491 lines
18 KiB
Python
Executable File
491 lines
18 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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AI Chat Module for 徵象防伪验证平台
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基于 Moonshot Kimi K2 API 的智能客服聊天系统
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"""
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import os
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import json
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import time
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from typing import List, Dict, Any, Optional
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from dataclasses import dataclass
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from openai import OpenAI
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from django.conf import settings
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@dataclass
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class ToolCall:
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"""工具调用数据结构"""
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id: str
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type: str
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function: Dict[str, Any]
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@dataclass
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class ToolResult:
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"""工具执行结果"""
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tool_call_id: str
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content: str
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class AIChatService:
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"""AI聊天服务主类"""
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def __init__(self, api_key: str = None, base_url: str = None, chat_type: str = 'platform', product_id: int = None):
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"""初始化AI聊天服务
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Args:
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api_key: Moonshot API密钥
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base_url: API基础URL
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chat_type: 聊天类型 ('platform' 或 'product')
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product_id: 产品ID，当chat_type为'product'时使用
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"""
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self.api_key = api_key or getattr(settings, 'KIMI_API_KEY', None)
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self.base_url = base_url or getattr(settings, 'KIMI_API_URL', None)
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if not self.api_key:
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raise ValueError("KIMI_API_KEY is required")
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# 聊天类型和产品ID
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self.chat_type = chat_type
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self.product_id = product_id
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# 验证参数
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if chat_type == 'product' and not product_id:
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raise ValueError("当chat_type为'product'时，必须提供product_id")
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self.client = OpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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self.model = "kimi-k2-0711-preview"
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self.conversation_history: List[Dict[str, str]] = []
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self.tools = self._define_tools()
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# 添加系统提示词
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self._add_system_message()
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def _define_tools(self) -> List[Dict[str, Any]]:
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"""定义可用工具"""
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return [
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{
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"type": "function",
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"function": {
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"name": "start_qr_scan",
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"description": "启动二维码扫描，暂停对话等待扫描结果",
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"parameters": {
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"type": "object",
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"properties": {},
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"required": []
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "search_knowledge_base",
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"description": "从平台知识库中检索相关信息",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {"type": "string", "description": "搜索查询"},
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"max_results": {"type": "integer", "description": "最大结果数", "default": 3}
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},
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"required": ["query"]
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}
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}
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}
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]
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def _add_system_message(self):
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"""添加系统提示词"""
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if self.chat_type == 'platform':
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system_prompt = """你是一个专业的徵象防伪验证平台AI客服助手。
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平台介绍：
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徵象是由广州市诚投科技有限公司开发的AI驱动的智能防伪平台，通过多模态特征识别构建新一代防伪验证体系，实现从物理防伪到数字认证的全链路保护。系统采用ISO 12931国际防伪标准，已获取国家发明专利(证书编号:CN 115222000 B)。
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服务范围：
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1. 商品防伪验证
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2. 证件安全验证
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3. 工业品防伪
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4. 品牌数字化服务
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服务原则：
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1. 严谨谦虚，基于知识库和真实数据回答，绝不捏造事实
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2. 优先使用官方信息和知识库内容回答
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3. 对于不确定或超出知识范围的信息，明确告知用户"我不确定"或"需要进一步确认"
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4. 如果知识库中没有相关信息，诚实告知用户，不要推测或编造
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5. 引导用户使用相关功能和服务
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6. 当用户需要验证产品真伪时，主动提供二维码扫描功能
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可用工具：
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- start_qr_scan: 启动二维码扫描功能，用于产品防伪验证
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- search_knowledge_base: 从平台知识库中检索相关信息，用于回答专业问题
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回答要求：
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- 严格基于知识库检索结果和真实数据回答
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- 如果知识库中没有相关信息，请明确告知用户
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- 对于专业问题，优先使用知识库搜索工具获取准确信息
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- 保持严谨谦虚的态度，不夸大、不推测、不编造
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- 如果用户需要验证产品，请使用二维码扫描工具"""
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elif self.chat_type == 'product':
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system_prompt = f"""你是一个专业的徵象防伪验证平台产品客服助手，专门为产品ID {self.product_id} 提供客服服务。
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平台介绍：
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徵象是由广州市诚投科技有限公司开发的AI驱动的智能防伪平台，通过多模态特征识别构建新一代防伪验证体系，实现从物理防伪到数字认证的全链路保护。
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当前服务产品：
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- 产品ID: {self.product_id}
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- 服务类型: 产品专属客服
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服务原则：
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1. 严谨谦虚，基于知识库和真实数据回答，绝不捏造事实
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2. 优先使用产品相关信息和知识库内容回答
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3. 对于不确定或超出知识范围的信息，明确告知用户"我不确定"或"需要进一步确认"
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4. 如果知识库中没有相关信息，诚实告知用户，不要推测或编造
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5. 引导用户使用相关功能和服务
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6. 当用户需要验证产品真伪时，主动提供二维码扫描功能
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可用工具：
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- start_qr_scan: 启动二维码扫描功能，用于产品防伪验证
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- search_knowledge_base: 从平台知识库中检索相关信息，用于回答专业问题
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回答要求：
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- 严格基于知识库检索结果和真实数据回答
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- 如果知识库中没有相关信息，请明确告知用户
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- 对于专业问题，优先使用知识库搜索工具获取准确信息
