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How are vector databases used?

 Vector Databases for Vector Search Use Cases:

  • Typically used to power vector search use cases like visual, semantic, and multimodal search.
  • Increasingly paired with generative artificial intelligence (AI) text models for conversational search experiences.

Pairing with Generative AI Text Models:

  • Vector databases are now paired with generative AI text models to create intelligent agents.

Development Process:

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

Application Usage:

  • Application developers use the database to search for similar products by encoding a product image.
  • The vector is then used to query for similar images.

k-Nearest Neighbor (k-NN) Indexes:

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

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  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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