diff --git a/backend/app/agent/agent_service.py b/backend/app/agent/agent_service.py index 376c6a1..3f0b1a6 100644 --- a/backend/app/agent/agent_service.py +++ b/backend/app/agent/agent_service.py @@ -16,7 +16,6 @@ from ..main_graph.main_graph_builder import build_react_main_graph from ..main_graph.tools.graph_tools import AVAILABLE_TOOLS, TOOLS_BY_NAME from ..main_graph.config import set_stream_writer from ..main_graph.utils.rag_initializer import init_rag_tool -from backend.app.core.intent_classifier import get_intent_classifier from backend.app.logger import debug, info, warning, error from ..main_graph.state import MainGraphState, CurrentAction @@ -66,8 +65,6 @@ class AIAgentService: self.chat_services = None # 缓存的模型字典 self.tools = AVAILABLE_TOOLS.copy() self.tools_by_name = TOOLS_BY_NAME.copy() - # 添加：意图分类器 - self.intent_classifier = get_intent_classifier() # RAG 管道（可选，需要时设置） self.rag_pipeline = None # Mem0 客户端 @@ -350,31 +347,6 @@ class AIAgentService: # 构建调用参数 config, input_state = self._build_invocation(message, thread_id, resolved_model, user_id) - # ========== 意图识别（保留用于日志和后续路由）========== - intent_result = await self.intent_classifier.classify(message) - info(f"🧠 意图识别: {intent_result.intent_type} (置信度: {intent_result.confidence:.2f})") - info(f"📝 推理: {intent_result.reasoning}") - - # 注入意图到状态（让 hybrid_router 可以利用） - input_state["intent_type"] = intent_result.intent_type.value - input_state["intent_confidence"] = intent_result.confidence - - # 发送意图分类事件 - yield { - "type": "intent_classified", - "intent": intent_result.intent_type.value, - "confidence": intent_result.confidence, - "reasoning": intent_result.reasoning - } - - # 发送路径决策事件（目前硬编码，但状态中有意图信息供后续使用） - yield { - "type": "path_decision", - "path": "react_loop", - "intent": intent_result.intent_type.value - } - # ============================================= - # ========== React 循环路径 ========== info(f"🚀 开始执行单图，指定模型: {resolved_model}") current_node = None diff --git a/backend/app/core/__init__.py b/backend/app/core/__init__.py index 7e0f5b3..1bfbf19 100644 --- a/backend/app/core/__init__.py +++ b/backend/app/core/__init__.py @@ -2,12 +2,6 @@ from .formatter import MarkdownFormatter from .state_base import BaseState -from .intent_classifier import ( - IntentType, - IntentResult, - IntentClassifier, - get_intent_classifier -) from .human_review import ( ReviewManager, InMemoryReviewStore, @@ -27,30 +21,9 @@ from .visualization import ( generate_chart ) -# 为了兼容性，添加 classify_intent 函数 -def classify_intent(user_input: str, context: str = None): - """兼容旧代码的 classify_intent 函数""" - from backend.app.core.intent_classifier import get_intent_classifier - import asyncio - classifier = get_intent_classifier() - try: - loop = asyncio.get_event_loop() - if loop.is_running(): - task = loop.create_task(classifier.classify(user_input, context)) - return asyncio.run_coroutine_threadsafe(task, loop).result() - except RuntimeError: - loop = asyncio.new_event_loop() - asyncio.set_event_loop(loop) - return loop.run_until_complete(classifier.classify(user_input, context)) - __all__ = [ "MarkdownFormatter", "BaseState", - "IntentType", - "IntentResult", - "IntentClassifier", - "classify_intent", - "get_intent_classifier", "ReviewManager", "InMemoryReviewStore", "ReviewStatus", @@ -62,5 +35,5 @@ __all__ = [ "VisualizationTool", "ChartData", "get_visualization_tool", - "generate_chart" + "generate_chart", ] diff --git a/backend/app/main_graph/nodes/hybrid_router.py b/backend/app/main_graph/nodes/hybrid_router.py index ce1e627..dc41b92 100644 --- a/backend/app/main_graph/nodes/hybrid_router.py +++ b/backend/app/main_graph/nodes/hybrid_router.py @@ -1,10 +1,10 @@ """ 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 + +复用 intent.py 的推理逻辑，保证判断一致！ """ -import re -import json from typing import Optional from dataclasses import dataclass, field from datetime import datetime @@ -12,9 +12,13 @@ from langchain_core.runnables.config import RunnableConfig from ..state import MainGraphState from backend.app.logger import info, debug -from ...model_services.chat_services import get_small_llm_service +# 直接复用 intent.py 的推理逻辑！ +from backend.app.core.intent import ( + react_reason_async, + ReasoningResult, + ReasoningAction, +) from ._utils import dispatch_custom_event -from backend.app.core.json_parser import extract_and_parse_json, safe_get, safe_get_float, safe_get_str # ========== 核心数据类型 ========== @@ -29,6 +33,7 @@ class HybridRouterResult: # ========== 规则配置 ========== +# 保留规则分流，保持快速响应 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", @@ -42,37 +47,48 @@ SUBGRAPH_KEYWORDS = { } -# ========== 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。 +# ========== 从 ReasoningResult 映射到 HybridRouterResult ========== +def _map_reasoning_to_router(reasoning_result: ReasoningResult) -> HybridRouterResult: + """将 intent.py 的推理结果映射为 hybrid_router 的结果""" -【格式要求】 -你必须严格输出 JSON 格式，不要加任何 Markdown 代码块标记（如 ```json）。 -仅输出纯 JSON 字符串，不要有其他解释文字。 + # ReasoningAction -> intent 映射 + intent_map = { + ReasoningAction.DIRECT_RESPONSE: "chitchat", + ReasoningAction.RETRIEVE_RAG: "knowledge", + ReasoningAction.RE_RETRIEVE_RAG: "knowledge", + ReasoningAction.WEB_SEARCH: "complex", # WEB_SEARCH 走 React循环 + ReasoningAction.ROUTE_SUBGRAPH: "tool", + ReasoningAction.CLARIFY: "chitchat", + ReasoningAction.UNKNOWN: "complex", + } -【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） + # ReasoningAction -> path 映射 + path_map = { + ReasoningAction.DIRECT_RESPONSE: "fast_chitchat", + ReasoningAction.RETRIEVE_RAG: "fast_rag", + ReasoningAction.RE_RETRIEVE_RAG: "fast_rag", + ReasoningAction.WEB_SEARCH: "react_loop", # WEB_SEARCH 走 React循环 + ReasoningAction.ROUTE_SUBGRAPH: "fast_tool", + ReasoningAction.CLARIFY: "fast_chitchat", + ReasoningAction.UNKNOWN: "react_loop", + } -【输出格式】 -{{ - "intent": "chitchat|knowledge|tool|complex", - "confidence": 0.85, - "reasoning": "简要说明理由", - "suggested_tools": ["contact|dictionary|news_analysis", "other"] -}} + intent = intent_map.get(reasoning_result.action, "complex") + path = path_map.get(reasoning_result.action, "react_loop") -【重要提示】 -- 如果不能100%确定意图，请选择 "complex"，置信度设低一些。 -- confidence 是你对当前分类的信心（0.0-1.0）。 -- suggested_tools 仅在 intent=tool 时提供，否则设为空数组。 + suggested_tools = [] + if reasoning_result.action == ReasoningAction.ROUTE_SUBGRAPH: + target_subgraph = reasoning_result.metadata.get("target_subgraph") + if target_subgraph: + suggested_tools = [target_subgraph] -【用户查询】 -{query} - -【现在开始】 -请根据以上信息，输出你的分类 JSON：""" + return HybridRouterResult( + intent=intent, + confidence=reasoning_result.confidence, + suggested_tools=suggested_tools, + path=path, + reasoning=reasoning_result.reasoning + ) # ========== 规则分流（<5ms） ========== @@ -112,66 +128,20 @@ def _rule_based_redirect(query: str) -> Optional[HybridRouterResult]: 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) - - # 使用新的 JSON 解析器 - parse_result = extract_and_parse_json(response.content) - if not parse_result.success or not parse_result.data: - return _default_result() - - return _parse_classification_result(parse_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 = safe_get_str(data, "intent", "complex") - confidence = safe_get_float(data, "confidence", 0.3) - suggested_tools = safe_get(data, "suggested_tools", []) - reasoning = safe_get_str(data, "reasoning", "") - - # 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=suggested_tools, - path=path_map.get(intent, "react_loop"), - reasoning=reasoning - ) - - +# ========== 默认结果 ========== def _default_result() -> HybridRouterResult: - """默认结果（LLM 失败时）""" + """默认结果""" return HybridRouterResult( intent="complex", confidence=0.3, path="react_loop", - reasoning="LLM 调用失败，降级到 React 循环" + reasoning="降级到默认值，走 React 循环" ) # ========== 主路由节点 ========== 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""" + """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 "" @@ -183,11 +153,17 @@ async def hybrid_router_node(state: MainGraphState, config: Optional[RunnableCon 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) + # 2. 复用 intent.py 的推理逻辑！保证判断一致！ + info("[Hybrid Router] 规则未命中，使用 intent.py 推理") + try: + reasoning_result = await react_reason_async(query, {}) + decision = _map_reasoning_to_router(reasoning_result) + info(f"[Hybrid Router] 推理结果: action={reasoning_result.action.name}, path={decision.path}") + except Exception as e: + debug(f"[Hybrid Router] intent.py 推理失败: {e}") + decision = _default_result() - # 步骤3: 更新状态 - 只使用新的结构化字段 + # 3. 更新状态 state.hybrid_router.decision = decision state.hybrid_router.start_time = datetime.now().isoformat() @@ -211,7 +187,7 @@ async def hybrid_router_node(state: MainGraphState, config: Optional[RunnableCon # ========== 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 - 使用新的结构化字段""" + """从混合路由决策获取下一步节点""" decision = state.hybrid_router.decision if decision and hasattr(decision, 'path'): return decision.path @@ -219,11 +195,11 @@ def route_from_hybrid_decision(state: MainGraphState) -> str: def check_fast_path_success(state: MainGraphState) -> str: - """检查快速路径是否成功 - 使用新的结构化字段""" + """检查快速路径是否成功""" if state.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"