DBMS > Weaviate

Weaviate Systemeigenschaften

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Unsere Besucher vergleichen Weaviate oft mit Milvus, Elasticsearch und Qdrant.

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Name

Kurzbeschreibung

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primäres Datenbankmodell

Vektor DBMS

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Punkte	1,89
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	#4	Vektor DBMS

Website

github.com/weaviate/weaviate
weaviate.io

Technische Dokumentation

weaviate.io/developers/weaviate

Entwickler

Weaviate B.V.

Erscheinungsjahr

2019

Aktuelle Version

1.19, Mai 2023

Lizenz

Commercial or Open Source

Open Source

commercial license available with Weaviate Enterprise

Ausschließlich ein Cloud-Service

Nur als Cloud-Service verfügbar

nein

DBaaS Angebote (gesponserte Links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementierungssprache

Datenschema

yes, maps to GraphQL interface

Typisierung

vordefinierte Datentypen, z.B. float oder date

string, int, float, geo point, date, cross reference, fuzzy references

XML Unterstützung

Verarbeitung von Daten in XML Format, beispielsweise Speicherung von XML-Strukturen und/oder Unterstützung von XPath, XQuery, XSLT

nein

Sekundärindizes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

GraphQL is used as query language

APIs und andere Zugriffskonzepte

GraphQL query language
RESTful HTTP/JSON API

Unterstützte Programmiersprachen

JavaScript / TypeScript
Python

Server-seitige Scripts

Stored Procedures

nein

Triggers

nein

Partitionierungsmechanismen

Methoden zum Speichern von unterschiedlichen Daten auf unterschiedlichen Knoten

Sharding

Replikationsmechanismen

Methoden zum redundanten Speichern von Daten auf mehreren Knoten

MapReduce

Bietet ein API für Map/Reduce Operationen

nein

Konsistenzkonzept

Methoden zur Sicherstellung der Konsistenz in einem verteilten System

Eventual Consistency

Fremdschlüssel

referenzielle Integrität

nein

Transaktionskonzept

Unterstützung zur Sicherstellung der Datenintegrität bei nicht-atomaren Datenmanipulationen

nein

Concurrency

Unterstützung von gleichzeitig ausgeführten Datenmanipulationen

Durability

Dauerhafte Speicherung der Daten

In-Memory Unterstützung

Gibt es Möglichkeiten einige oder alle Strukturen nur im Hauptspeicher zu halten

Berechtigungskonzept

Zugriffskontrolle

API Keys
OpenID Connect Discovery

Weitere Informationen bereitgestellt vom Systemhersteller

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native, and fast.
Is it an essential AI-native infrastructure component, used to store and searching embedding vectors and their corresponding objects. AI-native vector database capabilities include:

High performance – typically performs a 10-NN neighbor search out of millions of objects in single-digit milliseconds.
Extensible, built-in machine learning (ML) modules – Modules enable use of popular services and model hubs such as OpenAI, Cohere or HuggingFace and much more to vectorize data and perform generative AI operations for any data type or use case.
Rich vector search – Supports a variety of ML searches with the added benefit of being able to search vectors AND the source objects from which the vectors were generated.
Production ready - Built with scaling, replication, multi-tenancy, and security in mind.

Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service
Stores vectors AND source objects together - simplifies development by eliminating the need to host and integrate separate databases for source objects and their vectors.
Extensible modules architecture - intergrates with a variety of machine learning models from OpenAI, Cohere, HuggingFace, Google, and others to provide native vectorization and generative AI (results transformation) capabilities.

Typical application scenarios

As a database supporting the development of generative AI and semantic search applications processing any text, image, video, audio, code or other structured or unstructured information.

It is used for applications that automate or facilitate a wide range of use cases, such as first-line sales and customer service (chatbots), copywriting, language translation, named entity recognition, paraphrasing, text summarization, on-line research, and much more.

Key customers

All companies that have data.

Market metrics

As of mid 2023:

Over 2 million open source downloads
3500+ Weaviate Slack community members - https://weaviate.io/slack
6800 stars on Github - https://github.com/weaviate/weaviate

Licensing and pricing models

Weaviate is open-source, and free to use.
Weaviate is also available as a fully managed cloud vector database service, with prices starting at $25 per month - details are here: https://weaviate.io/pricing

Zugehörige Produkte und Dienstleistungen

Wir laden Vertreter von Anbietern von zugehörigen Produkten ein uns zu kontaktieren, um hier Informationen über ihre Angebote zu präsentieren.

Weitere Ressourcen

DB-Engines Blog Posts

Weaviate, an ANN Database with CRUD support
2. Februar 2021, Etienne Dilocker, SeMI Technologies (sponsor)

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8. Februar 2024, Datanami

Foley Represents Cortical Ventures in $50M Series B Round for Weaviate
17. Dezember 2023, Foley & Lardner LLP

Getting Started with Weaviate: A Beginner's Guide to Search with Vector Databases
18. Juli 2023, Towards Data Science

The 5 Best Vector Databases You Must Try in 2024
17. November 2023, KDnuggets

Weaviate Raises $50 Million Series B Funding to Meet Soaring Demand for AI Native Vector Database Technology ...
21. April 2023, PR Newswire

bereitgestellt von Google News

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