.

scripts/rag_build_query.py

@@ -15,6 +15,7 @@ import faiss
 from typing import List
 import asyncio
 from sentence_transformers import SentenceTransformer
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
 
 USER_INDEX_PATH = "index_data"
 USER_DOC_PATH = "docs"
@@ -54,7 +55,7 @@ def build_user_index(user_id: str):
         raise FileNotFoundError(f"文档目录不存在: {doc_dir}")
     
     documents = SimpleDirectoryReader(doc_dir).load_data()
-    embed_model = CustomEmbedding(model_name=settings.MODEL_NAME)
+    embed_model = HuggingFaceEmbedding(model_name=settings.MODEL_NAME)
 
     service_context = ServiceContext.from_defaults(embed_model=embed_model)
 
@@ -69,7 +70,7 @@ 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 = CustomEmbedding(model_name=settings.MODEL_NAME)
+    embed_model = HuggingFaceEmbedding(model_name=settings.MODEL_NAME)
     service_context = ServiceContext.from_defaults(embed_model=embed_model)
 
     all_nodes = []