Cyborg Vector Search was renamed to CyborgDB in
v0.9.0. This version of the docs will refer to the project as CyborgDB.
- Introduces a novel architecture to keep confidential inference data secure through zero-trust design.
- Keeps vector embeddings end-to-end encrypted throughout their lifecycle (including at search time).
- Exposes a familiar API, making it easy to integrate with existing AI workflows.
- Provides high-performance indexing and retrieval which can be GPU-accelerated with CUDA.
- Works with DBs such as Postgres and Redis, transforming traditional DBs into Confidential Vector DBs.
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Learn About CyborgDB
Learn about the architecture, design and deployment of CyborgDB.
Quickstart
Get started with CyborgDB and start building your first Confidential AI application.
API Docs
Explore the API reference to learn how to use CyborgDB in your applications.
