317 lines
13 KiB
Python
317 lines
13 KiB
Python
# ============================================================
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# 通用排产助理（moduleId: core-assistant, 可重生 ✅）
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# 像 Kimi：什么都能聊；带多轮上下文；排产是强项。
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# ============================================================
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from __future__ import annotations
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import re
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from datetime import datetime
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from typing import Any
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from server.agent_core.context import (
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format_history_for_llm,
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normalize_history,
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resolve_followup,
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wants_folder_listing,
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)
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from server.agent_core.providers import get_provider
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from server.aps_domain.readiness import analyze_project_data, check_readiness
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from server.contracts import AgentReply, UIBlock
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World = dict[str, Any]
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HistoryItem = dict[str, str]
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_WEEKDAYS = "一二三四五六日"
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_SYSTEM = """你是「工业智核」企业级排产决策助手，服务对象是计划员、生产经理和工厂管理者。
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输出要求：
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1. 先给结论，再给关键数据、阻断项/风险和下一步操作。
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2. 结合【对话上文】理解省略表达，但不要重复用户原话或暴露内部意图名。
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3. 排产数字只能来自【项目上下文】；文件夹内容只能来自【文件夹清单】。
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4. 所有数量、时长和日期都带单位或明确字段名；无法确认时直接说明“数据不足”。
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5. 使用专业、克制、可执行的中文。避免“啥、咱们、哟、随便、我就上手”等口语，不使用笑话、营销话术或 emoji。
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6. 不要每轮粘贴功能清单；只给当前目标最相关的一个下一步。
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7. 有【当前时间】时，问几点必须使用该时间。
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8. 只回答当前问题，不编造项目事实；没有依据时明确说明需要补充什么数据。
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"""
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def _now_text() -> str:
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n = datetime.now()
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wd = _WEEKDAYS[n.weekday()]
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return n.strftime(f"%Y年%m月%d日 星期{wd} %H:%M")
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def _world_brief(world: World) -> str:
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active = [o for o in (world.get("flexOrders") or [])
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if (o.get("status") or "") not in ("DONE", "CANCELLED")]
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mats = world.get("flexMaterials") or []
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routes = world.get("flexRoutings") or []
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equip = [e for e in (world.get("flexEquipment") or []) if e.get("status") == "RUNNING"]
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cal = world.get("flexCalendar") or []
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report = check_readiness(world)
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s = report["summary"]
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lines = [
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f"待排订单 {len(active)} 张，物料 {len(mats)} 种，工艺步骤 {len(routes)} 条，"
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f"能干活的设备 {len(equip)} 台，班次安排 {len(cal)} 条。",
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f"其中能直接开排约 {s.get('ready')} 张，卡住 {s.get('blocked')} 张，"
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f"工时未填 {s.get('timePending')} 步。",
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]
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for o in active[:8]:
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lines.append(
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f"- 订单 {o.get('orderNo')}：产品 {o.get('productCode')}，"
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f"数量 {o.get('quantity')}，交期 {o.get('dueDate') or o.get('deliveryDate') or '未填'}"
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)
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return "\n".join(lines)
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def _knowledge_snippets(query: str, top_k: int = 3) -> str:
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try:
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from server.knowledge import get_knowledge
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from server.knowledge.retrieval import hybrid_search
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units = get_knowledge().iter_search_units()
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hits = hybrid_search(units, query, top_k=top_k, min_score=0.04)
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except Exception:
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return ""
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if not hits:
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return ""
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parts = []
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for h in hits:
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title = h.get("title") or h.get("assetId") or "资料"
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body = (h.get("content") or "")[:400]
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parts.append(f"《{title}》\n{body}")
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return "\n\n".join(parts)
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def _is_greeting(t: str) -> bool:
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return bool(re.search(r"^(你好|您好|嗨|hi|hello|在吗)[\s!！.。？?]*$", t, re.I))
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def _is_time_query(t: str) -> bool:
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return bool(re.search(
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r"(现在|当前)?(几点|什么时候了|几时了)|现在几点|几点了|今日日期|今天(几号|日期|星期几)|现在是几号",
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t,
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))
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def _is_definition(t: str) -> bool:
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return bool(re.search(
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r"(什么叫|什么是|啥叫|啥是|解释(一下|下)?|怎么理解|啥意思|定义).{0,12}排产"
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r"|排产.{0,6}(什么意思|是什么|啥意思|怎么理解)",
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t,
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))
