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139eea92f6
H1: Delete 7 dead _pre_validate_* static methods from SchemaValidator
— exact duplicates of plugin pre_validate() methods, never called
after _pre_validate_chart_type() was updated to delegate to plugin.
H2: Inline DatasetValidator._normalize_xy_config/_normalize_table_config
into XYChartPlugin/TableChartPlugin.normalize_column_refs() and delete
both DatasetValidator helper methods. The 5 other plugins already
called _get_canonical_column_name directly; XY and Table now match.
H3: Add generate_name()/resolve_viz_type() to ChartTypePlugin protocol
and BaseChartPlugin, implement in all 7 plugins. Replace the 7-arm
isinstance chain in generate_chart_name() and the 7-arm elif chain
in _resolve_viz_type() with single-line registry dispatch.
H4: Add a sync comment above _CHART_TYPE_ERROR_HINTS to document that
it must stay in sync with the plugin registry.
M4: Move logger=logging.getLogger(__name__) from inside
XYChartPlugin.get_runtime_warnings() to module scope.
333 lines
15 KiB
Python
333 lines
15 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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"""
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Unified schema validation for chart configurations.
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Consolidates pre-validation, schema validation, and error enhancement.
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"""
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import logging
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from typing import Any, Dict, Tuple
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from pydantic import ValidationError as PydanticValidationError
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from superset.mcp_service.chart.schemas import (
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GenerateChartRequest,
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)
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from superset.mcp_service.common.error_schemas import ChartGenerationError
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logger = logging.getLogger(__name__)
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class SchemaValidator:
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"""Unified schema validator with pre-validation and enhanced error messages."""
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@staticmethod
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def validate_request(
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request_data: Dict[str, Any],
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) -> Tuple[bool, GenerateChartRequest | None, ChartGenerationError | None]:
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"""
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Validate request data with pre-validation and enhanced error handling.
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Returns:
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Tuple of (is_valid, parsed_request, error)
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"""
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# Pre-validate to catch common issues early
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is_valid, error = SchemaValidator._pre_validate(request_data)
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if not is_valid:
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return False, None, error
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# Try Pydantic validation
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try:
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request = GenerateChartRequest(**request_data)
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return True, request, None
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except PydanticValidationError as e:
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# Enhance the error message
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error = SchemaValidator._enhance_validation_error(e, request_data)
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return False, None, error
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@staticmethod
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def _pre_validate(
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data: Dict[str, Any],
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) -> Tuple[bool, ChartGenerationError | None]:
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"""Pre-validate request data before Pydantic processing."""
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if not isinstance(data, dict):
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return False, ChartGenerationError(
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error_type="invalid_request_format",
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message="Request must be a JSON object",
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details="The request body must be a valid JSON object, not a string "
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"or array",
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suggestions=[
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"Ensure you're sending a JSON object with 'dataset_id' and "
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"'config' fields",
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"Check that Content-Type header is set to 'application/json'",
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],
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error_code="INVALID_REQUEST_FORMAT",
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)
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# Check for required top-level fields
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if "dataset_id" not in data:
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return False, ChartGenerationError(
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error_type="missing_dataset_id",
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message="Missing required field: dataset_id",
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details="The 'dataset_id' field is required to identify which dataset "
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"to use",
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suggestions=[
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"Add 'dataset_id' field with the ID of your dataset",
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"Use list_datasets tool to find available dataset IDs",
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"Example: {'dataset_id': 1, 'config': {...}}",
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],
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error_code="MISSING_DATASET_ID",
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)
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if "config" not in data:
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return False, ChartGenerationError(
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error_type="missing_config",
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message="Missing required field: config",
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details="The 'config' field is required to specify chart configuration",
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suggestions=[
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"Add 'config' field with chart type and settings",
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"Example: {'dataset_id': 1, 'config': {'chart_type': 'xy', ...}}",
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],
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error_code="MISSING_CONFIG",
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)
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config = data.get("config", {})
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if not isinstance(config, dict):
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return False, ChartGenerationError(
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error_type="invalid_config_format",
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message="Config must be a JSON object",
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details="The 'config' field must be a valid JSON object with chart "
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"settings",
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suggestions=[
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"Ensure config is an object, not a string or array",
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"Example: 'config': {'chart_type': 'xy', 'x': {...}, 'y': [...]}",
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],
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error_code="INVALID_CONFIG_FORMAT",
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)
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# Check chart_type early
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chart_type = config.get("chart_type")
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if not chart_type:
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return False, ChartGenerationError(
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error_type="missing_chart_type",
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message="Missing required field: chart_type",
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details="Chart configuration must specify 'chart_type'",
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suggestions=[
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"Add 'chart_type': 'xy' for line/bar/area/scatter charts",
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"Add 'chart_type': 'table' for table visualizations",
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"Add 'chart_type': 'pie' for pie or donut charts",
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"Add 'chart_type': 'pivot_table' for interactive pivot tables",
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"Add 'chart_type': 'mixed_timeseries' for dual-series time charts",
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"Add 'chart_type': 'handlebars' for custom HTML template charts",
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"Add 'chart_type': 'big_number' for big number display",
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"Example: 'config': {'chart_type': 'xy', ...}",
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],
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error_code="MISSING_CHART_TYPE",
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)
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return SchemaValidator._pre_validate_chart_type(chart_type, config)
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@staticmethod
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def _pre_validate_chart_type(
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chart_type: str,
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config: Dict[str, Any],
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) -> Tuple[bool, ChartGenerationError | None]:
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"""Validate chart type and dispatch to plugin pre-validation."""
