majiahui@haimaqingfan.com
3 months ago
16 changed files with 1061 additions and 229 deletions
@ -0,0 +1,21 @@ |
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# 并行工作线程数 |
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workers = 16 |
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# 监听内网端口5000【按需要更改】 |
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bind = '0.0.0.0:12004' |
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loglevel = 'debug' |
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|
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worker_class = "gevent" |
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# 设置守护进程【关闭连接时，程序仍在运行】 |
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daemon = True |
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# 设置超时时间120s，默认为30s。按自己的需求进行设置 |
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timeout = 120 |
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# 设置访问日志和错误信息日志路径 |
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accesslog = './logs/acess_openbuddy_model.log' |
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errorlog = './logs/error_openbuddy_model.log' |
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# access_log_format = '%(h) - %(t)s - %(u)s - %(s)s %(H)s' |
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# errorlog = '-' # 记录到标准输出 |
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# 设置最大并发量 |
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worker_connections = 20000 |
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@ -0,0 +1,21 @@ |
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# 并行工作线程数 |
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workers = 16 |
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# 监听内网端口5000【按需要更改】 |
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bind = '0.0.0.0:12014' |
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|
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loglevel = 'debug' |
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|
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worker_class = "gevent" |
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# 设置守护进程【关闭连接时，程序仍在运行】 |
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daemon = True |
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# 设置超时时间120s，默认为30s。按自己的需求进行设置 |
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timeout = 120 |
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# 设置访问日志和错误信息日志路径 |
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accesslog = './logs/acess_openbuddy_model_llama_3_2.log' |
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errorlog = './logs/error_openbuddy_model_llama_3_2.log' |
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# access_log_format = '%(h) - %(t)s - %(u)s - %(s)s %(H)s' |
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# errorlog = '-' # 记录到标准输出 |
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|
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# 设置最大并发量 |
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worker_connections = 20000 |
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@ -0,0 +1,196 @@ |
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#coding:utf-8 |
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# 这是一个示例 Python 脚本。 |
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|
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# 按 Shift+F10 执行或将其替换为您的代码。 |
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# 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。 |
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|
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|
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# def request_api_chatgpt(api_key, prompt): |
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# print(api_key) |
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# print(prompt) |
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# OPENAI_API_KEY = api_key |
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# url = "https://api.openai.com/v1/chat/completions" |
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# # url = "https://one.aiskt.com" |
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# headers = { |
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# "Content-Type": "application/json", |
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# "Authorization": f"Bearer {OPENAI_API_KEY}" |
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# } |
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# data = { |
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# "model": "gpt-4-turbo-preview", |
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# "messages": [ |
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# {"role": "user", "content": "你好"}, |
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# {"role": "assistant", "content": "你好!有什么我可以帮助你的吗?"}, |
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# # {"role": "user", "content": prompt} |
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# {"role": "user", "content": "一张信用卡为多个gpt4账号付费会风控吗"} |
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# ], |
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# "top_p": 0.9, |
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# "temperature": 0.95 |
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# } |
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# response = requests.post(url, |
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# headers=headers, |
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# data=json.dumps(data), |
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# timeout=1200) |
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# |
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# return response |
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|
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from flask import Flask, jsonify |
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from flask import request |
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import requests |
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import time |
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import socket |
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import re |
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app = Flask(__name__) |
