Introduction
SRA_Recall is a recall algorithm library provided by Huawei and optimized based on the Kunpeng platform. This document provides the installation guide, interface definitions, and sample code of SRA_Recall to help you quickly get started with it.
SRA_Recall Overview
Table 1 describes the composition of SRA_Recall.
Algorithm |
Description |
Application Scenario |
|---|---|---|
KBest |
Kunpeng Blazing-fast embedding similarity search thruster (KBest) is an efficient, Huawei-developed graph search algorithm. It optimizes the performance and precision of the nearest neighbor search by using methods such as quantization and NUMA scheduling, which are used for multi-dimensional vector approximate nearest neighbor search. |
Applicable to various application fields of vector retrieval, including network search, multi-modal search, recommendation system, advertisement placement, and retrieval-augmented generation (RAG). |
KScaNN |
Kunpeng Scalable Nearest Neighbors (KScaNN) is a vector retrieval algorithm that is based on inverted indexes. It uses the Kunpeng architecture to deeply optimize the index layout, algorithm process, and computing process, fully unleashing the chip potential. |
|
KVecturbo |
KVecturbo is a vector retrieval acceleration component developed by Kunpeng and can be used together with the openGauss vector database. It quantifies and compresses high-dimensional vectors to quickly obtain the near neighbors of a query. In addition, KVecturbo uses the SIMD instructions to accelerate distance calculation for multidimensional vector nearest neighbor search. |
SRA_Recall applies to new Kunpeng 920 processor models and supports SVE instructions (256-bit width).
Application Scenarios
SRA_Recall is suited for the following scenarios:
- Search: network search and multi-modal search
- Recommendation: recommendation systems
- Advertising: advertisement placements