pengxiao
834cad729f
- data/: 非遗地理编码数据（GIS shapefile + CSV） - dofile/kg_project/: 知识图谱构建代码（纳入主仓库） - dofile/visulization/: 可视化数据与路线图 - officefile/: 文献、草稿、bib 文档 - officefile/latex/: Overleaf 同步目录（独立管理，不纳入） - output/: 输出目录 - logs/: 日志目录
242 lines
9.1 KiB
Python
242 lines
9.1 KiB
Python
# -*- coding: utf-8 -*-
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"""
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重新抽取失败项目的脚本
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"""
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import asyncio
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import pandas as pd
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import yaml
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import json
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from pathlib import Path
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from datetime import datetime
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import sys
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# 添加模块路径
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sys.path.append(str(Path(__file__).parent / 'src'))
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from knowledge_extraction.deep_entity_extractor import DeepEntityExtractor
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from data_processing.entity_normalizer import EntityNormalizer
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from data_processing.relationship_builder import RelationshipBuilder
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async def retry_and_merge():
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"""重新抽取失败项目并直接合并到现有文件"""
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# 加载配置
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config_file = Path(__file__).parent / 'config' / 'deep_extraction_config.yaml'
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with open(config_file, 'r', encoding='utf-8') as f:
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config = yaml.safe_load(f)
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# 读取原始数据
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data_file = Path(__file__).parent.parent.parent / 'data' / '黑龙江国家级和省级非遗名单.xlsx'
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df = pd.read_excel(data_file, engine='openpyxl')
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# 找到失败的项目
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failed_data = df[df.iloc[:, 0].isin([118, 229])]
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print(f"找到 {len(failed_data)} 个失败项目")
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print("=" * 60)
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# 初始化组件
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extractor = DeepEntityExtractor(str(config_file))
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ontology_file = Path(__file__).parent / 'config' / 'entity_ontology.yaml'
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normalizer = EntityNormalizer(str(ontology_file))
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builder = RelationshipBuilder(str(ontology_file))
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# 读取现有的实体注册表（如果存在）
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existing_nodes_file = Path(__file__).parent / 'output' / 'nodes_llm.csv'
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existing_rels_file = Path(__file__).parent / 'output' / 'rels_llm.csv'
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existing_nodes_df = pd.read_csv(existing_nodes_file, encoding='utf-8-sig')
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existing_rels_df = pd.read_csv(existing_rels_file, encoding='utf-8-sig')
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print(f"现有节点数: {len(existing_nodes_df)}")
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print(f"现有关系数: {len(existing_rels_df)}")
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# 将现有实体加载到规范化器中
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print("\n=== 加载现有实体到规范化器 ===")
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for idx, row in existing_nodes_df.iterrows():
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if row['type'] != 'ICH_Project':
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# 将现有实体添加到规范化器的注册表
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entity_type = row['type']
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entity_text = row['label']
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entity_id = row['id']
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# 规范化文本
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normalized_text = normalizer.normalize_text(entity_text)
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# 添加到映射表
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if normalized_text not in normalizer.text_to_id_map:
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normalizer.text_to_id_map[normalized_text] = entity_id
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normalizer.entity_registry[entity_id] = {
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'type': entity_type,
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'text': entity_text,
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'normalized_text': normalized_text,
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'canonical_name': normalized_text # 添加这个字段
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}
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print(f"已加载 {len(normalizer.entity_registry)} 个现有实体")
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# 重新抽取失败的项目
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extraction_results = []
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for idx, row in failed_data.iterrows():
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project_num = int(row.iloc[0])
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project_id = f'ICH-{project_num}'
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project_name = row.iloc[3]
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description = row.iloc[7] if len(row) > 7 else ""
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print(f"\n正在抽取: {project_id} - {project_name}")
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print(f"描述长度: {len(description)} 字符")
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try:
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result = await extractor.extract_from_remark(
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project_id=project_id,
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project_name=project_name,
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remark_text=description
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)
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if result:
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extraction_results.append({
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'project_id': project_id,
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'project_name': project_name,
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'extraction_result': result
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})
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print(f"[OK] 抽取成功: {len(result.get('entities', []))} 个实体, {len(result.get('relationships', []))} 条关系")
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else:
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print(f"[FAIL] 抽取失败")
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except Exception as e:
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print(f"[ERROR] 抽取异常: {str(e)}")
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if not extraction_results:
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print("\n没有成功抽取的项目")
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return
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# 规范化新抽取的实体（会自动去重）
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print("\n=== 规范化新抽取的实体 ===")
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for item in extraction_results:
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result = item['extraction_result']
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entities = result.get('entities', [])
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for entity in entities:
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# 实体名称可能在name字段或attributes.name字段
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entity_name = entity.get('name') or entity.get('attributes', {}).get('name', '')
