VectorNest is a privacy-first local vector database. Essentially, it serves as a long-term memory for LLMs. VectorNest is designed to be LLM-agnostic. This means that it can work seamlessly with any existing and future LLM. The major benefit of this approach is that VectorNest will be able to grow alongside future AI developments, always staying relevant and efficient.
Unique to VectorNest is that it is 100% Swift and utilizes Apple Silicon hardware acceleration. This ensures that VectorNest not only performs optimally but also integrates smoothly with Apple's ecosystem.
- LLM-Agnostic: Works with any existing and future LLMs.
- Vector Database: Acts as a long-term memory for LLMs.
- 100% Swift: Written entirely in Swift for optimal performance and integration.
- Apple Silicon Acceleration: Utilizes Apple Silicon hardware acceleration.
- Cost-effective: Provides flexible long-term memory at no cost.
- Privacy-Conscious: Prioritizes user privacy in all its operations.
We welcome contributions from the community. If you'd like to contribute, please check out our contributing guidelines.
VectorNest is licensed under MIT.