Indexing video embeddings is more complex than static image or text embeddings because each video contains many frames (or segments), and embeddings might change over time. When Sora content is dynamically generated, large volumes of embeddings arrive continuously; the vector database must insert them quickly and balance index rebuilding or incremental updates. For revocation (e.g. user withdraws permission or copyright takedown), embeddings must be deleted or invalidated cleanly without degrading index performance or leaving stale entries.
Deletion or update in high-dimensional indexes is nontrivial: some approximate nearest neighbor structures (e.g. HNSW) are optimized for insertion but make deletion expensive or lazy (marking nodes as inactive). Ensuring that deleted embeddings do not influence search results or pollute similarity rankings is a key challenge. Also, versioning or historical tracking might be needed—knowing which embedding belonged to which video version or prompt. Over time, indexes may become fragmented or unbalanced after many insertions and deletions; reindexing or compaction may be needed, but that must be scheduled carefully to avoid downtime or performance degradation.
Additionally, embedding drift over model updates (if embedding models evolve) makes earlier embeddings less comparable. The system may need migration strategies: reembedding historical videos, applying alignment, or maintaining multiple embedding versions and mapping between them. Handling all this at large scale, with consistency, low latency, and minimal disruption, is a significant challenge for vector database systems in a post-Sora landscape.