> For the complete documentation index, see [llms.txt](https://docs.graphite-note.com/graphite-note-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.graphite-note.com/graphite-note-documentation/rest-api/prediction-api/request-v2/body.md).

# Body

JSON request structures for various models

The request body should be passed using the -d option in the cURL command. Replace \[data] with the actual JSON-formatted content you want to send for prediction. The structure and required fields depend on the specific model’s configuration.&#x20;

The v2 request body uses a simplified, flat JSON structure. Each prediction row is represented as a single object containing direct key-value pairs, where:

* The keys are the exact column names used during model training
* The values are the inputs you want to use for prediction

***

#### v2 Example (Binary Classification / Multiclass Classification / Regression)

```
{
  "data": {
    "predict_values": [
      {
        "Lead ID": "21",
        "Lead Budget": 425.5130025,
        "Lead Source": "3",
        "Demo Request": 1,
        "Past Purchases": 0,
        "Website Visits": 1,
        "Email Open Rate": 49.38937152,
        "Interaction Score": 37.72859652,
        "Sales Rep Response Time": 10.99225024
      }
    ]
  }
}
```

This format is especially useful when exporting data from spreadsheets, CSV files, or databases. It eliminates the need to wrap each value in an alias/selectedValue structure, making it cleaner and easier to use in automated or programmatic scenarios.


---

# 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 by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.graphite-note.com/graphite-note-documentation/rest-api/prediction-api/request-v2/body.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.
