# Story 2.2: Summary Generation Pipeline ## Status Draft ## Story **As a** user **I want** an end-to-end pipeline that seamlessly processes YouTube URLs into high-quality summaries **so that** I can get from video link to summary in a single, streamlined workflow ## Acceptance Criteria 1. Integrated pipeline connects URL validation → transcript extraction → AI summarization 2. Pipeline handles the complete workflow asynchronously with progress tracking 3. System provides intelligent summary optimization based on transcript characteristics 4. Generated summaries include enhanced metadata (video info, processing stats, quality scores) 5. Pipeline includes quality validation and automatic retry for failed summaries 6. Users can monitor pipeline progress and receive completion notifications ## Tasks / Subtasks - [ ] **Task 1: Pipeline Orchestration Service** (AC: 1, 2) - [ ] Create `SummaryPipeline` orchestrator in `backend/services/summary_pipeline.py` - [ ] Implement workflow coordination between video, transcript, and AI services - [ ] Add pipeline state management with persistent job tracking - [ ] Create rollback and cleanup mechanisms for failed pipelines - [ ] **Task 2: Enhanced Video Metadata Integration** (AC: 4) - [ ] Integrate YouTube Data API for rich video metadata extraction - [ ] Add video categorization and content type detection - [ ] Implement thumbnail and channel information capture - [ ] Create metadata-driven summary customization logic - [ ] **Task 3: Intelligent Summary Optimization** (AC: 3, 5) - [ ] Implement transcript analysis for content type detection (educational, entertainment, technical) - [ ] Add automatic summary length optimization based on content complexity - [ ] Create quality scoring algorithm for generated summaries - [ ] Implement summary enhancement for poor-quality results - [ ] **Task 4: Progress Tracking and Notifications** (AC: 2, 6) - [ ] Create comprehensive pipeline progress tracking system - [ ] Implement WebSocket notifications for real-time updates - [ ] Add email notifications for completed summaries (optional) - [ ] Create detailed logging and audit trail for each pipeline run - [ ] **Task 5: Quality Assurance and Validation** (AC: 5) - [ ] Implement summary quality validation checks - [ ] Add automatic retry logic for failed or low-quality summaries - [ ] Create fallback strategies for different types of failures - [ ] Implement summary improvement suggestions and regeneration - [ ] **Task 6: API Integration and Frontend** (AC: 1, 2, 6) - [ ] Create `/api/process` endpoint for end-to-end pipeline processing - [ ] Update frontend to use integrated pipeline instead of separate services - [ ] Add pipeline status dashboard for monitoring active and completed jobs - [ ] Implement pipeline cancellation and cleanup functionality - [ ] **Task 7: Performance and Reliability** (AC: 2, 5) - [ ] Add comprehensive error handling and recovery mechanisms - [ ] Implement pipeline timeout and resource management - [ ] Create performance monitoring and optimization tracking - [ ] Add pipeline analytics and usage statistics ## Dev Notes ### Architecture Context This story creates the core user-facing workflow that demonstrates the full value of the YouTube Summarizer. The pipeline must be reliable, fast, and provide clear feedback while handling edge cases gracefully. ### Pipeline Orchestration Architecture [Source: docs/architecture.md#pipeline-architecture] ```python # backend/services/summary_pipeline.py import asyncio import uuid from datetime import datetime from enum import Enum from typing import Dict, Optional, List, Any from dataclasses import dataclass, asdict from ..services.video_service import VideoService from ..services.transcript_service import TranscriptService from ..services.openai_summarizer import OpenAISummarizer from ..services.cache_manager import CacheManager from ..core.exceptions import PipelineError class PipelineStage(Enum): INITIALIZED = "initialized" VALIDATING_URL = "validating_url" EXTRACTING_METADATA = "extracting_metadata" EXTRACTING_TRANSCRIPT = "extracting_transcript" ANALYZING_CONTENT = "analyzing_content" GENERATING_SUMMARY = "generating_summary" VALIDATING_QUALITY = "validating_quality" COMPLETED = "completed" FAILED = "failed" CANCELLED = "cancelled" @dataclass class PipelineConfig: summary_length: str = "standard" include_timestamps: bool = False focus_areas: Optional[List[str]] = None quality_threshold: float = 0.7 max_retries: int = 2 enable_notifications: bool = True @dataclass class PipelineProgress: stage: PipelineStage percentage: float