from fastapi import APIRouter, HTTPException, Depends from pydantic import BaseModel from typing import List, Optional, Dict, Any import httpx import os import logging from app.services.glm_service import get_glm_service, GLMService from app.models.base import BaseOut # 创建API路由，使用/glm前缀 router = APIRouter(prefix="/glm", tags=["GLM聊天"]) logger = logging.getLogger(__name__) # 数据模型 class Message(BaseModel): role: str content: str class ChatRequest(BaseModel): message: str conversation_id: Optional[str] = None enhanced_prompt: Optional[str] = None # 前端生成的完整提示词 customer_tags: Optional[List[Dict[str, Any]]] = None # 客户标签 message_history: Optional[List[Dict[str, Any]]] = None # 企业微信历史消息 workflow_context: Optional[Dict[str, Any]] = None # 工作流上下文 class ChatResponse(BaseModel): response: str conversation_id: str class RoleConfig(BaseModel): name: str description: str skills: str # 多行文本字符串 restrictions: str # 多行文本字符串 # 默认角色配置（使用字符串格式） DEFAULT_ROLE_CONFIG = RoleConfig( name="兜兜", 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。", 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", 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", ) # 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） conversations = {} def build_system_prompt(role_config: dict) -> str: """构建系统提示词 Args: role_config: 角色配置字典，包含name, description, skills, restrictions字段 Returns: 系统提示词字符串 """ # 解析技能字符串为列表 skills_list = [ skill.strip() for skill in role_config["skills"].split("\n") if skill.strip() ] restrictions_list = [ restriction.strip() for restriction in role_config["restrictions"].split("\n") if restriction.strip() ] # 构建技能部分 skills_section = "" for i, skill in enumerate(skills_list, 1): skills_section += f"\n### 技能 {i}: {skill}" # 添加技能描述（这里可以根据技能名称添加不同的描述） if "亲切温暖" in skill: 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。' 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。" 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。" elif "轻松友好" in skill: 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。' 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。' 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。' elif "简洁明了" in skill: 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。' 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。' 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。' elif "亲昵且口语化" in skill: 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。' 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"。' 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。' elif "乱码话术" in skill: 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"' else: skills_section += f"\n- **{skill}**：请根据这个技能特点来回答问题。" # 构建限制部分 restrictions_section = "\n".join( [f"- {restriction}" for restriction in restrictions_list] ) prompt = f"""# 角色 {role_config["description"]} ## 技能{skills_section} ## 限制 {restrictions_section} 请按照以上角色设定和限制来回答用户的问题。""" return prompt def get_deepseek_api_key() -> str: """获取DeepSeek API密钥""" api_key = os.getenv("DEEPSEEK_API_KEY", "your-deepseek-api-key") return api_key def is_api_key_configured() -> bool: """检查API密钥是否已配置""" api_key = get_deepseek_api_key() return api_key != "your-deepseek-api-key" async def call_deepseek_api(messages: List[dict]) -> str: """调用DeepSeek API""" # 直接使用环境变量获取API密钥 api_key = get_deepseek_api_key() if not is_api_key_configured(): # 模拟响应用于演示 return "宝，这个问题兜兜需要查一下资料哦，稍等哈~ 😊" url = "https://api.deepseek.com/v1/chat/completions" headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"} data = { "model": "deepseek-chat", "messages": messages, "stream": False, "max_tokens": 500, } try: async with httpx.AsyncClient() as client: response = await client.post(url, headers=headers, json=data, timeout=30.0) response.raise_for_status() result = response.json() return result["choices"][0]["message"]["content"] except Exception as e: raise HTTPException(status_code=500, detail=f"API调用失败: {str(e)}") @router.get("/") async def root(): return {"message": "DeepSeek AI Chat Assistant API"} @router.get("/role", response_model=BaseOut) async def get_role_config(service: GLMService = Depends(get_glm_service)): """获取当前角色配置""" try: role = await service.get_current_role() return