- Modified the `replace_activity_session_metrics` function to skip EAV inserts for parameters with a defined `source_field`, aligning with the existing logic in `upsert_session_metrics_from_csv_mapped`.
- Enhanced the frontend logic to filter out metrics associated with `source_field` during payload construction, improving data integrity and user experience.
- Added unit tests to validate the new behavior, ensuring that metrics with `source_field` are correctly excluded from inserts.
- Enhanced the `upsert_session_metrics_from_csv_mapped` function to skip EAV writes when a `source_field` is provided, ensuring canonical handling of activity logs.
- Updated the docstring to clarify the behavior regarding `source_field` and EAV logic.
- Added unit tests to validate the new behavior, ensuring correct functionality when `source_field` is set or not set during CSV imports.
- Replaced hardcoded keys in `activity_data_canon.py` with a dynamic retrieval method from the module registry, ensuring that `ACTIVITY_MODULE_REGISTRY_FIELD_KEYS` reflects the current configuration.
- Updated `activity_persistence_orchestrator.py` to utilize the new dynamic field retrieval function, enhancing consistency across the data layer.
- Modified `activity_session_metrics.py` to reference the dynamic field keys, improving maintainability and reducing redundancy in the codebase.
Reverts cd29c7d..026c51b per git revert. Alle zwischenliegenden Commits bleiben in Gitea sichtbar; der Arbeitsbaum entspricht wieder dem Stand von ca8cee9.
Made-with: Cursor
- Introduced `resolve_activity_attribute_schema_for_csv_import` to enhance the handling of training parameters during CSV imports, allowing for the inclusion of active parameters not present in the category/type profile.
- Updated `apply_activity_mapped_column_aliases` and `upsert_session_metrics_from_csv_mapped` to utilize the new CSV import function, ensuring comprehensive mapping and insertion of metrics.
- Added unit tests to validate the new functionality and ensure correct behavior when handling mapped training parameters during CSV imports.
- Updated the `_import_activity` function to include new metrics: duration_min, kcal_active, kcal_resting, hr_avg, hr_max, and distance_km during CSV imports.
- Modified the `insert_activity_csv_minimal` function to accept and store these additional metrics in the activity log.
- Enhanced the `run_activity_post_write_hooks_import` function to utilize the new metrics for auto-evaluation after activity imports.
- Updated the activity import router to pass the new metrics from the CSV file to the database functions, ensuring comprehensive data handling.
- Improved frontend handling of activity entry forms to accommodate the new metrics, enhancing user experience during activity log edits.
- Updated `ACTIVITY_PRODUCTION_ARCHITECTURE_AND_PHASES.md` to clarify the derivation of `ACTIVITY_MODULE_REGISTRY_FIELD_KEYS` from `csv_parser.module_registry`.
- Enhanced `activity_data_canon.py` to eliminate hardcoded key lists, ensuring all registry fields are derived dynamically.
- Refactored the `_import_activity` function to remove redundant parameters and streamline the import process.
- Improved the `insert_activity_csv_minimal` function to handle metrics exclusively through `update_activity_columns`, preventing hardcoded values.
- Updated frontend components to manage editable activity log fields more effectively, ensuring proper handling of metrics during CSV imports.
- Added unit tests to validate the new logic and ensure consistency in activity session metrics handling.
- Updated the `ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE` with new details on CSV import processes and EAV handling, improving documentation clarity.
- Refactored the `_import_activity` function to utilize `apply_activity_mapped_column_aliases`, ensuring proper mapping of training parameters and reducing redundancy.
- Introduced validation for numeric bounds in the `activity_csv_registry_updates_from_mapped` function, enhancing data integrity during CSV imports.
- Added new utility functions to manage column aliasing and streamline the upsert process for session metrics, preventing duplicate entries.
- Implemented unit tests to validate the new aliasing logic and ensure correct behavior during session metrics updates.
- Updated the README to include new activity production architecture and phases, improving clarity on the development roadmap.
- Enhanced the `ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE` with details on the target architecture and phase plan for production readiness.
- Introduced a new function `merge_column_backed_and_eav_metrics` to streamline the merging of metrics from column-backed and EAV sources, ensuring data integrity and reducing duplication.
- Refactored session metrics handling to eliminate deprecated synchronization methods, improving the overall efficiency of data processing.
