> For the complete documentation index, see [llms.txt](https://learn.getvectorai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://learn.getvectorai.com/vector-search/inserting-into-vector-ai.md).

# Inserting Into Vector AI

Before inserting into Vector AI, there are a few important things to note about Vector AI.&#x20;

1\) Document-based storage. Vector AI relies on a document-oriented storage. An example of a document can be seen below.&#x20;

```
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:&#x20;

| 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.                                                                   |


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://learn.getvectorai.com/vector-search/inserting-into-vector-ai.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
