Question

A financial services company runs a Bedrock Knowledge Base backed by an Amazon S3 data source and an OpenSearch Serverless vector store. Every night, a compliance team uploads a mix of brand-new policy PDFs, revised versions of existing PDFs (same object keys), and they delete a small number of retired policies from the S3 bucket. The developer needs the knowledge base to reflect all three types of changes with minimal cost and without re-embedding documents that have not changed. Which approach best meets these requirements?

Accepted Answer

Run a Bedrock Knowledge Base ingestion (sync) job on the data source after each nightly upload, relying on the service to add new files, re-embed modified files, and remove vectors for deleted objects.

Answer

Delete and recreate the entire knowledge base each night, then run a full ingestion job so the vector index always matches the current S3 contents.

Answer

Configure an S3 event notification to invoke a Lambda that calls the embedding model directly and writes vectors to OpenSearch, bypassing the knowledge base ingestion pipeline.

Answer

Schedule a nightly ingestion job but exclude deleted files, since the knowledge base cannot remove stale vectors and old policies must be manually purged from OpenSearch.