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app.config["JSON_AS_ASCII"] = False |
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|
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|
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def get_host_ip(): |
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""" |
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查询本机ip地址 |
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:return: ip |
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""" |
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try: |
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s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) |
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s.connect(('8.8.8.8', 80)) |
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ip = s.getsockname()[0] |
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finally: |
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s.close() |
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|
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return ip |
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|
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chatgpt_url_predict = "http://{}:12000/predict".format(str(get_host_ip())) |
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chatgpt_url_search = "http://{}:12000/search".format(str(get_host_ip())) |
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|
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|
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def request_api_chatgpt(content, model, top_p, temperature): |
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data = { |
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"content": content, |
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"model": model, |
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"top_p": top_p, |
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"temperature": temperature |
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} |
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response = requests.post( |
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chatgpt_url_predict, |
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json=data, |
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timeout=100000 |
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) |
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if response.status_code == 200: |
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return response.json() |
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else: |
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# logger.error( |
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# "【{}】 Failed to get a proper response from remote " |
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# "server. Status Code: {}. Response: {}" |
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# "".format(url, response.status_code, response.text) |
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# ) |
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print("Failed to get a proper response from remote " |
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"server. Status Code: {}. Response: {}" |
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"".format(response.status_code, response.text)) |
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return {} |
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|
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def uuid_search(uuid): |
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data = { |
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"id": uuid |
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} |
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response = requests.post( |
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chatgpt_url_search, |
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json=data, |
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timeout=100000 |
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) |
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if response.status_code == 200: |
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return response.json() |
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else: |
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# logger.error( |
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# "【{}】 Failed to get a proper response from remote " |
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# "server. Status Code: {}. Response: {}" |
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# "".format(url, response.status_code, response.text) |
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# ) |
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print("Failed to get a proper response from remote " |
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"server. Status Code: {}. Response: {}" |
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"".format(response.status_code, response.text)) |
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return {} |
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|
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|
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def uuid_search_mp(results): |
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|
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results_list = [""] * len(results) |
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while True: |
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tiaochu_bool = True |
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|
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for i in results_list: |
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if i == "": |
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tiaochu_bool = False |
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break |
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if tiaochu_bool == True: |
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break |
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for i in range(len(results)): |
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uuid = results[i]["texts"]["id"] |
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result = uuid_search(uuid) |
