This article delves into the technical challenge of ineffective data deletion using negative vector methods in FAISS (Facebook AI Similarity Search library) and proposes a weighted kernel as an innovative solution. The author elaborates on the algorithmic principles through a GitHub open-source project, revealing that in the field of AI vector search, the key to optimizing data management lies in technological innovation. This research holds significant reference value for AI developers, chip designers, and autonomous driving technology experts, offering new approaches to enhance search efficiency. The article also analyzes the advantages of weighted kernels in practical applications and provides code examples to help readers understand and implement this technology.
