马一丁
659 lines
24 KiB
Python
659 lines
24 KiB
Python
"""
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图表验证和修复工具。
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提供对Chart.js图表数据的验证和修复能力：
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1. 验证图表数据格式是否符合Chart.js要求
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2. 本地规则修复常见问题
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3. LLM API辅助修复复杂问题
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4. 遵循"宁愿不改，也不要改错"的原则
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支持的图表类型：
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- line (折线图)
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- bar (柱状图)
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- pie (饼图)
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- doughnut (圆环图)
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- radar (雷达图)
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- polarArea (极地区域图)
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- scatter (散点图)
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"""
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from __future__ import annotations
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import copy
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import json
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from typing import Any, Dict, List, Optional, Tuple, Callable
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from dataclasses import dataclass
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from loguru import logger
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@dataclass
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class ValidationResult:
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"""验证结果"""
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is_valid: bool
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errors: List[str]
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warnings: List[str]
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def has_critical_errors(self) -> bool:
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"""是否有严重错误（会导致渲染失败）"""
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return not self.is_valid and len(self.errors) > 0
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@dataclass
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class RepairResult:
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"""修复结果"""
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success: bool
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repaired_block: Optional[Dict[str, Any]]
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method: str # 'none', 'local', 'api'
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changes: List[str]
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def has_changes(self) -> bool:
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"""是否有修改"""
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return len(self.changes) > 0
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class ChartValidator:
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"""
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图表验证器 - 验证Chart.js图表数据格式是否正确。
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验证规则：
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1. 基本结构验证：widgetType, props, data字段
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2. 图表类型验证：支持的图表类型
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3. 数据格式验证：labels和datasets结构
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4. 数据一致性验证：labels和datasets长度匹配
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5. 数值类型验证：数据值类型正确
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"""
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# 支持的图表类型
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SUPPORTED_CHART_TYPES = {
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'line', 'bar', 'pie', 'doughnut', 'radar', 'polarArea', 'scatter',
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'bubble', 'horizontalBar'
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}
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# 需要labels的图表类型
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LABEL_REQUIRED_TYPES = {
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'line', 'bar', 'radar', 'polarArea', 'pie', 'doughnut'
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}
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# 需要数值数据的图表类型
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NUMERIC_DATA_TYPES = {
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'line', 'bar', 'radar', 'polarArea', 'pie', 'doughnut'
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}
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# 需要特殊数据格式的图表类型
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SPECIAL_DATA_TYPES = {
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'scatter': {'x', 'y'},
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'bubble': {'x', 'y', 'r'}
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}
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def __init__(self):
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pass
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def validate(self, widget_block: Dict[str, Any]) -> ValidationResult:
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"""
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验证图表格式。
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Args:
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widget_block: widget类型的block，包含widgetId/widgetType/props/data
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Returns:
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ValidationResult: 验证结果
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"""
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errors = []
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warnings = []
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# 1. 基本结构验证
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if not isinstance(widget_block, dict):
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errors.append("widget_block必须是字典类型")
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return ValidationResult(False, errors, warnings)
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# 2. 检查widgetType
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widget_type = widget_block.get('widgetType', '')
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if not widget_type or not isinstance(widget_type, str):
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errors.append("缺少widgetType字段或类型不正确")
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return ValidationResult(False, errors, warnings)
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# 检查是否是chart.js类型
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if not widget_type.startswith('chart.js'):
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# 不是图表类型，跳过验证
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return ValidationResult(True, errors, warnings)
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# 3. 提取图表类型
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chart_type = self._extract_chart_type(widget_block)
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if not chart_type:
