1448 lines
51 KiB
Python
1448 lines
51 KiB
Python
from __future__ import annotations
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import unittest
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from types import SimpleNamespace
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from unittest.mock import patch
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from models import AssistantConfig, RuntimeTool
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from pipecat.frames.frames import (
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LLMContextFrame,
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LLMFullResponseEndFrame,
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LLMFullResponseStartFrame,
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LLMMessagesAppendFrame,
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LLMMessagesUpdateFrame,
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LLMRunFrame,
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LLMTextFrame,
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OutputTransportMessageUrgentFrame,
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TTSSpeakFrame,
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)
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from pipecat.processors.aggregators.llm_context import LLMContext
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from pipecat.processors.frame_processor import FrameDirection
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from schemas import AssistantUpsert, REALTIME_CAPABLE_TYPES
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from services.brains import BrainRuntime, SPECS, build_brain
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from services.brains.base import GREETING_CONTEXT_MARKER
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from services.brains.dify_llm import (
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DifyLLMService,
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last_user_text,
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normalize_api_base,
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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.models import LLMRouteResult, RouteStatus
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class FakeLLM:
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def __init__(self):
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self.functions = {}
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def register_function(self, name, handler):
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self.functions[name] = handler
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class FakeCallEnd:
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def __init__(self):
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self.ending = False
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self.reason = ""
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self.armed = False
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self.finished = False
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self.response_started = False
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self.waited_for_text: bool | None = None
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self.tracked_speeches = 0
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def begin(self, reason: str) -> None:
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self.ending = True
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self.reason = reason
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def begin_response(self) -> None:
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self.response_started = True
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def arm_after_speech(self) -> None:
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self.armed = True
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def track_speech(self) -> None:
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self.tracked_speeches += 1
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async def arm_after_tracked_speech(self) -> None:
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self.armed = True
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if self.tracked_speeches == 0:
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await self.finish()
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async def finish_after_current_speech(self, *, has_text: bool) -> None:
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self.waited_for_text = has_text
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if has_text:
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self.armed = True
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else:
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await self.finish()
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async def finish(self) -> None:
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self.finished = True
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class FakeFunctionParams:
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def __init__(self, arguments=None):
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self.arguments = arguments or {}
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self.result = None
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self.properties = None
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async def result_callback(self, result, properties=None):
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self.result = result
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self.properties = properties
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class BrainRegistryTests(unittest.TestCase):
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def test_capability_matrix(self):
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self.assertEqual(
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{
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name: spec.supported_runtime_modes
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for name, spec in SPECS.items()
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},
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{
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"prompt": frozenset({"pipeline", "realtime"}),
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"workflow": frozenset({"pipeline"}),
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"dify": frozenset({"pipeline"}),
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"fastgpt": frozenset({"pipeline"}),
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},
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)
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self.assertEqual(
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REALTIME_CAPABLE_TYPES,
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{
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name
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for name, spec in SPECS.items()
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if "realtime" in spec.supported_runtime_modes
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},
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)
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def test_unknown_brain_does_not_fallback_to_prompt(self):
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with self.assertRaisesRegex(ValueError, "尚未实现"):
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build_brain(AssistantConfig(type="opencode"))
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def test_workflow_realtime_is_rejected_at_schema_boundary(self):
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with self.assertRaises(ValueError):
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AssistantUpsert(
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name="workflow",
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type="workflow",
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runtimeMode="realtime",
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)
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def test_prompt_realtime_keeps_dynamic_variable_definitions(self):
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assistant = AssistantUpsert(
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name="realtime prompt",
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type="prompt",
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runtimeMode="realtime",
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dynamicVariableDefinitions={
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"user_name": {
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"type": "string",
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"required": True,
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"default": None,
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}
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},
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)
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self.assertIn("user_name", assistant.dynamic_variable_definitions)
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def test_workflow_keeps_dynamic_variables_and_tool_bindings(self):
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assistant = AssistantUpsert(
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name="workflow",
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type="workflow",
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toolIds=["tool_a"],
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dynamicVariableDefinitions={
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"customer": {"type": "string", "required": False, "default": "王先生"}
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},
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graph={},
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)
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self.assertEqual(assistant.tool_ids, ["tool_a"])
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self.assertIn("customer", assistant.dynamic_variable_definitions)
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class DifyHelpersTests(unittest.TestCase):
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def test_normalize_api_base(self):
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self.assertEqual(
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normalize_api_base("https://api.dify.ai"),
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"https://api.dify.ai/v1",
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)
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self.assertEqual(
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normalize_api_base("https://example.test/v1/chat-messages"),
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"https://example.test/v1",
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)
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def test_last_user_text(self):
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self.assertEqual(
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last_user_text(
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[
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{"role": "user", "content": "first"},
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{"role": "assistant", "content": "answer"},
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{
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"role": "user",
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"content": [{"type": "text", "text": "latest"}],
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},
