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

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

frontend/src/api/endpoints.ts

@@ -2,6 +2,7 @@ const endpoints = {
   USER: {
     CONFIG: '/api/config',
     NEW_TOKEN: '/api/generate_token',
+    MODELS: '/api/models',
     DOCS: '/api/sources',
     DOCS_PAGINATED: '/api/sources/paginated',
     API_KEYS: '/api/get_api_keys',