Enhance conversation history and runtime variable management
- Update ConversationRecorder to include source and nodeId metadata in transcripts for better context tracking. - Introduce optional variable handling in DynamicVariableStore, allowing for unset variables to be rendered as empty without raising errors. - Refactor WorkflowBrain to apply turn configurations and manage interaction policies dynamically, improving agent responsiveness. - Implement tests to ensure proper handling of updated session variables and workflow metadata in various scenarios.
This commit is contained in:
Xin Wang
@@ -9,6 +9,7 @@ from pipecat.frames.frames import (
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LLMContextFrame,
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LLMFullResponseEndFrame,
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LLMFullResponseStartFrame,
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LLMMessagesUpdateFrame,
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LLMRunFrame,
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LLMTextFrame,
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OutputTransportMessageUrgentFrame,
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@@ -391,6 +392,78 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
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class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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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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name="查询订单",
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function_name="lookup_order",
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type="http",
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)
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cfg = prepare_dynamic_config(
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AssistantConfig(
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type="workflow",
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graph={
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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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{
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"id": "lookup_action",
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"type": "action",
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"data": {
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"toolId": "lookup",
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"resultAssignments": {
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"order_status": "order.status"
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},
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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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dynamic_variable_definitions={
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"order_status": {"type": "string", "default": "pending"}
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},
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tools=[tool],
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),
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{},
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assistant_id="asst_workflow_action",
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)
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brain = WorkflowBrain(cfg)
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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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async def execute(_tool, _arguments, *, result_assignments=None):
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self.assertEqual(result_assignments, {"order_status": "order.status"})
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brain._store.assign("order_status", "paid")
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return {
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"status": "ok",
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"updated_variables": ["order_status"],
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}
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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._tools.execute = execute
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await brain._enter_action("lookup_action")
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variable_events = [
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frame.message
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for frame in queued
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if isinstance(frame, OutputTransportMessageUrgentFrame)
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and frame.message.get("type") == "workflow-variables"
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]
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self.assertEqual(variable_events[-1]["reason"], "action")
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self.assertEqual(variable_events[-1]["changed"], ["order_status"])
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self.assertEqual(variable_events[-1]["variables"], {"order_status": "paid"})
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async def test_nodes_without_outgoing_edges_remain_active(self):
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queued = []
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@@ -492,6 +565,11 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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"defaultTtsResourceId": "tts_global",
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"knowledgeBaseId": "kb_global",
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"knowledgeMode": "automatic",
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"enableInterrupt": False,
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"turnConfig": {
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"bargeIn": {"strategy": "transcription"},
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"vad": {"confidence": 0.55},
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},
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},
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"nodes": [
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{
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@@ -551,6 +629,7 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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queued = []
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service_switches = []
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knowledge_scopes = []
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turn_configs = []
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call_end = FakeCallEnd()
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class FakeWorker:
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@@ -585,6 +664,9 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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async def switch_services(llm_id, asr_id, tts_id):
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service_switches.append((llm_id, asr_id, tts_id))
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async def apply_turn_config(enable_interrupt, turn_config):
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turn_configs.append((enable_interrupt, turn_config))
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runtime = BrainRuntime(
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context=context,
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llm=llm,
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@@ -596,15 +678,27 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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context_aggregator=pair,
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switch_services=switch_services,
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set_knowledge_scope=knowledge_scopes.append,
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apply_turn_config=apply_turn_config,
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)
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await brain.setup(cfg, runtime)
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await brain.on_connected()
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self.assertEqual(brain._manager.current_node, "agent")
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variable_events = [
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frame.message
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for frame in queued
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if isinstance(frame, OutputTransportMessageUrgentFrame)
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and frame.message.get("type") == "workflow-variables"
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]
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self.assertEqual(variable_events[0]["reason"], "initialized")
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self.assertEqual(variable_events[0]["variables"], {"user_name": "王先生"})
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self.assertNotIn("system__conversation_id", variable_events[0]["variables"])
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self.assertEqual(
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service_switches,
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[("llm_global", "asr_global", "tts_global")],
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)
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self.assertEqual(knowledge_scopes[-1]["knowledge_base_id"], "kb_global")
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self.assertEqual(turn_configs[-1][0], False)
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self.assertEqual(turn_configs[-1][1]["vad"]["confidence"], 0.55)
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brain._engine.data("agent").update(
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{
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@@ -614,6 +708,11 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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"ttsResourceId": "tts_agent",
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"knowledgeBaseId": "kb_agent",
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"knowledgeMode": "on_demand",
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"enableInterrupt": True,
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"turnConfig": {
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"bargeIn": {"strategy": "vad"},
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"turnDetection": {"strategy": "smart_turn"},
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},
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}
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)
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await brain._apply_agent_stage("agent")
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@@ -622,6 +721,11 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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("llm_agent", "asr_agent", "tts_agent"),
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)
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self.assertEqual(knowledge_scopes[-1]["knowledge_base_id"], "kb_agent")
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self.assertEqual(turn_configs[-1][0], True)
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self.assertEqual(
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turn_configs[-1][1]["turnDetection"]["strategy"],
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"smart_turn",
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)
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agent_config = brain._agent_config("agent")
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self.assertIn("王先生", agent_config["role_message"])
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self.assertIn("工作流路由已在用户一轮输入结束时完成", agent_config["role_message"])
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@@ -654,11 +758,30 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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fixed_config["task_messages"],
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[{"role": "assistant", "content": "您好，王先生"}],
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)
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self.assertEqual(fixed_config["pre_actions"][0]["node_id"], "agent")
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worker.frames.clear()
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queued.clear()
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await brain._manager.set_node_from_config(fixed_config)
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self.assertTrue(any(isinstance(frame, TTSSpeakFrame) for frame in queued))
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self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
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context_updates = [
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frame
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for frame in worker.frames
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if isinstance(frame, LLMMessagesUpdateFrame)
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]
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self.assertEqual(
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context_updates[-1].messages,
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[{"role": "assistant", "content": "您好，王先生"}],
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)
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fixed_reply_events = [
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frame.message
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for frame in queued
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if isinstance(frame, OutputTransportMessageUrgentFrame)
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and frame.message.get("source") == "workflow-fixed-reply"
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]
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self.assertEqual(fixed_reply_events[0]["content"], "您好，王先生")
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self.assertEqual(fixed_reply_events[0]["nodeId"], "agent")
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self.assertIn("您好，王先生", brain._store.values["system__conversation_history"])
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self.assertFalse(
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any(
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