151 lines
5.7 KiB
Python
151 lines
5.7 KiB
Python
from rag2_0.dify.dify_tool import NewWorkflowChat
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import pandas as pd
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from concurrent.futures import ThreadPoolExecutor
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from tqdm import tqdm
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import concurrent.futures
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class ChatDifyByWorkorder:
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def __init__(self, api_key=None, base_url="https://api.dify.ai/v1") -> None:
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"""
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初始化ChatDifyByWorkorder类
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Args:
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api_key: Dify API密钥，默认为None
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base_url: Dify API的基础URL，默认为"https://api.dify.ai/v1"
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"""
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baseurl = "http://172.20.0.145/v1"
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new_workflow_api_key = "app-qxsSybCs7ABiKlC1JabTYVn6"
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self.new_chat = NewWorkflowChat(api_key=new_workflow_api_key, base_url=baseurl)
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self.new_chat_answer = NewWorkflowChat(api_key=new_workflow_api_key, base_url=baseurl)
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def get_soft_name(self, row) -> str:
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if "博微配网计价通D3" in row["产品线"]:
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return "博微配网计价通D3"
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elif "博微电力建设计价通软件" in row["产品线"]:
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return "电力建设计价通软件"
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elif "新能源系列" in row["产品线"] and "博微新型储能电站建设计价通C1软件" in row["产品名称"]:
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return "储能C1软件"
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elif "博微西藏计价通Z1" in row["产品线"]:
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return "西藏计价通Z1"
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elif "博微技改检修计价通T1软件" in row["产品线"] and "技改检修计价通T1软件-概预算" in row["产品名称"]:
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return "技改检修工程计价通T1"
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elif "博微技改检修计价通T1软件" in row["产品线"] and "技改检修计价通T1软件-清单" in row["产品名称"]:
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return "检修清单计价通T1"
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return ""
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def process_query(self, q:str) -> dict:
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"""
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发送问题并获取回答及相关工作流信息
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Args:
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q: 用户问题
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Returns:
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dict: 包含问题、回答和工作流信息的字典
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"""
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retry_count = 0
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max_retries = 2
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while retry_count <= max_retries:
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try:
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# 发送问题获取回答和消息ID
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result = self.new_chat.process_question(q)
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return result
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except Exception as e:
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retry_count += 1
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if retry_count <= max_retries:
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continue
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else:
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raise e
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def process_answer(self, q:str) -> dict:
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"""
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发送问题并获取回答及相关工作流信息
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Args:
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q: 用户问题
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Returns:
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dict: 包含问题、回答和工作流信息的字典
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"""
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retry_count = 0
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max_retries = 2
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while retry_count <= max_retries:
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try:
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# 发送问题获取回答和消息ID
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result = self.new_chat_answer.process_question(q)
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return result
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except Exception as e:
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retry_count += 1
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if retry_count <= max_retries:
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continue
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else:
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raise
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def process_row(self, row):
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"""处理单行数据"""
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soft_name = self.get_soft_name(row=row)
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if soft_name == "":
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return None
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# 使用线程池并发执行查询
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with ThreadPoolExecutor() as executor:
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try:
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# 提交两个任务并获取Future对象
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query_future = executor.submit(self.process_query, q=f"{soft_name},{row['客户问题']}")
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answer_future = executor.submit(self.process_answer, q=f"{soft_name},{row['解决方案']}")
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# 获取结果
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query_result = query_future.result()
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answer_result = answer_future.result()
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except Exception as e:
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print(f"处理工单 {row.get('工单编号', '未知')} 时发生错误: {str(e)}")
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return None
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worker_id = str(row["工单编号"])
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if query_result is None or answer_result is None:
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print("处理对话出现错误")
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return None
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worker_order_info = {
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"工单编号": worker_id,
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"用户问题": row['客户问题'],
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"解决方案": row['解决方案'],
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"AI回答": query_result["新流程答案"],
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"用户问题检索到的词条": query_result["新检索词条"],
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"解决方案检索到的词条": answer_result["新检索词条"],
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}
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return worker_order_info
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def run(self, excel_path:str):
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df_data = pd.read_excel(excel_path)
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list_worker_order_info = []
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# 创建进度条
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with tqdm(total=len(df_data), desc="处理工单") as pbar:
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# 创建线程池，最大并发数可以根据需要调整
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with ThreadPoolExecutor(max_workers=5) as executor:
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# 提交所有任务
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future_to_row = {executor.submit(self.process_row, row): idx for idx, row in df_data.iterrows()}
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# 处理完成的任务
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for future in concurrent.futures.as_completed(future_to_row):
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result = future.result()
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if result is not None:
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list_worker_order_info.append(result)
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pbar.update(1)
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return list_worker_order_info
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if __name__=="__main__":
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worker_chat = ChatDifyByWorkorder()
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result = worker_chat.run(excel_path="data/excel/工单记录_均衡提取2000条.xlsx")
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# 可以选择保存结果到Excel
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if result:
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pd.DataFrame(result).to_excel("data/excel/工单处理结果.xlsx", index=False) |