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errors.append("无法确定图表类型")
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return ValidationResult(False, errors, warnings)
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# 4. 检查是否支持该图表类型
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if chart_type not in self.SUPPORTED_CHART_TYPES:
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warnings.append(f"图表类型 '{chart_type}' 可能不被支持，将尝试降级渲染")
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# 5. 验证数据结构
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data = widget_block.get('data')
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if not isinstance(data, dict):
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errors.append("data字段必须是字典类型")
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return ValidationResult(False, errors, warnings)
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# 检测是否使用了{x, y}形式的数据点（通常用于时间轴/散点）
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def contains_object_points(ds_list: List[Any] | None) -> bool:
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if not isinstance(ds_list, list):
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return False
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for point in ds_list:
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if isinstance(point, dict) and any(key in point for key in ('x', 'y', 't')):
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return True
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return False
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datasets_for_detection = data.get('datasets') or []
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uses_object_points = any(
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isinstance(ds, dict) and contains_object_points(ds.get('data'))
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for ds in datasets_for_detection
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)
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# 6. 根据图表类型验证数据
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if chart_type in self.SPECIAL_DATA_TYPES:
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# 特殊数据格式（scatter, bubble）
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self._validate_special_data(data, chart_type, errors, warnings)
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else:
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# 标准数据格式（labels + datasets）
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self._validate_standard_data(data, chart_type, errors, warnings, uses_object_points)
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# 7. 验证props
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props = widget_block.get('props')
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if props is not None and not isinstance(props, dict):
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warnings.append("props字段应该是字典类型")
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is_valid = len(errors) == 0
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return ValidationResult(is_valid, errors, warnings)
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def _extract_chart_type(self, widget_block: Dict[str, Any]) -> Optional[str]:
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"""
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提取图表类型。
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优先级：
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1. props.type
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2. widgetType中的类型（chart.js/bar -> bar）
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3. data.type
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"""
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# 1. 从props中获取
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props = widget_block.get('props') or {}
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if isinstance(props, dict):
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chart_type = props.get('type')
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if chart_type and isinstance(chart_type, str):
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return chart_type.lower()
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# 2. 从widgetType中提取
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widget_type = widget_block.get('widgetType', '')
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if '/' in widget_type:
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chart_type = widget_type.split('/')[-1]
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if chart_type:
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return chart_type.lower()
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# 3. 从data中获取
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data = widget_block.get('data') or {}
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if isinstance(data, dict):
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chart_type = data.get('type')
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if chart_type and isinstance(chart_type, str):
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return chart_type.lower()
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return None
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def _validate_standard_data(
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self,
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data: Dict[str, Any],
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chart_type: str,
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errors: List[str],
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warnings: List[str],
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uses_object_points: bool = False
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):
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"""验证标准数据格式（labels + datasets）"""
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labels = data.get('labels')
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datasets = data.get('datasets')
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# 验证labels
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if chart_type in self.LABEL_REQUIRED_TYPES:
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if not labels:
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if uses_object_points:
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warnings.append(
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f"{chart_type}类型图表缺少labels，已根据数据点渲染（使用x值）"
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)
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else:
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errors.append(f"{chart_type}类型图表必须包含labels字段")
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elif not isinstance(labels, list):
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errors.append("labels必须是数组类型")
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elif len(labels) == 0:
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warnings.append("labels数组为空，图表可能无法正常显示")
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# 验证datasets
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if datasets is None:
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errors.append("缺少datasets字段")
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return
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if not isinstance(datasets, list):
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errors.append("datasets必须是数组类型")
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return
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if len(datasets) == 0:
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errors.append("datasets数组为空")
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return
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# 验证每个dataset
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for idx, dataset in enumerate(datasets):
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if not isinstance(dataset, dict):
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errors.append(f"datasets[{idx}]必须是对象类型")
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continue
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# 验证data字段
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ds_data = dataset.get('data')
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if ds_data is None:
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errors.append(f"datasets[{idx}]缺少data字段")
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continue
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if not isinstance(ds_data, list):
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errors.append(f"datasets[{idx}].data必须是数组类型")
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continue
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if len(ds_data) == 0:
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warnings.append(f"datasets[{idx}].data数组为空")
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continue
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# 如果是{x, y}对象形式的数据点，默认允许跳过labels长度和数值校验
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object_points = any(
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isinstance(value, dict) and any(key in value for key in ('x', 'y', 't'))
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for value in ds_data
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)
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# 验证数据长度一致性
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if labels and isinstance(labels, list) and not object_points:
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if len(ds_data) != len(labels):
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warnings.append(
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f"datasets[{idx}].data长度({len(ds_data)})与labels长度({len(labels)})不匹配"
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)
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# 验证数值类型
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if chart_type in self.NUMERIC_DATA_TYPES and not object_points:
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for data_idx, value in enumerate(ds_data):
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if value is not None and not isinstance(value, (int, float)):
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errors.append(
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f"datasets[{idx}].data[{data_idx}]的值'{value}'不是有效的数值类型"
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)
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break # 只报告第一个错误
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def _validate_special_data(
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self,
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data: Dict[str, Any],
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chart_type: str,
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errors: List[str],
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warnings: List[str]
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):
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"""验证特殊数据格式（scatter, bubble）"""
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datasets = data.get('datasets')
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if not datasets:
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errors.append("缺少datasets字段")
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return
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if not isinstance(datasets, list):
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errors.append("datasets必须是数组类型")
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return
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if len(datasets) == 0:
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errors.append("datasets数组为空")
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return
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required_keys = self.SPECIAL_DATA_TYPES.get(chart_type, set())
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# 验证每个dataset
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for idx, dataset in enumerate(datasets):
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if not isinstance(dataset, dict):
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errors.append(f"datasets[{idx}]必须是对象类型")
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continue
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ds_data = dataset.get('data')
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if ds_data is None:
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errors.append(f"datasets[{idx}]缺少data字段")
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continue
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if not isinstance(ds_data, list):
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errors.append(f"datasets[{idx}].data必须是数组类型")
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continue
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if len(ds_data) == 0:
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warnings.append(f"datasets[{idx}].data数组为空")
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continue
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# 验证数据点格式
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for data_idx, point in enumerate(ds_data):
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if not isinstance(point, dict):
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errors.append(
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f"datasets[{idx}].data[{data_idx}]必须是对象类型（包含{required_keys}字段）"
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)
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break
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# 检查必需的键
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missing_keys = required_keys - set(point.keys())
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if missing_keys:
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errors.append(
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f"datasets[{idx}].data[{data_idx}]缺少必需字段: {missing_keys}"
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)
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break
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# 验证数值类型
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for key in required_keys:
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value = point.get(key)
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if value is not None and not isinstance(value, (int, float)):
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errors.append(
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f"datasets[{idx}].data[{data_idx}].{key}的值'{value}'不是有效的数值类型"
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)
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break
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def can_render(self, widget_block: Dict[str, Any]) -> bool:
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"""
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判断图表是否能正常渲染（快速检查）。
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Args:
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widget_block: widget类型的block
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Returns:
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bool: 是否能正常渲染
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"""
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result = self.validate(widget_block)
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return result.is_valid
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class ChartRepairer:
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"""
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图表修复器 - 尝试修复图表数据。
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修复策略：
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1. 本地规则修复：修复常见问题
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2. API修复：使用LLM修复复杂问题
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3. 验证修复结果：确保修复后能正常渲染
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"""
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def __init__(
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self,
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validator: ChartValidator,
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llm_repair_fns: Optional[List[Callable]] = None
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):
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"""
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初始化修复器。
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Args:
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validator: 图表验证器实例
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llm_repair_fns: LLM修复函数列表（对应4个Engine）
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"""
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self.validator = validator
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self.llm_repair_fns = llm_repair_fns or []
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def repair(
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self,
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widget_block: Dict[str, Any],
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validation_result: Optional[ValidationResult] = None
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) -> RepairResult:
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"""
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尝试修复图表数据。
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Args:
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widget_block: widget类型的block
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validation_result: 验证结果（可选，如果没有会先进行验证）
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Returns:
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RepairResult: 修复结果
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"""
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# 1. 如果没有验证结果，先验证
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if validation_result is None:
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validation_result = self.validator.validate(widget_block)
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# 2. 尝试本地修复（即使验证通过也尝试，因为可能有警告）
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logger.info(f"尝试本地修复图表")
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local_result = self.repair_locally(widget_block, validation_result)
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# 3. 验证修复结果
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if local_result.has_changes():
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repaired_validation = self.validator.validate(local_result.repaired_block)
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if repaired_validation.is_valid:
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logger.info(f"本地修复成功: {local_result.changes}")
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return RepairResult(True, local_result.repaired_block, 'local', local_result.changes)
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else:
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logger.warning(f"本地修复后仍然无效: {repaired_validation.errors}")
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# 4. 如果本地修复失败且有严重错误，尝试API修复
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if validation_result.has_critical_errors() and len(self.llm_repair_fns) > 0:
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logger.info("本地修复失败，尝试API修复")
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api_result = self.repair_with_api(widget_block, validation_result)
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if api_result.success:
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# 验证修复结果
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repaired_validation = self.validator.validate(api_result.repaired_block)
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if repaired_validation.is_valid:
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logger.info(f"API修复成功: {api_result.changes}")
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return api_result
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else:
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logger.warning(f"API修复后仍然无效: {repaired_validation.errors}")
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# 5. 如果验证通过，返回原始或修复后的数据
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if validation_result.is_valid:
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if local_result.has_changes():
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return RepairResult(True, local_result.repaired_block, 'local', local_result.changes)
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else:
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return RepairResult(True, widget_block, 'none', [])
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# 6. 所有修复都失败，返回原始数据
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logger.warning("所有修复尝试失败，保持原始数据")
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return RepairResult(False, widget_block, 'none', [])
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def repair_locally(
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self,
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widget_block: Dict[str, Any],
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validation_result: ValidationResult
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) -> RepairResult:
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"""
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使用本地规则修复。
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修复规则：
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1. 补全缺失的基本字段
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2. 修复数据类型错误
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3. 修复数据长度不匹配
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4. 清理无效数据
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5. 添加默认值
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"""
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repaired = copy.deepcopy(widget_block)
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changes = []
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# 1. 确保基本结构存在
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if 'props' not in repaired or not isinstance(repaired.get('props'), dict):
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repaired['props'] = {}
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changes.append("添加缺失的props字段")
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if 'data' not in repaired or not isinstance(repaired.get('data'), dict):
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repaired['data'] = {}
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changes.append("添加缺失的data字段")
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# 2. 确保图表类型存在
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chart_type = self.validator._extract_chart_type(repaired)
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props = repaired['props']
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if not chart_type:
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# 尝试从widgetType推断
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widget_type = repaired.get('widgetType', '')
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if '/' in widget_type:
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chart_type = widget_type.split('/')[-1].lower()
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props['type'] = chart_type
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changes.append(f"从widgetType推断图表类型: {chart_type}")
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else:
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# 默认使用bar类型
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chart_type = 'bar'
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||
props['type'] = chart_type
|
||
changes.append("设置默认图表类型: bar")
|
||
elif 'type' not in props or not props['type']:
|
||
# chart_type存在但props中没有type字段，需要添加
|
||
props['type'] = chart_type
|
||
changes.append(f"将推断的图表类型添加到props: {chart_type}")
|
||
|
||
# 3. 修复数据结构
|
||
data = repaired['data']
|
||
|
||
# 确保datasets存在
|
||
if 'datasets' not in data or not isinstance(data.get('datasets'), list):
|
||
data['datasets'] = []
|
||
changes.append("添加缺失的datasets字段")
|
||
|
||
# 如果datasets为空但data中有其他数据，尝试构造datasets
|
||
if len(data['datasets']) == 0:
|
||
constructed = self._try_construct_datasets(data, chart_type)
|
||
if constructed:
|
||
data['datasets'] = constructed
|
||
changes.append("从data中构造datasets")
|
||
elif 'labels' in data and isinstance(data.get('labels'), list) and len(data['labels']) > 0:
|
||
# 如果有labels但没有数据，创建一个空dataset
|
||
data['datasets'] = [{
|
||
'label': '数据',
|
||
'data': [0] * len(data['labels'])
|
||
}]
|
||
changes.append("根据labels创建默认dataset（使用零值）")
|
||
|
||
# 确保labels存在（如果需要）
|
||
if chart_type in ChartValidator.LABEL_REQUIRED_TYPES:
|
||
if 'labels' not in data or not isinstance(data.get('labels'), list):
|
||
# 尝试根据datasets长度生成labels
|
||
if data['datasets'] and len(data['datasets']) > 0:
|
||
first_ds = data['datasets'][0]
|
||
if isinstance(first_ds, dict) and isinstance(first_ds.get('data'), list):
|
||
data_len = len(first_ds['data'])
|
||
data['labels'] = [f"项目 {i+1}" for i in range(data_len)]
|
||
changes.append(f"生成{data_len}个默认labels")
|
||
|
||
# 4. 修复datasets中的数据
|
||
for idx, dataset in enumerate(data.get('datasets', [])):
|
||
if not isinstance(dataset, dict):
|
||
continue
|
||
|
||
# 确保有data字段
|
||
if 'data' not in dataset or not isinstance(dataset.get('data'), list):
|
||
dataset['data'] = []
|
||
changes.append(f"为datasets[{idx}]添加空data数组")
|
||
|
||
# 确保有label
|
||
if 'label' not in dataset:
|
||
dataset['label'] = f"系列 {idx + 1}"
|
||
changes.append(f"为datasets[{idx}]添加默认label")
|
||
|
||
# 修复数据长度不匹配
|
||
labels = data.get('labels', [])
|
||
ds_data = dataset.get('data', [])
|
||
if isinstance(labels, list) and isinstance(ds_data, list):
|
||
if len(ds_data) < len(labels):
|
||
# 数据不够，补null
|
||
dataset['data'] = ds_data + [None] * (len(labels) - len(ds_data))
|
||
changes.append(f"datasets[{idx}]数据长度不足，补充null")
|
||
elif len(ds_data) > len(labels):
|
||
# 数据过多，截断
|
||
dataset['data'] = ds_data[:len(labels)]
|
||
changes.append(f"datasets[{idx}]数据长度过长，截断")
|
||
|
||
# 转换非数值数据为数值（如果可能）
|
||
if chart_type in ChartValidator.NUMERIC_DATA_TYPES:
|
||
ds_data = dataset.get('data', [])
|
||
converted = False
|
||
for i, value in enumerate(ds_data):
|
||
if value is None:
|
||
continue
|
||
if not isinstance(value, (int, float)):
|
||
# 尝试转换
|
||
try:
|
||
if isinstance(value, str):
|
||
# 尝试转换字符串
|
||
ds_data[i] = float(value)
|
||
converted = True
|
||
except (ValueError, TypeError):
|
||
# 转换失败，设为null
|
||
ds_data[i] = None
|
||
converted = True
|
||
if converted:
|
||
changes.append(f"datasets[{idx}]包含非数值数据，已尝试转换")
|
||
|
||
# 5. 验证修复结果
|
||
success = len(changes) > 0
|
||
|
||
return RepairResult(success, repaired, 'local', changes)
|
||
|
||
def _try_construct_datasets(
|
||
self,
|
||
data: Dict[str, Any],
|
||
chart_type: str
|
||
) -> Optional[List[Dict[str, Any]]]:
|
||
"""尝试从data中构造datasets"""
|
||
# 如果data直接包含数据数组，尝试构造
|
||
if 'values' in data and isinstance(data['values'], list):
|
||
return [{
|
||
'label': '数据',
|
||
'data': data['values']
|
||
}]
|
||
|
||
# 如果data包含series字段
|
||
if 'series' in data and isinstance(data['series'], list):
|
||
datasets = []
|
||
for idx, series in enumerate(data['series']):
|
||
if isinstance(series, dict):
|
||
datasets.append({
|
||
'label': series.get('name', f'系列 {idx + 1}'),
|
||
'data': series.get('data', [])
|
||
})
|
||
elif isinstance(series, list):
|
||
datasets.append({
|
||
'label': f'系列 {idx + 1}',
|
||
'data': series
|
||
})
|
||
if datasets:
|
||
return datasets
|
||
|
||
return None
|
||
|
||
def repair_with_api(
|
||
self,
|
||
widget_block: Dict[str, Any],
|
||
validation_result: ValidationResult
|
||
) -> RepairResult:
|
||
"""
|
||
使用API修复（调用4个Engine的LLM）。
|
||
|
||
策略：按顺序尝试不同的Engine，直到修复成功
|
||
"""
|
||
if not self.llm_repair_fns:
|
||
return RepairResult(False, None, 'api', [])
|
||
|
||
for idx, repair_fn in enumerate(self.llm_repair_fns):
|
||
try:
|
||
logger.info(f"尝试使用Engine {idx + 1}修复图表")
|
||
repaired = repair_fn(widget_block, validation_result.errors)
|
||
|
||
if repaired and isinstance(repaired, dict):
|
||
# 验证修复结果
|
||
repaired_validation = self.validator.validate(repaired)
|
||
if repaired_validation.is_valid:
|
||
return RepairResult(
|
||
True,
|
||
repaired,
|
||
'api',
|
||
[f"使用Engine {idx + 1}修复成功"]
|
||
)
|
||
except Exception as e:
|
||
logger.error(f"Engine {idx + 1}修复失败: {e}")
|
||
continue
|
||
|
||
return RepairResult(False, None, 'api', [])
|
||
|
||
|
||
def create_chart_validator() -> ChartValidator:
|
||
"""创建图表验证器实例"""
|
||
return ChartValidator()
|
||
|
||
|
||
def create_chart_repairer(
|
||
validator: Optional[ChartValidator] = None,
|
||
llm_repair_fns: Optional[List[Callable]] = None
|
||
) -> ChartRepairer:
|
||
"""创建图表修复器实例"""
|
||
if validator is None:
|
||
validator = create_chart_validator()
|
||
return ChartRepairer(validator, llm_repair_fns)
|