166 lines
8.3 KiB
Python
166 lines
8.3 KiB
Python
import os
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import logging
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from typing import List, Optional, Any, Dict
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from pydantic_ai import Agent, RunContext
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.providers.openai import OpenAIProvider
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from core.schema import StandardMessage, StandardResponse
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from core.agent_tools import register_agent_tools
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logger = logging.getLogger("cs_agent")
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from core.skill_manager import skill_manager
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def _clip(text: str, limit: int = 1200) -> str:
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if text is None:
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return ""
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text = str(text)
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if len(text) <= limit:
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return text
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return f"{text[:limit]}...(截断, 共{len(text)}字)"
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def _fmt_time(ts: Any) -> str:
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s = str(ts or "").strip()
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if not s:
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return "--:--:--"
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if " " in s:
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return s.split(" ", 1)[1]
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return s
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class CustomerServiceBrain:
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"""
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重构后的单一 Agent 大脑：
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【全能终极版】统一称呼为“设计师”，支持下线安抚。
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"""
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def __init__(self, model_name: str = None):
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self.api_key = os.getenv("OPENAI_API_KEY")
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self.base_url = os.getenv("OPENAI_BASE_URL")
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self.model_name = model_name or os.getenv("OPENAI_MODEL", "gpt-4o-mini")
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model = OpenAIChatModel(
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model_name=self.model_name,
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provider=OpenAIProvider(api_key=self.api_key, base_url=self.base_url)
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)
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exclude_names = os.getenv("SKILL_EXCLUDE_FROM_PROMPT", "pricing-skill")
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excluded_skills = [s.strip().lower() for s in exclude_names.split(",") if s.strip()]
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all_skills = skill_manager.get_all_skills_text(exclude=excluded_skills)
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logger.info(f"[SkillManager] 已从提示词排除技能: {excluded_skills}")
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# --- 统一口径后的 System Prompt ---
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system_prompt = (
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"你是一位专注【高清修复】和【找原图】的专业店主。性格干脆，说话自然、专业。\n\n"
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"【统一称呼规范】\n"
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"1. 严禁使用'师傅'、'客服'、'专员'等词汇！必须统一称为【设计师】。\n"
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"2. 未转接前，用第一人称（我/我这边）。例如：'我叫设计师看下'。\n\n"
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"【核心逻辑】\n"
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"1. 业务：只聊高清修复和找原图。核心链路：引导发图 -> 问需求 -> 找设计师。\n"
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"2. **主动引导（关键）**：如果客户【没发图】就问能不能做、问收费，你必须回：'亲亲先发图我看下哈'。\n"
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"3. **非业务问题**：如果客户问招聘、合作、闲聊等与做图无关的话题，礼貌拒绝：'亲亲咱这边只做图哦，暂不招人哈'。\n"
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"4. **客户说没有参考图**：如果客户明确说'没有图'、'找不到'、'想让你们帮找'，直接转人工：'好的，我这就叫设计师帮您找哈'。\n"
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"5. **客户问尺寸/能否打印/退款**：这类问题需要设计师判断，直接转人工：'这个设计师帮您看下哈'。\n"
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"6. 转接时机：收到图片并明确需求后，立即调用转人工工具，并告知：'收到，正在呼叫设计师核价，稍等哈'。\n"
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"7. **下线安抚（重要）**：只有当【本次】工具返回 'ERROR_NO_DESIGNER_ONLINE' 时才能说下班。不能根据历史对话或自己猜测说下班！\n"
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"8. 正在转接中：如果系统提示已在转接，回：'设计师正在赶来，我再帮你催下哈！'。\n"
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"9. **每次转接必须调用工具**：不要根据之前的结果猜测，每次需要转接都必须重新调用工具检查设计师是否在线。\n\n"
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"【必杀令 - 严格遵守】\n"
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"1. 每句回复严禁超过15个字！语气淘宝亲切风，多用'哈'、'呢'。\n"
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"2. 严禁报价，严禁复读图片已收到的情况。\n"
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"3. 必须原样输出工具返回的'正在为您转接|'指令。\n"
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"4. **严禁**说'在呢铁子'！只能说'在呢'或'在呢亲'。\n"
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"5. **严禁**重复发送相同内容！如果刚说过的话，换一种说法。\n"
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"6. **严禁**输出任何代码、标记、括号等乱码！只输出自然语言。\n"
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"7. **严禁**自己臆造'下班'！只有工具返回ERROR才能说下班。\n\n"
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f"业务参考：\n{all_skills}"
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)
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self.agent = Agent(model=model, system_prompt=system_prompt)
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register_agent_tools(self.agent)
