macOS
brew install weaviatelocal Homebrew formula metadata
brew / rang 3874
Consultez les chemins d'installation, exécutables, métadonnées et notes de sécurité de weaviate pour les workflows d'agents IA.
installation
brew install weaviatelocal Homebrew formula metadata
nix profile install nixpkgs#weaviatenixpkgs package indexes · pkgs/by-name/we/weaviate/package.nix · Source: api.github.com
aperçu
Open-source vector database that stores both objects and vectors
historique
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.
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.
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.
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.
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.
posture de sécurité
Aucun manifest local de gestion des secrets correspondant n'a été trouvé pour weaviate. Les métadonnées de paquet Nucleus restent publiées ici afin que la couverture future dispose d'une URL stable.
Avant une utilisation sans surveillance par un agent, vérifiez si l'outil lit des identifiants en clair, écrit un état distant, publie des artefacts ou lance des plugins.
exécutables
| Commande | Type | Exposition | Note |
|---|---|---|---|
weaviate | exécutable | exécutable indexé | Découvert depuis l'index local des exécutables. |
fraîcheur
Ces signaux séparent l'âge de génération de la page, l'activité du gestionnaire de paquets et la comparaison avec les versions amont. Un retard de version n'est signalé que lorsqu'une URL de preuve et des versions comparables sont présentes.
métadonnées d'installation
| Clé du paquet | brew:weaviate |
|---|---|
| Version | 1.38.8 |
| Gestionnaire de paquets | Homebrew |
| Page d'accueil | https://weaviate.io/developers/weaviate/ |
| Dépôt | https://github.com/weaviate/weaviate |
| Dernière mise à jour | 2026-07-29T18:57:42Z |
| Pulse | updated |
| Bouteille | non enregistré |
| Service | aucun déclaré |
correspondances dans les bases sources
Les correspondances proviennent d’index externes de gestionnaires de paquets et restent séparées des liens de paquets Automic Vault locaux.
weaviate
nix profile install nixpkgs#weaviatepiste source
Cette page est servie par av-web depuis l'artéfact SQLite privé des paquets généré par scripts/generate-pkg-sqlite.py.
View the package source record on GitHub.