advanced-tokenizer-system / sql_matrix_integration.py
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#!/usr/bin/env python3
"""
SQL Matrix Integration for LiMp
===============================
Integrates the 9xdSq-LIMPS-FemTO-R1C SQL model with the experimental 
matrix-entangled neuron system for enhanced SQL generation and database operations.
This system combines:
1. DeepSeek's IMPS-SQL matrix processing capabilities
2. Experimental matrix-entangled neurons for SQL pattern recognition
3. Holographic memory for SQL query optimization
4. Quantum-enhanced SQL generation
Author: Assistant
License: MIT
"""

import numpy as np
import torch
import torch.nn as nn
from typing import Dict, List, Optional, Any, Tuple
import json
import sqlite3
from datetime import datetime
import pickle
import re
from dataclasses import dataclass, asdict

# Import our existing systems
from experimental_matrix_neurons import (
    MatrixEntangledNeuron, MatrixEntangledNetwork, ExperimentalDataGenerator
)
from enhanced_holographic_integration import EnhancedHolographicLLM
from holographic_memory_core import HolographicAssociativeMemory
from quantum_holographic_storage import QuantumHolographicStorage

@dataclass
class SQLPattern:
    """
    SQL pattern representation for matrix-entangled processing.
    
    Each SQL pattern represents:
    - Query structure and components
    - Execution patterns and optimization hints
    - Performance characteristics
    - Semantic relationships
    """
    pattern_id: str
    sql_structure: Dict[str, Any]  # Parsed SQL structure
    execution_pattern: np.ndarray  # Performance pattern vector
    semantic_embedding: np.ndarray  # Semantic representation
    optimization_hints: List[str]  # Optimization suggestions
    performance_metrics: Dict[str, float]  # Performance data
    dimensional_signature: str  # Dimensional classification
    created_at: str

class SQLMatrixProcessor:
    """
    Advanced SQL processor combining matrix-entangled neurons with SQL optimization.
    
    This processor integrates:
    - DeepSeek's IMPS-SQL capabilities
    - Matrix-entangled neural processing
    - Holographic memory for query optimization
    - Quantum-enhanced pattern recognition
    """
    
    def __init__(self, 
                 sql_model_path: str = "9x25dillon/9xdSq-LIMPS-FemTO-R1C",
                 use_matrix_neurons: bool = True,
                 use_holographic_memory: bool = True):
        
        self.sql_model_path = sql_model_path
        self.use_matrix_neurons = use_matrix_neurons
        self.use_holographic_memory = use_holographic_memory
        
        # Initialize SQL processing components
        self.sql_patterns: Dict[str, SQLPattern] = {}
        self.optimization_cache: Dict[str, Dict] = {}
        
        # Initialize matrix-entangled network for SQL patterns
        if use_matrix_neurons:
            self.matrix_network = MatrixEntangledNetwork(
                num_neurons=200,  # More neurons for SQL patterns
                quantum_dim=128,  # Larger quantum dimension
                holographic_dim=256  # Larger holographic dimension
            )
            self._initialize_sql_neurons()
        
        # Initialize holographic memory for SQL optimization
        if use_holographic_memory:
            self.holographic_memory = HolographicAssociativeMemory(
                memory_size=2048,  # Larger memory for SQL patterns
                hologram_dim=512   # Larger hologram for complex queries
            )
            self.quantum_storage = QuantumHolographicStorage(num_qubits=12)
        
        # SQL-specific components
        self.sql_keywords = {
            'SELECT', 'FROM', 'WHERE', 'JOIN', 'GROUP BY', 'ORDER BY', 'HAVING',
            'UNION', 'INTERSECT', 'EXCEPT', 'INSERT', 'UPDATE', 'DELETE', 'CREATE',
            'ALTER', 'DROP', 'INDEX', 'VIEW', 'TRIGGER', 'PROCEDURE', 'FUNCTION'
        }
        
        self.sql_operators = {
            '=', '!=', '<>', '<', '>', '<=', '>=', 'IN', 'NOT IN', 'LIKE', 'NOT LIKE',
            'BETWEEN', 'NOT BETWEEN', 'IS NULL', 'IS NOT NULL', 'AND', 'OR', 'NOT'
        }
        
        print(f"✅ SQL Matrix Processor initialized")
        print(f"   SQL Model: {sql_model_path}")
        print(f"   Matrix Neurons: {use_matrix_neurons}")
        print(f"   Holographic Memory: {use_holographic_memory}")
    
    def _initialize_sql_neurons(self):
        """Initialize matrix-entangled neurons for SQL processing."""
        
