To handle massive Sora-generated video embedding collections, vector databases must evolve in architecture, optimization, and storage strategies. First, sharding by modalities, time slices, or embedding clusters enables distributing load: e.g. dividing by date, by style cluster, or by video creator. This helps localize queries and reduce cross-shard cost. Second, compression and quantization techniques (e.g. product quantization, OPQ, IVF + PQ) become essential to reduce memory footprint while preserving similarity fidelity. Because video embeddings are high-dimensional and voluminous, aggressive but smart compression is required.
Third, approximate nearest neighbor (ANN) methods must be optimized for temporal and sequential embeddings. Hybrid index strategies—first coarse clustering, then fine-level search—can help speed up retrieval. Sparse or hierarchical structures, combined with pruning or caching, can reduce latency. Adaptively tuning index parameters (e.g. search depth, candidate set size) based on query patterns or embedding density can further optimize performance. Moreover, multi-tier storage strategies (hot embeddings in memory, cold ones on disk) can help manage cost vs latency trade-offs.
Finally, draining, compaction, rebalancing, and background reindexing must be supported. As new embeddings are inserted or old ones deleted, the system should perform compaction or reorganization without blocking reads. Incremental reindexing, background merges, and cold-hot transitions will be vital. In sum, vector databases must become more modular, adaptive, and efficient to handle the scale and dynamics of video embedding workloads.