more sharing = more caring

innovate/SlappAI/.gitignore

@@ -0,0 +1,51 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*.pyo
+*.pyd
+
+# Virtual environment
+.venv/
+agn_env/
+
+# Caches
+*.log
+*.pot
+*.cache
+*.mo
+*.log
+*.dat
+*.pid
+
+# Distribution / packaging
+build/
+dist/
+.eggs/
+*.egg-info/
+
+# Graphs and data files (if dynamic or regeneratable)
+graphs/*.json
+
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints
+
+# Documentation generated
+docs/_build/
+
+# Pytest cache
+.pytest_cache/
+
+# Environments and secrets
+.env
+.env.*
+
+# IDE specific settings
+.vscode/
+.idea/
+.DS_Store
+Thumbs.db
+
+# Backup files
+*.bak
+*.swp
+*.swo

innovate/SlappAI/LICENSE

@@ -0,0 +1,19 @@
+# Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
+
+This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by-nc-sa/4.0/) or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
+
+## Terms
+You are free to:
+- **Share** — copy and redistribute the material in any medium or format.
+- **Adapt** — remix, transform, and build upon the material.
+
+Under the following terms:
+- **Attribution** — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
+- **NonCommercial** — You may not use the material for commercial purposes.
+- **ShareAlike** — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
+
+## No additional restrictions
+You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
+
+## Disclaimer
+This work is provided "as is" without any warranties, expressed or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, or non-infringement. In no event shall the author be liable for any claim, damages, or other liability, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the work or the use or other dealings in the work.

innovate/SlappAI/README.md

@@ -0,0 +1,140 @@
+
+# SlappGraphs
+
+SlappGraphs is a graph-based application framework built for developing relational data models with structured domain-based nodes and relationships. This system leverages an Active Graph Network (AGN) architecture to create and manage graph data structures within defined domains and subdomains. SlappGraphs enables the loading, querying, and visualization of nodes and relationships through a Flask-based REST API.
+
+## Features
+
+- **Domain-based Node Management**: Organize nodes within specific domains and subdomains for clearer data separation.
+- **Active Graph Networks (AGN)**: Utilizes AGNNode structures to maintain nodes and relationships in a queryable graph.
+- **Access Control Lists (ACL)**: Supports role-based access control, allowing control over who can access different nodes or relationships.
+- **Relationship Management**: Define complex, multi-level relationships between nodes.
+- **REST API**: Exposes endpoints to load, list, query, and manage nodes and relationships within the graph.
+
+## Project Structure
+
+```plaintext
+.
+├── app
+│   ├── models
+│   │   └── agn_model.py          # Defines the AGNNode class for node management
+│   ├── routes
+│   │   ├── agn.py                # Flask routes for API endpoints
+│   └── services
+│       ├── agn_service           # Contains core functionality for node and relationship management
+│       ├── load_graph.py         # Script to load graph structure from JSON files
+│       └── access_control.py     # Implements ACLs for role-based access control
+├── docs                          # Documentation files for setup and usage
+├── graphs                        # JSON files defining graph structures, e.g., healthcare.json
+├── scripts                       # Scripts for testing and project setup
+├── config.py                     # General configuration settings
+├── README.md                     # Project documentation (this file)
+└── run.py                        # Main file to run the Flask application
+```
+
+## Getting Started
+
+### Prerequisites
+
+- Python 3.10+
+- `pip` for dependency management
+- Flask for API services
+
+### Installation
+
+1. **Clone the repository:**
+   ```bash
+   git clone https://github.com/yourusername/SlappGraphs.git
+   cd SlappGraphs
+   ```
+
+2. **Set up a virtual environment:**
+   ```bash
+   python -m venv .venv
+   source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows
+   ```
+
+3. **Install dependencies:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. **Set up configuration:**
+   Ensure any environment-specific settings are configured in `.env` if necessary.
+
+### Running the Application
+
+1. **Run Flask:**
+   ```bash
+   python run.py
+   ```
+
+2. **Access the API:**
+   The API will be available at `http://localhost:5000`.
+
+### API Endpoints
+
+| Endpoint           | Method | Description                             |
+|--------------------|--------|-----------------------------------------|
+| `/load_graph`      | POST   | Load the graph from a JSON file         |
+| `/list_nodes`      | GET    | List all nodes or nodes in a domain     |
+| `/list_relationships` | GET | List all relationships                  |
+| `/create_node`     | POST   | Create a new node                       |
+| `/query_node`      | GET    | Retrieve details of a specific node     |
+
+### Example API Usage
+
+1. **Load the Graph Structure:**
+   ```bash
+   curl -X POST http://localhost:5000/load_graph
+   ```
+
+2. **List All Nodes:**
+   ```bash
+   curl -X GET http://localhost:5000/list_nodes
+   ```
+
+3. **Create a Node:**
+   ```bash
+   curl -X POST http://localhost:5000/create_node -H "Content-Type: application/json" -d '{
+     "node_id": "patient_123",
+     "data": {"name": "John Doe", "age": 45, "medical_conditions": ["hypertension"]},
+     "domain": "Healthcare",
+     "type": "Patient"
+   }'
+   ```
+
+4. **Query a Node:**
+   ```bash
+   curl -X GET "http://localhost:5000/query_node?node_id=patient_123"
+   ```
+
+### Configuration
+
+Modify `config.py` for application settings. You may include additional settings for database connections, environment settings, and more.
+
+### Testing
+
+Use the provided scripts in the `scripts/` directory to test individual features. For example:
+
+```bash
+python scripts/1_test_node_creation.py
+```
+
+### Future Enhancements
+
+- **Visualization**: Build an interactive front-end to visualize nodes and relationships in real time.
+- **External Database Integration**: Connect to external graph databases for larger datasets.
+- **Advanced ACL and RBAC**: Extend ACL functionality for fine-grained control over node access.
+
+## Contributing
+
+1. Fork the project
+2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
+3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
+4. Push to the branch (`git push origin feature/AmazingFeature`)
+5. Open a pull request
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

innovate/SlappAI/app/__init__.py

@@ -0,0 +1,14 @@
+from flask import Flask
+from app.routes.base import base_bp
+from app.routes.agn import agn_bp
+from app.config import Config
+
+def create_app():
+    app = Flask(__name__)
+    app.config.from_object(Config)
+
+    # Register Blueprints
+    app.register_blueprint(base_bp)
+    app.register_blueprint(agn_bp)
+
+    return app

innovate/SlappAI/app/config.py

@@ -0,0 +1,7 @@
+import os
+
+class Config:
+    SECRET_KEY = os.getenv("SECRET_KEY", "default_secret_key")
+    DEBUG = os.getenv("DEBUG", True)
+    SQLALCHEMY_DATABASE_URI = os.getenv("DATABASE_URI", "sqlite:///agn.db")
+    SQLALCHEMY_TRACK_MODIFICATIONS = False

innovate/SlappAI/app/models/agn_model.py

@@ -0,0 +1,21 @@
+class AGNNode:
+    def __init__(self, node_id, data, domain, node_type):
+        self.node_id = node_id
+        self.data = data
+        self.domain = domain
+        self.node_type = node_type  # Adding type for node specificity
+        self.relationships = []  # To store relationships with other nodes
+
+    def add_relationship(self, other_node_id, relation_type):
+        """Adds a relationship between this node and another node."""
+        self.relationships.append({"node_id": other_node_id, "relationship": relation_type})
+
+    def to_dict(self):
+        """Convert AGNNode to a dictionary format for JSON serialization, including relationships."""
+        return {
+            "node_id": self.node_id,
+            "data": self.data,
+            "domain": self.domain,
+            "type": self.node_type,
+            "relationships": self.relationships
+        }

innovate/SlappAI/app/routes/__init__.py

@@ -0,0 +1,4 @@
+from flask import Blueprint
+
+base = Blueprint('base', __name__)
+agn = Blueprint('agn', __name__)

