Story 2.1: Single AI Model Integration #4

New Issue

demo · 2025-08-25T04:45:59Z

demo commented

2025-08-25 04:45:59 +00:00

User Story

As a user
I want the system to generate intelligent summaries from extracted transcripts using AI
so that I can quickly understand video content without watching the entire video

Acceptance Criteria

System integrates with OpenAI GPT-4o-mini for cost-effective summarization
AI generates structured summaries with key points, main themes, and actionable insights
Summary length is configurable (brief, standard, detailed) based on user preference
System handles long transcripts by intelligent chunking without losing context
AI processing includes error handling with graceful fallbacks and retry logic
Generated summaries include confidence scores and processing metadata

Tasks

Task 1: AI Service Foundation
- Create AIService base class in backend/services/ai_service.py
- Implement OpenAI client configuration with API key management
- Add retry logic with exponential backoff for API failures
- Create comprehensive error handling for API responses
Task 2: OpenAI Integration
- Create OpenAISummarizer class implementing AI service interface
- Configure GPT-4o-mini with optimal parameters for summarization
- Implement token counting and cost tracking for API usage
- Add response validation and quality checks
Task 3: Summary Generation Logic
- Create structured prompt templates for different summary types
- Implement summary length configuration (brief/standard/detailed)
- Add key point extraction and theme identification
- Create actionable insights generation from content
Task 4: Transcript Chunking Strategy
- Implement intelligent transcript splitting based on content boundaries
- Add context preservation between chunks for coherent summaries
- Create chunk overlap strategy to maintain narrative flow
- Implement map-reduce pattern for long transcript processing
Task 5: API Endpoints for Summarization
- Create /api/summarize POST endpoint for transcript processing
- Implement /api/summaries/{id} GET endpoint for result retrieval
- Add summary configuration options in request body
- Include processing metadata and confidence scores in response
Task 6: Background Processing
- Implement async summarization with job status tracking
- Create job queue system for managing AI processing requests
- Add progress updates via WebSocket for long-running summaries
- Implement cancellation support for running summarization jobs
Task 7: Integration Testing
- Test summarization with various transcript lengths and content types
- Validate summary quality and structure across different configurations
- Test error handling and fallback scenarios
- Verify cost tracking and token usage monitoring

Development Notes

Generated from BMad story: 2.1
Status: Draft
Created: 2025-08-25T00:45:58.242268

## User Story **As a** user **I want** the system to generate intelligent summaries from extracted transcripts using AI **so that** I can quickly understand video content without watching the entire video ## Acceptance Criteria 1. System integrates with OpenAI GPT-4o-mini for cost-effective summarization 2. AI generates structured summaries with key points, main themes, and actionable insights 3. Summary length is configurable (brief, standard, detailed) based on user preference 4. System handles long transcripts by intelligent chunking without losing context 5. AI processing includes error handling with graceful fallbacks and retry logic 6. Generated summaries include confidence scores and processing metadata ## Tasks - [ ] **Task 1: AI Service Foundation** - [ ] Create `AIService` base class in `backend/services/ai_service.py` - [ ] Implement OpenAI client configuration with API key management - [ ] Add retry logic with exponential backoff for API failures - [ ] Create comprehensive error handling for API responses - [ ] **Task 2: OpenAI Integration** - [ ] Create `OpenAISummarizer` class implementing AI service interface - [ ] Configure GPT-4o-mini with optimal parameters for summarization - [ ] Implement token counting and cost tracking for API usage - [ ] Add response validation and quality checks - [ ] **Task 3: Summary Generation Logic** - [ ] Create structured prompt templates for different summary types - [ ] Implement summary length configuration (brief/standard/detailed) - [ ] Add key point extraction and theme identification - [ ] Create actionable insights generation from content - [ ] **Task 4: Transcript Chunking Strategy** - [ ] Implement intelligent transcript splitting based on content boundaries - [ ] Add context preservation between chunks for coherent summaries - [ ] Create chunk overlap strategy to maintain narrative flow - [ ] Implement map-reduce pattern for long transcript processing - [ ] **Task 5: API Endpoints for Summarization** - [ ] Create `/api/summarize` POST endpoint for transcript processing - [ ] Implement `/api/summaries/{id}` GET endpoint for result retrieval - [ ] Add summary configuration options in request body - [ ] Include processing metadata and confidence scores in response - [ ] **Task 6: Background Processing** - [ ] Implement async summarization with job status tracking - [ ] Create job queue system for managing AI processing requests - [ ] Add progress updates via WebSocket for long-running summaries - [ ] Implement cancellation support for running summarization jobs - [ ] **Task 7: Integration Testing** - [ ] Test summarization with various transcript lengths and content types - [ ] Validate summary quality and structure across different configurations - [ ] Test error handling and fallback scenarios - [ ] Verify cost tracking and token usage monitoring ## Development Notes --- *Generated from BMad story: 2.1* *Status: Draft* *Created: 2025-08-25T00:45:58.242268*

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Reference: demo/youtube-summarizer#4