Root Cause:
- FastAPI cannot use same parameter name in endpoint and dependency
- Query param 'token' conflicted between endpoint and require_auth_flexible
- FastAPI cached dependency signatures at startup
Solution:
- Renamed to 'ssetoken' in require_auth_flexible (backend/auth.py)
- Updated frontend to use ssetoken (frontend/src/utils/api.js)
- Removed debug logging
- Added test endpoint /test-ssetoken
Testing:
✅ Header auth: X-Auth-Token works
✅ Query auth: ?ssetoken=XXX works
✅ SSE streaming: Ready for testing
Note: Required full rebuild, not just restart
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Backend:
- prompts.py: @router.post → @router.get für /execute-stream
- EventSource unterstützt nur GET-Requests
- modules/timeframes nutzen Defaults (SSE kann keine komplexen Params)
Fixes:
- "Connection to server lost" bei Analyse-Ausführung
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
- Updated `get_activity_session_logical_unit` to support optional parameters for form training context, allowing for more flexible schema resolution.
- Introduced a new endpoint `/attribute-schema` to fetch activity attribute schemas without an existing session, improving manual data entry capabilities.
- Enhanced the `getActivitySession` API method to accept query parameters for training category and type, facilitating dynamic schema retrieval.
- Updated the frontend `ActivityPage` to utilize the new schema fetching logic, ensuring a smoother user experience when managing activity sessions and metrics.
- Implemented JSON and CSV export features in `routers/exportdata.py`, including enriched session metrics for activity data.
- Updated `CLAUDE.md` to reflect new export capabilities and added details about the inclusion of `session_metrics_json` in the `activity.csv` file.
- Revised `ACTIVITY_PRODUCTION_ARCHITECTURE_AND_PHASES.md` to document the integration of export features and their relationship with session metrics.
- Improved the overall structure and clarity of the documentation to support the new export functionalities.
- Added new updates for Phase A and Phase B in `CLAUDE.md`, detailing the completion of Phase A and the introduction of enriched session metrics in the API response for `GET /api/activity`.
- Enhanced the README to include references to new documentation files for scalar canon and composite metrics implementation.
- Updated `ACTIVITY_PRODUCTION_ARCHITECTURE_AND_PHASES.md` to reflect the current status of phases and added navigation rules for data access.
- Improved `ACTIVITY_SESSION_METRICS_EAV_AGENT_GUIDE.md` with links to new implementation concepts for composite metrics.
- Refactored the activity router to integrate enriched session metrics into the activity list responses, ensuring a more comprehensive data presentation.
- 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 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.
- 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>
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 `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 `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.
- 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>
- 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.
- 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.
- 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 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.
- 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 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 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.
