Add Dify integration and enhance workflow node specifications
- Introduce new fields `dify_api_url` and `dify_api_key` in `AssistantConfig` for Dify API integration. - Update `requirements.txt` to include `dify-client-python` for Dify SDK support. - Modify `config_resolver` to handle Dify connection information. - Add a new `globalNode` type in workflow specifications to provide unified settings across workflows. - Enhance node specifications with additional constraints and default values for better configuration management. - Update frontend components to support the new `globalNode` type and its properties, improving workflow editor functionality.
This commit is contained in:
Xin Wang
303
backend/tests/test_brains.py
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303
backend/tests/test_brains.py
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from __future__ import annotations
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import unittest
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from types import SimpleNamespace
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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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LLMTextFrame,
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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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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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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_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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class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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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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"nodes": [
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{
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"id": "start",
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"type": "startCall",
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"data": {"name": "开始", "prompt": "收集需求"},
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},
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{
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"id": "end",
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"type": "endCall",
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"data": {"name": "结束", "prompt": "礼貌结束"},
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},
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],
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"edges": [
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{
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"id": "finish",
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"source": "start",
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"target": "end",
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"data": {"condition": "需求已收集"},
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}
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],
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}
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brain = WorkflowBrain(graph)
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llm = FakeLLM()
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context = LLMContext(messages=[])
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queued = []
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prompts = []
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visible_tools = []
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call_end = FakeCallEnd()
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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=context,
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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.append(tools or []),
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call_end=call_end,
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)
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await brain.setup(AssistantConfig(type="workflow", graph=graph), runtime)
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self.assertIn("goto_finish", llm.functions)
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self.assertIn("收集需求", prompts[-1])
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self.assertEqual(visible_tools[-1][0].name, "goto_finish")
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params = FakeFunctionParams()
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await llm.functions["goto_finish"](params)
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self.assertEqual(params.result, {"status": "ok"})
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self.assertIn("礼貌结束", prompts[-1])
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self.assertEqual(visible_tools[-1], [])
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await brain.on_assistant_text_start("closing-turn")
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await brain.on_assistant_text_end(
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"closing-turn",
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"感谢来电，再见。",
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False,
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)
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self.assertTrue(call_end.ending)
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self.assertTrue(call_end.armed)
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if __name__ == "__main__":
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unittest.main()
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