mirror of
https://github.com/apache/superset.git
synced 2026-04-20 16:44:46 +00:00
248 lines
9.7 KiB
Python
248 lines
9.7 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
|
|
# or more contributor license agreements. See the NOTICE file
|
|
# distributed with this work for additional information
|
|
# regarding copyright ownership. The ASF licenses this file
|
|
# to you under the Apache License, Version 2.0 (the
|
|
# "License"); you may not use this file except in compliance
|
|
# with the License. You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing,
|
|
# software distributed under the License is distributed on an
|
|
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
|
# KIND, either express or implied. See the License for the
|
|
# specific language governing permissions and limitations
|
|
# under the License.
|
|
|
|
"""
|
|
Chart resources for valid ChartConfig examples and templates
|
|
"""
|
|
|
|
import logging
|
|
|
|
from superset.mcp_service.app import mcp
|
|
from superset.mcp_service.auth import mcp_auth_hook
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
@mcp.resource("chart://configs")
|
|
@mcp_auth_hook
|
|
def get_chart_configs_resource() -> str:
|
|
"""
|
|
Provide valid ChartConfig examples that match the exact schema.
|
|
|
|
This resource gives LLMs access to:
|
|
- Valid ChartConfig examples for XYChartConfig and TableChartConfig
|
|
- Working configurations that pass schema validation
|
|
- Examples with proper ColumnRef, FilterConfig, AxisConfig, and LegendConfig
|
|
- Best practices for each chart type configuration
|
|
"""
|
|
|
|
# XY chart examples covering all chart kinds and features
|
|
xy_chart_configs = {
|
|
"line_chart": {
|
|
"description": "Line chart with daily time grain",
|
|
"config": {
|
|
"chart_type": "xy",
|
|
"kind": "line",
|
|
"x": {"name": "order_date", "label": "Date"},
|
|
"y": [
|
|
{
|
|
"name": "revenue",
|
|
"aggregate": "SUM",
|
|
"label": "Daily Revenue",
|
|
}
|
|
],
|
|
"time_grain": "P1D",
|
|
},
|
|
"use_cases": ["Time series trends", "Growth tracking"],
|
|
},
|
|
"bar_chart": {
|
|
"description": "Bar chart for category comparison with axis formatting",
|
|
"config": {
|
|
"chart_type": "xy",
|
|
"kind": "bar",
|
|
"x": {"name": "category", "label": "Category"},
|
|
"y": [{"name": "sales", "aggregate": "SUM", "label": "Total Sales"}],
|
|
"x_axis": {"title": "Product Categories"},
|
|
"y_axis": {"title": "Revenue ($)", "format": "$,.0f"},
|
|
},
|
|
"use_cases": ["Category comparison", "Rankings"],
|
|
},
|
|
"stacked_bar": {
|
|
"description": "Stacked bar chart with group_by dimension",
|
|
"config": {
|
|
"chart_type": "xy",
|
|
"kind": "bar",
|
|
"x": {"name": "quarter", "label": "Quarter"},
|
|
"y": [
|
|
{"name": "revenue", "aggregate": "SUM", "label": "Revenue"},
|
|
],
|
|
"group_by": {"name": "region", "label": "Region"},
|
|
"stacked": True,
|
|
"legend": {"show": True, "position": "right"},
|
|
},
|
|
"use_cases": ["Composition analysis", "Regional breakdown"],
|
|
},
|
|
"multi_metric_line": {
|
|
"description": "Multi-metric line chart with filters and monthly grain",
|
|
"config": {
|
|
"chart_type": "xy",
|
|
"kind": "line",
|
|
"x": {"name": "order_date", "label": "Date"},
|
|
"y": [
|
|
{"name": "revenue", "aggregate": "SUM", "label": "Revenue"},
|
|
{
|
|
"name": "customer_id",
|
|
"aggregate": "COUNT_DISTINCT",
|
|
"label": "Unique Customers",
|
|
},
|
|
],
|
|
"time_grain": "P1M",
|
|
"legend": {"show": True, "position": "top"},
|
|
"filters": [{"column": "status", "op": "=", "value": "active"}],
|
|
},
|
|
"use_cases": ["KPI tracking", "Multi-dimensional analysis"],
|
|
},
|
|
"scatter_plot": {
|
|
"description": "Scatter plot for correlation analysis",
|
|
"config": {
|
|
"chart_type": "xy",
|
|
"kind": "scatter",
|
|
"x": {
|
|
"name": "ad_spend",
|
|
"aggregate": "AVG",
|
|
"label": "Avg Ad Spend",
|
|
},
|
|
"y": [
|
|
{
|
|
"name": "conversion_rate",
|
|
"aggregate": "AVG",
|
|
"label": "Avg Conversion Rate",