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]
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),
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"latest",
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)
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class DifyLLMServiceTests(unittest.IsolatedAsyncioTestCase):
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async def test_streams_sdk_events_and_keeps_conversation_id(self):
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class FakeDifyClient:
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requests = []
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async def achat_messages(self, request, **_kwargs):
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self.requests.append(request)
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async def events():
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yield SimpleNamespace(
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event="message",
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answer="你好",
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conversation_id="conversation-1",
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)
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yield SimpleNamespace(
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event="message_end",
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conversation_id="conversation-1",
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)
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return events()
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client = FakeDifyClient()
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service = DifyLLMService(
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AssistantConfig(type="dify"),
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client=client,
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user_id="test-user",
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)
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frames = []
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async def push_frame(frame, *_args, **_kwargs):
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frames.append(frame)
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service.push_frame = push_frame
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context = LLMContext(messages=[{"role": "user", "content": "问题"}])
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await service.process_frame(
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LLMContextFrame(context),
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FrameDirection.DOWNSTREAM,
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)
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self.assertIsInstance(frames[0], LLMFullResponseStartFrame)
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self.assertIsInstance(frames[1], LLMTextFrame)
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self.assertEqual(frames[1].text, "你好")
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self.assertIsInstance(frames[-1], LLMFullResponseEndFrame)
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self.assertEqual(service._conversation_id, "conversation-1")
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context.add_message({"role": "user", "content": "追问"})
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await service.process_frame(
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LLMContextFrame(context),
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FrameDirection.DOWNSTREAM,
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)
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self.assertEqual(client.requests[-1].conversation_id, "conversation-1")
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class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
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async def test_realtime_prompt_brain_renders_dynamic_variables(self):
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cfg = prepare_dynamic_config(
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AssistantConfig(
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type="prompt",
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runtimeMode="realtime",
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prompt="服务用户 {{user_name}}",
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greeting="您好，{{user_name}}",
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dynamic_variable_definitions={
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"user_name": {"type": "string", "required": True}
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},
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),
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{"user_name": "王先生"},
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assistant_id="asst_realtime",
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)
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brain = build_brain(cfg)
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self.assertEqual(brain.system_prompt(cfg), "服务用户 王先生")
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self.assertEqual(await brain.greeting(cfg), "您好，王先生")
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context = LLMContext(
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messages=[{"role": "system", "content": brain.system_prompt(cfg)}]
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)
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brain.prepare_greeting_context("您好，王先生", context)
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self.assertEqual(
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[message["role"] for message in context.get_messages()],
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["system", "system"],
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)
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self.assertNotEqual(context.get_messages()[0]["role"], "assistant")
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self.assertEqual(
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context.get_messages()[1]["content"],
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f"{GREETING_CONTEXT_MARKER}\n您好，王先生",
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)
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async def test_end_call_tool_is_owned_by_prompt_brain(self):
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brain = build_brain(
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AssistantConfig(
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type="prompt",
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tools=[
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RuntimeTool(
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id="end-call",
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name="结束通话",
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function_name="end_call",
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type="end_call",
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definition={
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"config": {
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"message_type": "none",
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"capture_reason": True,
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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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llm = FakeLLM()
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call_end = FakeCallEnd()
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visible_tools = []
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async def queue_frame(_frame):
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pass
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await brain.setup(
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AssistantConfig(
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type="prompt",
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tools=[
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RuntimeTool(
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id="end-call",
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name="结束通话",
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function_name="end_call",
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type="end_call",
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definition={"config": {"capture_reason": True}},
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)
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],
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),
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BrainRuntime(
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context=LLMContext(messages=[]),
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llm=llm,
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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: visible_tools.extend(tools or []),
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call_end=call_end,
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),
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)
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self.assertEqual(visible_tools[0].name, "end_call")
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params = FakeFunctionParams({"reason": "用户已完成咨询"})
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await brain.on_assistant_text_start("closing-turn")
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await llm.functions["end_call"](params)
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self.assertEqual(call_end.reason, "用户已完成咨询")
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self.assertFalse(call_end.finished)
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await brain.on_assistant_text_end("closing-turn", "", False)
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self.assertTrue(call_end.finished)
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self.assertFalse(call_end.waited_for_text)
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self.assertTrue(call_end.response_started)
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self.assertEqual(params.result["action"], "ending_call")
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async def test_end_call_waits_for_prompt_generated_closing_speech(self):
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tool = RuntimeTool(
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id="end-call",
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name="结束通话",
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function_name="end_call",
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type="end_call",
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definition={"config": {"message_type": "none"}},
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)
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cfg = AssistantConfig(type="prompt", tools=[tool])
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brain = build_brain(cfg)
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llm = FakeLLM()
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call_end = FakeCallEnd()
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await brain.setup(
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cfg,
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BrainRuntime(
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context=LLMContext(messages=[]),
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llm=llm,
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queue_frame=lambda _frame: None,
