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Released under MIT by @Exios66.
literary-vault/
├── analysis/ # Detailed analysis of research papers
│ ├── AI-Research/ # AI-related research analysis
│ ├── EEG/ # EEG research analysis
│ └── Manipulation-Deception/ # Studies on manipulation/deception
├── docs/ # Repository documentation
│ ├── Analysis/ # Detailed research analysis
│ ├── api/ # API documentation and implementation
│ ├── Integration/ # Integration guides
│ ├── openai-functions/ # OpenAI function schemas
│ └── pdfs/ # PDF documentation
├── external-resources/ # Additional resources
│ ├── Math-PDFs/ # Mathematics resources
│ └── questions/ # Question datasets
├── scripts/ # Utility scripts
├── CHANGELOG.md # Project change history
├── CONTRIBUTING.md # Contribution guidelines
├── LICENSE # MIT License
└── README.md # Repository documentationAn active catalog and toolkit supporting neuroscience research with organized documentation, backend services, and infrastructure for comprehensive analysis and API services.
Literary Vault is structured to support data-driven neuroscience research. It provides categorized research analysis, curated datasets, external resources, and REST APIs for accessing and managing these resources. With detailed integration tools and utility scripts, it is designed for scalable and secure deployment in research and development environments.
The repository organizes neuroscience-related research into well-defined categories:
- AI Research: Covers neural networks, the ELIZA program, and neuropsychoanalysis.
- EEG Studies: Includes cognitive load studies, VR integration, and research referenced in the Oxford Handbook.
- Manipulation/Deception Studies: Focuses on models of persuasion and cognitive mechanisms behind deception.
A REST API that allows categorized access to a curated dataset of questions on topics including astronomy, literature, mathematics, and general knowledge.
- Endpoints:
GET /api/v1/questions/{category}- Retrieve questions by category.GET /api/v1/questions/{category}/random- Fetch random questions from a specified category.GET /api/v1/health- Health check endpoint for service status.
Programmatic management of the project's changelog, supporting:
- Adding new entries
- Creating releases
- Querying the change history with schema validation for consistent format.
The repository contains additional documentation and datasets to support research:
- Mathematics PDFs: Mathematical references and learning material.
- Question Datasets: Curated question sets in CSV format across various disciplines.
Tools to simplify integrations:
- OpenAI Function Schemas: Templates for OpenAI function integration.
- API Integration Guides: Step-by-step guides to integrate with external APIs.
- Changelog Automation: Scripts to automate changelog updates and versioning.
Scripts provided for streamlined repository management, including:
- Changelog Management: Automates updating and versioning for changelog entries.
- API Launchers: Scripts to start API servers.
- Documentation Generators: Helps maintain and organize documentation.
- Clone the repository:
git clone https://github.com/Exios66/Literary-Vault.git cd Literary-Vault
2.Install dependencies:
pip install -r requirements.txt- Start API Servers
Run API servers for different modules:
python docs/api/questions_api.py
python docs/api/changelog_api.py
Changelog Management
Use the provided scripts to add entries or create releases:
python scripts/update_changelog.py add "Added" "Description of new feature"
python scripts/update_changelog.py release "1.0.0"
The repository includes various question sets in CSV format, accessible either via the API or directly:
• Refined_Astronomy_Questions.csv
• Refined_Literature_Questions.csv
• Refined_Mathematics_Questions.csv
• Refined_General_Knowledge_Questions.csv
Development
• Python: Ensure Python 3.x is installed.
• Framework: FastAPI powers the API services.
• JSON Validation: JSON schema validation is used across APIs.
• Documentation: Markdown format for documentation.
• Version Control: Use Git for tracking changes.
Contribution Guidelines
We welcome contributions to enhance the Literary Vault. Please follow these guidelines:
1. Fork the repository and create a new branch for your feature or bug fix.
2. Follow existing naming conventions and structure.
3. Document any new additions or changes.
4. Submit a pull request with a clear description of the updates.
For more details, see CONTRIBUTING.md.
Comprehensive research analysis organized by topic:
- AI Research (neural networks, ELIZA program, neuropsychoanalysis)
- EEG Studies (cognitive load, VR integration, Oxford handbook)
- Manipulation/Deception (persuasion models, cognitive mechanisms)
REST API for accessing curated question datasets:
- Astronomy questions
- Literature questions
- Mathematics questions
- General Knowledge questions
Features:
- Category-based retrieval
- Random selection
- Customizable limits
- OpenAPI documentation
Endpoints:
GET /api/v1/questions/{category}
GET /api/v1/questions/{category}/random
GET /api/v1/healthProgrammatic changelog management:
- Add entries
- Create releases
- Query history
- Schema validation
- Mathematics PDFs
- Question datasets (CSV format)
- Reference materials
- Supplementary documentation
- OpenAI function schemas
- API integration guides
- Changelog automation
- Documentation templates
Utility scripts for repository management:
- Changelog management (cl)
- API servers
- Documentation generators
- Integration tools
Start the API servers:
# Questions API
python docs/api/questions_api.py
# Changelog API
python docs/api/changelog_api.pyAccess documentation:
- Questions API: http://localhost:8000/docs
- Changelog API: http://localhost:8001/docs
- Changelog Server API: https://exios66.github.io/Literary-Vault/
# Add entry
python scripts/update_changelog.py add "Added" "New feature"
# Create release
python scripts/update_changelog.py release "1.0.0"Access structured questions via API or direct CSV files:
- Refined_Astronomy_Questions.csv
- Refined_Literature_Questions.csv
- Refined_Mathematics_Questions.csv
- Refined_General Knowledge_Questions.csv
- Follow API documentation
- Use provided schemas
- Update changelog
- Maintain documentation
- Follow naming conventions
- Include proper citations
- MIT License compliance
- API authentication
- Data validation
- Error handling
- Secure endpoints
- API Documentation (/docs/api/)
- Integration Guides (/docs/Integration/)
- OpenAI Schemas (/docs/openai-functions/)
- Analysis Templates (/docs/Analysis/)
- Python 3.x required
- FastAPI for APIs
- JSON schema validation
- Markdown documentation
- Git integration
See CONTRIBUTING.md for guidelines.
Released under MIT License.