zbparse/flask_app/general/多线程提问.py

# 基于知识库提问的通用模板，
# assistant_id
import json
import os
import re
import queue
import concurrent.futures
import time
import requests
from dashscope import Assistants, Messages, Runs, Threads
from llama_index.indices.managed.dashscope import DashScopeCloudRetriever
from flask_app.general.通义千问long import qianwen_long

prompt = """
# 角色
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。

## 技能
### 技能 1：文档解析与摘要
- 深入理解并分析${documents}的内容，提取关键信息。
- 根据需求生成简洁明了的摘要，保持原文核心意义不变。

### 技能 2：信息检索与关联
- 在${documents}中高效检索特定信息或关键词。
- 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。

## 限制
- 所有操作均需基于${documents}的内容，不可超出此范围创造信息。
- 在处理敏感或机密信息时，需遵守严格的隐私和安全规定。
- 确保所有生成或改编的内容逻辑连贯，无误导性信息。

请注意，上述技能执行时将直接利用并参考${documents}的具体内容，以确保所有产出紧密相关且高质量。
"""
prom = '请记住以下材料，他们对回答问题有帮助，请你简洁准确地给出回答，不要给出无关内容。${documents}'

def read_questions_from_file(file_path):
    questions = []
    current_question = ""
    current_number = 0

    with open(file_path, 'r', encoding='utf-8') as file:
        for line in file:
            line = line.strip()

            if not line:  # 跳过空行
                continue

            if line.startswith('#'):  # 跳过以#开头的行
                continue

            # 检查是否是新的问题编号，例如 "1."
            match = re.match(r'^(\d+)\.', line)
            if match:
                # 如果有之前的问题，保存它
                if current_question:
                    questions.append(current_question.strip())

                # 开始新的问题
                current_number = int(match.group(1))
                # 提取问题内容，去掉编号和点
                current_question = line.split('.', 1)[1].strip() + "\n"
            else:
                # 继续添加到当前问题
                current_question += line + "\n"

    # 添加最后一个问题（如果存在）
    if current_question:
        questions.append(current_question.strip())

    return questions


#正文和文档名之间的内容

def send_message(assistant, message='百炼是什么？'):
    ans = []
    print(f"Query: {message}")

    # create thread.
    thread = Threads.create()
    print(thread)

    # create a message.
    message = Messages.create(thread.id, content=message)
    # create run

    run = Runs.create(thread.id, assistant_id=assistant.id)
    # print(run)

    # wait for run completed or requires_action
    run_status = Runs.wait(run.id, thread_id=thread.id)
    # print(run_status)
    # get the thread messages.
    msgs = Messages.list(thread.id)
    for message in msgs['data'][::-1]:
        ans.append(message['content'][0]['text']['value'])
    return ans

def rag_assistant(knowledge_name):
    retriever = DashScopeCloudRetriever(knowledge_name)
    pipeline_id = str(retriever.pipeline_id)
    assistant = Assistants.create(
        model='qwen-max',
        name='smart helper',
        description='智能助手，支持知识库查询和插件调用。',
        temperature='0.3',
        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。${documents}",
        tools=[
            {
                "type": "code_interpreter"
            },
            {
                "type": "rag",
                "prompt_ra": {
                    "pipeline_id": pipeline_id,
                    "parameters": {
                        "type": "object",
                        "properties": {
                            "query_word": {
                                "type": "str",
                                "value": "${documents}"
                            }
                        }
                    }
                }
            }]
    )
    return assistant

#TODO:http格式，有bug还没修改
def create_assistant(knowledge_name):
    """
    Create an assistant using DashScope API via HTTP request based on the provided knowledge name.

    Parameters:
        knowledge_name (str): The name of the knowledge base to associate with the assistant.

    Returns:
        dict: Response from the API containing assistant details.

    Raises:
        ValueError: If the DASHSCOPE_API_KEY environment variable is not set.
        Exception: If any error occurs during the HTTP request.
    """
    # Step 1: Initialize the Retriever and get the Pipeline ID
    try:
        retriever = DashScopeCloudRetriever(knowledge_name)
        pipeline_id = str(retriever.pipeline_id)
    except Exception as e:
        print(f"Error retrieving pipeline ID for knowledge '{knowledge_name}': {e}")
        return None

    # Step 2: Fetch the API Key from Environment Variables
    api_key = os.getenv("DASHSCOPE_API_KEY")
    if not api_key:
        raise ValueError("DASHSCOPE_API_KEY environment variable is not set.")

    # Step 3: Define the API Endpoint and Headers
    url = 'https://dashscope.aliyuncs.com/api/v1/assistants'
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }

    # Step 4: Construct the Instructions
    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}"
    )

    # Step 5: Define the Tools
    tools = [
        {
            "type": "code_interpreter"
        },
        {
            "type": "rag",
            "prompt_ra": {
                "pipeline_id": pipeline_id,
                "parameters": {
                    "type": "object",
                    "properties": {
                        "query_word": {
                            "type": "str",
                            "value": "${documents}"
                        }
                    }
                }
            }
        }
    ]

    # Step 6: Construct the Payload
    payload = {
        "model": "qwen-max",
        "name": "智能小助手",  # "Smart Helper" in Chinese
        "description": "智能助手，支持知识库查询和插件调用。",
        "temperature": 0.3,
        "instructions": instructions,
        "tools": tools,
        "file_ids": [],  # Add file IDs if necessary
        "metadata": {}    # Add metadata if necessary
    }

