# TagUI

Using aito.ai from the open-source RPA tool [TagUI](https://github.com/kelaberetiv/TagUI) is simple! This example uses an existing dataset in a demo instance, containing some thousands of purchase invoices that have already been categorised for `Product_Category` and `GL_Account`.

#### Uploading data to Aito

{% hint style="info" %}
**EVOLVING ARTICLE**

This article will evolve over time and contain more examples and material later. Data upload part will be added later.
{% endhint %}

#### Making predictions

Using aito.ai predictions as part of the TagUI workflows is as simple as using the built-in [api](https://tagui.readthedocs.io/en/latest/reference.html#api) keyword.

First, construct the Aito query using your input data for the predictions. For example like this:

```json
// Construct a predict query from your input data
linetext = "Rental car New York May 2021"
amount = 34.22
query = {"from": "invoice_data", "where": {"Item_Description": linetext, "Inv_Amt": amount}, "predict": "GL_Code", "limit": 1}
```

Next, consigure the HTTP call headers:

```json
// Configure HTTP headers
api_config = {method:'POST', header:['x-api-key: bvss2i2dIkaWUfBCdzEO89LpxUkwO3A24hYg8MBq','content-type: application/json'], body:query};
```

Then make a call using the `api` keyword. Note that the API key and the aito.ai instance in use here are publicly available for testing. Get your own instance for free at [Console](https://console.aito.ai/).

```json
// Make API call to Aito predict endpoint
api https://public-1.api.aito.ai/api/v1/_predict
```

The use of results is simple, as TagUI automatically returns the JSON version of the response as `api_json`. Here is where you would find the top result's predicted value and confidence:

```json
// Print out the top feature and confidence.
echo Aito predicts `api_json.hits[0].feature` with confidence `api_json.hits[0].$p`
```

#### All the code together

The entire code is here, try running it yourself! :)

```json
// This flow demonstrates the usage of Aito.ai _predict API endpoint with table already existing in Aito

// Construct a query from your input data
linetext = "Rental car New York May 2021"
amount = 34.22
query = {"from": "invoice_data", "where": {"Item_Description": linetext, "Inv_Amt": amount}, "predict": "GL_Code", "limit": 1}

// Configure HTTP headers
api_config = {method:'POST', header:['x-api-key: bvss2i2dIkaWUfBCdzEO89LpxUkwO3A24hYg8MBq','content-type: application/json'], body:query};

// Make API call to Aito predict endpoint
api https://public-1.api.aito.ai/api/v1/_predict

// Print out the top feature and confidence.
echo Aito predicts `api_json.hits[0].feature` with confidence `api_json.hits[0].$p`
```


---

# Agent Instructions: 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://docs.aito.ai/integrations/tagui.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.