refactor: 整理文件夹结构，修复 create_serde 导入问题
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- 移动 main_graph/tools/ 到 deprecated/main_graph_tools/（旧架构工具） - 移动 rag_initializer.py 和 retry_utils.py 到 core/ - 清理 main_graph/nodes/ 里的旧节点到 deprecated/ - 修复 backend.py 中 create_serde 导入问题
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backend/app/deprecated/rag_nodes.py
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backend/app/deprecated/rag_nodes.py
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"""
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RAG 检索节点模块
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包含：RAG 检索、置信度判断、重检索等节点
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"""
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import time
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import asyncio
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from typing import Optional
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from datetime import datetime
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from langchain_core.runnables.config import RunnableConfig
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from ...main_graph.state import MainGraphState, ErrorRecord, ErrorSeverity
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from ...main_graph.utils.retry_utils import RAG_RETRY_CONFIG
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from backend.app.logger import info, debug
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from ...model_services import get_small_llm_service
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from ._utils import dispatch_custom_event, make_react_event
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# 置信度阈值配置
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RAG_CONFIDENCE_THRESHOLD = 0.6 # 低于此值认为检索不相关
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# 全局 pipeline 实例
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_rag_pipeline = None
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def _get_rag_pipeline():
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"""获取 RAG Pipeline 实例"""
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global _rag_pipeline
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if _rag_pipeline is None:
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from backend.app.rag.pipeline import RAGPipeline
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_rag_pipeline = RAGPipeline(
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num_queries=3,
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rerank_top_n=5,
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use_rerank=True,
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return_parent_docs=True,
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)
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return _rag_pipeline
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def _get_rag_tool() -> Optional[callable]:
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"""获取 RAG 工具"""
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from backend.app.main_graph.utils.rag_initializer import get_rag_tool
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return get_rag_tool()
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# ========== RAG 检索核心逻辑 ==========
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async def _rag_retrieve_core(state: MainGraphState, pipeline) -> MainGraphState:
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info(f"[RAG Core] _rag_retrieve_core 开始")
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retrieval_query = state.user_query
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# 优先使用推理结果中的优化查询 - 从新的结构化字段获取
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reasoning_result = state.react_reasoning.reasoning_result
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if reasoning_result and hasattr(reasoning_result, "retrieval_config"):
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cfg = reasoning_result.retrieval_config
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if cfg and cfg.retrieval_query:
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retrieval_query = cfg.retrieval_query
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info(f"[RAG Core] 使用检索查询: {retrieval_query[:50]}...")
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# 直接调用 pipeline 获取文档和上下文
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info(f"[RAG Core] 调用 pipeline.aretrieve")
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documents = await pipeline.aretrieve(retrieval_query)
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info(f"[RAG Core] pipeline.aretrieve 返回，得到 {len(documents)} 个文档")
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info(f"[RAG Core] 调用 pipeline.format_context")
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rag_context = pipeline.format_context(documents)
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info(f"[RAG Core] pipeline.format_context 返回")
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info(f"[RAG Core] 获取到 rag_context: {type(rag_context)}, 长度={len(rag_context) if rag_context else 0}")
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info(f"[RAG Core] 获取到 rag_docs: {len(documents)} 个文档")
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# 更新状态
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state.rag_context = rag_context
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state.rag_docs = documents # 保存文档用于置信度评估
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state.rag_retrieved = bool(documents) # 有文档才算检索成功
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state.rag_attempts = getattr(state, 'rag_attempts', 0) + 1
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# 移除对 debug_info 的依赖，不再保存 rag_scores
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info(f"[RAG Core] _rag_retrieve_core 结束")
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return state
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# ========== RAG 检索节点 ==========
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async def rag_retrieve_node(state: MainGraphState, config: Optional[RunnableConfig] = None) -> MainGraphState:
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info(f"[RAG] rag_retrieve_node 开始")
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state.current_phase = "rag_retrieving"
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start_time = time.time()
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info(f"[RAG] 调用 _get_rag_pipeline")
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pipeline = _get_rag_pipeline()
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await dispatch_custom_event(
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"react_reasoning",
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make_react_event(state.reasoning_step, "rag_retrieve_start", 1.0, "开始执行 RAG 检索..."),
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config
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)
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try:
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info(f"[RAG] 调用 _rag_retrieve_core")
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state = await _rag_retrieve_core(state, pipeline)
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info(f"[RAG] _rag_retrieve_core 返回")
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# 评估置信度
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info(f"[RAG] 调用 _evaluate_rag_confidence")
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confidence = await _evaluate_rag_confidence(state)
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state.rag_confidence = confidence
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info(f"[RAG] 检索完成，置信度={confidence:.2f}，RAG尝试次数={state.rag_attempts}")
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state.reasoning_history.append({
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"step": state.reasoning_step,
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"action": "RETRIEVE_RAG",
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"confidence": confidence,
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"reasoning": f"RAG 检索完成，置信度={confidence:.2f}",
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"timestamp": datetime.now().isoformat()
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})
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await dispatch_custom_event(
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"react_reasoning",
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make_react_event(state.reasoning_step, "rag_retrieve_complete", confidence,
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f"RAG 检索完成，置信度={confidence:.2f}"),
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config
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)
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except Exception as e:
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info(f"[RAG] 检索失败: {e}", exc_info=True)
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state.rag_confidence = 0.0
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state.rag_retrieved = False
