![]() Vector search is not only applicable to image and text content. But vectors can capture and represent never-before-seen categories instantly. For example, emerging categories of content primarily attractive to kids, such as ASMR or slime, are really hard for adults or marketing professionals to predict beforehand, and going back through vast databases to manually update content with these new labels would be all but impossible to do quickly. ![]() Also, vectors have the flexibility to represent categories previously unknown to or undefined by service providers. Vectors can represent the meaning of content where “films”, “movies”, and “cinema” are all collected together. Vectors can represent a subset of content that contains "much about actors, some about movies, and a little about music". Vector search provides a much more refined way to find content, with subtle nuances and meanings. If you specify a keyword "films", for example, you would not see any content related to "movies" unless there was a synonyms dictionary that explicitly linked these two terms in the database or search engine. Also, you cannot express the actual "meaning" of the content to search. With keyword search, you can only specify a binary choice as an attribute of each piece of content it's either about a movie or not, either music or not, and so on. You’d then add those records to a database, so you could perform searches with those tags or keywords. For example, you would add tags or category keywords such as "movie", "music", or "actor" to each piece of content (image or text) or each entity (a product, user, IoT device, or anything really). So what's the difference between traditional keyword-based search and vector similarity search? For many years, relational databases and full-text search engines have been the foundation of information retrieval in modern IT systems. ![]() The technology is one of the most important components of Google's core services, and not just for Google: it is becoming a vital component of many popular web services that rely on content search and information retrieval accelerated by the power of deep neural networks. How can it find matches that fast? The trick is that the MatchIt Fast demo uses the vector similarity search (or nearest neighbor search or simply vector search) capabilities of the Vertex AI Matching Engine, which shares the same backend as Google Image Search, YouTube, Google Play, and more, for billions of recommendations and information retrievals for Google users worldwide. Text similarity search with MatchIt Fast Vector Search: the technology behind Google Search, YouTube, Play, and more
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