299 lines
12 KiB
Python
299 lines
12 KiB
Python
"""
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AI 左右互搏测试工具
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客服AI（真实） vs 买家AI（模拟）
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用来调试客服AI的回复质量，不需要真实客户
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"""
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import asyncio
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import os
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import random
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import httpx
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from openai import AsyncOpenAI
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from dotenv import load_dotenv
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from pydantic_ai_agent import CustomerServiceAgent, CustomerMessage
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load_dotenv()
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WECHAT_WEBHOOK = "https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=cc88bdef-a13f-4d7e-bdb6-ee51b68b8205"
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async def notify_wechat(content: str):
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"""发送企业微信机器人通知"""
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try:
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async with httpx.AsyncClient(timeout=10) as client:
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await client.post(WECHAT_WEBHOOK, json={
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"msgtype": "text",
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"text": {"content": content}
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})
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except Exception as e:
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print(f"[通知] 企业微信发送失败: {e}")
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# ========== 颜色输出 ==========
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RESET = "\033[0m"
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BLUE = "\033[94m" # 买家
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GREEN = "\033[92m" # 客服
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YELLOW = "\033[93m" # 系统提示
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GRAY = "\033[90m" # 分隔线
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def print_buyer(msg):
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print(f"{BLUE}【买家】{msg}{RESET}")
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def print_shop(msg):
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print(f"{GREEN}【客服】{msg}{RESET}")
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def print_system(msg):
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print(f"{YELLOW}{msg}{RESET}")
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def print_sep():
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print(f"{GRAY}{'─' * 50}{RESET}")
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# ========== 买家人设配置 ==========
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BUYER_PERSONAS = {
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"普通买家": """你是一个普通淘宝买家，想找一张图的高清版本。
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行为：先打招呼，发图问价，可能小砍一次价，觉得合适就说要拍了。
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说话简短自然，像手机上发消息，1-2句话。不要太正式。""",
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"砍价客": """你是个爱砍价的淘宝买家，总想便宜一点。
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行为：问价后嫌贵，砍价2-3次，最后可能接受也可能走人。
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说话直接，喜欢说"能不能便宜点""太贵了""别家更便宜"。""",
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"爱问细节": """你是个很谨慎的买家，买东西前喜欢问清楚。
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行为：先问在不在，问能不能找到，问格式，问效果，问不满意能退吗，最后才考虑下单。
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说话有点啰嗦，喜欢多问几个问题。""",
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"快节奏": """你是个很忙的买家，说话简短，不废话。
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行为：直接发图问多少钱，价格合适立刻说拍了，不合适直接走。
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每次只说3-5个字，极度简短。""",
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"售后投诉": """你是个已经付款的买家，但对收到的图片不满意，想要退款或重做。
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行为：先说拿到图了但效果不行，描述哪里不满意（模糊/颜色不对/尺寸不够），
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要求重做或者退款，态度有点不耐烦，反复追问进度。
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说话带点情绪，但不至于骂人，就是那种"花钱了结果不行"的委屈感。""",
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"多图打包": """你是个需要批量处理图片的买家，手头有好几张图要找高清版。
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行为：先问在不在，然后说有多张图要处理，询问能不能打包便宜点，
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逐步发图或询问价格，跟客服商量总价，最终决定下单还是再想想。
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说话随意，像在商量事情，不太在意每张的价格，更关注总价合不合适。""",
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"大批量砍价": """你是个手头有十几张图要处理的买家，量大，觉得自己有底气砍价。
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行为：上来就说"我有很多图，你们量大能便宜吗"，
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告知大概数量（10-20张），用量大作为筹码反复压价，
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问"做完这批还有下一批，能不能给个长期价""别家给我打6折了"，
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如果客服给的总价还算合理就下单，否则拉锯几个来回。
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说话有点强势，觉得自己是大客户，应该享受优惠。""",
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"要分层PSD": """你是个做设计的买家，需要带分层的PSD格式，不是普通jpg。
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行为：先问在不在，发图后问"这个能出PSD吗""要分层的那种"，
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追问能不能保留图层、格式是不是真的PSD，听到价格后可能嫌贵问能不能便宜，
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最终决定下单或者放弃。
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说话带点专业感，会说"图层""分层""PSD""源文件"之类的词。""",
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"问尺寸分辨率": """你是个有印刷需求的买家，非常在意图片的尺寸和分辨率。
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行为：发图后先不问价，反复问"这个能做多大""分辨率能到300dpi吗""印出来会不会模糊"，
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问完尺寸再问价格，如果客服说能满足需求就考虑下单，否则犹豫很久。
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说话里经常出现"厘米""像素""dpi""印刷""大图"。""",
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"问颜色效果": """你是个对颜色很挑剔的买家，担心高清版颜色和原图有色差。
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行为：发图后问"颜色会变吗""饱和度能保持吗""跟原图一样的色调吧"，
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让客服保证颜色效果，追问不满意能不能改，反复确认才肯下单。
