Commit_all_local_changes_after_PR_406_merge
This commit is contained in:
ajaysi
@@ -1,316 +0,0 @@
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# ALwrity Daily Workflow PR Merge Summary
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**Date:** March 9, 2026
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**Session Goal:** Review and integrate workflow enhancement PRs (#388-397)
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**Status:** ✅ COMPLETED - 9 PRs successfully merged
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---
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## Successfully Merged PRs (9 Total)
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### Core Workflow Enhancement Series
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| # | Title | Commit | Key Improvements |
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|---|-------|--------|-----------------|
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| #388 | Daily Workflow Integration & Enhanced Reliability | 8f6ed3a | Agent committee orchestration, robust task proposal handling, metadata normalization |
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| #389 | Committee Health Precheck & Simplified Architecture | 3558131 | Simplified schema, health precheck, removed complex dependency coercion |
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| #390 | Degraded-mode Workflow Regeneration Criteria | 56854df | Rate-limited `/regenerate` endpoint (3 req/60s), quality score tracking |
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| #391 | Workflow Provenance Quality Metrics | 2d4c83e | Provenance classification (agent vs fallback), quality ratio calculation |
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| #392 | Contextuality Validation & Low-context Status | 74b788a | Evidence-link grounding, plan contextuality scoring (65% threshold) |
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| #394 | Task Memory Feedback Scoring | 38444f4 | Proper self-learning: uses persisted task.status, handles all negative cases |
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| #395 | Dependencies Normalization | 0aaaf07 | Robust `_normalize_dependencies()` helper for consistent data types |
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| #396 | Date Validation & Error Handling | 9271566 | ISO date validation before yesterday indexing, narrower SQLAlchemyError handling |
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| #397 | Typed Request Model for Task Status | 39bc3e3 | Pydantic `TaskStatusEnum` & `TaskStatusUpdateRequest`, FastAPI auto-validation |
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---
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## System Architecture Evolution
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### From Simple to Sophisticated
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```
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PR #388 ─→ Agent Committee Orchestration
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PR #389 ─→ Clean Architecture
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PR #390 ─→ Regeneration Control
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PR #391 ─→ Quality Awareness
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PR #392 ─→ Evidence-Based Grounding
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PR #394 ─→ Proper Memory Learning
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PR #395 ─→ Data Consistency
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PR #396 ─→ Production Observability
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PR #397 ─→ API Type Safety
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```
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---
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## Key Features Implemented
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### 1. **Agent Committee (PR #388)**
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- Multi-agent orchestration with 5 specialized agents:
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- ContentStrategyAgent
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- StrategyArchitectAgent
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- SEOOptimizationAgent
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- SocialAmplificationAgent
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- CompetitorResponseAgent
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- Parallel proposal gathering with exception safety
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- Deduplication by priority and semantic ordering
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### 2. **Contextuality Validation (PR #392)**
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- Evidence-link framework:
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- `onboarding:{field_name}` references
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- `alert:{alert_id}` references
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- Task contextuality scoring: minimum 1 evidence link
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- Plan contextuality threshold: 65% of tasks must meet threshold
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- Automatic strict regeneration for low-context plans
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- Response fields: `quality_status`, `contextuality_validation`
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### 3. **Self-Learning Memory (PR #394)**
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- Uses canonical `task.status` from database (not request param)
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- Proper feedback scoring:
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- `completed` → +1 (positive learning)
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- `skipped`, `dismissed`, `rejected` → -1 (negative learning)
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- Other statuses → 0 (neutral)
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- Prevents inconsistent memory behavior from status normalization mismatches
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### 4. **Data Consistency (PR #395)**
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- `_normalize_dependencies()` helper handles all type variations:
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- `None` → `[]`
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- List → returned as-is
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- JSON string → parsed and validated
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- Invalid types → `[]`
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- Applied to today and yesterday task payloads
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- Ensures indexing pipeline receives consistent types
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### 5. **Production Observability (PR #396)**
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- Date validation:
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- ISO format check before computing yesterday
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- Clear warning logs (plan_id, user_id, plan_date, reason)
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- Graceful skip on parse failure
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- Narrower exception handling:
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- `SQLAlchemyError` instead of silent `except Exception: pass`
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- Detailed error logs with context
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- Non-fatal failures preserve today's indexing
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### 6. **API Type Safety (PR #397)**
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- `TaskStatusEnum` enumeration:
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- Constrains valid status values at type level
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- FastAPI auto-validation in OpenAPI
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- `TaskStatusUpdateRequest` Pydantic model:
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- `status: TaskStatusEnum` (auto-validated)
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- `completion_notes: Optional[str]` (max 4000 chars enforced)
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- Eliminates manual validation code
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---
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## Technical Highlights
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### Backend Services
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- **today_workflow_service.py**:
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- `generate_agent_enhanced_plan()` with agent committee + LLM fallback
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- `validate_plan_contextuality()` for evidence-link scoring
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- `_ensure_pillar_coverage()` with LLM backfill + controlled fallback
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- `update_task_status()` with memory integration
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- **API (today_workflow.py)**:
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- Type-safe endpoint handlers
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- Pydantic request/response validation
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- Comprehensive error handling
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- Normalized dependencies throughout
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- Detailed logging for observability
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### Database & ORM
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- Efficient schema after simplification (PR #389)
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- `plan_json` BLOB stores complete workflow metadata
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- Proper foreign key relationships
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- Transaction safety with SQLAlchemy
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### Frontend (TypeScript)
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- Zustand store for workflow state
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- Error boundary handling
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- Fallback logic for degraded mode
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- Type-safe API calls
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---
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## Quality Metrics
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### Code Quality
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- ✅ Type safety throughout (Pydantic, TypeScript)
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- ✅ Comprehensive error handling (narrower scopes)
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- ✅ Detailed observability logging
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- ✅ Non-fatal failure modes
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- ✅ Data consistency guarantees
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### Testing Coverage
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- ✅ Python static compile checks (all PRs)
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- ✅ Backend unit tests (scheduler, onboarding, database)
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- ✅ Frontend builds without errors (linting auto-fixed)
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### Production Readiness
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- ✅ Rate limiting for regeneration endpoint
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- ✅ Evidence-link grounding prevents hallucinations
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- ✅ Self-learning memory improves task proposals
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- ✅ Graceful degradation with fallback tasks
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- ✅ Detailed error logging for operations
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---
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## Skipped PRs & Rationale
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### PR #393: Improve indexing observability logs
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- **Status:** ❌ CLOSED (user decision)
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- **Reason:** Contextuality validation too important to remove
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- **Contains:** Good logging improvements, but removes core validation
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### PR #398: Resolve canonical user IDs in scheduler
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- **Status:** ⏸️ SKIPPED
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- **Reason:**
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- Codex flagged P1 concern: User ID filtering could drop legacy tasks
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- Codex flagged P2 concern: DB initialization as side effect in discovery
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- Causes regressions in API layer (removes Pydantic models, error handling)
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- Built from older main version
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- **Recommendation:** Await rebase on current main + Codex concerns addressed
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### PR #399: Centralize onboarding SEO task health
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- **Status:** ⏸️ SKIPPED
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- **Reason:**
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- Same regressions as PR #398 (removes API improvements)
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- Built from older main version
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- SEO dashboard improvements are solid but not worth losing workflow API enhancements
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- **Recommendation:** Rebase on current main when #398 is fixed
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---
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## Current State Summary
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### What We Have
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✅ **Agent Committee System**
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- 5 specialized agents with parallel proposal gathering
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- Semantic deduplication
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- Self-learning memory integration
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- Graceful fallback to LLM generation
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✅ **Evidence-Link Grounding**
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- Tasks reference onboarding data and system alerts
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- Contextuality scoring prevents hallucinations
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- Automatic strict regeneration for low-context workflows
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- Response metadata for monitoring
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✅ **Self-Learning Memory**
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- Proper feedback scoring from database state
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- Handles all task status outcomes
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- Prevents inconsistent learning from normalized statuses
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✅ **Data Consistency**
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- Normalized dependencies across all payloads
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- Type-safe API endpoints
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- Consistent data handling in indexing
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✅ **Production Observability**
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- Date validation before yesterday indexing
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- Narrower exception handling with detailed logs
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- Non-fatal error modes
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- Clear operational visibility
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✅ **API Type Safety**
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- Pydantic validation
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- OpenAPI documentation
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- No manual validation code needed
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- Better IDE support with TypeScript
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### System Capabilities
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- Daily workflow generation with 6 lifecycle pillars
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- Rate-limited on-demand regeneration
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- Evidence-based contextuality validation
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- Self-improving task proposals through memory
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- Graceful degradation with fallback tasks
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- Comprehensive logging and error handling
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- Type-safe endpoints with auto-validation
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---
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## Lessons Learned
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### PR Review Patterns
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1. **Check for regressions:** Several PRs removed recent improvements
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2. **Verify git history:** PRs #398-399 were built from older main
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3. **Surgical merges work:** Combining good parts while preserving improvements
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4. **Documentation matters:** Clear merge commit messages help understand evolution
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### Code Quality
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1. **Type safety prevents bugs:** Pydantic models caught issues early
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2. **Narrow exception scopes:** Better observability than broad catches
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3. **Evidence-based design:** Grounding prevents hallucination
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4. **Data consistency:** Normalization functions prevent downstream bugs
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### Architecture Decisions
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1. **Committee approach:** Multiple agents > single LLM
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2. **Evidence links:** Better than quality ratios for grounding
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3. **Memory learning:** Use DB state, not request params
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4. **Graceful degradation:** Fallback tasks > error states
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---
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## Next Steps (Future Work)
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### High Priority
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1. **PR #398 Rebase**: Wait for:
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- Rebase on current main
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- Codex P1 concern: Address user ID filtering for legacy tasks
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- Codex P2 concern: Avoid DB initialization in discovery
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2. **PR #399 Rebase**: Depends on #398
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- SEO dashboard improvements once #398 is fixed
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### Medium Priority
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1. **Performance Tuning**: Monitor agent committee query times
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2. **Memory Optimization**: Cache agent proposals for repeated patterns
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3. **Dashboard Enhancement**: Add contextuality metrics to UI
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### Low Priority
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1. **Documentation**: Update API docs with new models
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2. **Logging**: Expand observability for edge cases
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3. **Testing**: Add integration tests for committee scenarios
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---
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## Session Statistics
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| Metric | Value |
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|--------|-------|
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| **PRs Reviewed** | 12 (#388-397, #398-399) |
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| **PRs Merged** | 9 (#388-397, excluding #393) |
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| **PRs Skipped** | 3 (#393 closed by user, #398-399 due to regressions) |
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| **Merge Conflicts Resolved** | 11 |
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| **Surgical Merges** | 4 (#394-397) |
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| **Git Commits** | 9 merge commits |
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| **Files Modified** | 30+ across backend/frontend |
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| **Lines Added** | 1000+ |
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| **Lines Removed** | 1500+ |
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| **Time Span** | March 8-9, 2026 |
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---
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## Recommendation for Future Sessions
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1. **Before merging PRs:**
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- Check that PR is based on current main
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- Review for regressions in dependent code
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- Look for Codex review comments (P1/P2 flags)
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2. **When PRs conflict with improvements:**
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- Use surgical merge to extract good parts
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- Preserve working system over incomplete features
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3. **For architectural changes:**
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- Validate against existing patterns
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- Ensure data consistency maintained
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- Test against real workflows
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4. **Documentation:**
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- Update this file when significant changes occur
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- Keep git history clean with descriptive commits
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- Tag versions for major milestones
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---
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**Session Completed:** ✅
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**System State:** Production-ready with advanced features
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**Next Review:** When PR #398 is rebased on current main
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@@ -45,11 +45,17 @@ def _extract_error_message(exc: Exception) -> str:
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"""
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Extract user-friendly error message from exception.
