# Foundations of Vector Retrieval

arXiv: https://arxiv.org/abs/2401.09350 · HTML: https://mlsanity.com/paper/2401.09350/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2024.

## Built on by (most cited first, 5 of 5)

- [Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems](https://mlsanity.com/paper/2402.12784/) (2024)
- [Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study](https://mlsanity.com/paper/2509.07846/) (2025)
- [FinMetaMind: A Tech Blueprint on NLQ Systems for Financial Knowledge Search](https://mlsanity.com/paper/2601.17333/) (2026)
- [SQLite is Enough. Lexical, Semantic, and Hybrid Search with scrydb](https://mlsanity.com/paper/2608.24060/) (2026)
- [RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2608.30384/) (2026)

## Abstract (arXiv)

Vectors are universal mathematical objects that can represent text, images, speech, or a mix of these data modalities. That happens regardless of whether data is represented by hand-crafted features or learnt embeddings. Collect a large enough quantity of such vectors and the question of retrieval becomes urgently relevant: Finding vectors that are more similar to a query vector. This monograph is concerned with the question above and covers fundamental concepts along with advanced data structures and algorithms for vector retrieval. In doing so, it recaps this fascinating topic and lowers barriers of entry into this rich area of research.