> ## Documentation Index
> Fetch the complete documentation index at: https://docs.omni.co/llms.txt
> Use this file to discover all available pages before exploring further.
# Omni Agent
> Explore your data using conversational AI in a standalone chat experience.
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The Omni Agent provides a standalone, conversational interface for exploring your data. Ask questions, generate queries, and visualize results without needing to work directly in a workbook.
## Requirements
To use the Omni Agent, you'll need:
* **Restricted Querier**, **Querier**, **Modeler**, or **Connection Admin** permissions
* **To verify that the following settings in [AI Hub > General > Features](/ai/settings/features) are enabled**. These settings are enabled by default, but Organization Admins can modify them as needed.
* **Omni Agent** - Required to use the Omni Agent
* **Omni Agent > Chat** - Required to access the chat interface
* **Omni Agent > Chat > Chat in navigation** - Required to show the standalone chat in the main navigation
## Accessing the Omni Agent
To access the Omni Agent, click **Omni Agent** in Omni's main navigation. The chat interface opens in a dedicated page where you can begin asking questions about your data.
## Capabilities
The Omni Agent can:
* **Answer natural-language questions** about your data
* **Search existing dashboards** for tiles and visualizations that already answer your question before generating a new query
* **Generate queries** based on your prompts, routed through [topics](/modeling/topics) or any view in your model
* **Create visualizations**, including [KPI tiles](/visualize-present/visualizations/types/kpi), directly from your prompts
* **Create dashboards** based on the queries and visualizations in the session
* **Accept files and images as context**:
* **Upload files** including text and Markdown files to provide additional context alongside your queries
* **Attach images** by pasting from your clipboard to provide visual context for your questions
* **Search the Omni docs** to answer questions about how to use Omni
### Getting help
If you're stuck and can't remember how to do something in Omni, ask the Omni Agent. Questions like *"How do I do \[thing]?"* will prompt the agent to search the official Omni docs and provide you with an answer, all without leaving your Omni workflow.
You can also directly tell the agent to search the docs when researching the answer to your question.
**Working in an embedded context?** If you have the **Hide Omni watermark** setting enabled to provide a fully white-labeled experience, the AI doc search feature will respect it. Omni doc links will not be returned in chat, even if explicitly requested.
## Tuning responses
You can shape how the Omni Agent responds by scoping which data it draws from and choosing which underlying AI model handles your prompts.
### Scoping responses
The Omni Agent uses pickers to scope responses to specific connections, models, and topics. Click the button at the bottom of the chat box to open the pickers:
Up to three pickers can display in the chat interface:
* **Connections** - Lists the connections you have access to. Only displays if more than one connection is available. This setting can't be changed if you're using the chat in a workbook or dashboard.
Organization Admins can configure a default connection in [**AI Hub > Setup > Features**](/ai/settings/features#default-connection) to set which connection the Omni Agent opens with by default.
* **Models** - Lists the available models in the selected connection. Only displays if:
* The connection has more than one model, **and**
* At least one topic in the model is accessible. This could mean that the model's [`ai_chat_topics`](/modeling/models/ai-chat-topics) is unset, making all topics accessible, or that it specifies at least one topic:
```yaml theme={null}
ai_chat_topics: [ Products ]
```
* **Topics** - Lists the available topics in the model. Only displays if at least two topics in the model are accessible. This could mean that the model's [`ai_chat_topics`](/modeling/models/ai-chat-topics) is unset, or that it specifies at least two topics:
```yaml theme={null}
ai_chat_topics: [ Products, Orders ]
```
#### Scoping topics
If a prompt is entered and a topic isn't selected - meaning that **Auto-select a topic** is selected in the topic picker - the AI will attempt to select the most relevant topic.
To scope the AI's response to a specific dataset, use the pickers to select a specific topic. The AI will remain "locked" to the selected topic until the selection is changed.
Additionally, the picker menus won't display when a model only has one AI-accessible topic. If a connection has one model with a single AI-accessible topic - determined by the value of the model's [`ai_chat_topics`](/modeling/models/ai-chat-topics) parameter - the AI will be scoped only to that topic and the pickers will not display.
### Selecting an AI model tier
If your Omni organization uses [custom AI models](/ai/settings/model-providers), the model tier selector will be hidden in the chat interface. In this case, the AI will use the custom model configuration from your [AI settings](/administration/ai-hub).
The Omni Agent provides a model tier selector that allows you to choose between different AI models for your chat session. This lets you optimize for speed or intelligence based on your needs:
* **Smarter** - Uses the most capable model (Opus) for complex analysis and nuanced queries
* **Standard** - Uses a balanced model (Sonnet) that provides good performance for most queries
* **Faster** - Uses a fast model (Haiku) for quick responses to simpler questions
To select a model tier, click the model selector next to the (microphone) icon and choose your preferred tier from the dropdown menu. The selector displays the currently active tier, which defaults to your organization or model-level AI settings.
