Refactor workflow routing and greeting management in Brain classes
- Update WorkflowBrain to handle greeting playback more effectively, ensuring that the initial greeting completes before transitioning to the first node. - Introduce new methods for managing greeting states and conditions, enhancing the interaction flow for user turns. - Refactor WorkflowLLMRouter to improve routing logic and ensure proper handling of conditional paths. - Enhance tests to verify the correct behavior of greeting management and routing under various scenarios, including waiting for audio playback to finish. - Update frontend components to reflect changes in edge handling and improve user experience in workflow configurations.
This commit is contained in:
Xin Wang
@@ -28,6 +28,7 @@ from services.brains.dify_llm import (
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)
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from services.brains.workflow_brain import WorkflowBrain
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from services.runtime_variables import prepare_dynamic_config
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from services.workflow_router import STAY_ON_CURRENT_NODE
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class FakeLLM:
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@@ -460,6 +461,86 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
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class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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async def test_initial_fixed_speech_waits_for_start_greeting_to_finish(self):
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brain = WorkflowBrain(
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{
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"specVersion": 3,
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"settings": {"globalPrompt": "全局规则"},
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"nodes": [
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{
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"id": "start",
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"type": "start",
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"data": {"greeting": "欢迎使用"},
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},
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{
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"id": "agent",
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"type": "agent",
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"data": {
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"prompt": "收集用户信息",
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"entryMode": "fixed_speech",
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"entrySpeech": "请问您怎么称呼？",
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},
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},
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],
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"edges": [
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{
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"id": "begin",
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"source": "start",
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"target": "agent",
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"data": {"mode": "always", "priority": 0},
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}
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],
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}
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)
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queued = []
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async def queue_frame(frame):
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queued.append(frame)
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class FakeManager:
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def __init__(self):
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self.current_node = None
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async def initialize(self, config):
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self.current_node = config["name"]
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async def set_node_from_config(self, config):
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self.current_node = config["name"]
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for action in config.get("pre_actions", []):
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await action["handler"](action, self)
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brain._runtime = BrainRuntime(
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context=LLMContext(messages=[]),
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llm=FakeLLM(),
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queue_frame=queue_frame,
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set_system_prompt=lambda _prompt: None,
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set_tools=lambda _tools: None,
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call_end=FakeCallEnd(),
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)
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brain._manager = FakeManager()
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await brain.on_connected(greeting_pending=True)
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self.assertEqual(brain._manager.current_node, "start")
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self.assertFalse(any(isinstance(frame, TTSSpeakFrame) for frame in queued))
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await brain.on_greeting_finished()
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self.assertEqual(brain._manager.current_node, "agent")
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fixed_speech_frames = [
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frame for frame in queued if isinstance(frame, TTSSpeakFrame)
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]
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self.assertEqual(len(fixed_speech_frames), 1)
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self.assertEqual(fixed_speech_frames[0].text, "请问您怎么称呼？")
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# Playback notifications may be duplicated by a transport reconnect;
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# the initial entry behavior must still run only once.
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await brain.on_greeting_finished()
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self.assertEqual(
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len([frame for frame in queued if isinstance(frame, TTSSpeakFrame)]),
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1,
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)
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async def test_action_publishes_updated_session_variables(self):
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tool = RuntimeTool(
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id="lookup",
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@@ -652,6 +733,261 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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)
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)
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async def test_start_llm_conditions_wait_for_and_route_first_user_turn(self):
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brain = WorkflowBrain(
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{
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"specVersion": 3,
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"settings": {"globalPrompt": "全局规则"},
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"nodes": [
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{
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"id": "start",
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"type": "start",
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"data": {"name": "Start", "greeting": "你想做什么？"},
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},
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{
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"id": "eat",
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"type": "agent",
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"data": {
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"name": "点饭",
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"prompt": "帮助用户点饭",
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"contextPolicy": "fresh",
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"entryMode": "wait_user",
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},
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},
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{"id": "drink", "type": "agent", "data": {}},
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{"id": "run", "type": "agent", "data": {}},
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],
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"edges": [
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{
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"id": "eat",
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"source": "start",
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"target": "eat",
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"data": {
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"mode": "llm",
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"priority": 10,
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"condition": "用户想吃饭",
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},
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},
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{
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"id": "drink",
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"source": "start",
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"target": "drink",
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"data": {
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"mode": "llm",
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"priority": 20,
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"condition": "用户想喝水",
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},
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},
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{
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"id": "run",
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"source": "start",
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"target": "run",
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"data": {
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"mode": "llm",
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"priority": 30,
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"condition": "用户想跑步",
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},
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},
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],
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}
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)
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queued = []
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async def queue_frame(frame):
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queued.append(frame)
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brain._runtime = BrainRuntime(
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context=LLMContext(messages=[]),
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llm=FakeLLM(),
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queue_frame=queue_frame,
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set_system_prompt=lambda _prompt: None,
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set_tools=lambda _tools: None,
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call_end=FakeCallEnd(),
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)
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class FakeManager:
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def __init__(self):
