# Conversational

**Conversational** **labeling** allows you to label spans in a text document using a chat-style interface.

The interface displays each line as a message with bubbles and avatars, making it easier to work with conversational data. You can label individual spans within messages or label entire messages, which is useful for conversation-level classification.

Conversational labeling projects use the same label sets as span labeling projects, so existing label configurations are compatible.

![Conversational Overview](/files/g9SgFFNDK0eXhfHSh95u)

### Create a conversational labeling project

1. In step 1 of project creation, upload a file in a supported format for conversational labeling. You can use the sample `.json` file below to setup the project.
2. In step 3 of project creation, set up your label set by creating it from scratch or by uploading a `.csv` label set file.

{% file src="/files/4zldBLeR7cs12eWutJfE" %}

{% file src="/files/f7tUDqVwwe4ViS6dHzrf" %}

{% hint style="info" %}
For more detailed information on conversational labeling projects, refer to [this page](/data-studio-projects/lets-get-labeling/conversational-labeling.md).
{% endhint %}

### Additional settings

Conversational labeling projects support the same additional settings available in span labeling projects.

* **Limit selection to a span of 1 token** is useful when you want to ensureenforce that every token in the document is be labeled.
* **Spans should have at most one label** prevents you from adding multiple labels to a single span.
* **Allow arrows to be drawn between labels** lets you draw arrows from one label to another to annotate relationships between words.
* **Default text selection** allows you to choose between token-level and character-level selection.

![](/files/CwBXNbcDZKnzRARXI9tu)


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# 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.datasaur.ai/data-studio-projects/nlp-task-types/conversational.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.