chore: initial import of standalone agentscope project

qingjian_cs/app/agents.py

@@ -0,0 +1,192 @@
+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 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=8,
+        )
+
+    @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",
+            "你是客服路由Agent。只输出路由，不回复客户。必须先调用工具读取意图/风险/订单后再路由。",
+        )
+
+    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你是报价专家Agent。必须结合图片数量、尺寸和订单状态给出报价动作。",
+        )
+
+    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你是售后专家Agent。优先维护售后状态并给出下一步动作。",
+        )
+
+    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",
+            "你是风控Agent。遇到地图政治/黄暴/外联高风险优先给 transfer 或拒绝性 reply。",
+        )
+
+    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你是售前专家Agent。处理打招呼、询价前引导、收图承接。",
+        )
+
+    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(),
+    )