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if result["code"] == 200: |
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results_list[i] = result["text"] |
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time.sleep(3) |
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return results_list |
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|
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|
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@app.route("/predict", methods=["POST"]) |
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def handle_query(): |
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print(request.remote_addr) |
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model = request.json.get("model") |
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messages = request.json.get("messages") |
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top_p = request.json.get("top_p") |
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temperature = request.json.get("temperature") |
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|
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# "messages": [ |
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# {"role": "user", "content": "你好"}, |
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# {"role": "assistant", "content": "你好!有什么我可以帮助你的吗?"}, |
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# # {"role": "user", "content": prompt} |
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# {"role": "user", "content": "一张信用卡为多个gpt4账号付费会风控吗"} |
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# ], |
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content = "<|role|>user<|says|>{}<|end|>\n<|role|>assistant<|says|>".format(messages[-1]["content"]) |
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|
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print(model) |
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print(messages) |
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print(top_p) |
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print(temperature) |
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uid = request_api_chatgpt(content, model, top_p, temperature) |
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# { |
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# "probabilities": null, |
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# "status_code": 200, |
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# "texts": { |
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# "id": "29379d06-d08b-11ee-b56d-31fe0a8adccc" |
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# } |
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# } |
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results = uuid_search_mp([uid])[0] |
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|
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|
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return_text = { |
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'code': 200, |
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'id': uid["texts"]["id"], |
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'object': 0, |
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'created': 0, |
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'model': 0, |
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'choices': [ |
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{ |
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'index': 0, |
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'message': { |
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'role': 'assistant', |
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'content': results |
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}, |
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'logprobs': None, |
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'finish_reason': 'stop' |
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} |
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], |
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'usage': 0, |
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'system_fingerprint': 0 |
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} |
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return jsonify(return_text) |
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|
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|
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|
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if __name__ == '__main__': |
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app.run(host="0.0.0.0", port=12005, threaded=True, debug=False) |
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|
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@ -0,0 +1,273 @@ |
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#coding:utf-8 |
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# 这是一个示例 Python 脚本。 |
|||
|
|||
# 按 Shift+F10 执行或将其替换为您的代码。 |
|||
# 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。 |
|||
|
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|
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# def request_api_chatgpt(api_key, prompt): |
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# print(api_key) |
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# print(prompt) |
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# OPENAI_API_KEY = api_key |
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# url = "https://api.openai.com/v1/chat/completions" |
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# # url = "https://one.aiskt.com" |
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# headers = { |
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# "Content-Type": "application/json", |
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# "Authorization": f"Bearer {OPENAI_API_KEY}" |
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# } |
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# data = { |
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# "model": "gpt-4-turbo-preview", |
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# "messages": [ |
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# {"role": "user", "content": "你好"}, |
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# {"role": "assistant", "content": "你好!有什么我可以帮助你的吗?"}, |