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- 保持严谨谦虚的态度，不夸大、不推测、不编造
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- 如果用户需要验证产品，请使用二维码扫描工具"""
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self.conversation_history.append({
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"role": "system",
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"content": system_prompt
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})
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def _execute_tool(self, tool_call: ToolCall) -> ToolResult:
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"""执行工具调用
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Args:
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tool_call: 工具调用信息
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Returns:
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工具执行结果
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"""
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# Debug logging
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print(f"🔧 执行工具调用: {tool_call.function}")
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function_name = tool_call.function.name if hasattr(tool_call.function, 'name') else tool_call.function.get('name', '')
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print(f"🔧 工具名称: {function_name}")
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if function_name == "start_qr_scan":
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return self._execute_qr_scan(tool_call.id)
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elif function_name == "search_knowledge_base":
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return self._execute_search_knowledge_base(tool_call.id, tool_call.function)
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else:
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return ToolResult(
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tool_call_id=tool_call.id,
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content=f"未知工具: {function_name}"
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)
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def _execute_qr_scan(self, tool_call_id: str) -> ToolResult:
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"""执行二维码扫描（模拟实现）
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Args:
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tool_call_id: 工具调用ID
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Returns:
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扫描结果
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"""
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print("\n🔍 启动二维码扫描...")
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print("📱 请在客户端打开相机进行扫描...")
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# 模拟扫描过程
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for i in range(5, 0, -1):
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print(f"⏳ 扫描中... {i}秒后完成")
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time.sleep(1)
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print("✅ 扫描完成！")
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print("🎯 验证结果：通过")
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print("🏷️ 二维码标签：真品")
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# 返回验证结果
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result = {
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"status": "pass",
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"message": "二维码验证通过，产品为真品",
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"verification_time": time.strftime("%Y-%m-%d %H:%M:%S")
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}
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return ToolResult(
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tool_call_id=tool_call_id,
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content=json.dumps(result, ensure_ascii=False)
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)
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def _execute_search_knowledge_base(self, tool_call_id: str, function_data) -> ToolResult:
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"""执行知识库搜索工具"""
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print(f"\n🔍 开始执行知识库搜索工具...")
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print(f"📊 工具调用ID: {tool_call_id}")
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print(f"📋 函数数据: {function_data}")
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try:
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# 解析函数参数
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print(f"🔧 解析函数参数...")
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if hasattr(function_data, 'arguments'):
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arguments = function_data.arguments
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print(f"✅ 从function_data.arguments获取参数: {arguments}")
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else:
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arguments = function_data.get('arguments', '{}')
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print(f"✅ 从function_data.get获取参数: {arguments}")
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# 解析JSON参数
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print(f"🔧 解析JSON参数...")
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if isinstance(arguments, str):
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import json
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try:
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args = json.loads(arguments)
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print(f"✅ JSON解析成功: {args}")
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except json.JSONDecodeError as je:
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print(f"❌ JSON解析失败: {je}")
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return ToolResult(
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tool_call_id=tool_call_id,
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content=f"参数格式错误: {str(je)}"
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)
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else:
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args = arguments
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print(f"✅ 参数已经是字典格式: {args}")
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query = args.get('query', '')
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max_results = args.get('max_results', 3)
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print(f"📝 解析结果: query='{query}', max_results={max_results}")
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if not query:
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print(f"❌ 搜索查询为空")
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return ToolResult(
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tool_call_id=tool_call_id,
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content="搜索查询不能为空"
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)
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# 检查依赖
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print(f"🔧 检查RAG服务依赖...")
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try:
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from .rag_service import CachedLangChainRAG
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print(f"✅ RAG服务导入成功")
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except ImportError as ie:
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print(f"❌ RAG服务导入失败: {ie}")
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return ToolResult(
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tool_call_id=tool_call_id,
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content=f"RAG服务不可用: {str(ie)}"
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)
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# 创建RAG服务实例
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print(f"🔧 创建RAG服务实例...")
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try:
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rag_service = CachedLangChainRAG()
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print(f"✅ RAG服务实例创建成功")
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except Exception as e:
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print(f"❌ RAG服务实例创建失败: {e}")
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import traceback
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traceback.print_exc()
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return ToolResult(
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tool_call_id=tool_call_id,
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content=f"RAG服务初始化失败: {str(e)}"
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)
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# 根据聊天类型确定租户ID
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tenant_id = self.product_id if self.chat_type == 'product' else None
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print(f"🏢 租户ID: {tenant_id}")
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# 获取增强上下文
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print(f"🔍 开始知识库搜索...")
