Why is vector search useful?
Vector search is widely used for queries where relevance is important. For example, searching a large corpus of documents for documents related to a specific use case or generating recommended items for a shopping cart. Increasingly, vector stores are also being used as a persistent cache for generative AI models to prevent the need for recomputation.
What vector dataypes does Rockset support?
What are the max vector dimensions?
Vectors are syntactically identical to arrays and there is no limit on array size so there is no limit on vector size.
How do I ingest my vectors?
Can you update vectors?
Yes, you manipulate vectors in all of the same ways you would manipulate arrays. Rockset sits on top of an LSM tree based RocksdDB storage engine which means random mutations are fast. Updates, inserts and deletes are immediately visible in any ANN index associated with the vector.
Is there a limit on the number of vectors?
There is no general limit on the number of vectors that can be stored in Rockset. Rockset's disaggregated storage architecture allows your storage tier to scale independently of your compute needs.
Why use Rockset for vector search?
Rockset is already built for low-latency complex analytics on real-time data which perfectly complements vector search use cases.
Updated 1 day ago