Overview

Vearch is a scalable distributed system for efficient similarity search of deep learning vectors.

Architecture

• Data Model

  space, documents, vectors, scalars

• Components

  Master, Router and PartitionServer

• 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.

Quick start

• Quickly build a distributed vector search system with RESTful API, please see docs/Deploy.md.

• 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.

APIs and Use Cases

LowLevelAPI

• docs/APILowLevel.md

VisualSearchAPI

• docs/APIVisualSearch.md

Document

• https://vearch.readthedocs.io/en/latest
• https://vearch.readthedocs.io/zh_CN/latest

Benchmarks

• benchmarks

Publication

Jie Li, Haifeng Liu, Chuanghua Gui, Jianyu chen, Zhenyun Ni, Ning Wang, Yuan Chen. The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform. In the 19th International ACM Middleware Conference, December 10–14, 2018, Rennes, France. https://arxiv.org/abs/1908.07389

Community

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 vearch-maintainers@groups.io.

License

Licensed under the Apache License, Version 2.0. For detail see LICENSE and NOTICE.