97 lines
3.2 KiB
Markdown
97 lines
3.2 KiB
Markdown
# Story Writer Service
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Story generation service using prompt chaining approach, migrated from `ToBeMigrated/ai_writers/ai_story_writer/`.
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## Structure
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```
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backend/
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├── services/
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│ └── story_writer/
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│ ├── __init__.py
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│ ├── story_service.py # Core story generation logic
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│ └── README.md
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├── api/
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│ └── story_writer/
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│ ├── __init__.py
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│ ├── router.py # API endpoints
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│ ├── task_manager.py # Async task management
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│ └── cache_manager.py # Result caching
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└── models/
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└── story_models.py # Pydantic models
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```
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## Features
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- **Prompt Chaining**: Generates stories through premise → outline → start → continuation
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- **Multiple Personas**: Supports 11 predefined author personas/genres
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- **Configurable Parameters**:
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- Story setting, characters, plot elements
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- Writing style, tone, narrative POV
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- Audience age group, content rating, ending preference
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- **Subscription Integration**: Automatic usage tracking via `main_text_generation`
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- **Provider Support**: Works with both Gemini and HuggingFace
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- **Async Task Management**: Long-running story generation with polling
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- **Caching**: Result caching for identical requests
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## API Endpoints
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### Synchronous Endpoints
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- `POST /api/story/generate-premise` - Generate story premise
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- `POST /api/story/generate-outline` - Generate outline from premise
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- `POST /api/story/generate-start` - Generate story beginning
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- `POST /api/story/continue` - Continue story generation
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### Asynchronous Endpoints
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- `POST /api/story/generate-full` - Generate complete story (returns task_id)
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- `GET /api/story/task/{task_id}/status` - Get task status
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- `GET /api/story/task/{task_id}/result` - Get completed task result
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### Cache Management
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- `GET /api/story/cache/stats` - Get cache statistics
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- `POST /api/story/cache/clear` - Clear cache
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## Usage Example
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```python
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from services.story_writer.story_service import StoryWriterService
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service = StoryWriterService()
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# Generate full story
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result = service.generate_full_story(
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persona="Award-Winning Science Fiction Author",
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story_setting="A bustling futuristic city in 2150",
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character_input="John, a tall muscular man with a kind heart",
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plot_elements="The hero's journey, Good vs. evil",
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writing_style="Formal",
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story_tone="Suspenseful",
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narrative_pov="Third Person Limited",
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audience_age_group="Adults",
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content_rating="PG-13",
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ending_preference="Happy",
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user_id="clerk_user_id",
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max_iterations=10
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)
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print(result["premise"])
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print(result["outline"])
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print(result["story"])
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```
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## Migration Notes
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- Updated imports from legacy `...gpt_providers.text_generation.main_text_generation` to `services.llm_providers.main_text_generation`
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- Added `user_id` parameter to all LLM calls for subscription support
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- Removed Streamlit dependencies (UI moved to frontend)
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- Added proper error handling with HTTPException support
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- Added async task management for long-running operations
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- Added caching support for identical requests
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## Integration
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The router is automatically registered via `alwrity_utils/router_manager.py` in the optional routers section.
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