Vector databases manage large collections of embedding vectors. As AI
applications are growing rapidly, so are the number of embeddings that need to
be stored and indexed. The Faiss library is dedicated to vector similarity
search, a core functionality of vector databases. Faiss is a toolkit of
indexing methods and related primitives used to search, cluster, compress and
transform vectors. This paper first describes the tradeoff space of vector
search, then the design principles of Faiss in terms of structure, approach to
optimization and interfacing. We benchmark key features of the library and
discuss a few selected applications to highlight its broad applicability.

Faiss 是一个用于向量相似度搜索的工具库，它管理大型的嵌入向量集合，描述了向量搜索的权衡空间以及 Faiss 的设计原则和关键特性。

Faiss 库

The Faiss library

We present SkillGPT, a tool for skill extraction and standardization (SES)
from free-style job descriptions and user profiles with an open-source Large
Language Model (LLM) as backbone. Most previous methods for similar tasks
either need supervision or rely on heavy data-preprocessing and feature
engineering. Directly prompting the latest conversational LLM for standard
skills, however, is slow, costly and inaccurate. In contrast, SkillGPT utilizes
a LLM to perform its tasks in steps via summarization and vector similarity
search, to balance speed with precision. The backbone LLM of SkillGPT is based
on Llama, free for academic use and thus useful for exploratory research and
prototype development. Hence, our cost-free SkillGPT gives users the
convenience of conversational SES, efficiently and reliably.

使用开源的大型语言模型，以摘要和向量相似性搜索的方式，提供无监督的技能提取和标准化 SES 处理工具 SkillGPT。

SkillGPT：一种使用大型语言模型进行技能提取和标准化的 RESTful API 服务

SkillGPT: a RESTful API service for skill extraction and standardization  using a Large Language Model

There is an increasing adoption of machine learning for encoding data into
vectors to serve online recommendation and search use cases. As a result,
recent data management systems propose augmenting query processing with online
vector similarity search. In this work, we explore vector similarity search in
the context of Knowledge Graphs (KGs). Motivated by the tasks of finding
related KG queries and entities for past KG query workloads, we focus on hybrid
vector similarity search (hybrid queries for short) where part of the query
corresponds to vector similarity search and part of the query corresponds to
predicates over relational attributes associated with the underlying data
vectors. For example, given past KG queries for a song entity, we want to
construct new queries for new song entities whose vector representations are
close to the vector representation of the entity in the past KG query. But
entities in a KG also have non-vector attributes such as a song associated with
an artist, a genre, and a release date. Therefore, suggested entities must also
satisfy query predicates over non-vector attributes beyond a vector-based
similarity predicate. While these tasks are central to KGs, our contributions
are generally applicable to hybrid queries. In contrast to prior works that
optimize online queries, we focus on enabling efficient batch processing of
past hybrid query workloads. We present our system, HQI, for high-throughput
batch processing of hybrid queries. We introduce a workload-aware vector data
partitioning scheme to tailor the vector index layout to the given workload and
describe a multi-query optimization technique to reduce the overhead of vector
similarity computations. We evaluate our methods on industrial workloads and
demonstrate that HQI yields a 31x improvement in throughput for finding related
KG queries compared to existing hybrid query processing approaches.

本文介绍了一种名为 HQI 的系统，用于高吞吐量批处理混合查询，特别是在知识图谱中的向量相似度搜索。该系统使用一种基于负载的向量数据分区方案来量身定制向量索引布局，同时描述了一种多查询优化技术以减少向量相似度计算的开销。我们基于工业工作负载评估了 HQI 的方法，并证明与现有的混合查询处理方法相比，HQI 在查找相关 KG 查询方面的吞吐量提高了 31 倍。