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Handles HTTPException with nested error details from WaveSpeed API.
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Preserves subscription modal flags for frontend.
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"""
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if isinstance(exc, HTTPException):
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detail = exc.detail
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# If detail is a dict (from WaveSpeed client)
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if isinstance(detail, dict):
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# Check if this is a subscription/credit error
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if detail.get("error_type") == "insufficient_credits" or detail.get("show_subscription_modal"):
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# Return the error message with subscription modal flag
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return detail.get("message", "Insufficient credits. Please top up your account.")
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# Try to extract message from nested response JSON
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response_str = detail.get("response", "")
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if response_str:
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@@ -86,6 +92,27 @@ def _extract_error_message(exc: Exception) -> str:
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return error_str
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def _extract_error_metadata(exc: Exception) -> Dict[str, Any]:
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"""Extract structured error metadata for task polling clients."""
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if isinstance(exc, HTTPException):
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detail = exc.detail
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if isinstance(detail, dict):
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return {
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"error_status": exc.status_code,
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"error_data": detail,
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}
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if isinstance(detail, str):
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return {
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"error_status": exc.status_code,
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"error_data": {
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"error": detail,
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"message": detail,
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},
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}
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return {}
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def _execute_podcast_video_task(
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task_id: str,
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request: PodcastVideoGenerationRequest,
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@@ -229,9 +256,15 @@ def _execute_podcast_video_task(
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# Extract user-friendly error message from exception
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error_msg = _extract_error_message(exc)
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error_meta = _extract_error_metadata(exc)
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task_manager.update_task_status(
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task_id, "failed", error=error_msg, message=f"Video generation failed: {error_msg}"
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task_id,
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"failed",
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error=error_msg,
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message=f"Video generation failed: {error_msg}",
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error_status=error_meta.get("error_status"),
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error_data=error_meta.get("error_data"),
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)
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@@ -257,7 +290,7 @@ async def generate_podcast_video(
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try:
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if hasattr(request, 'headers') and hasattr(request.headers, 'get'):
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auth_header = request.headers.get("Authorization")
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except:
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except Exception:
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pass
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if auth_header and auth_header.startswith("Bearer "):
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@@ -76,6 +76,10 @@ class TaskManager:
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if task["status"] == "failed" and task.get("error"):
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response["error"] = task["error"]
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if task.get("error_status") is not None:
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response["error_status"] = task["error_status"]
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if task.get("error_data") is not None:
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response["error_data"] = task["error_data"]
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return response
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@@ -86,7 +90,9 @@ class TaskManager:
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progress: Optional[float] = None,
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message: Optional[str] = None,
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result: Optional[Dict[str, Any]] = None,
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error: Optional[str] = None
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error: Optional[str] = None,
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error_status: Optional[int] = None,
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error_data: Optional[Dict[str, Any]] = None,
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):
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"""Update the status of a task."""
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if task_id not in self.task_storage:
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@@ -112,6 +118,10 @@ class TaskManager:
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if error is not None:
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task["error"] = error
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logger.error(f"[StoryWriter] Task {task_id} error: {error}")
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if error_status is not None:
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task["error_status"] = error_status
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if error_data is not None:
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task["error_data"] = error_data
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||||
async def execute_story_generation_task(
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self,
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@@ -11,7 +11,7 @@ from sqlalchemy.exc import SQLAlchemyError
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from middleware.auth_middleware import get_current_user
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from services.database import get_db
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from services.today_workflow_service import get_or_create_daily_workflow_plan, update_task_status
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from services.today_workflow_service import get_or_create_daily_workflow_plan, update_task_status, _today_date_str
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from models.daily_workflow_models import DailyWorkflowPlan, DailyWorkflowTask
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import asyncio
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from services.intelligence.txtai_service import TxtaiIntelligenceService
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@@ -81,26 +81,7 @@ async def _index_tasks_to_sif(user_id: str, date: str, tasks: list[dict], label:
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logger.debug(f"Background indexing failed for user {user_id}: {e}")
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||||
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@router.get("")
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async def get_today_workflow(
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date: Optional[str] = None,
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current_user: dict = Depends(get_current_user),
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db: Session = Depends(get_db),
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) -> Dict[str, Any]:
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from starlette.concurrency import run_in_threadpool
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user_id = str(current_user.get("id"))
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plan, created = await get_or_create_daily_workflow_plan(db, user_id, date=date)
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def _fetch_tasks():
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return (
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db.query(DailyWorkflowTask)
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.filter(DailyWorkflowTask.plan_id == plan.id, DailyWorkflowTask.user_id == user_id)
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||||
.order_by(DailyWorkflowTask.created_at.asc())
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||||
.all()
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||||
)
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||||
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tasks = await run_in_threadpool(_fetch_tasks)
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||||
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||||
def _build_workflow_payload(user_id: str, plan: DailyWorkflowPlan, tasks: list[DailyWorkflowTask]) -> Dict[str, Any]:
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||||
response_tasks = []
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||||
for t in tasks:
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||||
response_tasks.append(
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||||
@@ -136,8 +117,156 @@ async def get_today_workflow(
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||||
workflow_status = "completed"
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||||
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||||
total_estimated = int(sum(int(t.get("estimatedTime") or 0) for t in response_tasks))
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||||
plan_json = plan.plan_json or {}
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||||
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||||
return {
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||||
"workflow": {
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||||
"id": f"daily-{user_id}-{plan.date}",
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||||
"date": plan.date,
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||||
"userId": user_id,
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||||
"tasks": response_tasks,
|
||||
"currentTaskIndex": current_index,
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||||
"completedTasks": completed,
|
||||
"totalTasks": total,
|
||||
"workflowStatus": workflow_status,
|
||||
"totalEstimatedTime": total_estimated,
|
||||
"actualTimeSpent": 0,
|
||||
},
|
||||
"plan": {
|
||||
"id": plan.id,
|
||||
"date": plan.date,
|
||||
"source": plan.source,
|
||||
"generation_mode": plan.generation_mode,
|
||||
"committee_agent_count": plan.committee_agent_count,
|
||||
"fallback_used": bool(plan.fallback_used),
|
||||
"quality_status": plan_json.get("quality_status", "contextual"),
|
||||
"contextuality_validation": plan_json.get("contextuality_validation"),
|
||||
"provenance_summary": {
|
||||
"generationMode": plan.generation_mode,
|
||||
"committeeAgentCount": plan.committee_agent_count,
|
||||
"fallbackUsed": bool(plan.fallback_used),
|
||||
"taskSourceBreakdown": {},
|
||||
},
|
||||
"created_at": plan.created_at.isoformat() if plan.created_at else None,
|
||||
"updated_at": plan.updated_at.isoformat() if plan.updated_at else None,
|
||||
},
|
||||
"schedule_status": {
|
||||
"date": plan.date,
|
||||
"generated": True,
|
||||
"scheduled_run_completed": plan.source == "scheduled",
|
||||
"source": plan.source,
|
||||
"created_at": plan.created_at.isoformat() if plan.created_at else None,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@router.get("")
|
||||
async def get_today_workflow(
|
||||
date: Optional[str] = None,
|
||||
current_user: dict = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""Get existing daily workflow for the specified date.
|
||||
Returns 404 if no workflow exists for the date.
|
||||
Workflow should only be created via explicit user action or scheduled job.
|
||||
"""
|
||||
from starlette.concurrency import run_in_threadpool
|
||||
user_id = str(current_user.get("id"))
|
||||
date_str = date or _today_date_str()
|
||||
|
||||
def _get_existing():
|
||||
return (
|
||||
db.query(DailyWorkflowPlan)
|
||||
.filter(DailyWorkflowPlan.user_id == user_id, DailyWorkflowPlan.date == date_str)
|
||||
.first()
|
||||
)
|
||||
|
||||
plan = await run_in_threadpool(_get_existing)
|
||||
|
||||
if not plan:
|
||||
raise HTTPException(
|
||||
status_code=404,
|
||||
detail=f"No workflow found for date {date_str}. Workflow should be generated via explicit user action or scheduled job."
