The AI-Native Vector Database for Next-Gen Applications
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Note: We are in the process of evaluating platforms based on our Core Criteria, Extended Criteria, and Code AI Criteria. The ratings for these categories will be published soon. Thank you for your patience as we ensure a thorough and accurate assessment.
Vector Search Engine: Weaviate is an open-source vector search engine that uses machine learning to facilitate semantic search through high-dimensional data.
Schema-less Data Management: It supports a schema-less approach to data management, allowing for the organization and retrieval of unstructured data.
GraphQL API: Weaviate provides a GraphQL API for easy data queries and interaction.
Integration with Various Data Sources: It can integrate with data from various sources, including databases and cloud storage.
Scalability: The architecture is designed to scale horizontally, allowing for the handling of large datasets effortlessly.
Real-time Updates: Supports real-time data updates, making it suitable for dynamic applications.
Built-in Machine Learning Capabilities: Incorporates built-in machine learning models for tasks such as text classification and clustering.
Support for Multiple Data Types: Facilitates working with different data types, including text, images, and other media formats.
Plug-in Architecture: Offers a modular plug-in architecture for extending functionality.
User Community and Documentation: Actively supported by a community and provides extensive documentation for users and developers.