OpenViking is an open-source context database engineered for efficient indexing and retrieval of large amounts of unstructured or semi-structured context data used by AI applications. It’s primarily designed to serve as a high-performance, scalable backend for storing app context, embeddings, conversational histories, and other textual artifacts that need rapid lookup and semantic search, which makes it especially useful for systems like chatbots or memory-augmented agents. The project is implemented with performance in mind, often leveraging optimized data structures that balance fast reads and writes with minimal resource consumption. Developers can integrate OpenViking into modern AI stacks to unify context storage across services, enabling consistent session history, personalized responses, and richer search experiences.
Features
- Efficient semantic context storage optimized for AI workflows
- Fast indexing and retrieval of text and embeddings
- Modular architecture for flexible integration
- Designed for scalable usage in production environments
- Support for unstructured and semi-structured data
- Lightweight footprint ideal for AI-centric infrastructure