|
||||
)
|
||||
|
||||
def _fetch_tasks():
|
||||
return (
|
||||
db.query(DailyWorkflowTask)
|
||||
.filter(DailyWorkflowTask.plan_id == plan.id, DailyWorkflowTask.user_id == user_id)
|
||||
.order_by(DailyWorkflowTask.created_at.asc())
|
||||
.all()
|
||||
)
|
||||
|
||||
tasks = await run_in_threadpool(_fetch_tasks)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"data": _build_workflow_payload(user_id, plan, tasks),
|
||||
"timestamp": datetime.utcnow().isoformat(),
|
||||
"user_id": user_id,
|
||||
}
|
||||
|
||||
|
||||
@router.get("/status")
|
||||
async def get_today_workflow_status(
|
||||
date: Optional[str] = None,
|
||||
current_user: dict = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
from starlette.concurrency import run_in_threadpool
|
||||
|
||||
user_id = str(current_user.get("id"))
|
||||
date_str = date or _today_date_str()
|
||||
|
||||
def _get_existing():
|
||||
return (
|
||||
db.query(DailyWorkflowPlan)
|
||||
.filter(DailyWorkflowPlan.user_id == user_id, DailyWorkflowPlan.date == date_str)
|
||||
.first()
|
||||
)
|
||||
|
||||
plan = await run_in_threadpool(_get_existing)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"data": {
|
||||
"date": date_str,
|
||||
"generated": plan is not None,
|
||||
"scheduled_run_completed": bool(plan and plan.source == "scheduled"),
|
||||
"source": plan.source if plan else None,
|
||||
"created_at": plan.created_at.isoformat() if plan and plan.created_at else None,
|
||||
},
|
||||
"timestamp": datetime.utcnow().isoformat(),
|
||||
"user_id": user_id,
|
||||
}
|
||||
|
||||
|
||||
@router.post("/generate")
|
||||
async def generate_workflow(
|
||||
date: Optional[str] = None,
|
||||
current_user: dict = Depends(get_current_user),
|
||||
db: Session = Depends(get_db),
|
||||
) -> Dict[str, Any]:
|
||||
"""Explicitly generate a new daily workflow for the specified date.
|
||||
This should only be called when the user explicitly requests workflow generation
|
||||
or via a scheduled job at night.
|
||||
"""
|
||||
from starlette.concurrency import run_in_threadpool
|
||||
user_id = str(current_user.get("id"))
|
||||
plan, created = await get_or_create_daily_workflow_plan(db, user_id, date=date, creation_source="manual")
|
||||
|
||||
def _fetch_tasks():
|
||||
return (
|
||||
db.query(DailyWorkflowTask)
|
||||
.filter(DailyWorkflowTask.plan_id == plan.id, DailyWorkflowTask.user_id == user_id)
|
||||
.order_by(DailyWorkflowTask.created_at.asc())
|
||||
.all()
|
||||
)
|
||||
|
||||
tasks = await run_in_threadpool(_fetch_tasks)
|
||||
|
||||
if created:
|
||||
response_tasks = _build_workflow_payload(user_id, plan, tasks)["workflow"]["tasks"]
|
||||
asyncio.create_task(_index_tasks_to_sif(user_id, plan.date, response_tasks, label="today"))
|
||||
from datetime import date as date_type, timedelta
|
||||
|
||||
@@ -200,29 +329,7 @@ async def get_today_workflow(
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"data": {
|
||||
"workflow": {
|
||||
"id": f"daily-{user_id}-{plan.date}",
|
||||
"date": plan.date,
|
||||
"userId": user_id,
|
||||
"tasks": response_tasks,
|
||||
"currentTaskIndex": current_index,
|
||||
"completedTasks": completed,
|
||||
"totalTasks": total,
|
||||
"workflowStatus": workflow_status,
|
||||
"totalEstimatedTime": total_estimated,
|
||||
"actualTimeSpent": 0,
|
||||
},
|
||||
"plan": {
|
||||
"id": plan.id,
|
||||
"date": plan.date,
|
||||
"source": plan.source,
|
||||
"quality_status": (plan.plan_json or {}).get("quality_status", "contextual"),
|
||||
"contextuality_validation": (plan.plan_json or {}).get("contextuality_validation"),
|
||||
"created_at": plan.created_at.isoformat() if plan.created_at else None,
|
||||
"updated_at": plan.updated_at.isoformat() if plan.updated_at else None,
|
||||
},
|
||||
},
|
||||
"data": _build_workflow_payload(user_id, plan, tasks),
|
||||
"timestamp": datetime.utcnow().isoformat(),
|
||||
"user_id": user_id,
|
||||
}
|
||||
|
||||
@@ -18,6 +18,26 @@ router = APIRouter()
|
||||
UPLOAD_DIR = Path("backend/data/video_studio/uploads")
|
||||
UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
|
||||
def _extract_error_metadata(exc: Exception) -> Dict[str, Any]:
|
||||
"""Extract structured HTTP error metadata for polling clients."""
|
||||
if isinstance(exc, HTTPException):
|
||||
detail = exc.detail
|
||||
if isinstance(detail, dict):
|
||||
return {
|
||||
"error_status": exc.status_code,
|
||||
"error_data": detail,
|
||||
}
|
||||
if isinstance(detail, str):
|
||||
return {
|
||||
"error_status": exc.status_code,
|
||||
"error_data": {
|
||||
"error": detail,
|
||||
"message": detail,
|
||||
},
|
||||
}
|
||||
return {}
|
||||
|
||||
def _process_avatar_generation(task_id: str, image_path: Path, audio_path: Path, user_id: str, resolution: str, model: str):
|
||||
"""
|
||||
Background task to process avatar generation using shared InfiniteTalk service.
|
||||
@@ -94,7 +114,15 @@ def _process_avatar_generation(task_id: str, image_path: Path, audio_path: Path,
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[VideoStudio] Avatar generation failed for task {task_id}: {e}", exc_info=True)
|
||||
task_manager.update_task(task_id, "failed", error=str(e), user_id=user_id)
|
||||
error_meta = _extract_error_metadata(e)
|
||||
task_manager.update_task(
|
||||
task_id,
|
||||
"failed",
|
||||
error=str(e),
|
||||
user_id=user_id,
|
||||
error_status=error_meta.get("error_status"),
|
||||
error_data=error_meta.get("error_data"),
|
||||
)
|
||||
finally:
|
||||
# Cleanup temp upload files
|
||||
try:
|
||||
|
||||
@@ -39,7 +39,18 @@ class TaskManager:
|
||||
logger.error(f"[VideoStudio] Failed to create task: {e}")
|
||||
raise
|
||||
|
||||
def update_task(self, task_id: str, status: str, result: Optional[Dict] = None, error: Optional[str] = None, user_id: str = None, progress: float = None, message: str = None):
|
||||
def update_task(
|
||||
self,
|
||||
task_id: str,
|
||||
status: str,
|
||||
result: Optional[Dict] = None,
|
||||
error: Optional[str] = None,
|
||||
user_id: str = None,
|
||||
progress: float = None,
|
||||
message: str = None,
|
||||
error_status: Optional[int] = None,
|
||||
error_data: Optional[Dict[str, Any]] = None,
|
||||
):
|
||||
"""Update an existing task."""
|
||||
if not user_id:
|
||||
logger.error(f"[VideoStudio] Cannot update task {task_id} without user_id")
|
||||
@@ -74,6 +85,13 @@ class TaskManager:
|
||||
task.result = result
|
||||
if error:
|
||||
task.error = error
|
||||
if error_status is not None or error_data is not None:
|
||||
result_payload = task.result if isinstance(task.result, dict) else {}
|
||||
if error_status is not None:
|
||||
result_payload["error_status"] = error_status
|
||||
if error_data is not None:
|
||||
result_payload["error_data"] = error_data
|
||||
task.result = result_payload
|
||||
if progress is not None:
|
||||
task.progress = progress
|
||||
if message:
|
||||
@@ -107,7 +125,7 @@ class TaskManager:
|
||||
if status_val == "processing":
|
||||
status_val = "running"
|
||||
|
||||
return {
|
||||
response = {
|
||||
"task_id": task.task_id,
|
||||
"status": status_val,
|
||||
"result": task.result,
|
||||
@@ -117,6 +135,12 @@ class TaskManager:
|
||||
"created_at": task.created_at,
|
||||
"updated_at": task.updated_at
|
||||
}
|
||||
if isinstance(task.result, dict):
|
||||
if task.result.get("error_status") is not None:
|
||||
response["error_status"] = task.result.get("error_status")
|
||||
if task.result.get("error_data") is not None:
|
||||
response["error_data"] = task.result.get("error_data")
|
||||
return response
|
||||
finally:
|
||||
db.close()
|
||||
except Exception as e:
|
||||
|
||||
@@ -4,6 +4,10 @@ import sqlite3
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
ROOT_DIR = Path(__file__).resolve().parent.parent
|
||||
WORKSPACE_DIR = ROOT_DIR / "workspace"
|
||||
|
||||
def migrate_database(db_path):
|
||||
"""Add missing columns to daily_workflow_plans table."""
|
||||
if not os.path.exists(db_path):
|
||||
@@ -46,14 +50,14 @@ def migrate_database(db_path):
|
||||
|
||||
def find_and_migrate_databases():
|
||||
"""Find all databases and apply migrations."""
|
||||
workspace_dir = r'c:\Users\diksha rawat\Desktop\ALwrity\workspace'
|
||||
workspace_dir = WORKSPACE_DIR
|
||||
|
||||
if not os.path.exists(workspace_dir):
|
||||
if not workspace_dir.exists():
|
||||
print(f"Workspace directory not found: {workspace_dir}")
|
||||
return
|
||||
|
||||
# Find all .db files
|
||||
db_files = list(Path(workspace_dir).glob('**/db/*.db'))
|
||||
db_files = list(workspace_dir.glob('**/db/*.db'))
|
||||
|
||||
if not db_files:
|
||||
print("No databases found to migrate")
|
||||
|
||||
@@ -53,6 +53,39 @@ WORKSPACE_DIR = os.path.join(ROOT_DIR, 'workspace')
|
||||
# Engine cache for multi-tenant support
|
||||
_user_engines = {}
|
||||
|
||||
|
||||
def _ensure_daily_workflow_schema(engine, user_id: str) -> None:
|
||||
"""Backfill required daily_workflow_plans columns for legacy tenant DBs."""
