had a look at the code, i think you almost got the index created correctly but the key step missing is vectorizing the data and store the embedding for semantic search. These steps are a little long, worth looking at the example here, it is a simple version of ingesting document and setuping index.

https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/basic-vector-workflow/azure-search-vector-python-sample.ipynb

lots of more advanced examples here: https://github.com/Azure/azure-search-vector-samples/tree/main/demo-python

At a very high level:

• a: create an index with text field and vector fields
• b1: prepare docs with corresponding embeddings, insert docs (with embedding into AI Search)
• b2: or create an indexer to carry out embedding generation
• c: query the data