推理优化

backend/app/main_graph/nodes/rag_nodes.py

@@ -19,6 +19,23 @@ from ._utils import dispatch_custom_event, make_react_event
 # 置信度阈值配置
 RAG_CONFIDENCE_THRESHOLD = 0.6  # 低于此值认为检索不相关
 
+# 全局 pipeline 实例
+_rag_pipeline = None
+
+
+def _get_rag_pipeline():
+    """获取 RAG Pipeline 实例"""
+    global _rag_pipeline
+    if _rag_pipeline is None:
+        from backend.app.rag.pipeline import RAGPipeline
+        _rag_pipeline = RAGPipeline(
+            num_queries=3,
+            rerank_top_n=5,
+            use_rerank=True,
+            return_parent_docs=True,
+        )
+    return _rag_pipeline
+
 
 def _get_rag_tool() -> Optional[callable]:
     """获取 RAG 工具"""
@@ -27,7 +44,7 @@ def _get_rag_tool() -> Optional[callable]:
 
 
 # ========== RAG 检索核心逻辑 ==========
-async def _rag_retrieve_core(state: MainGraphState, rag_tool: callable) -> MainGraphState:
+async def _rag_retrieve_core(state: MainGraphState, pipeline) -> MainGraphState:
     """执行 RAG 检索的核心逻辑"""
     retrieval_query = state.user_query
 
@@ -38,15 +55,20 @@ async def _rag_retrieve_core(state: MainGraphState, rag_tool: callable) -> MainG
         if cfg and cfg.retrieval_query:
             retrieval_query = cfg.retrieval_query
 
-    # 调用 RAG 工具
-    rag_context = await rag_tool.ainvoke(retrieval_query)
+    # 直接调用 pipeline 获取文档和上下文
+    documents = await pipeline.aretrieve(retrieval_query)
+    rag_context = pipeline.format_context(documents)
+
     info(f"[RAG Core] 获取到 rag_context: {type(rag_context)}, 长度={len(rag_context) if rag_context else 0}")
+    info(f"[RAG Core] 获取到 rag_docs: {len(documents)} 个文档")
 
     # 更新状态
     state.rag_context = rag_context
-    state.rag_retrieved = True
+    state.rag_docs = documents  # 保存文档用于置信度评估
+    state.rag_retrieved = bool(documents)  # 有文档才算检索成功
     state.rag_attempts = getattr(state, 'rag_attempts', 0) + 1
-    state.debug_info["rag_source"] = "tool"
+    state.debug_info["rag_source"] = "pipeline"
+    state.debug_info["rag_scores"] = pipeline.last_scores  # 保存分数信息
 
     return state
 
@@ -57,12 +79,7 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[RunnableConf
     state.current_phase = "rag_retrieving"
     start_time = time.time()
 
-    rag_tool = _get_rag_tool()
-    if not rag_tool:
-        info("[RAG] RAG 工具未初始化")
-        state.rag_confidence = 0.0
-        state.rag_retrieved = False
-        return state
+    pipeline = _get_rag_pipeline()
 
     await dispatch_custom_event(
         "react_reasoning",
@@ -71,7 +88,7 @@ async def rag_retrieve_node(state: MainGraphState, config: Optional[RunnableConf
     )
 
     try:
-        state = await _rag_retrieve_core(state, rag_tool)
+        state = await _rag_retrieve_core(state, pipeline)
 
         # 评估置信度
         confidence = await _evaluate_rag_confidence(state)
@@ -111,7 +128,7 @@ async def _evaluate_rag_confidence(state: MainGraphState) -> float:
         return 0.0
 
     # 方式1: 向量相似度（从 rag_docs 中获取）
-    embedding_score = _get_embedding_similarity(state, query)
+    embedding_score = _get_embedding_similarity(state)
     info(f"[RAG Confidence] 向量相似度={embedding_score:.3f}")
 
     # 方式2: 重排序分数（从 rag_docs 中获取）
@@ -131,36 +148,43 @@ async def _evaluate_rag_confidence(state: MainGraphState) -> float:
 
 
 def _get_embedding_similarity(state: MainGraphState) -> float:
-    """从 rag_docs 中获取向量相似度分数"""
-    rag_docs = getattr(state, "rag_docs", [])
+    """从 rag_scores 或 rag_docs 中获取向量相似度分数"""
+    # 优先从 pipeline 提供的分数中获取
+    rag_scores = state.debug_info.get("rag_scores", [])
+    if rag_scores:
+        scores = [s.get("embedding_score", 0.0) for s in rag_scores]
+        if scores:
+            # 归一化到 0-1
+            normalized = [min(s / 10.0, 1.0) if s > 1.0 else s for s in scores]
+            return max(normalized)
 
-    # 如果有多个文档，取最高分
+    # 降级：从 rag_docs 中获取
+    rag_docs = getattr(state, "rag_docs", [])
     scores = []
     for doc in rag_docs:
         if isinstance(doc, dict):
             score = doc.get("score", 0.0)
-            # 向量相似度通常在 0-1 之间，RRF 分数可能更高
-            # 归一化到 0-1
-            if score > 1.0:
-                score = min(score / 10.0, 1.0)  # 假设 max 约 10
-            scores.append(score)
         elif hasattr(doc, "metadata"):
-            score = doc.metadata.get("score", 0.0)
-            if score > 1.0:
-                score = min(score / 10.0, 1.0)
-            scores.append(score)
+            score = doc.metadata.get("embedding_score", doc.metadata.get("score", 0.0))
+        else:
+            continue
+        if score > 1.0:
+            score = min(score / 10.0, 1.0)
+        scores.append(score)
 
-    if scores:
-        # 取平均或最高分
-        return max(scores)  # 使用最高分更准确
-    return 0.0
+    return max(scores) if scores else 0.0
 
 
 def _get_rerank_score(state: MainGraphState) -> float:
-    """从 rag_docs 中获取重排序分数"""
-    rag_docs = getattr(state, "rag_docs", [])
+    """从 rag_scores 或 rag_docs 中获取重排序分数"""
+    # 优先从 pipeline 提供的分数中获取
+    rag_scores = state.debug_info.get("rag_scores", [])
+    if rag_scores:
+        scores = [s.get("rerank_score", 0.0) for s in rag_scores]
+        return max(scores) if scores else 0.0
 
-    # 重排分数通常在 0-1 之间
+    # 降级：从 rag_docs 中获取
+    rag_docs = getattr(state, "rag_docs", [])
     scores = []
     for doc in rag_docs:
         if isinstance(doc, dict):
@@ -168,14 +192,11 @@ def _get_rerank_score(state: MainGraphState) -> float:
         elif hasattr(doc, "metadata"):
             score = doc.metadata.get("rerank_score", 0.0)
         else:
-            score = 0.0
-
+            continue
         if score > 0:
             scores.append(score)
 
-    if scores:
-        return max(scores)  # 使用最高分
-    return 0.0
+    return max(scores) if scores else 0.0
 
 
 async def _get_llm_score(state: MainGraphState) -> float: