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.

backend/services/pipecat/service_factory.py

@@ -5,7 +5,7 @@
 """
 
 from loguru import logger
-from models import AssistantConfig
+from models import AssistantConfig, RuntimeModelResource
 
 from pipecat.services.openai.llm import OpenAILLMService
 from pipecat.services.openai.stt import OpenAISTTService
@@ -26,6 +26,35 @@ from services.pipecat.xfyun_tts import DEFAULT_XFYUN_TTS_URL, XfyunTTSService
 TTS_STOP_FRAME_TIMEOUT_S = 1.0
 
 
+def config_with_resource(
+    cfg: AssistantConfig, resource: RuntimeModelResource
+) -> AssistantConfig:
+    """Return a call-local config view for one workflow ASR/TTS resource."""
+    result = cfg.model_copy(deep=True)
+    values = resource.values or {}
+    secrets = resource.secrets or {}
+    if resource.capability == "ASR":
+        result.asr = str(values.get("modelId") or "")
+        result.stt_language = str(values.get("language") or "")
+        result.stt_interface_type = resource.interface_type
+        result.stt_values = values
+        result.stt_secrets = secrets
+        result.stt_api_key = str(secrets.get("apiKey") or "")
+        result.stt_base_url = str(values.get("apiUrl") or "")
+    elif resource.capability == "TTS":
+        result.tts_model = str(values.get("modelId") or "")
+        result.voice = str(values.get("voice") or "")
+        result.tts_speed = float(values.get("speed") or 1.0)
+        result.tts_interface_type = resource.interface_type
+        result.tts_values = values
+        result.tts_secrets = secrets
+        result.tts_api_key = str(secrets.get("apiKey") or "")
+        result.tts_base_url = str(values.get("apiUrl") or "")
+    else:
+        raise ValueError(f"工作流语音资源能力无效:{resource.capability}")
+    return result
+
+
 def _require(value: str, label: str) -> str:
     if value:
         return value