A vector database is a type of database that works with complex data like images, sounds, and text.
Embedding : This complex data is transformed into something called a vector, which is basically a list of numbers.
Think of a vector as a numerical representation or fingerprint of your data. For example, the sentence ?I love apples? could be represented as something like [0.7, 0.5, 0.2]. This list of numbers captures the meaning of the sentence in a way that a computer can understand.
These vectors are stored in the vector database in collections, which are similar to tables in a traditional database.
The key feature of a vector database is its ability to find similar items. When you query the database with a vector, it can find vectors close to it in the numerical space. The closeness of vectors is usually measured by something called cosine similarity or Euclidean distance.
For example, if you have a sentence ?I like apples? that is transformed into a vector [0.6, 0.5, 0.2], the vector database can find other sentences (represented as vectors) that are similar or close to it.
This is particularly useful for things like building recommendation systems, search engines, or any application where you need to find similar items in a large dataset.
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