Converse with Looker Explore data

Conversational Analytics, powered by Gemini for Google Cloud, lets you investigate your data by asking questions about it in conversational language through an intuitive chat interface. There are multiple ways to "have a conversation" using Conversational Analytics. When using Conversational Analytics with Explore data, you can ask questions about a single Explore in a Conversation or create a data Agent to chat with as many as five Explores at once. You can also use data agents that other users have created and shared with you.

This page discusses how to use Conversational Analytics to engage with Explore data. It covers the following topics:

• Starting a conversation with a single Explore
• Start a conversation with an existing data agent that is connected to Explores
• Asking questions about your data
• Managing queries within a conversation
• Understanding query results and calculations
• Managing conversations
• Known limitations with conversations

Learn how and when Gemini for Google Cloud uses your data.

As an early-stage technology, Gemini for Google Cloud products can generate output that seems plausible but is factually incorrect. We recommend that you validate all output from Gemini for Google Cloud products before you use it. For more information, see Gemini for Google Cloud and responsible AI.

Before you begin

Before you can use Conversational Analytics to engage with your Explore data, make sure that the setup and requirements for your Looker instance have been satisfied and that you have been granted the appropriate permissions to perform the tasks that are described on this page.

Start a conversation with a single Explore

To start a conversation — a discrete set of questions and answers about your data in regular, natural language in a chat interface — with a single Explore, follow these steps:

1. Navigate to the Conversational Analytics interface.

   • From the main navigation menu: Select Conversations. Select the Explores tab and then select the Explore that you want to query.
   • From the Create menu: Select Conversation. Select the Explores tab and then select the Explore that you want to query.
   • From a Looker Explore: Select Start a conversation. This option isn't available if you're using the new Explore experience.

2. By default the conversation is called "Untitled." After you ask your first question in the conversation, Conversational Analytics automatically generates a conversation title that is based on your question and response. To change the generated name, click the title at the top of the conversation page and enter a new conversation name. To save your changes, click elsewhere on the page, or press return (Mac) or Enter (PC).

Once you have created a conversation, you can ask questions about the data in the send_spark Ask a question field within the conversation. You can return to the conversation from the Recent Conversations section.

Start a conversation with an existing data agent that is connected to Explores

Note: To create a new data agent to chat with Explore data, see the Create and manage Explore data agents documentation page.

Data agents are customized with context and instructions that are specific to your data. You can start a conversation with an existing data agent that you created or that another user has shared with you. To start a conversation with an existing data agent that is connected to up to five Looker Explores follow these steps:

1. Navigate to the Conversational Analytics interface.

   • From the main navigation menu: Select Conversations. Select the Agents tab and then select the Explore data agent that you want to converse with.
   • From the Create menu: Select Conversation. Select the Agents tab and then select the Explore data agent that you want to converse with.

2. By default the conversation is called "Untitled." After you ask your first question in the conversation, Conversational Analytics automatically generates a conversation title that is based on your question and response. To change the generated name, click the title at the top of the conversation page and enter a new conversation name. To save your changes, click elsewhere on the page, or press return (Mac) or Enter (PC).

Once you have created a conversation, you can ask questions about the data in the send_spark Ask a question field within the conversation. You can return to the conversation from the Recent Conversations section.

Ask questions about Looker data

When you're beginning a new conversation, Conversational Analytics suggests some starting questions to ask. The questions don't need to be in a specific format or use a specific syntax. However, they do need to relate to the Explore that you've selected.

Type your question in natural language into the Ask a question field. Select a question mode and click send. After you submit your query, you can cancel Conversational Analytics' response by clicking Stop response. Conversational Analytics stops running the query and displays the following message: The query was cancelled.

For more guidance on the types of questions that you can ask, see Limitations on questions.

