Literary Structure Generator: Roadmap & Code Scaffolding

by Admin 57 views
Literary Structure Generator: Roadmap & Code Scaffolding

Alright guys, let's dive into creating a roadmap and code scaffolding plan for our literary structure generator! We've got a cool project on our hands, and this document will help us break it down into manageable steps. We'll be using the agentic_short_story_system_instruct_v_0.md document as our main guide, so keep that handy.

System Goals and Workflow Summary

First things first, let's quickly recap what we're trying to achieve with this system. In simple terms, we want to build a tool that can generate short stories with compelling literary structures. Think of it as a storytelling engine that can take some input and churn out a well-structured narrative.

The core workflow, as I understand it from the document, goes something like this:

  1. Story Specification: We start by defining what kind of story we want. This involves specifying the theme, characters, setting, and other key elements. Imagine it as filling out a form for your dream story!
  2. Exemplar Selection: Next, we'll look at existing stories that match our specification. These "exemplars" will serve as inspiration and guides for our generator. It's like researching what's already out there and learning from the best.
  3. Structure Generation: This is where the magic happens! Based on the story specification and the selected exemplars, our system will generate a potential literary structure. This involves outlining the plot points, character arcs, and other structural elements. We want to make sure the structure will give the story a proper foundation.
  4. Content Generation: With the structure in place, we can now start filling in the details. This involves generating the actual text of the story, including dialogue, descriptions, and actions. We can use techniques like large language models to make the language sound as good as possible.
  5. Evaluation and Refinement: Once we have a complete story, we need to evaluate its quality. This involves assessing the structure, coherence, and overall impact. We can then use this feedback to refine the generation process and make it even better. It's all about learning and improving!

Deep Dive into System Goals

To truly understand the roadmap, we need a solid grasp of the system's goals. This literary structure generator isn't just about spewing out words; it's about crafting narratives with meaningful structure. We aim to create a system that can:

  • Understand Story Specifications: This means the system needs to be able to interpret user inputs that define the kind of story they want. Think about the user specifying genres like mystery, sci-fi, or romance, or defining specific themes like love, loss, or redemption. The system should be able to translate these high-level concepts into concrete parameters for story generation.
  • Identify and Utilize Exemplars: The system needs to be smart about finding and using examples of well-structured stories. This isn't just about grabbing any story off the internet; it's about identifying stories that align with the user's specifications and showcase effective storytelling techniques. We're talking about analyzing existing literature to extract patterns and best practices.
  • Generate Coherent Structures: This is the heart of the project. The system should be able to create outlines that have a clear beginning, middle, and end, with well-defined plot points, character arcs, and thematic development. It's about moving beyond random sequences of events and creating narratives that resonate with readers.
  • Integrate with Content Generation: The structure is just the skeleton; the content is the flesh and blood. The system needs to seamlessly integrate with content generation modules (like large language models) to populate the structure with compelling text. This means ensuring that the generated content aligns with the structural framework and enhances the overall story.
  • Facilitate Iterative Refinement: Story generation is rarely a one-shot process. The system should allow for evaluation and refinement, enabling users to tweak the structure, regenerate content, and improve the story iteratively. It's about creating a feedback loop that leads to better and better narratives.

Workflow Elaboration

Let's break down the workflow steps a bit more to see how they translate into code.

  1. Story Specification:
    • The user provides input describing the desired story. This could be in the form of a structured document, a series of keywords, or even a natural language description.
    • The system parses this input and extracts key elements like genre, theme, characters, setting, and plot points.
    • This information is then used to create a StorySpec object, which serves as the blueprint for the story generation process.
  2. Exemplar Selection:
    • The system searches for existing stories that match the StorySpec. This might involve querying a database of literary works or using web scraping techniques.
    • The selected exemplars are analyzed to identify their structural elements, such as plot structure, character arcs, and thematic patterns.
    • This analysis is summarized in an ExemplarDigest object, which captures the key structural characteristics of the exemplars.
  3. Structure Generation:
    • The system uses the StorySpec and ExemplarDigest to generate a potential story structure. This could involve techniques like template-based generation, rule-based generation, or even using a machine learning model.
    • The generated structure is represented as a hierarchical outline, detailing the major plot points, scenes, and character interactions.
  4. Content Generation:
    • The system uses the generated structure as a guide for content generation. This might involve using a large language model to generate text for each scene or plot point.
    • The content is generated in a way that aligns with the structural framework, ensuring that the story is coherent and well-paced.
  5. Evaluation and Refinement:
    • The generated story is evaluated based on a set of criteria, such as structural integrity, coherence, and emotional impact.
    • This evaluation can be done automatically using metrics or manually by human reviewers.
    • The evaluation results are summarized in an EvalReport object, which provides feedback on the story's strengths and weaknesses.
    • The user can then use this feedback to refine the StorySpec, adjust the generation parameters, or even manually edit the generated content.

