Pine
18d28be943
- 引入新的`docker-compose.yml`文件，便于后端和MQTT代理（EMQX）的部署。 - 更新多个组件中的API基本检索，使用函数进行动态解析。 - 加强了多个组件中API调用的错误处理和日志记录。 - 优化了AI聊天面板离线场景的回退响应。 - 更新DataScreen和MediaScreen组件中的数据显示和统计，以反映准确的指标。 - 重构MQTT连接逻辑，以支持动态凭证和客户端ID。
210 lines
7.5 KiB
Python
210 lines
7.5 KiB
Python
# -*- coding: utf-8 -*-
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"""AI 对话引擎 —— 阿里云 DashScope（通义千问）+ 函数调用
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工具调用执行后通过 MQTT 广播，所有大屏同步响应
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DashScope 不可用时自动回退本地规则引擎（离线兜底）
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"""
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import json
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import logging
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import ssl
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import urllib.error
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import urllib.request
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import certifi
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from .config import settings
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log = logging.getLogger("dpm.llm")
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# macOS 系统 Python 无系统 CA，使用 certifi 提供的根证书
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_SSL_CTX = ssl.create_default_context(cafile=certifi.where())
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SYSTEM_PROMPT = (
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"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。"
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"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。"
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"回答园区相关问题时，必须严格依据【园区知识库】中的标准资料口径回答，"
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"不得使用资料外的数字；涉及运营数据时请区分口径（官方统计/省级绩效累计/AI平台指标），"
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"并在需要时调用 get_park_overview 工具获取实时准确数据。"
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"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；"
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"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。"
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)
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# 暴露给大模型的工具定义（执行时经 MQTT 广播到前端）
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TOOLS = [
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{
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"type": "function",
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"function": {
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"name": "navigate_page",
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"description": "切换大屏展示页面（数据大屏 / 数字孪生 / AI 助手 / 媒体轮播）",
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"parameters": {
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"type": "object",
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"properties": {
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"page": {"type": "string", "enum": ["/", "/twin", "/ai", "/screen"],
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"description": "目标页面路径"},
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},
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"required": ["page"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "media_control",
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"description": "控制媒体播放（播放/暂停/下一项/上一项）",
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"parameters": {
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"type": "object",
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"properties": {
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"action": {"type": "string", "enum": ["play", "pause", "next", "prev"]},
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},
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"required": ["action"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "show_card",
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"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）",
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"parameters": {
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"type": "object",
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"properties": {
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"card": {"type": "string", "enum": ["companies", "zones", "overview", "custom"]},
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"title": {"type": "string"},
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"content": {"type": "string"},
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},
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"required": ["card"],
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},
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},
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},
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{
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"type": "function",
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"function": {
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"name": "send_alert",
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"description": "在大屏上弹出通知提示",
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"parameters": {
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"type": "object",
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"properties": {
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"title": {"type": "string"},
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"content": {"type": "string"},
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},
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"required": ["title", "content"],
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},
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},
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},
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]
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_TOOL_MAP = {
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"navigate_page": "navigate",
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"media_control": "control",
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"show_card": "show_card",
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"send_alert": "alert",
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}
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def _chat_once(messages, with_tools=True):
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payload = {
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"model": settings.LLM_MODEL,
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"messages": messages,
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"temperature": 0.6,
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}
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if with_tools:
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payload["tools"] = TOOLS
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req = urllib.request.Request(
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f"{settings.LLM_BASE_URL}/chat/completions",
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data=json.dumps(payload, ensure_ascii=False).encode("utf-8"),
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {settings.DASHSCOPE_API_KEY}",
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},
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)
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with urllib.request.urlopen(req, timeout=60, context=_SSL_CTX) as resp:
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return json.loads(resp.read().decode("utf-8"))
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def _fallback(messages):
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"""DashScope 不可用时回退本地规则引擎"""
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from .ai_tools import run_chat as rule_run
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result = rule_run(messages)
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# 规则引擎产出的工具同样执行（MQTT 广播），并汇总发布结果
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_, all_ok = _exec_all(result.get("tools", []))
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result["model"] = "rule-engine"
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result["mqtt_published"] = all_ok
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return result
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def _exec_all(tools):
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"""执行工具列表，返回 (results, all_ok)"""
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from .ai_tools import run_tools
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results, all_ok = run_tools(tools)
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return results, all_ok
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def run_chat(messages):
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"""入口：{reply, tools, model}
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tools 已在后端执行（MQTT 广播），返回值供请求端本地同步执行"""
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if not settings.DASHSCOPE_API_KEY:
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log.warning("未配置 DASHSCOPE_API_KEY，使用本地规则引擎")
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return _fallback(messages)
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# 注入园区知识库摘要（标准资料口径），确保回答准确
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system = SYSTEM_PROMPT
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try:
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from .rag import brief
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kb = brief()
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if kb:
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system += f"\n\n【园区知识库】\n{kb}"
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except Exception as e: # noqa: BLE001
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log.warning("llm: 知识库注入失败（仅用基础提示词）: %s", e)
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msgs = [{"role": "system", "content": system}] + [
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{"role": m.get("role") == "me" and "user" or m.get("role", "user"), "content": m.get("content", "")}
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for m in (messages or [])
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]
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try:
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data = _chat_once(msgs)
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choice = data["choices"][0]["message"]
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reply = choice.get("content") or ""
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tool_calls = choice.get("tool_calls") or []
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executed = []
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if tool_calls:
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for tc in tool_calls:
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fn = tc.get("function", {})
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name = fn.get("name", "")
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try:
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args = json.loads(fn.get("arguments") or "{}")
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except json.JSONDecodeError:
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args = {}
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tool = {"type": _TOOL_MAP.get(name, name), "params": args}
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executed.append(tool)
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msgs.append({"role": "assistant", "content": None, "tool_calls": tool_calls})
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msgs.append({
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"role": "tool",
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"tool_call_id": tc.get("id", ""),
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"content": json.dumps({"ok": True}, ensure_ascii=False),
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})
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# 工具执行（MQTT 广播），并汇总发布结果
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_, all_ok = _exec_all(executed)
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# 二次调用：携带工具结果生成最终回复
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try:
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data2 = _chat_once(msgs, with_tools=False)
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reply = data2["choices"][0]["message"].get("content") or reply
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except Exception as e: # noqa: BLE001
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log.warning("LLM 二次调用失败（保留工具回复）: %s", e)
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else:
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all_ok = True
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if not reply:
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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。"
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return {"reply": reply, "tools": executed, "model": settings.LLM_MODEL, "mqtt_published": all_ok}
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except urllib.error.HTTPError as e:
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log.warning("DashScope HTTP %s: %s", e.code, e.read()[:300])
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except Exception as e: # noqa: BLE001
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log.warning("DashScope 调用失败，回退规则引擎: %s", e)
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return _fallback(messages)
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