.

scripts/rag_build_query.py

@@ -1,13 +1,16 @@
 import os
 
-from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, ServiceContext, PromptTemplate
+from llama_index.core import (
+    SimpleDirectoryReader,
+    VectorStoreIndex,
+    PromptTemplate,
+    Settings,
+)
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 from llama_index.vector_stores.faiss import FaissVectorStore
 from app.core.config import settings
 from scripts.permissions import get_user_allowed_indexes
-
-
-
+import faiss
 
 USER_INDEX_PATH = "index_data"
 USER_DOC_PATH = "docs"
@@ -19,11 +22,10 @@ def build_user_index(user_id: str):
     
     documents = SimpleDirectoryReader(doc_dir).load_data()
     embed_model = HuggingFaceEmbedding(model_name=settings.MODEL_NAME)
-    service_context = ServiceContext.from_defaults(embed_model=embed_model)
-    
+    Settings.embed_model = embed_model  # ✅ 新式配置
+
     index = VectorStoreIndex.from_documents(
         documents,
-        service_context=service_context,
         vector_store=FaissVectorStore()
     )
     
@@ -33,7 +35,7 @@ def build_user_index(user_id: str):
 
 def query_user_rag(user_id: str, question: str, top_k: int = 4) -> str:
     embed_model = HuggingFaceEmbedding(model_name=settings.MODEL_NAME)
-    service_context = ServiceContext.from_defaults(embed_model=embed_model)
+    Settings.embed_model = embed_model  # ✅ 全局设置一次即可
 
     all_nodes = []
 
@@ -42,7 +44,7 @@ def query_user_rag(user_id: str, question: str, top_k: int = 4) -> str:
     if not os.path.exists(index_path):
         raise FileNotFoundError(f"[ERROR] 用户 {user_id} 的索引不存在")
     user_store = FaissVectorStore.from_persist_path(index_path)
-    user_index = VectorStoreIndex.from_vector_store(user_store, service_context=service_context)
+    user_index = VectorStoreIndex.from_vector_store(user_store)
     all_nodes += user_index.as_retriever(similarity_top_k=top_k).retrieve(question)
 
     # 加载共享索引
@@ -52,7 +54,7 @@ def query_user_rag(user_id: str, question: str, top_k: int = 4) -> str:
             shared_path = os.path.join(USER_INDEX_PATH, shared_name)
             if os.path.exists(shared_path) and shared_path != index_path:
                 shared_store = FaissVectorStore.from_persist_path(shared_path)
-                shared_index = VectorStoreIndex.from_vector_store(shared_store, service_context=service_context)
+                shared_index = VectorStoreIndex.from_vector_store(shared_store)
                 all_nodes += shared_index.as_retriever(similarity_top_k=top_k).retrieve(question)
     else:
         print(f"[INFO] 用户 {user_id} 没有共享索引权限")