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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=call_end,
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),
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)
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await brain.on_assistant_text_start("closing-turn")
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await llm.functions["end_call"](FakeFunctionParams({}))
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await brain.on_assistant_text_end(
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"closing-turn",
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"感谢您的来电。祝您生活愉快，再见。",
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False,
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)
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self.assertFalse(call_end.finished)
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self.assertTrue(call_end.armed)
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self.assertTrue(call_end.waited_for_text)
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async def test_http_tool_renders_secrets_and_updates_prompt_variable(self):
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requests = []
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class FakeResponse:
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status_code = 200
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content = b'{"order":{"status":"paid"}}'
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def raise_for_status(self):
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return None
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def json(self):
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return {"order": {"status": "paid"}}
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|
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class FakeClient:
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def __init__(self, **_kwargs):
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pass
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async def __aenter__(self):
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return self
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async def __aexit__(self, *_args):
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return None
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async def request(self, method, url, **kwargs):
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requests.append((method, url, kwargs))
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return FakeResponse()
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cfg = prepare_dynamic_config(
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AssistantConfig(
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type="prompt",
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runtimeMode="pipeline",
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prompt="订单状态：{{order_status}}",
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dynamic_variable_definitions={
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"order_status": {"type": "string", "default": "unknown"}
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},
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tools=[
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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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description="查询订单状态",
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definition={
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"config": {
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"method": "GET",
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"url": "https://example.test/orders/{order_id}",
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"headers": {"Authorization": "Bearer {{secret__token}}"},
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"parameters": [
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{
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"name": "order_id",
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"type": "string",
|
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"location": "path",
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"required": True,
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},
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{
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"name": "Authorization",
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"type": "string",
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"location": "header",
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"required": False,
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},
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],
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"dynamic_variable_assignments": {
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"order_status": "response.order.status"
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},
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}
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},
|
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secrets={"dynamic_variables": {"secret__token": "server-token"}},
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)
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],
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),
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{},
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assistant_id="asst_1",
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)
|
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brain = build_brain(cfg)
|
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llm = FakeLLM()
|
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prompts = []
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visible_tools = []
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|
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async def queue_frame(_frame):
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pass
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|
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await brain.setup(
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cfg,
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BrainRuntime(
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context=LLMContext(messages=[]),
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llm=llm,
|
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queue_frame=queue_frame,
|
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set_system_prompt=prompts.append,
|
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set_tools=lambda tools: visible_tools.extend(tools or []),
|
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call_end=FakeCallEnd(),
|
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),
|
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)
|
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params = FakeFunctionParams(
|
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{"order_id": "A/1", "Authorization": "attacker-value"}
|
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)
|
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with patch("services.tool_executor.httpx.AsyncClient", FakeClient):
|
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await llm.functions["lookup_order"](params)
|
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|
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self.assertEqual(requests[0][1], "https://example.test/orders/A%2F1")
|
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self.assertEqual(
|
|
requests[0][2]["headers"]["Authorization"], "Bearer server-token"
|
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)
|
|
self.assertEqual(params.result["updated_variables"], ["order_status"])
|
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self.assertEqual(prompts[-1], "订单状态：paid")
|
|
|
|
|
|
class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
|
|
async def test_agent_vision_tool_is_scoped_to_effective_stage(self):
|
|
brain = WorkflowBrain(
|
|
{
|
|
"specVersion": 3,
|
|
"settings": {
|
|
"globalPrompt": "全局规则",
|
|
"defaultLlmResourceId": "llm_global",
|
|
"visionEnabled": True,
|
|
"visionModelResourceId": "vision_global",
|
|
},
|
|
"nodes": [
|
|
{"id": "start", "type": "start", "data": {}},
|
|
{
|
|
"id": "agent",
|
|
"type": "agent",
|
|
"data": {
|
|
"prompt": "观察用户需要展示的物品",
|
|
"inheritGlobalConfig": True,
|
|
},
|
|
},
|
|
],
|
|
"edges": [
|
|
{
|
|
"id": "begin",
|
|
"source": "start",
|
|
"target": "agent",
|
|
"data": {"mode": "always", "priority": 0},
|
|
}
|
|
],
|
|
}
|
|
)
|
|
scopes = []
|
|
vision_function = object()
|
|
|
|
async def queue_frame(_frame):
|
|
pass
|
|
|
|
brain._runtime = BrainRuntime(
|
|
context=LLMContext(messages=[]),
|
|
llm=FakeLLM(),
|
|
queue_frame=queue_frame,
|
|
set_system_prompt=lambda _prompt: None,
|
|
set_tools=lambda _tools: None,
|
|
call_end=FakeCallEnd(),
|
|
set_vision_scope=scopes.append,
|
|
vision_function=vision_function,
|
|
)
|
|
|
|
await brain._apply_agent_stage("agent")
|
|
inherited_config = brain._agent_config("agent")
|
|
self.assertIn(vision_function, inherited_config["functions"])
|
|
self.assertIn("fetch_user_image", inherited_config["role_message"])
|
|
self.assertEqual(
|
|
scopes[-1],
|
|
{
|
|
"enabled": True,
|
|
"vision_model_resource_id": "vision_global",
|
|
"llm_resource_id": "llm_global",
|
|
},
|
|
)
|
|
|
|
brain._engine.data("agent").update(
|
|
{
|
|
"inheritGlobalConfig": False,
|
|
"llmResourceId": "llm_agent",
|
|
"visionEnabled": False,
|
|
"visionModelResourceId": "",
|
|
}
|
|
)
|
|
await brain._apply_agent_stage("agent")
|
|
custom_config = brain._agent_config("agent")
|
|
self.assertNotIn(vision_function, custom_config["functions"])
|
|
self.assertNotIn("fetch_user_image", custom_config["role_message"])
|
|
self.assertFalse(scopes[-1]["enabled"])
|
|
|
|
async def test_initial_fixed_speech_waits_for_start_greeting_to_finish(self):
|
|
brain = WorkflowBrain(
|
|
{
|
|
"specVersion": 3,
|
|
"settings": {"globalPrompt": "全局规则"},
|
|
"nodes": [
|
|
{
|
|
"id": "start",
|
|
"type": "start",
|
|
"data": {"greeting": "欢迎使用"},
|
|
},
|
|
{
|
|
"id": "agent",
|
|
"type": "agent",
|
|
"data": {
|
|
"prompt": "收集用户信息",
|
|
"entryMode": "fixed_speech",
|
|
"entrySpeech": "请问您怎么称呼？",
|
|
},
|
|
},
|
|
],
|
|
"edges": [
|
|
{
|
|
"id": "begin",
|
|
"source": "start",
|
|
"target": "agent",
|
|
"data": {"mode": "always", "priority": 0},
|
|
}
|
|
],
|
|
}
|
|
)
|
|
queued = []
|
|
|
|
async def queue_frame(frame):
|
|
queued.append(frame)
|
|
|
|
class FakeManager:
|
|
def __init__(self):
|
|
self.current_node = None
|
|
|
|
async def initialize(self, config):
|
|
self.current_node = config["name"]
|
|
|
|
async def set_node_from_config(self, config):
|
|
self.current_node = config["name"]
|
|
for action in config.get("pre_actions", []):
|
|
await action["handler"](action, self)
|
|
|
|
brain._runtime = BrainRuntime(
|
|
context=LLMContext(messages=[]),
|
|
llm=FakeLLM(),
|
|
queue_frame=queue_frame,
|
|
set_system_prompt=lambda _prompt: None,
|
|
set_tools=lambda _tools: None,
|
|
call_end=FakeCallEnd(),
|
|
)
|
|
brain._manager = FakeManager()
|
|
|
|
await brain.on_connected(greeting_pending=True)
|
|
|
|
self.assertEqual(brain._manager.current_node, "start")
|
|
self.assertFalse(any(isinstance(frame, TTSSpeakFrame) for frame in queued))
|
|
|
|
await brain.on_greeting_finished()
|
|
|
|
self.assertEqual(brain._manager.current_node, "agent")
|
|
fixed_speech_frames = [
|
|
frame for frame in queued if isinstance(frame, TTSSpeakFrame)
|
|
]
|
|
self.assertEqual(len(fixed_speech_frames), 1)
|
|
self.assertEqual(fixed_speech_frames[0].text, "请问您怎么称呼？")
