post
https://example.com/api/v2/generate_chart
Generates an Apache ECharts option with the executed rows embedded for rendering
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The /generate_chart endpoint analyzes your question and data to produce an optimized visualization specification. It intelligently selects the appropriate chart type, colors, and layout based on your data characteristics and question intent.
Key Features
- Automatic chart selection: Chooses the best visualization type based on your data
- Embedded data: The executed rows are embedded in
dataset.sourcefor immediate rendering - Customizable instructions: Use
customInstructionto request specific chart tweaks (e.g., renaming titles, sorting, changing chart type)
Wren AI charts are based on the Apache ECharts option.
Example
Response will be an Apache ECharts option you could render with the ECharts library. For example:
{
"projectId": 1,
"question": "Show me total payments by customer state",
"sql": "SELECT customer_state, SUM(payment_value) AS total_payment_value FROM orders GROUP BY customer_state ORDER BY total_payment_value DESC",
"customInstruction": "Rename the chart title to 'Payments by State'",
"threadId": "75ab23c8-9124-4560-a125-fbe7e321dcba"
}Understanding arguments
- question: The original natural language query that was used to generate the SQL.
It reflects the user’s data intent, not a prompt for customizing the chart appearance. - sql: The actual SQL statement generated from the question, which retrieves the necessary data for visualization.
- customInstruction (optional)
Instructions for how the visualization should be adjusted (e.g., change chart type, rename title, filter top N).
This is separate from thequestion.
Response:
{
"id": "a9597146-03ee-4de7-bcd6-57d71bffe86a",
"chartSchema": {
"title": {
"text": "Payments by State",
"left": "center"
},
"tooltip": {
"trigger": "axis",
"axisPointer": {
"type": "shadow"
}
},
"dataset": {
"dimensions": [
"customer_state",
"total_payment_value"
],
"source": [
{ "customer_state": "SP", "total_payment_value": 5998226.96 },
{ "customer_state": "RJ", "total_payment_value": 2144379.69 },
{ "customer_state": "MG", "total_payment_value": 1872257.26 },
{ "customer_state": "RS", "total_payment_value": 890898.54 },
{ "customer_state": "PR", "total_payment_value": 811156.38 },
{ "customer_state": "SC", "total_payment_value": 623086.43 },
{ "customer_state": "BA", "total_payment_value": 616645.82 },
{ "customer_state": "DF", "total_payment_value": 355141.08 },
{ "customer_state": "GO", "total_payment_value": 350092.31 },
{ "customer_state": "ES", "total_payment_value": 325967.55 },
{ "customer_state": "PE", "total_payment_value": 324850.44 },
{ "customer_state": "CE", "total_payment_value": 279464.03 },
{ "customer_state": "PA", "total_payment_value": 218295.85 },
{ "customer_state": "MT", "total_payment_value": 187029.29 },
{ "customer_state": "MA", "total_payment_value": 152523.02 },
{ "customer_state": "PB", "total_payment_value": 141545.72 },
{ "customer_state": "MS", "total_payment_value": 137534.84 },
{ "customer_state": "PI", "total_payment_value": 108523.97 },
{ "customer_state": "RN", "total_payment_value": 102718.13 },
{ "customer_state": "AL", "total_payment_value": 96962.06 },
{ "customer_state": "SE", "total_payment_value": 75246.25 },
{ "customer_state": "TO", "total_payment_value": 61485.33 },
{ "customer_state": "RO", "total_payment_value": 60866.2 },
{ "customer_state": "AM", "total_payment_value": 27966.93 },
{ "customer_state": "AC", "total_payment_value": 19680.62 },
{ "customer_state": "AP", "total_payment_value": 16262.8 },
{ "customer_state": "RR", "total_payment_value": 10064.62 }
]
},
"xAxis": {
"type": "category"
},
"yAxis": {
"type": "value",
"name": "Total Payment Value"
},
"series": [
{
"name": "Total Payment Value",
"type": "bar",
"encode": {
"x": "customer_state",
"y": "total_payment_value"
}
}
]
},
"chartSchemaVersion": "v3",
"threadId": "bfbef4db-5bb4-4133-a65f-9dd813569727"
}The chart columns are bound through dataset.dimensions + series.encode, and the executed rows are injected into dataset.source. Pass the returned chartSchema straight to ECharts with myChart.setOption(chartSchema) and you'll see the rendered chart.
Error Handling
If chart generation fails, you'll receive an error response:
{
"id": "c4f82c31-a40d-4b8e-9e5f-c1d8a742db55",
"code": "INVALID_SQL_ERROR",
"error": "Invalid SQL, please check your SQL syntax"
}Error codes may include:
INVALID_SQL_ERROR: The SQL could not be executed (invalid syntax, or a table/column not defined in the MDL manifest)FAILED_TO_GENERATE_VEGA_SCHEMA: The result could not be turned into a valid chart specification (shared code — the ECharts engine reuses it)
Supported Chart Types
Check Wren AI / Generate Chart for reference.