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使用 Homebrew, Nix 安装 weaviate

查看 weaviate 的安装路径、可执行文件、元数据以及面向 AI 代理工作流的安全说明。

安装

其他安装命令

macOS

Homebrew已验证 · 100%
brew install weaviate

local Homebrew formula metadata

Linux

Nix已验证 · 92%
nix profile install nixpkgs#weaviate

nixpkgs package indexes · pkgs/by-name/we/weaviate/package.nix · 来源: api.github.com

概览

软件包摘要

Open-source vector database that stores both objects and vectors

命令和别名

  • weaviate

历史

项目历史与用法

Weaviate is an open-source vector database and semantic search engine built around storing data objects together with vector embeddings. Its package-manager identity sits at the intersection of databases, search engines, and AI infrastructure: developers install it when they want a local or self-hosted service that can combine vector similarity, keyword filtering, retrieval-augmented generation, and reranking.

项目历史

The Weaviate idea predates the modern RAG boom. Bob van Luijt's official project history traces an early line from 2017 writing about semantic-web-style 'things' to the end of 2018, when Weaviate entered a Dutch startup accelerator and the startup around it became SeMI Technologies, short for Semantic Machine Insights.

The project then narrowed from broad semantic data modeling toward natural-language processing, embeddings, and vector storage. Weaviate's own history describes this shift as the birth of the Weaviate Search Graph: a database/search system meant to make semantic search available as an open-source product rather than as a bespoke machine-learning pipeline.

In 2023, SeMI Technologies renamed itself Weaviate, adopting the name of the flagship open-source vector-search engine. The rename reflected the fact that the developer-facing product brand had become better known than the original company name.

采用历史

Weaviate's adoption rose with the wider normalization of embeddings in application development. The project describes use cases such as invoice classification, concept-based document search, site search, and product knowledge graphs; its current repository and platform positioning add RAG systems, semantic and image search, recommendation engines, chatbots, and content classification.

By July 2026 the public GitHub repository reported more than 16,000 stars and active releases in the 1.3x series, with release notes covering replication, vector-index work, BM25 optimization, and model-provider modules. That release cadence is a useful package-nerd signal: this is not just a research demo, but a packaged server with continuing operational and AI-integration work.

使用方式

In local development and self-hosted setups, Weaviate is typically run as a service behind an application, then addressed through client libraries or HTTP/gRPC APIs. The package supplies the server binary for developers who want to test schemas, indexes, vectorizers, hybrid search, and RAG retrieval locally before moving the same workload to containers, Kubernetes, or Weaviate Cloud.

The tool is usually selected when a conventional relational database or keyword search engine is not enough: users want nearest-neighbor search over embeddings, metadata filtering, and application-level retrieval in one system. The package is therefore most visible in AI application stacks, search prototypes, and MLOps/data-platform experiments.

为什么软件包爱好者会关心

For package nerds, Weaviate is one of the recognizable names in the first wave of vector databases that became ordinary installable infrastructure. Its significance is less the CLI itself than the fact that a vector database moved into the same packaging channels as Redis, PostgreSQL-adjacent tools, and search servers, making semantic search something developers could install and script locally.

时间线

  • 2017: Bob van Luijt wrote about the overlap between semantic-web objects and internet-of-things 'things', an idea later tied to Weaviate's conceptual roots.
  • 2018: Weaviate entered a Dutch startup accelerator, where the team and company around the open-source project began to form.
  • 2019: SeMI Technologies was founded around the Weaviate open-source vector database.
  • 2023: SeMI Technologies renamed itself Weaviate to match the better-known product brand.
  • 2026: The GitHub project remained actively released, with v1.37 and v1.38 release trains visible in June 2026.

Related projects

  • Weaviate belongs to the vector database and neural-search family alongside systems such as Milvus, Qdrant, Vespa, Elasticsearch/OpenSearch vector search, and managed embedding stores. It is also commonly paired with language-model orchestration frameworks and application code that performs RAG.

安全态势

尚未找到受保护工具覆盖

没有找到 weaviate 的匹配本地密钥处理 manifest。Nucleus 软件包元数据仍在此发布,以便未来覆盖拥有稳定的软件包 URL。

安装行为

  • 未记录 Homebrew bottle 元数据。

建议审查

在无人值守的代理使用前,请检查该工具是否读取明文凭据、写入远程状态、发布制品或调用插件。

可执行文件

已安装的可执行文件

命令类型暴露范围备注
weaviate可执行文件已索引可执行文件从本地可执行文件索引发现。

新鲜度

版本和新鲜度

这些信号区分页生成时间、软件包管理器活动和上游发布比较。只有存在证据 URL 和可比较版本时,才会提示版本落后。

页面生成时间2026-08-03
管理器版本1.38.8
管理器更新时间2026-07-29
本地数据未知
上游不可用
检测到的最新版本未检测到
  • OK没有生成新鲜度警告。

安装元数据

软件包元数据

软件包键brew:weaviate
版本1.38.8
软件包管理器Homebrew
主页https://weaviate.io/developers/weaviate/
仓库https://github.com/weaviate/weaviate
最后更新2026-07-29T18:57:42Z
Pulseupdated
Bottle未记录
服务未声明

源数据库匹配

其他软件包管理器记录

匹配项来自外部软件包管理器索引,并与本地 Automic Vault 软件包链接分开显示。

Nix95%

weaviate

nix profile install nixpkgs#weaviate
  • normalized package name match
  • 匹配方式:Weaviate
nixpkgs package indexes · api.github.com · nixpkgs package indexes: pkgs/by-name/we/weaviate/package.nix from https://api.github.com/repos/NixOS/nixpkgs/git/trees/master?recursive=1

来源线索

由仓库数据生成

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使用的来源

  • Geiger risk classifier
  • cross-ecosystem install command graph
  • curated package history
  • external package-manager database matches
  • pkg.so package database
  • pkgdb category and tag curation