205 lines
7.8 KiB
Python
205 lines
7.8 KiB
Python
# 基于知识库提问的通用模板，
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# assistant_id
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import re
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import queue
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import concurrent.futures
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import time
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from dashscope import Assistants, Messages, Runs, Threads
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from llama_index.indices.managed.dashscope import DashScopeCloudRetriever
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from flask_app.main.通义千问long import qianwen_long, upload_file
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prompt = """
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# 角色
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你是一个文档处理专家，专门负责理解和操作基于特定内容的文档任务，这包括解析、总结、搜索或生成与给定文档相关的各类信息。
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## 技能
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### 技能 1：文档解析与摘要
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- 深入理解并分析${documents}的内容，提取关键信息。
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- 根据需求生成简洁明了的摘要，保持原文核心意义不变。
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### 技能 2：信息检索与关联
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- 在${documents}中高效检索特定信息或关键词。
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- 能够识别并链接到文档内部或外部的相关内容，增强信息的连贯性和深度。
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## 限制
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- 所有操作均需基于${documents}的内容，不可超出此范围创造信息。
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- 在处理敏感或机密信息时，需遵守严格的隐私和安全规定。
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- 确保所有生成或改编的内容逻辑连贯，无误导性信息。
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请注意，上述技能执行时将直接利用并参考${documents}的具体内容，以确保所有产出紧密相关且高质量。
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"""
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prom = '请记住以下材料，他们对回答问题有帮助，请你简洁准确地给出回答，不要给出无关内容。${documents}'
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def read_questions_from_file(file_path):
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questions = []
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with open(file_path, 'r', encoding='utf-8') as file:
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for line in file:
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line = line.strip()
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# 使用正则表达式匹配以数字开头，后接一个点的行
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if re.match(r'\d+\.', line):
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# 从点后分割并去除前后空格获取问题部分
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question = line.split('.', 1)[1].strip()
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questions.append(question)
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return questions
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#正文和文档名之间的内容
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def send_message(assistant, message='百炼是什么？'):
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ans = []
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print(f"Query: {message}")
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# create thread.
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thread = Threads.create()
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print(thread)
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# create a message.
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message = Messages.create(thread.id, content=message)
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# create run
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run = Runs.create(thread.id, assistant_id=assistant.id)
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# print(run)
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# wait for run completed or requires_action
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run_status = Runs.wait(run.id, thread_id=thread.id)
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# print(run_status)
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# get the thread messages.
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msgs = Messages.list(thread.id)
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for message in msgs['data'][::-1]:
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ans.append(message['content'][0]['text']['value'])
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return ans
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def rag_assistant(knowledge_name):
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retriever = DashScopeCloudRetriever(knowledge_name)
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pipeline_id = str(retriever.pipeline_id)
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assistant = Assistants.create(
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model='qwen-max',
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name='smart helper',
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description='智能助手，支持知识库查询和插件调用。',
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temperature='0.3',
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instructions="请记住以下材料，他们对回答问题有帮助，请你简洁准确地给出回答，不要给出无关内容。${documents}",
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tools=[
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{
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"type": "code_interpreter"
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},
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{
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"type": "rag",
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"prompt_ra": {
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"pipeline_id": pipeline_id,
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"parameters": {
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"type": "object",
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"properties": {
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"query_word": {
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"type": "str",
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"value": "${documents}"
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}
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}
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}
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}
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}]
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)
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return assistant
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def pure_assistant():
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assistant = Assistants.create(
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model='qwen-max',
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name='smart helper',
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description='智能助手，能基于用户的要求精准简洁地回答用户的提问',
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instructions='智能助手，能基于用户的要求精准简洁地回答用户的提问',
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tools=[
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{
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"type": "code_interpreter"
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},
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]
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)
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return assistant
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def llm_call(question, knowledge_name,file_id, result_queue, ans_index, llm_type):
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if llm_type==1:
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print(f"rag_assistant! question:{question}")
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assistant = rag_assistant(knowledge_name)
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elif llm_type==2:
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print(f"qianwen_long! question:{question}")
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qianwen_res = qianwen_long(file_id,question)
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result_queue.put((ans_index,(question,qianwen_res)))
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return
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else :
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assistant = pure_assistant()
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ans = send_message(assistant, message=question)
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result_queue.put((ans_index, (question, ans))) # 在队列中添加索引 (question, ans)
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def multi_threading(queries, knowledge_name="", file_id="", llm_type=1):
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if not queries:
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return []
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print("多线程提问：starting multi_threading...")
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result_queue = queue.Queue()
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# 使用 ThreadPoolExecutor 管理线程
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with concurrent.futures.ThreadPoolExecutor(max_workers=15) as executor:
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# 逐个提交任务，每提交一个任务后休眠1秒
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future_to_query = {}
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for index, query in enumerate(queries):
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future = executor.submit(llm_call, query, knowledge_name, file_id, result_queue, index, llm_type)
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future_to_query[future] = index
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time.sleep(1) # 每提交一个任务后等待1秒
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# 收集每个线程的结果
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for future in concurrent.futures.as_completed(future_to_query):
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index = future_to_query[future]
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try:
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future.result() # 捕获异常或确认任务完成
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except Exception as exc:
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print(f"Query {index} generated an exception: {exc}")
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# 确保在异常情况下也向 result_queue 添加占位符
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result_queue.put((index, None))
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# 从队列中获取所有结果并按索引排序
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results = [None] * len(queries)
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while not result_queue.empty():
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index, result = result_queue.get()
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results[index] = result
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# 返回一个保证是列表的结构
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return results
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if __name__ == "__main__":
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# start_time=time.time()
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# # 读取问题列表
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# questions =read_questions_from_file('../static/提示词/前两章提问总结.txt')
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# for i in questions:
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# print(i)
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# knowledge_name = "招标解析5word"
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# llm_type=1
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# results = multi_threading(questions, knowledge_name)
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# end_time = time.time()
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# if not results:
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# print("errror!")
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# else:
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# print("elapsed time:"+str(end_time-start_time))
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# # 打印结果
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# for question, response in results:
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# print(f"Question: {question}")
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# print(f"Response: {response}")
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# file_path = "C:\\Users\\Administrator\\Desktop\\招标文件\\output1\\ztb_evaluation_method.pdf"
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# file_id = upload_file(file_path)
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# questions=["根据该文档中的评标办法前附表，请你列出该文件的技术标,以json的格式返回结果","根据该文档中的评标办法前附表，请你列出该文件的商务标,以json的格式返回结果","根据该文档中的评标办法前附表，请你列出该文件的投标报价,以json的格式返回结果"]
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# results=multi_threading(questions,"",file_id,2) #1代表使用百炼rag 2代表使用qianwen-long
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# if not results:
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# print("errror!")
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# else:
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# # 打印结果
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# for question, response in results:
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# print(f"Question: {question}")
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# print(f"Response: {response}")
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ques=[]
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results = multi_threading(ques, "招标解析5word")
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if not results:
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print("errror!")
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else:
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# 打印结果
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for question, response in results:
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print(f"Question: {question}")
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print(f"Response: {response}") |