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/agents/orchestrator.py

@@ -64,7 +64,7 @@ class Text2SQLOrchestrator:
             schema_manager: Schema管理器实例
             deepseek_api_key: DeepSeek API密钥（也可通过环境变量DEEPSEEK_API_KEY）
             deepseek_config: DeepSeek配置对象（优先于api_key）
-            embedding_model_path: Qwen3-Embedding模型路径
+            embedding_model_path: 本地 Embedding 模型目录（仅 USE_LOCAL_EMBEDDING=true）
             vector_db_path: 向量数据库路径
             max_retry: 最大重试次数（包含首次生成）
             use_vector_search: 是否使用向量检索粗筛
@@ -96,12 +96,22 @@ class Text2SQLOrchestrator:
 
         if self.fewshot_enabled:
             try:
-                path = fewshot_samples_path or os.getenv(
-                    "FEWSHOT_DATA_PATH",
-                    "./data/experiences/all_samples.jsonl"
+                use_chroma = os.getenv("FEWSHOT_USE_CHROMA", "false").lower() in (
+                    "1",
+                    "true",
+                    "yes",
                 )
+                if fewshot_samples_path is not None:
+                    path = str(fewshot_samples_path).strip()
+                else:
+                    env_p = os.getenv("FEWSHOT_DATA_PATH")
+                    if env_p is not None:
+                        path = env_p.strip()
+                    else:
+                        # Chroma 优先时默认不再依赖 JSONL；否则保留原默认路径
+                        path = "" if use_chroma else "./data/experiences/all_samples.jsonl"
                 self.fewshot_selector = FewShotSelector(
-                    path,
+                    path or None,
                     embedding_model_path=self._embedding_model_path,
                 )
                 logger.info(
@@ -119,6 +129,23 @@ class Text2SQLOrchestrator:
             + (", en→zh=on" if self.translate_english_to_zh else ", en→zh=off")
         )
 
+    @staticmethod
+    def _merge_dialog_for_model(
+        dialog_context: str, question: str, max_len: int
+    ) -> str:
+        """拼接上文与当前问句，控制总长，优先保留当前问句完整。"""
+        dc = (dialog_context or "").strip()
+        q = (question or "").strip()
+        if not dc:
+            return q
+        tail = "\n\n【当前用户问题】\n" + q
+        if len(dc) + len(tail) <= max_len:
+            return dc + tail
+        room = max_len - len(tail)
+        if room < 80:
+            return tail[-max_len:]
+        return dc[:room].rstrip() + tail
+
     def _get_vector_index(self) -> SchemaIndexer:
         """获取或创建向量索引（懒加载）"""
         if self._vector_index is None:
@@ -291,6 +318,7 @@ class Text2SQLOrchestrator:
         schema_str: str,
         dialect: str = "tsql",
         validation_feedback: Optional[str] = None,
+        dialog_context: Optional[str] = None,
     ) -> str:
         """
         SQL生成（SQL Generator Agent）
@@ -301,17 +329,24 @@ class Text2SQLOrchestrator:
             dialect: SQL方言
             validation_feedback: 非空时附加到用户提示（重试时传入上次校验错误）
 
+            dialog_context: 前几轮对话摘要；与 ``question`` 一并供指代消解与续问。
+
         Returns:
             SQL语句
         """
         from config.prompts import SQL_GENERATOR_SYSTEM, SQL_GENERATOR_USER
         from utils.sql_parser import normalize_sql_for_dialect
 
+        dc = (dialog_context or "").strip()
+        fewshot_question = question
+        if dc:
+            fewshot_question = f"{dc}\n\n【当前问】{question}"
+
         # Few-shot 增强
         if self.fewshot_enabled and self.fewshot_selector:
             try:
                 examples = self.fewshot_selector.select(
-                    question=question,
+                    question=fewshot_question,
                     top_k=self.fewshot_top_k,
                     min_rating=self.fewshot_min_rating
                 )
@@ -329,7 +364,15 @@ class Text2SQLOrchestrator:
         if dialect == "tsql":
             dialect_label = "Microsoft SQL Server (T-SQL)"
 
