Building a Tiny Vector Database in Rust: A Technical Walkthrough 🦀

Vector databases have existed for years, but recent advancements in scaling Retrieval Augmented Generation (RAG) systems have drawn significant attention from engineering teams seeking to implement AI solutions. These implementations employ distinct design philosophies. Some prioritize low-latency operations, while others emphasize scalability and distributed architecture support. This raises practical questions: How might researchers experiment with basic vector database implementations to test novel retrieval enhancements or semantic search improvements? What about developers seeking to understand the underlying mechanisms? Highly optimized production-grade systems present a challenge here – they often incorporate multiple abstraction layers that obscure implementation details from users.

nano-vectordb-rs provides a simple, minimalistic implementation using Rust - a systems programming language that ensures memory safety through its ownership and borrowing system.

This approach empowers developers with precise control over memory allocation/deallocation without garbage collection overhead, while zero-cost abstractions guarantee high-level code compiles to optimized machine instructions with no runtime penalty. These features make Rust particularly well-suited for building vector databases, where performance is critical. Additionally, Rust's built-in concurrency safety enables safe parallel execution, which we'll leverage to accelerate our algorithms through straightforward parallelism.

This implementation strips away the complexity to reveal core vector database mechanics through ~350 lines of focused code. Designed to educate, we'll explore:

• Basics of vector databases
• Design choices
• Implementation details