message: str estimated_time_remaining: Optional[float] = None current_step_details: Optional[Dict[str, Any]] = None @dataclass class PipelineResult: job_id: str video_url: str video_id: str status: PipelineStage # Video metadata video_metadata: Optional[Dict[str, Any]] = None # Processing results transcript: Optional[str] = None summary: Optional[str] = None key_points: Optional[List[str]] = None main_themes: Optional[List[str]] = None actionable_insights: Optional[List[str]] = None # Quality and metadata confidence_score: Optional[float] = None quality_score: Optional[float] = None processing_metadata: Optional[Dict[str, Any]] = None cost_data: Optional[Dict[str, Any]] = None # Timeline started_at: Optional[datetime] = None completed_at: Optional[datetime] = None processing_time_seconds: Optional[float] = None # Error information error: Optional[Dict[str, Any]] = None retry_count: int = 0 class SummaryPipeline: """Orchestrates the complete YouTube summarization workflow""" def __init__( self, video_service: VideoService, transcript_service: TranscriptService, ai_service: OpenAISummarizer, cache_manager: CacheManager ): self.video_service = video_service self.transcript_service = transcript_service self.ai_service = ai_service self.cache_manager = cache_manager # Active jobs tracking self.active_jobs: Dict[str, PipelineResult] = {} self.progress_callbacks: Dict[str, List[callable]] = {} async def process_video( self, video_url: str, config: PipelineConfig = None, progress_callback: callable = None ) -> str: """Start video processing pipeline and return job ID""" if config is None: config = PipelineConfig() job_id = str(uuid.uuid4()) # Initialize pipeline result result = PipelineResult( job_id=job_id, video_url=video_url, video_id="", # Will be populated during validation status=PipelineStage.INITIALIZED, started_at=datetime.utcnow(), retry_count=0 ) self.active_jobs[job_id] = result if progress_callback: self.progress_callbacks[job_id] = [progress_callback] # Start processing in background asyncio.create_task(self._execute_pipeline(job_id, config)) return job_id async def _execute_pipeline(self, job_id: str, config: PipelineConfig): """Execute the complete processing pipeline""" result = self.active_jobs[job_id] try: # Stage 1: URL Validation await self._update_progress(job_id, PipelineStage.VALIDATING_URL, 5, "Validating YouTube URL...") video_id = await self.video_service.extract_video_id(result.video_url) result.video_id = video_id # Stage 2: Extract Video Metadata await self._update_progress(job_id, PipelineStage.EXTRACTING_METADATA, 15, "Extracting video information...") metadata = await self._extract_enhanced_metadata(video_id) result.video_metadata = metadata # Stage 3: Extract Transcript await self._update_progress(job_id, PipelineStage.EXTRACTING_TRANSCRIPT, 35, "Extracting transcript...") transcript_result = await self.transcript_service.extract_transcript(video_id) result.transcript = transcript_result.transcript # Stage 4: Analyze Content for Optimization await self._update_progress(job_id, PipelineStage.ANALYZING_CONTENT, 50, "Analyzing content characteristics...") content_analysis = await self._analyze_content_characteristics(result.transcript, metadata) optimized_config = self._optimize_config_for_content(config, content_analysis) # Stage 5: Generate Summary await self._update_progress(job_id, PipelineStage.GENERATING_SUMMARY, 75, "Generating AI summary...") summary_result = await self._generate_optimized_summary(result.transcript, optimized_config, content_analysis) # Populate summary results result.summary = summary_result.summary result.key_points = summary_result.key_points result.main_themes = summary_result.main_themes result.actionable_insights = summary_result.actionable_insights result.confidence_score = summary_result.confidence_score result.processing_metadata = summary_result.processing_metadata result.cost_data = summary_result.cost_data # Stage 6: Quality Validation await self._update_progress(job_id, PipelineStage.VALIDATING_QUALITY, 90, "Validating summary quality...") quality_score = await self._validate_summary_quality(result, content_analysis) result.quality_score = quality_score # Check if quality meets threshold if quality_score < config.quality_threshold and result.retry_count < config.max_retries: await self._retry_with_improvements(job_id, config, "Low quality score") return # Stage 7: Complete result.completed_at = datetime.utcnow() result.processing_time_seconds = (result.completed_at - result.started_at).total_seconds() result.status = PipelineStage.COMPLETED await