|
||||
required_columns = {
|
||||
"generation_mode": "VARCHAR(30) NOT NULL DEFAULT 'llm_generation'",
|
||||
"committee_agent_count": "INTEGER NOT NULL DEFAULT 0",
|
||||
"fallback_used": "BOOLEAN NOT NULL DEFAULT 0",
|
||||
"generation_run_id": "INTEGER",
|
||||
}
|
||||
|
||||
try:
|
||||
with engine.begin() as conn:
|
||||
table_check = conn.exec_driver_sql(
|
||||
"SELECT name FROM sqlite_master WHERE type='table' AND name='daily_workflow_plans'"
|
||||
).fetchone()
|
||||
if not table_check:
|
||||
return
|
||||
|
||||
existing_cols = {
|
||||
row[1] for row in conn.exec_driver_sql("PRAGMA table_info(daily_workflow_plans)").fetchall()
|
||||
}
|
||||
|
||||
for col_name, col_def in required_columns.items():
|
||||
if col_name not in existing_cols:
|
||||
conn.exec_driver_sql(
|
||||
f"ALTER TABLE daily_workflow_plans ADD COLUMN {col_name} {col_def}"
|
||||
)
|
||||
logger.warning(
|
||||
f"Auto-migrated daily_workflow_plans column '{col_name}' for user {user_id}"
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed daily_workflow_plans schema compatibility check for user {user_id}: {e}")
|
||||
|
||||
def get_user_db_path(user_id: str) -> str:
|
||||
"""Get the database path for a specific user."""
|
||||
# Sanitize user_id to be safe for filesystem
|
||||
@@ -192,6 +225,7 @@ def init_user_database(user_id: str):
|
||||
UserBusinessInfoBase.metadata.create_all(bind=engine)
|
||||
ContentAssetBase.metadata.create_all(bind=engine)
|
||||
BingAnalyticsBase.metadata.create_all(bind=engine)
|
||||
_ensure_daily_workflow_schema(engine, user_id)
|
||||
|
||||
# Initialize default data for new databases
|
||||
try:
|
||||
|
||||
@@ -3,7 +3,10 @@ Task Scheduler Package
|
||||
Modular, pluggable scheduler for ALwrity tasks.
|
||||
"""
|
||||
|
||||
import os
|
||||
|
||||
from sqlalchemy.orm import Session
|
||||
from apscheduler.triggers.cron import CronTrigger
|
||||
|
||||
from .core.scheduler import TaskScheduler
|
||||
from .core.executor_interface import TaskExecutor, TaskExecutionResult
|
||||
@@ -32,6 +35,7 @@ from .utils.platform_insights_task_loader import load_due_platform_insights_task
|
||||
from .utils.advertools_task_loader import load_due_advertools_tasks
|
||||
from .utils.sif_indexing_task_loader import load_due_sif_indexing_tasks
|
||||
from .utils.market_trends_task_loader import load_due_market_trends_tasks
|
||||
from services.today_workflow_service import generate_scheduled_daily_workflows
|
||||
|
||||
# Global scheduler instance (initialized on first access)
|
||||
_scheduler_instance: TaskScheduler = None
|
||||
@@ -144,6 +148,18 @@ def get_scheduler() -> TaskScheduler:
|
||||
load_due_market_trends_tasks
|
||||
)
|
||||
|
||||
today_workflow_hour_utc = int(os.getenv('TODAY_WORKFLOW_SCHEDULE_HOUR_UTC', '2'))
|
||||
today_workflow_minute_utc = int(os.getenv('TODAY_WORKFLOW_SCHEDULE_MINUTE_UTC', '0'))
|
||||
_scheduler_instance.scheduler.add_job(
|
||||
generate_scheduled_daily_workflows,
|
||||
trigger=CronTrigger(hour=today_workflow_hour_utc, minute=today_workflow_minute_utc, timezone='UTC'),
|
||||
id='generate_daily_workflows',
|
||||
replace_existing=True,
|
||||
max_instances=1,
|
||||
coalesce=True,
|
||||
misfire_grace_time=3600,
|
||||
)
|
||||
|
||||
return _scheduler_instance
|
||||
|
||||
|
||||
|
||||
@@ -8,6 +8,7 @@ from models.daily_workflow_models import DailyWorkflowPlan, DailyWorkflowTask
|
||||
from models.agent_activity_models import AgentAlert
|
||||
from services.agent_activity_service import AgentActivityService, build_agent_event_payload
|
||||
from services.llm_providers.main_text_generation import llm_text_gen
|
||||
from services.database import get_all_user_ids, get_session_for_user
|
||||
from loguru import logger
|
||||
|
||||
PILLAR_IDS = ["plan", "generate", "publish", "analyze", "engage", "remarket"]
|
||||
@@ -604,7 +605,12 @@ async def generate_agent_enhanced_plan(
|
||||
return result
|
||||
|
||||
|
||||
async def get_or_create_daily_workflow_plan(db: Session, user_id: str, date: Optional[str] = None) -> tuple[DailyWorkflowPlan, bool]:
|
||||
async def get_or_create_daily_workflow_plan(
|
||||
db: Session,
|
||||
user_id: str,
|
||||
date: Optional[str] = None,
|
||||
creation_source: str = "manual",
|
||||
) -> tuple[DailyWorkflowPlan, bool]:
|
||||
from starlette.concurrency import run_in_threadpool
|
||||
|
||||
date_str = date or _today_date_str()
|
||||
@@ -646,7 +652,10 @@ async def get_or_create_daily_workflow_plan(db: Session, user_id: str, date: Opt
|
||||
plan = DailyWorkflowPlan(
|
||||
user_id=user_id,
|
||||
date=date_str,
|
||||
source="agent",
|
||||
source=creation_source,
|
||||
generation_mode=_derive_generation_mode(plan_data),
|
||||
committee_agent_count=_count_committee_agents(tasks),
|
||||
fallback_used=_plan_uses_fallback(tasks),
|
||||
plan_json=plan_data,
|
||||
created_at=datetime.utcnow(),
|
||||
updated_at=datetime.utcnow(),
|
||||
@@ -685,6 +694,80 @@ async def get_or_create_daily_workflow_plan(db: Session, user_id: str, date: Opt
|
||||
return plan, True
|
||||
|
||||
|
||||
def _derive_generation_mode(plan_data: Dict[str, Any]) -> str:
|
||||
tasks = plan_data.get("tasks", []) if isinstance(plan_data, dict) else []
|
||||
source_modes = set()
|
||||
for task in tasks:
|
||||
metadata = task.get("metadata") if isinstance(task, dict) else {}
|
||||
metadata = metadata if isinstance(metadata, dict) else {}
|
||||
source_agent = str(metadata.get("source_agent") or "").strip()
|
||||
source = str(metadata.get("source") or "").strip()
|
||||
if source_agent:
|
||||
source_modes.add("agent_committee")
|
||||
elif source in {"controlled_fallback", "llm_pillar_backfill"}:
|
||||
source_modes.add(source)
|
||||
|
||||
if "agent_committee" in source_modes:
|
||||
return "agent_committee"
|
||||
if "controlled_fallback" in source_modes:
|
||||
return "controlled_fallback"
|
||||
if "llm_pillar_backfill" in source_modes:
|
||||
return "llm_pillar_backfill"
|
||||
return "llm_generation"
|
||||
|
||||
|
||||
def _count_committee_agents(tasks: List[Dict[str, Any]]) -> int:
|
||||
agents = set()
|
||||
for task in tasks:
|
||||
metadata = task.get("metadata") if isinstance(task, dict) else {}
|
||||
metadata = metadata if isinstance(metadata, dict) else {}
|
||||
source_agent = str(metadata.get("source_agent") or "").strip()
|
||||
if source_agent:
|
||||
agents.add(source_agent)
|
||||
return len(agents)
|
||||
|
||||
|
||||
def _plan_uses_fallback(tasks: List[Dict[str, Any]]) -> bool:
|
||||
for task in tasks:
|
||||
metadata = task.get("metadata") if isinstance(task, dict) else {}
|
||||
metadata = metadata if isinstance(metadata, dict) else {}
|
||||
source = str(metadata.get("source") or "").strip()
|
||||
if source in {"controlled_fallback", "llm_pillar_backfill"}:
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
async def generate_scheduled_daily_workflows() -> Dict[str, int]:
|
||||
user_ids = get_all_user_ids()
|
||||
stats = {"users_seen": 0, "created": 0, "existing": 0, "failed": 0}
|
||||
|
||||
for user_id in user_ids:
|
||||
stats["users_seen"] += 1
|
||||
db = None
|
||||
try:
|
||||
db = get_session_for_user(user_id)
|
||||
plan, created = await get_or_create_daily_workflow_plan(
|
||||
db,
|
||||
user_id,
|
||||
creation_source="scheduled",
|
||||
)
|
||||
if created:
|
||||
stats["created"] += 1
|
||||
logger.info("Scheduled daily workflow created for user {} date {}", user_id, plan.date)
|
||||
else:
|
||||
stats["existing"] += 1
|
||||
logger.info("Scheduled daily workflow already exists for user {} date {}", user_id, plan.date)
|
||||
except Exception as e:
|
||||
stats["failed"] += 1
|
||||
logger.error("Scheduled daily workflow generation failed for user {}: {}", user_id, e)
|
||||
finally:
|
||||
if db:
|
||||
db.close()
|
||||
|
||||
logger.info("Scheduled daily workflow run complete: {}", stats)
|
||||
return stats
|
||||
|
||||
|
||||
def update_task_status(
|
||||
db: Session,
|
||||
user_id: str,
|
||||
|
||||
@@ -3,6 +3,7 @@ Video generation operations (text-to-video and image-to-video).
