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feat: add deterministic message interaction stages

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This commit is contained in:
Xin Wang
2026-08-03 07:10:41 +08:00
parent 479a516546
commit 9daa46ed4d
26 changed files with 1356 additions and 414 deletions
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183
backend/services/brains/workflow_brain.py
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@@ -3,6 +3,7 @@
from __future__ import annotations
import asyncio
from collections.abc import Awaitable
from copy import deepcopy
from dataclasses import replace
from typing import Any
@@ -40,7 +41,14 @@ from services.action_runtime import (
ActionRunner,
ActionStatus,
)
from services.action_stage import ActionStageRunner, ActionStageSpec, StageAction
from services.knowledge import search as search_knowledge
from services.message_stage import (
MessageDisplaySpec,
MessageStageResult,
MessageStageRunner,
MessageStageSpec,
)
from services.runtime_variables import DynamicVariableStore
from services.tool_executor import ToolExecutionError, ToolExecutor
from services.tool_policy import policy_for_tool
@@ -110,6 +118,8 @@ class WorkflowBrain(BaseBrain):
self._store = DynamicVariableStore.from_config(cfg or AssistantConfig(type="workflow"))
self._tools = ToolExecutor(self._store)
self._actions = ActionRunner(self._tools)
self._action_stages = ActionStageRunner(self._actions)
self._message_stages = MessageStageRunner()
self._tool_by_id: dict[str, RuntimeTool] = {
tool.id: tool for tool in (cfg.tools if cfg else [])
}
@@ -126,11 +136,10 @@ class WorkflowBrain(BaseBrain):
self._output: WorkflowOutput | None = None
self._agent_stage: WorkflowAgentStage | None = None
self._ended = False
self._greeting_context_message: dict[str, str] | None = None
self._startup_waiting_for_greeting = False
async def greeting(self, cfg: AssistantConfig) -> str:
return self._engine.greeting(self._store) or cfg.greeting
async def greeting(self, _cfg: AssistantConfig) -> str:
"""Workflow opening speech belongs to an explicit Message or Agent."""
return ""
def system_prompt(self, cfg: AssistantConfig) -> str:
return self._store.render(self._engine.global_prompt())
@@ -151,6 +160,8 @@ class WorkflowBrain(BaseBrain):
self._tools,
is_session_ending=lambda: runtime.call_end.ending,
)
self._action_stages = ActionStageRunner(self._actions)
self._message_stages = MessageStageRunner(runtime.client_tools)
self._tool_by_id = {tool.id: tool for tool in cfg.tools}
self._router = WorkflowLLMRouter(cfg)
self._edge_evaluator = WorkflowEdgeEvaluator(
@@ -168,8 +179,6 @@ class WorkflowBrain(BaseBrain):
runtime=runtime,
)
self._ended = False
self._greeting_context_message = None
self._startup_waiting_for_greeting = False
self._manager = ConfiguredFlowManager(
worker=runtime.worker,
llm=runtime.llm,
@@ -179,15 +188,6 @@ class WorkflowBrain(BaseBrain):
)
self._manager.state["variables"] = self._store.values
def prepare_greeting_context(
self,
greeting: str,
context: LLMContext,
) -> dict[str, str] | None:
message = super().prepare_greeting_context(greeting, context)
self._greeting_context_message = deepcopy(message) if message else None
return message
async def on_connected(self, *, greeting_pending: bool = False) -> None:
self._state.enter(self._engine.start_id, WorkflowStatus.STARTING)
await self._emit_node_active(self._engine.start_id)
@@ -198,39 +198,11 @@ class WorkflowBrain(BaseBrain):
if self._manager is None:
raise RuntimeError("Workflow FlowManager 尚未初始化")
self._startup_waiting_for_greeting = greeting_pending
if greeting_pending:
# Keep the Workflow on Start until the transport confirms that the
# shared greeting has finished. This prevents an initial Agent's
# fixed speech (or generated reply) from racing the greeting.
await self._manager.initialize(
self._passive_node_config(self._engine.start_id)
)
logger.info("工作流等待 Start 开场白播放完毕")
return
node_config = await self._initial_node_config()
await self._manager.initialize(node_config)
await self._after_node_activated(node_config)
logger.info(f"工作流模式启用: 当前节点={self._manager.current_node}")
async def on_greeting_finished(self) -> None:
"""Enter the first node only after Start's greeting reaches playback end."""
if not self._startup_waiting_for_greeting or self._ended:
return
self._startup_waiting_for_greeting = False
manager = self._require_manager()
if manager.current_node != self._engine.start_id:
return
node_config = await self._initial_node_config()
if node_config.get("name") == self._engine.start_id:
self._state.enter(self._engine.start_id, WorkflowStatus.WAITING_USER)
return
await manager.set_node_from_config(node_config)
await self._after_node_activated(node_config)
logger.info(f"Start 开场白结束,进入节点: {manager.current_node}")
async def _initial_node_config(self) -> NodeConfig:
"""Only a default-only Start advances before the first user turn."""
