调整代码结构，同时修改重定义提示词的方式。

backend/app/engine/retriever/HybridRetriever.py

@@ -0,0 +1,67 @@
+import os
+from typing import Optional, Any, Dict, List
+
+from llama_index.core.base.base_retriever import BaseRetriever
+from llama_index.core.schema import NodeWithScore, QueryBundle
+
+from app.engine.retriever import CHBM25Retriever
+
+
+class HybridRetriever(BaseRetriever):
+    def __init__(
+            self,
+            vector_index,
+            similarity_top_k: int = 2,
+            out_top_k: Optional[int] = None,
+            alpha: float = 0.5,
+            filters = None,
+            **kwargs: Any,
+    ) -> None:
+        super().__init__(**kwargs)
+        self._vector_index = vector_index
+        self._embed_model = vector_index._embed_model
+        self._out_top_k = out_top_k or similarity_top_k
+        self._vecRetriever = vector_index.as_retriever(
+            similarity_top_k=similarity_top_k,filters = filters
+        )
+
+        STORAGE_DIR = os.getenv("BM_RETRIEVER_PATH", "storage_bm")
+        if os.path.exists(STORAGE_DIR) and len(os.listdir(STORAGE_DIR)) > 0:
+            self._bm25Retriever = CHBM25Retriever.from_persist_dir(STORAGE_DIR)
+        else:
+            bmRetriver = CHBM25Retriever.from_defaults(similarity_top_k=similarity_top_k,nodes=self._vector_index.vector_store.get_nodes(None))
+            bmRetriver.persist(STORAGE_DIR)
+        self._alpha = alpha
+
+
+
+    def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
+        vecNodes:List[NodeWithScore] = self._vecRetriever.retrieve(query_bundle.query_str)
+        bmNodes:List[NodeWithScore] = self._bm25Retriever.retrieve(query_bundle.query_str)
+
+        bmDic:Dict[str,NodeWithScore] = {}
+        for node in bmNodes:
+            bmDic[node.node_id] = node
+
+        result_tups = []
+        for i in range(len(vecNodes)):
+            node = vecNodes[i]
+            bmScore = 0.0
+            if node.node_id in bmDic:
+                bmScore = bmDic[node.node_id].score
+                bmDic.pop(node.node_id)
+            else:
+                bmScore = 0.0
+            full_similarity = (self._alpha * node.score) + (
+                    (1 - self._alpha) * bmScore
+            )
+            result_tups.append((full_similarity, node))
+
+        for _,node in bmDic.items():
+            full_similarity = (1 - self._alpha) * node.score
+            result_tups.append((full_similarity, node))
+
+        result_tups = sorted(result_tups, key=lambda x: x[0], reverse=True)
+        for full_score, node in result_tups:
+            node.score = full_score
+        return [n for _, n in result_tups][:self._out_top_k]