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.

api_server.py

@@ -32,6 +32,10 @@ from main import setup_environment, load_schema, create_orchestrator, resolve_sq
 from agents.orchestrator import GenerationResult
 from nl_lite_store import lite_nl_store
 from utils.dialog_classifier import DialogIntent, classify_dialog
+from utils.dialog_context import (
+    last_assistant_was_data_query,
+    messages_to_text2sql_context,
+)
 
 logging.basicConfig(
     level=logging.INFO,
@@ -54,7 +58,12 @@ def get_orchestrator():
         return orchestrator
     
     if not setup_environment():
-        raise RuntimeError("环境检查失败，请检查配置")
+        raise RuntimeError(
+            "环境检查失败（请查看上方 WARNING/INFO，常见原因："
+            "USE_LOCAL_EMBEDDING=true 但未配置或找不到 EMBEDDING_MODEL_PATH；"
+            "或 USE_LOCAL_EMBEDDING=false 但未配好 OPENAI_* / MODELSCOPE_* / DASHSCOPE_*；"
+            "或 Schema 文件路径不对、DEEPSEEK_API_KEY 未设置）"
+        )
     
     schema_path = os.getenv("SCHEMA_PATH", "./data/schemas/G3SB_MCDataDictionary_table_structure.json")
     schema_meta_path = os.getenv("SCHEMA_META_PATH", None)
@@ -111,6 +120,20 @@ class DataQueryResult(BaseModel):
     truncated: bool = Field(False, description="是否截断")
     sql_explain: Optional[str] = Field(None, description="SQL自然语言说明")
     can_export: Optional[bool] = Field(True, description="是否允许导出")
+    db_execution_status: Optional[int] = Field(
+        None,
+        description="库探针：1=有数据行，0=无行需追问补充，-1=执行失败(重试)，None=未探针",
+    )
+    db_empty_feedback: Optional[str] = Field(
+        None, description="探针0时的无行说明与追问（含问题分析）"
+    )
+    follow_up_required: bool = Field(
+        False,
+        description="True 表示探针为0：需用户补充条件后重新提问以重新生成SQL",
+    )
+    sql_delivery_message: Optional[str] = Field(
+        None, description="探针为1时LLM生成的面向用户SQL交付说明"
+    )
 
 
 class NLChatSuccessData(BaseModel):
@@ -185,6 +208,32 @@ def _conversation_nl_dict(reply: str) -> Dict[str, Any]:
     }
 
 
+async def _load_session_text2sql_context(
+    request: NLChatRequest,
+) -> tuple[str, bool]:
+    """
+    在写入本轮之前读取会话历史，构造 Text2SQL 上文，并判断上一轮助手是否为数据查询。
+    """
+    sid = (request.session_id or "").strip()
+    if not sid:
+        return "", False
+    data = await lite_nl_store.get_messages(
+        request.user_id,
+        request.visitor_biz_id,
+        sid,
+        limit=200,
+        offset=0,
+    )
+    if not data:
+        return "", False
+    items = data.get("items") or []
+    if not items:
+        return "", False
+    last_data = last_assistant_was_data_query(items)
+    block, _n = messages_to_text2sql_context(items)
+    return block, last_data
+
+
 async def _append_session_if_needed(request: NLChatRequest, user_text: str, data: Dict[str, Any]) -> None:
     if not (request.session_id and request.session_id.strip()):
         return
@@ -226,6 +275,10 @@ def _nl_dict_from_generation(result: GenerationResult) -> Dict[str, Any]:
     dbe = result.metadata.get("db_empty_feedback")
     if dbe:
         branch_result["db_empty_feedback"] = dbe
+    branch_result["follow_up_required"] = bool(result.valid and dbs == 0)
+    sdm = result.metadata.get("sql_delivery_message")
+    if sdm:
+        branch_result["sql_delivery_message"] = str(sdm)
     conf = 1.0 if result.valid else 0.0
     if result.valid:
         reason = f"使用了 {len(result.tables_used)} 张表"
@@ -247,6 +300,10 @@ def _sql_gen_stream_html(result: GenerationResult) -> str:
     inner = json.dumps({"sql": sql}, ensure_ascii=False)
     parts = [f"<data>{inner}</data>"]
     explain = ""
+    if result.metadata.get("sql_delivery_message"):
+        parts.append(
+            f'<div class="sql-delivery-body">{html_lib.escape(str(result.metadata["sql_delivery_message"]))}</div>'
+        )
     if result.metadata.get("db_empty_feedback"):
         explain = str(result.metadata["db_empty_feedback"])
     elif result.warnings:
@@ -258,15 +315,21 @@ def _sql_gen_stream_html(result: GenerationResult) -> str:
     return "".join(parts)
 
