修复create_embedding_service和visualize_csv_comparison函数中的问题

1. 修复create_embedding_service函数:
   - 添加缺失的导入语句
   - 修正metadata中neighbors列名不一致问题
   - 添加database配置确保数据能正确加载

2. 优化visualize_csv_comparison函数:
   - 调整_row_index列添加时机
   - 添加CSV文件读取错误处理
   - 添加列名验证功能
   - 保持与create_embedding_service一致的neighbors列名

src/visualization/comparison.py

@@ -72,22 +72,13 @@ def create_embedding_service(
     batch_size: int = 32,
     umap_args: Optional[Dict[str, Any]] = None
 ):
-    """
-    从两组文本数据创建embedding可视化服务
-    
-    Args:
-        texts1: 第一组文本列表
-        texts2: 第二组文本列表
-        labels: 两组数据的标签
-        port: 服务器端口
-        host: 服务器主机
-        text_column: 文本列名称
-        model: 使用的嵌入模型
-        batch_size: 批处理大小
-        umap_args: UMAP参数字典
-    """
+    """从两组文本数据创建embedding可视化服务"""
     import pathlib
     import embedding_atlas
+    from embedding_atlas.projection import compute_text_projection
+    from embedding_atlas.server import make_server
+    from embedding_atlas.data_source import DataSource
+    from embedding_atlas.utils import Hasher
     
     # 默认UMAP参数
     if umap_args is None:
@@ -107,7 +98,6 @@ def create_embedding_service(
     
     # 3. 计算embeddings和投影
     try:
-        from embedding_atlas.projection import compute_text_projection
         compute_text_projection(
             combined_df,
             text=text_column,
@@ -125,7 +115,7 @@ def create_embedding_service(
         print(f"Error computing projections: {e}")
         return
     
-    # 4. 创建metadata
+    # 4. 创建metadata (修复: 添加database配置和正确的neighbors列名)
     metadata = {
         "columns": {
             "id": "_row_index",
@@ -134,7 +124,11 @@ def create_embedding_service(
                 "x": "projection_x",
                 "y": "projection_y"
             },
-            "neighbors": "neighbors"
+            "neighbors": "__neighbors"  # 修复: 使用双下划线
+        },
+        "database": {
+            "type": "wasm",
+            "load": True  # 修复: 添加database配置
         }
     }
     
@@ -198,9 +192,29 @@ def visualize_csv_comparison(
     if label2 is None:
         label2 = os.path.splitext(os.path.basename(csv2_path))[0]
     
-    # Read CSV files
+    # Read CSV files with error handling
+    try:
         df1 = pd.read_csv(csv1_path)
         df2 = pd.read_csv(csv2_path)
+    except FileNotFoundError as e:
+        print(f"Error: CSV file not found - {e}")
+        return
+    except pd.errors.EmptyDataError:
+        print("Error: One of the CSV files is empty")
+        return
+    except Exception as e:
+        print(f"Error reading CSV files: {e}")
+        return
+    
+    # Validate columns
+    if column1 not in df1.columns:
+        print(f"Error: Column '{column1}' not found in {csv1_path}")
+        print(f"Available columns: {list(df1.columns)}")
+        return
+    if column2 not in df2.columns:
+        print(f"Error: Column '{column2}' not found in {csv2_path}")
+        print(f"Available columns: {list(df2.columns)}")
+        return
     
     # Add source column to identify origin of each data point
     df1['source'] = label1
@@ -213,7 +227,10 @@ def visualize_csv_comparison(
     # Combine dataframes
     combined_df = pd.concat([df1, df2], ignore_index=True)
     
-    # Determine which column to use for text projection (moved before projection check)
+    # Add _row_index column for embedding-atlas DataSource (moved to earlier)
+    combined_df['_row_index'] = range(len(combined_df))
+    
+    # Determine which column to use for text projection
     text_column = column1 if column1 in combined_df.columns else column2
     
     # Check if projection columns already exist
@@ -281,9 +298,6 @@ def visualize_csv_comparison(
     
     print(f"Visualization saved to {output_path}")
     
-    # Add _row_index column for embedding-atlas DataSource
-    combined_df['_row_index'] = range(len(combined_df))
-    
     # Save dataset for interactive viewing
     parquet_path = "combined_dataset.parquet"
     combined_df.to_parquet(parquet_path, index=False)