Update app.py

app.py

@@ -1,12 +1,18 @@
-import os
-import json
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+from flask import Flask, request, make_response
 import hashlib
 import time
 import xml.etree.ElementTree as ET
-from flask import Flask, request, make_response
+import os
+import json
 from openai import OpenAI
 from dotenv import load_dotenv
-from functions import FUNCTIONS, FUNCTIONS_GROUP_1, FUNCTIONS_GROUP_2, process_function_call
+from duckduckgo_search import DDGS
+import requests
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
 
 # 加载环境变量
 load_dotenv()
@@ -17,18 +23,66 @@ app = Flask(__name__)
 TOKEN = os.getenv('TOKEN')
 API_KEY = os.getenv("API_KEY")
 BASE_URL = os.getenv("OPENAI_BASE_URL")
+emailkey = os.getenv("EMAIL_KEY")
 client = OpenAI(api_key=API_KEY, base_url=BASE_URL)
 
-# 定义可用的模型列表
-AVAILABLE_MODELS = {
-    'gpt-4o-mini': 'gpt-4o-mini',
-    'gpt-4o-mini': 'gpt-4o-mini',
-    'gpt-4o-mini': 'gpt-4o-mini',
-}
-
 # 存储用户会话信息
 user_sessions = {}
 
+FUNCTIONS = [
+    {
+        "name": "search_duckduckgo",
+        "description": "使用DuckDuckGo搜索引擎查询信息。可以搜索最新新闻、文章、博客等内容。",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "keywords": {
+                    "type": "array",
+                    "items": {"type": "string"},
+                    "description": "搜索的关键词列表。例如：['Python', '机器学习', '最新进展']。"
+                }
+            },
+            "required": ["keywords"]
+        }
+    },
+    {
+        "name": "search_papers",
+        "description": "使用Crossref API搜索学术论文。",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": "搜索查询字符串。例如：'climate change'。"
+                }
+            },
+            "required": ["query"]
+        }
+    },
+    {
+        "name": "send_email",
+        "description": "发送电子邮件。",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "to": {
+                    "type": "string",
+                    "description": "收件人邮箱地址"
+                },
+                "subject": {
+                    "type": "string",
+                    "description": "邮件主题"
+                },
+                "content": {
+                    "type": "string",
+                    "description": "邮件内容"
+                }
+            },
+            "required": ["to", "subject", "content"]
+        }
+    }
+]
+
 def verify_wechat(request):
     data = request.args
     signature = data.get('signature')
@@ -66,13 +120,14 @@ def generate_response_xml(from_user_name, to_user_name, output_content):
     response.content_type = 'application/xml'
     return response
 
-def get_openai_response(messages, model="gpt-4o-mini", functions=None, function_call=None):
+def get_openai_response(messages):
     try:
+        tools = [{"type": "function", "function": func} for func in FUNCTIONS]
         response = client.chat.completions.create(
-            model=model,
+            model="gpt-4o-mini",
             messages=messages,
-            functions=functions,
-            function_call=function_call
+            tools=tools,
+            tool_choice="auto"
         )
         return response.choices[0].message
     except Exception as e:
@@ -82,8 +137,47 @@ def get_openai_response(messages, model="gpt-4o-mini", functions=None, function_
 def split_message(message, max_length=500):
     return [message[i:i+max_length] for i in range(0, len(message), max_length)]
 
-def list_available_models():
-    return "\n".join([f"{key}: {value}" for key, value in AVAILABLE_MODELS.items()])
+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'][:5]  # 限制结果数量
+        return [{"title": paper.get('title', [''])[0], "DOI": paper.get('DOI', '')} for paper in 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 process_function_call(tool_call):
+    function_name = tool_call.function.name
+    function_args = json.loads(tool_call.function.arguments)
+    if function_name == "search_duckduckgo":
+        return search_duckduckgo(function_args.get('keywords', []))
+    elif function_name == "search_papers":
+        return search_papers(function_args.get('query', ''))
+    elif function_name == "send_email":
+        return send_email(function_args.get('to', ''), function_args.get('subject', ''), function_args.get('content', ''))
+    else:
+        return None
 