self._update_progress(job_id, PipelineStage.COMPLETED, 100, "Summary completed successfully!") # Cache the result await self.cache_manager.cache_pipeline_result(job_id, result) # Send completion notification if config.enable_notifications: await self._send_completion_notification(result) except Exception as e: await self._handle_pipeline_error(job_id, e, config) async def _extract_enhanced_metadata(self, video_id: str) -> Dict[str, Any]: """Extract rich video metadata using YouTube Data API""" # This would integrate with YouTube Data API v3 # For now, implementing basic structure try: # Simulate YouTube Data API call metadata = { "title": f"Video {video_id} Title", # Would come from API "description": "Video description...", "channel_name": "Channel Name", "published_at": datetime.utcnow().isoformat(), "duration": "PT10M30S", # ISO 8601 duration "view_count": 1000, "like_count": 50, "category": "Education", "tags": ["python", "tutorial", "coding"], "thumbnail_url": f"https://img.youtube.com/vi/{video_id}/maxresdefault.jpg", "language": "en", "default_language": "en" } return metadata except Exception as e: # Return basic metadata if enhanced extraction fails return { "video_id": video_id, "title": f"Video {video_id}", "error": f"Enhanced metadata extraction failed: {str(e)}" } async def _analyze_content_characteristics(self, transcript: str, metadata: Dict[str, Any]) -> Dict[str, Any]: """Analyze transcript and metadata to determine optimal processing strategy""" analysis = { "transcript_length": len(transcript), "word_count": len(transcript.split()), "estimated_reading_time": len(transcript.split()) / 250, # Words per minute "complexity_score": 0.5, # Would implement actual complexity analysis "content_type": "general", "language": metadata.get("language", "en"), "technical_indicators": [], "educational_indicators": [], "entertainment_indicators": [] } # Basic content type detection transcript_lower = transcript.lower() # Technical content indicators technical_terms = ["algorithm", "function", "variable", "database", "api", "code", "programming"] technical_count = sum(1 for term in technical_terms if term in transcript_lower) if technical_count >= 3: analysis["content_type"] = "technical" analysis["technical_indicators"] = [term for term in technical_terms if term in transcript_lower] # Educational content indicators educational_terms = ["learn", "tutorial", "explain", "understand", "concept", "example", "lesson"] educational_count = sum(1 for term in educational_terms if term in transcript_lower) if educational_count >= 3: analysis["content_type"] = "educational" analysis["educational_indicators"] = [term for term in educational_terms if term in transcript_lower] # Entertainment content indicators entertainment_terms = ["funny", "story", "experience", "adventure", "review", "reaction"] entertainment_count = sum(1 for term in entertainment_terms if term in transcript_lower) if entertainment_count >= 2: analysis["content_type"] = "entertainment" analysis["entertainment_indicators"] = [term for term in entertainment_terms if term in transcript_lower] # Complexity scoring based on sentence length and vocabulary sentences = transcript.split('.') avg_sentence_length = sum(len(s.split()) for s in sentences) / len(sentences) if sentences else 0 if avg_sentence_length > 20: analysis["complexity_score"] = min(1.0, analysis["complexity_score"] + 0.3) elif avg_sentence_length < 10: analysis["complexity_score"] = max(0.1, analysis["complexity_score"] - 0.2) return analysis def _optimize_config_for_content(self, base_config: PipelineConfig, analysis: Dict[str, Any]) -> PipelineConfig: """Optimize processing configuration based on content analysis""" optimized_config = PipelineConfig(**asdict(base_config)) # Adjust summary length based on content if analysis["word_count"] > 5000 and optimized_config.summary_length == "standard": optimized_config.summary_length = "detailed" elif analysis["word_count"] < 500 and optimized_config.summary_length == "standard": optimized_config.summary_length = "brief" # Add focus areas based on content type if not optimized_config.focus_areas: optimized_config.focus_areas = [] content_type = analysis.get("content_type", "general") if content_type == "technical": optimized_config.focus_areas.extend(["technical concepts", "implementation details"]) elif content_type == "educational": optimized_config.focus_areas.extend(["learning objectives", "key concepts", "practical applications"]) elif content_type == "entertainment": optimized_config.focus_areas.extend(["main highlights", "key moments", "overall message"]) # Adjust