#!/usr/bin/env python3
# ==============================================================================
# AEL CS Encyclopedia™ - © Ayman Elmasry
# Week 7 SQL & AI: ael_ai_search_llm.py (AI Search Engine & Embedding Matching)
# ==============================================================================

import math
import sys

class SearchNode:
    def __init__(self, state, parent=None, action=None, cost=0):
        self.state = state
        self.parent = parent
        self.action = action
        self.cost = cost

def calculate_euclidean_distance(vector_a, vector_b):
    """
    Computes the Euclidean distance between two multi-dimensional LLM embeddings.
    """
    if len(vector_a) != len(vector_b):
        raise ValueError("Dimensionality mismatch between embedding vectors.")
    return math.sqrt(sum((a - b) ** 2 for a, b in zip(vector_a, vector_b)))

def simulate_llm_vector_matching():
    print("================================================================")
    print("        AEL SOVEREIGN AI SEARCH ENGINE & LLM EMBEDDING MATRIX")
    print("================================================================\n")

    # Mock embeddings for user input and existing vector database documents
    target_prompt_embedding = [0.25, 0.58, 0.89, 0.12]

    corpus_vault = {
        "Doc A (AI Relational Theory)": [0.26, 0.59, 0.88, 0.14],
        "Doc B (Culinary Masterpieces)": [0.92, 0.11, 0.05, 0.85],
        "Doc C (Cyber Security Kernel)": [0.30, 0.50, 0.70, 0.22]
    }

    print("[LLM VECTOR EMBEDDING MATCHING (MINIMAL EUCLIDEAN DISTANCE)]")
    print(f"  Target Vector Prompt: {target_prompt_embedding}\n")

    best_match = None
    min_distance = float('inf')

    for doc_title, vector in corpus_vault.items():
        dist = calculate_euclidean_distance(target_prompt_embedding, vector)
        print(f"  ├── {doc_title} -> Distance: {dist:.4f}")
        if dist < min_distance:
            min_distance = dist
            best_match = doc_title

    print(f"\n  └── [OPTIMAL LLM MATCH]: {best_match} (Score: {min_distance:.4f})\n")

def simulate_decision_tree_traversal():
    print("[AI DECISION SEARCH TREE TRAVERSAL (MINIMAX GRAPH)]")
    root = SearchNode("INITIAL_QUERY", cost=0)
    child1 = SearchNode("BRANCH_SQL_JOIN", parent=root, action="EVAL_RELATION", cost=5)
    child2 = SearchNode("BRANCH_AI_VECTOR", parent=root, action="EVAL_EMBEDDING", cost=2)

    print(f"  ├── [Root Node]: {root.state} (Cost: {root.cost})")
    print(f"  ├── [Branch A]: {child1.state} via {child1.action} (Cost: {child1.cost})")
    print(f"  └── [Branch B]: {child2.state} via {child2.action} (Cost: {child2.cost})")

    # Greedy choice
    selected = child1 if child1.cost < child2.cost else child2
    print(f"\n  └── [AI Selected Minimal-Cost Path]: {selected.state} (Cost: {selected.cost})\n")
    print("Matrix execution completed successfully.")

def main():
    simulate_llm_vector_matching()
    simulate_decision_tree_traversal()

if __name__ == "__main__":
    main()