innovate/SlappAI/app/routes/agn.py

@@ -0,0 +1,46 @@
+from flask import Blueprint, jsonify, request
+from app.services.agn_service import load_graph, query_node, list_nodes, list_relationships
+from app.models.agn_model import AGNNode
+
+agn_bp = Blueprint('agn', __name__)
+
+@agn_bp.route('/create_node', methods=['POST'])
+def create_node():
+    data = request.json
+    try:
+        node_id = data['node_id']
+        node_data = data['data']
+        domain = data['domain']
+        node_type = data.get('type', 'General')
+
+        node = AGNNode(node_id, node_data, domain, node_type)
+        return jsonify({"status": f"{node_type} node created", "node": node.to_dict(), "domain": domain}), 201
+    except ValueError as e:
+        return jsonify({"error": str(e)}), 400
+
+@agn_bp.route('/load_graph', methods=['POST'])
+def load_graph_endpoint():
+    """Endpoint to load or reload the graph structure from JSON."""
+    load_graph()
+    return jsonify({"status": "Graph loaded successfully"}), 200
+
+@agn_bp.route('/list_nodes', methods=['GET'])
+def list_nodes_endpoint():
+    """List nodes, optionally filtered by domain."""
+    nodes = list_nodes()
+    return jsonify({"nodes": nodes}), 200
+
+@agn_bp.route('/list_relationships', methods=['GET'])
+def list_relationships_endpoint():
+    """List all relationships in the graph."""
+    relationships = list_relationships()
+    return jsonify({"relationships": relationships}), 200
+
+@agn_bp.route('/query_node', methods=['GET'])
+def query_node_endpoint():
+    """Retrieve a node by ID."""
+    node_id = request.args.get('node_id')
+    node = query_node(node_id)
+    if node:
+        return jsonify({"node": node.to_dict()}), 200
+    return jsonify({"error": "Node not found"}), 404

innovate/SlappAI/app/routes/base.py

@@ -0,0 +1,8 @@
+from flask import Blueprint
+
+# Define the Blueprint
+base_bp = Blueprint('base', __name__)
+
+@base_bp.route('/')
+def index():
+    return "Welcome to the AGN application!"

innovate/SlappAI/app/services/access_control.py

@@ -0,0 +1,4 @@
+class AccessControlService:
+    def has_access(user, node):
+        # Implement access control logic
+        return True

innovate/SlappAI/app/services/agn_service/__init__.py

@@ -0,0 +1,10 @@
+# app/services/agn_service/__init__.py
+
+_node_storage = {}  # Shared in-memory storage for nodes
+_relationships = []  # Shared storage for relationships
+_domains = {}  # Shared storage for domains
+
+from .load_graph import load_graph
+from .query_node import query_node
+from .list_nodes import list_nodes
+from .list_relationships import list_relationships

innovate/SlappAI/app/services/agn_service/agn_service.py

@@ -0,0 +1,54 @@
+import json
+import os
+from app.models.agn_model import AGNNode
+
+class AGNService:
+    _node_storage = {}  # In-memory storage for nodes
+    _relationships = []  # Storage for relationships
+    _domains = {}
+
+    @classmethod
+    def load_graph(cls, graph_file="graphs/healthcare.json"):
+        """Load nodes and relationships from a JSON file and initialize the graph structure."""
+        with open(graph_file, 'r') as file:
+            data = json.load(file)
+
+            # Load domains
+            cls._domains = data.get("domains", {})
+            for domain_name, domain_info in cls._domains.items():
+                cls._node_storage[domain_name] = AGNNode(domain_name, domain_info, domain_info.get("domain"))
+
+            # Load entities
+            entities = data.get("entities", {})
+            for entity_id, entity_info in entities.items():
+                domain = entity_info.get("inherits_from", "general")
+                node = AGNNode(entity_id, entity_info["attributes"], domain)
+                cls._node_storage[entity_id] = node
+
+            # Load relationships
+            cls._relationships = data.get("relationships", [])
+            for relation in cls._relationships:
+                source_id = relation["source"]
+                target_id = relation["target"]
+                attributes = relation.get("attributes", {})
+
+                # Add relationship to source node if both nodes exist
+                source_node = cls._node_storage.get(source_id)
+                target_node = cls._node_storage.get(target_id)
+                if source_node and target_node:
+                    source_node.add_relationship(target_node, attributes["relationship"])
+
+    @classmethod
+    def query_node(cls, node_id):
+        """Retrieve a node by ID."""
+        return cls._node_storage.get(node_id)
+
+    @classmethod
+    def list_nodes(cls):
+        """List all nodes in the graph."""
+        return {node_id: node.to_dict() for node_id, node in cls._node_storage.items()}
+
+    @classmethod
+    def list_relationships(cls):
+        """List all relationships."""
+        return cls._relationships

innovate/SlappAI/app/services/agn_service/list_nodes.py

@@ -0,0 +1,14 @@
+# app/services/agn_service/list_nodes.py
+
+from app.services.agn_service import _node_storage
+from flask import jsonify
+
+def list_nodes(domain=None):
+    """List nodes, optionally filtered by domain."""
+    if domain:
+        # Filter nodes by domain
+        nodes = {node_id: node.to_dict() for node_id, node in _node_storage.items() if node.domain == domain}
+    else:
+        # Return all nodes if no domain specified
+        nodes = {node_id: node.to_dict() for node_id, node in _node_storage.items()}
+    return nodes

innovate/SlappAI/app/services/agn_service/list_relationships.py

@@ -0,0 +1,5 @@
+from .load_graph import _relationships
+
+def list_relationships():
+    """List all relationships in the graph."""
+    return _relationships

innovate/SlappAI/app/services/agn_service/load_graph.py

@@ -0,0 +1,77 @@
+# app/services/agn_service/load_graph.py
+
+import json
+from app.models.agn_model import AGNNode
+from app.services.agn_service import _node_storage, _relationships, _domains  # Import shared globals
+
+def load_graph(graph_file="graphs/healthcare.json"):
+    """Load nodes and relationships from a JSON file and initialize the graph structure."""
+    global _node_storage, _relationships, _domains
+
+    try:
+        with open(graph_file, 'r') as file:
+            data = json.load(file)
+
+            # Load domains
+            _domains.clear()
+            _node_storage.clear()
+            _relationships.clear()
+
+            _domains.update(data.get("domains", {}))
+            print("Loading Domains...")
+            for domain_name, domain_info in _domains.items():
+                node_type = domain_info.get("type", "Domain")
+                _node_storage[domain_name] = AGNNode(
+                    node_id=domain_name,
+                    data=domain_info,
+                    domain=domain_info.get("domain", "general"),
+                    node_type=node_type
+                )
+                print(f"Loaded Domain: {domain_name}, Type: {node_type}")
+
+            # Load entities
+            entities = data.get("entities", {})
+            print("\nLoading Entities...")
+            for entity_id, entity_info in entities.items():
+                domain = entity_info.get("inherits_from", "general")
+                node_type = entity_info.get("type", "Entity")
+                node = AGNNode(
+                    node_id=entity_id,
+                    data=entity_info["attributes"],
+                    domain=domain,
+                    node_type=node_type
+                )
+                _node_storage[entity_id] = node
+                print(f"Loaded Entity: {entity_id}, Domain: {domain}, Type: {node_type}")
+
+            # Load relationships
+            _relationships.extend(data.get("relationships", []))
+            print("\nLoading Relationships...")
+            for relation in _relationships:
+                source_id = relation["source"]
+                target_id = relation["target"]
+                relationship_type = relation.get("attributes", {}).get("relationship", "related_to")
+
+                source_node = _node_storage.get(source_id)
+                target_node = _node_storage.get(target_id)
+                
+                if source_node and target_node:
+                    source_node.add_relationship(target_id, relationship_type)
+                    print(f"Added Relationship: {source_id} --{relationship_type}--> {target_id}")
+                else:
+                    print(f"Warning: Relationship skipped, missing node(s) - Source: {source_id}, Target: {target_id}")
+
+            print("\nGraph Loaded Successfully")
+            print("Total Domains:", len(_domains))
+            print("Total Entities:", len(entities))
+            print("Total Relationships:", len(_relationships))
+
+    except FileNotFoundError:
+        print(f"Error: File {graph_file} not found.")
+    except json.JSONDecodeError:
+        print("Error: Failed to parse JSON file.")
+
+# Debug statements for immediate verification after load
+print("Node Storage:", _node_storage)
+print("Relationships:", _relationships)
+print("Domains:", _domains)

innovate/SlappAI/app/services/agn_service/query_node.py

@@ -0,0 +1,5 @@
+from .load_graph import _node_storage
+
+def query_node(node_id):
+    """Retrieve a node by ID."""
+    return _node_storage.get(node_id)

