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/react_agent.py

@@ -86,7 +86,7 @@ class ReActAgent(BaseAgent):
         messages = [{"role": "user", "content": plan_prompt}]
 
         plan_stream = self.llm.gen_stream(
-            model=self.gpt_model,
+            model=self.model_id,
             messages=messages,
             tools=self.tools if self.tools else None,
         )
@@ -151,7 +151,7 @@ class ReActAgent(BaseAgent):
         messages = [{"role": "user", "content": final_prompt}]
 
         final_stream = self.llm.gen_stream(
-            model=self.gpt_model, messages=messages, tools=None
+            model=self.model_id, messages=messages, tools=None
         )
 
         if log_context: