HaHafeng
57fdc6ef00
Features: - Migrate 10 agent prompts from hardcoded to database - Add grayscale preview support (DRAFT/ACTIVE distribution) - Implement 3-tier fallback (DB -> Cache -> Hardcoded) - Add version management and rollback capability Files changed: - backend/scripts/migrate-aia-prompts.ts (new migration script) - backend/src/common/prompt/prompt.fallbacks.ts (add AIA fallbacks) - backend/src/modules/aia/services/agentService.ts (integrate PromptService) - backend/src/modules/aia/services/conversationService.ts (pass userId) - backend/src/modules/aia/types/index.ts (fix AgentStage type) Documentation: - docs/03-业务模块/AIA-AI智能问答/06-开发记录/2026-01-18-Prompt管理系统集成.md - docs/02-通用能力层/00-通用能力层清单.md (add FileCard, Prompt management) - docs/00-系统总体设计/00-系统当前状态与开发指南.md (update to v3.6) Prompt codes: - AIA_SCIENTIFIC_QUESTION, AIA_PICO_ANALYSIS, AIA_TOPIC_EVALUATION - AIA_OUTCOME_DESIGN, AIA_CRF_DESIGN, AIA_SAMPLE_SIZE - AIA_PROTOCOL_WRITING, AIA_METHODOLOGY_REVIEW - AIA_PAPER_POLISH, AIA_PAPER_TRANSLATE Tested: Migration script executed, all 10 prompts inserted successfully
166 lines
3.4 KiB
Python
166 lines
3.4 KiB
Python
"""
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高级筛选操作
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提供多条件筛选功能，支持AND/OR逻辑组合。
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"""
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import pandas as pd
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from typing import List, Dict, Any, Literal
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def apply_filter(
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df: pd.DataFrame,
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conditions: List[Dict[str, Any]],
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logic: Literal['and', 'or'] = 'and'
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) -> pd.DataFrame:
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"""
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应用筛选条件
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Args:
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df: 输入数据框
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conditions: 筛选条件列表，每个条件包含：
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- column: 列名
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- operator: 运算符 (=, !=, >, <, >=, <=, contains, not_contains,
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starts_with, ends_with, is_null, not_null)
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- value: 值（is_null和not_null不需要）
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logic: 逻辑组合方式 ('and' 或 'or')
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Returns:
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筛选后的数据框
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Examples:
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>>> df = pd.DataFrame({'年龄': [25, 35, 45], '性别': ['男', '女', '男']})
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>>> conditions = [
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... {'column': '年龄', 'operator': '>', 'value': 30},
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... {'column': '性别', 'operator': '=', 'value': '男'}
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... ]
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>>> result = apply_filter(df, conditions, logic='and')
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>>> len(result)
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1
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"""
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if not conditions:
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raise ValueError('筛选条件不能为空')
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if df.empty:
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return df
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# 生成各个条件的mask
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masks = []
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for cond in conditions:
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column = cond['column']
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operator = cond['operator']
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value = cond.get('value')
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# 验证列是否存在
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if column not in df.columns:
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raise KeyError(f"列 '{column}' 不存在")
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# 根据运算符生成mask
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if operator == '=':
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mask = df[column] == value
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elif operator == '!=':
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mask = df[column] != value
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elif operator == '>':
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mask = df[column] > value
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elif operator == '<':
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mask = df[column] < value
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elif operator == '>=':
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mask = df[column] >= value
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elif operator == '<=':
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mask = df[column] <= value
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elif operator == 'contains':
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mask = df[column].astype(str).str.contains(str(value), na=False)
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elif operator == 'not_contains':
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mask = ~df[column].astype(str).str.contains(str(value), na=False)
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elif operator == 'starts_with':
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mask = df[column].astype(str).str.startswith(str(value), na=False)
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elif operator == 'ends_with':
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mask = df[column].astype(str).str.endswith(str(value), na=False)
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elif operator == 'is_null':
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mask = df[column].isna()
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elif operator == 'not_null':
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mask = df[column].notna()
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else:
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raise ValueError(f"不支持的运算符: {operator}")
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masks.append(mask)
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# 组合所有条件
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if logic == 'and':
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final_mask = pd.concat(masks, axis=1).all(axis=1)
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elif logic == 'or':
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final_mask = pd.concat(masks, axis=1).any(axis=1)
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else:
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raise ValueError(f"不支持的逻辑运算: {logic}")
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# 应用筛选
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result = df[final_mask].copy()
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# 打印统计信息
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original_rows = len(df)
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filtered_rows = len(result)
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removed_rows = original_rows - filtered_rows
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print(f'原始数据: {original_rows} 行')
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print(f'筛选后: {filtered_rows} 行')
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print(f'删除: {removed_rows} 行 ({removed_rows/original_rows*100:.1f}%)')
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return result
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