Add workflow support and enhance runtime configuration in models and services
- Introduce RuntimeModelResource and RuntimeKnowledgeBase classes to manage workflow resources. - Update AssistantConfig to include workflow_model_resources and workflow_knowledge_bases for better integration. - Refactor validation and processing logic in routes and services to accommodate workflow types. - Implement dynamic variable support for workflow assistants and enhance graph normalization. - Add ToolExecutor for reusable tool execution across different assistant types. - Update various services to ensure compatibility with new workflow features and improve error handling.
This commit is contained in:
Xin Wang
@@ -111,6 +111,19 @@ class BrainRegistryTests(unittest.TestCase):
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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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@@ -363,7 +376,7 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
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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.brains.prompt_brain.httpx.AsyncClient", FakeClient):
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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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@@ -377,35 +390,91 @@ class PromptBrainTests(unittest.IsolatedAsyncioTestCase):
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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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"specVersion": 3,
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"settings": {"globalPrompt": "全局规则"},
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"nodes": [
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{
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"id": "start",
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"type": "startCall",
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"data": {"name": "开始", "prompt": "收集需求"},
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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": "endCall",
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"data": {"name": "结束", "prompt": "礼貌结束"},
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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": "finish",
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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": {"condition": "需求已收集"},
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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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},
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}
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],
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}
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brain = WorkflowBrain(graph)
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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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prompts = []
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visible_tools = []
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call_end = FakeCallEnd()
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class FakeWorker:
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def __init__(self):
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self.frames = []
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self.handlers = {}
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def set_reached_downstream_filter(self, *_args):
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pass
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def event_handler(self, name):
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def decorator(fn):
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self.handlers[name] = fn
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return fn
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return decorator
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async def queue_frame(self, frame):
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self.frames.append(frame)
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async def queue_frames(self, frames):
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self.frames.extend(frames)
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worker = FakeWorker()
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pair = SimpleNamespace(
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user=lambda: SimpleNamespace(_context=context),
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assistant=lambda: SimpleNamespace(has_function_calls_in_progress=False),
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)
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async def queue_frame(frame):
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queued.append(frame)
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@@ -413,30 +482,34 @@ class WorkflowBrainTests(unittest.IsolatedAsyncioTestCase):
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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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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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worker=worker,
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context_aggregator=pair,
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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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await brain.setup(cfg, runtime)
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await brain.on_connected()
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self.assertEqual(brain._manager.current_node, "agent")
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agent_config = brain._agent_config("agent")
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self.assertIn("王先生", agent_config["role_message"])
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self.assertIn("完成当前阶段任务", agent_config["role_message"])
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self.assertEqual(agent_config["task_messages"], [])
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self.assertEqual(
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agent_config["context_strategy"].strategy.value,
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"reset",
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)
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edge_function = next(
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function
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for function in brain._agent_config("agent")["functions"]
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if function.name == "goto_finish"
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)
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_, terminal = await edge_function.handler({}, brain._manager)
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self.assertEqual(terminal["name"], "end")
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self.assertTrue(call_end.ending)
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self.assertTrue(call_end.armed)
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self.assertTrue(any(getattr(frame, "text", "") == "感谢来电" for frame in queued))
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if __name__ == "__main__":
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