This session introduces the basics of vector indexes and vector databases and all the basic knowledge of how to use them. We will walk through the steps to setting up pg_vector, a vector extension for Postgres, and how to create embeddings from images. Then, we'll perform a similarity search on those images. We'll also cover the basics of distance algorithms and vector indexes.

Lastly, we'll go over how to use similarity search in real-time use cases and introduce Apache Pinot new vector index feature.

Technical details covered:
- Learn how to get started with pg_vector, the PostgreSQL vector extension.
- Learn how to create embeddings and perform similarity searches.

Takeaway:
How similarity search can be used in real-time

Target audience:
Architects and developers interested in vector databases, real-time OLAPs, and stream processing.