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/llm/handlers/base.py

@@ -282,7 +282,7 @@ class LLMHandler(ABC):
                     messages = e.value
                     break
             response = agent.llm.gen(
-                model=agent.gpt_model, messages=messages, tools=agent.tools
+                model=agent.model_id, messages=messages, tools=agent.tools
             )
             parsed = self.parse_response(response)
             self.llm_calls.append(build_stack_data(agent.llm))
@@ -337,7 +337,7 @@ class LLMHandler(ABC):
                 tool_calls = {}
 
                 response = agent.llm.gen_stream(
-                    model=agent.gpt_model, messages=messages, tools=agent.tools
+                    model=agent.model_id, messages=messages, tools=agent.tools
                 )
                 self.llm_calls.append(build_stack_data(agent.llm))