#vector store

A Reddit user shared practical pitfalls discovered while building an incremental indexing pipeline for vector stores, including issues with handling deletes, partial updates causing data drift, and the critical need for idempotency to prevent duplicate documents. These lessons are directly relevant to ML engineers and data pipeline practitioners who must keep vector indexes consistent with source data over time, highlighting often-overlooked bugs that degrade search accuracy in production. The user found that failing to handle upstream deletes causes the index to grow with stale entries, partial updates lead to drift when chunk boundaries change, and non-idempotent pipelines produce duplicate documents on retries or backfills.