-        user_content = SQL_GENERATOR_USER.format(
+        prefix = ""
+        if dc:
+            prefix = (
+                "【对话上文】（用于理解「这/那/同样/上面/刚才」等指代及续问条件；"
+                "请结合下文「当前用户问题」生成 SQL。）\n"
+                f"{dc}\n\n"
+            )
+
+        user_content = prefix + SQL_GENERATOR_USER.format(
             schema=schema_str,
             question=question,
             dialect=dialect_label,
@@ -380,6 +423,7 @@ class Text2SQLOrchestrator:
         schema_str: str,
         dialect: str = "tsql",
         question: str = "",
+        dialog_context: Optional[str] = None,
     ) -> Tuple[bool, List[str], List[str], Optional[int], Optional[str]]:
         """
         SQL验证（程序验证 + 库探针 + 按探针分支的 LLM）
@@ -389,6 +433,7 @@ class Text2SQLOrchestrator:
             schema_str: Schema描述
             dialect: 与生成一致的 SQL 方言（sqlglot 名，默认 tsql）
             question: 用户自然语言（探针为 0 时用于生成补充说明）
+            dialog_context: 会话上文；探针 0 时与 question 一并传入说明模型
 
         Returns:
             (是否通过, 错误列表, 警告列表, 库执行探针状态, 无数据时的用户说明)
@@ -433,11 +478,11 @@ class Text2SQLOrchestrator:
             db_execution_status, db_probe_err = probe_sql_execution_status_ex(sql)
             if db_execution_status == -1:
                 msg = (
-                    "数据库执行验证失败：SQL 在目标库执行报错（探针状态 -1），"
-                    "将据此重新生成 SQL。"
+                    "【库探针结果：-1 执行失败】SQL 在目标库执行报错，"
+                    "系统将依据下列错误**自动重新生成** SQL（请等待重试结果）。"
                 )
                 if db_probe_err:
-                    msg += f" 数据库返回：{db_probe_err}"
+                    msg += f"\n数据库返回：{db_probe_err}"
                 errors.append(msg)
             elif db_execution_status is None:
                 warnings.append(
@@ -482,23 +527,35 @@ class Text2SQLOrchestrator:
         # === 阶段2b：探针 0 时生成用户可读补充说明（仍返回 SQL，由 API/CLI 一并展示） ===
         if db_execution_status == 0 and len(errors) == 0:
             prefix = (
-                "该 SQL 已在数据库成功执行，但返回的数据行数为 0（未查到匹配记录）。"
-                "请将下方 SQL 与说明一并核对；若不符合预期，请补充或调整条件后再次提问。"
+                "【库探针结果：0 行】该 SQL 已在数据库成功执行，但**返回数据行数为 0**（未查到匹配记录）。"
+                "下方已附带完整 SQL 与原因分析，请一并阅读。"
             )
+            fb_q = question
+            dc = (dialog_context or "").strip()
+            if dc:
+                fb_q = f"{dc}\n\n【当前用户问题】\n{question}"
             try:
                 llm_fb = self.deepseek.empty_result_user_feedback(
-                    question=question,
+                    question=fb_q,
                     sql=sql,
                     schema=schema_str,
                 )
-                empty_feedback = f"{prefix}\n\n【分析与建议】\n{llm_fb}"
+                empty_feedback = f"{prefix}\n\n【问题分析】\n{llm_fb}"
             except Exception as e:
                 logger.warning(f"无数据说明生成失败: {e}")
                 empty_feedback = (
-                    f"{prefix}\n\n【分析与建议】\n"
+                    f"{prefix}\n\n【问题分析】\n"
                     "未能自动生成详细分析。请补充时间范围、筛选条件或业务对象后重新提问。"
                 )
 
+            follow = (
+                "\n\n【追问 — 请补充后再次提问以重新生成 SQL】\n"
+                "1. 请根据上述分析，尽量具体地补充或修正：**时间范围**、**业务对象**（账户/合约/代码等）、"
+                "**筛选口径** 或 **您认为 SQL 中不合理的条件**。\n"
+                "2. 补充说明后请**重新发起一次自然语言提问**（无需粘贴 SQL），系统会结合您的新描述**重新生成**查询。"
+            )
+            empty_feedback = (empty_feedback or prefix) + follow
+
         is_valid = len(errors) == 0
         return is_valid, errors, warnings, db_execution_status, empty_feedback
 
