Analyze data with conversations

This document describes how to create, edit, and delete conversations in BigQuery. Conversations are persisted chats with a data agent or data sources, such as tables or views, that you select.

You can ask data agents multi-part questions that use common terms—for example, "sales" or "most popular"—without specifying table field names, or defining conditions to filter the data. An agent can determine which data sources to query and take advantage of optimizations, such as table partitions, when it constructs a response. The chat response contains the answer to your question as text and code, and it includes the reasoning behind the results. The response can also include images and charts when appropriate.

You can create a conversation with a data agent, or a direct conversation with one or more tables. When you create a direct conversation, the Conversational Analytics API interprets your question without the context and processing instructions offered by a data agent.

Before you begin

1. Verify that billing is enabled for your Google Cloud project .

2. Enable the BigQuery, Gemini Data Analytics, and Gemini for Google Cloud APIs.

   Roles required to enable APIs

   To enable APIs, you need the Service Usage Admin IAM role ( roles/serviceusage.serviceUsageAdmin ), which contains the serviceusage.services.enable permission. Learn how to grant roles .

   Enable the APIs

Required roles

To create conversations, you must have one of the following Conversational Analytics API IAM roles :

• To view and create conversations with any data agent that has been shared with you, you must have the Gemini Data Analytics Data Agent User ( roles/geminidataanalytics.dataAgentUser ) role and the Gemini for Google Cloud User ( roles/cloudaicompanion.user ) role at the project level.
• To create a direct conversation, you must have the Gemini Data Analytics Stateless Chat User ( roles/geminidataanalytics.dataAgentStatelessUser ) role.

Additionally, in the following situations, you must have the following roles:

• If a data agent uses a data table as a knowledge source, you must have the BigQuery Data Viewer ( roles/bigquery.dataViewer ) role on that table.
• If a data table uses column-level access control , you need the Fine-Grained Reader ( roles/datacatalog.categoryFineGrainedReader ) role on the appropriate policy tag. For more information, see Roles used with column-level access control .
• If a data table uses row-level access control , you must have the role-level access policy on that table. For more information, see Create or update row-level access policies .
• If a data table uses data masking , you need the Masked Reader ( roles/bigquerydatapolicy.maskedReader ) role on the appropriate data policy. For more information, see Roles for querying masked data .

If you don't have appropriate roles on the source data tables used by the data agent, the system returns the following error when you chat with the data agent:

 Schema_Resolution: Access Denied 

Best practices

Conversational analytics automatically runs queries on your behalf to answer your questions. Consider the following factors that might increase query cost:

• Large table sizes
• Use of data joins in queries
• Frequent calls to AI functions within queries

Create conversations

You can create a conversation with an existing customized data agent. Alternatively, for quick, one-off questions, you can create a conversation directly with a single data source.

Create a conversation with a data agent

To create a conversation with a data agent, you first create a data agent and publish it. You can also initiate a conversation with agents that others share with you.

To create a conversation with an existing data agent in the Google Cloud console, follow these steps:

1. Go to the BigQuery Agentspage.

   Go to Agents

2. Select the Agent Catalogtab.

3. From either the My agentsor Shared by others in your organizationsection, click the agent card of the agent that you want to chat with.

4. Click Start a Conversation. A new chat panel opens.

Create a direct conversation with a data source

To create a conversation with a data source in the Google Cloud console, select one of the following options:

Create a data agent from a conversation

1. From within a conversation's Datapane, in the Quick Actionssection, click Create Agent.
2. Follow the steps to create an agent .

Have a conversation

In the Ask a questionfield, enter a question for the data agent. You can also click one of the Gemini-suggested questions to get started.

The Conversational Analytics API processes your question and returns the results. To see each step the data agent took to provide the answer to your question, click Show reasoning.

To see information about how the results were calculated, click keyboard_arrow_down How was this calculated?

The Summarysection includes the generated query followed by the query result. You can optionally open the query in the query editor.

When appropriate for the data, the response provides images, charts, tables, and other visualizations.

Manage conversations

You can open, rename, or delete a conversation on the Agentspage, and manage conversations in BigQuery Studio Explorer.

Open an existing conversation

1. In the Google Cloud console, go to the BigQuery Agentspage.

   Go to Agents

2. On the Conversationstab, in the conversations list, click the conversation you want to open.

Rename a conversation

1. In the Google Cloud console, go to the BigQuery Agentspage.

   Go to Agents

2. On the Conversationstab, in the conversations list, click the conversation you want to rename.

3. Click more_vert View actions > Rename.

4. In the Rename conversationdialog, enter a new name for the conversation in the Conversation namefield.

5. Click Rename.

Delete a conversation

Results from questions in a conversation persist even if the underlying data sources are deleted. To delete a conversation and all the results that it contains, follow these steps:

1. In the Google Cloud console, go to the BigQuery Agentspage.

   Go to Agents

2. On the Conversationstab, in the conversations list, click the conversation you want to delete.

3. Click more_vert View actions > Delete.

4. In the Delete conversation?dialog, click Delete.

If you don't update a conversation for 180 days, then BigQuery deletes it automatically.

Manage conversations using BigQuery Studio Explorer

Manage conversations using BigQuery Studio Explorer. This conversation list provides a central place to search for, open, or create conversations. You can also copy the conversation ID or refresh the conversations list.

To manage your conversations, follow these steps:

1. Go to the BigQuery Studio Explorer page.

   Go to Explorer

2. In the Explorerpane, expand a project name.

3. Click Conversations.

   1. To filter the conversation list, enter a property name or value in the filter field.
   2. To open a conversation, click more_vert View actions > Open.
   3. To copy a conversation ID, click more_vert View actions > Copy ID.
   4. To create a conversation, in the menu bar, click Create conversation.
   5. To refresh the list, in the menu bar, click Refresh.

Locations

Conversational analytics operates globally; you can't choose which region to use. Your conversations might not be stored in the same region as their data sources.

What's next

• Learn about Conversational analytics in BigQuery .
• Learn about the Conversational Analytics API .
• Create data agents .