refactor: remove legacy assistant compatibility

backend/services/brains/fastgpt_brain.py

@@ -1,6 +1,6 @@
 """FastGPT 大脑:外部托管,context/KB/tools 全在 FastGPT 服务端。
 
-cascade-only(realtime 不兼容外部大脑)。每通电话持有一个稳定 chatId:
+Pipeline-only（外部大脑不支持 Realtime）。每通电话持有一个稳定 chatId:
 greeting(get_chat_init)与后续每轮推理共用它,保证服务端上下文连续。
 """
 

backend/services/brains/prompt_brain.py

@@ -364,31 +364,14 @@ class PromptBrain(BaseBrain):
         )
 
     def _opening_message(self) -> dict[str, Any] | None:
-        startup = self._cfg.startup if isinstance(self._cfg.startup, dict) else {}
-        value = startup.get("opening_message", startup.get("openingMessage"))
+        value = self._cfg.startup["opening_message"]
         return value if isinstance(value, dict) else None
 
     def _opening_mode(self) -> str:
-        startup = self._cfg.startup if isinstance(self._cfg.startup, dict) else {}
-        value = str(startup.get("opening_mode", startup.get("openingMode")) or "")
-        if value in {"interruptible", "playback", "confirmation"}:
-            return value
-        # Before openingMode existed, an opening message meant confirmation;
-        # ordinary Prompt greetings were interruptible.
-        return (
-            "confirmation"
-            if self._opening_message() is not None
-            else "interruptible"
-        )
+        return str(self._cfg.startup["opening_mode"])
 
     def _entry_mode(self) -> str:
-        startup = self._cfg.startup if isinstance(self._cfg.startup, dict) else {}
-        value = str(startup.get("entry_mode", startup.get("entryMode")) or "")
-        if value in {"wait_user", "generate"}:
-            return value
-        # Raw runtime configs saved before entryMode existed generated a first
-        # reply after their opening confirmation. Keep that legacy behavior.
-        return "generate" if self._opening_mode() == "confirmation" else "wait_user"
+        return str(self._cfg.startup["entry_mode"])
 
     async def _enter_prompt_if_ready(self) -> None:
         if (

backend/services/brains/workflow_brain.py

@@ -1,4 +1,4 @@
-"""Pipecat Flows-backed Workflow v3 brain."""
+"""Pipecat Flows-backed Workflow v4 brain."""
 
 from __future__ import annotations
 
@@ -48,10 +48,8 @@ from services.action_stage import ActionStageRunner, ActionStageSpec, StageActio
 from services.fixed_speech import fixed_speech_context_message
 from services.knowledge import search as search_knowledge
 from services.message_policy import (
-    MESSAGE_COMPLETION_POLICIES,
     MESSAGE_CONFIRMATION,
     MESSAGE_INTERRUPTIBLE,
-    MESSAGE_PLAYBACK,
 )
 from services.message_stage import (
     MessageDisplaySpec,
@@ -160,12 +158,6 @@ class WorkflowBrain(BaseBrain):
     def __init__(self, cfg_or_graph: AssistantConfig | dict[str, Any]):
         cfg = cfg_or_graph if isinstance(cfg_or_graph, AssistantConfig) else None
         graph = deepcopy(cfg.graph if cfg is not None else cfg_or_graph)
-        if cfg is not None:
-            # Graph v3 owns Workflow defaults. Keep older saved graphs compatible
-            # by filling the new interaction settings from the assistant row.
-            settings = graph.setdefault("settings", {})
-            settings.setdefault("enableInterrupt", cfg.enableInterrupt)
-            settings.setdefault("turnConfig", deepcopy(cfg.turnConfig))
         self._engine = WorkflowEngine(graph or {})
         if not self._engine.has_graph() or not self._engine.start_id:
             raise ValueError("WorkflowBrain 缺少有效的 Start 节点")
@@ -722,7 +714,7 @@ class WorkflowBrain(BaseBrain):
     def _agent_runs_on_entry(self, node_id: str) -> bool:
         """Only explicit generate mode may start a reply on node entry."""
         entry_mode = str(
-            self._engine.data(node_id).get("entryMode") or "wait_user"
+            self._engine.data(node_id).get("entryMode", "wait_user")
         )
         return entry_mode == "generate"
 
@@ -1604,21 +1596,17 @@ class WorkflowBrain(BaseBrain):
         return result
 
     def _message_completion_policy(self, node_id: str) -> str:
-        value = str(
-            self._engine.data(node_id).get("completionPolicy")
-            or MESSAGE_PLAYBACK
-        )
-        return value if value in MESSAGE_COMPLETION_POLICIES else MESSAGE_PLAYBACK
+        return str(self._engine.data(node_id)["completionPolicy"])
 
     def _set_last_action(self, outcome: ActionOutcome) -> None:
-        legacy_status = {
+        status_value = {
             ActionStatus.SUCCESS: "ok",
             ActionStatus.FAILURE: "error",
             ActionStatus.CANCELLED: "cancelled",
         }[outcome.status]
         self._store.values.update(
             {
-                "system__last_action_status": legacy_status,
+                "system__last_action_status": status_value,
                 "system__last_action_invocation_id": outcome.invocation_id,
                 "system__last_action_duration_ms": outcome.duration_ms,
                 "system__last_action_error_code": (