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/worker.py

@@ -165,7 +165,7 @@ def run_agent_logic(agent_config, input_data):
             agent_type,
             endpoint="webhook",
             llm_name=settings.LLM_PROVIDER,
-            gpt_model=settings.LLM_NAME,
+            model_id=settings.LLM_NAME,
             api_key=settings.API_KEY,
             user_api_key=user_api_key,
             prompt=prompt,
@@ -180,7 +180,7 @@ def run_agent_logic(agent_config, input_data):
             prompt=prompt,
             chunks=chunks,
             token_limit=settings.DEFAULT_MAX_HISTORY,
-            gpt_model=settings.LLM_NAME,
+            model_id=settings.LLM_NAME,
             user_api_key=user_api_key,
             decoded_token=decoded_token,
         )