 
-async def _run_generate(question: str, top_k: int = 20) -> GenerationResult:
+async def _run_generate(
+    question: str,
+    top_k: int = 20,
+    dialog_context: Optional[str] = None,
+) -> GenerationResult:
     orch = get_orchestrator()
     dialect = resolve_sql_dialect(os.getenv("TEXT2SQL_DIALECT", "sqlserver"))
+    dc = (dialog_context or "").strip() or None
 
     def _call() -> GenerationResult:
         return orch.generate(
             question=question.strip(),
             dialect=dialect,
             top_k_candidates=top_k,
+            dialog_context=dc,
         )
 
     return await asyncio.to_thread(_call)
@@ -277,7 +340,15 @@ async def _chat_stream_events(request: NLChatRequest) -> AsyncIterator[bytes]:
         yield _sse_data({"code": 400, "msg": "消息内容不能为空", "data": None})
         return
     text = request.message.strip()
-    classified = classify_dialog(text)
+    orch = get_orchestrator()
+    dialog_block, last_data = await _load_session_text2sql_context(request)
+    classified = await asyncio.to_thread(
+        classify_dialog,
+        text,
+        last_turn_was_data_query=last_data,
+        dialog_context=dialog_block or None,
+        llm_client=orch.deepseek,
+    )
     if classified.intent == DialogIntent.CONVERSATION:
         reply = classified.reply_suggestion or ""
         logger.info("[API/stream] 对话意图: conversation（跳过 Text2SQL，与 CLI single_query 一致）")
@@ -290,7 +361,7 @@ async def _chat_stream_events(request: NLChatRequest) -> AsyncIterator[bytes]:
 
     yield _sse_data({"stage": "orchestrator", "stream_kind": "content", "content": "DATA_QUERY"})
     try:
-        result = await _run_generate(text)
+        result = await _run_generate(text, dialog_context=dialog_block or None)
     except Exception as e:
         logger.error(f"[API/stream] 生成异常: {e}")
         yield _sse_data({"code": 500, "msg": str(e), "data": None})
@@ -353,7 +424,15 @@ async def nl_chat(request: NLChatRequest):
             raise HTTPException(status_code=400, detail="消息内容不能为空")
         text = request.message.strip()
         logger.info(f"[API] 问题: {text[:100]}...")
-        classified = classify_dialog(text)
+        orch = get_orchestrator()
+        dialog_block, last_data = await _load_session_text2sql_context(request)
+        classified = await asyncio.to_thread(
+            classify_dialog,
+            text,
+            last_turn_was_data_query=last_data,
+            dialog_context=dialog_block or None,
+            llm_client=orch.deepseek,
+        )
         if classified.intent == DialogIntent.CONVERSATION:
             reply = classified.reply_suggestion or ""
             logger.info("[API/chat] 对话意图: conversation（跳过 Text2SQL）")
@@ -361,7 +440,7 @@ async def nl_chat(request: NLChatRequest):
             await _append_session_if_needed(request, text, data_dict)
             return {"code": 200, "msg": "success", "data": data_dict}
 
-        result = await _run_generate(text)
+        result = await _run_generate(text, dialog_context=dialog_block or None)
         data_dict = _nl_dict_from_generation(result)
         response_data = NLChatSuccessData.model_validate(data_dict)
         msg = "success" if result.valid else "partial"