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How are vector databases used?

 Vector Databases Usage:

  • Typically used for vector search use cases such as visual, semantic, and multimodal search.
  • More recently, they are paired with generative AI text models for conversational search experiences.

Development Process:

  • Begins with building an embedding model designed to encode a corpus (e.g., product images) into vectors.
  • The data import process is referred to as data hydration.

Application Development:

  • Application developers utilize the database to search for similar products.
  • This involves encoding a product image and using the vector to query for similar images.

k-Nearest Neighbor (k-NN) Indexes:

  • Within the model, k-nearest neighbor (k-NN) indexes facilitate efficient retrieval of vectors.
  • A distance function like cosine is applied to rank results by similarity.
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