outgoing = self._engine.outgoing(self._engine.start_id)
@@ -409,7 +381,6 @@ class WorkflowBrain(BaseBrain):
return self._require_agent_stage().node_config(
node_id,
functions=functions,
greeting_context_message=self._greeting_context_message,
leading_messages=leading_messages,
)
@@ -455,8 +426,8 @@ class WorkflowBrain(BaseBrain):
*,
source: str = "workflow-speech",
node_id: str | None = None,
) -> None:
await self._require_output().speak(
) -> Awaitable[None] | None:
return await self._require_output().speak(
text,
source=source,
node_id=node_id,
@@ -700,6 +671,14 @@ class WorkflowBrain(BaseBrain):
outcome = await self._enter_action(node_id)
if not outcome.should_route:
return self._passive_node_config(node_id, context_messages)
elif node_type == "message":
message_result = await self._enter_message(node_id)
if not message_result.succeeded:
return self._passive_node_config(node_id, context_messages)
if message_result.speech:
context_messages.append(
{"role": "assistant", "content": message_result.speech}
)
elif node_type == "handoff":
await self._enter_handoff(node_id)
elif node_type == "start":
@@ -735,29 +714,36 @@ class WorkflowBrain(BaseBrain):
data = self._engine.data(node_id)
runtime = self._require_runtime()
invocation_id = self._actions.new_invocation_id()
block_user_input = data.get("userInputPolicy") == "block"
if block_user_input and runtime.set_input_enabled:
# Blocking only suppresses new audio/text input while the Action
# runs. It deliberately does not cancel the tool. The default
# queue policy leaves input enabled; the turn lock serializes any
# completed user turn until this automatic path has finished.
runtime.set_input_enabled(False)
tool_id = str(data.get("toolId") or "")
tool = self._tool_by_id.get(tool_id)
try:
await self._emit_trace(
"action_started",
nodeId=node_id,
invocationId=invocation_id,
toolId=tool_id,
toolType=tool.type if tool else None,
)
outcome = await self._actions.execute(
tool,
data.get("arguments") or {},
result_assignments=self._action_result_assignments(data),
invocation_id=invocation_id,
stage_result = await self._action_stages.run(
ActionStageSpec(
actions=(
StageAction(
id=node_id,
tool=tool,
arguments=data.get("arguments") or {},
result_assignments=self._action_result_assignments(data),
invocation_id=invocation_id,
),
),
input_policy=(
"block"
if data.get("userInputPolicy") == "block"
else "queue"
),
),
set_input_enabled=runtime.set_input_enabled,
on_started=lambda: self._emit_trace(
"action_started",
nodeId=node_id,
invocationId=invocation_id,
toolId=tool_id,
toolType=tool.type if tool else None,
),
)
outcome = stage_result.outcomes[0]
updated_variables = list(outcome.updated_variables)
if updated_variables:
await self._emit_variables(
@@ -770,13 +756,74 @@ class WorkflowBrain(BaseBrain):
self._set_last_action(outcome)
await self._emit_action_outcome(node_id, outcome)
raise
finally:
if block_user_input and runtime.set_input_enabled:
runtime.set_input_enabled(True)
self._set_last_action(outcome)
await self._emit_action_outcome(node_id, outcome)
return outcome
async def _enter_message(self, node_id: str) -> MessageStageResult:
self._state.enter(node_id, WorkflowStatus.RUNNING_MESSAGE)
await self._emit_node_active(node_id)
data = self._engine.data(node_id)
runtime = self._require_runtime()
speech = self._store.render(str(data.get("speech") or "")).strip()
show_message = bool(data.get("showMessage", False))
require_confirmation = bool(data.get("requireConfirmation", False))
display = (
MessageDisplaySpec(
title=self._store.render(
str(data.get("title") or "重要提示")
).strip(),
message=self._store.render(
str(data.get("message") or "")
).strip(),
confirm_label=self._store.render(
str(data.get("confirmLabel") or "确认")
).strip(),
)
if show_message
else None
)
result = await self._message_stages.run(
MessageStageSpec(
speech=speech,
display=display,
require_confirmation=require_confirmation,
),
speak=lambda content: self._queue_visible_speech(
content,
source="workflow-message-speech",
node_id=node_id,
),
set_input_enabled=runtime.set_input_enabled,
on_started=lambda: self._emit_trace(
"message_started",
nodeId=node_id,
hasSpeech=bool(speech),
showsMessage=show_message,
requiresConfirmation=require_confirmation,
),
)
if result.succeeded:
await self._emit_trace(
"message_completed",
nodeId=node_id,
action=result.action,
)
return result
self._state.enter(node_id, WorkflowStatus.WAITING_USER)
await self._emit_trace(
"message_failed",
nodeId=node_id,
error=result.error or "Message 节点执行失败",
)
await self._require_output().emit_error(
result.error or "Message 节点执行失败",
node_id=node_id,
code="workflow_message_error",
)
return result
def _set_last_action(self, outcome: ActionOutcome) -> None:
legacy_status = {
ActionStatus.SUCCESS: "ok",