@Tariq Abuzaghleh Thanks for asking question!
Vector search is implemented at the field level. This definition puts the focus on vector fields. Vector fields must be of type Collection(Edm.Single)
used to store single-precision floating point values. Vector fields have a "dimensions" property that holds the number of output dimensions supported by the machine learning model used to generate embeddings.
For example, if you're using text-embedding-ada-002, the maximum number of output dimensions is 1536 per this document. The "algorithmConfiguration" is set to the name of the "vectorSearch" configuration in your index. You can define multiple in the index, and then specify one per field.
Check detail steps here- https://learn.microsoft.com/en-us/azure/search/vector-search-how-to-create-index?source=recommendations&tabs=config-2023-11-01%2Crest-2023-11-01%2Cpush%2Cportal-check-index#add-a-vector-field-to-the-fields-collection
"fieldMapping" property is required for vector embeddings, at least with how my vectors are configured.
"fieldsMapping": {
"vectorFields": [
"titleVector",
"contentVector"
]
},
Suggest you refer this detailed doc link would be helpful- https://learn.microsoft.com/en-us/rest/api/searchservice/preview-api/create-or-update-index
Please let us know if further query or issue remains.