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>

frontend/src/api/services/modelService.ts

@@ -0,0 +1,25 @@
+import apiClient from '../client';
+import endpoints from '../endpoints';
+
+import type { AvailableModel, Model } from '../../models/types';
+
+const modelService = {
+  getModels: (token: string | null): Promise<Response> =>
+    apiClient.get(endpoints.USER.MODELS, token, {}),
+
+  transformModels: (models: AvailableModel[]): Model[] =>
+    models.map((model) => ({
+      id: model.id,
+      value: model.id,
+      provider: model.provider,
+      display_name: model.display_name,
+      description: model.description,
+      context_window: model.context_window,
+      supported_attachment_types: model.supported_attachment_types,
+      supports_tools: model.supports_tools,
+      supports_structured_output: model.supports_structured_output,
+      supports_streaming: model.supports_streaming,
+    })),
+};
+
+export default modelService;