Introducing Vectors and Vector Search
This article explains how vectors can be used to encode similarities in unstructured data, enabling more effective search and retrieval. It discusses the limitations of traditional database queries and how vectors can address them.
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Why it matters
Vector search is a key technique in modern AI and information retrieval, enabling more intuitive and effective processing of unstructured data.
Key Points
- 1Vectors have both magnitude and direction, allowing for more nuanced representation of data relationships
- 2The angle between two vectors determines their similarity - the smaller the angle, the more similar the directions
- 3Vector search can handle unstructured data like
- 4 and
- 5 by encoding their semantic similarities
Details
The article discusses how traditional database queries often fail to capture the nuanced relationships between unstructured data points. It uses the example of a person with a dog that had puppies - while a human can easily understand the connection between
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