quality threshold based on complexity if analysis["complexity_score"] > 0.7: optimized_config.quality_threshold = max(0.6, optimized_config.quality_threshold - 0.1) return optimized_config async def _generate_optimized_summary( self, transcript: str, config: PipelineConfig, analysis: Dict[str, Any] ) -> Any: # Returns SummaryResult """Generate summary with content-aware optimizations""" from ..services.ai_service import SummaryRequest, SummaryLength # Map config to AI service parameters length_mapping = { "brief": SummaryLength.BRIEF, "standard": SummaryLength.STANDARD, "detailed": SummaryLength.DETAILED } summary_request = SummaryRequest( transcript=transcript, length=length_mapping[config.summary_length], focus_areas=config.focus_areas, language=analysis.get("language", "en"), include_timestamps=config.include_timestamps ) # Add content-specific prompt enhancements if analysis["content_type"] == "technical": summary_request.focus_areas.append("explain technical concepts clearly") elif analysis["content_type"] == "educational": summary_request.focus_areas.append("highlight learning outcomes") return await self.ai_service.generate_summary(summary_request) async def _validate_summary_quality(self, result: PipelineResult, analysis: Dict[str, Any]) -> float: """Validate and score summary quality""" quality_score = 0.0 # Check summary length appropriateness summary_word_count = len(result.summary.split()) if result.summary else 0 transcript_word_count = analysis["word_count"] # Good summary should be 5-15% of original length compression_ratio = summary_word_count / transcript_word_count if transcript_word_count > 0 else 0 if 0.05 <= compression_ratio <= 0.15: quality_score += 0.3 elif 0.03 <= compression_ratio <= 0.20: quality_score += 0.2 # Check key points availability and quality if result.key_points and len(result.key_points) >= 3: quality_score += 0.2 # Check main themes availability if result.main_themes and len(result.main_themes) >= 2: quality_score += 0.15 # Check actionable insights if result.actionable_insights and len(result.actionable_insights) >= 1: quality_score += 0.15 # Use AI confidence score if result.confidence_score and result.confidence_score > 0.8: quality_score += 0.2 elif result.confidence_score and result.confidence_score > 0.6: quality_score += 0.1 return min(1.0, quality_score) async def _retry_with_improvements(self, job_id: str, config: PipelineConfig, reason: str): """Retry pipeline with improved configuration""" result = self.active_jobs[job_id] result.retry_count += 1 await self._update_progress( job_id, PipelineStage.ANALYZING_CONTENT, 40, f"Retrying with improvements (attempt {result.retry_count + 1}/{config.max_retries + 1})" ) # Improve configuration for retry improved_config = PipelineConfig(**asdict(config)) improved_config.summary_length = "detailed" # Try more detailed summary improved_config.quality_threshold = max(0.5, config.quality_threshold - 0.1) # Lower threshold slightly # Continue pipeline with improved config await self._execute_pipeline(job_id, improved_config) async def _handle_pipeline_error(self, job_id: str, error: Exception, config: PipelineConfig): """Handle pipeline errors with retry logic""" result = self.active_jobs[job_id] result.status = PipelineStage.FAILED result.error = { "message": str(error), "type": type(error).__name__, "stage": result.status.value, "retry_count": result.retry_count } # Attempt retry if within limits if result.retry_count < config.max_retries: await asyncio.sleep(2 ** result.retry_count) # Exponential backoff await self._retry_with_improvements(job_id, config, f"Error: {str(error)}") else: result.completed_at = datetime.utcnow() await self._update_progress(job_id, PipelineStage.FAILED, 0, f"Failed after {result.retry_count + 1} attempts") async def _update_progress( self, job_id: str, stage: PipelineStage, percentage: float, message: str, details: Dict[str, Any] = None ): """Update pipeline progress and notify callbacks""" result = self.active_jobs.get(job_id) if result: result.status = stage progress = PipelineProgress( stage=stage, percentage=percentage, message=message, current_step_details=details ) # Notify all registered callbacks callbacks = self.progress_callbacks.get(job_id, []) for callback in callbacks: try: await callback(job_id, progress) except Exception as e: print(f"Progress callback error: {e}") async def get_pipeline_result(self, job_id: str) -> Optional[PipelineResult]: """Get pipeline result by job ID""" # Check active jobs first if job_id in self.active_jobs: return self.active_jobs[job_id] # Check cache for completed