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| # Generate Pulse Insight Brief | ||
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| Generates AI-powered conversational insights for Pulse metrics based on natural language questions. | ||
| This tool enables interactive, multi-turn conversations about your metrics data. | ||
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| ## What is an Insight Brief? | ||
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| An **insight brief** is an AI-generated response to questions about Pulse metrics that provides: | ||
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| - **Natural language answers** - Conversational responses to specific questions | ||
| - **Contextual summaries** - AI-powered analysis based on metric data | ||
| - **Action-oriented advice** - Recommendations and next steps | ||
| - **Conversational format** - Optimized for chat interfaces and follow-up questions | ||
| - **Multi-turn support** - Maintains conversation context across multiple questions | ||
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| ### Comparison with Other Bundle Types | ||
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| | Bundle Type | Purpose | Best For | | ||
| | ------------- | ---------------------------------- | ---------------------------------------------- | | ||
| | **Brief** | AI-powered conversational insights | Chat interfaces, Q&A, multi-turn conversations | | ||
| | **Detail** | Comprehensive analysis | Investigation, dashboard views, deep dives | | ||
| | **Ban** | Current value snapshot | Banner displays, at-a-glance metrics | | ||
| | **Breakdown** | Dimension analysis | Understanding categorical distributions | | ||
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| ## APIs called | ||
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| - [Generate insight brief](https://help.tableau.com/current/api/rest_api/en-us/REST/rest_api_ref_pulse.htm#PulseInsightsService_GenerateInsightBrief) | ||
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| ## Required arguments | ||
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| ### `briefRequest` | ||
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| The request to generate an insight brief. This includes the conversation history, language/locale | ||
| settings, and metric context. | ||
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| **Key fields:** | ||
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| - `language`: Language for the response (e.g., 'LANGUAGE_EN_US') | ||
| - `locale`: Locale for formatting (e.g., 'LOCALE_EN_US') | ||
| - `messages`: Array of conversation messages (see below) | ||
| - `now`: Optional current time in 'YYYY-MM-DD HH:MM:SS' or 'YYYY-MM-DD' format | ||
| - `time_zone`: Optional timezone for date/time calculations | ||
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| ### Message Structure | ||
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| Each message in the `messages` array contains: | ||
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| - `action_type`: Type of action | ||
| - `ACTION_TYPE_ANSWER` - Answer a specific question | ||
| - `ACTION_TYPE_SUMMARIZE` - Provide a summary | ||
| - `ACTION_TYPE_ADVISE` - Give recommendations | ||
| - `content`: The question or response text (string) | ||
| - `role`: Who sent the message | ||
| - `ROLE_USER` - User's question | ||
| - `ROLE_ASSISTANT` - AI's response | ||
| - `metric_group_context`: Array of metrics being analyzed | ||
| - `metric_group_context_resolved`: Whether metric context is resolved (boolean) | ||
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| ## Multi-Turn Conversations | ||
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| **Important:** To enable follow-up questions and richer insights, you must include the full | ||
| conversation history in the `messages` array: | ||
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| 1. Add the initial user question with `role: 'ROLE_USER'` | ||
| 2. Add the assistant's response with `role: 'ROLE_ASSISTANT'` and `content` containing the previous | ||
| response text | ||
| 3. Add the follow-up question with `role: 'ROLE_USER'` | ||
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| Without conversation history, follow-up questions may lack context and provide less detailed | ||
| answers. | ||
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| ### Example: Initial Question | ||
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| ```json | ||
| { | ||
| "language": "LANGUAGE_EN_US", | ||
| "locale": "LOCALE_EN_US", | ||
| "messages": [ | ||
| { | ||
| "action_type": "ACTION_TYPE_SUMMARIZE", | ||
| "content": "What are the key insights for Sales?", | ||
| "role": "ROLE_USER", | ||
| "metric_group_context": [ | ||
| { | ||
| "metadata": { | ||
| "name": "Sales", | ||
| "metric_id": "CF32DDCC-362B-4869-9487-37DA4D152552", | ||
| "definition_id": "BBC908D8-29ED-48AB-A78E-ACF8A424C8C3" | ||
| }, | ||
| "metric": { | ||
| "definition": { | ||
| "datasource": { | ||
| "id": "A6FC3C9F-4F40-4906-8DB0-AC70C5FB5A11" | ||
| }, | ||
| "basic_specification": { | ||
| "measure": { | ||
| "field": "Sales", | ||
| "aggregation": "AGGREGATION_SUM" | ||
| }, | ||
| "time_dimension": { | ||
| "field": "Order Date" | ||
| }, | ||
| "filters": [] | ||
| }, | ||
| "is_running_total": false | ||
| }, | ||
| "metric_specification": { | ||
| "filters": [], | ||
| "measurement_period": { | ||
| "granularity": "GRANULARITY_BY_MONTH", | ||
| "range": "RANGE_CURRENT_PARTIAL" | ||
| }, | ||
| "comparison": { | ||
| "comparison": "TIME_COMPARISON_PREVIOUS_PERIOD" | ||
| } | ||
| }, | ||
| "extension_options": { | ||
| "allowed_dimensions": ["Region", "Category"], | ||
| "allowed_granularities": ["GRANULARITY_BY_DAY", "GRANULARITY_BY_MONTH"], | ||
