> For the complete documentation index, see [llms.txt](https://docs.toucanai.cloud/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.toucanai.cloud/embed/embedding-overview/how-to/embed-an-ai-chat.md).

# Embed an AI Chat

{% hint style="info" %}
**Target Audience**: Developers
{% endhint %}

### Goal

Embed a self-service AI chat from Toucan AI into a host application to enable conversational analytics.

***

### Prerequisites

* At least one [connected and active database](/build/data-connections/how-to/add-a-database.md).
* A [valid API key](/embed/authentication/how-to/generate-an-api-key.md) for token generation.
* (Recommended): [Enriched metadata](/build/analyze-your-database-with-ai/how-to/analyze-your-database-with-ai.md) to improve the AI assistant's accuracy.
* (Recommended): [Row-Level Security (RLS) configured](/embed/permissions-and-row-level-security/how-to/apply-rls-to-your-database.md) for multi-tenant data isolation.

***

### Steps

#### 1. Prepare the data layer

* Execute an AI analysis on your datasets to generate semantic metadata.
* Review column descriptions and metric definitions to ensure the AI assistant has sufficient context.
* High-quality metadata directly improves the relevance of natural language answers.

#### 2. Configure security and RLS

* Define required token attributes (e.g., `customer_id` or `region`).
* Map these attributes to specific dataset columns using the RLS interface.
* Validate the configuration with different attribute values to confirm data isolation.

#### 3. Generate an authentication token

* Generate a token including all necessary user attributes.
* Set an appropriate expiration time for the session.
* Ensure the token scope includes permission for the AI assistant capability.

#### 4. Retrieve the embed code

* Locate the embed snippet in your admin panel or use the following standard web component format:

```html
<script type="module" src="https://toucanai.cloud/embed/embed.js"></script>
<link rel="stylesheet" href="https://toucanai.cloud/embed/embed.css" />

<tc-ai-assistant
  auth-token="YOUR_AUTH_TOKEN"
  server-url="https://toucanai.cloud/api"
  data-theme="light"
></tc-ai-assistant>
```

#### 5. Integrate and test

* Paste the embed code into your application's HTML.
* Replace `YOUR_AUTH_TOKEN` with a dynamic variable from your backend.
* Load your application and submit a test prompt (e.g., "How many hires last quarter?") to verify the connection.

<figure><img src="/files/N7je2tTfv7FdrMlQY1zx" alt="Embed AI Chat"><figcaption></figcaption></figure>

***

#### Multi-tenant Example: HR SaaS

In a multi-tenant environment, the integration follows this logic to ensure data privacy:

| Step            | Configuration                                                          |
| --------------- | ---------------------------------------------------------------------- |
| **Attribute**   | `customer_id` defined in the token.                                    |
| **RLS Mapping** | Token `customer_id` maps to database `customer_id` column.             |
| **User Query**  | "How many hires last quarter?".                                        |
| **Execution**   | The query is automatically filtered by `customer_id` before execution. |
| **Result**      | The user only sees data belonging to their specific organization.      |

***

### Conclusion

The AI assistant is now embedded and respects the security boundaries defined by your authentication tokens. Users can perform ad-hoc analysis through natural language while Toucan AI handles the underlying query generation and data visualization.