Select a question mode

When you ask a question, you can select the question mode from a drop-down menu that contains the options Fast and Thinking. The UI describes Fast as being intended for answering quickly and Thinking as being intended for solving complex problems. Conversational Analytics defaults to Thinking question mode. Conversational Analytics will maintain the same question mode throughout any multi-turn conversations, unless you manually change it.

Fast mode

When you ask a question in Fast mode, Conversational Analytics attempts to directly map your natural language query to the LookML parameters that are defined in the LookML models that underlie your conversation's Explores. Conversational Analytics is able to respond quickly because it relies on the governed definitions of your LookML, and it doesn't use or display any kind of reasoning.

For example, a query like "What was our total revenue last month?" can be quickly translated into a query that selects the total_revenue measure and filters for the previous month.

Select Fast mode if your query is asking for specific facts or predefined metrics from your data.

Thinking mode

Thinking mode is intended for more complex analytical requests that require analysis beyond direct LookML lookups. In this mode, the agent "plans" its approach, deciding which tools to use and how to combine results. This mode allows for solving multi-step problems and performing advanced data science tasks that may not be possible with a single SQL-based query.

Select Thinking mode when asking why about your data, when you're comparing trends, or when you're making more complex analytical requests that may require multiple steps. This mode is also especially helpful when testing an agent to understand how it's using the underlying LookML of its data sources.

How Conversational Analytics processes your questions

Conversational Analytics may rephrase your question after you've submitted a query, and the rephrased question will be displayed in the conversation window following your original question. For example, Conversational Analytics might rephrase the question "What is the mean of user ages?" to "What is the average user age?"

While Conversational Analytics runs your query, you can observe its reasoning and thought process. Conversational Analytics may ask follow-up questions to clarify any ambiguities in the original query. For example, if there are multiple, similarly named fields, Conversational Analytics may ask you to clarify which field is most appropriate to use in processing your query.

Multi-turn conversations

Conversational Analytics will take previous questions and answers into account as you continue the conversation. You can take previous answers and build on them by further refining results or changing the visualization type.

Important: You can only reference previous questions in answers from the active conversation. You cannot reference previous questions and answers from other conversations.

For more guidance on creating questions, see Limitations on questions.

Clarifying questions

While Conversational Analytics runs your query, you can observe its reasoning and thought process. Conversational Analytics may ask follow-up questions to clarify any ambiguities in the original query. For example, if there are multiple, similarly named fields, Conversational Analytics may ask you to clarify which field is most appropriate to use in processing your query.

Conversation metadata

When you converse with an Explore or a data agent, the collapsible storage Data panel shows the name of the Looker Explore that is being used by the conversation. The storage Data panel also provides the following options:

• open_in_new View fields: When chatting with an Explore, you can view the Explore in a new browser window by clicking open_in_new View fields.
• open_in_new Edit agent: When chatting with a data agent, you can edit the details about the data agent by clicking open_in_new Edit agent.
• chat_spark New conversation: Start a new conversation with the Looker Explore that the current conversation is using.

Manage queries within a conversation

When you converse with data, you can manage the conversation by stopping an active query response while it is running or by deleting the most recent question and its response.

Delete the most recent question

To delete the most recent question and its response, follow these steps:

1. Hold your cursor over the most recent question, and then click delete Delete message.
2. In the Permanently delete message? dialog, click Delete to permanently delete the question and its response.

Understand query results and calculations

When you ask questions about your data in Conversational Analytics, the response might include a visualization, a data table, or other details, depending on your specific query and the connected data. To open the query results as an Explore, click explore Open in Explore within the query results.

In addition to this query response, Conversational Analytics provides the following options for understanding query results and calculations:

• Details about how your query was interpreted
• Details about how the answer was calculated

Determine how your query was interpreted

If you use Thinking mode to ask your question, you can see how Conversational Analytics reasoned through your query. To see its reasoning, expand the Show reasoning option. To hide its reasoning, click Hide reasoning.

