# WebANNS: Fast and Efficient Approximate Nearest Neighbor Search in Web Browsers

arXiv: https://arxiv.org/abs/2507.00521 · HTML: https://mlsanity.com/paper/2507.00521/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Vector search and ANN indexes, one of 15 directions in Retrieval-augmented generation.
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Placed in

- Retrieval-augmented generation / [Vector search and ANN indexes](https://mlsanity.com/rag/vector-search-and-ann-indexes/)

## Builds on (most cited first, 12 of 12)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Embedding-based Retrieval in Facebook Search](https://mlsanity.com/paper/2006.11632/) (2020)
- [DocPrompting: Generating Code by Retrieving the Docs](https://mlsanity.com/paper/2207.05987/) (2022)
- [WikiChat: Stopping the Hallucination of Large Language Model Chatbots by Few-Shot Grounding on Wikipedia](https://mlsanity.com/paper/2305.14292/) (2023)
- [Visual Search at Alibaba](https://mlsanity.com/paper/2102.04674/) (2021)
- [Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search](https://mlsanity.com/paper/2202.06212/) (2022)
- [Rethinking Privacy in Machine Learning Pipelines from an Information Flow Control Perspective](https://mlsanity.com/paper/2311.15792/) (2023)
- [Constructing Tree-based Index for Efficient and Effective Dense Retrieval](https://mlsanity.com/paper/2304.11943/) (2023)
- [MeMemo: On-device Retrieval Augmentation for Private and Personalized Text Generation](https://mlsanity.com/paper/2407.01972/) (2024)
- [EdgeRAG: Online-Indexed RAG for Edge Devices](https://mlsanity.com/paper/2412.21023/) (2024)
- [Gender, Age, and Technology Education Influence the Adoption and Appropriation of LLMs](https://mlsanity.com/paper/2310.06556/) (2023)

## Built on by (most cited first, 1 of 1)

- [Beyond Static Endpoints: Tool Programs as an Interface for Flexible Agentic Web Services](https://mlsanity.com/paper/2606.19992/) (2026)

## Abstract (arXiv)

Approximate nearest neighbor search (ANNS) has become vital to modern AI infrastructure, particularly in retrieval-augmented generation (RAG) applications. Numerous in-browser ANNS engines have emerged to seamlessly integrate with popular LLM-based web applications, while addressing privacy protection and challenges of heterogeneous device deployments. However, web browsers present unique challenges for ANNS, including computational limitations, external storage access issues, and memory utilization constraints, which state-of-the-art (SOTA) solutions fail to address comprehensively. We propose WebANNS, a novel ANNS engine specifically designed for web browsers. WebANNS leverages WebAssembly to overcome computational bottlenecks, designs a lazy loading strategy to optimize data retrieval from external storage, and applies a heuristic approach to reduce memory usage. Experiments show that WebANNS is fast and memory efficient, achieving up to 743.8× improvement in 99th percentile query latency over the SOTA engine, while reducing memory usage by up to 39%. Note that WebANNS decreases query time from 10 seconds to the 10-millisecond range in browsers, making in-browser ANNS practical with user-acceptable latency.