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# # {"role": "user", "content": prompt} |
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# {"role": "user", "content": "一张信用卡为多个gpt4账号付费会风控吗"} |
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# ], |
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# "top_p": 0.9, |
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# "temperature": 0.95 |
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# } |
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# response = requests.post(url, |
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# headers=headers, |
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# data=json.dumps(data), |
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# timeout=1200) |
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# |
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# return response |
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|
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from flask import Flask, jsonify |
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from flask import request |
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import requests |
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import time |
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import socket |
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import re |
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|
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app = Flask(__name__) |
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app.config["JSON_AS_ASCII"] = False |
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|
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|
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def get_host_ip(): |
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""" |
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查询本机ip地址 |
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:return: ip |
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""" |
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try: |
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s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) |
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s.connect(('8.8.8.8', 80)) |
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ip = s.getsockname()[0] |
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finally: |
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s.close() |
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|
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return ip |
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|
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chatgpt_url_predict = "http://{}:12001/predict".format(str(get_host_ip())) |
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chatgpt_url_search = "http://{}:12001/search".format(str(get_host_ip())) |
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|
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|
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def check_problems(input, output): |
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pantten_formula = r'\\\[.*?\\\]' |
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pantten_picture = r'<mermaidStart>.*?<mermaidEnd>' |
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pantten_tb = r'<tbStart>.*?<tbEnd>' |
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|
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error_data = "" |
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# 判断是否是小标题任务 |
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if "任务：生成论文小标题内容" in input: |
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# 判断公式 |
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formula_bool_list = re.findall(pantten_formula, output, re.DOTALL) |
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tb_bool_list = re.findall(pantten_tb, output, re.DOTALL) |
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picture_bool_list = re.findall(pantten_picture, output, re.DOTALL) |
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|
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if "数学公式用\\[\\]进行包裹" not in input and formula_bool_list != []: |
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error_data += "多生成公式问题:\n" |
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error_data += "input:\n" |
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error_data += input |
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error_data += "output:\n" |
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error_data += output |
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error_data += "\n========================================================================\n" |
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# 判断公式 |
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|
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if "表格部分开始必须用<tbStart>标识，表格部分结束必须用<tbEnd>标识，必须返回html格式的表格" not in input and tb_bool_list != []: |
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error_data += "多生成表格问题:\n" |
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error_data += "input:\n" |
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error_data += input |
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error_data += "output:\n" |
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error_data += output |
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error_data += "\n========================================================================\n" |
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|
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if "图片要求在文字中插入一张图" not in input and picture_bool_list != []: |
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error_data += "多生成图片问题:\n" |
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error_data += "input:\n" |
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error_data += input |
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error_data += "output:\n" |
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error_data += output |
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error_data += "\n========================================================================\n" |
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if error_data != "": |