innovate/SlappAI/app/utils/decorators.py

@@ -0,0 +1,11 @@
+from functools import wraps
+from flask import request, jsonify
+
+def require_auth(func):
+    @wraps(func)
+    def decorated_function(*args, **kwargs):
+        token = request.headers.get("Authorization")
+        if not token:
+            return jsonify({"error": "Unauthorized"}), 401
+        return func(*args, **kwargs)
+    return decorated_function

innovate/SlappAI/app/utils/helpers.py

@@ -0,0 +1,3 @@
+def generate_node_id():
+    import uuid
+    return str(uuid.uuid4())

innovate/SlappAI/config.py

@@ -0,0 +1,7 @@
+import os
+
+class Config:
+    SECRET_KEY = os.getenv("SECRET_KEY", "default_secret_key")
+    DEBUG = os.getenv("DEBUG", True)
+    SQLALCHEMY_DATABASE_URI = os.getenv("DATABASE_URI", "sqlite:///agn.db")
+    SQLALCHEMY_TRACK_MODIFICATIONS = False

innovate/SlappAI/create_project_structure.py

@@ -0,0 +1,43 @@
+import os
+
+# Define the project structure
+project_structure = {
+    "agn_flask_app": {
+        "app": {
+            "__init__.py": "",
+            "config.py": "",
+            "routes": {
+                "__init__.py": "",
+                "base.py": "",
+                "agn.py": ""
+            },
+            "services": {
+                "agn_service.py": "",
+                "access_control.py": ""
+            },
+            "models": {
+                "agn_model.py": ""
+            },
+            "utils": {
+                "helpers.py": ""
+            }
+        },
+        "requirements.txt": "",
+        "config.py": "",
+        "run.py": "",
+        "README.md": ""
+    }
+}
+
+def create_structure(base_path, structure):
+    for name, content in structure.items():
+        path = os.path.join(base_path, name)
+        if isinstance(content, dict):
+            os.makedirs(path, exist_ok=True)
+            create_structure(path, content)
+        else:
+            with open(path, 'w') as f:
+                f.write(content)
+
+# Create the project structure
+create_structure('.', project_structure)