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info(f"[RAG] rag_retrieve_node 结束")
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return state
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async def _evaluate_rag_confidence(state: MainGraphState) -> float:
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"""评估 RAG 检索结果置信度（综合向量相似度 + 重排分数 + 小模型判断）"""
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query = state.user_query or ""
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rag_context = state.rag_context or ""
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if not rag_context:
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return 0.0
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# 方式1: 向量相似度（从 rag_docs 中获取）
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embedding_score = _get_embedding_similarity(state)
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info(f"[RAG Confidence] 向量相似度={embedding_score:.3f}")
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# 方式2: 重排序分数（从 rag_docs 中获取）
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rerank_score = _get_rerank_score(state)
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info(f"[RAG Confidence] 重排分数={rerank_score:.3f}")
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# 方式3: 小模型判断
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llm_score = await _get_llm_score(state)
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info(f"[RAG Confidence] LLM评估={llm_score:.3f}")
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# 综合得分（加权平均）
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# 向量相似度权重 0.3，重排权重 0.3，LLM 权重 0.4
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final_score = embedding_score * 0.3 + rerank_score * 0.3 + llm_score * 0.4
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info(f"[RAG Confidence] 综合置信度={final_score:.3f} (embedding={embedding_score:.3f}*0.3 + rerank={rerank_score:.3f}*0.3 + llm={llm_score:.3f}*0.4)")
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return final_score
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def _get_embedding_similarity(state: MainGraphState) -> float:
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"""从 rag_docs 中获取向量相似度分数（不再从 debug_info 获取）"""
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# 降级：从 rag_docs 中获取
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rag_docs = getattr(state, "rag_docs", [])
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scores = []
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for doc in rag_docs:
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if isinstance(doc, dict):
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score = doc.get("score", 0.0)
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elif hasattr(doc, "metadata"):
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score = doc.metadata.get("embedding_score", doc.metadata.get("score", 0.0))
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else:
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continue
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if score > 1.0:
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score = min(score / 10.0, 1.0)
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scores.append(score)
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return max(scores) if scores else 0.0
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def _get_rerank_score(state: MainGraphState) -> float:
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"""从 rag_docs 中获取重排序分数（不再从 debug_info 获取）"""
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# 降级：从 rag_docs 中获取
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rag_docs = getattr(state, "rag_docs", [])
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scores = []
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for doc in rag_docs:
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if isinstance(doc, dict):
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score = doc.get("rerank_score", 0.0)
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elif hasattr(doc, "metadata"):
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score = doc.metadata.get("rerank_score", 0.0)
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else:
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continue
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if score > 0:
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scores.append(score)
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return max(scores) if scores else 0.0
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async def _get_llm_score(state: MainGraphState) -> float:
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"""使用小模型评估检索结果相关性"""
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query = state.user_query or ""
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rag_context = state.rag_context or ""
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try:
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llm = get_small_llm_service()
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prompt = f"""评估以下检索结果与用户问题的相关性，返回 0.0-1.0 的分数：
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- 1.0 = 完全相关，能直接回答问题
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- 0.5 = 部分相关，有一定参考价值
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- 0.0 = 完全不相关，无法回答问题
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用户问题：{query}
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检索结果：{rag_context[:1500]}
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只返回一个数字："""
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response = await llm.ainvoke(prompt)
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content = response.content.strip()
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import re
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match = re.search(r'(\d+\.?\d*)', content)
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if match:
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score = float(match.group(1))
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return max(0.0, min(1.0, score))
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except Exception as e:
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info(f"[RAG Confidence] LLM评估失败: {e}")
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return 0.5 # 默认中等置信度
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# ========== 置信度判断节点 ==========
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def check_rag_confidence(state: MainGraphState) -> str:
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"""
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根据 RAG 置信度判断下一步
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Returns:
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"high_confidence" - 高置信度(>=0.6)，可直接生成回答
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"low_confidence" - 低置信度(<0.6)，需要联网搜索
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"no_rag" - 无检索结果，需要联网搜索
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"""
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rag_attempts = getattr(state, 'rag_attempts', 0)
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rag_confidence = getattr(state, 'rag_confidence', 0.0)
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info(f"[Confidence Check] rag_attempts={rag_attempts}, rag_confidence={rag_confidence:.2f}")
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# 情况1: 没有检索结果
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if not getattr(state, 'rag_retrieved', False) or not state.rag_context:
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info("[Confidence Check] 无检索结果，走联网")
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return "no_rag"
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# 情况2: 置信度低于阈值
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if rag_confidence < RAG_CONFIDENCE_THRESHOLD:
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if rag_attempts >= 2:
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info(f"[Confidence Check] 置信度={rag_confidence:.2f}<{RAG_CONFIDENCE_THRESHOLD}，且RAG尝试{rag_attempts}次，走联网")
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return "low_confidence"
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else:
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info(f"[Confidence Check] 置信度={rag_confidence:.2f}<{RAG_CONFIDENCE_THRESHOLD}，可再尝试RAG一次")
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return "retry_rag"
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# 情况3: 高置信度
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info(f"[Confidence Check] 高置信度={rag_confidence:.2f}>={RAG_CONFIDENCE_THRESHOLD}，直接生成回答")
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return "high_confidence"
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# ========== 导出 ==========
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__all__ = [
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"rag_retrieve_node",
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"check_rag_confidence",
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"RAG_CONFIDENCE_THRESHOLD",
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]
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