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with open("logs/error_xiaobiaoti.log", "a", encoding="utf-8") as f: |
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f.write(error_data) |
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def return_type(input, output): |
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pantten_formula = r'\\\[.*?\\\]' |
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pantten_picture = r'<mermaidStart>.*?<mermaidEnd>' |
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pantten_tb = r'<tbStart>.*?<tbEnd>' |
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|
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return_type_list = [] |
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# 判断是否是小标题任务 |
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if "任务：生成论文小标题内容" in input: |
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# 判断表格 |
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tb_bool_list = re.findall(pantten_tb, output, re.DOTALL) |
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formula_bool_list = re.findall(pantten_formula, output, re.DOTALL) |
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picture_bool_list = re.findall(pantten_picture, output, re.DOTALL) |
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|
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if tb_bool_list != []: |
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return_type_list.append("1") |
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|
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if formula_bool_list != []: |
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return_type_list.append("2") |
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|
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if picture_bool_list != []: |
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return_type_list.append("3") |
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return return_type_list |
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|
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|
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|
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def request_api_chatgpt(content, model, top_p, temperature): |
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data = { |
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"content": content, |
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"model": model, |
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"top_p": top_p, |
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"temperature": temperature |
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} |
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response = requests.post( |
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chatgpt_url_predict, |
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json=data, |
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timeout=100000 |
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) |
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if response.status_code == 200: |
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return response.json() |
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else: |
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# logger.error( |
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# "【{}】 Failed to get a proper response from remote " |
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# "server. Status Code: {}. Response: {}" |
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# "".format(url, response.status_code, response.text) |
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# ) |
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print("Failed to get a proper response from remote " |
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"server. Status Code: {}. Response: {}" |
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"".format(response.status_code, response.text)) |
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return {} |
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|
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|
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def uuid_search(uuid): |
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data = { |
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"id": uuid |
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} |
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response = requests.post( |
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chatgpt_url_search, |
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json=data, |
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timeout=100000 |
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) |
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if response.status_code == 200: |
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return response.json() |
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else: |
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# logger.error( |
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# "【{}】 Failed to get a proper response from remote " |
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# "server. Status Code: {}. Response: {}" |
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# "".format(url, response.status_code, response.text) |
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# ) |
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print("Failed to get a proper response from remote " |
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"server. Status Code: {}. Response: {}" |
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"".format(response.status_code, response.text)) |
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return {} |
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|
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|
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def uuid_search_mp(results): |
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|
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results_list = [""] * len(results) |
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while True: |
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tiaochu_bool = True |
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|
