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DBMS > Weaviate

Weaviate Systemeigenschaften

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

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NameWeaviate
KurzbeschreibungAn AI-native realtime vector database engine that integrates scalable machine learning models.
Primäres DatenbankmodellVektor DBMS
DB-Engines Ranking infomisst die Popularität von Datenbankmanagement- systemenranking trend
Trend Chart
Punkte1,73
Rang#143  Overall
#5  Vektor DBMS
Websitegithub.com/­weaviate/­weaviate
weaviate.io
Technische Dokumentationweaviate.io/­developers/­weaviate
EntwicklerWeaviate B.V.
Erscheinungsjahr2019
Aktuelle Version1.19, Mai 2023
Lizenz infoCommercial or Open SourceOpen Source infocommercial license available with Weaviate Enterprise
Ausschließlich ein Cloud-Service infoNur als Cloud-Service verfügbarnein
DBaaS Angebote (gesponserte Links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
ImplementierungsspracheGo
Datenschemayes, maps to GraphQL interface
Typisierung infovordefinierte Datentypen, z.B. float oder dateja infostring, int, float, geo point, date, cross reference, fuzzy references
XML Unterstützung infoVerarbeitung von Daten in XML Format, beispielsweise Speicherung von XML-Strukturen und/oder Unterstützung von XPath, XQuery, XSLTnein
Sekundärindizesja infoall data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed
SQL infoSupport of SQLGraphQL is used as query language
APIs und andere ZugriffskonzepteGraphQL query language
RESTful HTTP/JSON API
Unterstützte ProgrammiersprachenJavaScript / TypeScript
Python
Server-seitige Scripts infoStored Proceduresnein
Triggersnein
Partitionierungsmechanismen infoMethoden zum Speichern von unterschiedlichen Daten auf unterschiedlichen KnotenSharding
Replikationsmechanismen infoMethoden zum redundanten Speichern von Daten auf mehreren Knotenja
MapReduce infoBietet ein API für Map/Reduce Operationennein
Konsistenzkonzept infoMethoden zur Sicherstellung der Konsistenz in einem verteilten SystemEventual Consistency
Fremdschlüssel inforeferenzielle Integritätnein
Transaktionskonzept infoUnterstützung zur Sicherstellung der Datenintegrität bei nicht-atomaren Datenmanipulationennein
Concurrency infoUnterstützung von gleichzeitig ausgeführten Datenmanipulationenja
Durability infoDauerhafte Speicherung der Datenja
In-Memory Unterstützung infoGibt es Möglichkeiten einige oder alle Strukturen nur im Hauptspeicher zu haltenja
Berechtigungskonzept infoZugriffskontrolleAPI Keys
OpenID Connect Discovery
Weitere Informationen bereitgestellt vom Systemhersteller
Weaviate
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 togethersimplifies 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

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Weitere Ressourcen
Weaviate
DB-Engines Blog Posts

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

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Erwähnungen in aktuellen Nachrichten

Build enterprise-ready generative AI solutions with Cohere foundation models in Amazon Bedrock and Weaviate vector ...
24. Januar 2024, AWS Blog

Weaviate Partners with Snowflake to Bring Secure GenAI to Snowpark Container Services
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

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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