> For the complete documentation index, see [llms.txt](https://docs.dapta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.dapta.ai/product-guides/text-agents-legacy.md).

# Text Agents Legacy

- [How to create your first text Agent](https://docs.dapta.ai/product-guides/text-agents-legacy/how-to-create-your-first-text-agent.md): Create a text agent by choosing its purpose, editing the prompt template, and training it on your business information via URLs or uploaded files.
- [Setting up your first text agent](https://docs.dapta.ai/product-guides/text-agents-legacy/setting-up-your-first-text-agent.md): In this article, you will learn the initial setup steps required to configure your Dapta AI agent.
- [Legacy: Testing your AI Agent](https://docs.dapta.ai/product-guides/text-agents-legacy/legacy-testing-your-ai-agent.md): In this episode, you'll discover how to efficiently run tests on your agent and debug it to ensure that it's responding according to your desired tone, style, and guidelines.
- [How to embed your AI Agent to your website](https://docs.dapta.ai/product-guides/text-agents-legacy/how-to-embed-your-ai-agent-to-your-website.md): In this section, you will learn how to embed your AI agent into your website after thoroughly testing it.
- [Viewing Your AI Agent's Chat History](https://docs.dapta.ai/product-guides/text-agents-legacy/viewing-your-ai-agents-chat-history.md): In this episode, we will explore the process of reviewing your AI agents' conversation chat history, allowing you to track interactions and analyze agent performance effectively.


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# 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.dapta.ai/product-guides/text-agents-legacy.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.