PlanOpticon
Update implementation.md
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| --- implementation.md | ||
| +++ implementation.md | ||
| @@ -5,21 +5,25 @@ | ||
| 5 | 5 | video_processor/ |
| 6 | 6 | ├── extractors/ |
| 7 | 7 | │ ├── frame_extractor.py |
| 8 | 8 | │ ├── audio_extractor.py |
| 9 | 9 | │ └── text_extractor.py |
| 10 | +├── api/ | |
| 11 | +│ ├── transcription_api.py | |
| 12 | +│ ├── vision_api.py | |
| 13 | +│ ├── llm_api.py | |
| 14 | +│ └── api_manager.py | |
| 10 | 15 | ├── analyzers/ |
| 11 | -│ ├── visual_analyzer.py | |
| 12 | -│ ├── speech_analyzer.py | |
| 13 | -│ ├── text_analyzer.py | |
| 16 | +│ ├── content_analyzer.py | |
| 17 | +│ ├── diagram_analyzer.py | |
| 14 | 18 | │ └── action_detector.py |
| 15 | 19 | ├── integrators/ |
| 16 | 20 | │ ├── knowledge_graph.py |
| 17 | 21 | │ └── plan_generator.py |
| 18 | 22 | ├── utils/ |
| 19 | -│ ├── gpu_utils.py | |
| 20 | -│ ├── vector_store.py | |
| 23 | +│ ├── api_cache.py | |
| 24 | +│ ├── prompt_templates.py | |
| 21 | 25 | │ └── visualization.py |
| 22 | 26 | └── cli/ |
| 23 | 27 | ├── commands.py |
| 24 | 28 | └── output_formatter.py |
| 25 | 29 | Implementation Approach |
| @@ -198,32 +202,32 @@ | ||
| 198 | 202 | Create integration tests for component interactions |
| 199 | 203 | Implement performance benchmarks for critical paths |
| 200 | 204 | |
| 201 | 205 | |
| 202 | 206 | |
| 203 | -Model Development Considerations | |
| 204 | -When implementing AI components, consider: | |
| 205 | - | |
| 206 | -Model Selection | |
| 207 | - | |
| 208 | -Balance accuracy and performance requirements | |
| 209 | -Consider model quantization for ARM deployment | |
| 210 | -Design with graceful degradation for resource-constrained environments | |
| 211 | - | |
| 212 | - | |
| 213 | -Ensemble Approaches | |
| 214 | - | |
| 215 | -Use specialized models for different visual element types | |
| 216 | -Combine multiple techniques for robust action item detection | |
| 217 | -Implement voting mechanisms for increased accuracy | |
| 218 | - | |
| 219 | - | |
| 220 | -Domain Adaptation | |
| 221 | - | |
| 222 | -Design transfer learning approach for specialized vocabularies | |
| 223 | -Implement fine-tuning pipeline for domain-specific content | |
| 224 | -Consider few-shot learning techniques for flexibility | |
| 207 | +API Integration Considerations | |
| 208 | +When implementing cloud API components, consider: | |
| 209 | + | |
| 210 | +API Selection | |
| 211 | + | |
| 212 | +Balance capabilities, cost, and performance requirements | |
| 213 | +Implement appropriate rate limiting and quota management | |
| 214 | +Design with graceful fallbacks between different API providers | |
| 215 | + | |
| 216 | + | |
| 217 | +Efficient API Usage | |
| 218 | + | |
| 219 | +Create optimized prompts for different content types | |
| 220 | +Batch requests where possible to minimize API calls | |
| 221 | +Implement caching to avoid redundant API calls | |
| 222 | + | |
| 223 | + | |
| 224 | +Prompt Engineering | |
| 225 | + | |
| 226 | +Design effective prompt templates for consistent results | |
| 227 | +Implement few-shot examples for specialized content understanding | |
| 228 | +Create chain-of-thought prompting for complex analysis tasks | |
| 225 | 229 | |
| 226 | 230 | |
| 227 | 231 | |
| 228 | 232 | Prompting Guidelines |
| 229 | 233 | When developing complex AI systems, clear guidance helps ensure effective implementation. Consider these approaches: |
| 230 | 234 |
| --- implementation.md | |
| +++ implementation.md | |
| @@ -5,21 +5,25 @@ | |
| 5 | video_processor/ |
| 6 | ├── extractors/ |
| 7 | │ ├── frame_extractor.py |
| 8 | │ ├── audio_extractor.py |
| 9 | │ └── text_extractor.py |
| 10 | ├── analyzers/ |