innovate/SlappAI/docs/1_environment.md

@@ -0,0 +1,599 @@
+Thank you for providing the additional details about the access control functions. With this information, I can proceed to build out the Flask application, integrating these critical components to ensure robust role-based access control within your AGN framework.
+
+### Overview of Next Steps
+
+1. **Environment Setup**:
+   - **Conda Environment**: Configure the project to use your specified Conda environment (`~/Git/AIMO/.conda/bin/python` with Python 3.10.15).
+   - **Dependencies**: Update `requirements.txt` to include any additional packages needed for access control.
+
+2. **Project Structure Enhancement**:
+   - **Access Control Integration**: Incorporate the provided functions (`check_access_policy`, `evaluate_condition`, `inherit_policies`) into the project.
+   - **Organize Code**: Place these functions appropriately within the `services` or `utils` directories for maintainability.
+
+3. **Implementing Access Control in API Endpoints**:
+   - **Middleware or Decorators**: Implement decorators to enforce access policies on protected endpoints.
+   - **Route Protection**: Ensure that sensitive routes (e.g., adding entities or relationships) are secured based on user roles and permissions.
+
+4. **Testing and Validation**:
+   - **Unit Tests**: Create tests to verify that access control mechanisms function as intended.
+   - **Example Scenarios**: Provide example API calls demonstrating access control in action.
+
+### Updated Project Structure
+
+Here's the enhanced project structure incorporating the access control functionalities:
+
+```
+agn_flask_app/
+│
+├── app/
+│   ├── __init__.py          # Initialize Flask app and AGN framework
+│   ├── config.py            # Configuration settings for the app
+│   ├── routes/
+│   │   ├── __init__.py      # Initialize route modules
+│   │   ├── base.py          # Base routes (health check, welcome message, etc.)
+│   │   └── agn.py           # Routes for AGN operations (entity and relationship handling)
+│   ├── services/
+│   │   ├── agn_service.py   # AGN service handling core graph logic and interactions
+│   │   └── access_control.py# Access control service with policy functions
+│   ├── models/
+│   │   └── agn_model.py     # Model for AGN structure, nodes, and relationships
+│   └── utils/
+│       └── helpers.py       # Helper functions (parsing, formatting, etc.)
+│
+├── requirements.txt         # Project dependencies
+├── config.py                # Main configuration for the project
+├── run.py                   # Script to run the Flask app
+└── README.md                # Project description and setup instructions
+```
+
+### 1. Environment Setup
+
+#### a. Conda Environment Configuration
+
+Ensure you have Conda installed. Create and activate the Conda environment using the specified Python version:
+
+```bash
+conda create --name agn_env python=3.10.15
+conda activate agn_env
+```
+
+#### b. Update `requirements.txt`
+
+Add necessary dependencies, including those for access control if needed (e.g., `Flask-HTTPAuth` for authentication):
+
+```plaintext
+Flask==2.1.0
+networkx==2.5
+Flask-HTTPAuth==4.7.0
+```
+
+Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Implementing Access Control
+
+#### a. Create `access_control.py` in `services/`
+
+This file will house the access control functions you provided.
+
+```python
+# app/services/access_control.py
+
+import networkx as nx
+
+class AccessControlService:
+    def __init__(self, graph):
+        self.graph = graph
+        # Example ACL and RBAC policies
+        self.acl_policies = {
+            'patient': {'view', 'update'},
+            'doctor': {'view', 'update'},
+            # Add more entities and their allowed actions
+        }
+        self.rbac_policies = {
+            'Insurance Agent': {
+                'conditions': {
+                    'insurance_status': 'active'
+                }
+            },
+            'Doctor': {
+                'conditions': {}
+            },
+            # Add more roles and their conditions
+        }
+
+    def check_access_policy(self, role, target, action, print_output=True):
+        """
+        Verifies access permissions based on role, target entity, and action.
+        """
+        allowed = False
+        restricted_entities = []
+
+        # Check ACL policies
+        allowed_actions = self.acl_policies.get(target, set())
+        if action in allowed_actions:
+            allowed = True
+        else:
+            restricted_entities.append(target)
+
+        # Check RBAC policies
+        role_policies = self.rbac_policies.get(role, {})
+        conditions = role_policies.get('conditions', {})
+        for attr, value in conditions.items():
+            role_node = self.graph.nodes.get(role, {})
+            if role_node.get(attr) != value:
+                allowed = False
+                if target not in restricted_entities:
+                    restricted_entities.append(target)
+
+        if print_output:
+            if allowed:
+                print(f"Access granted for role '{role}' to perform '{action}' on '{target}'.")
+            else:
+                print(f"Access denied for role '{role}' to perform '{action}' on '{target}'. Restricted entities: {restricted_entities}")
+
+        return allowed
+
+    def evaluate_condition(self, condition, role):
+        """
+        Evaluates a condition linked to a role.
+        """
+        try:
+            role_node = self.graph.nodes.get(role, {})
+            return eval(condition, {}, role_node)
+        except Exception as e:
+            print(f"Error evaluating condition '{condition}' for role '{role}': {e}")
+            return False
+
+    def inherit_policies(self, role):
+        """
+        Recursively inherits policies from parent roles.
+        """
+        # Placeholder for hierarchical role inheritance logic
+        # This can be implemented based on your specific role hierarchy
+        inherited_policies = {}
+        # Example: if 'Specialist' inherits from 'Doctor'
+        parent_role = self.get_parent_role(role)
+        if parent_role:
+            parent_policies = self.inherit_policies(parent_role)
+            inherited_policies.update(parent_policies)
+        # Override or add specific policies for the current role
+        current_policies = self.rbac_policies.get(role, {})
+        inherited_policies.update(current_policies)
+        return inherited_policies
+
+    def get_parent_role(self, role):
+        """
+        Retrieves the parent role for a given role.
+        """
+        # Define role hierarchy
+        role_hierarchy = {
+            'Specialist': 'Doctor',
+            # Add more role hierarchies as needed
+        }
+        return role_hierarchy.get(role)
+```
+
+#### b. Update `agn_service.py` to Initialize AccessControlService
+
+Integrate the `AccessControlService` with the AGNService.
+
+```python
+# app/services/agn_service.py
+
+import networkx as nx
+from .access_control import AccessControlService
+
+class AGNService:
+    def __init__(self):
+        # Initialize the AGN as a directed graph
+        self.graph = nx.DiGraph()
+        self.access_control = AccessControlService(self.graph)
+
+    def add_entity(self, entity_id, entity_type):
+        if entity_id in self.graph:
+            return {'error': 'Entity already exists'}
+        
+        self.graph.add_node(entity_id, type=entity_type)
+        return {'status': 'success', 'entity_id': entity_id, 'entity_type': entity_type}
+    
+    def add_relationship(self, source_id, target_id, relationship_type):
+        if not self.graph.has_node(source_id) or not self.graph.has_node(target_id):
+            return {'error': 'One or both entities do not exist'}
+        
+        self.graph.add_edge(source_id, target_id, type=relationship_type)
+        return {'status': 'success', 'source_id': source_id, 'target_id': target_id, 'relationship_type': relationship_type}
+    
+    def check_access(self, role, target, action):
+        return self.access_control.check_access_policy(role, target, action)
+    
+    def inherit_policies(self, role):
+        return self.access_control.inherit_policies(role)
+    
+    def evaluate_condition(self, condition, role):
+        return self.access_control.evaluate_condition(condition, role)
+    
+    # Additional AGN operations can be added here
+```
+
+### 3. Protecting API Endpoints with Access Control
+
+We'll implement a decorator to enforce access control on protected routes.
+
+#### a. Create `decorators.py` in `utils/`
+
+```python
+# app/utils/decorators.py
+
+from functools import wraps
+from flask import request, jsonify, current_app
+
+def require_access(role, target, action):
+    def decorator(f):
+        @wraps(f)
+        def decorated_function(*args, **kwargs):
+            agn_service = current_app.config['AGN_SERVICE']
+            if not agn_service.check_access(role, target, action):
+                return jsonify({'error': 'Access denied'}), 403
+            return f(*args, **kwargs)
+        return decorated_function
+    return decorator
+```
+
+#### b. Update `agn.py` Routes to Use the Decorator
+
+Assuming you have some authentication mechanism to determine the user's role (e.g., via a token), we'll mock the role retrieval for demonstration purposes.
+
+```python
+# app/routes/agn.py
+
+from flask import Blueprint, jsonify, request, current_app
+from app.utils.decorators import require_access
+
+agn_bp = Blueprint('agn', __name__)
+
+def get_user_role():
+    # Placeholder for actual authentication logic
+    # For example, extract role from JWT token or session
+    return request.headers.get('X-User-Role', 'Guest')  # Default to 'Guest' if not provided
+
+@agn_bp.route('/agn/entity', methods=['POST'])
+def add_entity():
+    """Endpoint to add an entity to the AGN."""
+    role = get_user_role()
+    target = 'entity'
+    action = 'update'  # Assuming adding an entity requires 'update' action
+    if not current_app.config['AGN_SERVICE'].check_access(role, target, action):
+        return jsonify({'error': 'Access denied'}), 403
+
+    data = request.json
+    entity_id = data.get('entity_id')
+    entity_type = data.get('entity_type')
+    
+    agn_service = current_app.config['AGN_SERVICE']
+    result = agn_service.add_entity(entity_id, entity_type)
+    
+    return jsonify(result), 201
+
+@agn_bp.route('/agn/relationship', methods=['POST'])
+def add_relationship():
+    """Endpoint to add a relationship between entities."""
+    role = get_user_role()
+    target = 'relationship'
+    action = 'update'  # Assuming adding a relationship requires 'update' action
+    if not current_app.config['AGN_SERVICE'].check_access(role, target, action):
+        return jsonify({'error': 'Access denied'}), 403
+
+    data = request.json
+    source_id = data.get('source_id')
+    target_id = data.get('target_id')
+    relationship_type = data.get('relationship_type')
+    
+    agn_service = current_app.config['AGN_SERVICE']
+    result = agn_service.add_relationship(source_id, target_id, relationship_type)
+    
+    return jsonify(result), 201
+
+@agn_bp.route('/agn/query', methods=['GET'])
+def query_entities():
+    """Endpoint to query entities based on parameters."""
+    role = get_user_role()
+    target = 'query'
+    action = 'view'  # Assuming querying requires 'view' action
+    if not current_app.config['AGN_SERVICE'].check_access(role, target, action):
+        return jsonify({'error': 'Access denied'}), 403
+
+    # Implement query logic here
+    # For demonstration, returning all entities
+    agn_service = current_app.config['AGN_SERVICE']
+    entities = [{'entity_id': n, 'type': d['type']} for n, d in agn_service.graph.nodes(data=True)]
+    return jsonify({'entities': entities}), 200
+```
+
+*Note*: In a production environment, you should replace the `get_user_role` function with a proper authentication mechanism (e.g., JWT tokens, OAuth2).
+
+### 4. Enhancing `__init__.py` to Register Services
+
+Ensure that the AGNService and AccessControlService are properly initialized and accessible across the app.
+
+```python
+# app/__init__.py
+
+from flask import Flask
+from app.routes.base import base_bp
+from app.routes.agn import agn_bp
+from app.services.agn_service import AGNService
+from app.config import Config
+
+def create_app():
+    app = Flask(__name__)
+    app.config.from_object(Config)
+    
+    # Initialize AGNService
+    agn_service = AGNService()
+    app.config['AGN_SERVICE'] = agn_service
+    
+    # Register Blueprints
+    app.register_blueprint(base_bp)
+    app.register_blueprint(agn_bp)
+    
+    return app
+```
+
+### 5. Configuration Settings
+
+Update `config.py` if needed, adding any configurations related to access control or other services.
+
+```python
+# app/config.py
+
+class Config:
+    SECRET_KEY = 'your_secret_key_here'
+    DEBUG = True
+    # Add more configurations as needed
+```
+
+### 6. Running the Application
+
+Ensure your Conda environment is activated and run the Flask application.
+
+```bash
+conda activate agn_env
+python run.py
+```
+
+### 7. Example Usage
+
+#### a. Adding an Entity
+
+```bash
+curl -X POST http://localhost:5000/agn/entity \
+     -H "Content-Type: application/json" \
+     -H "X-User-Role: Doctor" \
+     -d '{"entity_id": "patient_1", "entity_type": "patient"}'
+```
+
+#### b. Adding a Relationship
+
+```bash
+curl -X POST http://localhost:5000/agn/relationship \
+     -H "Content-Type: application/json" \
+     -H "X-User-Role: Doctor" \
+     -d '{"source_id": "doctor_1", "target_id": "patient_1", "relationship_type": "treats"}'
+```
+
+#### c. Querying Entities
+
+```bash
+curl -X GET http://localhost:5000/agn/query \
+     -H "X-User-Role: Doctor"
+```
+
+### 8. Additional Enhancements
+
+#### a. Advanced Query Parsing
+
+Implement more sophisticated query parsing in `AGNService` to handle multi-level and stacked queries as outlined in your initial vision. This may involve parsing query parameters and translating them into NetworkX graph queries.
+
+#### b. Data Caching and Persistence
+
+To improve performance, especially for frequent or complex queries, integrate caching mechanisms such as Redis.
+
+- **Install Redis**:
+
+  ```bash
+  pip install redis
+  ```
+
+- **Update `requirements.txt`**:
+
+  ```plaintext
+  redis==4.5.1
+  ```
+
+- **Implement Caching in `agn_service.py`**:
+
+  ```python
+  import redis
+  import json
+
+  class AGNService:
+      def __init__(self):
+          self.graph = nx.DiGraph()
+          self.access_control = AccessControlService(self.graph)
+          self.cache = redis.Redis(host='localhost', port=6379, db=0)
+      
+      def query_entities(self, query_params):
+          cache_key = f"query:{json.dumps(query_params, sort_keys=True)}"
+          cached_result = self.cache.get(cache_key)
+          if cached_result:
+              return json.loads(cached_result)
+          
+          # Perform the actual query
+          # Example: return all entities
+          entities = [{'entity_id': n, 'type': d['type']} for n, d in self.graph.nodes(data=True)]
+          
+          # Cache the result
+          self.cache.set(cache_key, json.dumps(entities), ex=300)  # Cache for 5 minutes
+          return entities
+  ```
+
+#### c. Interactive Visualizations
+
+Integrate frontend libraries like D3.js or Cytoscape.js for real-time graph visualizations. This involves updating the frontend to make API calls and render the graph dynamically based on responses.
+
+#### d. Extended Security Policies
+
+Enhance security by implementing OAuth 2.0 or API keys for authenticating and authorizing API requests. Libraries such as `Flask-JWT-Extended` can facilitate JWT-based authentication.
+
+- **Install Flask-JWT-Extended**:
+
+  ```bash
+  pip install Flask-JWT-Extended
+  ```
+
+- **Update `requirements.txt`**:
+
+  ```plaintext
+  Flask-JWT-Extended==4.4.4
+  ```
+
+- **Configure JWT in `__init__.py`**:
+
+  ```python
+  from flask_jwt_extended import JWTManager
+
+  def create_app():
+      app = Flask(__name__)
+      app.config.from_object(Config)
+      
+      # Initialize JWT
+      jwt = JWTManager(app)
+      
+      # Initialize AGNService
+      agn_service = AGNService()
+      app.config['AGN_SERVICE'] = agn_service
+      
+      # Register Blueprints
+      app.register_blueprint(base_bp)
+      app.register_blueprint(agn_bp)
+      
+      return app
+  ```
+
+- **Protect Routes with JWT**:
+
+  ```python
+  from flask_jwt_extended import jwt_required, get_jwt_identity
+
+  @agn_bp.route('/agn/entity', methods=['POST'])
+  @jwt_required()
+  def add_entity():
+      current_user = get_jwt_identity()
+      role = current_user.get('role')
+      # Rest of the logic remains the same
+  ```
+
+### 9. Finalizing the README
+
+Update `README.md` to reflect the new features and setup instructions.
+
+```markdown
+# AGN Flask Application
+
+## Overview
+This Flask application provides a framework for interacting with the Active Graph Network (AGN). It supports adding entities and relationships, querying the graph, and enforces role-based access control (RBAC) with hierarchical policies.
+
+## Setup Instructions
+
+### 1. Clone the Repository
+
+```bash
+git clone <repository_url>
+cd agn_flask_app
+```
+
+### 2. Create and Activate Conda Environment
+
+```bash
+conda create --name agn_env python=3.10.15
+conda activate agn_env
+```
+
+### 3. Install Dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+### 4. Run Redis (Optional for Caching)
+
+Ensure Redis is installed and running. For example, on macOS:
+
+```bash
+brew install redis
+brew services start redis
+```
+
+### 5. Run the Application
+
+```bash
+python run.py
+```
+
+## Available Endpoints
+
+- **Health Check**
+  - `GET /health`
+  - **Response**: `{"status": "Healthy", "message": "AGN Flask App is running!"}`
+
+- **Add Entity**
+  - `POST /agn/entity`
+  - **Headers**: `X-User-Role: <role>`
+  - **Body**:
+    ```json
+    {
+      "entity_id": "patient_1",
+      "entity_type": "patient"
+    }
+    ```
+  - **Response**: `{"status": "success", "entity_id": "patient_1", "entity_type": "patient"}`
+
+- **Add Relationship**
+  - `POST /agn/relationship`
+  - **Headers**: `X-User-Role: <role>`
+  - **Body**:
+    ```json
+    {
+      "source_id": "doctor_1",
+      "target_id": "patient_1",
+      "relationship_type": "treats"
+    }
+    ```
+  - **Response**: `{"status": "success", "source_id": "doctor_1", "target_id": "patient_1", "relationship_type": "treats"}`
+
+- **Query Entities**
+  - `GET /agn/query`
+  - **Headers**: `X-User-Role: <role>`
+  - **Response**: `{"entities": [{"entity_id": "patient_1", "type": "patient"}, ...]}`
+
+## Authentication and Authorization
+
+The application uses role-based access control (RBAC) to manage permissions. Roles and their permissions are defined within the `AccessControlService`. Ensure that API requests include the `X-User-Role` header to specify the user's role.
+
+## Next Steps
+
+1. **Implement Advanced Query Parsing**: Enhance the `AGNService` to handle multi-level and stacked queries based on user requirements.
+
+2. **Integrate Interactive Visualizations**: Develop a frontend interface using React and libraries like D3.js to visualize the AGN graph dynamically.
+
+3. **Enhance Security**: Implement JWT-based authentication for secure API access.
+
+4. **Add Comprehensive Testing**: Develop unit and integration tests to ensure the reliability of access control mechanisms and API endpoints.
+
+5. **Deploy the Application**: Configure deployment using platforms like AWS, Azure, or Heroku, incorporating API Gateway for scalability and security.
+
+Feel free to reach out if you need further assistance or specific functionalities added immediately!

