feat: model registry and capabilities for multi-provider support (#2158)

* feat: Implement model registry and capabilities for multi-provider support

- Added ModelRegistry to manage available models and their capabilities.
- Introduced ModelProvider enum for different LLM providers.
- Created ModelCapabilities dataclass to define model features.
- Implemented methods to load models based on API keys and settings.
- Added utility functions for model management in model_utils.py.
- Updated settings.py to include provider-specific API keys.
- Refactored LLM classes (Anthropic, OpenAI, Google, etc.) to utilize new model registry.
- Enhanced utility functions to handle token limits and model validation.
- Improved code structure and logging for better maintainability.

* feat: Add model selection feature with API integration and UI component

* feat: Add model selection and default model functionality in agent management

* test: Update assertions and formatting in stream processing tests

* refactor(llm): Standardize model identifier to model_id

* fix tests

---------

Co-authored-by: Alex <a@tushynski.me>

application/agents/base.py

@@ -21,7 +21,7 @@ class BaseAgent(ABC):
         self,
         endpoint: str,
         llm_name: str,
-        gpt_model: str,
+        model_id: str,
         api_key: str,
         user_api_key: Optional[str] = None,
         prompt: str = "",
@@ -37,7 +37,7 @@ class BaseAgent(ABC):
     ):
         self.endpoint = endpoint
         self.llm_name = llm_name
-        self.gpt_model = gpt_model
+        self.model_id = model_id
         self.api_key = api_key
         self.user_api_key = user_api_key
         self.prompt = prompt
@@ -52,6 +52,7 @@ class BaseAgent(ABC):
             api_key=api_key,
             user_api_key=user_api_key,
             decoded_token=decoded_token,
+            model_id=model_id,
         )
         self.retrieved_docs = retrieved_docs or []
         self.llm_handler = LLMHandlerCreator.create_handler(
@@ -316,7 +317,7 @@ class BaseAgent(ABC):
         return messages
 
     def _llm_gen(self, messages: List[Dict], log_context: Optional[LogContext] = None):
-        gen_kwargs = {"model": self.gpt_model, "messages": messages}
+        gen_kwargs = {"model": self.model_id, "messages": messages}
 
         if (
             hasattr(self.llm, "_supports_tools")