- Consolidate node data retrieval from workflow_executions.output_data to unify storage access.
- Optimize the construction of messages and execution records to support opening suggestions.
- Eliminate redundant queries and storage logic to simplify the overall codebase structure.
- Augment workflow logs with execution status fields and loop node information.
- Refactor log service to handle distinct processing logic for workflows and agents.
- Construct message and node logs derived from workflow_executions data.
- Rectify exception propagation during node execution failures to ensure errors are correctly raised.
- Bolster workflow logging to support failed status records and persist node execution data, including loop nodes.
- Rectify exception propagation during node execution failures to ensure errors are correctly raised.
- Bolster workflow logging to support failed status records and persist node execution data, including loop nodes.
- Rectify exception propagation during node execution failures to ensure errors are correctly raised.
- Bolster workflow logging to support failed status records and persist node execution data, including loop nodes.
- feat(http_request): augment debugging capabilities with raw request generation and improved error handling.
- feat(app_log): extend session filtering logic to support retrieving all session types.
- feat(log): add 'process' field to node execution records for better data tracking.
- Augment HTTP request node capabilities and add generated curl commands for easier debugging.
feat(log): implement workflow execution logs and search functionality
- Add detailed logging for workflow node execution and enable search capabilities within application logs.
feat(auth): introduce middleware to verify application publication status
- Add a check to ensure the application is published before allowing access.
fix(converter): rectify variable handling logic in Dify converter
- Correct issues related to processing variables within the Dify converter module.
refactor(model): remove quota check decorator from model update operations
- Decouple quota validation from the model update process to streamline the logic.
- Refactor quota management logic to support usage checks scoped by workspace.
- Update quota statistics API to return granular quota details for each workspace.
- Revise default configuration settings for terminal user and model limits.
- Remove quota check decorators from the model controller.
- Refactor quota management logic to support usage checks scoped by workspace.
- Update quota statistics API to return granular quota details for each workspace.
- Revise default configuration settings for terminal user and model limits.
- Remove quota check decorators from the model controller.
- Refactor quota management logic to support usage checks scoped by workspace.
- Update quota statistics API to return granular quota details for each workspace.
- Revise default configuration settings for terminal user and model limits.
- Remove quota check decorators from the model controller.
* 'release/v0.3.1' of github.com:SuanmoSuanyangTechnology/MemoryBear:
fix(web): stream add default error message
fix(quota): restrict quota check to new terminal user creation only
fix(api): fix API Key rate limiting and terminal user quota checks
feat(exception): enhance I18nException response format and add error code mapping
feat(quota): add quota checks during app duplication and import operations
fix(知识服务): 添加工作空间模型配置的校验
refactor(knowledge_service): 简化模型绑定逻辑，直接使用工作区配置
fix(知识服务): 修复创建知识库时未检查视觉模型存在的错误
refactor(knowledge_service): 优化模型绑定逻辑，使用ID查询并简化回退机制
- Revert API Key rate limit handling to throw an error instead of auto-capping when exceeding the plan limit.
- Optimize terminal user quota check logic to validate only during new user creation, avoiding redundant checks.
- Add method to query terminal users by `workspace_id` and `other_id`.