@@ -507,7 +564,8 @@ class Text2SQLOrchestrator:
         question: str,
         dialect: str = "tsql",
         top_k_candidates: int = 20,
-        include_schema_in_result: bool = False
+        include_schema_in_result: bool = False,
+        dialog_context: Optional[str] = None,
     ) -> GenerationResult:
         """
         主生成流程
@@ -517,6 +575,7 @@ class Text2SQLOrchestrator:
             dialect: SQL方言（默认 tsql；与 sqlglot 一致）
             top_k_candidates: 粗筛候选表数量
             include_schema_in_result: 结果中是否包含使用的Schema字符串
+            dialog_context: 前几轮对话可读摘要；选表、向量粗筛、SQL 生成与无数据说明会参考
 
         Returns:
             GenerationResult对象
@@ -543,6 +602,9 @@ class Text2SQLOrchestrator:
                 logger.warning("[GEN] 英译中失败，使用原文: %s", e)
 
         question = work_question
+        dc_raw = (dialog_context or "").strip()
+        retrieval_question = self._merge_dialog_for_model(dc_raw, question, 4000)
+        linker_question = self._merge_dialog_for_model(dc_raw, question, 6000)
         logger.info(f"[GEN] 开始生成SQL：{question[:50]}...")
 
         attempt = 0
@@ -558,14 +620,18 @@ class Text2SQLOrchestrator:
             # === Step 1: Schema筛选（仅首次） ===
             if attempt == 0:
                 # 1.1 粗筛
-                candidate_tables = self._coarse_filter(question, top_k=top_k_candidates)
+                candidate_tables = self._coarse_filter(
+                    retrieval_question, top_k=top_k_candidates
+                )
 
                 # 1.2 LLM精筛
                 relevant_tables, reasoning = self._llm_select_tables(
-                    question,
+                    linker_question,
                     candidate_tables,
                 )
-                relevant_tables = self._prioritize_broker_tables(question, relevant_tables)
+                relevant_tables = self._prioritize_broker_tables(
+                    linker_question, relevant_tables
+                )
 
                 # 1.3 外键扩展
                 expanded_tables = self._expand_relations(relevant_tables)
@@ -613,6 +679,7 @@ class Text2SQLOrchestrator:
                     filtered_schema_str,
                     dialect,
                     validation_feedback=feedback,
+                    dialog_context=dc_raw or None,
                 )
                 last_sql = sql
             except Exception as e:
@@ -626,6 +693,7 @@ class Text2SQLOrchestrator:
                 filtered_schema_str,
                 dialect=dialect,
                 question=question,
+                dialog_context=dc_raw or None,
             )
             if db_probe is not None:
                 last_db_execution_status = db_probe
@@ -643,6 +711,19 @@ class Text2SQLOrchestrator:
                 meta["db_execution_status"] = db_probe
             if empty_feedback:
                 meta["db_empty_feedback"] = empty_feedback
+            if db_probe == 1:
+                try:
+                    meta["sql_delivery_message"] = (
+                        self.deepseek.sql_probe_success_delivery_message(
+                            question=question,
+                            sql=sql,
+                        )
+                    )
+                except Exception as e:
+                    logger.warning("[GEN] 探针1交付说明生成失败: %s", e)
+                    meta["sql_delivery_message"] = None
+            if dc_raw:
+                meta["dialog_context_chars"] = len(dc_raw)
 
             result = GenerationResult(
                 sql=sql,
@@ -663,6 +744,8 @@ class Text2SQLOrchestrator:
         fail_meta: Dict = dict(translation_meta)
         if last_db_execution_status is not None:
             fail_meta["db_execution_status"] = last_db_execution_status
+        if dc_raw:
+            fail_meta["dialog_context_chars"] = len(dc_raw)
         return GenerationResult(
             sql=last_sql or "",
             valid=False,