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self.current_node = None
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self.config = None
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async def initialize(self, config):
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self.current_node = config["name"]
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self.config = config
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async def set_node_from_config(self, config):
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self.current_node = config["name"]
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self.config = config
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class FakeRouter:
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def __init__(self):
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self.calls = 0
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async def select_edge(self, **_kwargs):
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self.calls += 1
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return "goto_eat"
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manager = FakeManager()
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router = FakeRouter()
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brain._manager = manager
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brain._router = router
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await brain.on_connected()
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self.assertEqual(manager.current_node, "start")
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self.assertEqual(router.calls, 0)
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handled = await brain.on_user_turn_end("我想吃饭")
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self.assertTrue(handled)
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self.assertEqual(router.calls, 1)
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self.assertEqual(manager.current_node, "eat")
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self.assertIn(
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{"role": "user", "content": "我想吃饭"},
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manager.config["task_messages"],
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)
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self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))
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self.assertIn("我想吃饭", brain._store.values["system__conversation_history"])
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async def test_automatic_node_can_follow_llm_condition(self):
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brain = WorkflowBrain(
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{
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"specVersion": 3,
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"settings": {"globalPrompt": "全局规则"},
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"nodes": [
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{"id": "start", "type": "start", "data": {}},
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{
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"id": "handoff",
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"type": "handoff",
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"data": {"name": "人工转接", "targetType": "human"},
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},
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{
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"id": "agent",
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"type": "agent",
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"data": {"name": "继续服务", "prompt": "继续处理"},
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},
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],
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"edges": [
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{
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"id": "to-agent",
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"source": "handoff",
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"target": "agent",
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"data": {
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"mode": "llm",
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"priority": 10,
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"condition": "转接后仍需 AI 继续服务",
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},
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}
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],
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}
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)
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queued = []
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async def queue_frame(frame):
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queued.append(frame)
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brain._runtime = BrainRuntime(
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context=LLMContext(messages=[]),
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llm=FakeLLM(),
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queue_frame=queue_frame,
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set_system_prompt=lambda _prompt: None,
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set_tools=lambda _tools: None,
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call_end=FakeCallEnd(),
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)
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class FakeRouter:
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async def select_edge(self, **kwargs):
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self.node_name = kwargs["node_name"]
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return "goto_to_agent"
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router = FakeRouter()
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brain._router = router
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config = await brain._resolve_path("handoff")
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self.assertEqual(config["name"], "agent")
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self.assertEqual(router.node_name, "人工转接")
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async def test_mixed_edge_conditions_follow_priority(self):
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brain = WorkflowBrain(
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{
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"specVersion": 3,
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"settings": {},
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"nodes": [
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{"id": "start", "type": "start", "data": {}},
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{"id": "agent", "type": "agent", "data": {}},
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{"id": "llm-target", "type": "end", "data": {}},
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{"id": "expression-target", "type": "end", "data": {}},
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{"id": "default-target", "type": "end", "data": {}},
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],
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"edges": [
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{
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"id": "llm",
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"source": "agent",
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"target": "llm-target",
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"data": {
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"mode": "llm",
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"priority": 10,
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"condition": "大模型条件成立",
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},
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},
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{
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"id": "expression",
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"source": "agent",
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"target": "expression-target",
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"data": {
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"mode": "expression",
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"priority": 20,
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"expression": {
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"combinator": "and",
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"rules": [
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{
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"variable": "route",
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"operator": "eq",
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"value": "expression",
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}
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],
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},
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},
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},
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{
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"id": "default",
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"source": "agent",
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"target": "default-target",
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"data": {"mode": "always", "priority": 30},
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},
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],
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}
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)
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brain._store.values["route"] = "expression"
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class FakeRouter:
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def __init__(self):
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self.result = STAY_ON_CURRENT_NODE
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self.edge_ids = []
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async def select_edge(self, **kwargs):
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self.edge_ids = [edge["id"] for edge in kwargs["edges"]]
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return self.result
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router = FakeRouter()
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brain._router = router
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selected = await brain._select_edge("agent")
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self.assertEqual(router.edge_ids, ["llm"])
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self.assertEqual(selected["id"], "expression")
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router.result = "goto_llm"
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selected = await brain._select_edge("agent")
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self.assertEqual(selected["id"], "llm")
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expression_edge = next(
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edge for edge in brain._engine.edges if edge["id"] == "expression"
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)
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expression_edge["data"]["priority"] = 5
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router.edge_ids = []
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selected = await brain._select_edge("agent")
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self.assertEqual(selected["id"], "expression")
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self.assertEqual(router.edge_ids, [])
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async def test_transition_and_end_are_owned_by_workflow_brain(self):
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graph = {
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"specVersion": 3,
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