- Added unit tests for the new merging logic, ensuring robust validation of metrics handling.
- 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.
- 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'
- 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
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
- Increased the maximum token limit in the `call_openrouter` function from 1500 to 4096 to allow for more extensive responses.
- Implemented robust error handling for API requests, including timeout and request errors, with detailed HTTP exceptions for better debugging.
- Improved JSON response handling to ensure valid data is returned, with specific error messages for missing content in the response.
- Enhanced the overall reliability of the OpenRouter API integration, providing clearer feedback for users in case of issues.
These changes improve the user experience by ensuring more comprehensive responses and clearer error reporting during API interactions.
- Updated the `_parse_float_auto` function in `type_converter.py` to better handle various decimal and thousand separators, particularly for cases with long decimal parts from sources like Apple Health.
- Enhanced the logic for splitting and processing numeric strings to ensure correct interpretation of values, including edge cases with multiple separators.
- Added handling for cases where numeric strings may contain both commas and periods, improving overall robustness in float parsing.
These changes enhance the accuracy of numeric conversions, ensuring more reliable data processing across the application.
- Updated the Gitea issues index to reflect the latest state as of 2026-04-11, adding issue #76 to the list.
- Refined data handling in `activity_metrics.py`, `body_metrics.py`, `nutrition_metrics.py`, and `scores.py` to ensure consistent float conversions for calculations, improving accuracy in metric evaluations.
- Enhanced the calculation logic for various metrics to handle potential None values more robustly, ensuring smoother data processing and improved reliability across the application.
These changes improve the clarity of the Gitea issues documentation and enhance the overall accuracy and reliability of health and fitness metrics.
- Updated `resolve_placeholders` in `prompt_executor.py` to support combined modifiers for placeholders, allowing for more flexible output formats.
- Enhanced `build_ai_placeholder_caption` in `placeholder_registry.py` to clarify the generation of AI context captions, focusing on descriptions and explanations.
- Introduced new helper functions in `placeholder_resolver.py` to streamline the retrieval of descriptions and explanations for placeholders.
- Modified tests to cover new functionality, ensuring accurate behavior for combined modifiers and improved placeholder resolution.
These changes enhance the usability and clarity of placeholder outputs, providing users with richer contextual information.
- Updated `build_ai_placeholder_caption` in `placeholder_registry.py` to improve the generation of AI context captions by prioritizing descriptions and avoiding redundancy.
- Introduced `format_value_with_d_modifier` in `placeholder_resolver.py` to format values with contextual information, enhancing the clarity of exported placeholder values.
- Modified `export_placeholder_values` in `prompts.py` to utilize the new formatting function, ensuring that exported data includes both raw values and contextual descriptions.
- Added tests for the new formatting function and updated existing tests to ensure accurate caption generation.
These changes improve the contextual relevance of placeholder data and enhance the user experience when interacting with exported values.
- Introduced `build_ai_placeholder_caption` function in `placeholder_registry.py` to generate AI context captions based on placeholder metadata.
- Updated `resolve_placeholders` in `placeholder_resolver.py` to support modifiers for AI context, allowing for enhanced descriptions when placeholders are resolved.
- Modified `get_placeholder_catalog` to include AI captions in the output, improving the metadata available for placeholders.
- Adjusted `export_placeholder_values` to include AI captions in the exported data, enhancing the information provided to users.
These changes improve the flexibility and functionality of the placeholder system, enabling richer context generation for dynamic content.
- Updated `get_sleep_avg_duration` and `get_sleep_avg_quality` functions in `placeholder_resolver.py` to provide clearer error messages when data is unavailable.
- Enhanced sleep quality calculations in `recovery_metrics.py` to handle cases with insufficient data more robustly.
- Improved data handling in various metrics files (`activity_metrics.py`, `body_metrics.py`, `nutrition_metrics.py`, `recovery_metrics.py`, and `scores.py`) to ensure consistent float conversions for calculations.
- Added utility functions in `recovery_metrics.py` for parsing and normalizing sleep segment data, enhancing the accuracy of sleep quality assessments.
These changes improve the reliability and clarity of sleep-related metrics and enhance overall data handling across the application.
- Updated `extract_value_raw` to improve JSON parsing and handle unavailable data more effectively.
- Introduced new functions in `placeholder_resolver.py` for standardized responses when data is unavailable, enhancing clarity for users and AI.