|
|
}
|
|
],
|
|
"group_by": {"name": "campaign_type", "label": "Campaign"},
|
|
"x_axis": {"format": "$,.0f"},
|
|
"y_axis": {"format": ".2%"},
|
|
},
|
|
"use_cases": ["Correlation analysis", "Outlier detection"],
|
|
},
|
|
"stacked_area": {
|
|
"description": "Stacked area chart for volume composition over time",
|
|
"config": {
|
|
"chart_type": "xy",
|
|
"kind": "area",
|
|
"x": {"name": "order_date", "label": "Date"},
|
|
"y": [{"name": "signups", "aggregate": "SUM", "label": "Signups"}],
|
|
"group_by": {"name": "channel", "label": "Channel"},
|
|
"stacked": True,
|
|
"time_grain": "P1W",
|
|
},
|
|
"use_cases": ["Volume trends", "Channel attribution"],
|
|
},
|
|
}
|
|
|
|
# Table chart examples
|
|
table_chart_configs = {
|
|
"basic_table": {
|
|
"description": "Standard table with dimensions and aggregated metrics",
|
|
"config": {
|
|
"chart_type": "table",
|
|
"columns": [
|
|
{"name": "customer_name", "label": "Customer"},
|
|
{"name": "orders", "aggregate": "COUNT", "label": "Total Orders"},
|
|
{"name": "revenue", "aggregate": "SUM", "label": "Total Revenue"},
|
|
],
|
|
"sort_by": ["Total Revenue"],
|
|
},
|
|
"use_cases": ["Detail views", "Customer lists"],
|
|
},
|
|
"aggregated_table": {
|
|
"description": "Table with multiple aggregations and filters",
|
|
"config": {
|
|
"chart_type": "table",
|
|
"columns": [
|
|
{"name": "region", "label": "Sales Region"},
|
|
{
|
|
"name": "sales_amount",
|
|
"aggregate": "SUM",
|
|
"label": "Total Sales",
|
|
},
|
|
{
|
|
"name": "sales_amount",
|
|
"aggregate": "AVG",
|
|
"label": "Average Sale",
|
|
},
|
|
{
|
|
"name": "customer_id",
|
|
"aggregate": "COUNT_DISTINCT",
|
|
"label": "Unique Customers",
|
|
},
|
|
],
|
|
"filters": [
|
|
{"column": "status", "op": "!=", "value": "cancelled"},
|
|
],
|
|
"sort_by": ["Total Sales"],
|
|
},
|
|
"use_cases": ["Summary reports", "Regional analysis"],
|
|
},
|
|
"ag_grid_table": {
|
|
"description": "Interactive AG Grid table with advanced features",
|
|
"config": {
|
|
"chart_type": "table",
|
|
"viz_type": "ag-grid-table",
|
|
"columns": [
|
|
{"name": "product_name", "label": "Product"},
|
|
{"name": "category", "label": "Category"},
|
|
{"name": "quantity", "aggregate": "SUM", "label": "Qty Sold"},
|
|
{"name": "revenue", "aggregate": "SUM", "label": "Revenue"},
|
|
],
|
|
},
|
|
"use_cases": [
|
|
"Interactive exploration",
|
|
"Large datasets with client-side sorting/filtering",
|
|
],
|
|
},
|
|
}
|
|
|
|
# Best practices
|
|
best_practices = {
|
|
"xy_charts": [
|
|
"Use time_grain for temporal x-axis columns (P1D, P1W, P1M, P1Y)",
|
|
"Limit Y-axis metrics to 3-5 maximum for readability",
|
|
"Use group_by to split data into series for comparison",
|
|
"Use stacked=true for bar/area charts showing composition",
|
|
"Configure axis format for readability ($,.0f for currency, .2% for pct)",
|
|
],
|
|
"table_charts": [
|
|
"Include only essential columns to avoid clutter",
|
|
"Use meaningful labels different from raw column names",
|
|
"Apply sort_by to highlight important data",
|
|
"Use ag-grid-table viz_type for large interactive datasets",
|
|
],
|
|
"general": [
|
|
"Always verify column names with get_dataset_info before charting",
|
|
"Use generate_explore_link for preview, generate_chart for saving",
|
|
"Each column label must be unique across the entire configuration",
|
|
"Column names must match: ^[a-zA-Z0-9_][a-zA-Z0-9_ \\-\\.]*$",
|
|
],
|
|
}
|
|
|
|
resource_data = {
|
|
"xy_chart_configs": xy_chart_configs,
|
|
"table_chart_configs": table_chart_configs,
|
|
"best_practices": best_practices,
|
|
"usage_notes": [
|
|
"All examples are valid ChartConfig objects that pass validation",
|
|
"Modify column names and labels to match your actual dataset",
|
|
"Use get_dataset_info to verify column names before charting",
|
|
"For complete schema details, see the generate_chart tool parameters",
|
|
],
|
|
}
|
|
|
|
from superset.utils import json
|
|
|
|
return json.dumps(resource_data, indent=2)
|