How to create an index and indexer for RAG solution with Azure OpenAI and Azure AI Search in Python?

Hello,

I am an enthusiastic supporter of the potential that Azure solutions, particularly regarding RAG, bring to the table.

However, my current approach to index creation appears somewhat rudimentary. I currently upload my source documents to Azure Blob Storage and proceed as follows: I navigate to AI Foundry, run Chat Playground, choose "Add a data source," select "Azure Blob Storage (preview)," and set up the process as below:

It creates an index like below; I failed to replicate this result by clicking it out manually:

Despite this, I have several concerns:

1. Azure Blob Storage is still in preview, making me hesitant to rely on it for a production environment.
2. The manual setup process is cumbersome and not scalable for multiple indexes.
3. The current setup lacks flexibility—I cannot specify advanced features such as chunk overlapping or define vector profiles during deployment.

Could you recommend any automation tools, preferably a Python module, that would allow me to streamline this process without manual intervention?