# Inserting 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.                                                                   |


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