- Introduced start_time and end_time fields in the activity log entry form, allowing users to input specific times for activities.
- Implemented utility functions to format and validate time inputs from the API and user input, ensuring proper handling of time data.
- Updated the activity display to show formatted start and end times, improving clarity for users reviewing their activity logs.
- Replaced the deprecated `resolve_activity_log_column_patch_from_csv` function with `activity_csv_registry_updates_from_mapped` to streamline updates from CSV mappings.
- Updated the `_import_activity` function to utilize the new registry updates, improving data integrity during activity imports.
- Enhanced the activity module registry by adding German labels for various fields, improving localization support.
- Refactored the session metrics handling to ensure only relevant fields are processed, enhancing the overall robustness of CSV imports.
- Enhanced the docstring for `upsert_session_metrics_from_csv_mapped` to clarify the handling of schema parameters and EAV logic.
- Modified the condition for skipping updates based on `source_field` to ensure only patchable columns are processed, improving data integrity during session metrics upsert operations.
- Introduced a new utility function `canonical_csv_header_label` to standardize CSV header labels, improving consistency in field mapping.
- Updated the `_lookup_db_field` function to support prefix matching for longer manual keys, enhancing the accuracy of field resolution.
- Added tests to validate handling of non-breaking space characters in CSV headers and ensure correct mapping to normalized keys, improving robustness of CSV parsing.
- Updated the `ACTIVITY_LOG_PATCHABLE_COLUMNS` and `ACTIVITY_LOG_PATCH_FORBIDDEN` sets to improve validation of CSV imports, ensuring only allowed fields are patched.
- Refactored the `_coerce_raw_value_for_parameter` function to handle string inputs for integer and float types, enhancing data coercion accuracy.
- Modified the `SessionMetricsFields` component to display orphan metrics that do not match the current schema, improving user visibility of imported data discrepancies.
- Enhanced the frontend to handle and display additional metrics, ensuring a more comprehensive representation of session data.
- Updated the CSV import logic to merge active training parameters with static fields for the activity module, improving field mapping accuracy.
- Enhanced validation functions to incorporate dynamic field definitions based on active training parameters, ensuring better data integrity during imports.
- Refactored related functions to streamline the process of handling CSV templates and field mappings, improving maintainability and clarity.
- Added new utility functions for resolving activity log column patches and upserting session metrics from CSV, enhancing the overall import functionality.
- Updated the `get_activity_detail` function to include session metrics in the activity detail output, allowing for enriched data representation.
- Refactored the activity import logic to streamline the process of inserting and updating activity records, utilizing new helper functions for better maintainability.
- Improved the handling of duplicate activity entries by implementing a more robust identification mechanism.
- Enhanced the metadata for activity detail registration to reflect the inclusion of EAV metrics and updated source tables.
- Added a new query parameter `collapseDuplicateSessions` to the activity listing endpoint to enable deduplication of sessions based on date, type, start time, duration, and calories.
- Enhanced backend logic to handle deduplication and return the most recent entry for duplicate sessions.
- Updated frontend to support the new deduplication feature, improving the clarity of displayed activity data.
- Modified API utility to include the new parameter in requests for activity data.
- Introduced a new query parameter for the activity listing endpoint to fetch entries by calendar month (format: YYYY-MM), excluding days and offset.
- Implemented backend validation for the month parameter to ensure correct format and range.
- Enhanced the frontend to support month selection, allowing users to load activities for specific months and dynamically update the displayed entries.
- Improved the user interface to show the selected month and the range of loaded months, enhancing user experience.
- Updated the `replace_activity_session_metrics` function to improve validation logic and error handling for required fields.
- Enhanced the activity listing query to order results by date, start time, and ID, ensuring consistent output.
- Modified the frontend to handle null values in metrics payload and improved the display of activity statistics, including total entries in profile and sample size.
- Added pagination support to the activity listing endpoint with `limit` and `offset` parameters.
- Introduced a `skip_quality_filter` option to allow retrieval of all entries without applying the quality filter.
- Updated the frontend to implement dynamic loading of activity entries and statistics without the quality filter.
- Improved user experience with a "Load More" button for fetching additional entries on the ActivityPage.
- Added new fields to the ActivityEntry model for improved tracking: hr_min, pace_min_per_km, cadence, avg_power, elevation_gain, temperature_celsius, humidity_percent, avg_hr_percent, and kcal_per_km.
- Updated the create_activity function to accommodate the new fields in the activity log.
- Modified session metrics handling to ensure accurate data retrieval and merging based on the updated schema.
