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import os
import json
import requests
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
from flask import Flask, request, jsonify, send_from_directory
from openai import OpenAI
from duckduckgo_search import DDGS
from functions import FUNCTIONS_GROUP_1, FUNCTIONS_GROUP_2, get_function_descriptions
app = Flask(__name__)
API_KEY = os.getenv("OPENAI_API_KEY")
BASE_URL = os.getenv("OPENAI_BASE_URL")
emailkey = os.getenv("EMAIL_KEY")
client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
def search_duckduckgo(keywords):
search_term = " ".join(keywords)
with DDGS() as ddgs:
return list(ddgs.text(keywords=search_term, region="cn-zh", safesearch="on", max_results=5))
def search_papers(query):
url = f"https://api.crossref.org/works?query={query}"
response = requests.get(url)
if response.status_code == 200:
data = response.json()
papers = data['message']['items']
processed_papers = []
for paper in papers:
processed_paper = {
"标题": paper.get('title', [''])[0],
"作者": ", ".join([f"{author.get('given', '')} {author.get('family', '')}" for author in paper.get('author', [])]),
"DOI": paper.get('DOI', ''),
"ISBN": ", ".join(paper.get('ISBN', [])),
"摘要": paper.get('abstract', '').replace('<p>', '').replace('</p>', '').replace('<italic>', '').replace('</italic>', '')
}
processed_papers.append(processed_paper)
return processed_papers
else:
return []
def send_email(to, subject, content):
try:
with smtplib.SMTP('106.15.184.28', 8025) as smtp:
smtp.login("jwt", emailkey)
message = MIMEMultipart()
message['From'] = "Me <[email protected]>"
message['To'] = to
message['Subject'] = subject
message.attach(MIMEText(content, 'html'))
smtp.sendmail("[email protected]", to, message.as_string())
return True
except Exception as e:
print(f"发送邮件时出错: {str(e)}")
return False
def get_openai_response(messages, model="gpt-4o-mini", functions=None, function_call=None):
try:
response = client.chat.completions.create(
model=model,
messages=messages,
functions=functions,
function_call=function_call
)
return response.choices[0].message
except Exception as e:
print(f"调用OpenAI API时出错: {str(e)}")
return None
def process_function_call(function_name, function_args):
if function_name == "search_duckduckgo":
keywords = function_args.get('keywords', [])
if not keywords:
return "搜索关键词为空，无法执行搜索。"
return json.dumps(search_duckduckgo(keywords), ensure_ascii=False)
elif function_name == "search_papers":
query = function_args.get('query', '')
if not query:
return "搜索查询为空，无法执行论文搜索。"
return json.dumps(search_papers(query), ensure_ascii=False)
elif function_name == "send_email":
to = function_args.get('to', '')
subject = function_args.get('subject', '')
content = function_args.get('content', '')
if not to or not subject or not content:
return "邮件信息不完整，无法发送邮件。"
success = send_email(to, subject, content)
return json.dumps({
"success": success,
"message": "邮件发送成功" if success else "邮件发送失败",
"to": to,
"subject": subject,
"content": content,
"is_email": True # 添加这个标记来识别邮件发送功能
}, ensure_ascii=False)
else:
return "未知的函数调用。"
@app.route('/')
def index():
return send_from_directory('.', 'index.html')
# @app.route('/chat', methods=['POST'])
# def chat():
# data = request.json
# question = data['question']
# history = data.get('history', [])
# messages = history + [{"role": "user", "content": question}]
# status_log = []
# # 次级模型1: 处理搜索相关函数
# status_log.append("次级模型1：正在判断是否需要选调第一组函数")
# sub_model_1_response = get_openai_response(messages, model="gpt-4o-mini", functions=FUNCTIONS_GROUP_1, function_call="auto")
# # 次级模型2: 处理邮件发送相关函数
# status_log.append("次级模型2：正在判断是否需要选调第二组函数")
# sub_model_2_response = get_openai_response(messages, model="gpt-4o-mini", functions=FUNCTIONS_GROUP_2, function_call="auto")
# function_call_1 = sub_model_1_response.function_call if sub_model_1_response and sub_model_1_response.function_call else None
# function_call_2 = sub_model_2_response.function_call if sub_model_2_response and sub_model_2_response.function_call else None
# final_function_call = None
# response = None
# if function_call_1 and function_call_2:
# # 裁决模型: 决定使用哪个函数调用
# status_log.append("裁决模型：正在决定使用哪个函数调用")
# arbitration_messages = messages + [
# {"role": "system", "content": "两个次级模型都建议使用函数。请决定使用哪个函数更合适。"