- Modified various data retrieval functions to utilize the new response format, providing detailed reasons for unavailability.
- Improved availability checks in `export_placeholder_values_extended` to account for new response formats.
These changes enhance the robustness of the placeholder system and improve user experience by providing clearer error messages and data handling.
- Adjusted the total number of placeholders from 116 to 114 across various documentation and code files to reflect the current state of the system.
- Enhanced TDEE calculation logic in `nutrition_metrics.py` to prioritize Mifflin–St Jeor BMR with PAL when demographic data is available, with a fallback to a weight-based estimate.
- Updated placeholder registrations to ensure consistency with the new metadata structure and improved data handling.
- Revised documentation to clarify the authoritative source of placeholder metadata and the implications of the changes on existing functionalities.
These updates improve the accuracy and consistency of the placeholder system and enhance the nutritional assessment capabilities within the application.
- Introduced functions to retrieve profile name, age, height, and gender for better placeholder resolution.
- Added functions for displaying current date and time period labels (last 7, 30, and 90 days).
- Updated PLACEHOLDER_MAP to utilize new functions for improved readability and maintainability.
- Enhanced placeholder registrations in __init__.py to include new modules for sleep, vital metrics, and profile time periods.
These changes enhance the flexibility and functionality of the placeholder system, allowing for more dynamic content generation.
- Added new functions for BMI and goal weight/body fat percentage retrieval in `body_metrics.py`.
- Introduced training frequency and inter-session gap calculations in `activity_metrics.py`.
- Updated placeholder registrations to include new metrics for nutrition and activity.
- Improved data handling in `placeholder_resolver.py` for better integration of new metrics.
- Enhanced documentation across modules to reflect the new functionalities.
These updates improve the accuracy and comprehensiveness of health and fitness assessments within the application.
- Introduced a single TDEE calculation based on current weight, replacing the fixed 2500 kcal value.
- Updated `get_energy_balance_data` to use daily totals for intake calculations and improved energy balance logic.
- Enhanced `get_nutrition_average_data` to calculate averages over calendar days instead of raw log entries.
- Adjusted placeholder resolution to ensure consistent metadata usage across requests.
- Fixed issues in the charts router to reflect the new energy balance logic and TDEE calculations.
These changes improve the accuracy of nutritional assessments and streamline data handling in the application.
**Error:**
```
psycopg2.ProgrammingError: can't adapt type 'dict'
```
**Root Cause:**
- duplicate_prompt passed Python dicts directly to SQL INSERT
- JSONB fields from r2d() are already deserialized by psycopg2
- PostgreSQL expects JSON strings for JSONB columns
**Fix:**
- Added json.dumps() for all JSONB fields before INSERT:
- stages, output_schema, question_augmentations, graph_data
- Same pattern as import function
Files changed:
- backend/routers/prompts.py: JSON-encode JSONB in duplicate_prompt
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
**Root Cause:**
- FastAPI route matching: /{prompt_id} caught ALL requests including /export-all
- Specific routes MUST be defined BEFORE path parameter routes
**Error:**
```
psycopg2.errors.InvalidTextRepresentation: invalid input syntax for type uuid: "export-all"
LINE 1: SELECT * FROM ai_prompts WHERE id='export-all'
```
**Fix:**
- Moved /export-all and /import endpoints to line 106 (BEFORE /{prompt_id} at ~260)
- Added warning comments to both functions
- Fixed typo: for r in → for row in
**Affected:**
- /export-all: Internal Server Error → now works ✅
- /import: Would have had same issue → preemptively fixed ✅
Files changed:
- backend/routers/prompts.py: Reordered route definitions
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Critical bug fix from pytest failures:
**Problem:**
- execute_end_node() tried to use 'graph' variable without defining it
- UnboundLocalError at line 602: "if graph:"
- Caused 2 test failures in test_end_node_template.py
**Root Cause:**
- In Issue #5 fix, added graph lookup for node labels in AUTO mode
- But forgot to get graph from context first
- TEMPLATE mode already had: graph = context.get("graph")
**Fix:**
- Added: graph = context.get("graph") at start of AUTO mode block
- Same pattern as TEMPLATE mode
- graph is optional (None if not in context), so if-check is safe
**Tests:**
- test_auto_mode_concatenates_all_analyses - should pass now
- test_auto_mode_skips_skipped_nodes - should pass now
Files changed:
- backend/workflow_executor.py: Added graph = context.get("graph") line 596
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Introduced a new utility function to streamline the extraction of user-facing content from aggregated workflow results.
