RAG ensures accurate, up-to-date responses using SQL data.

This setup is a solid way to bridge the gap between your SQL updates and your vector index. By using CDC (Change Data Capture), you avoid the heavy lifting of scanning the entire table for changes, which keeps your database CPU overhead low.
To make this work efficiently within the Azure Functions app, you'll typically want to:
Batch the updates: Instead of triggering a function for every single row change, process small batches of CDC records to reduce the number of calls to your embedding model (like OpenAI).
Use an Upsert logic: In your function, ensure the code handles both new inserts and updates by overwriting the existing embedding for a specific Article ID in the database.
Implement a "Cool-down" or Timer: If the "frequent updates" happen in bursts, you can use an Azure Service Bus or Storage Queue between the CDC process and the Function to smooth out the processing load.
Reference:
https://fivetran.com/docs/connectors/databases/troubleshooting/log-based-cdc-impact

Azure OpenAI Service allows users to query structured SQL data using natural language via LLMs.

Azure Key Vault securely stores secrets and keys.