MicroNN: An On-device Disk-resident Updatable Vector Database
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915233284489216 |
|---|---|
| author | Pound, Jeffrey Chabert, Floris Bhushan, Arjun Goswami, Ankur Pacaci, Anil Chowdhury, Shihabur Rahman |
| author_facet | Pound, Jeffrey Chabert, Floris Bhushan, Arjun Goswami, Ankur Pacaci, Anil Chowdhury, Shihabur Rahman |
| contents | Nearest neighbour search over dense vector collections has important applications in information retrieval, retrieval augmented generation (RAG), and content ranking. Performing efficient search over large vector collections is a well studied problem with many existing approaches and open source implementations. However, most state-of-the-art systems are generally targeted towards scenarios using large servers with an abundance of memory, static vector collections that are not updatable, and nearest neighbour search in isolation of other search criteria. We present Micro Nearest Neighbour (MicroNN), an embedded nearest-neighbour vector search engine designed for scalable similarity search in low-resource environments. MicroNN addresses the problem of on-device vector search for real-world workloads containing updates and hybrid search queries that combine nearest neighbour search with structured attribute filters. In this scenario, memory is highly constrained and disk-efficient index structures and algorithms are required, as well as support for continuous inserts and deletes. MicroNN is an embeddable library that can scale to large vector collections with minimal resources. MicroNN is used in production and powers a wide range of vector search use-cases on-device. MicroNN takes less than 7 ms to retrieve the top-100 nearest neighbours with 90% recall on publicly available million-scale vector benchmark while using ~10 MB of memory. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05573 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MicroNN: An On-device Disk-resident Updatable Vector Database Pound, Jeffrey Chabert, Floris Bhushan, Arjun Goswami, Ankur Pacaci, Anil Chowdhury, Shihabur Rahman Databases Artificial Intelligence Information Retrieval Nearest neighbour search over dense vector collections has important applications in information retrieval, retrieval augmented generation (RAG), and content ranking. Performing efficient search over large vector collections is a well studied problem with many existing approaches and open source implementations. However, most state-of-the-art systems are generally targeted towards scenarios using large servers with an abundance of memory, static vector collections that are not updatable, and nearest neighbour search in isolation of other search criteria. We present Micro Nearest Neighbour (MicroNN), an embedded nearest-neighbour vector search engine designed for scalable similarity search in low-resource environments. MicroNN addresses the problem of on-device vector search for real-world workloads containing updates and hybrid search queries that combine nearest neighbour search with structured attribute filters. In this scenario, memory is highly constrained and disk-efficient index structures and algorithms are required, as well as support for continuous inserts and deletes. MicroNN is an embeddable library that can scale to large vector collections with minimal resources. MicroNN is used in production and powers a wide range of vector search use-cases on-device. MicroNN takes less than 7 ms to retrieve the top-100 nearest neighbours with 90% recall on publicly available million-scale vector benchmark while using ~10 MB of memory. |
| title | MicroNN: An On-device Disk-resident Updatable Vector Database |
| topic | Databases Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2504.05573 |