You Probably Don’t Need a Vector Database for Your RAG — Yet

A dedicated vector database is often unnecessary for small-to-medium scale Retrieval Augmented Generation (RAG) applications. The retrieval component can be built more simply and efficiently using standard Python libraries like NumPy and SciKit-Learn. This approach treats vector search as a matrix multiplication problem, which is highly optimized in NumPy. The provided code demonstrates an in-memory `SimpleVectorStore` class that handles embedding, normalization, and fast retrieval without the complexity and network latency of external databases. This method is capable of searching millions of text strings in milliseconds, making it suitable for many internal tools and MVPs.