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for i in results_list: |
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if i == "": |
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tiaochu_bool = False |
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break |
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|
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if tiaochu_bool == True: |
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break |
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|
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for i in range(len(results)): |
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uuid = results[i]["texts"]["id"] |
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|
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result = uuid_search(uuid) |
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if result["code"] == 200: |
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results_list[i] = result["text"] |
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time.sleep(3) |
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return results_list |
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|
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|
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@app.route("/predict", methods=["POST"]) |
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def handle_query(): |
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print(request.remote_addr) |
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model = request.json.get("model") |
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messages = request.json.get("messages") |
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top_p = request.json.get("top_p") |
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temperature = request.json.get("temperature") |
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|
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print(model) |
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print(messages) |
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print(top_p) |
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print(temperature) |
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|
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# "messages": [ |
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# {"role": "user", "content": "你好"}, |
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# {"role": "assistant", "content": "你好!有什么我可以帮助你的吗?"}, |
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# # {"role": "user", "content": prompt} |
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# {"role": "user", "content": "一张信用卡为多个gpt4账号付费会风控吗"} |
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# ], |
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# text = "User: " + messages[-1]["content"] + "\nAssistant:" |
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content = "<|role|>user<|says|>{}<|end|>\n<|role|>assistant<|says|>".format(messages[-1]["content"]) |
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|
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print(model) |
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print(messages) |
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print(top_p) |
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print(temperature) |
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uid = request_api_chatgpt(content, model, top_p, temperature) |
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# { |
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# "probabilities": null, |
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# "status_code": 200, |
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# "texts": { |
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# "id": "29379d06-d08b-11ee-b56d-31fe0a8adccc" |
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# } |
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# } |
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results = uuid_search_mp([uid])[0] |
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# 检查输入输出 |
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check_problems(messages[0]["content"], results) |
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return_type_list = return_type(messages[0]["content"], results) |
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|
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return_text = { |
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'code': 200, |
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'id': uid["texts"]["id"], |
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'object': 0, |
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'created': 0, |
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'model': model, |
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'choices': [ |
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{ |
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'index': 0, |
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'message': { |
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'role': 'assistant', |
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'content': results |
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}, |
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'logprobs': None, |
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'finish_reason': 'stop' |
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} |
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], |
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'return_type_list': return_type_list, |
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'usage': 0, |
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'system_fingerprint': 0 |
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} |
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return jsonify(return_text) |
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|
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|
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|
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|
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if __name__ == '__main__': |
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app.run(host="0.0.0.0", port=12004, threaded=True, debug=False) |
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|
