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![Vector search in a nutshell
Vector search is a way to represent and search
your objects (documents, songs, images..) in a
geometric space (usually of high-dimension) in
the form of an embedding (a vector of
numbers: [0.9, -0.1, 0.15, …])
● At small scale you can apply exact KNN
search
● At larger scale you need to use ANN
search: trade some precision for speed
Credit: Weaviate V1.0 release - virtual meetup](https://image.slidesharecdn.com/vectordatabasesandneuralsearch-220121144201/85/Vector-databases-and-neural-search-7-320.jpg)
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![Get practical
● Code: https://github.com/DmitryKey/bert-solr-search
● Supported Engines: Solr, Elasticsearch, OpenSearch, GSI, [hnswlib]
● Supported LMs: BERT, SBERT, [theoretically any]](https://image.slidesharecdn.com/vectordatabasesandneuralsearch-220121144201/85/Vector-databases-and-neural-search-29-320.jpg)
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Uploaded byDmitry Kan
Vector databases and neural search
This document discusses vector databases and neural search, introducing key concepts like vector search representation, use cases, and major players in the field. It highlights various systems and algorithms for processing vector data, including open-source and managed solutions, as well as practical considerations for selecting a suitable vector database. Additionally, it touches on trends in machine learning and links to resources for further exploration.
![[DSC Europe 23] Spela Poklukar & Tea Brasanac - Retrieval Augmented Generation](https://cdn.slidesharecdn.com/ss_thumbnails/spelapoklukarteabrasanacprez-231129000106-d6582d1b-thumbnail.jpg?width=640&height=640&fit=bounds)
