.

scripts/rag_build_query.py

@@ -1,12 +1,13 @@
 import os
 
-from llama_index.core import (
+from llama_index import (
     SimpleDirectoryReader,
     VectorStoreIndex,
+    PromptHelper,
     PromptTemplate,
-    Settings,
+    ServiceContext,
 )
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.embeddings.base import BaseEmbedding
 from llama_index.vector_stores.faiss import FaissVectorStore
 from app.core.config import settings
 from scripts.permissions import get_user_allowed_indexes
@@ -15,18 +16,31 @@ import faiss
 USER_INDEX_PATH = "index_data"
 USER_DOC_PATH = "docs"
 
+class CustomEmbedding(BaseEmbedding):
+    def __init__(self, model_name: str):
+        from sentence_transformers import SentenceTransformer
+        self.model = SentenceTransformer(model_name)
+
+    def embed(self, text: str):
+        return self.model.encode(text).tolist()
+
+    def embed_batch(self, texts: list):
+        return self.model.encode(texts).tolist()
+
 def build_user_index(user_id: str):
     doc_dir = os.path.join(USER_DOC_PATH, user_id)
     if not os.path.exists(doc_dir):
         raise FileNotFoundError(f"文档目录不存在: {doc_dir}")
     
     documents = SimpleDirectoryReader(doc_dir).load_data()
-    embed_model = HuggingFaceEmbedding(model_name=settings.MODEL_NAME)
+    embed_model = CustomEmbedding(model_name=settings.MODEL_NAME)
-    Settings.embed_model = embed_model  # ✅ 新式配置
+
+    service_context = ServiceContext.from_defaults(embed_model=embed_model)
 
     index = VectorStoreIndex.from_documents(
         documents,
-        vector_store=FaissVectorStore()
+        vector_store=FaissVectorStore(),
+        service_context=service_context
     )
     
     index_path = os.path.join(USER_INDEX_PATH, f"{user_id}.index")
@@ -34,8 +48,8 @@ def build_user_index(user_id: str):
     print(f"[BUILD] 为用户 {user_id} 构建并保存了索引 → {index_path}")
 
 def query_user_rag(user_id: str, question: str, top_k: int = 4) -> str:
-    embed_model = HuggingFaceEmbedding(model_name=settings.MODEL_NAME)
+    embed_model = CustomEmbedding(model_name=settings.MODEL_NAME)
-    Settings.embed_model = embed_model  # ✅ 全局设置一次即可
+    service_context = ServiceContext.from_defaults(embed_model=embed_model)
 
     all_nodes = []
 
@@ -44,7 +58,7 @@ 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)
+    user_index = VectorStoreIndex.from_vector_store(user_store, service_context=service_context)
     all_nodes += user_index.as_retriever(similarity_top_k=top_k).retrieve(question)
 
     # 加载共享索引
@@ -54,7 +68,7 @@ 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)
+                shared_index = VectorStoreIndex.from_vector_store(shared_store, service_context=service_context)
                 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")
@@ -63,7 +77,7 @@ def query_user_rag(user_id: str, question: str, top_k: int = 4) -> str:
     sorted_nodes = sorted(all_nodes, key=lambda n: -(n.score or 0))
     top_nodes = sorted_nodes[:top_k]
 
-    context_str = "\n\n".join([n.get_content() for n in top_nodes])
+    context_str = "\n\n".join([n.get_text() for n in top_nodes])
     prompt_template = PromptTemplate(
         "请根据以下内容回答用户问题：\n\n{context}\n\n问题：{query}"
     )