Conversational Analytics analyzes each query and thinks about how to respond, using the keywords from your query to infer the relevant dimensions, measures, and other parameters from the semantic layer of the conversation's associated datasets and interpreting from your query what aggregations may need to be performed. When you expand Show reasoning, Conversational Analytics displays a plain text explanation of the steps that it took to interpret your query. The explanation also includes the duration that Conversational Analytics thought about the query.

Following its reasoning, Conversational Analytics generates a response, which may include a request for clarification about your query.

Determine how an answer was calculated

To see how Conversational Analytics arrived at an answer or created a visualization, click keyboard_arrow_down How was this calculated? within the query results.

When you click How was this calculated?, Conversational Analytics displays a Text section. The Text section provides a plain text explanation of the steps that were taken by Conversational Analytics to arrive at the given answer. This explanation includes the raw field names that were used, the calculations that were done, the filters that were applied, the sort order, and other details.

If Advanced Analytics is available for your data agent, the Code tab displays the additional generated Python code for any advanced queries.

Manage conversations

Conversations are listed by title in the schedule Recent section. You can change the names of conversations, delete conversations, or restore them from the trash folder.

Delete a conversation

To move a conversation to the trash, open the conversation and click Move to trash.

Note: Conversations that remain in the trash for more than 30 days are automatically deleted.

Restore or permanently delete a conversation

To restore or permanently delete a conversation from the trash, follow these steps:

1. Within Conversational Analytics, select Trash in the left navigation panel to view the list of conversations that have been moved to the trash.
2. In the Trash section, click the name of the conversation that you want to restore or permanently delete.

3. In the Are you sure? dialog, select one of the following options:

   • Cancel: Cancels the action.
   • Restore: Restores the conversation. The conversation can be accessed from the Recent section of the left navigation menu within Conversational Analytics.
   • Delete forever: Permanently deletes the conversation.

Known limitations

Conversational Analytics has the following known limitations.

Limitations on visualizations

Conversational Analytics leverages Vega-lite for conversation chart generation. The following Vega chart types are fully supported:

• Line chart (one or more series)
• Area chart
• Bar chart (horizontal, vertical, stacked)
• Scatter plot (one or more groups)
• Pie chart

The following Vega chart types are supported, but you may encounter unexpected behavior when rendering them:

• Maps
• Heatmaps
• Charts with tooltips

Chart types that exist outside the Vega catalog are not supported. Any charts that are not specified in this section are considered unsupported.

Limitations on data sources

Conversational Analytics has the following data source limitations:

• Conversational Analytics can return a maximum of 5,000 rows per query.
• Conversational Analytics cannot set the value of a filter-only field that is defined using the LookML parameter or filter parameters.

Limitations on questions

Conversational Analytics supports questions that can be answered by a single visualization, for example:

• Metric trends over time
• Breakdown or distribution of a metric by dimension
• Unique values for one or more dimensions
• Single metric values
• The top dimension values by metric

Conversational Analytics doesn't yet support questions that can only be answered with the following types of complicated visualizations:

• Prediction and forecasting
• Advanced statistical analysis, including correlation and anomaly detection

More advanced questions, such as forecasting, can be answered when Advanced Analytics is enabled.

Related resources

• Conversational Analytics in Looker overview: The landing page for Conversational Analytics with a list of key features links to all Conversational Analytics documentation.

• Create and manage Explore data agents: With Explore data agents, you can customize the AI-powered data querying agent by providing context and instructions that are specific to your Explore data, which helps Conversational Analytics generate more accurate and contextually relevant responses.

• Converse with Looker dashboard data: Chat with a Looker dashboard using a dashboard data agent.

• Best practices for configuring Conversational Analytics in Looker: Strategies and best practices to help Looker administrators and LookML developers successfully configure and optimize Conversational Analytics.

• Enable Advanced Analytics: Advanced Analytics within Conversational Analytics translates your natural language questions into Python code and executes that code. Compared to standard SQL-based queries, the Advanced Analytics feature's use of Python enables more complex analysis and visualizations.