This article explains how to build a simple local vector database in Python without using tools like FAISS or LangChain.
The focus is on understanding the fundamentals of vector databases using NumPy and a flat file, without relying on complex frameworks or installations.
A vector database stores data as vectors in multi-dimensional space, allowing similar items to be close together for efficient searching based on cosine similarity or Euclidean distance.
The tutorial aims to provide insight into the inner workings of vector search without using heavy tools, making it suitable for those interested in understanding the basics.