Embedding-based search outperforms traditional keyword-based methods across various domains by capturing semantic similarity using dense vector representations and approximate nearest neighbor (ANN) search. However, the ANN data structure brings excessive storage overhead, often 1.5 to 7 times the size of the original raw data. This overhead is manageable in large-scale web applications but becomes impractical […]
The post Meet LEANN: The Tiniest Vector Database that Democratizes Personal AI with Storage-Efficient Approximate Nearest Neighbor (ANN) Search Index appeared first on MarkTechPost.