72856bf3a7
- Introduce WorkflowLLMRouter for pre-response LLM routing, allowing agents to determine the appropriate function to call based on user input. - Implement UserTurnRoutingProcessor to manage user turns before reaching the LLM, ensuring proper routing and handling of user messages. - Refactor WorkflowBrain to integrate new routing logic and enhance agent stage configuration, including entry modes and resource management. - Update service factory to support dynamic LLM resource configuration based on workflow settings. - Add tests for new routing functionality and ensure proper handling of user messages in various scenarios.
707 lines
24 KiB
Python
707 lines
24 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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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.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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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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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 arm_after_speech(self) -> None:
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self.armed = True
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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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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 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.assertTrue(call_end.finished)
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self.assertEqual(params.result["action"], "ending_call")
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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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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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async def queue_frame(_frame):
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pass
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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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self.assertEqual(requests[0][1], "https://example.test/orders/A%2F1")
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self.assertEqual(
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requests[0][2]["headers"]["Authorization"], "Bearer server-token"
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)
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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")
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class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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async def test_nodes_without_outgoing_edges_remain_active(self):
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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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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, current_node=None):
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self.current_node = current_node
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async def initialize(self, config):
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self.current_node = config["name"]
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start_brain = WorkflowBrain(
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{
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"specVersion": 3,
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"settings": {},
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"nodes": [{"id": "start", "type": "start", "data": {}}],
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"edges": [],
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}
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)
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start_brain._runtime = runtime
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start_brain._manager = FakeManager()
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await start_brain.on_connected()
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self.assertEqual(start_brain._manager.current_node, "start")
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agent_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": "agent",
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"type": "agent",
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"data": {"prompt": "持续回答"},
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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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agent_brain._runtime = runtime
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agent_brain._manager = FakeManager("agent")
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queued.clear()
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handled = await agent_brain.on_user_turn_end("请继续回答")
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self.assertTrue(handled)
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self.assertEqual(agent_brain._manager.current_node, "agent")
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self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in queued))
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handoff_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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{
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"id": "handoff",
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"type": "handoff",
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"data": {"targetType": "human"},
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},
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],
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"edges": [],
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}
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)
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handoff_brain._runtime = runtime
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handoff_config = await handoff_brain._resolve_path("handoff")
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self.assertEqual(handoff_config["name"], "handoff")
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self.assertTrue(
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any(
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isinstance(frame, OutputTransportMessageUrgentFrame)
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and frame.message.get("type") == "handoff-requested"
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for frame in queued
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)
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)
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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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"settings": {
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"globalPrompt": "全局规则",
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"defaultLlmResourceId": "llm_global",
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"defaultAsrResourceId": "asr_global",
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"defaultTtsResourceId": "tts_global",
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"knowledgeBaseId": "kb_global",
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"knowledgeMode": "automatic",
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},
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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"},
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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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"name": "收集需求",
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"prompt": "服务 {{user_name}}",
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"contextPolicy": "fresh",
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},
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},
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{
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"id": "end",
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"type": "end",
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"data": {"name": "End", "message": "感谢来电", "scope": "session"},
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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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"id": "finish",
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"source": "agent",
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"target": "end",
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"data": {
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"mode": "llm",
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"priority": 10,
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"condition": "需求已收集",
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"transitionSpeech": "正在为你结束流程",
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},
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}
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],
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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=graph,
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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_workflow",
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)
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brain = WorkflowBrain(cfg)
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llm = FakeLLM()
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context = LLMContext(messages=[])
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queued = []
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service_switches = []
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knowledge_scopes = []
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call_end = FakeCallEnd()
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|
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class FakeWorker:
|
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def __init__(self):
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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))
|
||
|
||
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,
|
||
)
|
||
await brain.setup(cfg, runtime)
|
||
await brain.on_connected()
|
||
self.assertEqual(brain._manager.current_node, "agent")
|
||
self.assertEqual(
|
||
service_switches,
|
||
[("llm_global", "asr_global", "tts_global")],
|
||
)
|
||
self.assertEqual(knowledge_scopes[-1]["knowledge_base_id"], "kb_global")
|
||
|
||
brain._engine.data("agent").update(
|
||
{
|
||
"inheritGlobalConfig": False,
|
||
"llmResourceId": "llm_agent",
|
||
"asrResourceId": "asr_agent",
|
||
"ttsResourceId": "tts_agent",
|
||
"knowledgeBaseId": "kb_agent",
|
||
"knowledgeMode": "on_demand",
|
||
}
|
||
)
|
||
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")
|
||
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"], [])
|
||
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.assertTrue(generate_config["respond_immediately"])
|
||
worker.frames.clear()
|
||
await brain._manager.set_node_from_config(generate_config)
|
||
self.assertTrue(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
|
||
|
||
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.assertEqual(
|
||
fixed_config["pre_actions"][0]["type"],
|
||
"workflow_fixed_speech",
|
||
)
|
||
self.assertEqual(fixed_config["pre_actions"][0]["text"], "您好，王先生")
|
||
self.assertEqual(
|
||
fixed_config["task_messages"],
|
||
[{"role": "assistant", "content": "您好，王先生"}],
|
||
)
|
||
worker.frames.clear()
|
||
queued.clear()
|
||
await brain._manager.set_node_from_config(fixed_config)
|
||
self.assertTrue(any(isinstance(frame, TTSSpeakFrame) for frame in queued))
|
||
self.assertFalse(any(isinstance(frame, LLMRunFrame) for frame in worker.frames))
|
||
|
||
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 "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))
|
||
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()
|