 @app.route('/api/wx', methods=['GET', 'POST'])
 def wechatai():
@@ -91,28 +185,14 @@ def wechatai():
         return verify_wechat(request)
     else:
         xml_str = request.data
-        if not xml_str:
-            return ""
-        
         user_message_content, from_user_name, to_user_name = getUserMessageContentFromXML(xml_str)
-        
+
         if from_user_name not in user_sessions:
-            user_sessions[from_user_name] = {'model': 'gpt-4o-mini', 'messages': [], 'pending_response': []}
+            user_sessions[from_user_name] = {'messages': [], 'pending_response': []}
 
         session = user_sessions[from_user_name]
-        
-        if user_message_content.lower() == '/models':
-            response_content = f"可用的模型列表：\n{list_available_models()}\n\n使用 /model 模型名称 来切换模型"
-            return generate_response_xml(from_user_name, to_user_name, response_content)
-        elif user_message_content.lower().startswith('/model'):
-            model = user_message_content.split(' ')[1]
-            if model in AVAILABLE_MODELS:
-                session['model'] = model
-                response_content = f'模型已切换为 {AVAILABLE_MODELS[model]}'
-            else:
-                response_content = f'无效的模型名称。可用的模型有：\n{list_available_models()}'
-            return generate_response_xml(from_user_name, to_user_name, response_content)
-        elif user_message_content.lower() == '继续':
+
+        if user_message_content.lower() == '继续':
             if session['pending_response']:
                 response_content = session['pending_response'].pop(0)
                 if session['pending_response']:
@@ -121,58 +201,40 @@ def wechatai():
                     response_content += '\n\n回复结束。'
             else:
                 response_content = "没有待发送的消息。"
-            return generate_response_xml(from_user_name, to_user_name, response_content)
-
-        session['messages'].append({"role": "user", "content": user_message_content})
-        messages = session['messages']
-        
-        # 次级模型1: 处理搜索相关函数
-        sub_model_1_response = get_openai_response(messages, model=session['model'], functions=FUNCTIONS_GROUP_1, function_call="auto")
-        
-        # 次级模型2: 处理邮件发送相关函数
-        sub_model_2_response = get_openai_response(messages, model=session['model'], 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
-        
-        if function_call_1 and function_call_2:
-            # 裁决模型: 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=session['model'])
-            if arbitration_response and ("模型1" in arbitration_response.content or function_call_1.name in arbitration_response.content):
-                final_function_call = function_call_1
-            else:
-                final_function_call = function_call_2
-        elif function_call_1:
-            final_function_call = function_call_1
-        elif function_call_2:
-            final_function_call = function_call_2
-        
-        if final_function_call:
-            function_name = final_function_call.name
-            function_args = json.loads(final_function_call.arguments)
-            function_result = process_function_call(function_name, function_args)
+        else:
+            session['messages'].append({"role": "user", "content": user_message_content})
             
-            messages.append({"role": "function", "name": function_name, "content": json.dumps(function_result, ensure_ascii=False)})
-        
-        # 主模型: 生成最终回复
-        final_response = get_openai_response(messages, model=session['model'])
-        response_content = final_response.content if final_response else "Error occurred"
-        
-        session['messages'].append({"role": "assistant", "content": response_content})
-        
-        # 处理长消息
-        response_parts = split_message(response_content)
-        if len(response_parts) > 1:
-            session['pending_response'] = response_parts[1:]
-            response_content = response_parts[0] + '\n\n回复"继续"获取下一部分。'
-        
+            gpt_response = get_openai_response(session['messages'])
+
+            if gpt_response.tool_calls:
+                for tool_call in gpt_response.tool_calls:
+                    function_result = process_function_call(tool_call)
+                    session['messages'].append({
+                        "role": "assistant",
+                        "content": None,
+                        "tool_call_id": tool_call.id,
+                        "tool_calls": [tool_call]
+                    })
+                    session['messages'].append({
+                        "role": "tool",
+                        "content": json.dumps(function_result),
+                        "tool_call_id": tool_call.id
+                    })
+                
+                final_response = get_openai_response(session['messages'])
+                response_content = final_response.content
+            else:
+                response_content = gpt_response.content
+
+            session['messages'].append({"role": "assistant", "content": response_content})
+
+            response_parts = split_message(response_content)
+            if len(response_parts) > 1:
+                response_content = response_parts[0] + '\n\n回复"继续"获取下一部分。'
+                session['pending_response'] = response_parts[1:]
+            else:
+                response_content = response_parts[0]
+
         return generate_response_xml(from_user_name, to_user_name, response_content)
 
 if __name__ == '__main__':