268 lines
11 KiB
Python
268 lines
11 KiB
Python
from fastapi import APIRouter, HTTPException, Depends
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from pydantic import BaseModel
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from typing import List, Optional
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import httpx
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import os
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import logging
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from app.services.glm_service import get_glm_service, GLMService
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from app.models.base import BaseOut
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# 创建API路由，使用/glm前缀
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router = APIRouter(prefix="/glm", tags=["GLM聊天"])
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logger = logging.getLogger(__name__)
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# 数据模型
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class Message(BaseModel):
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role: str
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content: str
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class ChatRequest(BaseModel):
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message: str
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conversation_id: Optional[str] = None
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class ChatResponse(BaseModel):
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response: str
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conversation_id: str
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class RoleConfig(BaseModel):
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name: str
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description: str
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skills: str # 多行文本字符串
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restrictions: str # 多行文本字符串
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# 默认角色配置（使用字符串格式）
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DEFAULT_ROLE_CONFIG = RoleConfig(
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name="兜兜",
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description="你是兜兜，1989年出生，有一个5岁的女儿（2018年出生）。你是一位对用户负责、选品严格的宝妈客服。当用户给你发来疑问时，你总能第一时间回复，并以亲切温暖的风格与用户交流。",
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skills="亲切温暖的沟通\n轻松友好的语气\n简洁明了的描述\n亲昵且口语化的用词\n乱码话术重复问询",
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restrictions="回复必须基于提供的知识库中的内容\n每条回复应保持在80字以内，使用口语化语言\n回复中可以适当加入表情符号，但不要过度使用\n只讨论与宝妈相关的主题，如育儿、选品、团购等\n始终以用户的需求和满意度为优先",
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)
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# 全局变量存储对话历史（暂时保持内存存储，根据用户要求不持久化）
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conversations = {}
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def build_system_prompt(role_config: dict) -> str:
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"""构建系统提示词
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Args:
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role_config: 角色配置字典，包含name, description, skills, restrictions字段
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Returns:
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系统提示词字符串
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"""
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# 解析技能字符串为列表
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skills_list = [
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skill.strip() for skill in role_config["skills"].split("\n") if skill.strip()
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]
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restrictions_list = [
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restriction.strip()
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for restriction in role_config["restrictions"].split("\n")
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if restriction.strip()
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]
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# 构建技能部分
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skills_section = ""
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for i, skill in enumerate(skills_list, 1):
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skills_section += f"\n### 技能 {i}: {skill}"
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# 添加技能描述（这里可以根据技能名称添加不同的描述）
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if "亲切温暖" in skill:
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skills_section += '\n- **风格**：你的风格非常亲切，像是一个知心朋友在和大家聊天，给人一种温暖的感觉。你经常使用"宝"、"美妈"等亲昵的称呼，拉近了与用户的距离。'
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skills_section += "\n- **耐心细致**：你总是耐心解答用户的问题，尤其是对于新用户或不熟悉流程的用户，你会反复提醒他们查看群公告，保持安静等待开团。"
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skills_section += "\n- **负责靠谱**：你给人一种非常靠谱的感觉，尤其是在售后问题上，你会主动承担责任，承诺包处理满意，给用户很强的信任感。"
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elif "轻松友好" in skill:
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skills_section += '\n- **轻松友好**：你的语气非常轻松，像是在和朋友聊天，不会给人压力。比如"不着急哦"、"爱你！"等表达方式，让人感到轻松愉快。'
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skills_section += '\n- **安抚性**：当用户有疑问或遇到问题时，你的语气总是充满安抚性，比如"美妈不用担心"、"兜兜来处理哈"，给人一种安心感。'
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skills_section += '\n- **鼓励性**：你经常用"不着急"、"等开团的时候会有统一说明"等话语，鼓励用户耐心等待，保持积极的心态。'
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elif "简洁明了" in skill:
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skills_section += '\n- **简洁明了**：你的描述非常简洁，直接回答用户的问题，不会绕弯子。比如"贝亲是咱们深度合作品牌，经常上团的"，直接给出了明确的答案。'
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skills_section += '\n- **细节丰富**：在回答问题时，你会提供一些细节信息，比如"一般2天左右就会来"、"奶粉类的，贝亲，世喜，赫根，等常见品牌都有"，让用户对情况有更清晰的了解。'
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skills_section += '\n- **引导性**：你经常引导用户查看群公告，保持安静等待开团，比如"不熟悉的可以看一下群公告哦"、"现在安静的等开团就行啦"，帮助用户更好地参与活动。'
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elif "亲昵且口语化" in skill:
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skills_section += '\n- **亲昵称呼**：你经常使用"宝"、"美妈"、"大家"等亲昵的称呼，拉近与用户的距离，给人一种亲切感。'
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skills_section += '\n- **口语化**：你的用词非常口语化，像是日常聊天，比如"不着急哦"、"爱你！"、"兜兜来处理哈"，给人一种轻松自然的感觉，称呼自己为"兜兜"或者"我"。'
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skills_section += '\n- **积极正面**：你的用词总是积极正面的，比如"不用担心"、"包处理满意"、"很快"、"常见品牌都有"，给用户传递了积极的信息。'
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elif "乱码话术" in skill:
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skills_section += '\n- **确认问题**：用户说的看不懂的情况来进行再次确认，比如 "45615465" "分隔符规划规范的" "8分隔符公司的"等跟常规问题没关联的话术来重复确认，例如："宝看不懂你说的是什么啊，能在具体点嘛"'
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else:
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skills_section += f"\n- **{skill}**：请根据这个技能特点来回答问题。"
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# 构建限制部分
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restrictions_section = "\n".join(
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[f"- {restriction}" for restriction in restrictions_list]
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)
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prompt = f"""# 角色
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{role_config["description"]}
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## 技能{skills_section}
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## 限制
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{restrictions_section}
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请按照以上角色设定和限制来回答用户的问题。"""
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return prompt
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def get_deepseek_api_key() -> str:
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"""获取DeepSeek API密钥"""
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api_key = os.getenv("DEEPSEEK_API_KEY", "your-deepseek-api-key")
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return api_key
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def is_api_key_configured() -> bool:
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"""检查API密钥是否已配置"""
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api_key = get_deepseek_api_key()
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return api_key != "your-deepseek-api-key"
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async def call_deepseek_api(messages: List[dict]) -> str:
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"""调用DeepSeek API"""
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# 直接使用环境变量获取API密钥
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api_key = get_deepseek_api_key()
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if not is_api_key_configured():
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# 模拟响应用于演示
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return "宝，这个问题兜兜需要查一下资料哦，稍等哈~ 😊"
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url = "https://api.deepseek.com/v1/chat/completions"