    # Optional: If you have specific file_ids or metadata, you can modify the payload accordingly
    # For example:
    # payload["file_ids"] = ["file_id_1", "file_id_2"]
    # payload["metadata"] = {"key1": "value1", "key2": "value2"}

    # Step 7: Make the HTTP POST Request
    try:
        response = requests.post(url, headers=headers, data=json.dumps(payload))
        response.raise_for_status()  # Raises HTTPError for bad responses (4xx or 5xx)
        assistant = response.json()
        print("Assistant created successfully:")
        print(json.dumps(assistant, indent=4, ensure_ascii=False))
        return assistant
    except requests.exceptions.HTTPError as http_err:
        print(f"HTTP error occurred: {http_err} - Response: {response.text}")
    except Exception as err:
        print(f"An error occurred: {err}")


def pure_assistant():
    assistant = Assistants.create(
        model='qwen-max',
        name='smart helper',
        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',
        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',
        tools=[
            {
                "type": "code_interpreter"
            },
        ]
    )
    return assistant

def llm_call(question, knowledge_name,file_id, result_queue, ans_index, llm_type):
    if llm_type==1:
        print(f"rag_assistant! question:{question}")
        assistant = rag_assistant(knowledge_name)
        # assistant=create_assistant(knowledge_name)
    elif llm_type==2:
        print(f"qianwen_long! question:{question}")
        qianwen_res = qianwen_long(file_id,question)
        result_queue.put((ans_index,(question,qianwen_res)))
        return
    else :
        assistant = pure_assistant()
    ans = send_message(assistant, message=question)
    result_queue.put((ans_index, (question, ans)))  # 在队列中添加索引 (question, ans)

def multi_threading(queries, knowledge_name="", file_id="", llm_type=1):
    if not queries:
        return []

    print("多线程提问：starting multi_threading...")
    result_queue = queue.Queue()

    # 使用 ThreadPoolExecutor 管理线程
    with concurrent.futures.ThreadPoolExecutor(max_workers=15) as executor:
        # 逐个提交任务，每提交一个任务后休眠1秒
        future_to_query = {}
        for index, query in enumerate(queries):
            future = executor.submit(llm_call, query, knowledge_name, file_id, result_queue, index, llm_type)
            future_to_query[future] = index
            time.sleep(1)  # 每提交一个任务后等待1秒

        # 收集每个线程的结果
        for future in concurrent.futures.as_completed(future_to_query):
            index = future_to_query[future]
            try:
                future.result()  # 捕获异常或确认任务完成
            except Exception as exc:
                print(f"Query {index} generated an exception: {exc}")
                # 确保在异常情况下也向 result_queue 添加占位符
                result_queue.put((index, None))

    # 从队列中获取所有结果并按索引排序
    results = [None] * len(queries)
    while not result_queue.empty():
        index, result = result_queue.get()
        results[index] = result
    # 检查是否所有结果都是 None
    if all(result is None for result in results):
        return []
        # 过滤掉None值
    results = [r for r in results if r is not None]
    # 返回一个保证是列表的结构
    return results

if __name__ == "__main__":
    start_time=time.time()
    # # 读取问题列表
    baseinfo_file_path = '/flask_app/static/提示词/基本信息工程标.txt'
    questions =read_questions_from_file(baseinfo_file_path)
    knowledge_name = "招标解析5word"
    llm_type=1
    results = multi_threading(questions, knowledge_name)
    end_time = time.time()
    if not results:
        print("errror!")
    else:
        print("elapsed time:"+str(end_time-start_time))
        # 打印结果
        for question, response in results:
            print(f"Response: {response}")

    # file_path = "C:\\Users\\Administrator\\Desktop\\货物标\\zbfiles\\6.2定版视频会议磋商文件(1)\\6.2定版视频会议磋商文件_1-21.pdf"
    # file_id = upload_file(file_path)
    # 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'。"]
    # results=multi_threading(questions,"",file_id,2)  #1代表使用百炼rag 2代表使用qianwen-long
    # if not results:
    #     print("errror!")
    # else:
    #     # 打印结果
    #     for question, response in results:
    #         print(f"Question: {question}")
    #         print(f"Response: {response}")

    # ques=["关于'资格要求',本采购文件第一章第二款要求的内容是怎样的？请按json格式给我提供信息，键名为'资格要求'，而键值需要完全与原文保持一致，不要擅自总结、删减，如果存在未知信息，请在对应键值处填'未知'。"]
    # # ques=["该招标文件的工程名称（项目名称）是？招标编号是？招标人是？招标代理机构是？请按json格式给我提供信息，键名分别是'工程名称','招标编号','招标人','招标代理机构'，若存在未知信息，在对应的键值中填'未知'。","该招标文件的工程概况（或项目概况）是？招标范围是？请按json格式给我提供信息，键名分别为'工程概况','招标范围'，若存在嵌套信息，嵌套内容键名以文件中对应字段命名，若存在未知信息，在对应的键值中填'未知'。"]
    # results = multi_threading(ques, "6.2视频会议docx")
    # if not results:
    #     print("errror!")
    # else:
    #     # 打印结果
    #     for question, response in results:
    #         print(f"Question: {question}")
    #         print(f"Response: {response}")