| 11 | │ ├── visual_analyzer.py |
| 12 | │ ├── speech_analyzer.py |
| 13 | │ ├── text_analyzer.py |
| 14 | │ └── action_detector.py |
| 15 | ├── integrators/ |
| 16 | │ ├── knowledge_graph.py |
| 17 | │ └── plan_generator.py |
| 18 | ├── utils/ |
| 19 | │ ├── gpu_utils.py |
| 20 | │ ├── vector_store.py |
| 21 | │ └── visualization.py |
| 22 | └── cli/ |
| 23 | ├── commands.py |
| 24 | └── output_formatter.py |
| 25 | Implementation Approach |
| @@ -198,32 +202,32 @@ | |
| 198 | Create integration tests for component interactions |
| 199 | Implement performance benchmarks for critical paths |
| 200 | |
| 201 | |
| 202 | |
| 203 | Model Development Considerations |
| 204 | When implementing AI components, consider: |
| 205 | |
| 206 | Model Selection |
| 207 | |
| 208 | Balance accuracy and performance requirements |
| 209 | Consider model quantization for ARM deployment |
| 210 | Design with graceful degradation for resource-constrained environments |
| 211 | |
| 212 | |
| 213 | Ensemble Approaches |
| 214 | |
| 215 | Use specialized models for different visual element types |
| 216 | Combine multiple techniques for robust action item detection |
| 217 | Implement voting mechanisms for increased accuracy |
| 218 | |
| 219 | |
| 220 | Domain Adaptation |
| 221 | |
| 222 | Design transfer learning approach for specialized vocabularies |
| 223 | Implement fine-tuning pipeline for domain-specific content |
| 224 | Consider few-shot learning techniques for flexibility |
| 225 | |
| 226 | |
| 227 | |
| 228 | Prompting Guidelines |
| 229 | When developing complex AI systems, clear guidance helps ensure effective implementation. Consider these approaches: |
| 230 |
| --- implementation.md | |
| +++ implementation.md | |
| @@ -5,21 +5,25 @@ | |
| 5 | video_processor/ |
| 6 | ├── extractors/ |
| 7 | │ ├── frame_extractor.py |
| 8 | │ ├── audio_extractor.py |
| 9 | │ └── text_extractor.py |
| 10 | ├── api/ |
| 11 | │ ├── transcription_api.py |
| 12 | │ ├── vision_api.py |
| 13 | │ ├── llm_api.py |
| 14 | │ └── api_manager.py |
| 15 | ├── analyzers/ |
| 16 | │ ├── content_analyzer.py |
| 17 | │ ├── diagram_analyzer.py |
| 18 | │ └── action_detector.py |
| 19 | ├── integrators/ |
| 20 | │ ├── knowledge_graph.py |
| 21 | │ └── plan_generator.py |
| 22 | ├── utils/ |
| 23 | │ ├── api_cache.py |
| 24 | │ ├── prompt_templates.py |
| 25 | │ └── visualization.py |
| 26 | └── cli/ |
| 27 | ├── commands.py |
| 28 | └── output_formatter.py |
| 29 | Implementation Approach |
| @@ -198,32 +202,32 @@ | |
| 202 | Create integration tests for component interactions |
| 203 | Implement performance benchmarks for critical paths |
| 204 | |
| 205 | |
| 206 | |
| 207 | API Integration Considerations |
| 208 | When implementing cloud API components, consider: |
| 209 | |
| 210 | API Selection |
| 211 | |
| 212 | Balance capabilities, cost, and performance requirements |
| 213 | Implement appropriate rate limiting and quota management |
| 214 | Design with graceful fallbacks between different API providers |
| 215 | |
| 216 | |
| 217 | Efficient API Usage |
| 218 | |
| 219 | Create optimized prompts for different content types |
| 220 | Batch requests where possible to minimize API calls |
| 221 | Implement caching to avoid redundant API calls |
| 222 | |
| 223 | |
| 224 | Prompt Engineering |
| 225 | |
| 226 | Design effective prompt templates for consistent results |
| 227 | Implement few-shot examples for specialized content understanding |
| 228 | Create chain-of-thought prompting for complex analysis tasks |
| 229 | |
| 230 | |
| 231 | |
| 232 | Prompting Guidelines |
| 233 | When developing complex AI systems, clear guidance helps ensure effective implementation. Consider these approaches: |
| 234 |