1.10
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@ -5,8 +5,9 @@ from flask_app.general.json_utils import clean_json_string
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from flask_app.general.model_continue_query import process_continue_answers
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from flask_app.general.通义千问long import upload_file, qianwen_long_stream
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#提取两个大标题之间的内容
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def extract_between_sections(data, target_values):
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# 提取两个大标题之间的内容
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def extract_between_sections(data, target_values, flag=False):
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target_found = False
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extracted_data = {}
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current_section_title = ""
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@ -17,7 +18,7 @@ def extract_between_sections(data, target_values):
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# 遍历所有键值对
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for key, value in data.items():
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# 只匹配形如 "一": "竞争性磋商响应文件" 的章节标题
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if section_pattern.match(key): #匹配到大标题...
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if section_pattern.match(key): # 匹配到大标题...
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if target_found:
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# 如果已经找到了符合的章节，并且遇到了另一个章节
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# 保存当前块并重置
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@ -27,7 +28,8 @@ def extract_between_sections(data, target_values):
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target_found = False
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# 检查当前标题是否包含 target_values 中的任意关键词
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if any(tv in value for tv in target_values) and not file_pattern.search(value):
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if any(tv in value for tv in target_values):
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if (flag and not file_pattern.search(value)) or not flag:
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target_found = True # 找到了目标章节，开始捕获后续内容
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current_section_title = value # 保存章节标题内容
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@ -40,7 +42,8 @@ def extract_between_sections(data, target_values):
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return extracted_data
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#生成无结构的数据货物标
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# 生成无结构的数据货物标
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def postprocess_formatted1(section_content):
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# print(json.dumps(section_content, ensure_ascii=False, indent=4))
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"""
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@ -77,7 +80,8 @@ def postprocess_formatted1(section_content):
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return concatenated
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#-----------以下为直接从序号中提取，无大标题的相关函数----------
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# -----------以下为直接从序号中提取，无大标题的相关函数----------
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# 对于每个target_value元素，如果有完美匹配json_data中的键，那就加入这个完美匹配的键名，否则，把全部模糊匹配到的键名都加入
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def find_keys_by_value(target_value, json_data):
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@ -92,6 +96,8 @@ def find_keys_by_value(target_value, json_data):
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def find_keys_with_prefix(key_prefix, json_data):
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subheadings = [k for k in json_data if k.startswith(key_prefix)]
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return subheadings
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def extract_json(data, target_values):
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results = {}
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for target_value in target_values:
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@ -113,7 +119,8 @@ def extract_json(data, target_values):
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results[subkey] = data[subkey]
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return results
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#生成无结构的数据工程标,对提取出的若干键值对，生成外键为target_value，值为列表的新键值对
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# 生成无结构的数据工程标,对提取出的若干键值对，生成外键为target_value，值为列表的新键值对
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def postprocess_formatted2(data, target_values):
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"""
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从输入字典中提取目标值对应的章节，并为每个子章节添加缩进。
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@ -183,6 +190,7 @@ def postprocess_formatted2(data, target_values):
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return result
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def get_requirements_with_gpt(merged_baseinfo_path, selection):
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"""
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根据 selection 的值选择相应的用户查询，并调用大模型获取要求。
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@ -216,7 +224,7 @@ def get_requirements_with_gpt(merged_baseinfo_path, selection):
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# }
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# }
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# """,
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1:"""请根据提供的招标文件内容，提取其中有关投标文件的要求、内容，并以 JSON 格式返回结果。
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1: """请根据提供的招标文件内容，提取其中有关投标文件的要求、内容，并以 JSON 格式返回结果。
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格式要求：
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- 外层键名应为文中提到的有关投标文件相关要求、内容的大项概览性标题。
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- 对于每个大项下的具体要求内容，有以下两种组织方式：
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@ -271,7 +279,7 @@ def get_requirements_with_gpt(merged_baseinfo_path, selection):
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# }
