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text: 00:00 Search professional with expertise in vectors and engines.09:59 Elasticsearch: scalable, weak consistency, prefer vector search.12:53 Optimize data structures for faster processing efficiency.21:41 Vector indexes require special treatment, like HNSW’s proximity graph and greedy search.23:16 HNSW index: approximate, precision control, CPU intensive.30:06 Post-filtering inefficient, prefiltering costly.34:01 Metadata-based filters; creating additional connecting links.41:41 Vector dimension impacts comparison speed, indexing complexity high.46:53 Overlapping intervals and subgraphs for precision.53:18 Postgres limits scalability, additional indexing engines provide faster queries.59:55 Embedding models for time series data explained.01:02:01 Cheaper system for serving billion vectors.