Topic
Retrieval over corpora that keep growing: chunking, metadata filters, index choice, and the TTL policy that stops a vector store becoming a landfill.
A retrieval index is not something you build once; it is a data pipeline with additional ways to be confidently wrong. Chunking should respect source structure, metadata filters often carry more precision than embeddings, and retention needs an owner before the index grows without bound. These notes focus on evaluation, provenance, deletion, re-indexing, and the operational half of retrieval that short demos usually skip.
Scrape to bronze, silver, gold on Spark 4 and Delta, embed into Chroma, and serve answers that cite their sources or decline to answer.
~1 h, most of it pulling images and the first Ivy resolution