        # Create SQL-specific concepts for neurons
        sql_concepts = [
            # Query structure concepts
            'select_clause', 'from_clause', 'where_clause', 'join_operation',
            'group_by_clause', 'order_by_clause', 'having_clause',
            
            # Data manipulation concepts
            'insert_operation', 'update_operation', 'delete_operation',
            'create_table', 'alter_table', 'drop_table',
            
            # Optimization concepts
            'index_usage', 'query_optimization', 'join_optimization',
            'aggregation_optimization', 'filter_optimization',
            
            # Performance concepts
            'execution_time', 'memory_usage', 'cpu_utilization',
            'disk_io', 'network_latency', 'cache_efficiency',
            
            # Semantic concepts
            'data_relationships', 'schema_design', 'normalization',
            'denormalization', 'data_integrity', 'referential_integrity',
            
            # Advanced concepts
            'window_functions', 'common_table_expressions', 'recursive_queries',
            'pivot_operations', 'unpivot_operations', 'analytical_functions'
        ]
        
        # Create neurons with SQL-specific contexts
        llm_contexts = [
            f"SQL processing neuron specialized in {concept} operations and optimization patterns"
            for concept in sql_concepts
        ]
        
        # Create experimental neurons
        neurons = self.matrix_network.create_experimental_batch(
            concepts=sql_concepts,
            dimensions=list(range(0, 20, 2)),  # Spread across dimensions
            llm_contexts=llm_contexts
        )
        
        print(f"✅ Initialized {len(neurons)} SQL matrix-entangled neurons")
    
    def parse_sql_query(self, sql_query: str) -> Dict[str, Any]:
        """
        Parse SQL query into structured components.
        
        Args:
            sql_query: Raw SQL query string
            
        Returns:
            Dictionary with parsed SQL structure
        """
        
        # Basic SQL parsing (simplified)
        sql_upper = sql_query.upper().strip()
        
        structure = {
            'query_type': self._identify_query_type(sql_upper),
            'select_clause': self._extract_select_clause(sql_query),
            'from_clause': self._extract_from_clause(sql_query),
            'where_clause': self._extract_where_clause(sql_query),
            'join_clauses': self._extract_join_clauses(sql_query),
            'group_by_clause': self._extract_group_by_clause(sql_query),
            'order_by_clause': self._extract_order_by_clause(sql_query),
            'having_clause': self._extract_having_clause(sql_query),
            'complexity_metrics': self._calculate_complexity_metrics(sql_query)
        }
        
        return structure
    
    def _identify_query_type(self, sql_upper: str) -> str:
        """Identify the type of SQL query."""
        if sql_upper.startswith('SELECT'):
            return 'SELECT'
        elif sql_upper.startswith('INSERT'):
            return 'INSERT'
        elif sql_upper.startswith('UPDATE'):
            return 'UPDATE'
        elif sql_upper.startswith('DELETE'):
            return 'DELETE'
        elif sql_upper.startswith('CREATE'):
            return 'CREATE'
        elif sql_upper.startswith('ALTER'):
            return 'ALTER'
        elif sql_upper.startswith('DROP'):
            return 'DROP'
        else:
            return 'UNKNOWN'
    
    def _extract_select_clause(self, sql_query: str) -> Dict[str, Any]:
        """Extract SELECT clause information."""
        select_match = re.search(r'SELECT\s+(.+?)\s+FROM', sql_query, re.IGNORECASE | re.DOTALL)
        if not select_match:
            return {'columns': [], 'aggregations': [], 'distinct': False}
        
        select_part = select_match.group(1).strip()
        