- Updated backend prompt handling to utilize the new function for improved clarity and maintainability.
- Adjusted frontend analysis component to leverage the utility for consistent content display across different workflow result formats.
These changes enhance the overall user experience by ensuring more reliable and readable output from workflow executions.
- Added support for handling aggregated results in workflow prompts, allowing for various data formats (string, object).
- Introduced a utility function to filter active prompts for both pipeline and workflow types in the analysis page.
- Updated content handling in the analysis component to accommodate new workflow data structures.
This improves the flexibility and usability of the prompt execution process in both backend and frontend components.
ISSUE: Inline templates referencing node outputs ({{ node_id.analysis_core }},
{{ node_id.signal_xyz }}) were not resolved - AI received empty data from
previous workflow stages.
ROOT CAUSE: load_prompt_template() only loaded system placeholders
(name, age, etc.) but not node execution results from context['node_results'].
FIX:
- Extract node outputs from context['node_results']
- Add as placeholders: node_id.analysis_core, node_id.signal_xyz, node_id.question_xyz
- Format matches PlaceholderPicker extraction logic
- Debug logging shows which node placeholders are added
TESTING:
- System placeholder test: ✅ SUCCESS (name, age, geschlecht resolved)
- Node output placeholders: Fixed (previously missing)
- User workflow: Join → Analysis → End now receives upstream data
Part 3: Inline Prompts - placeholder resolution completion
- Updated the `validate_csv_template` function to normalize both the column signature and field mappings for accurate comparison, preventing false warnings about mismatches.
- Enhanced warning messages to provide clearer guidance on the relationship between normalized signatures and raw field mappings.
- Added a new test to ensure that normalized signatures do not trigger false warnings when compared to raw mappings.
- Introduced a new endpoint for validating CSV templates without saving, allowing users to check field mappings and type conversions.
- Updated the `create_system_template` and `update_system_template` functions to include validation reports in responses.
- Enhanced error handling in CSV import processes by integrating `enrich_row_error` for more informative error messages.
- Improved the AdminCsvTemplateEditorPage to support format checking and display validation results, enhancing user experience.
- Incremented version numbers for `csv_import` and `admin_csv_templates` to reflect these updates.
- Increased precision for `kcal_active`, `kcal_resting`, `hr_avg`, and `hr_max` fields in the activity log schema.
- Added a new function `_activity_hr_bpm` to validate heart rate values during CSV import, ensuring they fall within plausible ranges.
- Updated the CSV parser to utilize the new heart rate validation function for improved data integrity.
- Enhanced the type converter to accommodate additional aliases for energy fields in CSV imports.
- Added a test to verify conversion of active energy from kJ to kcal, ensuring accurate data handling.
- Added SAVEPOINT management to the `_import_activity` function to improve error handling during CSV imports.
- Moved the training type resolution logic to occur within a transaction block, ensuring that errors can be rolled back without affecting the entire import process.
- Enhanced error logging to capture evaluation failures, providing better insights during CSV import operations.
- Modified `_resolve_training_type_for_activity` to accept a database cursor, improving efficiency and avoiding potential deadlocks during CSV imports.
- Introduced `get_training_type_for_activity_with_cursor` to handle training type resolution with an existing cursor, streamlining database interactions.
- Updated related calls in the activity import logic to utilize the new function, ensuring consistent behavior across the application.
- Introduced `diagnose_blood_pressure_row` and `diagnose_activity_row` functions to validate and analyze blood pressure and activity data from CSV imports.
- Updated the CSV import logic to handle combined datetime columns for blood pressure and activity, improving data integrity during import.
- Enhanced type conversion specifications to include `start_time` for blood pressure and activity, ensuring accurate data mapping.
- Added tests to validate the new diagnosis functions and their integration with existing import processes, ensuring robustness and reliability.
- Updated frontend messages to provide clearer guidance on blood pressure and activity data handling during CSV imports.
- Implemented a new endpoint for diagnosing CSV imports without writing to the database, allowing users to validate mappings and type conversions.