- Added a new function to synchronize session metrics with activity log entries, ensuring data consistency.
- Updated the create and update activity endpoints to call the synchronization function after inserting or modifying activity logs.
- Introduced a set of allowed keys for activity log payloads to streamline data handling in the frontend.
- Improved data coercion logic for various data types in the frontend to ensure accurate data submission.
- Implemented a new endpoint to retrieve activity statistics for the last 30 entries, including total calories and duration by activity type.
- Added an endpoint to list activities without assigned training types, grouped by activity type.
- Removed deprecated versions of the statistics and uncategorized activities endpoints for cleaner code.
- Introduced new endpoints for updating training category and type parameters in the backend.
- Added corresponding update functions in the frontend API utility.
- Enhanced the Admin Activity Attribute Profiles page to support editing and saving changes for category and type parameters.
- Implemented state management for editing parameters and improved error handling during updates.
- Added new Admin UI for managing Activity Attribute Profiles.
- Enhanced ActivityPage to support dynamic loading and editing of session metrics.
- Updated API utility functions to handle new endpoints for training parameters and metrics.
- Improved form handling for session metrics, including validation and error management.
- Updated documentation to reflect new features and changes in session metrics handling.
- Introduced Activity Session Metrics for enhanced tracking of session data.
- Updated backend to support new API endpoints for managing session metrics.
- Added new Pydantic models for activity metrics and replaced metrics functionality.
- Enhanced data layer to include session metrics in recent training session data.
- Updated documentation to reflect changes in session metrics handling.
- Added a function to calculate goal progress percentage based on start, target, and current values.
- Updated GoalsPage to display progress in a user-friendly format, including visual progress bars.
- Implemented error handling for goal progress updates in the backend to ensure robustness.
- Add name field to WorkflowNode model
- Extend node_label priority: node.name > prompt_slug > node_type-id
- No new fields in NodeExecutionState (uses existing debug_prompt_slug)
- Simpler approach than previous attempt to avoid 504 timeout
- Add name field to WorkflowNode model
- Add node_name field to NodeExecutionState
- Set node_name in execute_workflow from node.name
- Display priority: node_name > debug_prompt_slug > node_label > node_id
User sees 'Qualitätseinschätzung' instead of 'node_abc123'
Display per node:
- debug_prompt (prompt sent to AI)
- debug_raw_response (raw AI response)
- analysis_core (parsed results)
- normalized_signals (decision signals with status)
- Failed nodes: red border + red background
NO other changes - executeWorkflow still used
- Failed nodes now have:
- Red border (2px instead of 1px)
- Light red background (#D85A3010)
- Red shadow/glow effect
Makes it immediately obvious which nodes had errors.
- workflow_executor.py: Store prompt_slug or generated label in debug_prompt_slug for all nodes
- This makes it easy to identify nodes in the debug panel
- Example: 'wf_nutrition_basis' instead of 'node_5'
- Helps identify which node is which when debugging workflows
- Created WorkflowDebugPanel.jsx: Collapsible panel showing debug info for each workflow node
- Shows prompt sent to AI
- Shows raw AI response
- Shows parsed results
- Shows normalized signals
- Color-coded status (executed/failed/skipped)
- Expandable/collapsible per node
- Updated Analysis.jsx:
- Added WorkflowDebugPanel import
- Store node_states in newResult for debugging
- Display WorkflowDebugPanel below InsightCard (both locations)
This makes it easy to debug workflow issues by seeing exactly what happened at each node.
Backend changes:
- workflow_models.py: Add debug_prompt, debug_raw_response, debug_node_type, debug_prompt_slug, metadata fields to NodeExecutionState
- workflow_executor.py: Capture and store debug info for analysis, logic, and join nodes when enable_debug=True
- Analysis nodes: store full prompt + raw AI response
- Logic nodes: store expression + evaluation result
- Join nodes: store strategy + path statistics
Frontend changes:
- Analysis.jsx: Enable debug mode by default (debug=true) for all workflow executions
This allows developers to see exactly what prompt was sent to the AI, what response was received, and how each node was processed - essential for debugging workflow issues.
- workflow_executor.py: Generate node_label from prompt_slug or node.type (WorkflowNode has no label attribute)
- prompts.py: Fix INSERT statement - use 'created' column instead of 'created_at'
SSE endpoint now works correctly for workflow execution streaming.
