Vector embedding databases, commonly known as vector databases, are designed to deliver advanced search capabilities beyond traditional keyword or synonym-based searches. They aim to interpret the user’s intent or directly answer user queries by understanding the underlying meaning.
Semantic search and question-answering systems rely on similarity-based searches, which focus on the meaning of text or the content within images. For instance, imagine a database containing descriptions of various teas, one of which mentions that a particular tea “pairs nicely with desserts.” A simple keyword search for “tea for sweets” or even a synonym-based search might miss this tea. However, a semantic search would recognize that “desserts” and “sweets” share similar meanings, and “pairs nicely with X” implies suitability for “X,” thus successfully retrieving the relevant tea.
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