# A Comprehensive Survey on Vector Database: Storage and Retrieval Technique, Challenge

arXiv: https://arxiv.org/abs/2310.11703 · HTML: https://mlsanity.com/paper/2310.11703/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 114 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 27 of 27)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Recommender Systems in the Era of Large Language Models (LLMs)](https://mlsanity.com/paper/2307.02046/) (2023)
- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [Talking About Large Language Models](https://mlsanity.com/paper/2212.03551/) (2022)
- [Does Synthetic Data Generation of LLMs Help Clinical Text Mining?](https://mlsanity.com/paper/2303.04360/) (2023)
- [How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings](https://mlsanity.com/paper/2305.11853/) (2023)
- [LLM-powered Data Augmentation for Enhanced Cross-lingual Performance](https://mlsanity.com/paper/2305.14288/) (2023)
- [A Survey on Deep Hashing Methods](https://mlsanity.com/paper/2003.03369/) (2020)
- [Optimal Data-Dependent Hashing for Approximate Near Neighbors](https://mlsanity.com/paper/1501.01062/) (2015)
- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [A Survey on Locality Sensitive Hashing Algorithms and their Applications](https://mlsanity.com/paper/2102.08942/) (2021)
- [Multimodal Recommender Systems: A Survey](https://mlsanity.com/paper/2302.03883/) (2023)
- [High-Dimensional Approximate Nearest Neighbor Search: with Reliable and Efficient Distance Comparison Operations](https://mlsanity.com/paper/2303.09855/) (2023)
- [ParlayANN: Scalable and Deterministic Parallel Graph-Based Approximate Nearest Neighbor Search Algorithms](https://mlsanity.com/paper/2305.04359/) (2023)
- [Beyond Locality-Sensitive Hashing](https://mlsanity.com/paper/1306.1547/) (2013)
- [High-Throughput Vector Similarity Search in Knowledge Graphs](https://mlsanity.com/paper/2304.01926/) (2023)
- [Manu: A Cloud Native Vector Database Management System](https://mlsanity.com/paper/2206.13843/) (2022)
- [Online Product Quantization](https://mlsanity.com/paper/1711.10775/) (2017)
- [Ball*-tree: Efficient spatial indexing for constrained nearest-neighbor search in metric spaces](https://mlsanity.com/paper/1511.00628/) (2015)
- [Vector database management systems: Fundamental concepts, use-cases, and current challenges](https://mlsanity.com/paper/2309.11322/) (2023)
- [Interactive Question Answering Systems: Literature Review](https://mlsanity.com/paper/2209.01621/) (2022)
- [For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions](https://mlsanity.com/paper/2103.06875/) (2021)
- [A Revisit of Hashing Algorithms for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/1612.07545/) (2016)
- [Dumpy: A Compact and Adaptive Index for Large Data Series Collections](https://mlsanity.com/paper/2304.08264/) (2023)
- [Consistent Hashing with Bounded Loads](https://mlsanity.com/paper/1608.01350/) (2016)
- [Tree-Based Optimization: A Meta-Algorithm for Metaheuristic Optimization](https://mlsanity.com/paper/1809.09284/) (2018)