},
# {"role": "assistant", "content": f"次级模型1建议使用函数：{function_call_1.name}"},
# {"role": "assistant", "content": f"次级模型2建议使用函数：{function_call_2.name}"}
# ]
# arbitration_response = get_openai_response(arbitration_messages, model="gpt-4o-mini")
# if "模型1" in arbitration_response.content or function_call_1.name in arbitration_response.content:
# final_function_call = function_call_1
# status_log.append(f"裁决模型：决定使用函数 {function_call_1.name}")
# else:
# final_function_call = function_call_2
# status_log.append(f"裁决模型：决定使用函数 {function_call_2.name}")
# elif function_call_1:
# final_function_call = function_call_1
# status_log.append(f"次级模型1：决定使用函数 {function_call_1.name}")
# elif function_call_2:
# final_function_call = function_call_2
# status_log.append(f"次级模型2：决定使用函数 {function_call_2.name}")
# else:
# status_log.append("次级模型：判断不需要进行搜索或发送邮件")
# if final_function_call:
# function_name = final_function_call.name
# function_args = json.loads(final_function_call.arguments)
# status_log.append(f"正在执行函数 {function_name}")
# result = process_function_call(function_name, function_args)
# status_log.append(f"函数 {function_name} 执行完成")
# # 检查是否为邮件发送功能
# result_dict = json.loads(result)
# if result_dict.get("is_email", False):
# response = f"邮件{'已成功' if result_dict['success'] else '未能成功'}发送到 {result_dict['to']}。\n\n主题：{result_dict['subject']}\n\n内容：\n{result_dict['content']}"
# else:
# messages.append({
# "role": "function",
# "name": function_name,
# "content": result
# })
# # 只有在没有邮件发送结果时才调用主模型
# if not response:
# status_log.append("主模型：正在生成回答")
# final_response = get_openai_response(messages, model="gpt-4o-mini")
# response = final_response.content if final_response else "Error occurred"
# status_log.append("主模型：回答生成完成")
# return jsonify({
# "response": response,
# "status_log": status_log
# })
@app.route('/chat', methods=['POST'])
def chat():
data = request.json
question = data['question']
history = data.get('history', [])
messages = history + [{"role": "user", "content": question}]
status_log = []
# 次级模型1: 处理搜索相关函数
status_log.append("次级模型1：正在判断是否需要选调第一组函数")
sub_model_1_response = get_openai_response(messages, model="gpt-4o-mini", functions=FUNCTIONS_GROUP_1, function_call="auto")
# 次级模型2: 处理邮件发送相关函数
status_log.append("次级模型2：正在判断是否需要选调第二组函数")
sub_model_2_response = get_openai_response(messages, model="gpt-4o-mini", functions=FUNCTIONS_GROUP_2, function_call="auto")
function_call_1 = sub_model_1_response.function_call if sub_model_1_response and sub_model_1_response.function_call else None
function_call_2 = sub_model_2_response.function_call if sub_model_2_response and sub_model_2_response.function_call else None
if not function_call_1:
status_log.append("次级模型1：判断不需要选调第一组函数")
if not function_call_2:
status_log.append("次级模型2：判断不需要选调第二组函数")
final_function_call = None
response = None
if function_call_1 and function_call_2:
# 裁决模型: 决定使用哪个函数调用
status_log.append("裁决模型：正在决定使用哪个函数调用")
arbitration_messages = messages + [
{"role": "system", "content": "两个次级模型都建议使用函数。请决定使用哪个函数更合适。"},
{"role": "assistant", "content": f"次级模型1建议使用函数：{function_call_1.name}"},
{"role": "assistant", "content": f"次级模型2建议使用函数：{function_call_2.name}"}
]
arbitration_response = get_openai_response(arbitration_messages, model="gpt-4o-mini")
if "模型1" in arbitration_response.content or function_call_1.name in arbitration_response.content:
final_function_call = function_call_1
status_log.append(f"裁决模型：决定使用函数 {function_call_1.name}")
else:
final_function_call = function_call_2
status_log.append(f"裁决模型：决定使用函数 {function_call_2.name}")
elif function_call_1:
final_function_call = function_call_1
status_log.append(f"次级模型1：决定使用函数 {function_call_1.name}")
elif function_call_2:
final_function_call = function_call_2
status_log.append(f"次级模型2：决定使用函数 {function_call_2.name}")
else:
status_log.append("所有次级模型：判断不需要进行任何函数调用")
if final_function_call:
function_name = final_function_call.name
function_args = json.loads(final_function_call.arguments)
status_log.append(f"正在执行函数 {function_name}")
result = process_function_call(function_name, function_args)
status_log.append(f"函数 {function_name} 执行完成")
# 检查是否为邮件发送功能
result_dict = json.loads(result)
if result_dict.get("is_email", False):
response = f"邮件{'已成功' if result_dict['success'] else '未能成功'}发送到 {result_dict['to']}。\n\n主题：{result_dict['subject']}\n\n内容：\n{result_dict['content']}"
else:
messages.append({
"role": "function",
"name": function_name,
"content": result
})
# 只有在没有邮件发送结果时才调用主模型
if not response:
status_log.append("主模型：正在生成回答")
final_response = get_openai_response(messages, model="gpt-4o-mini")
response = final_response.content if final_response else "Error occurred"
status_log.append("主模型：回答生成完成")
return jsonify({
"response": response,
"status_log": status_log
})
@app.route('/settings', methods=['POST'])
def update_settings():
data = request.json
max_history = data.get('max_history', 10)
return jsonify({"status": "success", "max_history": max_history})
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860, debug=True) |