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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@@ -1,7 +1,11 @@
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import unittest
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from models import AssistantConfig
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from services.pipecat.pipeline import _knowledge_tool_description
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from services.pipecat.pipeline import (
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KNOWLEDGE_CONTEXT_MARKER,
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KnowledgeRetrievalProcessor,
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_knowledge_tool_description,
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)
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class KnowledgeToolDescriptionTest(unittest.TestCase):
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@@ -33,6 +37,25 @@ class KnowledgeToolDescriptionTest(unittest.TestCase):
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self.assertNotIn("\n ", description)
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self.assertLess(len(description), 1000)
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def test_workflow_knowledge_uses_system_role(self):
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processor = KnowledgeRetrievalProcessor(None)
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messages = [
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{"role": "assistant", "content": "你好"},
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{
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"role": "developer",
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"content": f"{KNOWLEDGE_CONTEXT_MARKER}\n旧检索结果",
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},
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]
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processor._set_context(
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messages,
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f"{KNOWLEDGE_CONTEXT_MARKER}\n新检索结果",
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)
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self.assertEqual(messages[0]["role"], "system")
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self.assertIn("新检索结果", messages[0]["content"])
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self.assertFalse(any(message["role"] == "developer" for message in messages))
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if __name__ == "__main__":
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unittest.main()
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118
backend/tests/test_workflow_v3.py
Normal file
118
backend/tests/test_workflow_v3.py
Normal file
@@ -0,0 +1,118 @@
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from __future__ import annotations
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import unittest
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from models import AssistantConfig, RuntimeModelResource
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from services.pipecat.service_factory import config_with_resource
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from services.node_specs import normalize_graph, validate_graph
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from services.runtime_variables import DynamicVariableStore, prepare_dynamic_config
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from services.workflow_engine import WorkflowEngine
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def valid_graph():
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return {
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"specVersion": 3,
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"settings": {"globalPrompt": "服务 {{customer}}"},
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"nodes": [
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{"id": "start", "type": "start", "data": {"name": "Start"}},
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{
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"id": "agent",
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"type": "agent",
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"data": {"name": "Agent", "prompt": "处理订单", "contextPolicy": "fresh"},
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},
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{"id": "end", "type": "end", "data": {"name": "End", "scope": "flow"}},
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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": "paid",
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"source": "agent",
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"target": "end",
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"data": {
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"mode": "expression",
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"priority": 10,
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"expression": {
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"combinator": "and",
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"rules": [
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{"variable": "order_status", "operator": "eq", "value": "paid"}
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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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class WorkflowGraphTests(unittest.TestCase):
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def test_voice_resource_creates_isolated_runtime_config(self):
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base = AssistantConfig(type="workflow", asr="default", voice="default")
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asr = RuntimeModelResource(
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id="asr_1",
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capability="ASR",
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interface_type="openai-asr",
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values={"modelId": "sensevoice", "language": "zh", "apiUrl": "https://asr.test"},
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secrets={"apiKey": "secret"},
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)
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resolved = config_with_resource(base, asr)
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self.assertEqual(resolved.asr, "sensevoice")
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self.assertEqual(resolved.stt_api_key, "secret")
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self.assertEqual(base.asr, "default")
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def test_v2_start_prompt_is_preserved_in_synthetic_agent(self):
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graph = normalize_graph(
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{
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"nodes": [
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{"id": "s", "type": "startCall", "data": {"prompt": "询问需求"}},
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{"id": "e", "type": "endCall", "data": {"prompt": "再见"}},
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],
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"edges": [
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{"id": "done", "source": "s", "target": "e", "data": {"condition": "完成"}}
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],
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}
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)
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migrated = next(node for node in graph["nodes"] if node["type"] == "agent")
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self.assertEqual(migrated["data"]["prompt"], "询问需求")
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self.assertEqual(validate_graph(graph), [])
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def test_rejects_automatic_cycle_and_duplicate_priorities(self):
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graph = valid_graph()
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graph["nodes"].insert(1, {"id": "action", "type": "action", "data": {}})
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graph["edges"] = [
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{"id": "a", "source": "start", "target": "action", "data": {"mode": "always", "priority": 0}},
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{"id": "b", "source": "action", "target": "start", "data": {"mode": "always", "priority": 0}},
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{"id": "c", "source": "agent", "target": "end", "data": {"mode": "always", "priority": 10}},
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]
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errors = validate_graph(graph)
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self.assertTrue(any("无等待循环" in error for error in errors))
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def test_expression_routes_using_session_variables(self):
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cfg = prepare_dynamic_config(
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AssistantConfig(
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type="workflow",
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graph=valid_graph(),
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dynamic_variable_definitions={
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"customer": {"type": "string", "default": "王先生"},
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"order_status": {"type": "string", "default": "pending"},
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},
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),
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{},
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assistant_id="asst_test",
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)
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store = DynamicVariableStore.from_config(cfg)
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engine = WorkflowEngine(cfg.graph)
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self.assertIsNone(engine.deterministic_edge("agent", store, include_default=False))
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store.assign("order_status", "paid")
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self.assertEqual(
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engine.deterministic_edge("agent", store, include_default=False)["target"],
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"end",
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)
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self.assertIn("王先生", engine.prompt_for("agent", store))
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if __name__ == "__main__":
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unittest.main()
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