#### Selection reset
Your selected model tier will persist throughout the session, applying to prompts sent after the tier is selected. However, the selection automatically resets to the default tier when you:
* Switch to a different data model using the model picker
* Start a new chat session
The default tier for each model is determined by the model's [`ai_settings`](/modeling/models/ai-settings) configuration, falling back to organization-level settings when not specified.
## Searching existing dashboards
Before generating new queries, the Omni Agent can search for existing dashboards that might already answer your question. When you ask a question, the AI analyzes your prompt and searches through:
* **Dashboard names** - Searches the content index for dashboards with relevant titles
* **Tile and chart names** - Searches for specific visualizations that match your question
The AI uses multiple search variations of your keywords to improve the chance of finding relevant content. For example, if you ask about "inventory items count", it might search for "inventory items", "items count", and "inventory" separately. You can also provide the AI with a dashboard's ID or [custom identifier](/share#changing-document-urls-and-identifiers).
When the AI finds potentially relevant dashboards, it:
Uses semantic matching to identify which specific tile or chart from a dashboard best matches your question
Executes the underlying query and displays the actual chart or table visualization inline in the chat
If a tile directly answers your question, it displays in a highlighted card with an AI-generated explanation of why it's relevant
This helps you discover existing content before creating new queries, promoting reuse of trusted dashboards and reducing duplicate analysis work.
## Creating queries and dashboards
When the Omni Agent answers a question, it generates a live query you can inspect, refresh, or roll up into a dashboard alongside other charts from the session.
### Viewing query field details
When the Omni Agent generates a query, you can view details about the fields used in the results. This helps you understand what data the AI selected and how each field is defined.
To view field details, click the icon near the top-right corner of the query results, next to the icon. A panel displays information for each field in the query, including:
* **Field name** - The field's label and identifier in the model
* **Definition** - The SQL or calculation that defines the field
* **Description** - Additional context about the field, if defined in the model
This feature provides transparency into AI-generated queries, helping you verify the AI selected appropriate fields and understand the underlying data structure. To improve the quality of field definitions displayed here, refer to [Optimizing your models for AI](/modeling/develop/ai-optimization).
### Refreshing query results
When you revisit a previous chat session, query results may be stale. To update the results with the latest data, click the icon floating above the chat box:
Clicking this icon re-runs all queries in the conversation, updating visualizations and results with current data. To optimize performance, only the most recent queries are re-run.
### Creating dashboards
The Omni Agent can build dashboards. There are a few ways to get started:
* **Ask the agent directly to build a dashboard** — describe the dashboard you want in the chat and the agent plans the layout, generates queries, and publishes a complete dashboard to your [**My documents**](/content/navigate#personal) folder:
> *"Create a dashboard showing sales performance by region with monthly trends and a top customers table"* After the dashboard is created, you can ask the agent to iterate on it in the same session — rearranging tiles, editing charts, or adding new ones.
* **From an existing chat** — you've been exploring data and created a few visualizations in a chat session, ask the agent to pull them into a dashboard
* **Upload an image** — drop in an image of a dashboard you'd like to replicate. The agent parses what it sees and uses it as a starting point
## Adding files as context
The **Workbook Agent > File uploads** [AI setting](/ai/settings/features#file-uploads) must be enabled to upload files and attach images.
You can upload text and Markdown files or paste images from your clipboard to give the Omni Agent additional context for your questions.
### Uploading files
You can upload text (`.txt`), Markdown (`.md`), LookML (`.lkml`), XML (`.xml`), HTML (`.html`, `.htm`), and [images](#attaching-images) to the Omni Agent to provide additional context for your questions. This is useful when you need to reference additional information alongside your data queries.
To upload a file:
1. Click the icon at the bottom of the chat box
2. Select **Attach file**
3. Select a file from your computer
4. The file appears as a file chip in the chat box, indicating it's ready to be sent with your message
The Omni Agent can then read and reference the contents of the uploaded files when generating responses, allowing you to ask questions that combine your data with external context or documentation.
### Attaching images
You can paste images directly from your clipboard into the chat to provide visual context alongside your questions. This works with screenshots, images copied from web pages, or any image data on your clipboard.
To attach an image:
1. Copy an image to your clipboard (for example, take a screenshot or copy an image from a web page)
2. Click into the chat box
3. Paste the image using your system's paste command (`Cmd+V` on Mac, `Ctrl+V` on Windows/Linux)
The image appears as a file chip in the chat box, indicating it's ready to be sent with your message. You can attach multiple images to a single message by pasting them one after another.
