The ALKB Curator

Project Overview

The ALKB Curator is a Python project designed to build a dynamic, secure, and compliant knowledge base for AI systems. The project ensures that AI interactions are precise, regulated, and enhanced by expert contributions, creating an evolving knowledge base that adapts to changing business and regulatory needs.

Key Features:

• Insightful AI Interactions: Tailor AI responses to fit your specific business context.
• Compliance & Security: Ensure all data meets regulatory requirements (e.g., GDPR, HIPAA) and is stored securely.
• Expert Contributions: Easily incorporate expert feedback to prioritize and enhance the AI knowledge base.
• Knowledge Base Evolution: Seamlessly update the knowledge base as regulations or business needs change.
• Data Integrity: Robust security mechanisms to safeguard your knowledge base and monitor interactions.

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Table of Contents

• Project Overview
• Key Features
• Project Structure
• Installation
• Configuration
• Usage
• Testing
• Contributing
• License

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Project Structure

ALKB-Curator/
│
├── README.md                # Project documentation
├── .gitignore               # Files to ignore in version control
├── requirements.txt         # Python dependencies
├── setup.py                 # Package setup and metadata
│
├── src/
│   ├── __init__.py
│   ├── knowledge_base/
│   │   ├── __init__.py
│   │   ├── curator.py         # Knowledge curator logic
│   │   ├── knowledge_manager.py   # Handles knowledge base operations
│   │   ├── compliance_checker.py  # Ensures data compliance
│   │   ├── expert_contributor.py  # Manages expert feedback integration
│   └── utils/
│       ├── __init__.py
│       ├── data_parser.py     # Handles data transformations (JSON, XML)
│       └── security.py        # Security and encryption mechanisms
│
└── tests/
    ├── test_curator.py        # Unit tests for curator
    ├── test_knowledge_manager.py   # Tests for knowledge manager
    └── test_compliance_checker.py  # Tests for compliance checks

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Installation

To set up and run the project, follow these steps:

Prerequisites

Make sure you have Python 3.8 or higher installed. You can check your Python version by running:

python --version

Step 1: Clone the Repository

Clone this repository to your local machine using Git:

git clone https://github.com/yourusername/ALKB-Curator.git
cd ALKB-Curator

Step 2: Create a Virtual Environment

Create a virtual environment to isolate your project’s dependencies:

python -m venv venv

Activate the virtual environment:

• On macOS/Linux:

  source venv/bin/activate

• On Windows:

  venv\Scripts\activate

Step 3: Install Dependencies

Install the project dependencies using `pip`:

pip install -r requirements.txt

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Configuration

Compliance Rules

To set up compliance rules, modify the `compliance_rules` parameter in the `ComplianceChecker` class. You can specify any regulation or policy your system must adhere to, such as GDPR, HIPAA, or internal corporate policies.

For example:

compliance_rules = ["GDPR", "HIPAA", "Internal Policy"]
compliance_checker = ComplianceChecker(compliance_rules)

Security Configuration

In `utils/security.py`, you can configure encryption methods to secure the knowledge base. This project uses placeholder methods for encryption and monitoring, but you can replace them with actual security frameworks like AES encryption from the `cryptography` library.

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Usage

Step 1: Curating Knowledge

Use the `KnowledgeCurator` to curate and update your knowledge base.

from knowledge_base.curator import KnowledgeCurator
from knowledge_base.knowledge_manager import KnowledgeManager
from knowledge_base.compliance_checker import ComplianceChecker

# Initialize components
compliance_rules = ["GDPR", "HIPAA"]
compliance_checker = ComplianceChecker(compliance_rules)
knowledge_manager = KnowledgeManager()
curator = KnowledgeCurator(knowledge_manager, compliance_checker)

# Curate and update knowledge
new_data = {
    # Your new incoming data
}
curated_data = curator.curate_knowledge(new_data)

Step 2: Handling Expert Contributions

You can gather expert feedback to refine and prioritize the knowledge base.

from knowledge_base.expert_contributor import ExpertContributor

contributor = ExpertContributor()
expert_refined_data = contributor.gather_expert_feedback(new_data)

Step 3: Checking Compliance

Ensure that incoming data meets regulations before updating the knowledge base.

if compliance_checker.is_compliant(new_data):
    print("Data is compliant!")
else:
    print("Data does not meet regulatory standards.")

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Testing

Unit tests are provided to ensure that each component of the system works as expected.

Running Tests

You can run tests using `pytest`:

pytest tests/

Example tests include:

• Curator Tests: Ensure the curator correctly updates the knowledge base.
• Knowledge Manager Tests: Check if data transformation and storage are correct.
• Compliance Checker Tests: Validate that the system catches non-compliant data.

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Contributing

We welcome contributions to improve the project! To contribute:

1. Fork the repository.
2. Create a feature branch (`git checkout -b feature/new-feature`).
3. Commit your changes (`git commit -am 'Add new feature'`).
4. Push to the branch (`git push origin feature/new-feature`).
5. Create a new Pull Request.

Make sure to write unit tests for any new features or changes, and ensure that all tests pass.

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License

This project is licensed under the BSD License. See the LICENSE file for details.

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Contact

For any inquiries or issues, please reach out to:

• Project Maintainer: Richardson Lima (Rick)
• Email: contato at richardsonlima dot com dot br