- Introduced LLMErrorCategory and adapt_llm_exception to normalize LLM exceptions.
- Updated llm_retryable_message and llm_permanent_message to utilize the new adaptation logic.
- Enhanced classify_stream_exception to classify provider errors and return user-friendly messages.
- Added tests for error classification and adaptation to ensure robustness.
- Updated frontend error handling to display appropriate messages based on new classifications.
- Added currency parameter to the Stripe checkout session for auto-reload setup.
- Integrated AutoReloadSettings component into the BuyMorePage for improved user experience.
- Removed deprecated AutoReloadSettings component from user settings directory.
- Updated import paths for AutoReloadSettings in purchases page to reflect new structure.
- Updated environment variables and - configurations for credit purchases via Stripe, replacing legacy page pack system.
- Introduced auto-reload feature for credit top-ups and modified database models to track credit transactions.
- Updated notification system to handle insufficient credits and auto-reload failures.
- Adjusted API routes and schemas to reflect changes in credit management.
- Introduced lazy knowledge base retrieval mode, allowing the main agent to fetch KB content on demand via the `search_knowledge_base` tool, improving performance by skipping expensive pre-injection processes.
- Added cross-thread caching capability, enabling reuse of compiled graphs across different user chats, reducing latency for returning users.
- Updated middleware to support new lazy loading and caching features, ensuring efficient resource utilization and improved response times.
- Enhanced logging for performance tracking during knowledge retrieval and agent interactions.
- Added a new backend URL import to the route file for better configuration.
- Updated the Composer component to use a relative positioning class for improved layout.
- Refactored the ChatExamplePrompts component to enhance the display of active categories and prompts, including a close button for better user interaction.
- Added a new utility function `isLlmOnboardingComplete` to determine if the onboarding process is complete based on the agent LLM ID and the presence of global configurations.
- Updated the onboarding logic in the `OnboardPage` and `DashboardClientLayout` components to utilize the new utility function for improved readability and maintainability.
- Updated the backend URL initialization to use a function that retrieves the URL from environment variables, enhancing configurability for different environments.
- Modified backend URL assignment to ensure it resolves correctly within the internal Docker network, preventing 503 errors for authenticated Zero queries.
- Added comments to clarify the routing behavior and the necessity of using the internal backend URL.
Step results now render from the synced Zero row so the panel ticks
forward as the run progresses. The REST getRun call is gated on the
run reaching a terminal status, since output/artifacts/error are only
written at terminal mark.
- Revised the pricing page title and description to reflect new features including AI workspace, automations, and agents.
- Enhanced the FAQ section with detailed information about AI automations and agents, including scheduling and event-triggered workflows.
- Updated demo plans to include features related to AI automations and agents, ensuring clarity on capabilities and costs.
- Added a new automation illustration to the homepage features grid, emphasizing the automation capabilities of SurfSense.
- Removed the eligibility gate for model selection in the automation creation process, allowing users to choose models directly in the builder.
- Updated the `AutomationBuilderForm` to incorporate model selection logic, ensuring that selected models are validated and preserved during automation creation and editing.
- Simplified the `AutomationsContent` and `AutomationNewContent` components by eliminating unnecessary eligibility checks and alerts.
- Enhanced the user experience by integrating model selection directly into the automation approval process, ensuring that only billable models are used.
- Refactored related tests to cover new model selection behavior and ensure proper validation of user-selected models.