Image pasting is supported in Chrome and Safari. Firefox does not currently support pasting images into textarea elements due to browser limitations.
## Managing and sharing chat sessions
Each conversation with the Omni Agent is saved as a chat session you can rename, revisit, and share with other users in your organization.
### Navigating prompt history
You can quickly recall previous prompts using keyboard shortcuts, similar to a command-line interface:
* Press the **Up arrow** key to cycle backward through your previous prompts
* Press the **Down arrow** key to cycle forward through your prompt history
When you reach your oldest prompt and press Up arrow again, the navigation wraps to your most recent prompt. Pressing Down arrow after your newest prompt restores any in-progress text you were typing before navigating the history.
Arrow key navigation only works when your cursor is at the beginning of the input field or when the field is empty. This preserves normal cursor movement when editing text.
The prompt history displays only your own prompts from the current chat session.
### Renaming chat sessions
The Omni Agent automatically generates a name for each chat session based on your conversation. You can customize these names to make it easier to identify and return to specific conversations.
To rename a chat session, double-click on the active session name in the Omni Agent sidebar. An inline text field appears where you can enter a new name. Press Enter to save the new name, or press Escape to revert to the original name.
Session names persist and display whenever you return to the Omni Agent. You can only rename chat sessions that you own.
### Managing long conversations
As your conversation with the Omni Agent grows, it consumes more of the available context window. When a chat session reaches 75% of the context window, a visual indicator appears on the **New chat** button to help you recognize when starting a fresh conversation would improve performance. Context window size is determined by the [`conversation_prune_length`](/modeling/models/ai-settings#param-conversation-prune-length) parameter.
This feature is automatic and requires no configuration. Starting a new chat when prompted helps maintain optimal AI performance, especially for complex queries and analysis.
### Sharing chat sessions
You can share a chat session by copying the URL from your browser's address bar and sending it to another user.
Query results stored in the chat history are cached from when the creator ran them. The viewer of the shared chat will see those same cached results.
Therefore, Omni requires that the viewer of the shared chat have **Querier**, **Modeler**, or **Connection Admin** permissions, as well as the same [connection environment](/connect-data/dynamic-environments), to view the session. Users with **Restricted Querier** permissions or lower will not be able to access shared chat sessions.
The following rules determine who can see a chat:
1. The **owner** of the chat can always view the chat.
2. Any user with the **Querier role or above on all relevant models** can view the chat. Because Queriers can already write SQL queries directly in Omni, this doesn't expose any data they couldn't otherwise access.
3. **Viewers and Restricted Queriers** are blocked from viewing, preventing access to data they are not authorized to see.
## Customizing and embedding the Omni Agent
Organization Admins can tailor the agent's appearance to match their brand and surface it inside external applications.
### Applying branding
Organization Admins can customize the appearance of the AI chat interface, including the agent's name, icon, and greeting messages. Refer to the [AI branding settings](/ai/settings/branding) for more information.
### Embedding the agent
The Omni Agent can also be embedded into external applications. Refer to the [embedding the Omni Agent guide](/embed/customization/ai-chat) for setup instructions.
## Next steps
* [Optimize your models for AI](/modeling/develop/ai-optimization) to improve the quality of AI-generated responses
* [Configure topics](/modeling/topics) to organize and scope the data the Omni Agent can access
* [Customize the Omni Agent appearance](/ai/settings/branding) with your own branding
Up to three pickers can display in the chat interface:
* **Connections** - Lists the connections you have access to. Only displays if more than one connection is available. This setting can't be changed if you're using the chat in a workbook or dashboard.
This feature provides transparency into AI-generated queries, helping you verify the AI selected appropriate fields and understand the underlying data structure. To improve the quality of field definitions displayed here, refer to [Optimizing your models for AI](/modeling/develop/ai-optimization).
### Refreshing query results
When you revisit a previous chat session, query results may be stale. To update the results with the latest data, click the 
Clicking this icon re-runs all queries in the conversation, updating visualizations and results with current data. To optimize performance, only the most recent queries are re-run.
### Creating dashboards
The Omni Agent can build dashboards. There are a few ways to get started:
* **Ask the agent directly to build a dashboard** — describe the dashboard you want in the chat and the agent plans the layout, generates queries, and publishes a complete dashboard to your [**My documents**](/content/navigate#personal) folder:
> *"Create a dashboard showing sales performance by region with monthly trends and a top customers table"* After the dashboard is created, you can ask the agent to iterate on it in the same session — rearranging tiles, editing charts, or adding new ones.
* **From an existing chat** — you've been exploring data and created a few visualizations in a chat session, ask the agent to pull them into a dashboard
* **Upload an image** — drop in an image of a dashboard you'd like to replicate. The agent parses what it sees and uses it as a starting point
## Adding files as context