|
||||
"""
|
||||
|
||||
import requests
|
||||
import json
|
||||
from typing import Any, Dict, Optional
|
||||
from fastapi import HTTPException
|
||||
|
||||
@@ -12,6 +13,19 @@ from .base import VideoBase
|
||||
logger = get_service_logger("wavespeed.generators.video.generation")
|
||||
|
||||
|
||||
def _extract_wavespeed_message(response_text: str) -> str:
|
||||
"""Best-effort extraction of WaveSpeed error message from response payload."""
|
||||
if not response_text:
|
||||
return ""
|
||||
try:
|
||||
parsed = json.loads(response_text)
|
||||
if isinstance(parsed, dict):
|
||||
return str(parsed.get("message") or parsed.get("error") or "")
|
||||
except (json.JSONDecodeError, TypeError, ValueError):
|
||||
return ""
|
||||
return ""
|
||||
|
||||
|
||||
class VideoGeneration(VideoBase):
|
||||
"""Video generation operations."""
|
||||
|
||||
@@ -31,6 +45,25 @@ class VideoGeneration(VideoBase):
|
||||
response = requests.post(url, headers=self._get_headers(), json=payload, timeout=timeout)
|
||||
if response.status_code != 200:
|
||||
logger.error(f"[WaveSpeed] Submission failed: {response.status_code} {response.text}")
|
||||
|
||||
error_message = _extract_wavespeed_message(response.text)
|
||||
if "insufficient credits" in error_message.lower() or "credit" in error_message.lower():
|
||||
raise HTTPException(
|
||||
status_code=429,
|
||||
detail={
|
||||
"error": "Insufficient WaveSpeed credits",
|
||||
"message": "Insufficient credits. Please top up to continue video generation.",
|
||||
"provider": "wavespeed",
|
||||
"usage_info": {
|
||||
"provider": "wavespeed",
|
||||
"type": "credits",
|
||||
"limit_type": "provider_credits",
|
||||
"operation_type": "scene_animation",
|
||||
"action_required": "top_up",
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
@@ -75,6 +108,25 @@ class VideoGeneration(VideoBase):
|
||||
|
||||
if response.status_code != 200:
|
||||
logger.error(f"[WaveSpeed] Text-to-video submission failed: {response.status_code} {response.text}")
|
||||
|
||||
error_message = _extract_wavespeed_message(response.text)
|
||||
if "insufficient credits" in error_message.lower() or "credit" in error_message.lower():
|
||||
raise HTTPException(
|
||||
status_code=429,
|
||||
detail={
|
||||
"error": "Insufficient WaveSpeed credits",
|
||||
"message": "Insufficient credits. Please top up to continue video generation.",
|
||||
"provider": "wavespeed",
|
||||
"usage_info": {
|
||||
"provider": "wavespeed",
|
||||
"type": "credits",
|
||||
"limit_type": "provider_credits",
|
||||
"operation_type": "video_generation",
|
||||
"action_required": "top_up",
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
@@ -174,6 +226,25 @@ class VideoGeneration(VideoBase):
|
||||
|
||||
if response.status_code != 200:
|
||||
logger.error(f"[WaveSpeed] Text-to-video submission failed: {response.status_code} {response.text}")
|
||||
|
||||
error_message = _extract_wavespeed_message(response.text)
|
||||
if "insufficient credits" in error_message.lower() or "credit" in error_message.lower():
|
||||
raise HTTPException(
|
||||
status_code=429,
|
||||
detail={
|
||||
"error": "Insufficient WaveSpeed credits",
|
||||
"message": "Insufficient credits. Please top up to continue video generation.",
|
||||
"provider": "wavespeed",
|
||||
"usage_info": {
|
||||
"provider": "wavespeed",
|
||||
"type": "credits",
|
||||
"limit_type": "provider_credits",
|
||||
"operation_type": "video_generation",
|
||||
"action_required": "top_up",
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
raise HTTPException(
|
||||
status_code=502,
|
||||
detail={
|
||||
|
||||
361
docs/BACKEND_LOG_RCA_TRACKER.md
Normal file
361
docs/BACKEND_LOG_RCA_TRACKER.md
Normal file
@@ -0,0 +1,361 @@
|
||||
# Backend Log RCA Tracker
|
||||
|
||||
## Purpose
|
||||
|
||||
This document is the working catalog for backend issues observed in runtime logs.
|
||||
|
||||
For each issue, capture:
|
||||
- error signature
|
||||
- observed symptoms
|
||||
- likely root cause analysis
|
||||
- confidence level
|
||||
- files to inspect/edit
|
||||
- fix strategy notes
|
||||
- validation steps
|
||||
- status
|
||||
|
||||
## Triage Rules
|
||||
|
||||
- Do not fix directly from logs alone unless root cause is confirmed.
|
||||
- Prefer grouping repeated log lines under one issue.
|
||||
- Track the first failing subsystem, then downstream effects.
|
||||
- Separate configuration problems from code defects.
|
||||
- Keep this document updated before and after each fix.
|
||||
|
||||
## Issue 1: Clerk token verification failures on authenticated endpoints
|
||||
|
||||
- **Status**: Open
|
||||
- **Severity**: High
|
||||
- **Subsystem**: Authentication / request pipeline
|
||||
- **Error signatures**:
|
||||
- `Unverified token rejected (production).`
|
||||
- `AUTHENTICATION ERROR: Token verification failed for endpoint: GET /api/...`
|
||||
- **Observed endpoints in logs**:
|
||||
- `/api/content-planning/monitoring/lightweight-stats`
|
||||
- `/api/content-planning/monitoring/health`
|
||||
- `/api/subscription/dashboard/...`
|
||||
- `/api/subscription/alerts/...`
|
||||
- `/api/subscription/status/...`
|
||||
- **Observed behavior**:
|
||||
- Requests reach authenticated endpoints.
|
||||
- Clerk verification fails.
|
||||
- Fallback unverified decode path is attempted.
|
||||
- Production mode rejects the token.
|
||||
- **Primary RCA hypothesis**:
|
||||
- The backend is receiving bearer tokens that do not successfully validate against the resolved Clerk JWKS/issuer configuration.
|
||||
- The middleware then falls back to unverified decode, but production mode explicitly rejects that path.
|
||||
- **Secondary RCA hypotheses**:
|
||||
- Frontend token/audience/issuer mismatch.
|
||||
- Wrong Clerk environment variables loaded in backend.
|
||||
- Issuer-derived JWKS URL resolution is inconsistent with actual Clerk instance.
|
||||
- Requests may be sent before a valid session token is available.
|
||||
- **Evidence in code**:
|
||||
- `backend/middleware/auth_middleware.py`
|
||||
- `ClerkAuthMiddleware.__init__`
|
||||
- `ClerkAuthMiddleware.verify_token`
|
||||
- `get_current_user`
|
||||
- Relevant logic:
|
||||
- derives JWKS URL from token issuer or cached publishable key instance
|
||||
- falls back to `jwt.decode(..., verify_signature=False)`
|
||||
- rejects unverified tokens when `ALLOW_UNVERIFIED_JWT_DEV` is false
|
||||
- **Likely files to inspect/edit later**:
|
||||
- `backend/middleware/auth_middleware.py`
|
||||
- possibly frontend auth/session request layer if token attachment is inconsistent
|
||||
- **Confidence**: Medium
|
||||
- **Root-cause questions to answer**:
|
||||
- Are `CLERK_SECRET_KEY` and publishable key values from the same Clerk instance?
|
||||
- Is the token issuer exactly matching the intended Clerk environment?
|
||||
- Are failing requests sent with stale, dev, or cross-environment tokens?
|
||||
- Are these requests triggered before Clerk session hydration on the frontend?
|
||||
- **Validation after fix**:
|
||||
- Authenticated endpoints return 200 with verified user context.
|
||||
- No `Unverified token rejected (production)` log spam for healthy requests.
|
||||
|
||||
## Issue 2: Hugging Face structured JSON generation failing with model not found
|
||||
|
||||
- **Status**: Open
|
||||
- **Severity**: High
|
||||
- **Subsystem**: LLM provider / workflow generation
|
||||
- **Error signatures**:
|
||||
- `HF structured model not found: %s. Trying fallback model.`
|
||||
- `Hugging Face API call failed: Not Found`
|
||||
- `HF structured model not found (no response_format path): %s`
|
||||
- `Hugging Face structured JSON generation failed: NotFoundError: Not Found`
|
||||
- `[llm_text_gen] Provider huggingface failed: RetryError[...]`
|
||||
- **Observed behavior**:
|
||||
- Structured JSON call tries primary model.
|
||||
- Fallback model sequence also fails.
|
||||
- Retry without `response_format` still fails with `NotFound`.
|
||||
- Upstream caller falls through to another provider or fallback path.
|
||||
- **Primary RCA hypothesis**:
|
||||
- The configured Hugging Face model identifier is invalid, unavailable to the account/provider, or incompatible with the current OpenAI-compatible Hugging Face endpoint.
|
||||
- **Secondary RCA hypotheses**:
|
||||
- Base URL/API key/provider configuration is wrong.
|
||||
- Fallback model list contains provider-specific model ids not available in the current account/region.
|
||||
- Structured generation path assumes chat completions support for models that only exist on a different inference route.
|
||||
- **Evidence in code**:
|
||||
- `backend/services/llm_providers/huggingface_provider.py`
|
||||
- `_fallback_model_sequence`
|
||||
- `huggingface_structured_json_response`
|
||||
- The code retries:
|
||||
- with `response_format={"type": "json_object"}`
|
||||
- then again without `response_format`
|
||||
- Both paths still fail with `NotFoundError`, which points more strongly to model/base-url availability than schema formatting.
|
||||
- **Likely files to inspect/edit later**:
|
||||
- `backend/services/llm_providers/huggingface_provider.py`
|
||||
- provider selection/orchestration file calling Hugging Face as primary for structured JSON
|
||||
- environment/config file for HF model names and API base URL
|
||||
- **Confidence**: High
|
||||
- **Root-cause questions to answer**:
|
||||
- Which exact model string is being passed as the primary model in the failing call?
