Hello @Francesco Granozio
are you using the same type of data when using vector search? what is the size of your data set
In meantime, here are a few things to consider:
- There might be errors during the indexing process. Azure AI Search stops indexing when the error count exceeds ‘maxFailedItems’. If you want indexers to ignore these errors (and skip over “failed documents”), consider updating the
maxFailedItems
andmaxFailedItemsPerBatch
- Azure AI Search imposes limits on vector storage. If vector size exceeds this limit, any further indexing requests result in failure. You can resume indexing once you free up available quota by either deleting some vector documents or by scaling up in partitions
- Some older search services created before January 1, 2019 are deployed on infrastructure that doesn’t support vector workloads. If you try to add a vector field to a schema and get an error, it’s a result of outdated services. In this situation, you must create a new search service to try out the vector feature
- Data Formats and File Types: Azure OpenAI on your data supports several file types including .txt, .md, .html, Microsoft Word files, Microsoft PowerPoint files, and PDF4. Make sure your data is in a supported format.
Please let us know if you have further questions
Best,
Grace