tw2/qingjian_cs/app/agents.py

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(),
    )