|
|
|
|
# Playback notifications may be duplicated by a transport reconnect;
|
|
# the initial entry behavior must still run only once.
|
|
await brain.on_greeting_finished()
|
|
self.assertEqual(
|
|
len([frame for frame in queued if isinstance(frame, TTSSpeakFrame)]),
|
|
1,
|
|
)
|
|
|
|
async def test_action_publishes_updated_session_variables(self):
|
|
tool = RuntimeTool(
|
|
id="lookup",
|
|
name="查询订单",
|
|
function_name="lookup_order",
|
|
type="http",
|
|
)
|
|
cfg = prepare_dynamic_config(
|
|
AssistantConfig(
|
|
type="workflow",
|
|
graph={
|
|
"specVersion": 3,
|
|
"settings": {},
|
|
"nodes": [
|
|
{"id": "start", "type": "start", "data": {}},
|
|
{
|
|
"id": "lookup_action",
|
|
"type": "action",
|
|
"data": {
|
|
"toolId": "lookup",
|
|
"resultAssignments": {
|
|
"order_status": "order.status"
|
|
},
|
|
},
|
|
},
|
|
],
|
|
"edges": [],
|
|
},
|
|
dynamic_variable_definitions={
|
|
"order_status": {"type": "string", "default": "pending"}
|
|
},
|
|
tools=[tool],
|
|
),
|
|
{},
|
|
assistant_id="asst_workflow_action",
|
|
)
|
|
brain = WorkflowBrain(cfg)
|
|
queued = []
|
|
|
|
async def queue_frame(frame):
|
|
queued.append(frame)
|
|
|
|
async def execute(_tool, _arguments, *, result_assignments=None):
|
|
self.assertEqual(result_assignments, {"order_status": "order.status"})
|
|
brain._store.assign("order_status", "paid")
|
|
return {
|
|
"status": "ok",
|
|
"updated_variables": ["order_status"],
|
|
}
|
|
|
|
brain._runtime = BrainRuntime(
|
|
context=LLMContext(messages=[]),
|
|
llm=FakeLLM(),
|
|
queue_frame=queue_frame,
|
|
set_system_prompt=lambda _prompt: None,
|
|
set_tools=lambda _tools: None,
|
|
call_end=FakeCallEnd(),
|
|
)
|
|
brain._tools.execute = execute
|
|
|
|
await brain._enter_action("lookup_action")
|
|
|
|
variable_events = [
|
|
frame.message
|
|
for frame in queued
|
|
if isinstance(frame, OutputTransportMessageUrgentFrame)
|
|
and frame.message.get("type") == "workflow-variables"
|
|
]
|
|
self.assertEqual(variable_events[-1]["reason"], "action")
|
|
self.assertEqual(variable_events[-1]["changed"], ["order_status"])
|
|
self.assertEqual(variable_events[-1]["variables"], {"order_status": "paid"})
|
|
|
|
async def test_nodes_without_outgoing_edges_remain_active(self):
|
|
queued = []
|
|
|
|
async def queue_frame(frame):
|
|
queued.append(frame)
|
|
|
|
runtime = BrainRuntime(
|
|
context=LLMContext(messages=[]),
|
|
llm=FakeLLM(),
|
|
queue_frame=queue_frame,
|
|
set_system_prompt=lambda _prompt: None,
|
|
set_tools=lambda _tools: None,
|
|
call_end=FakeCallEnd(),
|
|
)
|
|
|
|
class FakeManager:
|
|
def __init__(self, current_node=None):
|
|
self.current_node = current_node
|
|
|
|
async def initialize(self, config):
|
|
self.current_node = config["name"]
|
|
|
|
start_brain = WorkflowBrain(
|
|
{
|
|
"specVersion": 3,
|
|
"settings": {},
|
|
"nodes": [{"id": "start", "type": "start", "data": {}}],
|
|
"edges": [],
|
|
}
|
|
)
|
|
start_brain._runtime = runtime
|
|
start_brain._manager = FakeManager()
|
|
await start_brain.on_connected()
|
|
self.assertEqual(start_brain._manager.current_node, "start")
|
|
queued.clear()
|
|
await start_brain.on_client_ready()
|
|
ready_messages = [
|
|
frame.message
|
|
for frame in queued
|
|
if isinstance(frame, OutputTransportMessageUrgentFrame)
|
|
]
|
|
self.assertIn(
|
|
{"type": "node-active", "nodeId": "start"},
|
|
ready_messages,
|
|
)
|
|
self.assertTrue(
|
|
any(
|
|
message.get("type") == "workflow-variables"
|
|
and message.get("reason") == "client_ready"
|
|
and message.get("nodeId") == "start"
|
|
for message in ready_messages
|
|
)
|
|
)
|
|
|
|
agent_brain = WorkflowBrain(
|
|
{
|
|
"specVersion": 3,
|
|
"settings": {"globalPrompt": "全局规则"},
|
|
"nodes": [
|
|
{"id": "start", "type": "start", "data": {}},
|
|
{
|
|
"id": "agent",
|
|
"type": "agent",
|
|
"data": {"prompt": "持续回答"},
|
|
},
|
|
],
|
|
"edges": [
|
|
{
|
|
"id": "begin",
|
|
"source": "start",
|
|
"target": "agent",
|
|
"data": {"mode": "always", "priority": 0},
|
|
}
|
|
],
|
|
}
|
|
)
|
|
agent_brain._runtime = runtime
|
|
agent_brain._manager = FakeManager("agent")
|
|
queued.clear()
|
|
await agent_brain.on_client_ready()
|
|
self.assertTrue(
|
|
any(
|
|
isinstance(frame, OutputTransportMessageUrgentFrame)
|
|
and frame.message
|
|
== {"type": "node-active", "nodeId": "agent"}
|
|
for frame in queued
|
|
)
|
|
)
|
|
queued.clear()
|
|