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@ -0,0 +1,273 @@ |
|||
#coding:utf-8 |
|||
# 这是一个示例 Python 脚本。 |
|||
|
|||
# 按 Shift+F10 执行或将其替换为您的代码。 |
|||
# 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。 |
|||
|
|||
|
|||
# def request_api_chatgpt(api_key, prompt): |
|||
# print(api_key) |
|||
# print(prompt) |
|||
# OPENAI_API_KEY = api_key |
|||
# url = "https://api.openai.com/v1/chat/completions" |
|||
# # url = "https://one.aiskt.com" |
|||
# headers = { |
|||
# "Content-Type": "application/json", |
|||
# "Authorization": f"Bearer {OPENAI_API_KEY}" |
|||
# } |
|||
# data = { |
|||
# "model": "gpt-4-turbo-preview", |
|||
# "messages": [ |
|||
# {"role": "user", "content": "你好"}, |
|||
# {"role": "assistant", "content": "你好!有什么我可以帮助你的吗?"}, |
|||
# # {"role": "user", "content": prompt} |
|||
# {"role": "user", "content": "一张信用卡为多个gpt4账号付费会风控吗"} |
|||
# ], |
|||
# "top_p": 0.9, |
|||
# "temperature": 0.95 |
|||
# } |
|||
# response = requests.post(url, |
|||
# headers=headers, |
|||
# data=json.dumps(data), |
|||
# timeout=1200) |
|||
# |
|||
# return response |
|||
|
|||
from flask import Flask, jsonify |
|||
from flask import request |
|||
import requests |
|||
import time |
|||
import socket |
|||
import re |
|||
|
|||
app = Flask(__name__) |
|||
app.config["JSON_AS_ASCII"] = False |
|||
|
|||
|
|||
def get_host_ip(): |
|||
""" |
|||
查询本机ip地址 |
|||
:return: ip |
|||
""" |
|||
try: |
|||
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) |
|||
s.connect(('8.8.8.8', 80)) |
|||
ip = s.getsockname()[0] |
|||
finally: |
|||
s.close() |
|||
|
|||
return ip |
|||
|
|||
chatgpt_url_predict = "http://{}:12001/predict".format(str(get_host_ip())) |
|||
chatgpt_url_search = "http://{}:12001/search".format(str(get_host_ip())) |
|||
|
|||
|
|||
def check_problems(input, output): |
|||
pantten_formula = r'\\\[.*?\\\]' |
|||
pantten_picture = r'<mermaidStart>.*?<mermaidEnd>' |
|||
pantten_tb = r'<tbStart>.*?<tbEnd>' |
|||
|
|||
error_data = "" |
|||
# 判断是否是小标题任务 |
|||
if "任务：生成论文小标题内容" in input: |
|||
# 判断公式 |
|||
formula_bool_list = re.findall(pantten_formula, output, re.DOTALL) |
|||
tb_bool_list = re.findall(pantten_tb, output, re.DOTALL) |
|||
picture_bool_list = re.findall(pantten_picture, output, re.DOTALL) |
|||
|
|||
if "数学公式用\\[\\]进行包裹" not in input and formula_bool_list != []: |
|||
error_data += "多生成公式问题:\n" |
|||
error_data += "input:\n" |
|||
error_data += input |
|||
error_data += "output:\n" |
|||
error_data += output |
|||
error_data += "\n========================================================================\n" |
|||
# 判断公式 |
|||
|
|||
if "表格部分开始必须用<tbStart>标识，表格部分结束必须用<tbEnd>标识，必须返回html格式的表格" not in input and tb_bool_list != []: |
|||
error_data += "多生成表格问题:\n" |
|||
error_data += "input:\n" |
|||
error_data += input |
|||
error_data += "output:\n" |
|||
error_data += output |
|||
error_data += "\n========================================================================\n" |
|||
|
|||
if "图片要求在文字中插入一张图" not in input and picture_bool_list != []: |
|||
error_data += "多生成图片问题:\n" |
|||
error_data += "input:\n" |
|||
error_data += input |
|||
error_data += "output:\n" |
|||
error_data += output |
|||
error_data += "\n========================================================================\n" |
|||
if error_data != "": |
|||
with open("logs/error_xiaobiaoti.log", "a", encoding="utf-8") as f: |
|||
f.write(error_data) |
|||
|
|||
|
|||
def return_type(input, output): |
|||
pantten_formula = r'\\\[.*?\\\]' |
|||
pantten_picture = r'<mermaidStart>.*?<mermaidEnd>' |
|||
pantten_tb = r'<tbStart>.*?<tbEnd>' |
|||
|
|||
return_type_list = [] |
|||
# 判断是否是小标题任务 |
|||
if "任务：生成论文小标题内容" in input: |
|||
# 判断表格 |
|||
tb_bool_list = re.findall(pantten_tb, output, re.DOTALL) |
|||
formula_bool_list = re.findall(pantten_formula, output, re.DOTALL) |
|||
picture_bool_list = re.findall(pantten_picture, output, re.DOTALL) |
|||
|
|||
if tb_bool_list != []: |
|||
return_type_list.append("1") |
|||
|
|||
if formula_bool_list != []: |
|||
return_type_list.append("2") |
|||
|
|||
if picture_bool_list != []: |
|||
return_type_list.append("3") |
|||
|
|||
return return_type_list |
|||
|
|||
|
|||
|
|||
def request_api_chatgpt(content, model, top_p, temperature): |
|||
data = { |
|||
"content": content, |
|||
"model": model, |
|||
"top_p": top_p, |
|||
"temperature": temperature |
|||
} |
|||
response = requests.post( |
|||
chatgpt_url_predict, |
|||
json=data, |
|||
timeout=100000 |
|||
) |
|||
if response.status_code == 200: |
|||
return response.json() |
|||
else: |
|||
# logger.error( |
|||
# "【{}】 Failed to get a proper response from remote " |
|||
# "server. Status Code: {}. Response: {}" |
|||
# "".format(url, response.status_code, response.text) |
|||
# ) |
|||
print("Failed to get a proper response from remote " |
|||
"server. Status Code: {}. Response: {}" |
|||
"".format(response.status_code, response.text)) |
|||
return {} |
|||
|
|||
|
|||
def uuid_search(uuid): |
|||
data = { |
|||
"id": uuid |
|||
} |
|||
response = requests.post( |
|||
chatgpt_url_search, |
|||
json=data, |
|||
timeout=100000 |
|||
) |
|||
if response.status_code == 200: |
|||
return response.json() |
|||
else: |
|||
# logger.error( |
|||
# "【{}】 Failed to get a proper response from remote " |
|||
# "server. Status Code: {}. Response: {}" |
|||
# "".format(url, response.status_code, response.text) |
|||
# ) |
|||
print("Failed to get a proper response from remote " |
|||
"server. Status Code: {}. Response: {}" |
|||
"".format(response.status_code, response.text)) |
|||
return {} |
|||
|
|||
|
|||
def uuid_search_mp(results): |
|||
|
|||
results_list = [""] * len(results) |
|||
while True: |
|||
tiaochu_bool = True |
|||
|
|||
for i in results_list: |
|||
if i == "": |
|||
tiaochu_bool = False |
|||
break |
|||
|
|||
if tiaochu_bool == True: |
|||
break |
|||
|
|||
for i in range(len(results)): |
|||
uuid = results[i]["texts"]["id"] |
|||
|
|||
result = uuid_search(uuid) |
|||
if result["code"] == 200: |
|||
results_list[i] = result["text"] |
|||
time.sleep(3) |
|||
return results_list |
|||
|
|||
|
|||
@app.route("/predict", methods=["POST"]) |
|||
def handle_query(): |
|||
print(request.remote_addr) |
|||
model = request.json.get("model") |
|||
messages = request.json.get("messages") |
|||
top_p = request.json.get("top_p") |
|||
temperature = request.json.get("temperature") |
|||
|
|||
print(model) |
|||
print(messages) |
|||
