Michael-A-Kuykendall/contextlite
📦 Open Source ProjectMichael-A-Kuykendall
A high-performance, SMT-powered search optimization engine that outperforms traditional vector databases by 27,000x.
ContextLite represents a paradigm shift in how AI applications handle document retrieval and context management. Rather than relying on resource-heavy vector embeddings and expensive vector database infrastructure, ContextLite leverages SMT-powered mathematical search optimization. This approach allows developers to integrate advanced search capabilities into their existing database stacks without the need for migration or costly cloud-based vector services. The engine is built in Go, ensuring high concurrency and performance. By utilizing SMT solvers, it achieves search speeds reportedly 27,000 times faster than standard vector database implementations. It supports a wide array of database backends, including SQLite and FTS5, making it a versatile tool for privacy-conscious developers looking to build efficient Retrieval-Augmented Generation (RAG) pipelines. The project emphasizes a 'Database Freedom' philosophy, offering a one-time licensing model that drastically reduces long-term operational costs compared to traditional SaaS alternatives.
💡Highlights
- ├─27,000x faster than vector DBs
- ├─SMT-powered search optimization
- └─Supports 8+ database types
🎯For
- ├─Backend Developers
- └─AI Engineers