handled = await agent_brain.on_user_turn_end("请继续回答")
|
|
self.assertTrue(handled)
|
|
self.assertEqual(agent_brain._manager.current_node, "agent")
|
|
self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))
|
|
|
|
handoff_brain = WorkflowBrain(
|
|
{
|
|
"specVersion": 3,
|
|
"settings": {},
|
|
"nodes": [
|
|
{"id": "start", "type": "start", "data": {}},
|
|
{
|
|
"id": "handoff",
|
|
"type": "handoff",
|
|
"data": {"targetType": "human"},
|
|
},
|
|
],
|
|
"edges": [],
|
|
}
|
|
)
|
|
handoff_brain._runtime = runtime
|
|
handoff_config = await handoff_brain._resolve_path("handoff")
|
|
self.assertEqual(handoff_config["name"], "handoff")
|
|
self.assertTrue(
|
|
any(
|
|
isinstance(frame, OutputTransportMessageUrgentFrame)
|
|
and frame.message.get("type") == "handoff-requested"
|
|
for frame in queued
|
|
)
|
|
)
|
|
|
|
async def test_start_llm_conditions_wait_for_and_route_first_user_turn(self):
|
|
brain = WorkflowBrain(
|
|
{
|
|
"specVersion": 3,
|
|
"settings": {"globalPrompt": "全局规则"},
|
|
"nodes": [
|
|
{
|
|
"id": "start",
|
|
"type": "start",
|
|
"data": {"name": "Start", "greeting": "你想做什么？"},
|
|
},
|
|
{
|
|
"id": "eat",
|
|
"type": "agent",
|
|
"data": {
|
|
"name": "点饭",
|
|
"prompt": "帮助用户点饭",
|
|
"contextPolicy": "fresh",
|
|
"entryMode": "wait_user",
|
|
},
|
|
},
|
|
{"id": "drink", "type": "agent", "data": {}},
|
|
{"id": "run", "type": "agent", "data": {}},
|
|
],
|
|
"edges": [
|
|
{
|
|
"id": "eat",
|
|
"source": "start",
|
|
"target": "eat",
|
|
"data": {
|
|
"mode": "llm",
|
|
"priority": 10,
|
|
"condition": "用户想吃饭",
|
|
},
|
|
},
|
|
{
|
|
"id": "drink",
|
|
"source": "start",
|
|
"target": "drink",
|
|
"data": {
|
|
"mode": "llm",
|
|
"priority": 20,
|
|
"condition": "用户想喝水",
|
|
},
|
|
},
|
|
{
|
|
"id": "run",
|
|
"source": "start",
|
|
"target": "run",
|
|
"data": {
|
|
"mode": "llm",
|
|
"priority": 30,
|
|
"condition": "用户想跑步",
|
|
},
|
|
},
|
|
],
|
|
}
|
|
)
|
|
queued = []
|
|
|
|
async def queue_frame(frame):
|
|
queued.append(frame)
|
|
|
|
brain._runtime = BrainRuntime(
|
|
context=LLMContext(messages=[]),
|
|
llm=FakeLLM(),
|
|
queue_frame=queue_frame,
|
|
set_system_prompt=lambda _prompt: None,
|
|
set_tools=lambda _tools: None,
|
|
call_end=FakeCallEnd(),
|
|
)
|
|
|
|
class FakeManager:
|
|
def __init__(self):
|
|
self.current_node = None
|
|
self.config = None
|
|
|
|
async def initialize(self, config):
|
|
self.current_node = config["name"]
|
|
self.config = config
|
|
|
|
async def set_node_from_config(self, config):
|
|
self.current_node = config["name"]
|
|
self.config = config
|
|
|
|
class FakeRouter:
|
|
def __init__(self):
|
|
self.calls = 0
|
|
|
|
async def select_edge(self, **_kwargs):
|
|
self.calls += 1
|
|
return LLMRouteResult(
|
|
status=RouteStatus.MATCHED,
|
|
function_name="goto_eat",
|
|
)
|
|
|
|
manager = FakeManager()
|
|
router = FakeRouter()
|
|
brain._manager = manager
|
|
brain._router = router
|
|
|
|
await brain.on_connected()
|
|
self.assertEqual(manager.current_node, "start")
|
|
self.assertEqual(router.calls, 0)
|
|
|
|
handled = await brain.on_user_turn_end("我想吃饭")
|
|
|
|
self.assertTrue(handled)
|
|
self.assertEqual(router.calls, 1)
|
|
self.assertEqual(manager.current_node, "eat")
|
|
self.assertIn(
|
|
{"role": "user", "content": "我想吃饭"},
|
|
manager.config["task_messages"],
|
|
)
|
|
self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))
|
|
self.assertIn("我想吃饭", brain._store.values["system__conversation_history"])
|
|
|
|
async def test_start_expression_condition_also_waits_for_user_turn(self):
|
|
brain = WorkflowBrain(
|
|
{
|
|
"specVersion": 3,
|
|
"settings": {},
|
|
"nodes": [
|
|
{"id": "start", "type": "start", "data": {}},
|
|
{"id": "agent", "type": "agent", "data": {}},
|
|
],
|
|
"edges": [
|
|
{
|
|
"id": "route",
|
|
"source": "start",
|
|
"target": "agent",
|
|
"data": {
|
|
"mode": "expression",
|
|
"priority": 10,
|
|
"expression": {
|
|
"combinator": "and",
|
|
"rules": [
|
|
{
|
|
"variable": "route",
|
|
"operator": "eq",
|
|
"value": "agent",
|
|
}
|
|
],
|
|
},
|
|
},
|
|
}
|
|
],
|
|
}
|
|
)
|
|
brain._store.values["route"] = "agent"
|
|
|
|
config = await brain._initial_node_config()
|
|
|
|
self.assertEqual(config["name"], "start")
|
|
self.assertEqual(brain._state.status.value, "waiting_user")
|
|
|
|
async def test_automatic_node_can_follow_llm_condition(self):
|
|
brain = WorkflowBrain(
|
|
{
|
|
"specVersion": 3,