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def _wants_inventory(t: str) -> bool:
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return bool(re.search(
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r"分析|盘点|现状|手头|缺什么|缺啥|齐不齐|能不能开排|现在(能不能|可以)排|"
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r"看看(现在|项目|数据|订单)|有多少(单|订单|料)|数据内容|项目里面的数据",
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t,
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)) or "【文件解析】" in t
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def _simple_math(t: str) -> str | None:
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m = re.fullmatch(r"\s*(\d+(?:\.\d+)?)\s*([+\-*/×÷])\s*(\d+(?:\.\d+)?)\s*[=？?]?\s*", t)
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if not m:
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return None
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a, op, b = float(m.group(1)), m.group(2), float(m.group(3))
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try:
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if op == "+":
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r = a + b
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elif op == "-":
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r = a - b
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elif op in ("*", "×"):
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r = a * b
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elif op in ("/", "÷"):
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if b == 0:
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return "除数不能为 0。"
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r = a / b
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else:
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return None
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except Exception:
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return None
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if abs(r - round(r)) < 1e-9:
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return f"**{int(round(r))}**"
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return f"**{r:.4g}**"
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def _explain_scheduling() -> str:
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return (
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"## 什么是排产？\n\n"
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"排产是将订单需求转换为可执行的生产计划，明确产品、数量、工艺路线、"
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"设备资源、开工时间和完工时间，并在交期、产能和物料约束之间做出安排。\n\n"
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"**一份可执行的排产计划至少需要：**\n"
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"- **需求**：订单、产品、数量和交期\n"
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"- **工艺**：工艺路线、标准工时和前后置关系\n"
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"- **资源**：设备能力、班次日历、模具和物料齐套状态\n\n"
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"如果你提供当前项目数据，我可以先检查齐备度，再生成试排方案。"
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)
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def _explain_can_schedule() -> str:
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return (
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"**可以。**\n\n"
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"我可以检查订单、工艺、设备、班次和物料数据，识别排产阻断项，"
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"并按交期、产能或瓶颈策略生成试排方案。\n\n"
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"建议先说「分析当前项目数据」，确认数据齐备度后再生成方案。"
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)
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def _answer_time() -> str:
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return f"现在是 **{_now_text()}**（按你这台电脑的本地时间）。"
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def _readiness_block(world: World) -> UIBlock:
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report = check_readiness(world)
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return UIBlock(
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blockId="assistant-readiness", type="readiness",
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props={"summary": report["summary"], "orders": report["orders"][:12],
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"globalIssues": report["globalIssues"]},
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)
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def _offline_reply(
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world: World, query: str, *, session_id: str | None = None,
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) -> tuple[str, bool]:
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t = query.strip()
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if _is_greeting(t):
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return (
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"您好，我是工业智核排产助手。\n\n"
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"我可以协助分析订单与生产数据、检查排产条件、生成试排方案并解释冲突。\n\n"
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"请直接描述目标，例如「分析当前项目数据」或「生成交期优先试排方案」。",
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False,
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)
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if _is_time_query(t):
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return _answer_time(), False
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if wants_folder_listing(t):
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from server.aps_domain.folder_pack import analyze_work_dir
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report = analyze_work_dir(world, session_id)
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return report.get("markdown") or report.get("error") or "目录解析完成。", False
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if _is_definition(t):
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return _explain_scheduling(), False
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if re.search(r"你会排产|会不会排|能不能排产|你会排吗", t):
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return _explain_can_schedule(), False
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if re.search(r"你是谁|你能做什么|怎么用|如何使用|介绍一下|你会什么", t):
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return (
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"我是工业智核的排产决策助手。\n\n"
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"主要能力包括：分析项目数据、检查排产齐备度、生成和比较试排方案、"
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"解释交期风险与产能瓶颈。\n\n"
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"建议从「分析当前项目数据」开始。",
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False,
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)
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math = _simple_math(t)
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if math:
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return f"算下来是 {math}。", False
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if re.search(r"讲个笑话|来个笑话|说个笑话", t):
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return (
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"我目前专注于生产计划、排产数据和交期分析。\n\n"
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"如果你有具体订单或现场问题，请直接描述，我会给出可执行的分析结果。",
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False,
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)