| "offset_from_today": 0 | ||
| }, | ||
| "representation_options": { | ||
| "type": "NUMBER_FORMAT_TYPE_NUMBER", | ||
| "number_units": { | ||
| "singular_noun": "dollar", | ||
| "plural_noun": "dollars" | ||
| }, | ||
| "sentiment_type": "SENTIMENT_TYPE_NONE", | ||
| "row_level_id_field": { | ||
| "identifier_col": "Order ID" | ||
| }, | ||
| "row_level_entity_names": { | ||
| "entity_name_singular": "Order", | ||
| "entity_name_plural": "Orders" | ||
| }, | ||
| "row_level_name_field": { | ||
| "name_col": "Order Name" | ||
| }, | ||
| "currency_code": "CURRENCY_CODE_USD" | ||
| }, | ||
| "insights_options": { | ||
| "settings": [ | ||
| { "type": "INSIGHT_TYPE_TOP_DRIVERS", "disabled": false }, | ||
| { "type": "INSIGHT_TYPE_METRIC_FORECAST", "disabled": false } | ||
| ] | ||
| } | ||
| } | ||
| } | ||
| ], | ||
| "metric_group_context_resolved": true | ||
| } | ||
| ] | ||
| } | ||
| ``` | ||
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| ### Example: Follow-up Question with Conversation History | ||
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| ```json | ||
| { | ||
| "language": "LANGUAGE_EN_US", | ||
| "locale": "LOCALE_EN_US", | ||
| "messages": [ | ||
| { | ||
| "action_type": "ACTION_TYPE_SUMMARIZE", | ||
| "content": "What are the key insights for Sales?", | ||
| "role": "ROLE_USER", | ||
| "metric_group_context": [ | ||
| /* ... */ | ||
| ], | ||
| "metric_group_context_resolved": true | ||
| }, | ||
| { | ||
| "action_type": "ACTION_TYPE_SUMMARIZE", | ||
| "content": "Sales increased 5% with growth in Region A and B...", | ||
| "role": "ROLE_ASSISTANT", | ||
| "metric_group_context": [ | ||
| /* ... */ | ||
| ], | ||
| "metric_group_context_resolved": true | ||
| }, | ||
| { | ||
| "action_type": "ACTION_TYPE_ANSWER", | ||
| "content": "What factors contributed to the increase?", | ||
| "role": "ROLE_USER", | ||
| "metric_group_context": [ | ||
| /* ... */ | ||
| ], | ||
| "metric_group_context_resolved": true | ||
| } | ||
| ] | ||
| } | ||
| ``` | ||
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| ## Use Cases | ||
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| ### Conversational Analytics | ||
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| Interactive Q&A about metrics: | ||
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| ``` | ||
| User: "What are the key insights for Sales?" | ||
| AI: "Sales is up 5% with growth in West region..." | ||
| User: "What factors contributed to the increase?" | ||
| AI: "The increase was driven by Technology category growth..." | ||
| ``` | ||
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| ### Executive Briefings | ||
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| Natural language metric summaries: | ||
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| ``` | ||
| "What should I know about my metrics today?" | ||
| → AI-generated summary of key changes, trends, and recommendations | ||
| ``` | ||
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| ## Example Response | ||
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| ```json | ||
| { | ||
| "data": { | ||
| "markup": "- **Forecast for November 22, 2025**: The forecasted value for Sales is $150K, with a confidence range of $145K to $155K.\n\n- **Month-to-Date Comparison**: Sales for November 2025 month-to-date is $150K, which is a 5.0% increase compared to October 2025 month-to-date ($142.9K).\n\nOverall, the metric shows a positive trend with a slight increase month-to-date and a stable forecast.", | ||
| "generation_id": "abc123...", | ||
| "source_insights": [ | ||
| { | ||
| "type": "forecast", | ||
| "markup": "The forecast for Sales for November 22, 2025 is $150K with a confidence range of $145K to $155K.", | ||
| "viz": { | ||
| /* Vega-Lite visualization spec */ | ||
| }, | ||
| "facts": { | ||
| /* Insight facts and data */ | ||
| } | ||
| }, | ||
| { | ||
| "type": "popc", | ||
| "markup": "Sales was $150K (November 2025 month to date), up 5.0% ($7.1K) compared to the prior period.", | ||
| "viz": { | ||
| /* Vega-Lite visualization spec */ | ||
| }, | ||
| "facts": { | ||
| /* Insight facts and data */ | ||
| } | ||
| } | ||
| ], | ||
| "follow_up_questions": [ | ||
| { "content": "What factors contributed to the increase in Sales?" }, | ||
| { "content": "How does the forecast compare to historical trends?" } | ||
| ], | ||
| "group_context": [ | ||
| /* Full metric context */ | ||
| ], | ||
| "not_enough_information": false | ||
| } | ||
| } | ||
| ``` | ||
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| ## Response Fields | ||
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| - `markup`: AI-generated markdown text with the answer | ||
| - `generation_id`: Unique ID for this generation | ||
| - `source_insights`: Array of underlying insights used to generate the response | ||
| - Each insight includes `type`, `markup`, `viz` (Vega-Lite spec), and `facts` | ||
| - `follow_up_questions`: Suggested next questions to continue the conversation | ||
| - `group_context`: The full metric context used | ||
| - `not_enough_information`: Boolean indicating if the AI had insufficient data | ||
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| ## Notes | ||
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| - **Conversational**: Designed for multi-turn Q&A about metrics | ||
| - **Context-aware**: Maintains conversation history for richer responses | ||
| - **AI-powered**: Uses natural language understanding to answer questions | ||
| - **Visualization-rich**: Includes Vega-Lite specs for charts | ||
| - **Follow-up suggestions**: Provides relevant next questions | ||
| - **Ideal for chat interfaces**: Slack, Teams, ChatGPT, Claude, etc. | ||
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