Weaviate
Weaviate is an open-source vector database designed for storing data objects and their vector embeddings. It enables high-performance semantic search, allowing users to query data based on meaning and context rather than just keywords, making it ideal for AI-native applications like RAG systems…
Key facts
- First appeared
- 2019
- Category
- database
- Problem solved
- Traditional databases and search engines are optimized for keyword-based search or structured data queries, making them inefficient for handling high-dimensional vector embeddings and performing semantic similarity searches at scale. Weaviate was created to bridge this gap, offering a specialized database capable of storing and indexing millions of vector representations, enabling applications to understand and retrieve information based on conceptual meaning rather than exact matches.
- Platforms
- macOS (development), AWS, GCP, Kubernetes, Linux, Azure, Docker, Self-hosted
Related technologies
Notable users
- Ubermetrics
- Many startups and enterprises building AI-powered applications
- Swisens
- Enqio