doc/AGDB/3_AGDB_defining_thefuture.md · SlappAI/Singularity at main

The agn.py file you've developed for the API interface is a solid foundation for interacting with AGNs through endpoints, and it includes essential functions such as node creation, graph loading, and querying. To adapt this for time series data and AGDBs, we can draw inspiration from your existing routes and structure additional endpoints for AGDB-specific functionality. This will allow us to interface with both AGN and AGDB seamlessly within the same framework. Here’s how we can proceed:

Enhancing `agn.py` with AGDB Functionality

Additional Routes for AGDB Operations

AGDB Loading and Validation: This route would load AGDBs, validate the JSON structure, and initialize the graph with predefined relationships.
Time-Series Data Querying: These endpoints will query specific nodes based on timestamps, such as finding data for a specific minute/hour/day.
Feature-Based Querying and Inference: This will allow querying based on node features and synthesized relationships inferred from policies.
Relationship Exploration and Traversal: Enhance relationship inspection to account for synthetic relationships and policy-based connections within AGDB.

Sample Code for New AGDB Routes in `agn.py`

Here's how we can extend agn.py with some AGDB-specific routes, using concepts from your existing code.

# app/routes/agn.py
from app.services.agdb_service import (
    load_agdb, validate_agdb, query_time_node, create_agdb_node, traverse_agdb_node
)

# New Blueprint for AGDB routes
@agn_bp.route('/load_agdb', methods=['POST'])
def load_agdb_endpoint():
    """Endpoint to load an AGDB from a JSON file."""
    data = request.json
    agdb_file = data.get("agdb_file", "graphs/time_series_agdb.json")
    agdb = load_agdb(agdb_file)
    
    if agdb:
        return jsonify({"status": f"AGDB '{agdb_file}' loaded successfully"}), 200
    return jsonify({"error": "Failed to load AGDB"}), 400

@agn_bp.route('/validate_agdb', methods=['GET'])
def validate_agdb_endpoint():
    """Endpoint to validate the loaded AGDB structure."""
    is_valid = validate_agdb()
    status = "valid" if is_valid else "invalid"
    return jsonify({"status": f"AGDB is {status}"}), 200 if is_valid else 400

@agn_bp.route('/query_time_node', methods=['GET'])
def query_time_node_endpoint():
    """Endpoint to query a specific time node in the AGDB."""
    year = request.args.get('year')
    month = request.args.get('month')
    day = request.args.get('day')
    hour = request.args.get('hour')
    minute = request.args.get('minute')

    time_node = query_time_node(year, month, day, hour, minute)
    if time_node:
        return jsonify({"time_node": time_node}), 200
    return jsonify({"error": "Time node not found"}), 404

@agn_bp.route('/create_agdb_node', methods=['POST'])
def create_agdb_node_endpoint():
    """Endpoint to create a new node in the AGDB."""
    data = request.json
    node_id = data['node_id']
    node_data = data['data']
    domain = data['domain']
    node_type = data.get('type', 'TimeSeriesNode')
    
    node = create_agdb_node(node_id, node_data, domain, node_type)
    return jsonify({"status": f"{node_type} node created", "node": node.to_dict(), "domain": domain}), 201

@agn_bp.route('/traverse_agdb_node', methods=['GET'])
def traverse_agdb_node_endpoint():
    """Endpoint to traverse the AGDB based on a node ID and direction."""
    node_id = request.args.get('node_id')
    direction = request.args.get('direction', 'forward')
    
    traversal_path = traverse_agdb_node(node_id, direction)
    if traversal_path:
        return jsonify({"traversal_path": traversal_path}), 200
    return jsonify({"error": "Traversal failed or node not found"}), 404

Explanation of Key Additions

load_agdb_endpoint: This endpoint loads an AGDB from a JSON file, initializing the structure and verifying that the AGDB schema aligns with the time-series or generalized data requirements.
validate_agdb_endpoint: Checks that the AGDB schema is valid, ensuring data integrity and conformity to expected fields, attributes, and relationships.
query_time_node_endpoint: Allows querying based on a specific timestamp (year, month, day, hour, minute). This enables quick retrieval of data nodes at a particular time point, useful for time-series analysis.
create_agdb_node_endpoint: Creates a new node within the AGDB, specifying the node type (e.g., TimeSeriesNode). This is helpful for dynamic data insertion, where new nodes might be created as data is ingested.
traverse_agdb_node_endpoint: Traverses nodes within the AGDB in a specified direction, providing a path for navigating the graph based on time progression or predefined relationships.

Expanding on the JSON Schema for AGDBs

To align with this architecture, we could refine the JSON template you provided by ensuring that metadata, node attributes, and relationships are well-defined. Here’s a more structured template:

{
    "metadata": {
        "title": "Financial Time Series AGDB",
        "source": "Sample Data Source",
        "description": "A structured AGDB for storing time-series financial data.",
        "timezone": "UTC"
    },
    "time_nodes": [
        {
            "timestamp": "2024-11-04 10:45:00",
            "attributes": {
                "open": 1.12,
                "close": 1.15,
                "high": 1.17,
                "low": 1.10,
                "volume": 50000
            },
            "relationships": {
                "next": "2024-11-04 10:46:00",
                "previous": "2024-11-04 10:44:00",
                "related_features": ["open", "close"]
            }
        },
        ...
    ],
    "features": {
        "nodes": [
            {"name": "open", "type": "float", "unit": "USD"},
            {"name": "close", "type": "float", "unit": "USD"},
            {"name": "volume", "type": "integer", "unit": "shares"}
        ],
        "relationships": [
            {"source": "open", "target": "close", "type": "correlation"},
            {"source": "volume", "target": "price", "type": "impact"}
        ]
    }
}

Next Steps for Development

Refine AGDB Service Modules: Build out additional modules in agdb_service for enhanced functionality, especially around relationship handling, querying, and time-based traversal.
Develop Synthetic Relationships: Define policies to establish synthetic relationships within AGDBs, such as correlations between features or inferred trends over time.
Testing and Validation: Test the endpoints with various AGDB JSON files to ensure that querying, traversal, and relationships are working correctly.
Documentation and Examples: Write documentation to clearly explain how to use the AGDB endpoints, including JSON examples and query examples.
UI Integration: If you're planning to visualize these relationships and data nodes, consider enhancing the web app to display AGDBs alongside AGNs.

This approach sets a solid foundation for integrating AGDBs with the existing AGN framework, providing a unified querying and data management interface that can handle both time-series and relational data structures.

Enhancing agn.py with AGDB Functionality