tchigher / vearch

A distributed system for efficient similarity search of deep learning vectors

master:     dev:

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 [email protected].

License

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