|
|
"settings": {"globalPrompt": "全局规则"},
|
|
"nodes": [
|
|
{"id": "start", "type": "start", "data": {}},
|
|
{
|
|
"id": "handoff",
|
|
"type": "handoff",
|
|
"data": {"name": "人工转接", "targetType": "human"},
|
|
},
|
|
{
|
|
"id": "agent",
|
|
"type": "agent",
|
|
"data": {"name": "继续服务", "prompt": "继续处理"},
|
|
},
|
|
],
|
|
"edges": [
|
|
{
|
|
"id": "to-agent",
|
|
"source": "handoff",
|
|
"target": "agent",
|
|
"data": {
|
|
"mode": "llm",
|
|
"priority": 10,
|
|
"condition": "转接后仍需 AI 继续服务",
|
|
},
|
|
}
|
|
],
|
|
}
|
|
)
|
|
queued = []
|
|
|
|
async def queue_frame(frame):
|
|
queued.append(frame)
|
|
|
|
brain._runtime = BrainRuntime(
|
|
context=LLMContext(messages=[]),
|
|
llm=FakeLLM(),
|
|
queue_frame=queue_frame,
|
|
set_system_prompt=lambda _prompt: None,
|
|
set_tools=lambda _tools: None,
|
|
call_end=FakeCallEnd(),
|
|
)
|
|
|
|
class FakeRouter:
|
|
async def select_edge(self, **kwargs):
|
|
self.node_name = kwargs["node_name"]
|
|
return LLMRouteResult(
|
|
status=RouteStatus.MATCHED,
|
|
function_name="goto_to_agent",
|
|
)
|
|
|
|
router = FakeRouter()
|
|
brain._router = router
|
|
|
|
config = await brain._resolve_path("handoff")
|
|
|
|
self.assertEqual(config["name"], "agent")
|
|
self.assertEqual(router.node_name, "人工转接")
|
|
|
|
async def test_mixed_edge_conditions_follow_priority(self):
|
|
brain = WorkflowBrain(
|
|
{
|
|
"specVersion": 3,
|
|
"settings": {},
|
|
"nodes": [
|
|
{"id": "start", "type": "start", "data": {}},
|
|
{"id": "agent", "type": "agent", "data": {}},
|
|
{"id": "llm-target", "type": "end", "data": {}},
|
|
{"id": "expression-target", "type": "end", "data": {}},
|
|
{"id": "default-target", "type": "end", "data": {}},
|
|
],
|
|
"edges": [
|
|
{
|
|
"id": "llm",
|
|
"source": "agent",
|
|
"target": "llm-target",
|
|
"data": {
|
|
"mode": "llm",
|
|
"priority": 10,
|
|
"condition": "大模型条件成立",
|
|
},
|
|
},
|
|
{
|
|
"id": "expression",
|
|
"source": "agent",
|
|
"target": "expression-target",
|
|
"data": {
|
|
"mode": "expression",
|
|
"priority": 20,
|
|
"expression": {
|
|
"combinator": "and",
|
|
"rules": [
|
|
{
|
|
"variable": "route",
|
|
"operator": "eq",
|
|
"value": "expression",
|
|
}
|
|
],
|
|
},
|
|
},
|
|
},
|
|
{
|
|
"id": "default",
|
|
"source": "agent",
|
|
"target": "default-target",
|
|
"data": {"mode": "always", "priority": 30},
|
|
},
|
|
],
|
|
}
|
|
)
|
|
brain._store.values["route"] = "expression"
|
|
|
|
class FakeRouter:
|
|
def __init__(self):
|
|
self.result = LLMRouteResult(status=RouteStatus.NO_MATCH)
|
|
self.edge_ids = []
|
|
|
|
async def select_edge(self, **kwargs):
|
|
self.edge_ids = [edge["id"] for edge in kwargs["edges"]]
|
|
return self.result
|
|
|
|
router = FakeRouter()
|
|
brain._router = router
|
|
|
|
selected = await brain._select_edge("agent")
|
|
self.assertEqual(router.edge_ids, ["llm"])
|
|
self.assertEqual(selected["id"], "expression")
|
|
|
|
router.result = LLMRouteResult(
|
|
status=RouteStatus.MATCHED,
|
|
function_name="goto_llm",
|
|
)
|
|
selected = await brain._select_edge("agent")
|
|
self.assertEqual(selected["id"], "llm")
|
|
|
|
expression_edge = next(
|
|
edge for edge in brain._engine.edges if edge["id"] == "expression"
|
|
)
|
|
expression_edge["data"]["priority"] = 5
|
|
router.edge_ids = []
|
|
selected = await brain._select_edge("agent")
|
|
self.assertEqual(selected["id"], "expression")
|
|
self.assertEqual(router.edge_ids, [])
|
|
|
|
async def test_transition_and_end_are_owned_by_workflow_brain(self):
|
|
graph = {
|
|
"specVersion": 3,
|
|
"settings": {
|
|
"globalPrompt": "全局规则",
|
|
"defaultLlmResourceId": "llm_global",
|
|
"defaultAsrResourceId": "asr_global",
|
|
"defaultTtsResourceId": "tts_global",
|
|
"knowledgeBaseId": "kb_global",
|
|
"knowledgeMode": "automatic",
|
|
"enableInterrupt": False,
|
|
"turnConfig": {
|
|
"bargeIn": {"strategy": "transcription"},
|
|
"vad": {"confidence": 0.55},
|
|
},
|
|
},
|
|
"nodes": [
|
|
{
|
|
"id": "start",
|
|
"type": "start",
|
|
"data": {
|
|
"name": "Start",
|
|
"greeting": "欢迎，{{user_name}}",
|
|
},
|
|
},
|
|
{
|
|
"id": "agent",
|
|
"type": "agent",
|
|
"data": {
|
|
"name": "收集需求",
|
|
"prompt": "服务 {{user_name}}",
|
|
"contextPolicy": "fresh",
|
|
},
|
|
},
|
|
{
|
|
"id": "end",
|
|
"type": "end",
|
|
"data": {"name": "End", "message": "感谢来电", "scope": "session"},
|
|
},
|
|
],
|
|
"edges": [
|
|
{
|
|
"id": "begin",
|
|
"source": "start",
