A Comprehensive Survey on Vector Database
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.
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A Comprehensive Survey on Vector Database
Category: Research Papers & Surveys
Tags: vector-databases, survey, ANNS, architecture
Source: arXiv:2310.11703
Overview
This academic survey provides an in-depth review of vector databases, focusing on their architecture, storage, retrieval techniques, and the challenges they face. Vector databases are specialized systems for storing and retrieving high-dimensional data, a task not well-handled by traditional database management systems.
Features
- Comprehensive Overview: Covers the current landscape of vector database architectures.
- Algorithmic Approaches for ANNS: Categorizes and reviews major approaches to the Approximate Nearest Neighbor Search (ANNS) problem, including:
- Hash-based methods
- Tree-based methods
- Graph-based methods
- Quantization-based methods
- Challenges: Discusses existing challenges in vector database design and implementation.
- Integration with Large Language Models: Explores how vector databases can be combined with large language models, opening up new application possibilities.
- Foundational Knowledge: Offers valuable insights for understanding and building vector database systems.
Pricing
n/a (This is a research paper freely available on arXiv)
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