tos-vectors

What problem does it solve?

Efficiently manage and search high-dimensional embeddings by providing a cohesive pipeline to store, index, and retrieve semantically similar vectors with TOS Vectors.

Core Features & Use Cases

• Vector bucket management: create, list, and delete vector buckets with policy controls and permissions.
• Indexing and data operations: create indexes, insert, query, get, and delete vectors with metadata, and perform batch operations.
• End-to-end workflows: semantic search, RAG, and recommendations across ML apps with ready-made scripts and workflows.
• Use Case Examples: build document search, power LLM-assisted retrieval, and personalized recommendations across products.

Quick Start

Create a VectorClient with your credentials, configure the endpoint and region, and run the sample workflow to initialize a bucket, create an index, and insert sample vectors.