print(top_p) |
|||
print(temperature) |
|||
|
|||
# "messages": [ |
|||
# {"role": "user", "content": "你好"}, |
|||
# {"role": "assistant", "content": "你好!有什么我可以帮助你的吗?"}, |
|||
# # {"role": "user", "content": prompt} |
|||
# {"role": "user", "content": "一张信用卡为多个gpt4账号付费会风控吗"} |
|||
# ], |
|||
# text = "User: " + messages[-1]["content"] + "\nAssistant:" |
|||
content = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n".format(messages[-1]["content"]) |
|||
|
|||
print(model) |
|||
print(messages) |
|||
print(top_p) |
|||
print(temperature) |
|||
uid = request_api_chatgpt(content, model, top_p, temperature) |
|||
# { |
|||
# "probabilities": null, |
|||
# "status_code": 200, |
|||
# "texts": { |
|||
# "id": "29379d06-d08b-11ee-b56d-31fe0a8adccc" |
|||
# } |
|||
# } |
|||
results = uuid_search_mp([uid])[0] |
|||
# 检查输入输出 |
|||
check_problems(messages[0]["content"], results) |
|||
return_type_list = return_type(messages[0]["content"], results) |
|||
|
|||
return_text = { |
|||
'code': 200, |
|||
'id': uid["texts"]["id"], |
|||
'object': 0, |
|||
'created': 0, |
|||
'model': model, |
|||
'choices': [ |
|||
{ |
|||
'index': 0, |
|||
'message': { |
|||
'role': 'assistant', |
|||
'content': results |
|||
}, |
|||
'logprobs': None, |
|||
'finish_reason': 'stop' |
|||
} |
|||
], |
|||
'return_type_list': return_type_list, |
|||
'usage': 0, |
|||
'system_fingerprint': 0 |
|||
} |
|||
return jsonify(return_text) |
|||
|
|||
|
|||
|
|||
|
|||
if __name__ == '__main__': |
|||
app.run(host="0.0.0.0", port=12004, threaded=True, debug=False) |
|||
|
|||
@ -0,0 +1 @@ |
|||
gunicorn main_mulu_openbuddy.py:app -c gunicorn_config_mulu_openbuddy.py |
|||
@ -0,0 +1 @@ |
|||
gunicorn main_openbuddy:app -c gunicorn_config_openbuddy.py |
|||
@ -0,0 +1 @@ |
|||
gunicorn main_openbuddy_llama_3_2_1b:app -c gunicorn_config_openbuddy_model_llama_3_2.py |
|||
@ -0,0 +1 @@ |
|||
gunicorn main_qwen:app -c gunicorn_config_qwen.py |
|||
@ -0,0 +1,42 @@ |
|||
# coding:utf-8 |
|||
import json |
|||
import re |
|||
|
|||
pan = r'\\\[.*?\\\]' |
|||
|
|||
# sentence = "2.4.1 时域有限差分原理\n\n时域有限差分（FDTD）方法是一种计算电磁波传播和散射问题的数值分析技术。该方法基于Maxwell方程在时域内的直接求解，通过离散化时间和空间来模拟电磁场的行为。FDTD方法的核心在于将连续的电磁场方程转换为可以在计算机上求解的离散形式。\n\nMaxwell方程的基本形式包括：\n\\[\n\\nabla \\times \\mathbf{E} = -\\frac{\\partial \\mathbf{B}}{\\partial t}\n\\]\n\\[\n\\nabla \\times \\mathbf{H} = \\frac{\\partial \\mathbf{D}}{\\partial t} + \\mathbf{J}\n\\]\n其中，\\(\\mathbf{E}\\) 和 \\(\\mathbf{H}\\) 分别是电场和磁场，\\(\\mathbf{D}\\) 和 \\(\\mathbf{B}\\) 是电位移场和磁感应场，\\(\\mathbf{J}\\) 是电流密度。\n\n在FDTD方法中，空间被划分为一个由Yee格子组成的网格，每个网格点上的电场和磁场分量都被独立更新。时间也被离散化，更新算法交替在电场和磁场之间执行，形成一个可以迭代的计算过程。\n\n更新电场和磁场的离散方程如下：\n\\[\nE_x^{n+1}(i, j, k) = E_x^n(i, j, k) + \\frac{\\Delta t}{\\epsilon} \\left( \\frac{H_z^{n+0.5}(i, j+1, k) - H_z^{n+0.5}(i, j, k)}{\\Delta y} - \\frac{H_y^{n+0.5}(i, j, k+1) - H_y^{n+0.5}(i, j, k)}{\\Delta z} \\right)\n\\]\n\\[\nH_z^{n+0.5}(i, j, k) = H_z^n(i, j, k) - \\frac{\\Delta t}{\\mu} \\left( \\frac{E_x^{n+1}(i, j, k) - E_x^{n+1}(i, j-1, k)}{\\Delta y} - \\frac{E_y^{n+1}(i, j, k) - E_y^{n+1}(i, j, k-1)}{\\Delta x} \\right)\n\\]\n\n<mermaidStart>\nstateDiagram-v2\n [*] --> MaxwellEquations: Maxwell方程\n MaxwellEquations --> YeeGrid: Yee格子划分\n YeeGrid --> UpdateE: 更新电场E\n UpdateE --> UpdateH: 更新磁场H\n UpdateH --> CheckConvergence: 检查收敛性\n CheckConvergence --> [*]\n CheckConvergence --> UpdateE\n<mermaidEnd>\n\n通过上述迭代过程，FDTD方法能够模拟复杂介质中电磁波的传播和相互作用，广泛应用于光学、天线设计、微波工程等领域。该方法的优点在于其直观性和相对简单的实现方式，但同时也存在时间步长和空间步长受稳定性条件限制的缺点。" |
|||
|
|||
sentence = "4.3 德国破产管理人选任和责任追究制度的改革\n\n德国在破产管理人选任和责任追究方面的制度改革，体现了对破产程序透明度和效率的重视。该国通过立法明确了破产管理人的职责范围、选任标准以及违规行为的法律后果，从而提高了破产管理人的专业性和责任感。\n\n在选任方面，德国规定破产管理人必须具备特定的资格和经验。这包括但不限于财务管理、法律知识以及破产程序的理解。此外，破产管理人的选任还需经过相关机构的严格审核，确保其具备履行职责的能力。具体的选任流程可以用以下公式表示：\n\n\\[\n选任流程 = \\frac{申请者资格审核}{专业能力评估} \\times \\frac{背景调查}{最终确认}\n\\]\n\n在责任追究方面，德国法律对破产管理人的行为设定了明确的法律责任。一旦破产管理人未能恰当履行职责，或因疏忽导致破产程序的延误或其他负面影响，将面临民事赔偿责任甚至刑事责任。此外，破产管理人还需向破产清算委员会报告其工作进展和遇到的问题，接受监督和评估。\n\n德国的破产管理人选任和责任追究制度改革，有效提升了破产管理人的专业水平和工作效率，同时也增强了破产程序的公正性和透明度。以下是一个简单的代码示例，展示了如何使用Python来模拟破产管理人选任的过程：\n\n```python\ndef select_bankruptcy_manager(qualifications, experience, legal_knowledge, bankruptcy_program_understanding):\n # 定义选任标准\n qualification_threshold = 0.6\n experience_threshold = 0.5\n legal_knowledge_threshold = 0.7\n bankruptcy_program_understanding_threshold = 0.8\n\n # 计算申请者符合标准的程度\n qualifications_score = max(qualifications, 1) if qualifications >= qualification_threshold else 0\n experience_score = max(experience, 1) if experience >= experience_threshold else 0\n legal_knowledge_score = max(legal_knowledge, 1) if legal_knowledge >= legal_knowledge_threshold else 0\n bankruptcy_program_understanding_score = max(bankruptcy_program_understanding, 1) if bankruptcy_program_understanding >= bankruptcy_program_understanding_threshold else 0\n\n # 综合评分\n total_score = sum([qualifications_score, experience_score, legal_knowledge_score, bankruptcy_program_understanding_score])\n\n # 根据总分决定是否选任\n if total_score >= 4:\n return \"合格\"\n else:\n return \"不合格\"\n\n# 示例应用\napplicant_qualifications = 0.75\napplicant_experience = 0.6\napplicant_legal_knowledge = 0.9\napplicant_bankruptcy_program_understanding = 0.85\nresult = select_bankruptcy_manager(applicant_qualifications, applicant_experience, applicant_legal_knowledge, applicant_bankruptcy_program_understanding)\nprint(result)\n```\n\n通过上述代码，我们可以模拟破产管理人的选任过程，根据申请者的不同资质和经验，计算出其是否符合选任标准。这种方法有助于提高破产管理人选任的科学性和公正性。" |
|||
|
|||
print(re.findall(pan, sentence, re.DOTALL)) |
|||
|
|||
# sentence_new = sentence.replace("\\[", "<formulaStart>").replace("\\]", "<formulaEnd>") |
|||
# print(sentence_new) |
|||
|
|||
b = '''背景:我是一名博士生，我想写一篇论文。 |
|||
要求：根据论文题目《论破产管理人的规制》，目录是“一、绪论 |
|||
1.1 研究背景与研究意义 |
|||
1.2 研究综述 |
|||
1.3 研究方法与研究思路 |
|||
二、破产管理人的规制的理论依据 |
|||
2.1 破产管理人的特殊性 |
|||
2.2 破产程序法定主义 |
|||
2.3 破产管理人的独立性原则 |
|||
三、破产管理人监管的现状及问题分析 |
|||
3.1 监管主体 |
|||
3.2 监管方式 |
|||
3.3 监管内容 |
|||
3.4 监管结果 |
|||
四、国外破产管理人规制经验借鉴 |
|||
4.1 美国破产管理人监督体系的构建 |
|||
4.2 英国破产管理人和破产清算委员会制度的设立 |
|||
4.3 德国破产管理人选任和责任追究制度的改革 |
|||
五、我国破产管理人监管机制的改进建议 |
|||
5.1 构建多元主体监督制度 |
|||
5.2 建立专业化破产管理人队伍 |
|||
5.3 完善破产管理人入选制度 |
|||
5.4 制度完善后的保障措施 |
|||
六、结论 |
|||
6.1 研究结论 |
|||
6.2 研究不足”，为小标题“4.3 德国破产管理人选任和责任追究制度的改革”填充1000字左右的中文内容，内容第一行返回“4.3 德国破产管理人选任和责任追究制度的改革”且不包含目录中其他标题，禁止出现“首先”，“其次”，等字样，必要时候应根据条数罗列。这段内容中必须包含公式、代码、其中，数学公式用\[\]进行包裹。代码要求在文中合理位置插入一段代码，代码要求质量要高。''' |
|||
print(json.dumps(b,ensure_ascii=False)) |
|||
Loading…
Reference in new issue