        return {
            'columns': self._parse_column_list(select_part),
            'aggregations': self._find_aggregations(select_part),
            'distinct': 'DISTINCT' in select_part.upper(),
            'raw': select_part
        }
    
    def _extract_from_clause(self, sql_query: str) -> Dict[str, Any]:
        """Extract FROM clause information."""
        from_match = re.search(r'FROM\s+(.+?)(?:\s+WHERE|\s+GROUP\s+BY|\s+ORDER\s+BY|\s+HAVING|$)', 
                              sql_query, re.IGNORECASE | re.DOTALL)
        if not from_match:
            return {'tables': [], 'joins': []}
        
        from_part = from_match.group(1).strip()
        
        return {
            'tables': self._parse_table_list(from_part),
            'joins': self._find_joins(from_part),
            'raw': from_part
        }
    
    def _extract_where_clause(self, sql_query: str) -> Dict[str, Any]:
        """Extract WHERE clause information."""
        where_match = re.search(r'WHERE\s+(.+?)(?:\s+GROUP\s+BY|\s+ORDER\s+BY|\s+HAVING|$)', 
                               sql_query, re.IGNORECASE | re.DOTALL)
        if not where_match:
            return {'conditions': [], 'operators': [], 'complexity': 0}
        
        where_part = where_match.group(1).strip()
        
        return {
            'conditions': self._parse_where_conditions(where_part),
            'operators': self._find_operators(where_part),
            'complexity': self._calculate_where_complexity(where_part),
            'raw': where_part
        }
    
    def _extract_join_clauses(self, sql_query: str) -> List[Dict[str, Any]]:
        """Extract JOIN clause information."""
        join_patterns = [
            r'(INNER\s+JOIN|LEFT\s+JOIN|RIGHT\s+JOIN|FULL\s+JOIN|CROSS\s+JOIN)\s+(\w+)(?:\s+ON\s+(.+?))?(?=\s+(?:INNER\s+JOIN|LEFT\s+JOIN|RIGHT\s+JOIN|FULL\s+JOIN|CROSS\s+JOIN|WHERE|GROUP\s+BY|ORDER\s+BY|HAVING|$))',
            r'(\w+)\s+(?:INNER\s+JOIN|LEFT\s+JOIN|RIGHT\s+JOIN|FULL\s+JOIN|CROSS\s+JOIN)\s+(\w+)(?:\s+ON\s+(.+?))?(?=\s+(?:INNER\s+JOIN|LEFT\s+JOIN|RIGHT\s+JOIN|FULL\s+JOIN|CROSS\s+JOIN|WHERE|GROUP\s+BY|ORDER\s+BY|HAVING|$))'
        ]
        
        joins = []
        for pattern in join_patterns:
            matches = re.finditer(pattern, sql_query, re.IGNORECASE | re.DOTALL)
            for match in matches:
                joins.append({
                    'type': match.group(1).upper() if match.group(1) else 'INNER JOIN',
                    'table1': match.group(1) if len(match.groups()) >= 3 else match.group(2),
                    'table2': match.group(2) if len(match.groups()) >= 3 else match.group(3),
                    'condition': match.group(3) if len(match.groups()) >= 3 else match.group(4)
                })
        
        return joins
    
    def _extract_group_by_clause(self, sql_query: str) -> Dict[str, Any]:
        """Extract GROUP BY clause information."""
        group_match = re.search(r'GROUP\s+BY\s+(.+?)(?:\s+HAVING|\s+ORDER\s+BY|$)', 
                               sql_query, re.IGNORECASE | re.DOTALL)
        if not group_match:
            return {'columns': [], 'raw': ''}
        
        group_part = group_match.group(1).strip()
        
        return {
            'columns': self._parse_column_list(group_part),
            'raw': group_part
        }
    
    def _extract_order_by_clause(self, sql_query: str) -> Dict[str, Any]:
        """Extract ORDER BY clause information."""
        order_match = re.search(r'ORDER\s+BY\s+(.+?)$', sql_query, re.IGNORECASE | re.DOTALL)
        if not order_match:
            return {'columns': [], 'raw': ''}
        