Proposed Roadmap

Okay, now for the exciting part – the roadmap! We want to approach this project in an incremental and realistic way, so Copilot can handle it in small, manageable chunks. Here’s a step-by-step plan:

Phase 1: Core Data Structures and Infrastructure

This phase focuses on setting up the basic building blocks of our system. We'll define the core data structures and create the initial project structure.

  • Step 1: Project Setup and Core Data Models
    • Set up the basic Python project structure (folders, pyproject.toml, etc.).
    • Define the StorySpec Pydantic model to represent the story specification.
    • Define the ExemplarDigest Pydantic model to represent the exemplar analysis.
    • Define the GenerationConfig Pydantic model to hold generation parameters.
    • Define the EvalReport Pydantic model to represent the evaluation results.
  • Step 2: Logging and Configuration
    • Implement a basic logging system for debugging and monitoring.
    • Set up a configuration system to manage parameters (e.g., API keys, file paths).
  • Step 3: Initial Tests
    • Write basic unit tests for the data models to ensure they work as expected.

Phase 2: Story Specification and Exemplar Selection

This phase focuses on the first two steps of our workflow: story specification and exemplar selection.

  • Step 4: StorySpec Parsing
    • Implement a module to parse story specifications from different input formats (e.g., JSON, YAML, natural language).
    • Add tests to ensure the parsing logic works correctly.
  • Step 5: Exemplar Database Interface
    • Create an interface to interact with a database of literary works (e.g., a local database or a remote API).
    • Implement basic search functionality to find exemplars based on keywords and metadata.
  • Step 6: Exemplar Digest Generation (Initial)
    • Implement a basic module to generate ExemplarDigest objects from selected exemplars.
    • This initial implementation can focus on extracting basic information like plot points and character arcs.

Phase 3: Structure Generation and Content Integration

This is where the core generation logic comes into play. We'll focus on generating story structures and integrating with content generation models.

  • Step 7: Structure Generation Module (Basic)
    • Implement a basic structure generation module using a simple technique like template-based generation.
    • Add tests to ensure the module generates valid story structures.
  • Step 8: Content Generation Integration (Placeholder)
    • Create a placeholder module for content generation that can integrate with a language model (e.g., OpenAI GPT-3).
    • This placeholder can simply return dummy content for now.
  • Step 9: Structure-Content Integration
    • Implement logic to integrate the generated structure with the content generation module.
    • This involves mapping the structural elements to content elements and ensuring a coherent flow.

Phase 4: Evaluation and Refinement

In this phase, we'll focus on evaluating the generated stories and refining the system based on feedback.

  • Step 10: Evaluation Metrics
    • Define a set of metrics to evaluate the generated stories (e.g., structural integrity, coherence, emotional impact).
    • Implement functions to calculate these metrics automatically.
  • Step 11: EvalReport Generation
    • Implement a module to generate EvalReport objects based on the evaluation metrics.
  • Step 12: Refinement Loop (Initial)
    • Implement a basic refinement loop that allows users to provide feedback and regenerate the story.

Phase 5: Advanced Features and Optimization

This phase is for adding more advanced features and optimizing the system for performance and quality.

  • Step 13: Advanced Structure Generation Techniques
    • Explore and implement more advanced structure generation techniques, such as rule-based generation or machine learning models.
  • Step 14: Improved Exemplar Selection
    • Improve the exemplar selection process by incorporating more sophisticated search and filtering criteria.
  • Step 15: Content Generation Refinement
    • Refine the content generation integration by using more advanced prompting techniques or fine-tuning the language model.
  • Step 16: User Interface (Optional)
    • Develop a user interface to make the system more accessible and user-friendly.

This roadmap is a living document, guys! We can adjust it as we go, based on our progress and any new insights we gain. The key is to keep moving forward in small, manageable steps.

Initial File/Folder Structure

To keep things organized, let's establish an initial file and folder structure for our Python project:

literary_structure_generator/
 ├── literary_structure_generator/
 │   ├── __init__.py
 │   ├── core/
 │   │   ├── __init__.py
 │   │   ├── models.py  # Data models (StorySpec, ExemplarDigest, etc.)
 │   ├── exemplar_selection/
 │   │   ├── __init__.py
 │   │   ├── exemplar_db.py  # Interface to the exemplar database
 │   │   ├── exemplar_digest.py  # Exemplar digest generation
 │   ├── structure_generation/
 │   │   ├── __init__.py
 │   │   ├── structure_generator.py  # Structure generation logic
 │   ├── content_generation/
 │   │   ├── __init__.py
 │   │   ├── content_generator.py  # Content generation integration
 │   ├── evaluation/
 │   │   ├── __init__.py
 │   │   ├── metrics.py  # Evaluation metrics
 │   │   ├── evaluator.py  # Evaluation logic
 │   ├── utils/
 │   │   ├── __init__.py
 │   │   ├── config.py  # Configuration management
 │   │   ├── logging.py  # Logging utilities
 │   ├── main.py  # Main application entry point
 ├── tests/
 │   ├── __init__.py
 │   ├── core/
 │   │   ├── test_models.py
 │   ├── exemplar_selection/
 │   ├── structure_generation/
 │   ├── content_generation/
 │   ├── evaluation/
 ├── pyproject.toml  # Project configuration
 └── README.md

This structure gives us a good starting point. We can always add or modify folders as needed. The key is to keep the codebase organized and modular.

Minimal Starter Scaffolding

Alright, let's get our hands dirty and generate some code scaffolding! We'll start with empty classes, docstrings, and Pydantic placeholders. This will give us a solid foundation to build upon.

Here's the minimal scaffolding for our core modules:

literary_structure_generator/core/models.py

from typing import List, Optional
from pydantic import BaseModel


class StorySpec(BaseModel):
    """Represents the specification for a story."""
    title: str
    genre: str
    theme: str
    characters: List[str]
    setting: str
    plot_summary: str


class ExemplarDigest(BaseModel):
    """Represents a digest of structural elements from an exemplar story."""
    title: str
    author: str
    plot_points: List[str]
    character_arcs: List[str]
    themes: List[str]


class GenerationConfig(BaseModel):
    """Represents the configuration for story generation."""
    temperature: float = 0.7
    max_length: int = 1000


class EvalReport(BaseModel):
    """Represents the evaluation report for a generated story."""
    structural_integrity: float
    coherence: float
    emotional_impact: float
    feedback: Optional[str] = None

literary_structure_generator/exemplar_selection/exemplar_db.py

from typing import List
from literary_structure_generator.core.models import StorySpec


class ExemplarDatabase:
    """Interface for interacting with a database of literary works."""

    def search_exemplars(self, story_spec: StorySpec) -> List[str]:
        """Searches for exemplars based on the story specification."""
        raise NotImplementedError

literary_structure_generator/exemplar_selection/exemplar_digest.py

from literary_structure_generator.core.models import ExemplarDigest, StorySpec


class ExemplarDigestGenerator:
    """Generates ExemplarDigest objects from selected exemplars."""

    def generate_digest(self, story_spec: StorySpec, exemplar_text: str) -> ExemplarDigest:
        """Generates a digest of structural elements from an exemplar story."""
        raise NotImplementedError

literary_structure_generator/structure_generation/structure_generator.py

from typing import List
from literary_structure_generator.core.models import StorySpec, ExemplarDigest


class StructureGenerator:
    """Generates story structures based on the story specification and exemplars."""

    def generate_structure(self, story_spec: StorySpec, exemplar_digests: List[ExemplarDigest]) -> List[str]:
        """Generates a story structure as a list of plot points."""
        raise NotImplementedError

literary_structure_generator/content_generation/content_generator.py

from typing import List


class ContentGenerator:
    """Generates story content based on the structure."""

    def generate_content(self, structure: List[str]) -> str:
        """Generates story content from the given structure."""
        raise NotImplementedError

literary_structure_generator/evaluation/metrics.py

from typing import List


def calculate_structural_integrity(structure: List[str]) -> float:
    """Calculates the structural integrity of a story."""
    raise NotImplementedError


def calculate_coherence(story_text: str) -> float:
    """Calculates the coherence of a story."""
    raise NotImplementedError


def calculate_emotional_impact(story_text: str) -> float:
    """Calculates the emotional impact of a story."""
    raise NotImplementedError

literary_structure_generator/evaluation/evaluator.py

from typing import List
from literary_structure_generator.core.models import EvalReport


class Evaluator:
    """Evaluates a generated story."""

    def evaluate(self, story_text: str, structure: List[str]) -> EvalReport:
        """Evaluates a generated story and returns an EvalReport."""
        raise NotImplementedError

This scaffolding provides a starting point for each of our major modules. We've defined the basic classes and methods, along with docstrings to explain their purpose. Now, we can start filling in the implementation details in subsequent issues and pull requests. This is all about incremental progress, folks!

Conclusion

So, there you have it – a roadmap and code scaffolding plan for our literary structure generator! We've broken down the project into manageable steps, defined our core data structures, and created a basic file and folder structure. We've also generated some minimal scaffolding to get us started with the coding. Remember, this is a collaborative effort, and we'll be refining things as we go. Let's build something amazing, guys!