Enhance dialog context handling for Text2SQL queries by integrating session history. Introduce new methods for summarizing previous assistant messages and determining if the last interaction was a data query. Update environment configuration for embedding options and improve error handling in SQL generation. Add user-facing delivery messages for successful SQL execution. This update supports more coherent follow-up questions and improves user experience in conversational interactions.

backend/schema/indexer.py

@@ -40,6 +40,7 @@ class SchemaIndexer:
         self.embedder = embedder
         self.persist_dir = Path(persist_dir)
         self.persist_dir.mkdir(parents=True, exist_ok=True)
+        self.collection_name = collection_name
 
         # 初始化ChromaDB客户端
         self.client = chromadb.PersistentClient(
@@ -49,11 +50,11 @@ class SchemaIndexer:
 
         # 获取或创建集合
         self.collection = self.client.get_or_create_collection(
-            name=collection_name,
+            name=self.collection_name,
             metadata={"hnsw:space": "cosine"},  # 使用余弦相似度
         )
 
-        logger.info(f"[OK] 初始化SchemaIndexer: collection={collection_name}")
+        logger.info(f"[OK] 初始化SchemaIndexer: collection={self.collection_name}")
 
     def build_index(
         self,
@@ -80,7 +81,12 @@ class SchemaIndexer:
 
         if force_rebuild and existing_ids:
             logger.info(f"强制重建索引，删除{len(existing_ids)}条旧记录")
-            self.collection.delete()
+            # Chroma 新版本要求 delete 必须带 ids/where；整库清空用删集合再建
+            self.client.delete_collection(self.collection_name)
+            self.collection = self.client.get_or_create_collection(
+                name=self.collection_name,
+                metadata={"hnsw:space": "cosine"},
+            )
 
         # 准备数据
         table_texts = []
@@ -97,7 +103,9 @@ class SchemaIndexer:
 
         # 批量计算embedding
         logger.info(f"计算{len(table_texts)}张表的embedding...")
-        embeddings = self.embedder.encode(table_texts, batch_size=batch_size)
+        embeddings = self.embedder.encode(
+            table_texts, batch_size=batch_size, normalize=True
+        )
 
         # 存入ChromaDB
         self.collection.add(
@@ -136,8 +144,8 @@ class SchemaIndexer:
                 ...
             ]
         """
-        # 编码查询文本
-        query_embedding = self.embedder.encode([query])
+        # 编码查询文本（与建库时一致：L2 归一化 + 余弦空间）
+        query_embedding = self.embedder.encode([query], normalize=True)
 
         # 执行检索
         results = self.collection.query(
@@ -205,7 +213,12 @@ class SchemaIndexer:
 
     def clear(self):
         """清空索引"""
-        self.collection.delete()
+        if self.collection.count() > 0:
+            self.client.delete_collection(self.collection_name)
+            self.collection = self.client.get_or_create_collection(
+                name=self.collection_name,
+                metadata={"hnsw:space": "cosine"},
+            )
         logger.info("索引已清空")
 
     def count(self) -> int:
@@ -218,8 +231,18 @@ class SchemaIndexer:
         """获取索引统计信息"""
         count = self.collection.count()
         result = self.collection.get(include=["metadatas"])
+        metas = result.get("metadatas") or []
 
-        total_columns = sum(m.get("column_count", 0) for m in result["metadatas"])
+        def _col_count(m: Optional[Dict]) -> int:
+            if not m:
+                return 0
+            v = m.get("column_count", 0)
+            try:
+                return int(v)
+            except (TypeError, ValueError):
+                return 0
+
+        total_columns = sum(_col_count(m) for m in metas)
 
         return {
             "indexed_tables": count,