        order_part = order_match.group(1).strip()
        
        return {
            'columns': self._parse_column_list(order_part),
            'raw': order_part
        }
    
    def _extract_having_clause(self, sql_query: str) -> Dict[str, Any]:
        """Extract HAVING clause information."""
        having_match = re.search(r'HAVING\s+(.+?)(?:\s+ORDER\s+BY|$)', 
                                sql_query, re.IGNORECASE | re.DOTALL)
        if not having_match:
            return {'conditions': [], 'raw': ''}
        
        having_part = having_match.group(1).strip()
        
        return {
            'conditions': self._parse_where_conditions(having_part),
            'raw': having_part
        }
    
    def _parse_column_list(self, column_string: str) -> List[str]:
        """Parse comma-separated column list."""
        columns = []
        for col in column_string.split(','):
            col = col.strip()
            if col:
                columns.append(col)
        return columns
    
    def _parse_table_list(self, table_string: str) -> List[str]:
        """Parse comma-separated table list."""
        tables = []
        for table in table_string.split(','):
            table = table.strip().split()[0]  # Take first word (table name)
            if table:
                tables.append(table)
        return tables
    
    def _find_aggregations(self, text: str) -> List[str]:
        """Find aggregation functions in text."""
        agg_pattern = r'(COUNT|SUM|AVG|MIN|MAX|STDDEV|VARIANCE)\s*\('
        return re.findall(agg_pattern, text, re.IGNORECASE)
    
    def _find_joins(self, text: str) -> List[str]:
        """Find JOIN operations in text."""
        join_pattern = r'(INNER\s+JOIN|LEFT\s+JOIN|RIGHT\s+JOIN|FULL\s+JOIN|CROSS\s+JOIN)'
        return re.findall(join_pattern, text, re.IGNORECASE)
    
    def _parse_where_conditions(self, where_text: str) -> List[str]:
        """Parse WHERE conditions."""
        # Split by AND/OR but preserve the operators
        conditions = re.split(r'\s+(AND|OR)\s+', where_text, flags=re.IGNORECASE)
        return [cond.strip() for cond in conditions if cond.strip()]
    
    def _find_operators(self, text: str) -> List[str]:
        """Find SQL operators in text."""
        operators = []
        for op in self.sql_operators:
            if op in text.upper():
                operators.append(op)
        return operators
    
    def _calculate_where_complexity(self, where_text: str) -> int:
        """Calculate complexity of WHERE clause."""
        complexity = 0
        complexity += len(re.findall(r'\s+AND\s+', where_text, re.IGNORECASE))
        complexity += len(re.findall(r'\s+OR\s+', where_text, re.IGNORECASE))
        complexity += len(re.findall(r'\s+NOT\s+', where_text, re.IGNORECASE))
        complexity += len(re.findall(r'\(', where_text))
        return complexity
    
    def _calculate_complexity_metrics(self, sql_query: str) -> Dict[str, int]:
        """Calculate overall complexity metrics."""
        return {
            'total_length': len(sql_query),
            'keyword_count': sum(1 for keyword in self.sql_keywords if keyword in sql_query.upper()),
            'join_count': len(self._find_joins(sql_query)),
            'aggregation_count': len(self._find_aggregations(sql_query)),
            'subquery_count': sql_query.upper().count('SELECT') - 1,
            'nested_level': sql_query.count('(')
        }
    
    def generate_sql_with_matrix_neurons(self, 
                                       natural_language: str,
                                       schema_context: str = "",
                                       optimization_level: str = "balanced") -> Dict[str, Any]:
        """
        Generate SQL using matrix-entangled neurons.
        
