Status: Done


This talk provides a brief introduction to vector embeddings, search and storage. In it, I show how to

  • Create vector embeddings
  • Perform vector similarity locally
  • Upload and index vectors in a vector database (Redis + RediSearch).

and also show a couple example of applications that utilize Redis for vector search capability.


Talk given at the Southern Data Science Conference about AI-powered Search and vector embeddings