Perpetual Sandbox

Indexing strategy where vector indexes are stored in the same partitions as corresponding table rows, ensuring data locality and operational advantages in distributed databases.

Agent architecture pattern in AG2 where agents respond to events rather than polling, enabling better async execution, scalability, and resource efficiency.

Memory system architecture used in Mastra's Observational Memory with two background agents that compress and garbage collect conversation history achieving 5-40x compression.

The size of vector embeddings, typically ranging from 128 to 1536 dimensions for text models. Higher dimensions capture more nuanced semantics but require more storage and computation. Modern techniques like Matryoshka embeddings allow flexible dimension selection from a single model.

A retrieval paradigm where query and document encodings are kept separate until a late interaction stage, enabling more expressive and efficient similarity computations. Pioneered by ColBERT and extended by ColPali and ColQwen, this approach maintains fine-grained representations while enabling fast retrieval.

Architectural patterns for isolating and managing data for multiple customers (tenants) in shared vector database infrastructure. Multi-tenancy strategies include namespace isolation, metadata filtering, and separate collections, each offering different trade-offs between performance, cost, and data isolation.