Foundations of Vector Retrieval
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jan 2024.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 3.
5 papers.
- 2024Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systemscited by 9
- 2025Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Studycited by 1
- 2026FinMetaMind: A Tech Blueprint on NLQ Systems for Financial Knowledge Searchcited by 0
- 2026SQLite is Enough. Lexical, Semantic, and Hybrid Search with scrydbcited by 0
- 2026RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Searchcited by 0
- 2026RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Search31 Aug 2026
- 2026SQLite is Enough. Lexical, Semantic, and Hybrid Search with scrydb25 Aug 2026
- 2026FinMetaMind: A Tech Blueprint on NLQ Systems for Financial Knowledge Search24 Jan 2026
- 2025Aligning LLMs for the Classroom with Knowledge-Based Retrieval -- A Comparative RAG Study9 Sep 2025
- 2024Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems20 Feb 2024