|
|
"target": "agent",
|
|
"data": {"mode": "always", "priority": 0},
|
|
},
|
|
{
|
|
"id": "finish",
|
|
"source": "agent",
|
|
"target": "end",
|
|
"data": {
|
|
"mode": "llm",
|
|
"priority": 10,
|
|
"condition": "需求已收集",
|
|
"transitionSpeech": "正在为你结束流程",
|
|
},
|
|
}
|
|
],
|
|
}
|
|
cfg = prepare_dynamic_config(
|
|
AssistantConfig(
|
|
type="workflow",
|
|
graph=graph,
|
|
dynamic_variable_definitions={
|
|
"user_name": {"type": "string", "required": True}
|
|
},
|
|
),
|
|
{"user_name": "王先生"},
|
|
assistant_id="asst_workflow",
|
|
)
|
|
brain = WorkflowBrain(cfg)
|
|
llm = FakeLLM()
|
|
context = LLMContext(messages=[])
|
|
queued = []
|
|
service_switches = []
|
|
knowledge_scopes = []
|
|
turn_configs = []
|
|
call_end = FakeCallEnd()
|
|
|
|
class FakeWorker:
|
|
def __init__(self):
|
|
self.frames = []
|
|
self.handlers = {}
|
|
|
|
def set_reached_downstream_filter(self, *_args):
|
|
pass
|
|
|
|
def event_handler(self, name):
|
|
def decorator(fn):
|
|
self.handlers[name] = fn
|
|
return fn
|
|
return decorator
|
|
|
|
async def queue_frame(self, frame):
|
|
self.frames.append(frame)
|
|
|
|
async def queue_frames(self, frames):
|
|
self.frames.extend(frames)
|
|
|
|
worker = FakeWorker()
|
|
pair = SimpleNamespace(
|
|
user=lambda: SimpleNamespace(_context=context),
|
|
assistant=lambda: SimpleNamespace(has_function_calls_in_progress=False),
|
|
)
|
|
|
|
async def queue_frame(frame):
|
|
queued.append(frame)
|
|
|
|
async def switch_services(llm_id, asr_id, tts_id):
|
|
service_switches.append((llm_id, asr_id, tts_id))
|
|
|
|
async def apply_turn_config(enable_interrupt, turn_config):
|
|
turn_configs.append((enable_interrupt, turn_config))
|
|
|
|
runtime = BrainRuntime(
|
|
context=context,
|
|
llm=llm,
|
|
queue_frame=queue_frame,
|
|
set_system_prompt=lambda _prompt: None,
|
|
set_tools=lambda _tools: None,
|
|
call_end=call_end,
|
|
worker=worker,
|
|
context_aggregator=pair,
|
|
switch_services=switch_services,
|
|
set_knowledge_scope=knowledge_scopes.append,
|
|
apply_turn_config=apply_turn_config,
|
|
)
|
|
await brain.setup(cfg, runtime)
|
|
greeting = await brain.greeting(cfg)
|
|
self.assertEqual(greeting, "欢迎，王先生")
|
|
greeting_message = {
|
|
"role": "system",
|
|
"content": f"{GREETING_CONTEXT_MARKER}\n欢迎，王先生",
|
|
}
|
|
brain.prepare_greeting_context(greeting, context)
|
|
self.assertEqual(context.get_messages(), [greeting_message])
|
|
await brain.on_connected()
|
|
self.assertEqual(brain._manager.current_node, "agent")
|
|
variable_events = [
|
|
frame.message
|
|
for frame in queued
|
|
if isinstance(frame, OutputTransportMessageUrgentFrame)
|
|
and frame.message.get("type") == "workflow-variables"
|
|
]
|
|
self.assertEqual(variable_events[0]["reason"], "initialized")
|
|
self.assertEqual(variable_events[0]["variables"], {"user_name": "王先生"})
|
|
self.assertNotIn("system__conversation_id", variable_events[0]["variables"])
|
|
self.assertEqual(
|
|
service_switches,
|
|
[("llm_global", "asr_global", "tts_global")],
|
|
)
|
|
self.assertEqual(knowledge_scopes[-1]["knowledge_base_id"], "kb_global")
|
|
self.assertEqual(turn_configs[-1][0], False)
|
|
self.assertEqual(turn_configs[-1][1]["vad"]["confidence"], 0.55)
|
|
|
|
brain._engine.data("agent").update(
|
|
{
|
|
"inheritGlobalConfig": False,
|
|
"llmResourceId": "llm_agent",
|
|
"asrResourceId": "asr_agent",
|
|
"ttsResourceId": "tts_agent",
|
|
"knowledgeBaseId": "kb_agent",
|
|
"knowledgeMode": "on_demand",
|
|
"enableInterrupt": True,
|
|
"turnConfig": {
|
|
"bargeIn": {"strategy": "vad"},
|
|
"turnDetection": {"strategy": "smart_turn"},
|
|
},
|
|
}
|
|
)
|
|
await brain._apply_agent_stage("agent")
|
|
self.assertEqual(
|
|
service_switches[-1],
|
|
("llm_agent", "asr_agent", "tts_agent"),
|
|
)
|
|
self.assertEqual(knowledge_scopes[-1]["knowledge_base_id"], "kb_agent")
|
|
self.assertEqual(turn_configs[-1][0], True)
|
|
self.assertEqual(
|
|
turn_configs[-1][1]["turnDetection"]["strategy"],
|
|
"smart_turn",
|
|
)
|
|
agent_config = brain._agent_config("agent")
|
|
self.assertIn("王先生", agent_config["role_message"])
|
|
self.assertIn("工作流路由已在用户一轮输入结束时完成", agent_config["role_message"])
|
|
self.assertEqual(agent_config["task_messages"], [greeting_message])
|
|
self.assertFalse(agent_config["respond_immediately"])
|
|
self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
|
|
self.assertEqual(
|
|
