* 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>
- Added functionality to select agents in the Navigation component, allowing users to reset conversations and set the selected agent.
- Updated the MessageInput component to conditionally show source and tool buttons based on the selected agent.
- Modified the Conversation component to handle agent-specific queries and manage file uploads.
- Improved conversation fetching logic to include agent IDs and handle attachments.
- Introduced new types for conversation summaries and results to streamline API responses.
- Refactored Redux slices to manage selected agent state and improve overall state management.
- Enhanced error handling and loading states across components for better user experience.
