Vector databases 2022-02-15
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Weaviate

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Store vectors with fast search.
Generated by ChatGPT

Weaviate is an open-source vector database that allows users to store data objects and vector embeddings from ML-models and scale to billions of data objects seamlessly.

The tool provides lightning-fast pure vector similarity search over data objects or raw vectors and supports a combination of keyword-based search and vector search techniques for state-of-the-art search results.

Weaviate also enables users to use any generative model in combination with your data to create next-gen search experiences. The tool has integrations with a wide variety of well-known neural search frameworks and provides out-of-the-box support for vectorization.

Users can also choose from Weaviate's modules, which have extensive support for vectorization. Weaviate is designed to give developers an excellent experience and enable them to go from zero to production seamlessly.

The tool is designed with community and open-source principles in mind, and users can join the Weaviate community on Slack. The tool currently has backup and restore capabilities, making it a robust solution for data-intensive applications.

Weaviate has a vast library of resources that help users learn how to use the tool and get inspiration from other users' innovative apps. Finally, Weaviate is available for use anywhere as an open-source tool.

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Weaviate was manually vetted by our editorial team and was first featured on May 29th 2023.
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Pros and Cons

Pros

Stores vector embeddings
Scales to billions objects
Lightning-fast vector similarity search
Supports keyword-based search
Supports vector search
Allows any generative model
Wide neural search integrations
Supports vectorization
Zero to production design
Community and open-source focus
Backup and restore capabilities
Variety of learning resources
Free to use
Well-integrated with embedding providers
Simultaneous keyword and vector search
Provides state-of-the-art search experiences
Efficient Q&A over dataset
Supports innovative applications development
Seamless vector indexing
Fast pure vector search
Extensive module support
User-friendly developer experience
Open-source with Slack community
Provides SaaS services
Good for data-intensive applications
Community inspirations for usage

Cons

Limited integrations
No commercial support
Open-source drawbacks
Requires ML model building
Learning curve
Limited search options
Inadequate community support
Insufficient documentation

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