## Built on by (most cited first, 30 of 55)

- [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://mlsanity.com/paper/2401.05459/) (2024)
- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [Synthetic continued pretraining](https://mlsanity.com/paper/2409.07431/) (2024)
- [When Large Language Models Meet Vector Databases: A Survey](https://mlsanity.com/paper/2402.01763/) (2024)
- [M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions](https://mlsanity.com/paper/2405.16420/) (2024)
- [A Survey on Knowledge-Oriented Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.10677/) (2025)
- [Large language models for artificial general intelligence (AGI): A survey of foundational principles and approaches](https://mlsanity.com/paper/2501.03151/) (2025)
- [Eliza: A Web3 friendly AI Agent Operating System](https://mlsanity.com/paper/2501.06781/) (2025)
- [From Questions to Insightful Answers: Building an Informed Chatbot for University Resources](https://mlsanity.com/paper/2405.08120/) (2024)
- [Human-inspired Perspectives: A Survey on AI Long-term Memory](https://mlsanity.com/paper/2411.00489/) (2024)
- [AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents](https://mlsanity.com/paper/2512.23343/) (2025)
- [Vectorizing string entries for data processing on tables: when are larger language models better?](https://mlsanity.com/paper/2312.09634/) (2023)
- [VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework](https://mlsanity.com/paper/2406.13399/) (2024)
- [Personalization Toolkit: Training Free Personalization of Large Vision Language Models](https://mlsanity.com/paper/2502.02452/) (2025)
- [A systematic review of geospatial location embedding approaches in large language models: A path to spatial AI systems](https://mlsanity.com/paper/2401.10279/) (2024)
- [Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding](https://mlsanity.com/paper/2505.19825/) (2025)
- [VDTuner: Automated Performance Tuning for Vector Data Management Systems](https://mlsanity.com/paper/2404.10413/) (2024)
- [Retrieval-Augmented Mining of Temporal Logic Specifications from Data](https://mlsanity.com/paper/2405.14355/) (2024)
- [RAGent: Retrieval-based Access Control Policy Generation](https://mlsanity.com/paper/2409.07489/) (2024)
- [LSHBloom: Memory-efficient, Extreme-scale Document Deduplication](https://mlsanity.com/paper/2411.04257/) (2024)
- [Doc2Agent: Scalable Generation of Tool-Using Agents from API Documentation](https://mlsanity.com/paper/2506.19998/) (2025)
- [Pancake: Hierarchical Memory System for Multi-Agent LLM Serving](https://mlsanity.com/paper/2602.21477/) (2026)
- [Retrieval-Augmented Feature Generation for Domain-Specific Classification](https://mlsanity.com/paper/2406.11177/) (2024)
- [Using predefined vector systems as latent space configuration for neural network supervised training on data with arbitrarily large number of classes](https://mlsanity.com/paper/2510.04090/) (2025)
- [A critical review of methods and challenges in large language models](https://mlsanity.com/paper/2404.11973/) (2024)
- [Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models](https://mlsanity.com/paper/2503.16581/) (2025)
- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Benchmarking Filtered Approximate Nearest Neighbor Search Algorithms on Transformer-based Embedding Vectors](https://mlsanity.com/paper/2507.21989/) (2025)
- [Panorama: Fast-Track Nearest Neighbors](https://mlsanity.com/paper/2510.00566/) (2025)
- [B+ANN: A Fast Billion-Scale Disk-based Nearest-Neighbor Index](https://mlsanity.com/paper/2511.15557/) (2025)

## Abstract (arXiv)

As high-dimensional vector data increasingly surpasses the processing capabilities of traditional database management systems, Vector Databases (VDBs) have emerged and become tightly integrated with large language models, being widely applied in modern artificial intelligence systems. However, existing research has primarily focused on underlying technologies such as approximate nearest neighbor search, with relatively few studies providing a systematic architectural-level review of VDBs or analyzing how these core technologies collectively support the overall capacity of VDBs. This survey aims to offer a comprehensive overview of the core designs and algorithms of VDBs, establishing a holistic understanding of this rapidly evolving field. First, we systematically review the key technologies and design principles of VDBs from the two core dimensions of storage and retrieval, tracing their technological evolution. Next, we conduct an in-depth comparison of several mainstream VDB architectures, summarizing their strengths, limitations, and typical application scenarios. Finally, we explore emerging directions for integrating VDBs with large language models, including open research challenges and trends such as novel indexing strategies. This survey serves as a systematic reference guide for researchers and practitioners, helping readers quickly grasp the technological landscape and development trends in the field of vector databases, and promoting further innovation in both theoretical and applied aspects.