Enhance workflow routing and agent configuration management

- 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.

backend/tests/test_workflow_router.py

@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+import unittest
+from types import SimpleNamespace
+from unittest.mock import patch
+
+from models import AssistantConfig
+from services.workflow_router import WorkflowLLMRouter
+
+
+class WorkflowLLMRouterTest(unittest.IsolatedAsyncioTestCase):
+    async def test_uses_required_tool_choice_without_developer_messages(self):
+        requests = []
+
+        class FakeCompletions:
+            async def create(self, **kwargs):
+                requests.append(kwargs)
+                return SimpleNamespace(
+                    choices=[
+                        SimpleNamespace(
+                            message=SimpleNamespace(
+                                tool_calls=[
+                                    SimpleNamespace(
+                                        function=SimpleNamespace(name="goto_age", arguments="{}")
+                                    )
+                                ]
+                            )
+                        )
+                    ]
+                )
+
+        class FakeClient:
+            def __init__(self, **_kwargs):
+                self.chat = SimpleNamespace(completions=FakeCompletions())
+                self.closed = False
+
+            async def close(self):
+                self.closed = True
+
+        cfg = AssistantConfig(
+            type="workflow",
+            model="deepseek-chat",
+            llm_api_key="secret",
+            llm_base_url="https://llm.test/v1",
+        )
+        router = WorkflowLLMRouter(cfg)
+        edges = [
+            {
+                "id": "age",
+                "data": {"condition": "用户已经回答姓名", "priority": 10},
+            }
+        ]
+
+        with patch("services.workflow_router.AsyncOpenAI", FakeClient):
+            selected = await router.select_edge(
+                node_name="询问姓名",
+                node_prompt="询问用户姓名",
+                edges=edges,
+                history=[{"role": "user", "message": "我叫李白"}],
+                variables={"customer_type": "new"},
+                edge_name=lambda _edge: "goto_age",
+                edge_description=lambda _edge: "用户已经回答姓名",
+            )
+
+        self.assertEqual(selected, "goto_age")
+        self.assertEqual(requests[0]["tool_choice"], "required")
+        self.assertEqual(
+            [message["role"] for message in requests[0]["messages"]],
+            ["system", "user"],
+        )
+        self.assertNotIn("developer", str(requests[0]["messages"]))
+
+
+if __name__ == "__main__":
+    unittest.main()