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try:
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context = rag_service.get_enhanced_context(query, max_results, tenant_id)
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print(f"✅ 知识库搜索成功，上下文长度: {len(context)}")
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return ToolResult(
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tool_call_id=tool_call_id,
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content=context
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)
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except Exception as e:
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print(f"❌ 知识库搜索失败: {e}")
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import traceback
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traceback.print_exc()
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return ToolResult(
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tool_call_id=tool_call_id,
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content=f"知识库搜索执行失败: {str(e)}"
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)
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except Exception as e:
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print(f"❌ 知识库搜索工具执行异常: {e}")
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import traceback
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traceback.print_exc()
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return ToolResult(
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tool_call_id=tool_call_id,
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content=f"知识库搜索工具异常: {str(e)}"
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)
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def chat(self, user_message: str) -> str:
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"""发送消息给AI并获取回复
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Args:
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user_message: 用户消息
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Returns:
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AI回复内容
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"""
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# 添加用户消息到历史
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self.conversation_history.append({
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"role": "user",
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"content": user_message
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})
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try:
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# 调用Moonshot API
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response = self.client.chat.completions.create(
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model=self.model,
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messages=self.conversation_history,
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tools=self.tools,
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tool_choice="auto",
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temperature=0.7,
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max_tokens=2000
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)
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assistant_message = response.choices[0].message
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# 检查是否有工具调用
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if assistant_message.tool_calls:
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# 执行工具调用
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tool_results = []
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for tool_call in assistant_message.tool_calls:
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# 将function对象转换为字典格式
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function_dict = {
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'name': tool_call.function.name,
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'arguments': tool_call.function.arguments
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}
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tool_call_obj = ToolCall(
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id=tool_call.id,
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type=tool_call.type,
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function=function_dict
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)
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result = self._execute_tool(tool_call_obj)
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tool_results.append(result)
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# 将工具结果发送给AI获取最终回复
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tool_messages = []
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for result in tool_results:
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tool_messages.append({
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"role": "tool",
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"content": result.content,
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"tool_call_id": result.tool_call_id
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})
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# 添加助手消息和工具结果到历史
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self.conversation_history.append({
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"role": "assistant",
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"content": assistant_message.content or "",
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"tool_calls": [
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{
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"id": tc.id,
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"type": tc.type,
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"function": {
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"name": tc.function.name,
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"arguments": tc.function.arguments
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}
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} for tc in assistant_message.tool_calls
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]
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})
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self.conversation_history.extend(tool_messages)
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# 获取最终回复
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final_response = self.client.chat.completions.create(
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model=self.model,
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messages=self.conversation_history,
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temperature=0.7,
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max_tokens=2000
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)
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final_message = final_response.choices[0].message.content
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# 添加最终回复到历史
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self.conversation_history.append({
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"role": "assistant",
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"content": final_message
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})
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return final_message
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else:
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# 没有工具调用，直接返回回复
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content = assistant_message.content or "抱歉，我没有获取到有效回复。"
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# 添加助手回复到历史
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self.conversation_history.append({
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"role": "assistant",
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"content": content
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})
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return content
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except Exception as e:
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error_msg = f"AI服务调用失败: {str(e)}"
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print(f"❌ {error_msg}")
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return error_msg
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def get_conversation_history(self) -> List[Dict[str, str]]:
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"""获取对话历史
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Returns:
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对话历史列表
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"""
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return self.conversation_history.copy()
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def clear_history(self):
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"""清空对话历史"""
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self.conversation_history = []
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self._add_system_message()
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def print_history(self):
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"""打印对话历史"""
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print("\n" + "="*50)
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print("对话历史:")
|
||
print("="*50)
|
||
|
||
for i, message in enumerate(self.conversation_history):
|
||
role = message["role"]
|
||
content = message["content"]
|
||
|
||
if role == "system":
|
||
print(f"🤖 系统: {content[:100]}...")
|
||
elif role == "user":
|
||
print(f"👤 用户: {content}")
|
||
elif role == "assistant":
|
||
print(f"🤖 AI助手: {content}")
|
||
elif role == "tool":
|
||
print(f"🔧 工具结果: {content}")
|
||
|
||
if i < len(self.conversation_history) - 1:
|
||
print("-" * 30)
|
||
|
||
print("="*50)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
"""直接运行时的简单测试"""
|
||
print("🧪 AI聊天库测试模式")
|
||
print("=" * 30)
|
||
print("这是一个库模块，建议使用以下方式运行:")
|
||
print("1. Django管理命令: python manage.py chat")
|
||
print("2. 作为库导入: from products.aichat import AIChatService")
|
||
print("=" * 30)
|
||
|
||
# 简单的功能测试
|
||
try:
|
||
service = AIChatService()
|
||
print("✅ AIChatService 初始化成功")
|
||
print(f"📝 系统提示词长度: {len(service.conversation_history[0]['content'])} 字符")
|
||
print(f"🔧 可用工具数量: {len(service.tools)}")
|
||
except Exception as e:
|
||
print(f"❌ 初始化失败: {e}")
|
||
|