Backend:
- workflow_executor.py: Add progress_callback parameter, emit events for execution_started, node_complete, execution_complete, execution_failed
- prompt_executor.py: Thread progress_callback through execute chain
- routers/prompts.py: New /execute-stream endpoint with asyncio Queue for SSE
Frontend:
- utils/api.js: New executeUnifiedPromptStream() function with EventSource
- pages/Analysis.jsx: Use SSE with live progress display (X/Y Nodes)
Fixes:
- No more gateway timeouts for complex workflows (10+ nodes)
- Live progress feedback for users
- Unlimited workflow complexity
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Bug: debug=true in URL was ignored because FastAPI expected it in
request body (POST without Query() expects body params by default).
Result: node_states were never returned, even with ?debug=true
Fix: Changed debug and save to Query parameters:
- debug: bool = Query(False, ...)
- save: bool = Query(False, ...)
Now ?debug=true in URL correctly enables debug output with node_states.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Root cause: UI saves LogicExpression directly as condition:
{operands: [...], operator: "and"}
But Pydantic model expected Condition with wrapped expression:
{expression: {operands: [...], operator: "and"}}
Result: Pydantic deserialized it as Condition with expression=None
→ Logic-Nodes failed with "'NoneType' object has no attribute 'operator'"
Fix:
1. Changed WorkflowNode.condition type from Condition to Any
2. Executor now handles both dict and Pydantic model formats
3. Detects UI format (operator+operands) vs legacy format (expression wrapper)
4. Converts dict to LogicExpression before evaluation
Fixes: Logic-Node execution failures in Training-Tiefenanalyse workflow
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Previous fix handled hasattr() but didn't check for None values.
Now explicitly checks that operator/expression is not None before using it.
Error was: "'NoneType' object has no attribute 'operator'"
Clearer error message: "condition is None or missing"
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Logic-Nodes were timing out because UI saves condition as:
{operands: [...], operator: "and"}
But Backend expected:
{expression: {operands: [...], operator: "and"}}
This caused node.condition.expression to be None, triggering:
- Logic-Node failures
- Join-Node wait_all timeout
- 504 Gateway Timeout
Fix: Accept both formats by checking for operator/operands attributes
directly on condition, falling back to condition.expression.
Fixes: 504 Gateway Timeout in Training-Tiefenanalyse workflow
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Problem: Logic nodes without logic_expression defined caused AttributeError
"'NoneType' object has no attribute 'operator'" when evaluating condition.
Solution: Check both node.condition AND node.condition.expression before
calling evaluate_logic_expression(). Return clear FAILED state with error
message instead of crashing.
Impact: Workflows with incomplete logic node definitions now fail gracefully
with clear error message instead of cryptic AttributeError.
Problem: Parser converted question IDs to lowercase ('qAnalyst' → 'qanalyst'),
causing normalization to fail because id_catalog lookup is case-sensitive.
Impact: All workflow question signals were lost - normalized_signals stayed empty,
so template placeholders like {{node_2.signal_qAnalyst}} remained unresolved.
Solution: Removed .lower() call in parse_decision_questions() to preserve
original case from AI response.
Root cause: Line 162 in result_container_parser.py
Fixes: Question augmentation signals not appearing in workflow end nodes
Problem: Workflows executed via /api/prompts/execute (not /api/workflows/execute)
were passing call_openrouter directly to execute_prompt_with_data, which then
passes it to workflow_executor. workflow_executor expects (prompt, model) signature
but call_openrouter has (prompt, max_tokens=4096) signature.
Previous fix in workflows.py was correct but unused - workflows use prompts.py endpoint.
Solution: Added workflow_llm_call() wrapper in execute_unified_prompt() endpoint
that matches expected (prompt, model) -> str signature.
Related: cb3aa48 (workflows.py fix for different endpoint)
Problem: workflow_executor calls openrouter_call_func(prompt, model) but
call_openrouter expects (prompt, max_tokens=4096). This caused the model string
'anthropic/claude-sonnet-4' to be passed as max_tokens, resulting in OpenRouter
requesting 64000 tokens and failing with 402 credit errors.
Solution: Added workflow_llm_call() wrapper in workflows.py that matches the
expected (prompt, model) -> str signature and calls call_openrouter correctly.
Fixes: All workflows failing with 402 'insufficient credits' errors
Problem: Cursor springt nach jedem Tastendruck aus dem ID-Feld
Ursache: key={q.id} in QuestionEditor map
- Wenn ID geändert wird, ändert sich der React Key
- React unmountet alte Component und mountet neue
- Focus geht verloren
Lösung: key={idx} verwenden
- Stabiler Key während Editing
- Komponente bleibt gemountet
- Cursor bleibt im Feld
UX: Jetzt kann man IDs flüssig editieren ohne Unterbrechung
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