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headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}
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data = {
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"model": "deepseek-chat",
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"messages": messages,
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"stream": False,
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"max_tokens": 500,
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}
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try:
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async with httpx.AsyncClient() as client:
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response = await client.post(url, headers=headers, json=data, timeout=30.0)
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response.raise_for_status()
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result = response.json()
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return result["choices"][0]["message"]["content"]
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"API调用失败: {str(e)}")
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@router.get("/")
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async def root():
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return {"message": "DeepSeek AI Chat Assistant API"}
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@router.get("/role", response_model=BaseOut)
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async def get_role_config(service: GLMService = Depends(get_glm_service)):
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"""获取当前角色配置"""
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try:
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role = await service.get_current_role()
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return BaseOut(code=200, message="ok", result=role.to_role_config_dict())
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except Exception as e:
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logger.error(f"获取角色配置失败: {e}")
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return BaseOut(code=500, message=f"获取角色配置失败: {str(e)}", result=None)
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@router.put("/role", response_model=BaseOut)
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async def update_role_config(
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role_config: RoleConfig, service: GLMService = Depends(get_glm_service)
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):
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"""更新角色配置"""
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try:
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# 将Pydantic模型转换为字典
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role_data = role_config.dict()
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updated_role = await service.update_role(role_data)
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return BaseOut(
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code=200,
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message="角色配置已更新",
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result={
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"message": "角色配置已更新",
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"role_config": updated_role.to_role_config_dict(),
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},
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)
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except Exception as e:
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logger.error(f"更新角色配置失败: {e}")
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return BaseOut(code=500, message=f"更新角色配置失败: {str(e)}", result=None)
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@router.post("/chat", response_model=ChatResponse)
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async def chat(request: ChatRequest, service: GLMService = Depends(get_glm_service)):
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"""处理聊天请求"""
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# 创建或获取对话历史
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if request.conversation_id and request.conversation_id in conversations:
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conversation_history = conversations[request.conversation_id]
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else:
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conversation_history = []
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request.conversation_id = f"conv_{len(conversations) + 1}"
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conversations[request.conversation_id] = conversation_history
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# 获取当前角色配置
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try:
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role = await service.get_current_role()
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system_prompt = build_system_prompt(role.to_role_config_dict())
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except Exception as e:
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logger.error(f"获取角色配置失败: {e}")
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raise HTTPException(status_code=500, detail=f"获取角色配置失败: {str(e)}")
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# 构建消息列表
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messages = [{"role": "system", "content": system_prompt}]
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# 添加历史消息
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for msg in conversation_history[-10:]: # 保留最近10条历史
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messages.append({"role": msg["role"], "content": msg["content"]})
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# 添加当前用户消息
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messages.append({"role": "user", "content": request.message})
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conversation_history.append({"role": "user", "content": request.message})
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# 调用DeepSeek API
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try:
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response_text = await call_deepseek_api(messages)
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# 添加助手回复到历史
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conversation_history.append({"role": "assistant", "content": response_text})
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return ChatResponse(
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response=response_text, conversation_id=request.conversation_id
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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@router.get("/conversations/{conversation_id}")
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async def get_conversation(conversation_id: str):
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"""获取特定对话的历史"""
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if conversation_id not in conversations:
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raise HTTPException(status_code=404, detail="对话不存在")
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return {
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"conversation_id": conversation_id,
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"messages": conversations[conversation_id],
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}
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@router.post("/role/reset", response_model=BaseOut)
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async def reset_role_config(service: GLMService = Depends(get_glm_service)):
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"""重置角色配置为默认值"""
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try:
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# 使用 DEFAULT_ROLE_CONFIG 更新角色
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role_data = DEFAULT_ROLE_CONFIG.dict()
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updated_role = await service.update_role(role_data)
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return BaseOut(
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code=200,
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message="角色配置已重置为默认值",
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result={
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"message": "角色配置已重置为默认值",
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"role_config": updated_role.to_role_config_dict(),
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},
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)
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except Exception as e:
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logger.error(f"重置角色配置失败: {e}")
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return BaseOut(code=500, message=f"重置角色配置失败: {str(e)}", result=None)
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