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Description
This issue is for a: (mark with an x)
- bug report -> please search issues before submitting
- feature request
- documentation issue or request
- regression (a behavior that used to work and stopped in a new release)
Expected/desired behavior
Eventually, users often want continuous indexing of a blob storage. The local script could ingest the files to a blob storage and configure the indexers and skillsets (e.g. Form Recognizer/OCR) for continuous reindexing.
Azure AI Search has an integrated chunking and vectorization engine in public preview, thus not ready for production yet. However would be good to investigate if more native AI Search features can be incorporated in this example in the future.
Documentation: https://learn.microsoft.com/en-us/azure/search/vector-search-integrated-vectorization
Example notebook: https://github.com/Azure/azure-search-vector-samples/blob/main/demo-python/code/azure-search-integrated-vectorization-sample.ipynb
Advantages
- Continous reindexing out of the box
- Easy to index new documents (even via UI), by uploading to blob storage
Disadvantages
- Harder to test different chunking strategies
andremarques023waqasjavedkhan
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