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from superset.mcp_service.chart.registry import get_registry
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registry = get_registry()
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if not isinstance(chart_type, str) or not registry.is_registered(chart_type):
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valid_types = ", ".join(registry.all_types())
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return False, ChartGenerationError(
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error_type="invalid_chart_type",
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message=f"Invalid chart_type: '{chart_type}'",
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details=f"Chart type '{chart_type}' is not supported. "
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f"Must be one of: {valid_types}",
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suggestions=[
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"Use 'chart_type': 'xy' for line, bar, area, or scatter charts",
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"Use 'chart_type': 'table' for tabular data display",
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"Use 'chart_type': 'pie' for pie or donut charts",
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"Use 'chart_type': 'pivot_table' for interactive pivot tables",
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"Use 'chart_type': 'mixed_timeseries' for dual-series time charts",
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"Use 'chart_type': 'handlebars' for custom HTML template charts",
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"Use 'chart_type': 'big_number' for big number display",
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"Check spelling and ensure lowercase",
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],
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error_code="INVALID_CHART_TYPE",
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)
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plugin = registry.get(chart_type)
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if plugin is None:
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return False, ChartGenerationError(
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error_type="invalid_chart_type",
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message=f"Chart type '{chart_type}' has no registered plugin",
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details="Internal error: chart type is listed but has no plugin",
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suggestions=["Use a supported chart_type"],
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error_code="INVALID_CHART_TYPE",
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)
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if (error := plugin.pre_validate(config)) is not None:
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return False, error
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return True, None
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# Per-chart-type error details used by _enhance_validation_error.
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# Keyed by chart_type discriminator value.
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# NOTE: Keep this dict in sync with the plugin registry in
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# superset/mcp_service/chart/plugins/ — each registered chart_type must
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# have a corresponding entry here so Pydantic parse errors produce
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# helpful, type-specific messages.
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_CHART_TYPE_ERROR_HINTS: Dict[str, Dict[str, Any]] = {
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"xy": {
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"error_type": "xy_validation_error",
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"message": "XY chart configuration validation failed",
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"details": "The XY chart configuration is missing required "
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"fields or has invalid structure",
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"suggestions": [
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"Ensure 'x' field exists with {'name': 'column_name'}",
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"Ensure 'y' is an array: [{'name': 'metric', 'aggregate': 'SUM'}]",
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"Check that all column names are strings",
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"Verify aggregate functions are valid: SUM, COUNT, AVG, MIN, MAX",
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],
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"error_code": "XY_VALIDATION_ERROR",
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},
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"table": {
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"error_type": "table_validation_error",
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"message": "Table chart configuration validation failed",
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"details": "The table chart configuration is missing required "
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"fields or has invalid structure",
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"suggestions": [
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"Ensure 'columns' field is an array of column specifications",
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"Each column needs {'name': 'column_name'}",
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"Optional: add 'aggregate' for metrics",
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"Example: 'columns': [{'name': 'product'}, "
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"{'name': 'sales', 'aggregate': 'SUM'}]",
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],
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"error_code": "TABLE_VALIDATION_ERROR",
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},
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"pie": {
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"error_type": "pie_validation_error",
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"message": "Pie chart configuration validation failed",
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"details": "The pie chart configuration is missing required "
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"fields or has invalid structure",
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"suggestions": [
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"Ensure 'dimension' field has 'name' for the slice label",
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"Ensure 'metric' field has 'name' and 'aggregate'",
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"Example: {'chart_type': 'pie', 'dimension': {'name': 'category'}, "
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"'metric': {'name': 'revenue', 'aggregate': 'SUM'}}",
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],
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"error_code": "PIE_VALIDATION_ERROR",
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},
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"pivot_table": {
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"error_type": "pivot_table_validation_error",
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"message": "Pivot table configuration validation failed",
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"details": "The pivot table configuration is missing required "
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"fields or has invalid structure",
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"suggestions": [
