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Research paper proposing a decoupled storage architecture for vector databases that improves update speed by 10.05x for insertions and 6.89x for deletions through innovative design.

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.

Sandbox architecture that maintains state indefinitely while scaling costs to zero during idle periods. Pioneered by Blaxel with sub-25ms resume times from standby mode.

YugabyteDB's pluggable vector indexing architecture that separates vector search logic from the database engine, enabling integration with multiple ANN backends like USearch.

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.

Approaches for distributing vectors across multiple nodes including horizontal sharding, data partitioning, and routing strategies for scaling vector search to billions of vectors.

Architectural patterns for isolating data between different tenants (customers/organizations) in vector databases. Includes collection-per-tenant, partition-per-tenant, and filter-based approaches with different trade-offs.

Maximum number of tokens an embedding model or LLM can process in a single input. Critical parameter for vector databases affecting chunk sizes, with modern models supporting 512 to 32,000+ tokens for long-document understanding.

Number of components in an embedding vector, typically ranging from 128 to 4096 dimensions. Higher dimensions can capture more information but increase storage, computation, and costs. Critical design parameter for vector databases.

An overview of the architectural components common to Vector Database Management Systems (VDBMS), which are designed to efficiently store, index, and query high-dimensional vector embeddings. This provides foundational knowledge for anyone interested in the internal workings of vector databases.