tos-vectors

Manage vector storage and indexing for RAG workflows by creating buckets, defining indexes, and inserting embeddings. This Skill handles the end-to-end vector lifecycle, enabling semantic search and LLM-assisted retrieval.

Perform semantic search on high-dimensional embeddings in cloud environments by using TOS Vectors to store, index, and retrieve semantically similar vectors. It provides ready-made scripts to execute similarity queries with metadata.

Yes, you can use TOS to build personalized recommendation systems with vector embeddings. It applies to recommendation workflows across ML apps, supporting batch vector operations and similarity retrieval to match user preferences.

Yes, you need pre-configured environment credentials to run vector CRUD operations. The quick start requires creating a VectorClient with your specific endpoint and region configured before initializing buckets and inserting sample vectors.

Vector bucket management handles policy controls and permissions by allowing you to create, list, and delete vector buckets with integrated policy configurations. This ensures secure access control over your stored embeddings and indexes.