from __future__ import annotations import json import re import sys from pathlib import Path from typing import Any from .agent_tools import ( tool_detect_external_contact, tool_detect_intent, tool_detect_order_status, tool_detect_risk, tool_extract_image_urls, tool_extract_size_pairs, tool_is_meaningless_short, ) from .config import AGENT_MAX_ITERS, OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL_NAME from .models import Decision, DecisionModel, RouteModel from .rules import rules_prompt def _ensure_agentscope_importable() -> None: repo_root = Path(__file__).resolve().parents[2] src_dir = repo_root / "src" if src_dir.exists() and str(src_dir) not in sys.path: sys.path.insert(0, str(src_dir)) class _AgentRuntime: def __init__(self, name: str, sys_prompt: str): _ensure_agentscope_importable() from agentscope.agent import ReActAgent from agentscope.formatter import OpenAIChatFormatter from agentscope.memory import InMemoryMemory from agentscope.message import Msg from agentscope.model import OpenAIChatModel from agentscope.tool import Toolkit if not OPENAI_API_KEY: raise RuntimeError("OPENAI_API_KEY 未设置") self.Msg = Msg toolkit = Toolkit() toolkit.register_tool_function(tool_detect_intent) toolkit.register_tool_function(tool_extract_image_urls) toolkit.register_tool_function(tool_detect_order_status) toolkit.register_tool_function(tool_extract_size_pairs) toolkit.register_tool_function(tool_detect_risk) toolkit.register_tool_function(tool_detect_external_contact) toolkit.register_tool_function(tool_is_meaningless_short) model = OpenAIChatModel( model_name=OPENAI_MODEL_NAME, api_key=OPENAI_API_KEY, stream=False, client_kwargs={"base_url": OPENAI_BASE_URL}, generate_kwargs={"temperature": 0.1}, ) self.agent = ReActAgent( name=name, sys_prompt=sys_prompt, model=model, formatter=OpenAIChatFormatter(), toolkit=toolkit, memory=InMemoryMemory(), max_iters=max(1, AGENT_MAX_ITERS), ) @staticmethod def _extract_json(text: str) -> dict[str, Any] | None: m = re.search(r"\{[\s\S]*\}", text or "") if not m: return None try: return json.loads(m.group(0)) except Exception: return None @staticmethod def _msg_to_text(msg: Any) -> str: try: if hasattr(msg, "get_text_content"): v = msg.get_text_content() if isinstance(v, str): return v except Exception: pass c = getattr(msg, "content", None) if isinstance(c, str): return c if isinstance(c, list): out: list[str] = [] for b in c: t = getattr(b, "text", None) if isinstance(t, str) and t.strip(): out.append(t) return "\n".join(out) return str(msg) @staticmethod def _extract_structured(metadata: dict[str, Any] | None) -> dict[str, Any] | None: if not isinstance(metadata, dict): return None candidates = [metadata, metadata.get("structured_output"), metadata.get("result"), metadata.get("output"), metadata.get("json")] for obj in candidates: if isinstance(obj, dict): return obj return None class RouterAgent(_AgentRuntime): def __init__(self) -> None: super().__init__( "RouterAgent", rules_prompt() + "\n你是路由Agent。只输出路由 pre_sales/quote/after_sales/risk，不直接回复客户。" + " 你必须基于上下文语义路由，禁止关键词硬匹配。", ) async def route(self, context: dict[str, Any]) -> tuple[str, str]: prompt = f"按上下文路由到 pre_sales/quote/after_sales/risk。\n上下文:\n{json.dumps(context, ensure_ascii=False)}" res = await self.agent(self.Msg("user", prompt, "user"), structured_model=RouteModel) obj = self._extract_structured(getattr(res, "metadata", None)) or self._extract_json(self._msg_to_text(res)) or {} route = str(obj.get("route", "pre_sales") or "pre_sales") if route not in {"pre_sales", "quote", "after_sales", "risk"}: route = "pre_sales" return route, str(obj.get("reason", "") or "") class QuoteAgent(_AgentRuntime): def __init__(self) -> None: super().__init__( "QuoteAgent", rules_prompt() + "\n你是报价Agent。负责收图、报价触发、报价回复和报价阶段状态更新。", ) async def decide(self, context: dict[str, Any]) -> Decision: prompt = f"你负责报价相关决策。\n上下文:\n{json.dumps(context, ensure_ascii=False)}" return await _decide_with_model(self, prompt) class AfterSalesAgent(_AgentRuntime): def __init__(self) -> None: super().__init__( "AfterSalesAgent", rules_prompt() + "\n你是售后Agent。负责退款/重发/不满意等售后处理与状态推进。", ) async def decide(self, context: dict[str, Any]) -> Decision: prompt = f"你负责售后相关决策。\n上下文:\n{json.dumps(context, ensure_ascii=False)}" return await _decide_with_model(self, prompt) class RiskAgent(_AgentRuntime): def __init__(self) -> None: super().__init__( "RiskAgent", rules_prompt() + "\n你是风控Agent。专注风险识别与风险动作决策。", ) async def decide(self, context: dict[str, Any]) -> Decision: prompt = f"你负责风控相关决策。\n上下文:\n{json.dumps(context, ensure_ascii=False)}" return await _decide_with_model(self, prompt) class PreSalesAgent(_AgentRuntime): def __init__(self) -> None: super().__init__( "PreSalesAgent", rules_prompt() + "\n你是售前Agent。处理咨询承接、收图、澄清需求与转报价前动作。", ) async def decide(self, context: dict[str, Any]) -> Decision: prompt = f"你负责售前相关决策。\n上下文:\n{json.dumps(context, ensure_ascii=False)}" return await _decide_with_model(self, prompt) async def _decide_with_model(rt: _AgentRuntime, prompt: str) -> Decision: res = await rt.agent(rt.Msg("user", prompt, "user"), structured_model=DecisionModel) obj = rt._extract_structured(getattr(res, "metadata", None)) or rt._extract_json(rt._msg_to_text(res)) or {} action = str(obj.get("action", "reply") or "reply").strip().lower() if action not in {"reply", "quote", "transfer", "noop", "update_state"}: action = "reply" return Decision( action=action, reply=str(obj.get("reply", "") or "").strip(), transfer_msg=str(obj.get("transfer_msg", "") or "").strip(), quote_mode=str(obj.get("quote_mode", "") or "").strip(), state_patch=obj.get("state_patch") if isinstance(obj.get("state_patch"), dict) else {}, reason=str(obj.get("reason", "") or "").strip(), )