agent_config["context_strategy"].strategy.value,
|
|
"reset",
|
|
)
|
|
|
|
brain._engine.data("agent")["entryMode"] = "generate"
|
|
generate_config = brain._agent_config("agent")
|
|
self.assertFalse(generate_config["respond_immediately"])
|
|
worker.frames.clear()
|
|
await brain._manager.set_node_from_config(generate_config)
|
|
self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
|
|
await brain._after_node_activated(generate_config)
|
|
self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))
|
|
|
|
brain._engine.data("agent").update(
|
|
{"entryMode": "fixed_speech", "entrySpeech": "您好，{{user_name}}"}
|
|
)
|
|
fixed_config = brain._agent_config("agent")
|
|
self.assertFalse(fixed_config["respond_immediately"])
|
|
self.assertNotIn("pre_actions", fixed_config)
|
|
self.assertEqual(
|
|
fixed_config["task_messages"],
|
|
[
|
|
greeting_message,
|
|
{"role": "assistant", "content": "您好，王先生"},
|
|
],
|
|
)
|
|
self.assertEqual(
|
|
brain._agent_config(
|
|
"agent",
|
|
[{"role": "assistant", "content": "正在进入下一阶段"}],
|
|
)["task_messages"],
|
|
[
|
|
greeting_message,
|
|
{"role": "assistant", "content": "正在进入下一阶段"},
|
|
{"role": "assistant", "content": "您好，王先生"},
|
|
],
|
|
)
|
|
worker.frames.clear()
|
|
queued.clear()
|
|
await brain._manager.set_node_from_config(fixed_config)
|
|
await brain._after_node_activated(fixed_config)
|
|
self.assertTrue(any(isinstance(frame, TTSSpeakFrame) for frame in queued))
|
|
self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
|
|
context_updates = [
|
|
frame
|
|
for frame in worker.frames
|
|
if isinstance(frame, LLMMessagesUpdateFrame)
|
|
]
|
|
self.assertEqual(
|
|
context_updates[-1].messages,
|
|
[
|
|
greeting_message,
|
|
{"role": "assistant", "content": "您好，王先生"},
|
|
],
|
|
)
|
|
self.assertFalse(
|
|
any(
|
|
isinstance(frame, OutputTransportMessageUrgentFrame)
|
|
and frame.message.get("source") == "workflow-fixed-reply"
|
|
for frame in queued
|
|
)
|
|
)
|
|
await brain.on_client_ready()
|
|
fixed_reply_events = [
|
|
frame.message
|
|
for frame in queued
|
|
if isinstance(frame, OutputTransportMessageUrgentFrame)
|
|
and frame.message.get("source") == "workflow-fixed-reply"
|
|
]
|
|
self.assertEqual(fixed_reply_events[0]["content"], "您好，王先生")
|
|
self.assertEqual(fixed_reply_events[0]["nodeId"], "agent")
|
|
self.assertIn("您好，王先生", brain._store.values["system__conversation_history"])
|
|
|
|
self.assertFalse(
|
|
any(
|
|
function.name == "goto_finish"
|
|
for function in brain._agent_config("agent")["functions"]
|
|
)
|
|
)
|
|
await brain.on_assistant_text_end("old-turn", "需求已收集", False)
|
|
self.assertEqual(brain._manager.current_node, "agent")
|
|
|
|
class FakeRouter:
|
|
async def select_edge(self, **_kwargs):
|
|
return LLMRouteResult(
|
|
status=RouteStatus.MATCHED,
|
|
function_name="goto_finish",
|
|
)
|
|
|
|
brain._router = FakeRouter()
|
|
handled = await brain.on_user_turn_end("我的需求已经说完了")
|
|
self.assertTrue(handled)
|
|
self.assertEqual(brain._manager.current_node, "end")
|
|
self.assertIn("我的需求已经说完了", brain._store.values["system__conversation_history"])
|
|
self.assertTrue(call_end.ending)
|
|
self.assertTrue(call_end.armed)
|
|
self.assertTrue(any(getattr(frame, "text", "") == "感谢来电" for frame in queued))
|
|
transition_context_frames = [
|
|
frame
|
|
for frame in worker.frames
|
|
if isinstance(frame, LLMMessagesAppendFrame)
|
|
and frame.messages
|
|
== [{"role": "assistant", "content": "正在为你结束流程"}]
|
|
]
|
|
self.assertTrue(transition_context_frames)
|
|
transition_events = [
|
|
frame.message
|
|
for frame in queued
|
|
if isinstance(frame, OutputTransportMessageUrgentFrame)
|
|
and frame.message.get("source") == "workflow-edge-transition"
|
|
]
|
|
self.assertEqual(transition_events[0]["content"], "正在为你结束流程")
|
|
self.assertEqual(transition_events[0]["nodeId"], "end")
|
|
assistant_transcripts = [
|
|
frame.message.get("content")
|
|
for frame in queued
|
|
if isinstance(frame, OutputTransportMessageUrgentFrame)
|
|
and frame.message.get("type") == "transcript"
|
|
and frame.message.get("role") == "assistant"
|
|
]
|
|
self.assertEqual(
|
|
assistant_transcripts,
|
|
["您好，王先生", "正在为你结束流程", "感谢来电"],
|
|
)
|
|
self.assertIn(
|
|
"正在为你结束流程",
|
|
brain._store.values["system__conversation_history"],
|
|
)
|
|
self.assertIn(
|
|
"感谢来电",
|
|
brain._store.values["system__conversation_history"],
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|