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>

tests/conftest.py

@@ -150,7 +150,7 @@ def agent_base_params(decoded_token):
     return {
         "endpoint": "https://api.example.com",
         "llm_name": "openai",
-        "gpt_model": "gpt-4",
+        "model_id": "gpt-4",
         "api_key": "test_api_key",
         "user_api_key": None,
         "prompt": "You are a helpful assistant.",