🔍Inserting Into Vector AI

How to insert your data into Vector AI

Before inserting into Vector AI, there are a few important things to note about Vector AI.

1) Document-based storage. Vector AI relies on a document-oriented storage. An example of a document can be seen below.

document_example = {
    "car": {
        "wheels":
            {
                "number": 4
            }
    }
}

2) When uploading documents into VectorAI, the schema is automatically determined. The schema itself relies on the following built-in concepts:

Field Name	Description
_id	This refers to the unique identity of the document. Every document should have its own ID value.
contains _vector_	If labelled with _vector_ then the value will be treated as a vector and will be treated as a variable. Note: This needs to be an array of floats. (Note: In Python, lists are arrays and we are not referring to NumPy arrays.)
contains _chunkvector_	A _chunkvector_ refers to a vector that is part of a larger document. Documents can have multiple chunkvectors where each chunk is part of a larger document.