270 lines
11 KiB
Python
270 lines
11 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.general.llm.doubao import read_txt_to_string
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from flask_app.general.llm.通义千问long_plus import qianwen_long, upload_file, qianwen_plus
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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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current_question = ""
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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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if not line: # 跳过空行
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continue
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if line.startswith('#'): # 跳过以#开头的行
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continue
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# 检查是否是新的问题编号，例如 "1."
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match = re.match(r'^(\d+)\.', line)
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if match:
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# 如果有之前的问题，保存它
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if current_question:
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questions.append(current_question.strip())
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# 提取问题内容，去掉编号和点
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current_question = line.split('.', 1)[1].strip() + "\n"
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else:
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# 继续添加到当前问题
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current_question += line + "\n"
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# 添加最后一个问题（如果存在）
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if current_question:
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questions.append(current_question.strip())
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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,need_extra=False):
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"""
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调用不同的 LLM 模型并将结果放入结果队列。
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"""
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try:
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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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# assistant=create_assistant(knowledge_name)
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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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elif llm_type==2:
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# print(f"qianwen_long! question:{question}")
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# qianwen_res,usage = qianwen_long(file_id,question) #有bug
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qianwen_res = qianwen_long(file_id, question,2,1,need_extra)
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if not qianwen_res:
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result_queue.put((ans_index, None)) # 如果为空字符串，直接返回 None
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else:
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result_queue.put((ans_index, (question, qianwen_res)))
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elif llm_type==3:
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# print(f"doubao! question:{question}") #暂时废弃doubao！
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# doubao_res=doubao_model(question,need_extra)
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qianwen_plus_res=qianwen_plus(question,need_extra)
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if not qianwen_plus_res:
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result_queue.put((ans_index, None)) # 如果为空字符串，直接返回 None
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else:
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result_queue.put((ans_index, (question, qianwen_plus_res)))
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elif llm_type==4:
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qianwen_plus_res = qianwen_plus(question, need_extra)
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if not qianwen_plus_res:
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result_queue.put((ans_index, None)) # 如果为空字符串，直接返回 None
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else:
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result_queue.put((ans_index, (question, qianwen_plus_res)))
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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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except Exception as e:
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print(f"LLM 调用失败，查询索引 {ans_index}，错误：{e}")
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result_queue.put((ans_index, None)) # 使用 None 作为失败的占位符
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def multi_threading(queries, knowledge_name="", file_id="", llm_type=1, need_extra=False):
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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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def submit_task(executor, query, idx):
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# 提交任务，并返回对应的 future
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return executor.submit(llm_call, query, knowledge_name, file_id, result_queue, idx, llm_type, need_extra)
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future_to_index = {}
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with concurrent.futures.ThreadPoolExecutor(max_workers=40) as executor:
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if llm_type == 4:
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# 每组10个任务，组与组之间间隔5秒,提高缓存命中率
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group_size = 10
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for i in range(0, len(queries), group_size):
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group = queries[i:i + group_size]
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for idx, query in enumerate(group, start=i):
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future = submit_task(executor, query, idx)
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future_to_index[future] = idx
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# 若还有后续组，则等待5秒
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if i + group_size < len(queries):
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time.sleep(5)
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else:
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# 直接一次性提交所有任务
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for index, query in enumerate(queries):
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future = submit_task(executor, query, index)
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future_to_index[future] = index
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# 收集所有任务的异常
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for future in concurrent.futures.as_completed(future_to_index):
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idx = future_to_index[future]
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try:
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future.result()
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except Exception as exc:
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print(f"查询索引 {idx} 生成了一个异常：{exc}")
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result_queue.put((idx, None))
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# 根据任务索引初始化结果列表
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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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# 如果所有结果都为 None，则返回空列表；否则过滤掉 None 项
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if all(result is None for result in results):
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return []
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return [r for r in results if r is not None]
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if __name__ == "__main__":
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start_time=time.time()
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# file_path = r"C:\Users\Administrator\Desktop\fsdownload\39b0c3b4-1807-456c-8330-c5c7d1b7a2ca\ztbfile_procurement\ztbfile_procurement_1.pdf"
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# file_id = upload_file(file_path)
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# questions=["该招标文件的项目名称是？项目编号（或招标编号）是？采购人（或招标人）是？采购代理机构（或招标代理机构）是？请按json格式给我提供信息，键名分别是'项目名称','项目编号','采购人','采购代理机构'，若存在未知信息，在对应的键值中填'未知'。","该招标文件的项目概况是？项目基本情况是？请按json格式给我提供信息，键名分别为'项目概况','项目基本情况'，若存在嵌套信息，嵌套内容键名以文件中对应字段命名，而嵌套键值必须与原文保持一致，若存在未知信息，在对应的键值中填'未知'。"]
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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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#
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query=[]
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processed_filepath=r"C:\Users\Administrator\Desktop\货物标\extract_files\107国道.txt"
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full_text=read_txt_to_string(processed_filepath)
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temp="请告诉我LED 全彩显示屏的功能是怎样的，请以JSON格式返回，键名为'LED 全彩显示屏'，键值为字符串。"
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user_query = f"文本内容：{full_text}\n" + temp
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# user_query='''
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# </tr><tr>
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# <td colspan="1" rowspan="1">3</td>
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# <td colspan="1" rowspan="1">大屏播控系统</td>
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# <td colspan="1" rowspan="1">1、具有多用户多权限管理功能,支持多用户同时登录客户端,每个用户根据自身不同权限管理显示屏;2、系统对输入信号源进行预监视,实现在播控前预先查看的功能。</td>
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# <td colspan="1" rowspan="1">中国</td>
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# <td colspan="1" rowspan="1">1</td>
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# <td colspan="1" rowspan="1">套</td>
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# </tr><tr>
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# 请告诉我大屏播控系统的功能是怎样的，请以JSON格式返回，键名为'大屏播控系统'，键值为字符串。
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# '''
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for i in range(1,15):
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query.append(user_query)
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res=multi_threading(query,"","",4)
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for _,response in res:
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print(response)
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# end_time = time.time()
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