A storage engine for vector machine learning embeddings
Embeddinghub is a database built for machine learning embeddings. It is built with four goals in mind.
- Store embeddings durably and with high availability
- Allow for approximate nearest neighbor operations
- Enable other operations like partitioning, sub-indices, and averaging
- Manage versioning, access control, and rollbacks painlessly
Features
- Supported Operations: Run approximate nearest neighbor lookups, average multiple embeddings, partition tables (spaces), cache locally while training, and more.
- Storage: Store and index billions vectors embeddings from our storage layer.
- Versioning: Create, manage, and rollback different versions of your embeddings.
- Access Control: Encode different business logic and user management directly into Embeddinghub.
- Monitoring: Keep track of how embeddings are being used, latency, throughput, and feature drift over time.