Problem Statement
Users frequently modify their Genie Rooms, which includes adding, removing, or updating underlying tables and columns. Currently, when these modifications occur, the downstream AI agent remains unaware of the latest data changes. Without a mechanism to capture these updates, the agent relies on stale context, leading to inaccurate or outdated responses.
Proposed Solution
We need to implement an enrichment ETL pipeline that can selectively pick up these schema and configuration changes and run on a regular schedule.
Specifically, the pipeline should:
- Change Detection: Selectively identify incremental changes made to Genie Rooms, tables, and columns since the last run.
- Scheduled Execution: Run on a configurable schedule to process only the updated entities, avoiding the overhead of a full data refresh.
- Downstream Synchronization: Automatically update the corresponding Metadata Tables and Vector Search (VS) indexes with the new context.
Impact
Implementing this pipeline will ensure that the agent always has access to the most up-to-date schema and data context, significantly improving the accuracy, reliability, and relevance of its responses to user queries.
Problem Statement
Users frequently modify their Genie Rooms, which includes adding, removing, or updating underlying tables and columns. Currently, when these modifications occur, the downstream AI agent remains unaware of the latest data changes. Without a mechanism to capture these updates, the agent relies on stale context, leading to inaccurate or outdated responses.
Proposed Solution
We need to implement an enrichment ETL pipeline that can selectively pick up these schema and configuration changes and run on a regular schedule.
Specifically, the pipeline should:
Impact
Implementing this pipeline will ensure that the agent always has access to the most up-to-date schema and data context, significantly improving the accuracy, reliability, and relevance of its responses to user queries.