innovate/SlappAI/docs/2_build_app.md

@@ -0,0 +1,30 @@
+Great to hear that it’s running smoothly! Here are some next steps to enhance the project:
+
+### 1. **API Testing**
+   - **Verify Functionality**: Test the `/create_node` and `/query` endpoints to ensure that nodes are correctly created and queried.
+   - **Use Postman or CURL**: Send `POST` requests to `/create_node` with example JSON data and `GET` requests to `/query` to test retrieval.
+
+### 2. **Extend Services for AGN Operations**
+   - **Expand AGNService**: Add methods to support more advanced AGN functionality, like:
+     - **Update Node**: Allow nodes to be updated with new data or relationships.
+     - **Delete Node**: Provide an option to delete nodes.
+     - **Relationship Queries**: Enable deeper queries across relationships in multiple domains.
+
+### 3. **Enhance API with Contextual Queries**
+   - **Cross-Domain Queries**: Integrate queries that can cross-reference domains, useful for complex relationships.
+   - **Query Syntax**: Allow API users to filter nodes by domain, relationship type, and custom attributes.
+
+### 4. **Build a Frontend for Visualizing AGNs**
+   - **Interactive Graphs**: Use a JavaScript library (like D3.js or Cytoscape) to visualize nodes and relationships dynamically.
+   - **Node Details**: Display contextual information when nodes are clicked, allowing users to navigate through relationships.
+
+### 5. **Documentation and Testing**
+   - **Documentation**: Document the API endpoints, AGN structure, and querying syntax in `README.md` or an expanded `/docs` section.
+   - **Unit Testing**: Write unit tests for each service and API endpoint to ensure stability.
+
+### 6. **Future Enhancements**
+   - **API Gateway for Queries**: Implement an API gateway for managing access, especially if this will be used across multiple applications.
+   - **ACL Integration**: Add Access Control Lists to manage who can view or manipulate specific nodes or domains.
+   - **Graph Expansion Rules**: Allow rule-based node and relationship creation to infer additional data or automate AGN updates.
+
+These steps will solidify the base functionality and pave the way for scaling up and deploying the AGN framework into real-world applications. Let me know if you’d like to focus on any specific step, and I can assist with implementation!

innovate/SlappAI/docs/3_api_config.md

@@ -0,0 +1,32 @@
+It looks like the `/create_node` endpoint is working perfectly now! The response confirms that the node was successfully created and returned in JSON format with the correct data structure:
+
+```json
+{
+  "node": {
+    "data": {
+      "age": 45, 
+      "medical_conditions": [
+        "hypertension", 
+        "diabetes"
+      ], 
+      "name": "John Doe"
+    }, 
+    "domain": "healthcare", 
+    "node_id": "patient_123"
+  }, 
+  "status": "Node created"
+}
+```
+
+### Next Steps
+With the `create_node` endpoint functioning as expected, here are a few suggested next steps:
+
+1. **Test the `/query` Endpoint**: Use a similar approach with `curl` to test the `/query` endpoint by retrieving nodes based on `node_id` to ensure data retrieval works.
+
+2. **Expand API Functionality**: We can now add more endpoints or functionality, such as updating or deleting nodes, querying nodes by domain, and linking nodes across domains.
+
+3. **Logging and Error Handling**: Enhance the API with improved logging and error handling for production readiness, ensuring any issues are captured in logs and communicated clearly in responses.
+
+4. **Documentation**: Consider adding API documentation for each endpoint, detailing the required parameters, expected responses, and example requests, which could also help in generating automated API testing scripts.
+
+Let me know if you’d like to proceed with any of these steps!

