to date ranges, categories, geographic distances, access control groups, etc. • Rich filter expressions • Pre-/post-filtering • Pre-filter: great for selective filters, no recall disruption • Post-filter: better for low-selectivity filters, but watch for empty results r = search_client.search( None, top=5, vector_queries=[VectorizedQuery( vector=query_vector, k_nearest_neighbors=5, fields="embedding")], vector_filter_mode=VectorFilterMode.PRE_FILTER, filter= "tag eq 'perks' and created gt 2023-11-15T00:00:00Z") r = search_client.search( None, top=5, vector_queries=[ VectorizedQuery( vector=query1, fields="body_vector", k_nearest_neighbors=5,), VectorizedQuery( vector=query2, fields="title_vector", k_nearest_neighbors=5,) ]) Multi-vector scenarios Multiple vector fields per document Multi-vector queries Can mix and match as needed Filters in vector queries (aka.ms/aisearch/vectorfilters)