In this post, Frank Liu. ML Architect at Zilliz, discusses vector databases and different indexing strategies for approximate nearest neighbor search. The options mentioned include brute-force search, inverted file index, scalar quantization, product quantization, HNSW, and Annoy. Liu emphasizes the importance of considering application requirements when choosing the appropriate index.
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In this post, Frank Liu. ML Architect at Zilliz, discusses vector databases and different indexing strategies for approximate nearest neighbor search. The options mentioned include brute-force search, inverted file index, scalar quantization, product quantization, HNSW, and Annoy. Liu emphasizes the importance of considering application requirements when choosing the appropriate index.