        Args:
            natural_language: Natural language description of query
            schema_context: Database schema context
            optimization_level: Optimization level (basic, balanced, aggressive)
            
        Returns:
            Dictionary with generated SQL and metadata
        """
        
        print(f"🔍 Generating SQL with matrix-entangled neurons...")
        print(f"   Input: {natural_language[:100]}...")
        print(f"   Schema context: {'Yes' if schema_context else 'No'}")
        print(f"   Optimization: {optimization_level}")
        
        # Step 1: Extract concepts from natural language
        concepts = self._extract_concepts_from_nl(natural_language)
        
        # Step 2: Find relevant matrix neurons
        relevant_neurons = self._find_relevant_neurons(concepts)
        
        # Step 3: Generate SQL structure using matrix neurons
        sql_structure = self._generate_sql_structure(relevant_neurons, concepts, schema_context)
        
        # Step 4: Generate actual SQL query
        sql_query = self._construct_sql_query(sql_structure, optimization_level)
        
        # Step 5: Optimize using holographic memory
        if self.use_holographic_memory:
            optimized_query = self._optimize_with_holographic_memory(sql_query, sql_structure)
        else:
            optimized_query = sql_query
        
        # Step 6: Calculate performance metrics
        performance_metrics = self._calculate_performance_metrics(optimized_query, sql_structure)
        
        # Step 7: Store pattern in holographic memory
        if self.use_holographic_memory:
            self._store_sql_pattern(optimized_query, sql_structure, performance_metrics)
        
        result = {
            'sql_query': optimized_query,
            'sql_structure': sql_structure,
            'performance_metrics': performance_metrics,
            'relevant_neurons': [neuron.neuron_id for neuron in relevant_neurons],
            'optimization_applied': optimization_level,
            'concepts_used': concepts,
            'generation_method': 'matrix_entangled_neurons'
        }
        
        print(f"✅ SQL generated successfully")
        print(f"   Query length: {len(optimized_query)} characters")
        print(f"   Relevant neurons: {len(relevant_neurons)}")
        print(f"   Performance score: {performance_metrics.get('overall_score', 0.0):.3f}")
        
        return result
    
    def _extract_concepts_from_nl(self, natural_language: str) -> List[str]:
        """Extract SQL-related concepts from natural language."""
        
        # Convert to lowercase for processing
        nl_lower = natural_language.lower()
        
        concepts = []
        
        # Map natural language to SQL concepts
        concept_mappings = {
            'show': ['select_clause'],
            'display': ['select_clause'],
            'get': ['select_clause'],
            'find': ['select_clause', 'where_clause'],
            'filter': ['where_clause'],
            'where': ['where_clause'],
            'group': ['group_by_clause'],
            'summarize': ['group_by_clause', 'aggregation_optimization'],
            'count': ['aggregation_optimization'],
            'average': ['aggregation_optimization'],
            'join': ['join_operation'],
            'connect': ['join_operation'],
            'order': ['order_by_clause'],
            'sort': ['order_by_clause'],
            'insert': ['insert_operation'],
            'add': ['insert_operation'],
            'update': ['update_operation'],
            'modify': ['update_operation'],
            'delete': ['delete_operation'],
            'remove': ['delete_operation']
        }
        
        for keyword, sql_concepts in concept_mappings.items():
            if keyword in nl_lower:
                concepts.extend(sql_concepts)
        
        # Add general SQL concepts
        concepts.extend(['query_optimization', 'execution_time'])
        
        return list(set(concepts))  # Remove duplicates
    
    def _find_relevant_neurons(self, concepts: List[str]) -> List[MatrixEntangledNeuron]:
        """Find relevant matrix neurons for given concepts."""
        
        if not self.use_matrix_neurons:
            return []
        
        relevant_neurons = []
        
        for neuron in self.matrix_network.neurons.values():
            neuron_concept = neuron.metadata.get('concept', '')
            
            # Check if neuron concept matches any of the input concepts
            for concept in concepts:
                if concept in neuron_concept or neuron_concept in concept:
                    relevant_neurons.append(neuron)
                    break
        
        # If no direct matches, find neurons with high quantum coherence
        if not relevant_neurons:
            sorted_neurons = sorted(
                self.matrix_network.neurons.values(),
                key=lambda n: n.emergence_level,
                reverse=True
            )
            relevant_neurons = sorted_neurons[:5]  # Top 5 by emergence level
        
        return relevant_neurons
    
    def _generate_sql_structure(self, 
                               neurons: List[MatrixEntangledNeuron],
                               concepts: List[str],
                               schema_context: str) -> Dict[str, Any]:
        """Generate SQL structure using matrix neurons."""
        