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if re.search(r"天气|下雨|降温|热不热", t):
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return (
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"当前未接入实时天气数据，无法提供可靠天气判断。\n\n"
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"如果你担心天气影响交期，请提供订单号，我可以检查对应计划和缓冲时间。",
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False,
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)
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if _wants_inventory(t):
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return analyze_project_data(world, t), True
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# 祈使开排不应落到助理菜单（意图层应已拦截；此处再兜底一句口令）
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if re.search(
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r"给我排产|我要你排产|你给我排|帮我排产|开始排产|直接排|马上排|赶紧排|排产吧",
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t,
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):
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return (
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"我理解你的目标是生成排产方案。\n\n"
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"请确认使用当前项目数据生成一版柔性试排方案；系统会先校验订单、工艺、"
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"设备和班次条件，缺失数据会在结果中明确列出。",
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False,
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)
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if re.search(r"排产|订单|交期|设备|工艺|物料|工时", t) and re.search(
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r"什么|怎么|如何|吗|呢|？|\?", t,
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):
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return (
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f"我理解你关注的是「{t[:40]}」。\n\n"
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"请明确需要执行的操作：生成试排方案、检查数据齐备度，或查看项目文件。",
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False,
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)
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# 开放问题：结合上文语气，不再甩「没配 Kimi」
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return (
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f"我暂未识别到「{t[:48]}」对应的明确业务目标。\n\n"
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"请补充以下任一信息：订单号或产品、期望交期、需要检查的数据，或希望采用的排产策略。",
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False,
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)
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async def reply(
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world: World,
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query: str,
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*,
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history: list[Any] | None = None,
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session_id: str | None = None,
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) -> AgentReply:
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"""带多轮上下文的通用作答。"""
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hist = normalize_history(history)
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# 历史不含本轮用户句时，resolve 用 hist；前端传来的 history 通常不含当前句
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follow = resolve_followup(query, hist)
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if follow.get("kind") == "deny" and follow.get("reply"):
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return AgentReply(text=str(follow["reply"]))
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q = str(follow.get("text") or query or "").strip() or "你好"
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note = str(follow.get("note") or "")
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# 短确认被改写成「直接排一版 / 带我排一版」等 → 交给工作流口令识别更稳
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# 此处若已是明确排产口令，仍可由 assistant 引导；gateway 会在 resolve 后重新 recognize。
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# 助理层只负责问答类。
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if _is_time_query(q) or _is_greeting(q) or _is_definition(q) \
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or re.search(r"你会排产|会不会排|能不能排产|你会排吗", q) \
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or wants_folder_listing(q):
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text, with_card = _offline_reply(world, q, session_id=session_id)
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if note and follow.get("kind") == "affirm":
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text = f"已根据上一轮确认，继续执行：{q}\n\n" + text
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return AgentReply(text=text, blocks=[_readiness_block(world)] if with_card else [])
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provider = get_provider()
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if provider.enabled:
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brief = _world_brief(world)
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knowledge = _knowledge_snippets(q)
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hist_blob = format_history_for_llm(hist)
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folder_hint = ""
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if wants_folder_listing(q):
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from server.aps_domain.folder_pack import analyze_work_dir
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folder_hint = analyze_work_dir(world, session_id).get("markdown") or ""
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user = (
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f"【当前时间】{_now_text()}\n\n"
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+ (f"【对话上文】\n{hist_blob}\n\n" if hist_blob else "")
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+ (f"【上下文备注】{note}\n\n" if note else "")
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+ f"【项目上下文】\n{brief}\n\n"
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+ (f"【文件夹清单】\n{folder_hint}\n\n" if folder_hint else "")
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+ (f"【厂内资料摘录】\n{knowledge}\n\n" if knowledge else "")
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+ f"【用户本轮】\n{q}\n\n"
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"请结合上文正面回答。用户说「是的」时，按上一轮建议执行或确认，不要反问「你问的是是的」。"
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)
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text = await provider.chat_text(_SYSTEM, user, timeout=45.0)
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if text and text.strip():
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blocks = [_readiness_block(world)] if _wants_inventory(q) else []
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return AgentReply(text=text.strip(), blocks=blocks)
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text, with_card = _offline_reply(world, q, session_id=session_id)
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if note and follow.get("kind") == "affirm":
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text = f"已根据上一轮确认，继续执行：{q}\n\n" + text
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blocks = [_readiness_block(world)] if with_card else []
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return AgentReply(text=text, blocks=blocks)
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