- Introduced the `diagnose_vitals_row` function to analyze vital metrics and provide detailed feedback on data validity.
- Enhanced the CSV import logic to include alias handling for vital fields, improving compatibility with different CSV formats.
- Updated the frontend to support the new diagnosis feature, including UI elements for displaying diagnosis results and error details.
- Added tests to ensure the correctness of the new diagnosis functionality and its integration with existing import processes.
- Updated migration scripts to ensure idempotent behavior for the source CHECK constraint, allowing for consistent application even if previous migrations were partially successful.
- Enhanced SQL logic to drop existing constraints safely and re-add them, ensuring compatibility with the universal CSV import.
- Clarified comments for better understanding of migration context and functionality.
- Updated the CSV import logic to include SAVEPOINT management, allowing for better error handling during the vitals baseline import process.
- Enhanced the SQL migration script to drop existing CHECK constraints related to the 'source' field, ensuring compatibility with the new universal CSV import.
- Incremented DB_SCHEMA_VERSION to "20260409c" to reflect these changes and improve the import process reliability.
- Incremented DB_SCHEMA_VERSION to "20260409b" to reflect changes related to the vitals_baseline.source CSV migration.
- Updated comments to clarify the migration context for better maintainability.
- Updated the aggregate_mapped_rows function to support multiple row policies, allowing for flexible handling of duplicate keys during CSV imports.
- Introduced deduplication of identical rows before aggregation, improving data integrity.
- Enhanced validation for multi_row_policy and dedupe_identical_rows in import_row_processing specifications.
- Updated the AdminCsvTemplateEditorPage to include options for multi-row policies and deduplication settings, improving user experience in template management.
- Added comprehensive tests to validate new aggregation behaviors and ensure correct error handling for multiple rows.
- Bumped version numbers for csv_import to 0.3.1 and admin_csv_templates to 0.2.0, reflecting recent enhancements.
- Added support for import_row_processing_default in the CSV modules endpoint, improving data handling capabilities.
- Introduced new row aggregation operations in the AdminCsvTemplateEditorPage, allowing for more flexible data processing options.
- Implemented parsing and validation for custom row processing configurations, enhancing user experience in template management.
- Updated the CSV import logic to support new row processing specifications for weight and vitals baseline, allowing for better data aggregation and validation.
- Implemented handling for multiple rows on the same day, enabling aggregation of values such as averages for vitals and last values for weight.
- Enhanced test coverage for the new import functionalities, ensuring correct behavior during data processing and validation.
- Refactored the module registry to include default import row processing options for better flexibility in handling CSV data.
- Updated the nutrition import logic to utilize a new row processing specification, improving data aggregation and validation.
- Refactored the template rendering process in the workflow executor to use Jinja2's Environment with ChainableUndefined for better handling of missing attributes.
- Added backward-compatible shortcuts for accessing decision signals in node contexts, enhancing flexibility in template usage.
- Introduced import row processing options in CSV templates, allowing for more customizable data handling during imports.
- Updated the test execution command in the CI workflow to run all tests excluding slow ones, improving efficiency.
- Enhanced the AdminCsvTemplateEditorPage to support custom equivalence for unit conversions, allowing for more flexible data handling.
- Added markers in pytest configuration for categorizing tests, facilitating better test management.
- Adjusted the extract_value_raw function to return failure for unavailable values in strict mode.
- Expanded the circumference detection logic in infer_unit_strict to include additional terms for better accuracy in unit inference.
- Updated the source_unit_choices_for_field function to include a custom option for user-defined conversion factors, improving flexibility in unit conversions.
- Modified the AdminCsvTemplateEditorPage to support custom conversion factors, allowing users to input specific scaling factors for their data.
- Added tests to ensure the custom option is correctly included in the source unit choices and functions as expected in the template editor.
- Changed source unit representation from "kJ" to "kj" for consistency across CSV templates and migrations.
- Updated CI workflow to enhance testing conditions, ensuring tests run in the correct environment based on deployment context.
- Improved job steps for backend testing and syntax checking by utilizing deployed application directories, streamlining the CI process.
- Added workflow_run triggers for "Deploy Development" and "Deploy Production" to ensure tests run only after successful deployments.
- Updated Python version in CI from 3.12 to 3.11 for better compatibility with the Debian 12 ARM64 runner.
- Enhanced job conditions to skip tests on failed workflow runs.