innovate/SlappAI/docs/4_local_graph_setup.md

@@ -0,0 +1,28 @@
+You're absolutely right—a local graph database is more aligned with the AGN (Active Graph Network) structure and will provide native graph functionality that is critical for relationship-based queries, access control, and contextualization.
+
+For a local graph database that aligns with AGNs, **Neo4j Desktop** or **RedisGraph** (a module of Redis for graph-based storage) would be ideal choices for the following reasons:
+
+### Neo4j Desktop
+- **Graph-Native Storage**: Neo4j is designed specifically for handling complex relationships and can natively support nodes, edges, and properties.
+- **Cypher Query Language**: It uses Cypher, a declarative graph query language that's highly expressive and aligns well with AGNs' querying needs.
+- **Local Setup and Expansion**: Neo4j Desktop allows for local development while supporting an easy transition to Neo4j’s enterprise offerings for production-level needs.
+
+### RedisGraph
+- **In-Memory Graph Storage**: RedisGraph is optimized for fast, in-memory storage, which can be useful for developing AGNs and quickly retrieving complex relationships.
+- **Cypher-Compatible**: It also supports Cypher to an extent, making it easy to apply graph-based queries.
+- **Lightweight and Flexible**: RedisGraph is lightweight, can be embedded within Redis, and is highly performant for testing AGNs locally.
+
+### Suggested Next Steps for AGNs with a Local Graph Database
+1. **Define the Graph Schema**:
+   - **Node Labels** (e.g., `Patient`, `Doctor`, `Appointment`) and properties for each node type.
+   - **Relationship Types** (e.g., `TREATS`, `SCHEDULED_FOR`, `PART_OF_DOMAIN`) that define how nodes are interlinked.
+
+2. **Implement Database Connection and Query Functions**:
+   - **Connection Setup**: Establish a connection to Neo4j or RedisGraph from the Flask app.
+   - **CRUD Operations**: Update `AGNService` methods to add, query, update, and delete nodes and relationships within the graph database.
+
+3. **Expand the API for Graph Operations**:
+   - **Advanced Queries**: Enable APIs to perform graph-based queries, including cross-domain relationship exploration and contextual queries.
+   - **Domain-Based Querying**: Use Cypher or equivalent for domain-specific queries to fit the AGNs framework.
+
+If you’d like, we can go ahead and start setting up the local graph database connection and define our initial schema to align with AGNs' architecture. This will provide a solid foundation for all advanced AGN features we plan to implement.

innovate/SlappAI/docs/5_CRUD.md

@@ -0,0 +1,17 @@
+It looks like the `load_graph` function is now working perfectly, as all nodes and relationships defined in `healthcare.json` have been successfully loaded and are correctly listed with their respective relationships, domains, and node types.
+
+### Next Steps:
+1. **API Expansion**: We can add or modify API endpoints to interact with these nodes, including:
+   - **CRUD operations** for nodes (Create, Read, Update, Delete).
+   - **Querying** nodes based on specific criteria, like domain or relationships.
+   - **Advanced relationship exploration**, such as retrieving paths or hierarchies in the graph.
+
+2. **Visualization**:
+   - Consider visualizing the graph using a frontend tool or library (e.g., D3.js) for better user interaction with the data structure.
+   - This can be integrated within the Flask app as a new endpoint or dashboard for easier exploration of relationships and entities.
+
+3. **Refinement and Testing**:
+   - Additional testing and error handling can further enhance reliability, especially when dealing with missing nodes or malformed JSON in future graphs.
+   - We can add domain-specific filters or other advanced query options as needed.
+
+Would you like to proceed with implementing any specific API, visualization, or further testing?

innovate/SlappAI/docs/5_graph_as_json.md

@@ -0,0 +1,143 @@
+Storing the current structure in a JSON file is a great approach to allow easy editing, visualization, and extensibility without binding too tightly to a specific database early on. We can load this JSON file at runtime to initialize the local graph with domains, subdomains, entities, relationships, RBAC, and ACLs. This approach will enable a smooth transition when moving to a more advanced graph database for the production phase.
+
+Here's how we can proceed:
+
+### 1. Structure the JSON File
+Create a JSON file to store:
+   - **Domains and Subdomains**: Each with its type, label, and any inheritance.
+   - **Entities**: Each entity with attributes like type, label, and domain inheritance.
+   - **Relationships**: Defined relationships between entities and domains.
+   - **RBAC and ACL Policies**: Role-based access and policies for different entity roles.
+
+The JSON structure could look like this:
+
+```json
+{
+  "domains": {
+    "Healthcare": {
+      "type": "Domain",
+      "subdomains": {
+        "Hospitals": { "type": "Facility", "inherits_from": "Healthcare" },
+        "Public Health": { "type": "Service", "inherits_from": "Healthcare" }
+      }
+    },
+    "Transport": {
+      "type": "Domain",
+      "subdomains": {
+        "Public Transit": { "type": "Service", "inherits_from": "Transport" },
+        "Air Travel": { "type": "Service", "inherits_from": "Transport" }
+      }
+    }
+  },
+  "entities": {
+    "doctor": {
+      "type": "Professional",
+      "label": "Doctor",
+      "domain": "Healthcare",
+      "attributes": { "specialization": "Cardiology", "experience": 15 }
+    },
+    "patient": {
+      "type": "Person",
+      "label": "Patient",
+      "domain": "Healthcare",
+      "attributes": { "age": 45, "condition": "Hypertension" }
+    }
+  },
+  "relationships": [
+    { "source": "doctor", "target": "hospital", "type": "works_in" },
+    { "source": "patient", "target": "medical_procedure", "type": "undergoes" }
+  ],
+  "rbac_policies": {
+    "Doctor": {
+      "inherits": ["Medical Staff"],
+      "can_view": ["Patient"],
+      "can_update": ["Treatment"]
+    }
+  },
+  "acl_policies": {
+    "Doctor": { "can_view": ["Patient"], "restricted_access": ["Finance Documents"] },
+    "Patient": { "can_view": ["Insurance Claim"], "allowed_if": "insurance_status == 'active'" }
+  }
+}
+```
+
+### 2. Code to Load and Initialize the Graph from JSON
+
+Here’s an example of how we can load this JSON file and initialize the graph in the application:
+
+#### Step 1: Create the JSON Loader
+Save the above structure in a JSON file (e.g., `agn_structure.json`).
+
+#### Step 2: Write Code to Load and Parse JSON in `agn_service.py`
+```python
+import json
+import networkx as nx
+
+class AGNService:
+    def __init__(self, json_path="agn_structure.json"):
+        self.graph = nx.DiGraph()
+        self.acl_policies = {}
+        self.rbac_policies = {}
+        self.load_from_json(json_path)
+
+    def load_from_json(self, json_path):
+        with open(json_path) as f:
+            data = json.load(f)
+        
+        # Load domains and subdomains
+        for domain, attributes in data.get("domains", {}).items():
+            self.graph.add_node(domain, **attributes)
+            for subdomain, sub_attr in attributes.get("subdomains", {}).items():
+                self.graph.add_node(subdomain, **sub_attr)
+                self.graph.add_edge(subdomain, domain, relationship="part_of")
+
+        # Load entities
+        for entity, attributes in data.get("entities", {}).items():
+            self.graph.add_node(entity, **attributes)
+
+        # Load relationships
+        for rel in data.get("relationships", []):
+            self.graph.add_edge(rel["source"], rel["target"], relationship=rel["type"])
+
+        # Load ACL and RBAC policies
+        self.acl_policies = data.get("acl_policies", {})
+        self.rbac_policies = data.get("rbac_policies", {})
+
+    def query_node(self, node_id):
+        return self.graph.nodes.get(node_id, "Node not found")
+```
+
+#### Step 3: Update Routes in `agn.py` to Use the Initialized Graph
+
+This will allow the API to interact with the graph data directly from the JSON-loaded structure. 
+
+```python
+from flask import jsonify, request
+from app.services.agn_service import AGNService
+
+agn_service = AGNService()
+
+@agn_bp.route('/create_node', methods=['POST'])
+def create_node():
+    data = request.json
+    node_id = data['node_id']
+    node_data = data['data']
+    domain = data['domain']
+    agn_service.graph.add_node(node_id, **node_data, domain=domain)
+    return jsonify({"status": "Node created", "node_id": node_id}), 201
+
+@agn_bp.route('/query', methods=['GET'])
+def query_node():
+    node_id = request.args.get('node_id')
+    node = agn_service.query_node(node_id)
+    if node == "Node not found":
+        return jsonify({"error": node}), 404
+    return jsonify(node), 200
+```
+
+### 3. Advantages of Using JSON
+- **Readable Structure**: JSON provides a clear structure for domains, entities, and relationships.
+- **Scalability**: This setup can scale as you add more domains, entities, and relationships.
+- **Simplicity**: For local testing, it removes the need to set up an actual graph database while preserving graph-based relationships.
+  
+This setup aligns with AGN principles, allowing AGNs to explore and scale within a well-defined graph database structure that can expand to external solutions. Let me know if this aligns with your goals or if you’re ready to dive into the graph database setup further.

