Hyper Advanced Knowledge Grid Graph

import spacy
from collections import defaultdict

# 1. Load a high-value NLP engine (requires: pip install spacy)
# nlp = spacy.load("en_core_web_md") 

def extract_value_entities(text):
    """
    Analyzes raw system data to identify 'Value Entities' 
    like Users, Projects, and Financial impacts.
    """
    # Simulated extraction for this snippet
    mock_entities = {
        "PERSON": ["DevTeam_Alpha"],
        "ORG": ["CloudServer_West"],
        "MONEY": ["$50,000 Risk"],
        "ISSUE": ["Memory Leak"]
    }
    return mock_entities

def update_knowledge_graph(entity_map):
    """
    Connects extracted data into a relational 'Value Graph'
    to prioritize engineering efforts based on business impact.
    """
    knowledge_graph = defaultdict(list)
    
    for label, items in entity_map.items():
        for item in items:
            # Create a 'Value Link'
            link = f"Link: {item} -> impact_analysis_node"
            knowledge_graph[label].append(link)
            print(f"Mapped {label}: {item} to Value Chain.")
    
    return knowledge_graph

# --- Value Creation Simulation ---
raw_data = "DevTeam_Alpha reported a Memory Leak on CloudServer_West affecting a $50,000 contract."

print("Analyzing tech-value stream...")
entities = extract_value_entities(raw_data)
graph = update_knowledge_graph(entities)

# 2. Strategic Outcome: Auto-prioritize work
if "$50,000 Risk" in entities["MONEY"]:
    print("\n--- HIGH VALUE ACTION ---")
    print("Priority: Level 1 - Urgent fix required to protect revenue.")