- Improved frontend build process by updating Node.js setup and ensuring correct directory navigation.
- Refined CSV parsing logic to handle custom and unknown source units, enhancing conversion flexibility.
- Added new tests for custom source unit handling in CSV conversions, ensuring accurate processing.
- Updated the CSV import architecture to clarify the distinction between import and data layer responsibilities, as outlined in the new section of ARCHITECTURE.md.
- Enhanced the build_row_after_mapping function to include module-specific context for improved data processing.
- Introduced source unit options in the admin CSV template editor to facilitate user-defined conversions, improving flexibility in handling various data formats.
- Added new tests to validate the handling of source units and ensure accurate conversions during CSV imports.
- Updated module definitions to include unit specifications for nutritional and activity data fields, enhancing data integrity.
- Integrated date parsing improvements using dateutil for better handling of various date formats in sleep data.
- Added total sleep hours to the nights dictionary for comprehensive sleep analysis.
- Updated the import logic to handle cases where sleep duration is zero, providing appropriate warnings.
- Enhanced the CSV import interface to detect Apple sleep CSV format and provide user feedback on template selection.
- Improved the admin CSV template editor to accommodate new sleep import requirements and clarify usage instructions.
- Added support for new CSV import modules: sleep and vitals_baseline, expanding the import capabilities.
- Implemented backend logic for handling CSV imports related to sleep and vitals baseline, including error handling and data processing.
- Updated frontend components to include new modules in the CSV import interface, improving user experience.
- Introduced unit tests for the new import functionalities to ensure reliability and correctness.
- Enhanced existing CSV analysis features to accommodate the new modules, ensuring consistent behavior across the application.
- Added new functions for calculating header signature recall and ranking metrics, improving the analysis of CSV templates.
- Updated existing CSV analysis endpoints to utilize the new ranking metrics, enhancing the accuracy of template matching.
- Refactored related code to replace Jaccard score calculations with the new metrics, providing a more comprehensive evaluation of CSV structure.
- Improved documentation for new functions to clarify their purpose and usage in the context of CSV template analysis.
- Added a new endpoint for analyzing uploaded CSV files, providing suggestions for field mappings and type conversions.
- Implemented validation for required field targets to ensure all mandatory fields are mapped correctly.
- Enhanced the admin CSV templates interface with new routes and navigation options in the frontend.
- Updated API utility functions to support the new CSV analysis functionality.
- Improved error handling for CSV uploads, including file size and row count checks.
- Added a new function to strip header keys of unwanted characters, improving CSV import consistency.
- Updated CSV row iteration to utilize the new header normalization function, ensuring cleaner data processing.
- Enhanced date parsing capabilities to support flexible formats, accommodating various date representations in CSV files.
- Introduced additional tests to validate the new header normalization and date parsing functionalities.
- Bumped version of csv_import to 0.3.0, reflecting new analysis capabilities.
- Modified analyze_csv endpoint to allow optional module filtering, improving flexibility in template selection.
- Enhanced the import process to support both system and user-defined templates, ensuring backward compatibility.
- Updated frontend to streamline mapping choices and improve user experience during CSV analysis and import.
- Added detailed error handling and user feedback for import operations.
- Updated version for csv_import to 0.2.0, reflecting new features.
- Implemented a new POST endpoint for universal CSV import, supporting nutrition, weight, and blood pressure modules.
- Added CSV parsing function to yield rows as dictionaries for easier data handling.
- Enhanced error handling and logging for import operations.
- Introduced tests for the new CSV parsing functionality to ensure reliability.
- Changed feature ID from 'csv_import' to 'data_import' in the features table.
- Updated foreign key references in tier_limits, user_feature_restrictions, user_feature_usage, and widget_feature_requirements.
- Removed the old 'csv_import' feature entry after ensuring all references are updated.
- Simplified the migration process by using an INSERT with ON CONFLICT for the new feature entry.
- Added permissions for editing and deleting CSV field mappings.
- Created type converter for CSV cells to handle various data types.
- Implemented database migrations for CSV field mappings and import logs.
- Seeded initial system templates for nutrition and activity data imports.
- Developed admin endpoints for managing system CSV templates.
- Introduced user endpoints for CSV import analysis and mapping retrieval.
- Added tests for core CSV parser functionalities, including delimiter detection and value conversion.