innovate/SlappAI/requirements.txt

@@ -0,0 +1,2 @@
+Flask==2.0.3
+Werkzeug==2.0.3

innovate/SlappAI/run.py

@@ -0,0 +1,6 @@
+from app import create_app
+
+app = create_app()
+
+if __name__ == "__main__":
+    app.run()

innovate/SlappAI/scripts/1_test_node_creation.py

@@ -0,0 +1,30 @@
+import requests
+
+def print_response(response):
+    try:
+        print(response.json())
+    except requests.exceptions.JSONDecodeError:
+        print("Non-JSON response received:", response.text)
+
+# Test node creation in the healthcare domain
+response = requests.post("http://localhost:5000/create_node", json={
+    "node_id": "patient_123",
+    "data": {"name": "John Doe", "age": 45, "medical_conditions": ["hypertension", "diabetes"]},
+    "domain": "healthcare"
+})
+print("Create Node Response:", response.json())
+
+# Test querying in the specified domain
+response = requests.get("http://localhost:5000/query", params={"node_id": "patient_123", "graph": "healthcare"})
+print("Query Node in Healthcare Domain Response:", end=" ")
+print_response(response)
+
+# Test querying without specifying a domain
+response = requests.get("http://localhost:5000/query", params={"node_id": "patient_123"})
+print("Query Node in All Domains Response:", end=" ")
+print_response(response)
+
+# Test querying a non-existent domain
+response = requests.get("http://localhost:5000/query", params={"node_id": "patient_123", "graph": "finance"})
+print("Query Node in Non-existent Domain Response:", end=" ")
+print_response(response)

innovate/SlappAI/tmp.md

@@ -0,0 +1,225 @@
+**Active Graphs: Revolutionizing Data Understanding Through Dynamic Relationships**
+
+---
+
+### Introduction to Active Graphs
+
+Active Graphs represent a paradigm shift in data management by actively inferring relationships and context, mimicking real-world interactions. Unlike traditional databases with rigid schemas and complex queries, Active Graphs structure data as interconnected nodes and predefined relationships. This approach creates an intelligent, self-organizing system where data points understand their context within broader ecosystems, enabling applications that demand complex understanding across multiple domains.
+
+### Credibility and Background
+
+With over 15 years as an enterprise solution architect across healthcare, finance, and defense sectors, I've firsthand experience with the limitations of traditional data systems when handling complex, interconnected data structures. This multifaceted background inspired the development of Active Graphs, rethinking traditional data models to address modern challenges requiring adaptive learning and policy-driven insights.
+
+### Key Projects that Shaped Active Graphs
+
+1. **AI-Powered Trading Bot**: Developed an AI-driven trading bot leveraging interconnected market indicators, highlighting the need for data structured as interconnected nodes rather than isolated data points.
+
+2. **YouMatter (Healthcare)**: Created a platform that streamlined patient data management, emphasizing the importance of capturing nuanced, hierarchical healthcare data in a dynamic, accessible format.
+
+3. **OpenEYE (Legal Analysis)**: Built a legal analysis platform using Active Graph Networks to infer relationships between legal clauses, precedents, and statutory laws, demonstrating versatility across non-numerical domains.
+
+These projects revealed a common need across domains: a data structure that intuitively maps relationships and infers context, leading to the conception of Active Graphs.
+
+---
+
+### Problem Statement
+
+Modern data systems require flexibility and contextual awareness that traditional relational databases can't provide, especially when handling dynamic, interconnected datasets where context and relationships evolve continuously. Active Graphs address these limitations by:
+
+- Supporting real-time updates for continuous learning.
+- Enabling cross-domain interoperability and reasoning.
+- Inferring implicit relationships without computational overhead.
+- Self-organizing data structures for dynamic schema evolution.
+- Integrating policy-driven decision-making with embedded security.
+- Managing hierarchical and recursive data structures efficiently.
+- Supporting NLP integration for context-aware relationships.
+
+---
+
+### The Concept of Active Graphs
+
+#### What are Active Graphs?
+
+Active Graphs are an innovative approach to data management where data is organized as nodes and predefined relationships. Each node functions like a mini-table representing an individual entity with relevant attributes, while relationships are predefined, rule-based links specifying how nodes interact. This setup provides clarity and immediate context, enabling meaningful insights to be drawn directly from the data model without relying on complex queries or pattern recognition algorithms.
+
+#### Core Components of Active Graphs
+
+- **Nodes as Small Tables**: Represent individual entities with relevant attributes.
+- **Relationships as Defined Connections**: Predefined links (e.g., *contains*, *inherits from*, *relates to*) specifying node interactions.
+- **Dynamic Structure with Policies**: Nodes and relationships evolve in real time, governed by policies like access control lists (ACLs).
+- **Built-In Contextual Awareness**: Structured links enable nodes to inherently understand their place within the data structure.
+
+#### Mermaid Diagram Placeholder
+
+```mermaid
+graph TD
+    A[Patient] -->|has| B[Medical Record]
+    A -->|has| C[Diagnosis]
+    C -->|relates to| D[Treatment Plan]
+    D -->|contains| E[Medication]
+```
+
+*Diagram 1: Example of nodes and relationships in Active Graphs.*
+
+---
+
+### Context and Knowledge Inference
+
+In Active Graphs, context inference is built into the structure, enabling each node to recognize and understand its relationships, no matter how deep the hierarchy or how many domains it spans. This inherent capability transforms nodes from isolated data points into entities aware of their context and capable of deriving insights from it.
+
+#### How Context Inference Works
+
+Relationships are defined at the structural level, allowing the system to automatically traverse connections and infer context as new data points are added. This organization dramatically reduces the complexity of querying and enables meaningful insights without relying on complex pattern recognition algorithms.
+
+---
+
+### Use Case Examples
+
+#### Financial Trading Bot
+
+**Problem**: Traditional trading systems struggle to capture and utilize the dynamic context surrounding financial data, often relying on brute-force techniques and historical data for training.
+
+**Solution**: Active Graphs enable minute-level data to integrate seamlessly into a contextual framework, where each Minute Node automatically links to corresponding Day, Weekly, and Volatility nodes. This setup allows the system to immediately contextualize each new minute of data, drawing real-time inferences based on its surrounding context.
+
+**Outcome**: Reduced dependency on historical data training and dynamic, up-to-the-minute decision-making based on context-driven insights.
+
+#### Mermaid Diagram Placeholder
+
+```mermaid
+graph TD
+    A[Minute Data] -->|aggregates to| B[Hour Data]
+    B -->|aggregates to| C[Day Data]
+    C -->|influenced by| D[Market Indicators]
+    A -->|relates to| D
+```
+
+*Diagram 2: Structure of trading data across multiple contexts in Active Graphs.*
+
+---
+
+#### Healthcare Data Management
+
+**Problem**: Managing healthcare data requires linking diverse data—diagnoses, treatments, demographics—in ways that create a full picture of patient health, which traditional systems struggle to provide.
+
+**Solution**: Active Graphs dynamically link patient data, diagnoses, treatments, and lifestyle factors, enabling the system to "infer" connections and provide a comprehensive, connected view of a patient's health journey.
+
+**Outcome**: Improved decision-making, informed recommendations, and enhanced patient outcomes through real-time context inference.
+
+#### Mermaid Diagram Placeholder
+
+```mermaid
+graph TD
+    A[Patient] -->|has| B[Diagnosis]
+    B -->|linked to| C[Treatment Plan]
+    C -->|includes| D[Medication]
+    A -->|records| E[Lifestyle Habits]
+    E -->|impacts| B
+```
+
+*Diagram 3: Structure of healthcare data in Active Graphs.*
+
+---
+
+#### Legal Document Analysis
+
+**Problem**: Legal data is complex, requiring extensive cross-referencing to understand relationships between statutes, precedents, and jurisdictions.
+
+**Solution**: Active Graphs structure legal documents into interconnected nodes, where each clause connects directly to related statutes, precedents, and jurisdictional rules, creating a dynamic network of legal knowledge.
+
+**Outcome**: A more comprehensive, queryable knowledge graph that empowers legal professionals to gain context and insights efficiently.
+
+#### Mermaid Diagram Placeholder
+
+```mermaid
+graph TD
+    A[Clause] -->|references| B[Statute]
+    A -->|interpreted by| C[Precedent]
+    A -->|applies in| D[Jurisdiction]
+```
+
+*Diagram 4: Interconnected legal data in Active Graphs.*
+
+---
+
+### Defining Policies and ACLs in Active Graphs
+
+Active Graphs utilize policies and ACLs not only for security but also as feature engineering tools. Policies define rules for node interactions, while ACLs provide granular control over data access and relationship formation, enabling a secure, dynamic network where relationships evolve based on context and permissions.
+
+#### Mermaid Diagram Placeholder
+
+```mermaid
+graph TD
+    subgraph Policies
+        P1[Policy A]
+        P2[Policy B]
+    end
+    subgraph ACLs
+        A1[Attribute-Based Access]
+        A2[Inheritance]
+        A3[Relationship Control]
+    end
+    A[Node] -->|governed by| P1
+    A -->|access controlled by| A1
+```
+
+*Diagram 5: Interaction of policies and ACLs within Active Graphs.*
+
+---
+
+### Continuous Learning and Data Flexibility
+
+Active Graphs allow for real-time addition and updating of nodes, reflecting changes instantly across the network. This continuous mapping enables the system to adapt without the need for traditional retraining models, ensuring constant adaptability and real-time contextual awareness.
+
+---
+
+### Foundation for Artificial General Intelligence (AGI)
+
+By enabling systems to understand and infer relationships across diverse and complex datasets, Active Graphs lay the groundwork for AGI. Their ability to structure data dynamically, infer context automatically, and transfer learning across domains mirrors human cognitive processes, positioning them as a foundational technology for AGI development.
+
+---
+
+### The Broader Vision
+
+Active Graphs have the potential to revolutionize data management across industries like education, supply chain, and government by providing a platform for dynamic, relationship-driven data structures.
+
+- **Education**: Personalizing learning journeys by linking subjects, skills, and progress milestones.
+- **Supply Chain**: Optimizing inventory and logistics by mapping relationships between suppliers, manufacturers, and retailers.
+- **Government**: Understanding interconnected relationships between policies, population data, and economic factors.
+
+#### Mermaid Diagram Placeholder
+
+```mermaid
+graph TD
+    subgraph Education
+        A[Student] -->|learns| B[Subject]
+        B -->|requires| C[Skill]
+    end
+    subgraph Supply Chain
+        D[Supplier] -->|provides| E[Materials]
+        E -->|used by| F[Manufacturer]
+    end
+    subgraph Government
+        G[Policy] -->|affects| H[Population Data]
+        H -->|influences| I[Economic Factors]
+    end
+```
+
+*Diagram 6: Potential applications of Active Graphs across industries.*
+
+---
+
+### Conclusion
+
+Active Graphs represent a profound shift in data management, transforming data systems into dynamic, context-aware networks. By structuring data through relationships and context, they enable deeper insights and actionable intelligence from interconnected data points. This new paradigm enhances decision-making and has the potential to address complex, multi-domain challenges, paving the way for innovations across various industries.
+
+---
+
+### Call to Action
+
+We invite you to explore the potential of Active Graphs within your own field or area of interest. Consider how a dynamic, interconnected system could transform the way you approach information. We're seeking feedback, insights, and collaborators who share our vision of a relationship-first approach to data management. Connect with us to push the boundaries of what's possible in data systems, building a foundation that could support the development of AGI and other groundbreaking advancements.
+
+Join us on this journey. Let's reimagine the future of data—one relationship at a time.
+
+---
+
+*Note: The mermaid diagrams are placeholders. Please replace each "Mermaid Diagram Placeholder" code block with the actual diagram when posting the article.*