        # Initialize SQL structure
        structure = {
            'query_type': 'SELECT',
            'select_clause': {'columns': [], 'aggregations': []},
            'from_clause': {'tables': []},
            'where_clause': {'conditions': []},
            'join_clauses': [],
            'group_by_clause': {'columns': []},
            'order_by_clause': {'columns': []},
            'dimensional_signature': 'D0-D2-D4',  # Default signature
            'neuron_contributions': []
        }
        
        # Use neuron quantum states to influence structure
        for neuron in neurons:
            quantum_state = neuron.quantum_state
            
            # Extract information from quantum state
            real_part = np.real(quantum_state)
            imag_part = np.imag(quantum_state)
            
            # Use quantum state to determine SQL components
            if np.mean(np.abs(real_part)) > 0.5:
                # High real component suggests SELECT operations
                structure['select_clause']['columns'].append(f"column_{len(structure['select_clause']['columns'])}")
            
            if np.mean(np.abs(imag_part)) > 0.5:
                # High imaginary component suggests WHERE conditions
                structure['where_clause']['conditions'].append(f"condition_{len(structure['where_clause']['conditions'])}")
            
            # Track neuron contributions
            structure['neuron_contributions'].append({
                'neuron_id': neuron.neuron_id,
                'concept': neuron.metadata.get('concept', ''),
                'emergence_level': neuron.emergence_level,
                'quantum_coherence': float(np.abs(np.vdot(quantum_state, quantum_state)))
            })
        
        # Extract dimensional signature
        if neurons:
            dimensions = [neuron.metadata.get('dimension', 0) for neuron in neurons]
            unique_dims = sorted(set(dimensions))
            structure['dimensional_signature'] = f"D{'-'.join(map(str, unique_dims[:3]))}"
        
        return structure
    
    def _construct_sql_query(self, structure: Dict[str, Any], optimization_level: str) -> str:
        """Construct actual SQL query from structure."""
        
        # Start with basic SELECT
        sql_parts = ["SELECT"]
        
        # Add SELECT clause
        select_columns = structure['select_clause']['columns']
        if select_columns:
            sql_parts.append(", ".join(select_columns))
        else:
            sql_parts.append("*")
        
        # Add FROM clause
        from_tables = structure['from_clause']['tables']
        if from_tables:
            sql_parts.append("FROM " + ", ".join(from_tables))
        else:
            sql_parts.append("FROM table_name")  # Placeholder
        
        # Add WHERE clause
        where_conditions = structure['where_clause']['conditions']
        if where_conditions:
            sql_parts.append("WHERE " + " AND ".join(where_conditions))
        
        # Add GROUP BY clause
        group_columns = structure['group_by_clause']['columns']
        if group_columns:
            sql_parts.append("GROUP BY " + ", ".join(group_columns))
        
        # Add ORDER BY clause
        order_columns = structure['order_by_clause']['columns']
        if order_columns:
            sql_parts.append("ORDER BY " + ", ".join(order_columns))
        
        # Construct final query
        sql_query = " ".join(sql_parts)
        
        # Apply optimizations based on level
        if optimization_level == "aggressive":
            sql_query = self._apply_aggressive_optimizations(sql_query)
        elif optimization_level == "balanced":
            sql_query = self._apply_balanced_optimizations(sql_query)
        
        return sql_query
    
    def _apply_balanced_optimizations(self, sql_query: str) -> str:
        """Apply balanced SQL optimizations."""
        # Add LIMIT if not present
        if 'LIMIT' not in sql_query.upper():
            sql_query += " LIMIT 1000"
        
        return sql_query
    
    def _apply_aggressive_optimizations(self, sql_query: str) -> str:
        """Apply aggressive SQL optimizations."""
        # Add LIMIT
        if 'LIMIT' not in sql_query.upper():
            sql_query += " LIMIT 100"
        
        # Add hints for optimization
        if 'SELECT' in sql_query.upper():
            sql_query = sql_query.replace('SELECT', 'SELECT /*+ USE_INDEX */', 1)
        
        return sql_query
    
    def _optimize_with_holographic_memory(self, sql_query: str, structure: Dict[str, Any]) -> str:
        """Optimize SQL query using holographic memory."""
        
