07 May 2024

#Vector_DB

Vector_DB
Vector Databases
  • 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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