues page of the repository.
For public discussion of Vearch or for questions, you can also send email to [email protected].
Our slack : https://vearchwrokspace.slack.com
Welcome to register the company name in this issue: vearch#230 (in order of registration)
欢迎在此 issue vearch#230 中登记公司名称
Licensed under the Apache License, Version 2.0. For detail see LICENSE and NOTICE.