        # Convert SQL query to embedding for holographic processing
        sql_embedding = self._sql_to_embedding(sql_query)
        
        # Recall similar queries from holographic memory
        similar_queries = self.holographic_memory.recall_associative(
            sql_embedding, 
            similarity_threshold=0.6
        )
        
        # Apply optimizations from similar queries
        optimized_query = sql_query
        for similar in similar_queries:
            # Extract optimization hints from similar query metadata
            if 'optimization_hints' in similar.get('metadata', {}):
                hints = similar['metadata']['optimization_hints']
                optimized_query = self._apply_optimization_hints(optimized_query, hints)
        
        # Store optimized query in holographic memory
        self.holographic_memory.store_holographic(
            self._sql_to_embedding(optimized_query),
            metadata={
                'original_query': sql_query,
                'optimization_method': 'holographic_memory',
                'dimensional_signature': structure.get('dimensional_signature', 'D0'),
                'performance_improvement': 0.1  # Placeholder
            }
        )
        
        return optimized_query
    
    def _sql_to_embedding(self, sql_query: str) -> np.ndarray:
        """Convert SQL query to embedding vector."""
        
        # Simple embedding based on SQL structure
        embedding = np.zeros(256)
        
        # Count SQL keywords
        for i, keyword in enumerate(self.sql_keywords):
            count = sql_query.upper().count(keyword)
            embedding[i] = min(count / 10.0, 1.0)  # Normalize
        
        # Add query length and complexity
        embedding[100] = min(len(sql_query) / 1000.0, 1.0)
        embedding[101] = min(sql_query.count('(') / 10.0, 1.0)
        embedding[102] = min(sql_query.count(',') / 10.0, 1.0)
        
        # Add dimensional signature encoding
        dim_sig = sql_query.count('D')  # Simple signature
        embedding[103] = min(dim_sig / 10.0, 1.0)
        
        return embedding
    
    def _apply_optimization_hints(self, sql_query: str, hints: List[str]) -> str:
        """Apply optimization hints to SQL query."""
        
        optimized_query = sql_query
        
        for hint in hints:
            if hint == 'add_limit' and 'LIMIT' not in optimized_query.upper():
                optimized_query += " LIMIT 1000"
            elif hint == 'add_index_hint' and 'SELECT' in optimized_query.upper():
                optimized_query = optimized_query.replace('SELECT', 'SELECT /*+ USE_INDEX */', 1)
        
        return optimized_query
    
    def _calculate_performance_metrics(self, sql_query: str, structure: Dict[str, Any]) -> Dict[str, float]:
        """Calculate performance metrics for SQL query."""
        
        metrics = {
            'query_length': len(sql_query),
            'complexity_score': self._calculate_complexity_score(structure),
            'optimization_potential': self._calculate_optimization_potential(sql_query),
            'dimensional_coherence': self._calculate_dimensional_coherence(structure),
            'overall_score': 0.0
        }
        
        # Calculate overall score
        metrics['overall_score'] = (
            0.3 * (1.0 - min(metrics['complexity_score'], 1.0)) +  # Lower complexity is better
            0.3 * metrics['optimization_potential'] +
            0.2 * metrics['dimensional_coherence'] +
            0.2 * (1.0 - min(metrics['query_length'] / 1000.0, 1.0))  # Shorter queries preferred
        )
        
        return metrics
    
    def _calculate_complexity_score(self, structure: Dict[str, Any]) -> float:
        """Calculate complexity score of SQL structure."""
        
        complexity = 0.0
        
        # Count components
        complexity += len(structure['select_clause']['columns']) * 0.1
        complexity += len(structure['where_clause']['conditions']) * 0.2
        complexity += len(structure['join_clauses']) * 0.3
        complexity += len(structure['group_by_clause']['columns']) * 0.2
        
        return min(complexity, 1.0)
    
    def _calculate_optimization_potential(self, sql_query: str) -> float:
        """Calculate optimization potential of SQL query."""
        
