DPM/backend/app/llm.py

# -*- coding: utf-8 -*-
"""AI 对话引擎 —— 阿里云 DashScope（通义千问）+ 函数调用
   工具调用执行后通过 MQTT 广播，所有大屏同步响应
   DashScope 不可用时自动回退本地规则引擎（离线兜底）
"""

import json
import logging
import ssl
import urllib.error
import urllib.request

import certifi

from .config import settings

log = logging.getLogger("dpm.llm")

# macOS 系统 Python 无系统 CA，使用 certifi 提供的根证书
_SSL_CTX = ssl.create_default_context(cafile=certifi.where())

SYSTEM_PROMPT = (
    "你是「昆明市大学生创业园 · OPC 智能园区」的 AI 智能助手，运行在大屏展播系统上。"
    "园区提供空间、孵化、融资、政策、资源、AI 赋能、综合服务七位一体服务，"
    "在园项目 158 家，累计带动就业 2,186 人，累计营收 2.08 亿元。"
    "当用户要求「切换页面 / 控制播放 / 展示卡片 / 弹出通知」时，必须调用对应工具；"
    "其余园区相关问题用简洁、专业的中文回答，可适当使用列表。"
)

# 暴露给大模型的工具定义（执行时经 MQTT 广播到前端）
TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "navigate_page",
            "description": "切换大屏展示页面（数据大屏 / 数字孪生 / AI 助手 / 媒体轮播）",
            "parameters": {
                "type": "object",
                "properties": {
                    "page": {"type": "string", "enum": ["/", "/twin", "/ai", "/screen"],
                             "description": "目标页面路径"},
                },
                "required": ["page"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "media_control",
            "description": "控制媒体播放（播放/暂停/下一项/上一项）",
            "parameters": {
                "type": "object",
                "properties": {
                    "action": {"type": "string", "enum": ["play", "pause", "next", "prev"]},
                },
                "required": ["action"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "show_card",
            "description": "在大屏上展示信息卡片（企业分布 / 分区介绍 / 园区总览 / 自定义内容）",
            "parameters": {
                "type": "object",
                "properties": {
                    "card": {"type": "string", "enum": ["companies", "zones", "overview", "custom"]},
                    "title": {"type": "string"},
                    "content": {"type": "string"},
                },
                "required": ["card"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "send_alert",
            "description": "在大屏上弹出通知提示",
            "parameters": {
                "type": "object",
                "properties": {
                    "title": {"type": "string"},
                    "content": {"type": "string"},
                },
                "required": ["title", "content"],
            },
        },
    },
]

_TOOL_MAP = {
    "navigate_page": "navigate",
    "media_control": "control",
    "show_card": "show_card",
    "send_alert": "alert",
}


def _chat_once(messages, with_tools=True):
    payload = {
        "model": settings.LLM_MODEL,
        "messages": messages,
        "temperature": 0.6,
    }
    if with_tools:
        payload["tools"] = TOOLS
    req = urllib.request.Request(
        f"{settings.LLM_BASE_URL}/chat/completions",
        data=json.dumps(payload, ensure_ascii=False).encode("utf-8"),
        headers={
            "Content-Type": "application/json",
            "Authorization": f"Bearer {settings.DASHSCOPE_API_KEY}",
        },
    )
    with urllib.request.urlopen(req, timeout=60, context=_SSL_CTX) as resp:
        return json.loads(resp.read().decode("utf-8"))


def _fallback(messages):
    """DashScope 不可用时回退本地规则引擎"""
    from .ai_tools import run_chat as rule_run

    result = rule_run(messages)
    # 规则引擎产出的工具同样执行（MQTT 广播），并汇总发布结果
    _, all_ok = _exec_all(result.get("tools", []))
    result["model"] = "rule-engine"
    result["mqtt_published"] = all_ok
    return result


def _exec_all(tools):
    """执行工具列表，返回 (results, all_ok)"""
    from .ai_tools import run_tools

    results, all_ok = run_tools(tools)
    return results, all_ok


def run_chat(messages):
    """入口：{reply, tools, model}
       tools 已在后端执行（MQTT 广播），返回值供请求端本地同步执行"""
    if not settings.DASHSCOPE_API_KEY:
        log.warning("未配置 DASHSCOPE_API_KEY，使用本地规则引擎")
        return _fallback(messages)

    msgs = [{"role": "system", "content": SYSTEM_PROMPT}] + [
        {"role": m.get("role") == "me" and "user" or m.get("role", "user"), "content": m.get("content", "")}
        for m in (messages or [])
    ]

    try:
        data = _chat_once(msgs)
        choice = data["choices"][0]["message"]
        reply = choice.get("content") or ""
        tool_calls = choice.get("tool_calls") or []
        executed = []

        if tool_calls:
            for tc in tool_calls:
                fn = tc.get("function", {})
                name = fn.get("name", "")
                try:
                    args = json.loads(fn.get("arguments") or "{}")
                except json.JSONDecodeError:
                    args = {}
                tool = {"type": _TOOL_MAP.get(name, name), "params": args}
                executed.append(tool)
                msgs.append({"role": "assistant", "content": None, "tool_calls": tool_calls})
                msgs.append({
                    "role": "tool",
                    "tool_call_id": tc.get("id", ""),
                    "content": json.dumps({"ok": True}, ensure_ascii=False),
                })
            # 工具执行（MQTT 广播），并汇总发布结果
            _, all_ok = _exec_all(executed)
            # 二次调用：携带工具结果生成最终回复
            try:
                data2 = _chat_once(msgs, with_tools=False)
                reply = data2["choices"][0]["message"].get("content") or reply
            except Exception as e:  # noqa: BLE001
                log.warning("LLM 二次调用失败（保留工具回复）: %s", e)
        else:
            all_ok = True

        if not reply:
            reply = "已完成操作。您还可以让我切换页面、控制播放或展示园区卡片。"

        return {"reply": reply, "tools": executed, "model": settings.LLM_MODEL, "mqtt_published": all_ok}
    except urllib.error.HTTPError as e:
        log.warning("DashScope HTTP %s: %s", e.code, e.read()[:300])
    except Exception as e:  # noqa: BLE001
        log.warning("DashScope 调用失败，回退规则引擎: %s", e)

    return _fallback(messages)