feat: Enhance OpenAI compatibility and introduce unified LLM service data models

- Refactored model fetching logic to support various authentication methods for OpenAI-compatible APIs.
- Introduced new data models for LLM service requests and responses to standardize interactions across layers.
- Added an adapter base class for unified request execution across different providers.
- Implemented utility functions for building OpenAI-compatible client configurations and request overrides.

src/plugin_runtime/host/component_registry.py

@@ -91,7 +91,7 @@ class ToolEntry(ComponentEntry):
     def __init__(self, name: str, component_type: str, plugin_id: str, metadata: Dict[str, Any]) -> None:
         self.description: str = metadata.get("description", "")
         self.parameters: List[Dict[str, Any]] = metadata.get("parameters", [])
-        self.parameters_raw: List[Dict[str, Any]] = metadata.get("parameters_raw", [])
+        self.parameters_raw: Dict[str, Any] | List[Dict[str, Any]] = metadata.get("parameters_raw", {})
         super().__init__(name, component_type, plugin_id, metadata)