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说话谨慎，总要客服给"保证"，但说话不凶，就是很在意品质。""",
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"来源质疑": """你是个对店铺服务有疑虑的买家，总觉得有猫腻，想搞清楚原理。
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行为：问"你们是怎么找的""原图是从哪里来的""是AI弄的吗还是真的原图"，
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追问来源和方法，对客服的回答将信将疑，继续追问，
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最终可能被说服下单，也可能觉得不靠谱走人。
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说话带点怀疑，喜欢反问，但不是来找茬的，是真的想搞清楚。""",
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}
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# 用于测试的图片URL
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TEST_IMAGE_URLS = [
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"https://img.alicdn.com/imgextra/i1/O1CN01OilxfD1kr8tM4Ugg2_!!4611686018427387680-0-amp.jpg",
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]
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class BuyerAgent:
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"""模拟买家的AI"""
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def __init__(self, persona_name: str = "普通买家"):
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self.client = AsyncOpenAI(
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL"),
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)
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self.model = os.getenv("OPENAI_MODEL", "gpt-4o-mini")
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self.persona_name = persona_name
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self.persona = BUYER_PERSONAS.get(persona_name, BUYER_PERSONAS["普通买家"])
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self.history = []
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self.image_sent = False
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self.rounds = 0
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async def next_message(self, shop_reply: str = None) -> str:
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"""根据客服回复，生成下一条买家消息"""
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self.rounds += 1
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if shop_reply:
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self.history.append({"role": "assistant", "content": f"客服说：{shop_reply}"})
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# 第一轮：打招呼或直接发问
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if self.rounds == 1:
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first_msgs = ["在不在", "在吗", "你好", "有人吗", "亲在吗"]
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msg = random.choice(first_msgs)
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self.history.append({"role": "user", "content": msg})
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return msg
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# 第二轮：发图+问有没有
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if self.rounds == 2 and not self.image_sent:
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self.image_sent = True
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url = random.choice(TEST_IMAGE_URLS)
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msg = f"有吗#*#{url}"
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self.history.append({"role": "user", "content": msg})
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return msg
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# 后续：让AI根据对话历史自由生成
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system = f"""{self.persona}
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你正在和一家找图店的客服聊天，对话历史如下。
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请根据你的人设生成下一条消息，简短自然，像真人发消息。
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如果对话已经快结束（超过10轮），可以说"好的拍了"或"算了不要了"结束对话。
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只输出你要发的消息内容，不要加任何前缀说明。"""
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resp = await self.client.chat.completions.create(
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model=self.model,
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messages=[
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{"role": "system", "content": system},
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*self.history,
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{"role": "user", "content": "（请生成你的下一条消息）"}
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],
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max_tokens=80,
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temperature=0.9,
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)
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msg = resp.choices[0].message.content.strip()
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self.history.append({"role": "user", "content": msg})
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return msg
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async def run_battle(persona_name: str = "普通买家", max_rounds: int = 8):
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"""运行一场对话模拟"""
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print_sep()
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print_system(f" 开始模拟 | 买家人设：{persona_name} | 最多 {max_rounds} 轮")
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print_sep()
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# 初始化双方
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buyer = BuyerAgent(persona_name)
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shop = CustomerServiceAgent()
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buyer_id = "test_buyer_001"
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shop_id = "test_shop_001"
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shop_reply = None
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for i in range(max_rounds):
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# 买家发消息
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buyer_msg = await buyer.next_message(shop_reply)
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print_buyer(buyer_msg)
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# 判断对话是否结束
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end_words = ["算了", "不要了", "不买了", "拍了", "好的拍了", "下单了", "谢谢"]
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if any(w in buyer_msg for w in end_words):
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print_system(f"\n 对话结束（第 {i+1} 轮）")