BaseOut(code=200, message="ok", result=role.to_role_config_dict()) except Exception as e: logger.error(f"获取角色配置失败: {e}") return BaseOut(code=500, message=f"获取角色配置失败: {str(e)}", result=None) @router.put("/role", response_model=BaseOut) async def update_role_config( role_config: RoleConfig, service: GLMService = Depends(get_glm_service) ): """更新角色配置""" try: # 将Pydantic模型转换为字典 role_data = role_config.dict() updated_role = await service.update_role(role_data) return BaseOut( code=200, message="角色配置已更新", result={ "message": "角色配置已更新", "role_config": updated_role.to_role_config_dict(), }, ) except Exception as e: logger.error(f"更新角色配置失败: {e}") return BaseOut(code=500, message=f"更新角色配置失败: {str(e)}", result=None) @router.post("/chat", response_model=ChatResponse) async def chat(request: ChatRequest, service: GLMService = Depends(get_glm_service)): """处理聊天请求""" # 步骤1: 记录请求开始 logger.info( f"收到聊天请求: conversation_id={request.conversation_id or 'None'}, " f"message_length={len(request.message)}, " f"message_preview='{request.message[:50]}{'...' if len(request.message) > 50 else ''}'" ) # 记录增强参数信息 has_enhanced_prompt = request.enhanced_prompt is not None has_customer_tags = ( request.customer_tags is not None and len(request.customer_tags) > 0 ) has_message_history = ( request.message_history is not None and len(request.message_history) > 0 ) has_workflow_context = request.workflow_context is not None logger.info( f"增强参数状态: enhanced_prompt={has_enhanced_prompt}, " f"customer_tags={has_customer_tags} ({len(request.customer_tags) if has_customer_tags else 0}个标签), " f"message_history={has_message_history} ({len(request.message_history) if has_message_history else 0}条历史), " f"workflow_context={has_workflow_context}" ) # 记录客户标签摘要 if has_customer_tags: tag_categories = {} for tag in request.customer_tags: category = tag.get("TagCategory", "未分类") name = tag.get("TagName", "未命名") if category not in tag_categories: tag_categories[category] = [] tag_categories[category].append(name) tag_summary = [] for category, names in tag_categories.items(): tag_summary.append( f"{category}: {', '.join(names[:3])}{'...' if len(names) > 3 else ''}" ) # 检查是否使用mock数据 is_mock_data = False for tag in request.customer_tags: if tag.get("TagName", "").startswith("mock_") or tag.get( "TagCategory", "" ).startswith("mock_"): is_mock_data = True break mock_note = " [mock数据]" if is_mock_data else "" logger.info(f"客户标签摘要{mock_note}: {', '.join(tag_summary)}") # 记录历史消息摘要 if has_message_history: # 按时间排序，获取最近的消息 sorted_history = sorted( request.message_history, key=lambda x: x.get("msgtime", ""), reverse=True )[ :5 ] # 只显示最近5条 history_summary = [] for i, msg in enumerate(sorted_history): from_user = msg.get("from_user", "未知用户") user_id = ( msg.get("user_id") or msg.get("userId") or msg.get("from_user_id") or "无ID" ) content = msg.get("text", {}).get("content", "") or msg.get("content", "") content_preview = content[:30] + ("..." if len(content) > 30 else "") history_summary.append(f"{from_user}(ID:{user_id}): '{content_preview}'") # 检查是否使用mock数据 is_mock_data = False for msg in sorted_history: if msg.get("from_user", "").startswith("mock_") or msg.get( "user_id", "" ).startswith("mock_"): is_mock_data = True break mock_note = " [mock数据]" if is_mock_data else "" logger.info(f"历史消息摘要{mock_note}: {', '.join(history_summary)}") # 步骤2: 创建或获取对话历史 if request.conversation_id and request.conversation_id in conversations: conversation_history = conversations[request.conversation_id] logger.info( f"使用现有对话: {request.conversation_id}, 历史消息数: {len(conversation_history)}" ) # 记录历史消息摘要 if conversation_history: last_messages = conversation_history[-3:] # 显示最近3条消息 history_summary = [] for i, msg in enumerate(last_messages, 1): role = msg["role"] content_preview = msg["content"][:30] + ( "..." if len(msg["content"]) > 30 else "" ) history_summary.append(f"{role}: '{content_preview}'") logger.debug(f"最近历史消息摘要: {', '.join(history_summary)}") else: conversation_history = [] request.conversation_id = f"conv_{len(conversations) + 1}" conversations[request.conversation_id] = conversation_history logger.info(f"新建对话: {request.conversation_id}") # 步骤3: 构建系统提示词 try: if has_enhanced_prompt: # 使用前端生成的增强提示词 system_prompt = request.enhanced_prompt logger.info(f"使用前端增强提示词, 长度: {len(system_prompt)} 字符") logger.debug( f"增强提示词预览: '{system_prompt[:200]}{'...' if len(system_prompt) > 200 else ''}'" ) else: # 获取当前角色配置并构建系统提示词 role = await service.get_current_role() role_config = role.to_role_config_dict() logger.info( f"获取角色配置: name='{role.name}', " f"技能数: {len(role_config['skills'].split('\\n')) if role_config['skills'] else 0}, " f"限制数: {len(role_config['restrictions'].split('\\n')) if role_config['restrictions'] else 0}" ) system_prompt = build_system_prompt(role_config) logger.debug(f"系统提示词构建完成, 长度: {len(system_prompt)} 字符") except Exception as e: logger.error( f"构建系统提示词失败: {e}, conversation_id={request.conversation_id}" ) raise HTTPException(status_code=500, detail=f"构建系统提示词失败: {str(e)}") # 步骤4: 构建消息列表 messages = [{"role": "system", "content": system_prompt}] # 初始化企业微信历史消息变量 selected_wework_messages = [] # 添加企业微信历史消息（如果提供） if has_message_history: logger.info(f"添加企业微信历史消息到上下文: {len(request.message_history)} 条") # 将企业微信消息转换为大模型消息格式 wework_messages = [] for msg in request.message_history: from_user = msg.get("from_user", "未知用户") content = msg.get("text", {}).get("content", "") or msg.get("content", "") msgtime = msg.get("msgtime", "") if content: # 确定角色：如果是客户发送的，则为user；否则为assistant # 这里需要根据实际业务逻辑判断，暂时假设外部用户是user role = ( "user" if "customer" in from_user.lower() or "user" in from_user.lower() else "assistant" ) # 格式化消息内容 formatted_content = f"[{msgtime}] {from_user}: {content}" wework_messages.append({"role": role, "content": formatted_content}) # 添加到消息列表（限制数量，避免过长） max_wework_messages = 10 selected_wework_messages = wework_messages[ -max_wework_messages: ] # 取最近的消息 messages.extend(selected_wework_messages) logger.info( f"已添加 {len(selected_wework_messages)} 条企业微信历史消息到上下文" ) # 添加与大模型的对话历史 history_messages = conversation_history[-10:] # 保留最近10条历史 for msg in history_messages: messages.append({"role": msg["role"], "content": msg["content"]}) # 添加当前用户消息 messages.append({"role": "user", "content": request.message}) conversation_history.append({"role": "user", "content": request.message}) logger.info( f"构建消息列表完成: 系统消息1条, 企业微信历史{len(selected_wework_messages)}条, " f"对话历史{len(history_messages)}条, 用户消息1条, 总消息数: {len(messages)}" ) # 记录消息列表摘要 if logger.isEnabledFor(logging.DEBUG): for i, msg in enumerate(messages): role = msg["role"] content_preview = msg["content"][:50] + ( "..." if len(msg["content"]) > 50 else "" ) logger.debug(f"消息{i+1}: {role} - '{content_preview}'") # 步骤5: 调用DeepSeek API try: logger.info( f"调用DeepSeek API, conversation_id={request.conversation_id}, 总消息数: {len(messages)}" ) response_text = await call_deepseek_api(messages) # 步骤6: 记录API响应 logger.info( f"DeepSeek API调用成功, conversation_id={request.conversation_id}, " f"响应长度: {len(response_text)} 字符, " f"响应预览: '{response_text[:100]}{'...' if len(response_text) > 100 else ''}'" ) # 添加助手回复到历史 conversation_history.append({"role": "assistant", "content": response_text}) # 步骤7: 返回响应 logger.info( f"返回聊天响应: conversation_id={request.conversation_id}, 响应长度: {len(response_text)} 字符" ) return ChatResponse( response=response_text, conversation_id=request.conversation_id ) except Exception as e: logger.error( f"DeepSeek API调用失败: {e}, conversation_id={request.conversation_id}, message_preview='{request.message[:50]}...'" ) raise HTTPException(status_code=500, detail=str(e)) @router.get("/conversations/{conversation_id}") async def get_conversation(conversation_id: str): """获取特定对话的历史""" if conversation_id not in conversations: raise HTTPException(status_code=404, detail="对话不存在") return { "conversation_id": conversation_id, "messages": conversations[conversation_id], } @router.post("/role/reset", response_model=BaseOut) async def reset_role_config(service: GLMService = Depends(get_glm_service)): """重置角色配置为默认值""" try: # 使用 DEFAULT_ROLE_CONFIG 更新角色 role_data = DEFAULT_ROLE_CONFIG.dict() updated_role = await service.update_role(role_data) return BaseOut( code=200, message="角色配置已重置为默认值", result={ "message": "角色配置已重置为默认值", "role_config": updated_role.to_role_config_dict(), }, ) except Exception as e: logger.error(f"重置角色配置失败: {e}") return BaseOut(code=500, message=f"重置角色配置失败: {str(e)}", result=None)