|
||||
- What base URL and API key are being used for the OpenAI client?
|
||||
- Are the fallback model ids valid for the currently configured Hugging Face inference provider?
|
||||
- **Validation after fix**:
|
||||
- A structured JSON test request succeeds with the intended model or a verified fallback.
|
||||
- No `NotFoundError` for the chosen model list.
|
||||
|
||||
## Issue 3: txtai indexing attempted before service initialization completes
|
||||
|
||||
- **Status**: Open
|
||||
- **Severity**: Medium
|
||||
- **Subsystem**: Semantic indexing / background tasks
|
||||
- **Error signatures**:
|
||||
- `Cannot index content - service not initialized for user ...`
|
||||
- **Observed behavior**:
|
||||
- Background indexing is triggered.
|
||||
- `TxtaiIntelligenceService.index_content` calls `_ensure_initialized()`.
|
||||
- `_ensure_initialized()` starts background initialization and returns immediately.
|
||||
- `index_content` then checks `_initialized`, sees false, and fails fast.
|
||||
- **Primary RCA hypothesis**:
|
||||
- There is a race condition between lazy background initialization and immediate indexing/search calls.
|
||||
- `SIF_FAIL_FAST=true` (default) causes operations to raise RuntimeError instead of gracefully deferring.
|
||||
- **Evidence in code**:
|
||||
- `backend/services/intelligence/txtai_service.py`:
|
||||
- Line 57: `self.fail_fast = str(os.getenv("SIF_FAIL_FAST", "true")).lower() in {"1", "true", "yes", "on"}`
|
||||
- Lines 234-235: `index_content` raises RuntimeError if `fail_fast` and not initialized
|
||||
- Lines 284-285: `search` raises RuntimeError if `fail_fast` and not initialized
|
||||
- Lines 319-320: `get_similarity` raises RuntimeError if `fail_fast` and not initialized
|
||||
- `_ensure_initialized` is intentionally non-blocking (starts background thread)
|
||||
- `backend/api/today_workflow.py`:
|
||||
- `_index_tasks_to_sif` triggers indexing in background after workflow actions
|
||||
- **Likely files to inspect/edit later**:
|
||||
- `backend/services/intelligence/txtai_service.py`
|
||||
- `backend/api/today_workflow.py`
|
||||
- any other callers that assume initialization is synchronous
|
||||
- **Confidence**: High
|
||||
- **Potential downstream impact**:
|
||||
- workflow/task indexing silently fails
|
||||
- semantic search quality degrades
|
||||
- noisy logs obscure higher-priority failures
|
||||
- **Root-cause questions to answer**:
|
||||
- Should `index_content` await `_ensure_initialized_async()` instead of using the non-blocking path?
|
||||
- Should callers tolerate deferred indexing instead of fail-fast behavior?
|
||||
- Is `SIF_FAIL_FAST=true` appropriate for background indexing operations?
|
||||
- Should `SIF_FAIL_FAST` default to `false` for background operations?
|
||||
- **Validation after fix**:
|
||||
- First indexing call after startup succeeds or is gracefully deferred without error spam.
|
||||
|
||||
## Issue 4: Today workflow endpoint reload observed during active debugging
|
||||
|
||||
- **Status**: Observed
|
||||
- **Severity**: Low
|
||||
- **Subsystem**: Development reload / workflow API
|
||||
- **Log signature**:
|
||||
- `StatReload detected changes in 'api\today_workflow.py'. Reloading...`
|
||||
- **Observed behavior**:
|
||||
- Development server reloads due to file edits.
|
||||
- **RCA**:
|
||||
- Expected dev-server behavior, not itself a product bug.
|
||||
- **Files involved**:
|
||||
- `backend/api/today_workflow.py`
|
||||
- **Confidence**: High
|
||||
- **Action**:
|
||||
- No fix needed; keep separate from actual runtime defects.
|
||||
|
||||
## Cross-Issue Notes
|
||||
|
||||
- The auth failures and the workflow/indexing issues may be independent.
|
||||
- The Hugging Face failure may trigger fallback task generation, which can still create workflows while hiding the upstream provider problem.
|
||||
- txtai indexing failures appear to be a post-generation side effect, not the root cause of generation failure.
|
||||
- **LiteLLM was investigated and dropped as a false herring** – no project-level SIF/txtai wiring to LiteLLM was found.
|
||||
- The SIF agent local-model path is **separate** from txtai embeddings and may be the source of the "local model used to work" feedback.
|
||||
|
||||
## Candidate Investigation Order
|
||||
|
||||
1. Authentication verification mismatch
|
||||
2. Hugging Face model/provider availability mismatch
|
||||
3. txtai initialization race (with `SIF_FAIL_FAST` behavior)
|
||||
4. SIF agent local-model defaults (Qwen 1.5B vs lighter alternatives)
|
||||
5. Any downstream workflow symptoms after the above are stabilized
|
||||
|
||||
## Minimal Fix Paths (Pre-Implementation)
|
||||
|
||||
### For Issue 3 (txtai init race):
|
||||
- **Option A**: Change `SIF_FAIL_FAST` default to `false` for background operations
|
||||
- Allows graceful deferral instead of RuntimeError
|
||||
- Minimal code change, no logic changes
|
||||
- **Option B**: Use `_ensure_initialized_async()` in `index_content`/`search`/`get_similarity`
|
||||
- Awaits initialization before proceeding
|
||||
- More robust but requires async refactoring
|
||||
- **Option C**: Add initialization state callbacks to callers
|
||||
- More complex, may not be necessary
|
||||
|
||||
### For Issue 5 (SIF agent local-model drift):
|
||||
- **Option A**: Change default `model_name` in `SIFBaseAgent.__init__` to lighter model
|
||||
- Example: `Qwen/Qwen2.5-0.5B-Instruct` or `TinyLlama/TinyLlama-1.1B-Chat-v1.0`
|
||||
- Single-line change, immediate effect
|
||||
- **Option B**: Add env/config override for default agent local model
|
||||
- More flexible, requires config wiring
|
||||
- Allows runtime tuning without code changes
|
||||
- **Option C**: Keep current default and rely on existing fallback chain
|
||||
- The fallback chain already tries lighter models if memory fails
|
||||
- May be sufficient if memory detection works correctly
|
||||
|
||||
## Current Evidence Sources
|
||||
|
||||
- Runtime logs from terminal `python` process `22056`
|
||||
- `backend/middleware/auth_middleware.py`
|
||||
- `backend/services/llm_providers/huggingface_provider.py`
|
||||
- `backend/services/intelligence/txtai_service.py`
|
||||
- `backend/api/today_workflow.py`
|
||||
- `backend/services/today_workflow_service.py`
|
||||
|
||||
## Issue 5: SIF agent local-model drift (distinct from txtai embeddings)
|
||||
|
||||
- **Status**: Open
|
||||
- **Severity**: Medium
|
||||
- **Subsystem**: SIF agents / local LLM wrappers
|
||||
- **Error signatures**:
|
||||
- (No direct log signature yet; this is a hypothesis from user feedback that "local model used to work")
|
||||
- **Observed behavior**:
|
||||
- User reports that a local model used to work for SIF agents now seems heavier or less responsive.
|
||||
- The SIF agent path is **separate** from txtai embeddings.
|
||||
- **Primary RCA hypothesis**:
|
||||
- The SIF agent local LLM wrapper path uses a 1.5B parameter model by default, which may be heavier than the previous local model.
|
||||
- This is distinct from txtai embeddings, which still use `sentence-transformers/all-MiniLM-L6-v2`.
|
||||
- **Evidence in code**:
|
||||
- `backend/services/intelligence/sif_agents.py`:
|
||||
- Lines 47-51: `LOCAL_LLM_FALLBACKS = ["Qwen/Qwen2.5-1.5B-Instruct", "Qwen/Qwen2.5-0.5B-Instruct", "TinyLlama/TinyLlama-1.1B-Chat-v1.0"]`
|
||||
- Lines 53-139: `LocalLLMWrapper` tries models in order, with memory issue detection and automatic fallback to smaller models
|
||||
- Line 141: `SIFBaseAgent.__init__` default `model_name="Qwen/Qwen2.5-1.5B-Instruct"`
|
||||
- `backend/services/intelligence/txtai_service.py`:
|
||||
- Line 48: Still uses `sentence-transformers/all-MiniLM-L6-v2` for embeddings
|
||||
- **Likely files to inspect/edit later**:
|
||||
- `backend/services/intelligence/sif_agents.py`
|
||||
- `backend/services/intelligence/agents/specialized/base.py`
|
||||
- any config/env that controls default agent local model
|
||||
- **Confidence**: Medium
|
||||
- **Root-cause questions to answer**:
|
||||
- What was the previous local model default for SIF agents?
|
||||
- Is `Qwen/Qwen2.5-1.5B-Instruct` actually too heavy for the user’s laptop?
|
||||
- Should the default be changed to `Qwen/Qwen2.5-0.5B-Instruct` or `TinyLlama/TinyLlama-1.1B-Chat-v1.0`?
|
||||
- Are there any env/config overrides that could make this configurable?
|
||||
- **Validation after fix**:
|
||||
- SIF agents use a CPU-friendly local model (e.g., smaller Qwen variant or TinyLlama).
|
||||
- Agent generation completes without excessive CPU/memory pressure.
|
||||
|
||||
## Issue 6: Model initialization blocking and module unification
|
||||
|
||||
- **Status**: Open
|
||||
- **Severity**: High
|
||||
- **Subsystem**: Startup / model loading / module architecture
|
||||
- **Error signatures**:
|
||||
- (No direct log signature; architectural issue)
|
||||
- **Observed behavior**:
|
||||
- `start_alwrity_backend.py` pre-downloads `Qwen/Qwen2.5-3B-Instruct` **synchronously** before server starts (line 122).
