# weaviate を Homebrew, Nix でインストール weaviate のインストール経路、実行ファイル、メタデータ、AI エージェント向けセキュリティノートを確認します。 ## インストール ```sh sudo av install brew:weaviate ``` 追加のインストールコマンド: ### macOS - Homebrew (100%): ```sh brew install weaviate ``` 証拠: local Homebrew formula metadata ### Linux - Nix (92%): ```sh nix profile install nixpkgs#weaviate ``` 証拠: nixpkgs package indexes: pkgs/by-name/we/weaviate/package.nix from https://api.github.com/repos/NixOS/nixpkgs/git/trees/master?recursive=1 ## パッケージ情報 - **パッケージキー:** brew:weaviate - **パッケージマネージャ:** Homebrew - **バージョン:** 1.38.8 - **ソース概要:** Open-source vector database that stores both objects and vectors - **ホームページ:** - **リポジトリ:** - **最終更新:** 2026-07-29T18:57:42Z - **生成日時:** 2026-08-03T19:37:03+00:00 ## 実行可能ファイル - weaviate (エイリアス) ## インストール挙動 - Bottle: 利用不可 ## バージョンと鮮度 - ページ生成日: 2026-08-03 - マネージャ版: 1.38.8 ## プロジェクトの歴史と使われ方 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. ### プロジェクトの歴史 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. ### 採用の歴史 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. ### 使われ方 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. ### パッケージ好きにとっての重要性 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. ### タイムライン - 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. ### ソース - - - - - ## セキュリティノート weaviate に一致するローカルシークレット処理マニフェストは見つかりませんでした。将来の対応で安定したパッケージ URL を使えるよう、Nucleus パッケージメタデータはここに公開されています。 ## 他のパッケージマネージャ記録 - Nix - weaviate: normalized package name match | nixpkgs package indexes: pkgs/by-name/we/weaviate/package.nix from https://api.github.com/repos/NixOS/nixpkgs/git/trees/master?recursive=1 ## Combined YAML source View the package source record on GitHub. [combined/weaviate.yml](https://github.com/mxcl/pkgdb/blob/main/combined/weaviate.yml) ## ソース - pkg.so package database - Geiger risk classifier - curated package history - pkgdb category and tag curation - external package-manager database matches - cross-ecosystem install command graph