Challenge 5: Implement AI-Powered Vector Search with pgvector

Store high-dimensional vector embeddings alongside your time-series data using Tiger Data’s native pgvector and pgvectorscale support. Perform similarity searches that filter by time ranges, metadata, and distance metrics directly inside PostgreSQL. Taking on this challenge allows you to build hybrid Retrieval-Augmented Generation (RAG) systems that can search both semantic meanings and time-series context simultaneously, eliminating the need to maintain a separate vector database sidecar.

Relevant Resources

• Turning PostgreSQL Into a Vector Database: Step-by-step tutorial on using pgvector with hypertables.
• Deploying TimescaleDB Vector Search: Guide on scaling AI vector applications with Tiger Data.