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* Enhance GGUF model handling with timestamps, metadata and memory training status * Check if is_trained exists * fix * cloud service * Change the data type of the is_trained field to boolean and update the related logic to reflect this change * Change the data type of the is_trained field to boolean and update the related logic to reflect this change * Add gguf path to json file * Added model selection function, updated model list acquisition logic, and enhanced model information display * Update the model service startup logic, add integrity check for the model path, and support obtaining the model path from different fields * Service Change * full cloud service * feat: implement async cloud training process with job tracking and API key management * Progress bar modification * feat: Add Local and Cloud Training Configuration Components - Introduced LocalTrainingConfig component for configuring local training parameters. - Updated TrainingConfiguration component to include tabs for Local and Cloud training configurations. - Added API functions for setting and getting cloud service API keys. - Created useCloudProviderStore for managing cloud provider configurations. - Enhanced event utility to include a new event for showing cloud provider modal. * Refactor cloud provider and training configuration components - Updated CloudProviderModal to handle cloud service API key management. - Replaced API key handling with model configuration updates in CloudProviderModal. - Enhanced CloudTrainingConfig to manage cloud models based on API key availability. - Introduced new cloud service functions for listing available models and managing training jobs. - Modified LocalTrainingConfig to ensure default model selection and synchronization. - Updated TrainingConfiguration to manage model switching between local and cloud environments. - Refactored useCloudProviderStore to integrate cloud service API key handling. - Adjusted useTrainingStore to prioritize model name selection based on the active environment. * Stream Output * feat: Enhance training configuration and progress components - Updated LocalTrainingConfig to improve default model handling and avoid unnecessary updates. - Introduced LocalTrainingProgress component to manage local training progress display. - Refactored TrainingConfiguration to support both local and cloud training types, including updated button text and actions. - Modified TrainingProgress to conditionally render local or cloud training progress based on the selected training type. - Added cloud service functions for starting training and managing job information. - Adjusted training parameter interfaces to ensure consistency across local and cloud models. * Stream response change * feat: Enhance cloud training and inference capabilities - Updated TrainingProgress component to handle cloud training progress data and job ID. - Modified trainExposureModel to allow nullable path and added optional stageName. - Enhanced useSSE hook to support cloud model inference with new parameters. - Introduced CloudProgressData type to align cloud training progress with local training structure. - Implemented cloud inference request handling with local knowledge retrieval in cloudService. - Added utility functions for managing active cloud model state in cloudModelUtils. - Updated cloud inference endpoint to support local knowledge retrieval before cloud inference. - Refactored advanced chat service to utilize new message structure for cloud inference. - Enhanced prompt strategies to incorporate knowledge retrieval based on user messages. * feat: Delete the training parameter debugging information component * Resume training at breakpoint * Repair data redundancy * Stop system modification * fix error: reset training * fix stop and reset * Change chat reply format * Enhance cloud and local service management with status tracking and improved progress reporting - Implemented service status file management in cloud and local services to track active status and model information. - Added endpoints to start and stop cloud services, including validation for existing services. - Enhanced local service management with status checks and progress updates during document processing and chunk embedding. - Introduced real-time progress tracking for document embedding and chunk processing, allowing for incremental updates. - Improved error handling and logging throughout the service management processes. - Refactored chat request handling to intelligently route between local and cloud services based on current status. * feat:Cleaned up code comment * translate Chinese comments to English in cloud service modules * translate into chinese * feat: Enhance cloud provider configuration and training management with API key handling and tab switching logic * bug fix * Add is_trained field modification in the cloud * feat: Refactor training parameters management to separate local and cloud configurations * feat: Update training parameter types to improve type safety and consistency * feat: Add data synthesis mode to cloud training parameters and update related components * feat: The document embedding part is restored to its original state * refactor: optimize cloud training process with improved stop handling and file path updates * feat: Update the default values and merging logic of cloud training parameters to ensure parameter consistency * feat: Add API key preloading function to optimize the loading experience when the modal box is opened * feat: Optimize CloudProviderModal component, add API key preloading and state management * fix: Simplify cloud provider display by removing conditional rendering for Alibaba Cloud * feat: Update .gitignore to include job_id.json and add .gitkeep for gguf directory --------- Co-authored-by: wyx-hhhh <1360479992@qq.com> |
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| .. | ||
| __init__.py | ||
| V20250420221300__add_thinking_fields_to_user_llm_configs.py | ||
| V20250519144900__add_is_trained_field_to_models.py | ||
| V20250521170000__add_cloud_service_api_key_to_user_llm_configs.py | ||