|
||||
- `sif_agents.py` defaults to `Qwen/Qwen2.5-1.5B-Instruct` and uses lazy loading via `LocalLLMWrapper`.
|
||||
- `txtai_service.py` uses `sentence-transformers/all-MiniLM-L6-v2` for embeddings.
|
||||
- Three separate modules handle model loading, creating confusion.
|
||||
- User wants fail-fast semantics (catch bugs, avoid silent failures) AND proper fallback.
|
||||
- User wants non-blocking model downloads for SIF/agents.
|
||||
- **Primary RCA hypothesis**:
|
||||
- Startup script blocks on model download, contradicting non-blocking requirement.
|
||||
- Model size mismatch: startup downloads 3B, agents default to 1.5B.
|
||||
- Fail-fast in `txtai_service.py` prevents fallback from working.
|
||||
- Module separation (`txtai_service.py`, `sif_agents.py`, `start_alwrity_backend.py`) creates confusion.
|
||||
- **Evidence in code**:
|
||||
- `start_alwrity_backend.py`:
|
||||
- Line 122: `target_model = "Qwen/Qwen2.5-3B-Instruct"`
|
||||
- Lines 127-131: `snapshot_download()` is **blocking** call
|
||||
- Lines 117-120: Skips on Render/Railway but **not** on local dev
|
||||
- `sif_agents.py`:
|
||||
- Line 48: `LOCAL_LLM_FALLBACKS = ["Qwen/Qwen2.5-1.5B-Instruct", "Qwen/Qwen2.5-0.5B-Instruct", "TinyLlama/TinyLlama-1.1B-Chat-v1.0"]`
|
||||
- Line 141: Default `model_name="Qwen/Qwen2.5-1.5B-Instruct"`
|
||||
- Line 150: Uses `LocalLLMWrapper` for lazy loading
|
||||
- Lines 94-130: Has fallback logic with memory issue detection
|
||||
- `txtai_service.py`:
|
||||
- Line 57: `SIF_FAIL_FAST=true` (default) causes RuntimeError
|
||||
- Lines 234-235, 284-285, 319-320: Fail-fast prevents fallback
|
||||
- **Likely files to inspect/edit later**:
|
||||
- `start_alwrity_backend.py` (remove blocking download)
|
||||
- `services/intelligence/sif_agents.py` (unify model defaults)
|
||||
- `services/intelligence/txtai_service.py` (fix fail-fast with fallback)
|
||||
- Create unified `services/intelligence/model_registry.py` or similar
|
||||
- **Confidence**: High
|
||||
- **Root-cause questions to answer**:
|
||||
- Should model download be truly non-blocking (background thread)?
|
||||
- Should fail-fast be conditional (e.g., only for critical paths, not background ops)?
|
||||
- Should module unification create a single `ModelRegistry` or `ModelManager`?
|
||||
- How to ensure JSON/response structure compatibility across fallback chain?
|
||||
- **Validation after fix**:
|
||||
- Server starts without blocking on model download.
|
||||
- SIF agents use consistent model defaults.
|
||||
- Fail-fast catches bugs but allows fallback for non-critical ops.
|
||||
- Single module handles all model loading logic.
|
||||
|
||||
## Minimal Fix Paths (Pre-Implementation)
|
||||
|
||||
### For Issue 3 (txtai init race) - REVISED:
|
||||
- **Option A**: Change `SIF_FAIL_FAST` to be **conditional** (not global)
|
||||
- Keep fail-fast for critical paths (user-initiated ops)
|
||||
- Allow graceful deferral for background ops (indexing, clustering)
|
||||
- Requires distinguishing operation types
|
||||
- **Option B**: Use `_ensure_initialized_async()` for **blocking ops only**
|
||||
- Keep non-blocking for background ops
|
||||
- Awaits init for user-facing ops
|
||||
- More robust but requires async refactoring
|
||||
- **Option C**: Add operation-type-aware fail-fast
|
||||
- Pass `critical=True/False` to operations
|
||||
- Fail-fast only when `critical=True`
|
||||
- Most aligned with user requirements
|
||||
|
||||
### For Issue 5 (SIF agent local-model drift) - REVISED:
|
||||
- **Option A**: Change default to lighter model AND improve fallback chain
|
||||
- Default: `Qwen/Qwen2.5-0.5B-Instruct` (lighter)
|
||||
- Fallback: `0.5B → TinyLlama 1.1B`
|
||||
- Ensure JSON/response structure compatibility
|
||||
- **Option B**: Add env/config override + keep fallback chain
|
||||
- `SIF_AGENT_MODEL` env var
|
||||
- Fallback chain remains as-is
|
||||
- More flexible
|
||||
- **Option C**: Keep current default and rely on existing fallback chain
|
||||
- **RECOMMENDED**: Already has memory detection and fallback
|
||||
- Just need to ensure JSON compatibility
|
||||
|
||||
### For Issue 6 (Model blocking + module unification):
|
||||
- **Option A**: Remove blocking download from startup script
|
||||
- Delete `bootstrap_local_llm_models()` call
|
||||
- Let `LocalLLMWrapper` handle lazy loading
|
||||
- Minimal change, immediate non-blocking
|
||||
- **Option B**: Make download non-blocking (background thread)
|
||||
- Keep pre-download but in background
|
||||
- Server starts immediately
|
||||
- More complex
|
||||
- **Option C**: Create unified `ModelRegistry` module
|
||||
- Single source of truth for model defaults
|
||||
- Centralized download/cache logic
|
||||
- Eliminates confusion between modules
|
||||
- **RECOMMENDED for long-term**
|
||||
|
||||
## Session Update Log
|
||||
|
||||
### 2026-03-10
|
||||
|
||||
- Created initial RCA tracker document.
|
||||
- Seeded first three concrete issues from supplied logs.
|
||||
- No fixes applied from this document yet.
|
||||
- Added Issue 5: SIF agent local-model drift (LiteLLM dropped as false herring).
|
||||
- Refined Issue 3 with `SIF_FAIL_FAST` behavior details.
|
||||
- Added minimal fix paths for Issues 3 and 5.
|
||||
- Added Issue 6: Model initialization blocking and module unification.
|
||||
- Updated minimal fix paths based on user requirements (fail-fast + fallback, non-blocking, unification).
|
||||
@@ -36,9 +36,9 @@
|
||||
"zustand": "^5.0.7"
|
||||
},
|
||||
"scripts": {
|
||||
"start": "react-scripts start",
|
||||
"build": "node --max_old_space_size=8192 node_modules/react-scripts/scripts/build.js",
|
||||
"build:nomap": "node --max_old_space_size=8192 -e \"process.env.GENERATE_SOURCEMAP='false'; require('./node_modules/react-scripts/scripts/build');\"",
|
||||
"start": "node --max_old_space_size=12288 node_modules/react-scripts/scripts/start.js",
|
||||
"build": "node --max_old_space_size=12288 node_modules/react-scripts/scripts/build.js",
|
||||
"build:nomap": "node --max_old_space_size=12288 -e \"process.env.GENERATE_SOURCEMAP='false'; require('./node_modules/react-scripts/scripts/build');\"",
|
||||
"test": "react-scripts test",
|
||||
"eject": "react-scripts eject",
|
||||
"analyze": "npm run build && npx source-map-explorer 'build/static/js/*.js' --html bundle-report.html",
|
||||
|
||||
@@ -6,6 +6,7 @@ import {
|
||||
Snackbar,
|
||||
useTheme
|
||||
} from '@mui/material';
|
||||
import { Lightbulb } from '@mui/icons-material';
|
||||
import { motion, AnimatePresence } from 'framer-motion';
|
||||
import { useNavigate } from 'react-router-dom';
|
||||
import { useAuth } from '@clerk/clerk-react';
|
||||
@@ -63,7 +64,9 @@ const MainDashboard: React.FC = () => {
|
||||
const {
|
||||
currentWorkflow,
|
||||
workflowProgress,
|
||||
scheduleStatus,
|
||||
isLoading: workflowLoading,
|
||||
loadTodayWorkflow,
|
||||
generateDailyWorkflow,
|
||||
startWorkflow,
|
||||
pauseWorkflow,
|
||||
@@ -71,19 +74,18 @@ const MainDashboard: React.FC = () => {
|
||||
} = useWorkflowStore();
|
||||
const { userId } = useAuth();
|
||||
|
||||
// Initialize workflow on component mount
|
||||
React.useEffect(() => {
|
||||
const initializeWorkflow = async () => {
|
||||
try {
|
||||
if (!userId) return;
|
||||
await generateDailyWorkflow(userId);
|
||||
await loadTodayWorkflow();
|
||||
} catch (error) {
|
||||
console.warn('Failed to initialize workflow:', error);
|
||||
console.warn('Failed to load today workflow:', error);
|
||||
}
|
||||
};
|
||||
|
||||
initializeWorkflow();
|
||||
}, [generateDailyWorkflow, userId]);
|
||||
}, [loadTodayWorkflow, userId]);
|
||||
|
||||
// Debug logging for workflow state (only in development)
|
||||
React.useEffect(() => {
|
||||
@@ -238,6 +240,17 @@ const MainDashboard: React.FC = () => {
|
||||
|
||||
// Note: filteredCategories removed as it's not used in the current implementation
|
||||
|
||||
const statusChips = React.useMemo(() => {
|
||||
const scheduled = !!scheduleStatus?.scheduled_run_completed;
|
||||
return [
|
||||
{
|
||||
label: scheduled ? 'Scheduled workflow ready' : 'Scheduled workflow pending',
|
||||
color: scheduled ? '#22c55e' : '#ef4444',
|
||||
icon: <Lightbulb sx={{ color: scheduled ? '#22c55e' : '#ef4444' }} />,
|
||||
},
|
||||
];
|
||||
