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class CerenceXUI:
"""
Cerence xUI 混合 LLM 平台
架构: 嵌入式LLM(车端) + 云端LLM(Azure) + Nvidia AI Enterprise
CES 2026 发布, Q3 FY2026 已部署10万辆
"""
def __init__(self):
self.embedded_llm = {
"model": "Cerenceembedded LLM (on-device)",
"params": "7B-13B (量化版)",
"latency": "<500ms",
"offline": True,
"use_cases": ["基础控制", "导航", "媒体", "简单问答"],
}
self.cloud_llm = {
"model": "Nvidia AI Enterprise on Azure",
"params": "70B+ (云端大模型)",
"latency": "1-3s (网络依赖)",
"offline": False,
"use_cases": ["复杂推理", "多轮对话", "知识问答", "创意生成"],
}
self.routing_engine = {
"type": "AI Intent Router",
"decision_factors": [
"query_complexity",
"network_availability",
"latency_requirement",
"privacy_sensitivity",
],
}
def process_query(self, query: str, context: dict):
"""路由查询到合适的LLM"""
complexity = self._assess_complexity(query)
network = context.get("network", "5G")
if complexity < 0.3 or not network:
return self._embedded_inference(query)
else:
return self._cloud_inference(query)
def _assess_complexity(self, query: str) -> float:
"""评估查询复杂度 (0-1)"""
complex_indicators = ["为什么", "解释", "比较", "计划", "如果"]
score = sum(0.2 for ind in complex_indicators if ind in query)
return min(score, 1.0)
def _embedded_inference(self, query: str):
return {"source": "embedded", "response": f"[本地推理] {query}", "latency_ms": 300}
def _cloud_inference(self, query: str):
return {"source": "cloud", "response": f"[云端推理] {query}", "latency_ms": 1500}
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