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/main.py

@@ -27,9 +27,6 @@ logging.basicConfig(
 )
 logger = logging.getLogger(__name__)
 
-_DEFAULT_EMBEDDING_PATH = "./data/models/Qwen3-Embedding-0.6B"
-
-
 def _repo_root() -> Path:
     """仓库根目录（含 data/、.env、api_server.py 的目录）。"""
     return Path(__file__).resolve().parent.parent
@@ -41,7 +38,8 @@ def _load_project_env():
 
 
 def _embedding_model_path() -> str:
-    return os.getenv("EMBEDDING_MODEL_PATH", _DEFAULT_EMBEDDING_PATH).strip()
+    """仅 ``USE_LOCAL_EMBEDDING=true`` 时需要；远程 Embedding 可为空。"""
+    return os.getenv("EMBEDDING_MODEL_PATH", "").strip()
 
 
 def resolve_sql_dialect(name: str) -> str:
@@ -55,20 +53,23 @@ def resolve_sql_dialect(name: str) -> str:
 def setup_environment():
     """环境检查"""
     _load_project_env()
-    use_local_emb = os.getenv("USE_LOCAL_EMBEDDING", "true").strip().lower() in (
+    use_local_emb = os.getenv("USE_LOCAL_EMBEDDING", "false").strip().lower() in (
         "1", "true", "yes", "on",
     )
     if use_local_emb:
-        # 与 .env 中 EMBEDDING_MODEL_PATH 及 utils.embedding 一致
-        model_path = Path(_embedding_model_path())
+        mp_str = _embedding_model_path()
+        if not mp_str:
+            logger.warning(
+                "USE_LOCAL_EMBEDDING=true 但未设置 EMBEDDING_MODEL_PATH（本地模型目录）"
+            )
+            return False
+        model_path = Path(mp_str)
         if not model_path.exists():
-            logger.warning(f"Embedding模型不存在: {model_path}")
-            logger.info("请先下载模型：")
-            logger.info("  modelscope download --model 'Qwen/Qwen3-Embedding-0.6B' "
-                        f"--local_dir '{model_path}'")
-            logger.info("或使用远程 Embedding API：USE_LOCAL_EMBEDDING=false，并配置 "
-                        "MODELSCOPE_API_KEY、或 OPENAI_API_KEY+OPENAI_EMBEDDING_MODEL"
-                        "（及可选 OPENAI_BASE_URL）、或 DASHSCOPE_*（百炼）")
+            logger.warning("本地 Embedding 目录不存在: %s", model_path)
+            logger.info(
+                "请设置正确的 EMBEDDING_MODEL_PATH，或改用 USE_LOCAL_EMBEDDING=false，"
+                "并配置 OPENAI_* / MODELSCOPE_* / DASHSCOPE_* 远程 Embedding"
+            )
             return False
     else:
         ms_key = os.getenv("MODELSCOPE_API_KEY", "").strip()
@@ -206,7 +207,7 @@ def single_query(orchestrator, question: str, dialect: str = "tsql"):
 
     logger.info(f"[Q] 问题: {question}")
 
-    classified = classify_dialog(question)
+    classified = classify_dialog(question, llm_client=orchestrator.deepseek)
     if classified.intent == DialogIntent.CONVERSATION:
         reply = classified.reply_suggestion or ""
         logger.info("[Q] 意图: conversation（跳过 SQL 生成）")