LPC/services/deepseek_service.py

import requests
import json
from typing import List, Dict, Optional, Generator
from config import Config, SYSTEM_PROMPTS, get_api_key


class DeepSeekService:
    def __init__(self):
        self.api_key = get_api_key()
        self.api_base = Config.DEEPSEEK_API_BASE
        self.model = Config.MODEL_NAME
        self.max_tokens = Config.MAX_TOKENS
        self.temperature = Config.TEMPERATURE
    
    def _get_headers(self) -> Dict[str, str]:
        return {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
    
    def _build_messages(self, system_prompt: str, conversation_history: List[Dict[str, str]], 
                        user_input: str) -> List[Dict[str, str]]:
        messages = [{"role": "system", "content": system_prompt}]
        messages.extend(conversation_history)
        messages.append({"role": "user", "content": user_input})
        return messages
    
    def _call_api(self, messages: List[Dict[str, str]], stream: bool = False) -> Dict:
        payload = {
            "model": self.model,
            "messages": messages,
            "max_tokens": self.max_tokens,
            "temperature": self.temperature,
            "stream": stream
        }
        
        # 创建会话对象并设置超时
        session = requests.Session()
        session.timeout = 120  # 会话级别的超时设置
        
        response = session.post(
            f"{self.api_base}/chat/completions",
            headers=self._get_headers(),
            json=payload
        )
        
        if response.status_code != 200:
            error_msg = f"API调用失败：状态码 {response.status_code}"
            try:
                error_detail = response.json().get("error", {})
                error_msg += f"，错误信息：{error_detail.get('message', '未知错误')}"
            except:
                error_msg += f"，响应内容：{response.text}"
            raise Exception(error_msg)
        
        return response.json()
    
    def chat(self, user_input: str, conversation_history: List[Dict[str, str]] = None,
             system_type: str = "general_assistant") -> Dict[str, str]:
        if conversation_history is None:
            conversation_history = []
        
        system_prompt = SYSTEM_PROMPTS.get(system_type, SYSTEM_PROMPTS["general_assistant"])
        messages = self._build_messages(system_prompt, conversation_history, user_input)
        
        response = self._call_api(messages)
        assistant_message = response["choices"][0]["message"]["content"]
        
        return {"role": "assistant", "content": assistant_message}
    
    def chat_with_feedback(self, user_input: str, user_answer: str,
                          conversation_history: List[Dict[str, str]] = None) -> Dict[str, str]:
        if conversation_history is None:
            conversation_history = []
        
        feedback_prompt = SYSTEM_PROMPTS["answer_feedback"]
        
        context = f"面试问题：{user_input}\n\n候选人回答：{user_answer}"
        messages = self._build_messages(feedback_prompt, conversation_history, context)
        
        response = self._call_api(messages)
        feedback_content = response["choices"][0]["message"]["content"]
        
        return {"role": "assistant", "content": feedback_content}
    
    def generate_interview_question(self, job_position: str, difficulty: str,
                                    conversation_history: List[Dict[str, str]] = None,
                                    phase: str = "intro") -> str:
        if conversation_history is None:
            conversation_history = []
        
        system_prompt = SYSTEM_PROMPTS["interview_simulation"]
        
        context = f"""
目标岗位：{job_position}
难度级别：{difficulty}
面试阶段：{phase}

请根据以上信息，提出一个针对性的面试问题。
"""
        messages = self._build_messages(system_prompt, conversation_history, context)
        
        response = self._call_api(messages)
        question = response["choices"][0]["message"]["content"]
        
        return question
    
    def optimize_resume(self, resume_content: str, target_position: str = None) -> Dict[str, str]:
        system_prompt = SYSTEM_PROMPTS["resume_optimization"]
        
        if target_position:
            user_input = f"目标岗位：{target_position}\n\n简历内容：\n{resume_content}"
        else:
            user_input = f"请分析以下简历内容，提供优化建议：\n\n{resume_content}"
        
        messages = [{"role": "system", "content": system_prompt}]
        messages.append({"role": "user", "content": user_input})
        
        response = self._call_api(messages)
        optimization_suggestions = response["choices"][0]["message"]["content"]
        
        return {
            "resume_content": resume_content,
            "target_position": target_position,
            "suggestions": optimization_suggestions
        }


deepseek_service = DeepSeekService()