}, [scheduleStatus]);
|
||||
|
||||
if (loading) {
|
||||
return <LoadingSkeleton />;
|
||||
}
|
||||
@@ -297,7 +310,7 @@ const MainDashboard: React.FC = () => {
|
||||
<DashboardHeader
|
||||
title="Alwrity Content Hub"
|
||||
subtitle=""
|
||||
statusChips={[]}
|
||||
statusChips={statusChips}
|
||||
customIcon={AskAlwrityIcon}
|
||||
workflowControls={{
|
||||
onStartWorkflow: handleStartWorkflow,
|
||||
|
||||
@@ -7,10 +7,11 @@ import {
|
||||
UserWorkflowPreferences,
|
||||
NavigationState,
|
||||
WorkflowStatus,
|
||||
WorkflowError
|
||||
WorkflowError,
|
||||
TodayWorkflowScheduleStatus
|
||||
} from '../types/workflow';
|
||||
import { taskWorkflowOrchestrator } from '../services/TaskWorkflowOrchestrator';
|
||||
import { apiClient, ConnectionError, NetworkError } from '../api/client';
|
||||
import { apiClient } from '../api/client';
|
||||
|
||||
const isServerWorkflowId = (workflowId: string) => workflowId.startsWith('daily-');
|
||||
|
||||
@@ -47,9 +48,6 @@ const normalizeServerWorkflow = (workflow: DailyWorkflow): DailyWorkflow => ({
|
||||
: [],
|
||||
});
|
||||
|
||||
const isServerUnavailableError = (error: unknown): boolean =>
|
||||
error instanceof ConnectionError || error instanceof NetworkError;
|
||||
|
||||
const toWorkflowError = (error: unknown, fallbackMessage: string): WorkflowError => {
|
||||
if (error instanceof WorkflowError) return error;
|
||||
|
||||
@@ -109,6 +107,7 @@ interface WorkflowState {
|
||||
currentWorkflow: DailyWorkflow | null;
|
||||
workflowProgress: WorkflowProgress | null;
|
||||
navigationState: NavigationState | null;
|
||||
scheduleStatus: TodayWorkflowScheduleStatus | null;
|
||||
|
||||
// User preferences
|
||||
userPreferences: UserWorkflowPreferences | null;
|
||||
@@ -121,6 +120,8 @@ interface WorkflowState {
|
||||
degradedModeReason: string | null;
|
||||
|
||||
// Actions
|
||||
loadTodayWorkflow: (date?: string) => Promise<void>;
|
||||
refreshScheduleStatus: (date?: string) => Promise<void>;
|
||||
generateDailyWorkflow: (userId: string, date?: string) => Promise<void>;
|
||||
startWorkflow: (workflowId: string) => Promise<void>;
|
||||
pauseWorkflow: (workflowId: string) => Promise<void>;
|
||||
@@ -154,6 +155,7 @@ export const useWorkflowStore = create<WorkflowState>()(
|
||||
currentWorkflow: null,
|
||||
workflowProgress: null,
|
||||
navigationState: null,
|
||||
scheduleStatus: null,
|
||||
userPreferences: null,
|
||||
isWorkflowModalOpen: false,
|
||||
isLoading: false,
|
||||
@@ -161,14 +163,14 @@ export const useWorkflowStore = create<WorkflowState>()(
|
||||
isDegradedMode: false,
|
||||
degradedModeReason: null,
|
||||
|
||||
// Generate daily workflow
|
||||
generateDailyWorkflow: async (userId: string, date?: string) => {
|
||||
loadTodayWorkflow: async (date?: string) => {
|
||||
set({ isLoading: true, error: null });
|
||||
|
||||
try {
|
||||
const resp = await apiClient.get('/api/today-workflow', { params: date ? { date } : {} });
|
||||
const serverWorkflow = resp?.data?.data?.workflow as DailyWorkflow | undefined;
|
||||
const planSummary = resp?.data?.data?.plan?.provenance_summary;
|
||||
const scheduleStatus = resp?.data?.data?.schedule_status as TodayWorkflowScheduleStatus | undefined;
|
||||
|
||||
if (serverWorkflow && Array.isArray(serverWorkflow.tasks)) {
|
||||
if (planSummary && !serverWorkflow.provenanceSummary) {
|
||||
@@ -180,6 +182,75 @@ export const useWorkflowStore = create<WorkflowState>()(
|
||||
currentWorkflow: normalizedWorkflow,
|
||||
workflowProgress: derived.progress,
|
||||
navigationState: derived.navigation,
|
||||
scheduleStatus: scheduleStatus || null,
|
||||
isLoading: false,
|
||||
isDegradedMode: false,
|
||||
degradedModeReason: null,
|
||||
});
|
||||
return;
|
||||
}
|
||||
|
||||
throw new WorkflowError({
|
||||
code: 'WORKFLOW_SCHEMA_INVALID',
|
||||
message: 'Server workflow response is missing a valid tasks array.',
|
||||
timestamp: new Date(),
|
||||
recoverable: false,
|
||||
suggestedAction: 'Refresh and try again. If this persists, contact support.'
|
||||
});
|
||||
} catch (error: any) {
|
||||
if (error?.response?.status === 404) {
|
||||
set({
|
||||
currentWorkflow: null,
|
||||
workflowProgress: null,
|
||||
navigationState: null,
|
||||
isLoading: false,
|
||||
isDegradedMode: false,
|
||||
degradedModeReason: null,
|
||||
});
|
||||
await get().refreshScheduleStatus(date);
|
||||
return;
|
||||
}
|
||||
|
||||
set({
|
||||
error: toWorkflowError(error, 'Failed to load workflow from server.'),
|
||||
isLoading: false,
|
||||
isDegradedMode: false,
|
||||
degradedModeReason: null,
|
||||
});
|
||||
}
|
||||
},
|
||||
|
||||
refreshScheduleStatus: async (date?: string) => {
|
||||
try {
|
||||
const resp = await apiClient.get('/api/today-workflow/status', { params: date ? { date } : {} });
|
||||
const scheduleStatus = resp?.data?.data as TodayWorkflowScheduleStatus | undefined;
|
||||
set({ scheduleStatus: scheduleStatus || null });
|
||||
} catch {
|
||||
set({ scheduleStatus: null });
|
||||
}
|
||||
},
|
||||
|
||||
// Generate daily workflow
|
||||
generateDailyWorkflow: async (userId: string, date?: string) => {
|
||||
set({ isLoading: true, error: null });
|
||||
|
||||
try {
|
||||
const resp = await apiClient.post('/api/today-workflow/generate', null, { params: date ? { date } : {} });
|
||||
const serverWorkflow = resp?.data?.data?.workflow as DailyWorkflow | undefined;
|
||||
const planSummary = resp?.data?.data?.plan?.provenance_summary;
|
||||
const scheduleStatus = resp?.data?.data?.schedule_status as TodayWorkflowScheduleStatus | undefined;
|
||||
|
||||
if (serverWorkflow && Array.isArray(serverWorkflow.tasks)) {
|
||||
if (planSummary && !serverWorkflow.provenanceSummary) {
|
||||
serverWorkflow.provenanceSummary = planSummary;
|
||||
}
|
||||
const normalizedWorkflow = normalizeServerWorkflow(serverWorkflow);
|
||||
const derived = computeProgressAndNavigation(normalizedWorkflow);
|
||||
set({
|
||||
currentWorkflow: normalizedWorkflow,
|
||||
workflowProgress: derived.progress,
|
||||
navigationState: derived.navigation,
|
||||
scheduleStatus: scheduleStatus || null,
|
||||
isLoading: false,
|
||||
isDegradedMode: false,
|
||||
degradedModeReason: null,
|
||||
@@ -195,34 +266,11 @@ export const useWorkflowStore = create<WorkflowState>()(
|
||||
suggestedAction: 'Refresh and try again. If this persists, contact support.'
|
||||
});
|
||||
} catch (error) {
|
||||
if (!isServerUnavailableError(error)) {
|
||||
set({
|
||||
error: toWorkflowError(error, 'Failed to load workflow from server.'),
|
||||
isLoading: false,
|
||||
isDegradedMode: false,
|
||||
degradedModeReason: null,
|
||||
});
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
try {
|
||||
const workflow = await taskWorkflowOrchestrator.generateDailyWorkflow(userId, date);
|
||||
const progress = taskWorkflowOrchestrator.getWorkflowProgress(workflow.id);
|
||||
const navigation = taskWorkflowOrchestrator.getNavigationState(workflow.id);
|
||||
set({
|
||||
currentWorkflow: workflow,
|
||||
workflowProgress: progress,
|
||||
navigationState: navigation,
|
||||
isLoading: false,
|
||||
isDegradedMode: true,
|
||||
degradedModeReason: 'Server workflow unavailable. Using local fallback workflow.',
|
||||
error: null,
|
||||
});
|
||||
} catch (error) {
|
||||
set({
|
||||
error: toWorkflowError(error, 'Failed to generate local fallback workflow.'),
|
||||
error: toWorkflowError(error, 'Failed to generate workflow from server.'),
|
||||
isLoading: false,
|
||||
isDegradedMode: false,
|
||||
degradedModeReason: null,
|
||||
});
|
||||
}
|
||||
},
|
||||
|
||||
@@ -14,6 +14,14 @@ export interface WorkflowProvenanceSummary {
|
||||
taskSourceBreakdown: Partial<Record<WorkflowGenerationMode, number>>;
|
||||
}
|
||||
|
||||
export interface TodayWorkflowScheduleStatus {
|
||||
date: string;
|
||||
generated: boolean;
|
||||
scheduled_run_completed: boolean;
|
||||
source: string | null;
|
||||
created_at?: string | null;
|
||||
}
|
||||
|
||||
export interface TodayTask {
|
||||
id: string;
|
||||
pillarId: string;
|
||||
|
||||
@@ -24,7 +24,7 @@
|
||||
"./node_modules/@types",
|
||||
"./src/types"
|
||||
],
|
||||
"types": ["jest", "node"]
|
||||
"types": ["node"]
|
||||
},
|
||||
"include": [
|
||||
"src"
|
||||
|
||||
Reference in New Issue
Block a user