# VectorSearch: Enhancing Document Retrieval with Semantic Embeddings and Optimized Search

arXiv: https://arxiv.org/abs/2409.17383 · HTML: https://mlsanity.com/paper/2409.17383/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer](https://mlsanity.com/paper/2105.11741/) (2021)
- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [A Neural Corpus Indexer for Document Retrieval](https://mlsanity.com/paper/2206.02743/) (2022)
- [An Unsupervised Sentence Embedding Method by Mutual Information Maximization](https://mlsanity.com/paper/2009.12061/) (2020)
- [Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives](https://mlsanity.com/paper/2206.02457/) (2022)

## Built on by (most cited first, 10 of 10)

- [Search Arena: Analyzing Search-Augmented LLMs](https://mlsanity.com/paper/2506.05334/) (2025)
- [AgroLLM: Connecting Farmers and Agricultural Practices through Large Language Models for Enhanced Knowledge Transfer and Practical Application](https://mlsanity.com/paper/2503.04788/) (2025)
- [AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys](https://mlsanity.com/paper/2510.26012/) (2025)
- [Investigating Retrieval-Augmented Generation in Quranic Studies: A Study of 13 Open-Source Large Language Models](https://mlsanity.com/paper/2503.16581/) (2025)
- [VecLSTM: Trajectory Data Processing and Management for Activity Recognition through LSTM Vectorization and Database Integration](https://mlsanity.com/paper/2409.19258/) (2024)
- [MedCoAct: Confidence-Aware Multi-Agent Collaboration for Complete Clinical Decision](https://mlsanity.com/paper/2510.10461/) (2025)
- [NexusIndex: Integrating Advanced Vector Indexing and Multi-Model Embeddings for Robust Fake News Detection](https://mlsanity.com/paper/2410.18294/) (2024)
- [WindVE: Collaborative CPU-NPU Vector Embedding](https://mlsanity.com/paper/2504.14941/) (2025)
- [Progressive Searching for Retrieval in RAG](https://mlsanity.com/paper/2602.07297/) (2026)
- [RSLM: Training-Free Vector Quantization for Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2608.30384/) (2026)

## Abstract (arXiv)

Traditional retrieval methods have been essential for assessing document similarity but struggle with capturing semantic nuances. Despite advancements in latent semantic analysis (LSA) and deep learning, achieving comprehensive semantic understanding and accurate retrieval remains challenging due to high dimensionality and semantic gaps. The above challenges call for new techniques to effectively reduce the dimensions and close the semantic gaps. To this end, we propose VectorSearch, which leverages advanced algorithms, embeddings, and indexing techniques for refined retrieval. By utilizing innovative multi-vector search operations and encoding searches with advanced language models, our approach significantly improves retrieval accuracy. Experiments on real-world datasets show that VectorSearch outperforms baseline metrics, demonstrating its efficacy for large-scale retrieval tasks.