.

app/core/config.py

@@ -4,7 +4,7 @@ class Settings(BaseSettings):
     EMBEDDING_DIM: int = 768           # 嵌入维度（取决于模型）
     TOP_K: int = 5                      # 默认检索 top K 个段落
     DOC_PATH: str = "docs/"            # 默认文档根目录
-    DEVICE: str = "cpu"                # 可改为 "cuda" 使用 GPU
+    DEVICE: str = "cuda"                # 可改为 "cuda" 使用 GPU
     MODEL_NAME: str = "BAAI/bge-m3"    # 多语种语义嵌入模型
 
 settings = Settings()

rag_build_query.py

@@ -12,23 +12,47 @@ from scripts.permissions import get_user_allowed_indexes  # 导入权限函数
 USER_INDEX_PATH = "index_data"  # 用户索引存储路径
 USER_DOC_PATH = "docs"  # 用户文档存储路径
 
+
+# 设置日志记录器，记录操作步骤和错误信息
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+
+# # BGEEmbedding 类，继承自 HuggingFaceEmbedding，用于生成查询的嵌入
+# class BGEEmbedding(HuggingFaceEmbedding):
+#     def _get_query_embedding(self, query: str) -> List[float]:
+#         # 在查询前加上前缀，生成嵌入向量
+#         prefix = "Represent this sentence for searching relevant passages: "
+#         return super()._get_query_embedding(prefix + query)
+
+#     def _get_query_embeddings(self, queries: List[str]) -> List[List[float]]:
+#         # 批量生成嵌入向量
+#         prefix = "Represent this sentence for searching relevant passages: "
+#         return super()._get_query_embeddings([prefix + q for q in queries])
+
+
 # BGEEmbedding 类，继承自 HuggingFaceEmbedding，用于生成查询的嵌入
 class BGEEmbedding(HuggingFaceEmbedding):
     def _get_query_embedding(self, query: str) -> List[float]:
         # 在查询前加上前缀，生成嵌入向量
         prefix = "Represent this sentence for searching relevant passages: "
-        return super()._get_query_embedding(prefix + query)
+        embedding = super()._get_query_embedding(prefix + query)
+
+        # 使用logger打印数据类型
+        logger.info(f"Query embedding dtype: {embedding.dtype}")
+        return embedding
 
     def _get_query_embeddings(self, queries: List[str]) -> List[List[float]]:
         # 批量生成嵌入向量
         prefix = "Represent this sentence for searching relevant passages: "
-        return super()._get_query_embeddings([prefix + q for q in queries])
+        embeddings = super()._get_query_embeddings([prefix + q for q in queries])
+
+        # 使用logger打印数据类型
+        logger.info(f"Batch query embeddings dtype: {embeddings[0].dtype}")
+        return embeddings
+
 
 def build_user_index(user_id: str):
-    # 设置日志记录器，记录操作步骤和错误信息
-    logger = logging.getLogger(__name__)
-    logger.setLevel(logging.INFO)
-
     logger.info(f"开始为用户 {user_id} 构建索引...")
 
     # 确认文档目录是否存在