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entity_type = entity.get('type', '')
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# 规范化实体（会自动去重）
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normalizer.normalize_entity(entity, similarity_threshold=0.85)
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# 获取所有实体节点（包括新增的）
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all_entity_nodes = normalizer.get_entity_nodes()
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print(f"规范化后实体节点数: {len(all_entity_nodes)}")
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# 构建关系
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print("\n=== 构建关系 ===")
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all_new_relationships = []
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for item in extraction_results:
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project_id = item['project_id']
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result = item['extraction_result']
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entities = result.get('entities', [])
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relationships = result.get('relationships', [])
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# 构建实体名称到ID的映射
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entity_name_to_id = {}
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for entity in entities:
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entity_name = entity.get('name') or entity.get('attributes', {}).get('name', '')
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entity_type = entity.get('type', '')
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normalized_name = normalizer.normalize_text(entity_name)
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entity_id = normalizer.text_to_id_map.get(normalized_name)
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if entity_id:
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entity_name_to_id[entity_name] = entity_id
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# 手动构建关系
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for rel in relationships:
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source_name = rel.get('source_entity') or rel.get('source')
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target_name = rel.get('target_entity') or rel.get('target')
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rel_type = rel.get('type')
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rel_props = rel.get('properties', {})
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# 查找源实体ID
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if source_name == project_id:
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source_id = project_id
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else:
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normalized_name = normalizer.normalize_text(source_name)
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source_id = normalizer.text_to_id_map.get(normalized_name)
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# 查找目标实体ID
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normalized_name = normalizer.normalize_text(target_name)
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target_id = normalizer.text_to_id_map.get(normalized_name)
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# 如果都找到了，添加关系
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if source_id and target_id:
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all_new_relationships.append({
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'source': source_id,
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'target': target_id,
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'type': rel_type,
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'properties': json.dumps(rel_props, ensure_ascii=False) if rel_props else '{}'
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})
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print(f"项目 {project_id}: {len([r for r in all_new_relationships if r['source'] == project_id])} 条关系")
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# 合并节点和关系
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print("\n=== 合并数据 ===")
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# 项目节点：只添加缺失的项目
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existing_project_ids = set(existing_nodes_df[existing_nodes_df['type'] == 'ICH_Project']['id'].tolist())
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new_project_nodes = [
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{'id': item['project_id'], 'label': item['project_name'], 'type': 'ICH_Project', 'properties': '{}'}
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for item in extraction_results
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if item['project_id'] not in existing_project_ids
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]
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# 合并所有节点
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all_nodes_df = pd.concat([
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existing_nodes_df[existing_nodes_df['type'] != 'ICH_Project'], # 现有实体节点
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pd.DataFrame(all_entity_nodes), # 所有实体节点（包括新增和去重后的现有）
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pd.DataFrame(new_project_nodes), # 新增项目节点
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existing_nodes_df[existing_nodes_df['type'] == 'ICH_Project'] # 现有项目节点
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], ignore_index=True)
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# 去重节点（按ID）
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all_nodes_df = all_nodes_df.drop_duplicates(subset=['id'], keep='first')
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# 合并关系
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all_rels_df = pd.concat([
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existing_rels_df,
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pd.DataFrame(all_new_relationships)
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], ignore_index=True)
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# 去重关系
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all_rels_df = all_rels_df.drop_duplicates(subset=['source', 'target', 'type'], keep='first')
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print(f"合并后节点数: {len(all_nodes_df)} (新增 {len(all_entity_nodes) - len(existing_nodes_df[existing_nodes_df['type'] != 'ICH_Project'])} 个实体)")
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print(f"合并后关系数: {len(all_rels_df)} (新增 {len(all_new_relationships)} 条)")
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# 保存结果
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print("\n=== 保存结果 ===")
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all_nodes_df.to_csv(existing_nodes_file, index=False, encoding='utf-8-sig')
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all_rels_df.to_csv(existing_rels_file, index=False, encoding='utf-8-sig')
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print(f"节点已保存: {existing_nodes_file}")
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print(f"关系已保存: {existing_rels_file}")
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# 验证
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print("\n=== 验证 ===")
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for item in extraction_results:
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project_id = item['project_id']
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rel_count = len(all_rels_df[all_rels_df['source'] == project_id])
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print(f"{project_id}: {rel_count} 条关系")
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print("\n完成！")
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# 保存抽取结果以供检查
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output_file = Path(__file__).parent / 'output' / 'retry_projects.json'
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with open(output_file, 'w', encoding='utf-8') as f:
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json.dump(extraction_results, f, ensure_ascii=False, indent=2)
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if __name__ == '__main__':
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asyncio.run(retry_and_merge())
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