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_pipeline_knowledge.py

@@ -1,9 +1,13 @@
 import unittest
 
 from models import AssistantConfig
+from pipecat.frames.frames import LLMContextFrame
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.frame_processor import FrameDirection
 from services.pipecat.pipeline import (
     KNOWLEDGE_CONTEXT_MARKER,
     KnowledgeRetrievalProcessor,
+    UserTurnRoutingProcessor,
     _knowledge_tool_description,
 )
 
@@ -57,5 +61,37 @@ class KnowledgeToolDescriptionTest(unittest.TestCase):
         self.assertFalse(any(message["role"] == "developer" for message in messages))
 
 
+class UserTurnRoutingProcessorTest(unittest.IsolatedAsyncioTestCase):
+    async def test_routes_each_user_message_once_before_response_run(self):
+        class FakeBrain:
+            def __init__(self):
+                self.turns = []
+
+            async def on_user_turn_end(self, content):
+                self.turns.append(content)
+                return True
+
+        brain = FakeBrain()
+        processor = UserTurnRoutingProcessor(brain)
+        forwarded = []
+
+        async def push_frame(frame, direction):
+            forwarded.append((frame, direction))
+
+        processor.push_frame = push_frame
+        context = LLMContext(messages=[{"role": "user", "content": "我叫李白"}])
+        frame = LLMContextFrame(context)
+
+        await processor.process_frame(frame, FrameDirection.DOWNSTREAM)
+        self.assertEqual(brain.turns, ["我叫李白"])
+        self.assertEqual(forwarded, [])
+
+        # A queued LLMRunFrame after the transition uses the same context. It
+        # must reach the target Agent without invoking routing a second time.
+        await processor.process_frame(frame, FrameDirection.DOWNSTREAM)
+        self.assertEqual(brain.turns, ["我叫李白"])
+        self.assertEqual(forwarded, [(frame, FrameDirection.DOWNSTREAM)])
+
+
 if __name__ == "__main__":
     unittest.main()