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async def think_and_reply(self, msg: StandardMessage, history: List[dict] = []) -> StandardResponse:
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try:
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# 构造增强上下文
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user_content = msg.content
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if msg.image_urls:
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user_content = f"【系统通知：收到客户 {len(msg.image_urls)} 张图】\n{user_content}"
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recent_context = ""
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if history:
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lines = [
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f"[{_fmt_time(h.get('timestamp'))}] {('客户' if h['role']=='user' else '我')}：{h['content']}"
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for h in history[-6:]
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]
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recent_context = "【近期对话回顾】\n" + "\n".join(lines) + "\n----------------\n"
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full_input = f"{recent_context}现在的对话：{user_content}"
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logger.info(
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f"[PROMPT->AI] user={msg.user_id} acc={msg.acc_id} images={len(msg.image_urls)}\n"
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f"{_clip(full_input)}"
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)
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result = await self.agent.run(full_input, message_history=history)
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# --- 终极修复：强制截获工具返回的转接指令 ---
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reply_text = ""
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# pydantic-ai 1.x 使用 result.output（旧版 0.x 使用 result.data）
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raw_output = getattr(result, 'output', None) or getattr(result, 'data', None)
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if isinstance(raw_output, str):
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reply_text = raw_output
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# 暴力扫描所有消息片段，寻找转接暗号
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found_magic = ""
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for m in result.all_messages():
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if hasattr(m, 'parts'):
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for part in m.parts:
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# 检查是否是工具返回片段
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if getattr(part, 'part_kind', '') == 'tool-return':
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content = str(getattr(part, 'content', ''))
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if "[转移会话]" in content:
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found_magic = content
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# 如果 AI 弄丢了暗号，我们强行给它补回来
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if found_magic and "[转移会话]" not in reply_text:
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logger.info(f"[Brain] 检测到 AI 弄丢了转接暗号，正在强制恢复: {found_magic[:30]}...")
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reply_text = found_magic
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# ----------------------------------------
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# 清理可能的乱码/代码标记
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import re
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reply_text = re.sub(r'\[\]<\|[^|]+\|>', '', reply_text) # 清理 []<|xxx|>
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reply_text = re.sub(r'<\|[^|]+\|>', '', reply_text) # 清理 <|xxx|>
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reply_text = re.sub(r'\[Function[^\]]*\]', '', reply_text) # 清理 [FunctionXxx]
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reply_text = re.sub(r'<think[^>]*>.*', '', reply_text, flags=re.DOTALL) # 清理 <think_xxx>内部思考泄漏
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reply_text = re.sub(r'</?think[^>]*>', '', reply_text) # 清理 think 标签
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reply_text = re.sub(r'```[^`]*```', '', reply_text) # 清理代码块
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reply_text = re.sub(r'\{["\'][^}]+\}', '', reply_text) # 清理 JSON
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reply_text = reply_text.strip()
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# 过滤"在呢铁子"
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if "在呢铁子" in reply_text:
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reply_text = reply_text.replace("在呢铁子", "在呢亲")
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if not reply_text:
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reply_text = "稍等我看看。"
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logger.info(f"[THINK/RAW_OUTPUT] user={msg.user_id}\n{_clip(reply_text)}")
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need_transfer = "[转移会话]" in reply_text
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return StandardResponse(
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reply_content=reply_text,
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need_transfer=need_transfer,
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metadata={"acc_id": msg.acc_id, "acc_type": msg.acc_type}
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)
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except Exception as e:
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logger.error(f"[Brain Error]: {e}")
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return StandardResponse(reply_content="好哒，设计师正在看图，稍等回你。", metadata={"acc_id": msg.acc_id})
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