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ALwrity/backend/services/story_writer/README.md

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