Question

A software company wants to build a RAG-based internal support assistant using Amazon Bedrock Knowledge Bases. Their engineering documentation lives in Atlassian Confluence, and content is updated frequently by multiple teams. The team wants to minimize custom code for ingestion, keep the knowledge base current with Confluence changes, and avoid building and maintaining an ETL pipeline to export documents into Amazon S3. What is the MOST operationally efficient approach?

Accepted Answer

Configure a native Confluence data source connector in the Bedrock Knowledge Base and schedule periodic syncs to ingest content directly.

Answer

Build an AWS Glue job that exports Confluence pages nightly to an S3 bucket, then configure the S3 bucket as the Knowledge Base data source.

Answer

Use a Lambda function triggered on a schedule to call the Confluence API, chunk documents, generate embeddings with Titan, and write vectors directly to OpenSearch Serverless.

Answer

Export Confluence content manually to PDF files, upload them to S3, and run a full ingestion job whenever documentation changes.