1// Multi-Agent Orchestration System
2class OrchestratorAgent:
3    def __init__(self):
4        self.query_planner = QueryPlannerAgent()
5        self.sql_validator = SQLValidatorAgent()
6        self.analytics_service = AnalyticsService()
7
8    async def process_query(self, user_query: str, schema_context: dict) -> dict:
9        # Step 1: Plan the query with AI assistance
10        planned_query = await self.query_planner.plan_query(
11            user_query, schema_context
12        )
13
14        # Step 2: Validate and optimize SQL
15        validated_query = await self.sql_validator.validate_sql(
16            planned_query['sql'], schema_context
17        )
18
19        # Step 3: Execute with error recovery
20        try:
21            results = await self.execute_query(validated_query)
22        except Exception as e:
23            recovery_result = await self.sql_validator.recover_from_error(
24                e, validated_query, schema_context
25            )
26            results = await self.execute_query(recovery_result)
27
28        # Step 4: Generate comprehensive analytics
29        insights = await self.analytics_service.generate_insights(
30            results, user_query
31        )
32
33        return {
34            'results': results,
35            'insights': insights,
36            'performance_metrics': planned_query.get('metrics', {}),
37            'optimized_sql': validated_query
38        }

1// Advanced Analytics Engine with Safe Serialization
2class AnalyticsService:
3    @staticmethod
4    def safe_float(value: any) -> float:
5        """Safely convert values to float, handling NaN/Inf"""
6        try:
7            result = float(value)
8            if not (isfinite(result)):
9                return 0.0
10            return result
11        except (ValueError, TypeError, OverflowError):
12            return 0.0
13
14    async def generate_comprehensive_insights(self, data: pd.DataFrame) -> dict:
15        insights = {
16            'basic_stats': {},
17            'correlations': {},
18            'outliers': {},
19            'data_quality': {},
20            'ai_recommendations': []
21        }
22
23        # Safe statistical analysis
24        for column in data.columns:
25            if data[column].dtype in ['int64', 'float64']:
26                values = [self.safe_float(x) for x in data[column].dropna()]
27                if values:
28                    insights['basic_stats'][column] = {
29                        'mean': safe_mean(values),
30                        'median': safe_median(values),
31                        'std': safe_std(values),
32                        'min': min(values),
33                        'max': max(values)
34                    }
35
36        # Correlation analysis with safe matrix handling
37        numeric_data = data.select_dtypes(include=[np.number])
38        if not numeric_data.empty:
39            corr_matrix = numeric_data.corr()
40            # Convert to safe JSON-serializable format
41            insights['correlations'] = {
42                col: {k: self.safe_float(v) for k, v in corr_matrix[col].items()}
43                for col in corr_matrix.columns
44            }
45
46        # Outlier detection using IQR method
47        for column in numeric_data.columns:
48            Q1 = numeric_data[column].quantile(0.25)
49            Q3 = numeric_data[column].quantile(0.75)
50            IQR = Q3 - Q1
51            outliers = numeric_data[
52                (numeric_data[column] < (Q1 - 1.5 * IQR)) |
53                (numeric_data[column] > (Q3 + 1.5 * IQR))
54            ]
55            insights['outliers'][column] = len(outliers)
56
57        return insights

1// AI-Powered Query Optimization System
2class PerformanceOptimizer:
3    def __init__(self):
4        self.ai_service = OpenRouterService()
5        self.query_analyzer = QueryAnalyzer()
6
7    async def optimize_query_performance(self, sql: str, schema: dict) -> dict:
8        optimization_report = {
9            'original_query': sql,
10            'execution_plan': {},
11            'anti_patterns': [],
12            'optimization_suggestions': [],
13            'performance_score': 0,
14            'estimated_improvement': 0
15        }
16
17        # Analyze query structure
18        analysis = self.query_analyzer.analyze_query(sql)
19
20        # Detect anti-patterns
21        optimization_report['anti_patterns'] = self.detect_anti_patterns(analysis)
22
23        # Get execution plan (if possible)
24        try:
25            plan = await self.get_execution_plan(sql, schema)
26            optimization_report['execution_plan'] = plan
27        except Exception as e:
28            optimization_report['execution_plan'] = {'error': str(e)}
29
30        # AI-powered optimization suggestions
31        ai_suggestions = await self.ai_service.generate_optimization_suggestions(
32            sql, analysis, schema
33        )
34
35        optimization_report['optimization_suggestions'] = ai_suggestions
36
37        # Calculate performance score
38        optimization_report['performance_score'] = self.calculate_performance_score(
39            analysis, optimization_report['anti_patterns']
40        )
41
42        # Estimate improvement potential
43        optimization_report['estimated_improvement'] = self.estimate_improvement(
44            optimization_report['optimization_suggestions']
45        )
46
47        return optimization_report
48
49    def detect_anti_patterns(self, analysis: dict) -> list:
50        patterns = []
51
52        # SELECT * detection
53        if analysis.get('select_star', False):
54            patterns.append({
55                'type': 'SELECT_STAR',
56                'severity': 'HIGH',
57                'description': 'Using SELECT * can impact performance',
58                'suggestion': 'Specify only required columns'
59            })
60
61        # Missing indexes on WHERE clauses
62        if analysis.get('missing_indexes', []):
63            patterns.append({
64                'type': 'MISSING_INDEX',
65                'severity': 'MEDIUM',
66                'description': f'Consider indexes on: {analysis["missing_indexes"]}',
67                'suggestion': 'Create indexes on frequently filtered columns'
68            })
69
70        return patterns