The Excellence
Directory Platform Template

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.

A comprehensive academic survey that explores the architecture, storage, retrieval techniques, and challenges associated with vector databases. It categorizes algorithmic approaches to approximate nearest neighbor search (ANNS) and discusses how vector databases can be integrated with large language models, offering valuable insights and foundational knowledge for understanding and building vector database systems.