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break
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# 构建消息对象
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customer_msg = CustomerMessage(
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msg_id=f"test_{i}",
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acc_id=shop_id,
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msg=buyer_msg,
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from_id=buyer_id,
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from_name="测试买家",
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cy_id=buyer_id,
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acc_type="AliWorkbench",
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msg_type=0,
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cy_name="测试买家",
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goods_name="模糊图清晰处理专业代找原图素材淘宝图片找图修复服务",
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)
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# 含图片URL时先打印等待语（模拟真实客服的即时回应）
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if "#*#" in buyer_msg or (buyer_msg.startswith(("http://", "https://")) and any(
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h in buyer_msg for h in ("alicdn.com", "imgextra", ".jpg", ".jpeg", ".png")
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)):
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print_shop("我找一下看看")
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print()
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# 客服AI处理
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response = await shop.process_message(customer_msg)
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if response.need_transfer:
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print_shop("[转人工]")
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print_system("\n 客服决定转人工，对话结束")
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break
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shop_reply = response.reply if response.should_reply else None
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# 过滤 AI 误输出的内部独白（与生产环境保持一致）
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nonsense_patterns = [
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"无需", "流程已完成", "不需要回复", "无需额外", "已完成",
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"无需回复", "不需要额外", "已经完成", "无需再", "操作已完成",
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"任务完成", "流程完成", "记录完成", "报价已",
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]
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if shop_reply and any(p in shop_reply for p in nonsense_patterns):
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print_system(f" [已拦截无效回复: {shop_reply}]")
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shop_reply = None
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if shop_reply:
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print_shop(shop_reply)
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else:
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print_system(" （客服决定不回复）")
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shop_reply = ""
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print()
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await asyncio.sleep(0.3)
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print_sep()
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print_system(" 模拟结束")
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print_sep()
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async def main():
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import sys
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personas = list(BUYER_PERSONAS.keys())
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print(f"\n{YELLOW}{'=' * 50}")
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print(" AI 左右互搏测试工具")
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print(f"{'=' * 50}{RESET}")
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print(f"{GRAY}用法: python test_battle.py [人设编号|all]")
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print(f" 1=普通买家 2=砍价客 3=爱问细节 4=快节奏")
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print(f" 5=售后投诉 6=多图打包 7=大批量砍价 8=要分层PSD")
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print(f" 9=问尺寸分辨率 10=问颜色效果 11=来源质疑 0=全部{RESET}\n")
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arg = sys.argv[1] if len(sys.argv) > 1 else "1"
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if arg == "0" or arg == "all":
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for persona in personas:
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await run_battle(persona, max_rounds=14)
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print()
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await asyncio.sleep(1)
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else:
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try:
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idx = int(arg) - 1
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persona = personas[idx] if 0 <= idx < len(personas) else personas[0]
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except ValueError:
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persona = personas[0]
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await run_battle(persona, max_rounds=14)
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if __name__ == "__main__":
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try:
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asyncio.run(main())
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except Exception as e:
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err_str = str(e)
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if "AccountOverdueError" in err_str or "overdue" in err_str.lower():
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msg = "⚠️ 火山引擎 API 欠费，请立即充值！\n地址：https://console.volcengine.com/ark"
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else:
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msg = f"⚠️ 测试脚本异常退出：{err_str[:200]}"
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print(f"\n[通知] {msg}")
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asyncio.run(notify_wechat(msg))
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raise
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