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ailine/backend/app/main_graph/nodes/hybrid_router.py

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"""
混合路由节点模块 - 前置路由决策
负责决定走快速路径还是 React 循环
"""
import re
import json
from typing import Optional
from dataclasses import dataclass, field
from datetime import datetime
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from langchain_core.runnables.config import RunnableConfig
from ..state import MainGraphState
from ...logger import info, debug
from ...model_services.chat_services import get_small_llm_service
from ._utils import dispatch_custom_event
# ========== 核心数据类型 ==========
@dataclass
class HybridRouterResult:
"""混合路由结果"""
intent: str = "complex" # chitchat / knowledge / tool / complex
confidence: float = 0.0
suggested_tools: list = field(default_factory=list)
path: str = "react_loop" # fast_chitchat / fast_rag / fast_tool / react_loop
reasoning: str = ""
# ========== 规则配置 ==========
CHITCHAT_KEYWORDS = {
"你好", "您好", "hi", "hello", "hey", "早上好", "晚上好", "下午好",
"谢谢", "感谢", "多谢", "thanks", "thank you",
"再见", "拜拜", "goodbye", "bye"
}
SUBGRAPH_KEYWORDS = {
"contact": ["通讯录", "联系人", "contact", "email", "邮件", "邮箱"],
"dictionary": ["词典", "单词", "翻译", "dictionary", "translate", "生词"],
"news_analysis": ["资讯", "新闻", "分析", "news", "report", "热点"]
}
# ========== 意图分类 Prompt 模板 ==========
INTENT_CLASSIFICATION_PROMPT = """你是一个专业的意图分类助手。请分析用户的查询,并输出 JSON 格式的结果。
意图类型4选一
- chitchat: 闲聊问候感谢道别不需要工具
- knowledge: 知识查询需要查询知识库
- tool: 工具操作需要调用通讯录/词典/新闻等子图
- complex: 复杂任务多步骤不确定或需要推理
用户查询:
{query}
输出格式 JSON不要其他内容
{{
"intent": "chitchat|knowledge|tool|complex",
"confidence": 0.0-1.0,
"reasoning": "简要说明理由",
"suggested_tools": ["contact|dictionary|news_analysis", "other"]
}}
注意如果不能100%确定意图请选择 "complex"置信度设低一些"""
# ========== 规则分流(<5ms ==========
def _rule_based_redirect(query: str) -> Optional[HybridRouterResult]:
"""规则分流:处理明显不需要推理的情况"""
query_clean = query.strip().lower()
# 1. 闲聊
if query_clean in CHITCHAT_KEYWORDS or any(kw in query_clean for kw in CHITCHAT_KEYWORDS):
return HybridRouterResult(
intent="chitchat",
confidence=1.0,
path="fast_chitchat",
reasoning="规则匹配:闲聊类请求"
)
# 2. 子图关键词
for subgraph_name, keywords in SUBGRAPH_KEYWORDS.items():
if any(kw in query_clean for kw in keywords):
return HybridRouterResult(
intent="tool",
confidence=0.9,
suggested_tools=[subgraph_name],
path="fast_tool",
reasoning=f"规则匹配:{subgraph_name} 子图关键词"
)
# 3. 短问题
if len(query_clean) < 3 or (query_clean.endswith("?") and len(query_clean) < 5):
return HybridRouterResult(
intent="complex",
confidence=0.3,
path="react_loop",
reasoning="规则匹配:问题过于简短"
)
return None
# ========== LLM 分类 ==========
async def _classify_with_llm(query: str) -> HybridRouterResult:
"""使用轻量级 LLM 进行意图分类"""
try:
llm = get_small_llm_service()
prompt = INTENT_CLASSIFICATION_PROMPT.format(query=query)
response = await llm.ainvoke(prompt)
# 解析 JSON
json_match = re.search(r'\{[\s\S]*?\}', response.content)
if not json_match:
return _default_result()
data = json.loads(json_match.group())
return _parse_classification_result(data)
except Exception as e:
debug(f"LLM 分类失败: {e}")
return _default_result()
def _parse_classification_result(data: dict) -> HybridRouterResult:
"""解析分类结果"""
intent = data.get("intent", "complex")
confidence = float(data.get("confidence", 0.3))
# 置信度低于阈值,走 complex
if confidence < 0.5:
intent = "complex"
# intent -> path 映射
path_map = {
"chitchat": "fast_chitchat",
"knowledge": "fast_rag",
"tool": "fast_tool",
}
return HybridRouterResult(
intent=intent,
confidence=confidence,
suggested_tools=data.get("suggested_tools", []),
path=path_map.get(intent, "react_loop"),
reasoning=data.get("reasoning", "")
)
def _default_result() -> HybridRouterResult:
"""默认结果LLM 失败时)"""
return HybridRouterResult(
intent="complex",
confidence=0.3,
path="react_loop",
reasoning="LLM 调用失败,降级到 React 循环"
)
# ========== 主路由节点 ==========
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async def hybrid_router_node(state: MainGraphState, config: Optional[RunnableConfig] = None) -> MainGraphState:
"""混合路由节点:前置路由,决定走快速路径还是 React 循环"""
state.current_phase = "hybrid_router"
query = state.user_query or ""
info(f"[Hybrid Router] 开始路由: {query[:50]}...")
# 1. 规则分流
rule_result = _rule_based_redirect(query)
if rule_result:
decision = rule_result
info(f"[Hybrid Router] 规则命中: {decision.path}")
else:
# 2. LLM 分类
info("[Hybrid Router] 规则未命中,使用 LLM 分类")
decision = await _classify_with_llm(query)
# 3. 更新状态
state.debug_info["hybrid_decision"] = {
"intent": decision.intent,
"confidence": decision.confidence,
"path": decision.path,
"reasoning": decision.reasoning,
"suggested_tools": decision.suggested_tools
}
state.debug_info["hybrid_start_time"] = datetime.now().isoformat()
# 4. 发送事件
await dispatch_custom_event("intent_classified", {
"intent": decision.intent,
"confidence": decision.confidence,
"reasoning": decision.reasoning,
"suggested_tools": decision.suggested_tools
}, config)
await dispatch_custom_event("path_decision", {
"path": decision.path,
"intent": decision.intent,
"reasoning": decision.reasoning
}, config)
info(f"[Hybrid Router] 路由决策: {decision.path} (intent={decision.intent}, confidence={decision.confidence})")
return state
# ========== 条件路由函数 ==========
def route_from_hybrid_decision(state: MainGraphState) -> str:
"""从混合路由决策获取下一步节点"""
decision = state.debug_info.get("hybrid_decision", {})
return decision.get("path", "react_loop")
def check_fast_path_success(state: MainGraphState) -> str:
"""检查快速路径是否成功"""
if state.debug_info.get("fast_path_failed"):
info("[Fast Path Check] 快速路径失败,升级到 React 循环")
return "escalate"
info("[Fast Path Check] 快速路径成功,进入 llm_call")
return "llm_call"
# ========== 导出 ==========
__all__ = [
"hybrid_router_node",
"route_from_hybrid_decision",
"check_fast_path_success",
"HybridRouterResult",
]