        potential = 0.0
        
        # Check for optimization opportunities
        if 'SELECT *' in sql_query.upper():
            potential += 0.3  # Column selection optimization
        
        if sql_query.upper().count('JOIN') > 2:
            potential += 0.2  # Join optimization
        
        if 'WHERE' not in sql_query.upper():
            potential += 0.2  # Filtering optimization
        
        if 'LIMIT' not in sql_query.upper():
            potential += 0.1  # Result limiting
        
        if sql_query.upper().count('SELECT') > 1:
            potential += 0.2  # Subquery optimization
        
        return min(potential, 1.0)
    
    def _calculate_dimensional_coherence(self, structure: Dict[str, Any]) -> float:
        """Calculate dimensional coherence of SQL structure."""
        
        # Extract dimensional signature
        dim_sig = structure.get('dimensional_signature', 'D0')
        
        # Count dimensions
        dim_count = len(dim_sig.split('-'))
        
        # More dimensions = higher coherence
        coherence = min(dim_count / 5.0, 1.0)
        
        return coherence
    
    def _store_sql_pattern(self, sql_query: str, structure: Dict[str, Any], metrics: Dict[str, float]):
        """Store SQL pattern in holographic memory."""
        
        # Create SQL pattern
        pattern_id = f"sql_{hashlib.md5(sql_query.encode()).hexdigest()[:12]}"
        
        pattern = SQLPattern(
            pattern_id=pattern_id,
            sql_structure=structure,
            execution_pattern=self._sql_to_embedding(sql_query),
            semantic_embedding=self._sql_to_embedding(sql_query),
            optimization_hints=['add_limit', 'add_index_hint'],
            performance_metrics=metrics,
            dimensional_signature=structure.get('dimensional_signature', 'D0'),
            created_at=datetime.now().isoformat()
        )
        
        # Store in pattern dictionary
        self.sql_patterns[pattern_id] = pattern
        
        # Store in holographic memory
        if self.use_holographic_memory:
            self.holographic_memory.store_holographic(
                pattern.execution_pattern,
                metadata={
                    'pattern_id': pattern_id,
                    'sql_query': sql_query,
                    'performance_metrics': metrics,
                    'dimensional_signature': pattern.dimensional_signature
                }
            )

def demo_sql_matrix_integration():
    """Demonstrate SQL matrix integration system."""
    
    print("🗄️ SQL Matrix Integration Demo")
    print("=" * 50)
    
    # Initialize SQL matrix processor
    processor = SQLMatrixProcessor(
        sql_model_path="9x25dillon/9xdSq-LIMPS-FemTO-R1C",
        use_matrix_neurons=True,
        use_holographic_memory=True
    )
    
    # Test cases
    test_queries = [
        "Show me all customers from California with orders over $100",
        "Get the total sales by month for electronics category",
        "Find products that are out of stock and need reordering",
        "Display the top 10 performing sales representatives",
        "Calculate average order value by customer segment"
    ]
    
    print(f"\n🔍 Processing {len(test_queries)} test queries...")
    
    results = []
    for i, query in enumerate(test_queries, 1):
        print(f"\n--- Test {i}/{len(test_queries)} ---")
        print(f"Input: {query}")
        
        # Generate SQL with matrix neurons
        result = processor.generate_sql_with_matrix_neurons(
            natural_language=query,
            schema_context="customers, orders, products, categories",
            optimization_level="balanced"
        )
        
        results.append(result)
        
        print(f"Generated SQL: {result['sql_query']}")
        print(f"Performance Score: {result['performance_metrics']['overall_score']:.3f}")
        print(f"Relevant Neurons: {len(result['relevant_neurons'])}")
    
    # Summary
    print(f"\n📊 Summary:")
    print(f"   Total queries processed: {len(results)}")
    print(f"   Average performance score: {np.mean([r['performance_metrics']['overall_score'] for r in results]):.3f}")
    print(f"   Total neurons involved: {sum(len(r['relevant_neurons']) for r in results)}")
    
    return results

if __name__ == "__main__":
    demo_sql_matrix_integration()