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# }
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# """,
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2:"""该招标文件中有关开标、评标、定标、中标要求、内容(或磋商流程内容)是什么？请以 JSON 格式返回结果。
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2: """该招标文件中有关开标、评标、定标、中标要求、内容(或磋商流程内容)是什么？请以 JSON 格式返回结果。
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格式要求：
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- 外层键名应为文中提到的有关开标、评标、定标、中标要求或磋商流程内容的大项概览性标题。
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- 对于每个大项下的子要求，使用嵌套的键值对进行组织。
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@ -324,11 +332,11 @@ def get_requirements_with_gpt(merged_baseinfo_path, selection):
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# 调用大模型并处理响应
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try:
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questions_to_continue = []
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qianwen_res = qianwen_long_stream(file_id, user_query,2,1,True)
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qianwen_res = qianwen_long_stream(file_id, user_query, 2, 1, True)
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message = qianwen_res[0]
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parsed = clean_json_string(message)
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total_tokens = qianwen_res[1]
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if not parsed and total_tokens >5900:
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if not parsed and total_tokens > 5900:
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questions_to_continue.append((user_query, message))
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if questions_to_continue:
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continued_results = process_continue_answers(questions_to_continue, 3, file_id)
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@ -338,6 +346,7 @@ def get_requirements_with_gpt(merged_baseinfo_path, selection):
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except Exception as e:
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return {"error": "调用大模型失败"}
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# 读取JSON数据，提取内容，转换结构，并打印结果
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def extract_from_notice(merged_baseinfo_path, clause_path, type):
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"""
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@ -361,12 +370,12 @@ def extract_from_notice(merged_baseinfo_path, clause_path, type):
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# 映射 type 到 target_values
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type_target_map = {
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1: ["投标", "投标文件", "响应文件"],
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2: ["开标", "评标", "定标", "评审", "成交", "合同", "磋商","谈判","中标", "程序", "步骤"],
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2: ["开标", "评标", "定标", "评审", "成交", "合同", "磋商", "谈判", "中标", "程序", "步骤"],
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3: ["重新招标、不再招标和终止招标", "重新招标", "重新采购", "不再招标", "不再采购", "终止招标", "终止采购"],
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4: ["评标"] # 测试
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}
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# 获取对应 type 的 target_values
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flag = (type == 2)
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target_values = type_target_map.get(type)
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if not target_values:
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print(f"Error: Invalid type specified: {type}. Use 1, 2, 3, or 4.")
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@ -378,7 +387,7 @@ def extract_from_notice(merged_baseinfo_path, clause_path, type):
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data = json.load(file)
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if len(data) >= 60:
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# 尝试使用大章节筛选
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extracted_data = extract_between_sections(data, target_values)
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extracted_data = extract_between_sections(data, target_values, flag)
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if extracted_data:
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# 后处理并返回结果
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extracted_data_concatenated = {
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@ -402,12 +411,13 @@ def extract_from_notice(merged_baseinfo_path, clause_path, type):
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print(f"Error occurred while processing clause_path '{clause_path}': {e}")
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return DEFAULT_RESULT
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if __name__ == "__main__":
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# file_path = 'C:\\Users\\Administrator\\Desktop\\fsdownload\\3bffaa84-2434-4bd0-a8ee-5c234ccd7fa0\\clause1.json'
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merged_baseinfo_path=r"C:\Users\Administrator\Desktop\fsdownload\ec7d5328-9c57-450f-baf4-2e5a6f90ed1d\tmp\merged_baseinfo_path_more.pdf"
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clause_path=r"C:\Users\Administrator\Desktop\fsdownload\ec7d5328-9c57-450f-baf4-2e5a6f90ed1d\tmp\clause1.json"
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merged_baseinfo_path = r"C:\Users\Administrator\Desktop\fsdownload\b29de31a-297e-42cf-b9ba-6859e530a472\ztbfile_merged_baseinfo.pdf"
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clause_path = r"C:\Users\Administrator\Desktop\fsdownload\b29de31a-297e-42cf-b9ba-6859e530a472\clause1.json"
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try:
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res = extract_from_notice(merged_baseinfo_path,clause_path, 2) # 可以改变此处的 type 参数测试不同的场景
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res = extract_from_notice(merged_baseinfo_path, clause_path, 1) # 可以改变此处的 type 参数测试不同的场景
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res2 = json.dumps(res, ensure_ascii=False, indent=4)
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print(res2)
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except ValueError as e:
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