jobs cached_result = await self.cache_manager.get_cached_pipeline_result(job_id) return cached_result async def cancel_pipeline(self, job_id: str) -> bool: """Cancel running pipeline""" if job_id in self.active_jobs: result = self.active_jobs[job_id] result.status = PipelineStage.CANCELLED result.completed_at = datetime.utcnow() await self._update_progress(job_id, PipelineStage.CANCELLED, 0, "Pipeline cancelled by user") return True return False async def _send_completion_notification(self, result: PipelineResult): """Send completion notification (email, webhook, etc.)""" # This would integrate with notification service notification_data = { "job_id": result.job_id, "video_title": result.video_metadata.get("title", "Unknown") if result.video_metadata else "Unknown", "status": result.status.value, "processing_time": result.processing_time_seconds, "summary_preview": result.summary[:100] + "..." if result.summary else None } # Log completion for now (would send actual notifications) print(f"Pipeline completed: {notification_data}") ``` ### API Integration [Source: docs/architecture.md#api-specification] ```python # backend/api/pipeline.py from fastapi import APIRouter, HTTPException, BackgroundTasks, Depends from pydantic import BaseModel, Field, HttpUrl from typing import Optional, List, Dict, Any from ..services.summary_pipeline import SummaryPipeline, PipelineConfig, PipelineStage from ..core.websocket_manager import WebSocketManager router = APIRouter(prefix="/api", tags=["pipeline"]) class ProcessVideoRequest(BaseModel): video_url: HttpUrl = Field(..., description="YouTube video URL to process") summary_length: str = Field("standard", description="Summary length preference") focus_areas: Optional[List[str]] = Field(None, description="Areas to focus on in summary") include_timestamps: bool = Field(False, description="Include timestamps in summary") enable_notifications: bool = Field(True, description="Enable completion notifications") quality_threshold: float = Field(0.7, description="Minimum quality score threshold") class ProcessVideoResponse(BaseModel): job_id: str status: str message: str estimated_completion_time: Optional[float] = None class PipelineStatusResponse(BaseModel): job_id: str status: str progress_percentage: float current_message: str video_metadata: Optional[Dict[str, Any]] = None result: Optional[Dict[str, Any]] = None error: Optional[Dict[str, Any]] = None processing_time_seconds: Optional[float] = None @router.post("/process", response_model=ProcessVideoResponse) async def process_video( request: ProcessVideoRequest, pipeline: SummaryPipeline = Depends(), websocket_manager: WebSocketManager = Depends() ): """Process YouTube video through complete pipeline""" try: config = PipelineConfig( summary_length=request.summary_length, focus_areas=request.focus_areas or [], include_timestamps=request.include_timestamps, quality_threshold=request.quality_threshold, enable_notifications=request.enable_notifications ) # Create progress callback for WebSocket notifications async def progress_callback(job_id: str, progress): await websocket_manager.send_progress_update(job_id, { "stage": progress.stage.value, "percentage": progress.percentage, "message": progress.message, "details": progress.current_step_details }) # Start pipeline processing job_id = await pipeline.process_video( video_url=str(request.video_url), config=config, progress_callback=progress_callback ) return ProcessVideoResponse( job_id=job_id, status="processing", message="Video processing started", estimated_completion_time=120.0 # 2 minutes estimate ) except Exception as e: raise HTTPException( status_code=500, detail=f"Failed to start processing: {str(e)}" ) @router.get("/process/{job_id}", response_model=PipelineStatusResponse) async def get_pipeline_status( job_id: str, pipeline: SummaryPipeline = Depends() ): """Get pipeline processing status and results""" result = await pipeline.get_pipeline_result(job_id) if not result: raise HTTPException(status_code=404, detail="Pipeline job not found") # Calculate progress percentage based on stage stage_percentages = { PipelineStage.INITIALIZED: 0, PipelineStage.VALIDATING_URL: 5, PipelineStage.EXTRACTING_METADATA: 15, PipelineStage.EXTRACTING_TRANSCRIPT: 35, PipelineStage.ANALYZING_CONTENT: 50, PipelineStage.GENERATING_SUMMARY: 75, PipelineStage.VALIDATING_QUALITY: 90, PipelineStage.COMPLETED: 100, PipelineStage.FAILED: 0, PipelineStage.CANCELLED: 0 } response_data = { "job_id": job_id, "status": result.status.value, "progress_percentage": stage_percentages.get(result.status, 0), "current_message": f"Status: {result.status.value}", "video_metadata": result.video_metadata, "processing_time_seconds": result.processing_time_seconds } # Include results if completed if result.status == PipelineStage.COMPLETED: response_data["result"] = { "summary": result.summary, "key_points": result.key_points, "main_themes": result.main_themes, "actionable_insights": result.actionable_insights, "confidence_score": result.confidence_score, "quality_score": result.quality_score, "cost_data": result.cost_data } # Include error if failed if result.status == PipelineStage.FAILED and result.error: response_data["error"] = result.error return PipelineStatusResponse(**response_data) @router.delete("/process/{job_id}") async def cancel_pipeline( job_id: str, pipeline: SummaryPipeline = Depends() ): """Cancel running pipeline""" success = await pipeline.cancel_pipeline(job_id) if not success: raise HTTPException(status_code=404, detail="Pipeline job not found or already completed") return {"message": "Pipeline cancelled successfully"} ``` ### File Locations and Structure [Source: docs/architecture.md#project-structure] **Backend Files**: - `backend/services/summary_pipeline.py` - Main pipeline orchestration service - `backend/api/pipeline.py` - Pipeline management endpoints - `backend/core/websocket_manager.py` - WebSocket progress notifications - `backend/models/pipeline.py` - Pipeline result storage models - `backend/services/notification_service.py` - Completion notifications - `backend/tests/unit/test_summary_pipeline.py` - Unit tests - `backend/tests/integration/test_pipeline_api.py` - Integration tests ### Frontend Integration [Source: docs/architecture.md#frontend-architecture] ```typescript // frontend/src/hooks/usePipelineProcessor.ts import { useState, useCallback } from 'react'; import { useMutation, useQuery } from '@tanstack/react-query'; import { apiClient } from '@/services/apiClient'; import { useWebSocket } from './useWebSocket'; interface PipelineConfig { summary_length: 'brief' | 'standard' | 'detailed'; focus_areas?: string[]; include_timestamps: boolean; enable_notifications: boolean; quality_threshold: number; } interface PipelineProgress { stage: string; percentage: number; message: string; details?: any; } export function usePipelineProcessor() { const [activeJobId, setActiveJobId] = useState(null); const [progress, setProgress] = useState(null); const { connect, disconnect } = useWebSocket({ onProgressUpdate: (update: PipelineProgress) => { setProgress(update); } }); const startProcessing = useMutation({ mutationFn: async ({ url, config }: { url: string; config: PipelineConfig }) => { const response = await apiClient.processVideo(url, config); return response; }, onSuccess: (data) => { setActiveJobId(data.job_id); connect(data.job_id); } }); const { data: pipelineStatus } = useQuery({ queryKey: ['pipeline-status', activeJobId], queryFn: () => activeJobId ? apiClient.getPipelineStatus(activeJobId) : null, enabled: !!activeJobId, refetchInterval: (data) => data?.status === 'completed' || data?.status === 'failed' ? false : 2000 }); const cancelProcessing = useCallback(async () => { if (activeJobId) { await apiClient.cancelPipeline(activeJobId); setActiveJobId(null); setProgress(null); disconnect(); } }, [activeJobId, disconnect]); return { startProcessing: startProcessing.mutateAsync, cancelProcessing, isProcessing: startProcessing.isPending || (pipelineStatus?.status === 'processing'), progress: progress || (pipelineStatus ? { stage: pipelineStatus.status, percentage: pipelineStatus.progress_percentage, message: pipelineStatus.current_message } : null), result: pipelineStatus?.result, error: startProcessing.error || pipelineStatus?.error, pipelineStatus }; } ``` ### Quality Assurance Features - **Automatic Retry Logic**: Failed or low-quality summaries automatically retried with improved parameters - **Content-Aware Processing**: Different strategies for technical, educational, and entertainment content - **Quality Scoring**: Multi-factor quality assessment ensures consistent results - **Progress Transparency**: Detailed progress tracking keeps users informed throughout the process - **Error Recovery**: Comprehensive error handling with graceful degradation ## Change Log | Date | Version | Description | Author | |------|---------|-------------|--------| | 2025-01-25 | 1.0 | Initial story creation | Bob (Scrum Master) | ## Dev Agent Record *This section will be populated by the development agent during implementation* ## QA Results *Results from QA Agent review of the completed story implementation will be added here*