---
title: Type Convert
slug: /type-convert
---

import Icon from "@site/src/components/icon";
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
import PartialParams from '@site/docs/_partial-hidden-params.mdx';
import PartialCurlyBraces from '@site/docs/_partial-escape-curly-braces.mdx';

The **Type Convert** component converts data from one type to another.
It supports `JSON`, `Table`, and `Message` data types.

<Tabs>
<TabItem value="data" label="Data" default>

A `JSON` object is a structured object that contains a primary `text` key and other key-value pairs:

```json
"data": {
  "text": "User Profile",
  "name": "Charlie Lastname",
  "age": 28,
  "email": "charlie.lastname@example.com"
},
```

The larger context associated with a component's `data` dictionary also identifies which key is the primary `text_key`, and it can provide an optional default value if the primary key isn't specified.
For example:

```json
{
  "text_key": "text",
  "data": {
    "text": "User Profile",
    "name": "Charlie Lastname",
    "age": 28,
    "email": "charlie.lastname@example.com"
  },
  "default_value": ""
}
```

</TabItem>
<TabItem value="dataframe" label="DataFrame">

A `Table` is an array that represents a tabular data structure with rows and columns.

It consists of a list (array) of dictionary objects, where each dictionary represents a row.
Each key in the dictionaries corresponds to a column name.
For example, the following `Table` contains two rows with columns for `name`, `age`, and `email`:

```json
[
  {
    "name": "Charlie Lastname",
    "age": 28,
    "email": "charlie.lastname@example.com"
  },
  {
    "name": "Bobby Othername",
    "age": 25,
    "email": "bobby.othername@example.com"
  }
]
```

</TabItem>
<TabItem value="message" label="Message">

A `Message` is primarily for passing a `text` string, such as`"Name: Charlie Lastname, Age: 28, Email: charlie.lastname@example.com"`.
However, the entire `Message` object can include metadata about the message, particularly when used as chat input or output.

</TabItem>
</Tabs>

For more information, see [Langflow data types](/data-types).

## Use the Type Convert component in a flow

The **Type Convert** component is typically used to transform data into a format required by a downstream component.
For example, if a component outputs a `Message`, but the following component requires `JSON`, then you can use the **Type Convert** component to reformat the `Message` as `JSON` before passing it to the downstream component.

The following example uses the **Type Convert** component to convert the `Table` output from a **Web Search** component into `Message` data that is passed as text input for an LLM:

1. Create a flow based on the **Basic prompting** template.

2. Add a **Web Search** component to the flow, and then enter a search query, such as `environmental news`.

3. In the **Prompt Template** component, replace the contents of the **Template** field with the following text:

    ```text
    Answer the user's question using the {context}
    ```

    The curly braces define a [prompt variable](/components-prompts#define-variables-in-prompts) that becomes an input field on the **Prompt Template** component.
    In this example, you will use the **context** field to pass the search results into the template, as explained in the next steps.

3. Add a **Type Convert** component to the flow, and then set the **Output Type** to **Message**.

    Because the **Web Search** component's `Table` output is incompatible with the **context** variable's `Message` input, you must use the **Type Convert** component to change the `Table` to a `Message` in order to pass the search results to the **Prompt Template** component.

4. Connect the additional components to the rest of the flow:

    * Connect the **Web Search** component's output to the **Type Convert** component's input.
    * Connect the **Type Convert** component's output to the **Prompt Template** component's **context** input.

    ![Convert web search output to text input](/img/component-type-convert-and-web-search.png)

5. In the **Language Model** component, add your OpenAI API key.

    If you want to use a different provider or model, edit the **Model Provider**, **Model Name**, and **API Key** fields accordingly.

6. Click **Playground**, and then ask something relevant to your search query, such as `latest news` or `what's the latest research on the environment?`.

    <details>
    <summary>Result</summary>

    The LLM uses the search results context, your chat message, and it's built-in training data to respond to your question.
    For example:

    ```text
    Here are some of the latest news articles related to the environment:
    Ozone Pollution and Global Warming: A recent study highlights that ozone pollution is a significant global environmental concern, threatening human health and crop production while exacerbating global warming. Read more
    ...
    ```

    </details>

## Type Convert parameters

| Name | Display Name | Info |
|------|--------------|------|
| input_data | Input Data | Input parameter. The data to convert. Accepts `JSON`, `Table`, or `Message` input. |
| output_type | Output Type | Input parameter. The desired output type, as one of **Data**, **DataFrame** or **Message**. |
| output | Output | Output parameter. The converted data in the specified format. The output port changes depending on the selected **Output Type**. |