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"Ensure 'rows' field is an array of column specs",
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"Ensure 'metrics' field is an array with aggregate funcs",
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"Optional: add 'columns' for column grouping",
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"Example: {'chart_type': 'pivot_table', 'rows': [{'name': 'region'}], "
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"'metrics': [{'name': 'revenue', 'aggregate': 'SUM'}]}",
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],
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"error_code": "PIVOT_TABLE_VALIDATION_ERROR",
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},
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"mixed_timeseries": {
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"error_type": "mixed_timeseries_validation_error",
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"message": "Mixed timeseries chart configuration validation failed",
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"details": "The mixed timeseries configuration is missing "
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"required fields or has invalid structure",
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"suggestions": [
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"Ensure 'x' field has 'name' for the time axis column",
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"Ensure 'y' is an array of primary-axis metrics",
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"Ensure 'y_secondary' is an array of secondary-axis metrics",
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"Example: {'chart_type': 'mixed_timeseries', "
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"'x': {'name': 'order_date'}, "
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"'y': [{'name': 'revenue', 'aggregate': 'SUM'}], "
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"'y_secondary': [{'name': 'orders', 'aggregate': 'COUNT'}]}",
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],
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"error_code": "MIXED_TIMESERIES_VALIDATION_ERROR",
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},
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"handlebars": {
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"error_type": "handlebars_validation_error",
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"message": "Handlebars chart configuration validation failed",
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"details": "The handlebars chart configuration is missing "
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"required fields or has invalid structure",
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"suggestions": [
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"Ensure 'handlebars_template' is a non-empty string",
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"For aggregate mode: add 'metrics' with aggregate functions",
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"For raw mode: set 'query_mode': 'raw' and add 'columns'",
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"Example: {'chart_type': 'handlebars', "
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"'handlebars_template': '<ul>{{#each data}}<li>"
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"{{this.name}}</li>{{/each}}</ul>', "
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"'metrics': [{'name': 'sales', 'aggregate': 'SUM'}]}",
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],
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"error_code": "HANDLEBARS_VALIDATION_ERROR",
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},
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"big_number": {
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"error_type": "big_number_validation_error",
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"message": "Big Number chart configuration validation failed",
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"details": "The Big Number chart configuration is missing required "
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"fields or has invalid structure",
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"suggestions": [
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"Ensure 'metric' field has 'name' and 'aggregate'",
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"Example: 'metric': {'name': 'revenue', 'aggregate': 'SUM'}",
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"For trendline: add show_trendline=true and temporal_column='col'",
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"Without trendline: just provide the metric",
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],
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"error_code": "BIG_NUMBER_VALIDATION_ERROR",
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},
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}
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@staticmethod
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def _enhance_validation_error(
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error: PydanticValidationError, request_data: Dict[str, Any]
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) -> ChartGenerationError:
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"""Convert Pydantic validation errors to user-friendly messages."""
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errors = error.errors()
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# Check for discriminated union errors (generic "'table' was expected")
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for err in errors:
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if err.get("type") == "union_tag_invalid" or "discriminator" in str(
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err.get("ctx", {})
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):
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chart_type = request_data.get("config", {}).get("chart_type", "")
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hint = SchemaValidator._CHART_TYPE_ERROR_HINTS.get(chart_type)
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if hint:
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return ChartGenerationError(**hint)
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# Default enhanced error
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error_details = []
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for err in errors[:3]: # Show first 3 errors
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loc = " -> ".join(str(location) for location in err.get("loc", []))
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msg = err.get("msg", "Validation failed")
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error_details.append(f"{loc}: {msg}" if loc else msg)
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return ChartGenerationError(
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error_type="validation_error",
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message="Chart configuration validation failed",
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details="; ".join(error_details) or "Invalid chart configuration structure",
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suggestions=[
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"Check that all required fields are present",
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"Ensure field types match the schema",
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"Use get_dataset_info to verify column names",
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"Refer to the API documentation for field requirements",
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],
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error_code="VALIDATION_ERROR",
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)
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