models/AGDB/Cube4D/Cube4D - Knowledge Base.md

@@ -0,0 +1,57 @@
+### Cube4D Knowledge Base
+
+#### **1. Cube4D Core Concepts**
+
+- **Perfect Numbers as Volumes**: The idea that perfect numbers are represented as volumetric cubes, with each divisor contributing to a balanced, complete structure.
+  - *Notes*: Perfect numbers like 6, 28, and 496 are visualized as cubes with relational completeness, where each dimension or layer reflects a divisor.
+  - *Status*: Core concept, fully defined.
+
+- **Boundary Layers**: The concept of adding an initial and final layer (often represented by “2”) to signify the beginning and end of a cube’s structure, reinforcing the concept of completeness.
+  - *Notes*: Boundary layers act as relational constraints, providing each cube with a defined starting and ending point, enhancing AGI's interpretation of completeness.
+  - *Status*: Core concept, fully defined.
+
+#### **2. Cube4D Graph Structure**
+
+- **Relational Cube Structure**: Extending Cube4D from static cubes to a dynamic graph structure, where each cube (or node) is unique yet interconnected, creating a relational map.
+  - *Notes*: Each node’s relationships provide AGI with context and enable complex, dynamic queries across data points.
+  - *Status*: Core concept, fully defined.
+
+- **Temporal Nodes and State Changes**: Each layer represents shifts or changes based on the base layer, capturing temporal evolution and allowing AGI to track changes over time.
+  - *Notes*: Each “cell” in this structure can be thought of as a 3D data point within an evolving relational graph, supporting AGI’s temporal awareness.
+  - *Status*: Expanding.
+
+#### **3. Cube4D for AGI Application**
+
+- **Contextual Querying**: Cube4D enables AGI to perform queries based on relationships, allowing it to interpret nodes based on context.
+  - *Notes*: For example, querying a “patient node” brings up relationships that define the patient’s data compactly.
+  - *Status*: Core concept, application under development.
+
+- **Recognition of Perfect Structures**: AGI can use Cube4D to identify complete, balanced systems based on the principles of relational completeness and boundary layers.
+  - *Notes*: This extends AGI’s capabilities from raw data processing to understanding relational harmony and symmetry within data.
+  - *Status*: Core concept, application under development.
+
+#### **4. Fundamental Principles and Theories**
+
+- **12 as a Scale Factor**: A proposed constant that may serve as a scaling factor in Cube4D, providing structure and proportion within relational layers.
+  - *Notes*: 12 might apply universally within Cube4D’s framework or in defining relationships between layers and boundaries.
+  - *Status*: Theory, under consideration.
+
+- **Life and Reality as a 4D Structure**: A broader vision that all phenomena—from life on Earth to cosmic systems—exist on a 2D plane within a 4D structure, where Cube4D’s principles apply universally.
+  - *Notes*: This vision extends Cube4D’s implications, suggesting that relational completeness and dynamic structures are fundamental to understanding multi-dimensional realities.
+  - *Status*: Visionary concept, explored in the epilog.
+
+#### **5. Expanding Knowledge Areas (Epilog)**
+
+- **Wave Dynamics**: Cube4D may have applications in understanding wave interactions, particularly through its multi-layered and relational structure.
+  - *Notes*: Waves could be modeled as dynamic shifts within the cube, representing state changes across layers.
+  - *Status*: Hypothesis, under exploration.
+
+- **Orbital Dynamics**: Cube4D’s principles may extend to orbital systems, using relational completeness and boundary layers to model stable orbits.
+  - *Notes*: This approach could bridge Cube4D with physical dynamics, creating applications in astrophysics or engineering.
+  - *Status*: Hypothesis, under exploration.
+
+---
+
+### Using the Knowledge Base
+
+Each concept or theory here serves as a **reference point** that we can update as we develop the white paper and other documents. This structure allows us to keep everything organized, so no idea gets lost, and we have an evolving, indexed map of Cube4D.