from typing import List, Optional from . import data_operation from .db_manager import DatabaseManager1 from .models1 import Post, PostDecodeTopicPoint, PostDecodeTopicPointElement, ElementClassificationMapping, \ GlobalCategory, GlobalElement db_manager = DatabaseManager1() def export_post_elements( post_ids: List[str], merge_leve2: Optional[str] = None, platform: Optional[str] = None, account_name: Optional[str] = None, post_limit: int = 500, ): """导出帖子元素数据（含分类路径）+ 分类树快照 + 元素快照 返回: - data: 与 build_transactions_at_depth(data=...) 输入一致的帖子数据 - categories: 涉及的所有分类节点（含完整祖先链，构成完整树） - elements: 涉及分类下的所有全局元素 """ try: session = db_manager.get_session() try: from collections import defaultdict # 1. 取目标帖子 post_query = session.query(Post) if merge_leve2: post_query = post_query.filter(Post.merge_leve2 == merge_leve2) if platform: post_query = post_query.filter(Post.platform == platform) if account_name: post_query = post_query.filter(Post.platform_account_name == account_name) # 先获取分类完成度 >= 80% 的帖子集合 qualified_post_ids = set(data_operation.get_fully_classified_post_ids( required_types=['实质', '形式'], min_ratio=0.8 )) if not qualified_post_ids: return {"success": True, "post_count": 0, "data": {}, "categories": [], "elements": []} # 在合格帖子范围内按条件筛选并限制数量 post_query = post_query.filter(Post.post_id.in_(qualified_post_ids)) if post_ids: post_query = post_query.filter(Post.post_id.in_(post_ids)) post_rows = post_query.order_by(Post.id.desc()).limit(post_limit).all() post_ids = [p.post_id for p in post_rows] post_obj_map = {p.post_id: p for p in post_rows} if not post_ids: return {"success": True, "post_count": 0, "data": {}, "categories": [], "elements": []} # 2. 查询所有选题点 points = session.query(PostDecodeTopicPoint).filter( PostDecodeTopicPoint.post_id.in_(post_ids) ).all() point_ids = [p.id for p in points] points_map = defaultdict(lambda: defaultdict(list)) for p in points: points_map[p.post_id][p.topic_point_type].append(p) # 3. 查询所有元素 elements = session.query(PostDecodeTopicPointElement).filter( PostDecodeTopicPointElement.topic_point_id.in_(point_ids) ).all() if point_ids else [] elem_ids = [e.id for e in elements] elems_map = defaultdict(list) for e in elements: elems_map[e.topic_point_id].append(e) # 4. 查询分类映射 mappings = session.query(ElementClassificationMapping).filter( ElementClassificationMapping.post_decode_topic_point_element_id.in_(elem_ids) ).all() if elem_ids else [] mapping_map = {m.post_decode_topic_point_element_id: m for m in mappings} # 5. 查询涉及的分类（当前有效版本） direct_stable_ids = {m.global_category_stable_id for m in mappings if m.global_category_stable_id} cat_map = {} # stable_id → GlobalCategory row if direct_stable_ids: cats = session.query(GlobalCategory).filter( GlobalCategory.stable_id.in_(direct_stable_ids), GlobalCategory.retired_at_execution_id.is_(None), ).all() cat_map = {c.stable_id: c for c in cats} # 5b. 补全祖先节点，使树完整 all_stable_ids = set(cat_map.keys()) missing_parents = set() for c in cat_map.values(): if c.parent_stable_id and c.parent_stable_id not in all_stable_ids: missing_parents.add(c.parent_stable_id) while missing_parents: parent_cats = session.query(GlobalCategory).filter( GlobalCategory.stable_id.in_(missing_parents), GlobalCategory.retired_at_execution_id.is_(None), ).all() if not parent_cats: break next_missing = set() for c in parent_cats: cat_map[c.stable_id] = c all_stable_ids.add(c.stable_id) if c.parent_stable_id and c.parent_stable_id not in all_stable_ids: next_missing.add(c.parent_stable_id) missing_parents = next_missing # 5c. 查询涉及分类下的 GlobalElement global_elements = [] if all_stable_ids: global_elements = session.query(GlobalElement).filter( GlobalElement.belong_category_stable_id.in_(all_stable_ids), GlobalElement.retired_at_execution_id.is_(None), ).all() # 6. 为未分类的意图元素生成虚拟分类节点 # 虚拟 stable_id 使用负数自增，避免与真实 ID 冲突 cat_path_map = {sid: c.path for sid, c in cat_map.items()} virtual_stable_id_counter = -1 intent_name_to_virtual_sid = {} # 意图元素名称 → 虚拟 stable_id # 先扫描一遍，收集所有需要虚拟分类的意图元素名称 for post_id in post_ids: for point_type in ['灵感点', '目的点', '关键点']: for point in points_map[post_id].get(point_type, []): for elem in elems_map.get(point.id, []): if elem.element_type != '意图': continue mapping = mapping_map.get(elem.id) path_str = cat_path_map.get(mapping.global_category_stable_id) if mapping else None if not path_str: name = elem.element_name if name and name not in intent_name_to_virtual_sid: intent_name_to_virtual_sid[name] = virtual_stable_id_counter virtual_stable_id_counter -= 1 # 6b. 组装帖子数据 result = {} for post_id in post_ids: post_data = {} for point_type in ['灵感点', '目的点', '关键点']: point_list = [] for point in points_map[post_id].get(point_type, []): point_item = { "点": point.topic_point_result, "实质": [], "形式": [], "意图": [], } for elem in elems_map.get(point.id, []): mapping = mapping_map.get(elem.id) path_str = cat_path_map.get(mapping.global_category_stable_id) if mapping else None path_list = [s for s in path_str.split('/') if s] if path_str else [] if not path_list and elem.element_type == '意图': path_list = [elem.element_name] elem_item = { "名称": elem.element_name, "详细描述": elem.element_description or "", "分类路径": path_list, } if elem.element_type in point_item: point_item[elem.element_type].append(elem_item) point_list.append(point_item) if point_list: post_data[point_type] = point_list if post_data: result[post_id] = post_data # 7. 序列化分类树（真实分类 + 虚拟意图分类） categories_out = [] for c in cat_map.values(): categories_out.append({ "stable_id": c.stable_id, "name": c.name, "description": c.description or "", "category_nature": c.category_nature, "source_type": c.source_type, "path": c.path, "level": c.level, "parent_stable_id": c.parent_stable_id, }) # 追加虚拟意图分类节点 for intent_name, v_sid in intent_name_to_virtual_sid.items(): categories_out.append({ "stable_id": v_sid, "name": intent_name, "description": "", "category_nature": None, "source_type": "意图", "path": f"/{intent_name}", "level": 1, "parent_stable_id": None, }) # 8. 序列化元素 elements_out = [] for ge in global_elements: elements_out.append({ "id": ge.id, "name": ge.name, "description": ge.description or "", "element_type": ge.source_type, "element_sub_type": ge.element_sub_type, "belong_category_stable_id": ge.belong_category_stable_id, "occurrence_count": ge.occurrence_count or 1, }) finally: session.close() return { "success": True, "post_count": len(result), "data": result, "post_metadata": { post_id: { "account_name": post.platform_account_name, "merge_leve2": post.merge_leve2, "platform": post.platform, } for post_id, post in post_obj_map.items() }, "categories": categories_out, "elements": elements_out, } except Exception as e: import traceback traceback.print_exc()