⚙️ Session 0: Advanced RAG Patterns¶

# Advanced Adaptive RAG with Self-Correction
import asyncio
from typing import List, Dict, Any, Optional
from enum import Enum

class ConfidenceLevel(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"

class AdaptiveRAGSystem:
    def __init__(self, llm, retriever, critic_model):
        self.llm = llm
        self.retriever = retriever
        self.critic = critic_model
        self.confidence_thresholds = {
            ConfidenceLevel.HIGH: 0.85,
            ConfidenceLevel.MEDIUM: 0.65,
            ConfidenceLevel.LOW: 0.45
        }

    async def adaptive_generate(self, query: str, max_iterations: int = 3) -> Dict[str, Any]:
        """Self-correcting RAG with adaptive retrieval"""
        iteration = 0
        context_history = []
        response_history = []

        while iteration < max_iterations:
            # Step 1: Intelligent retrieval decision
            retrieval_decision = await self.critic.should_retrieve(
                query, context_history, response_history
            )

            if retrieval_decision['should_retrieve']:
                # Step 2: Adaptive retrieval strategy
                retrieval_strategy = self.determine_retrieval_strategy(
                    query, retrieval_decision['confidence']
                )

                context = await self.execute_retrieval_strategy(
                    query, retrieval_strategy
                )

                # Step 3: Context quality assessment
                quality_assessment = await self.critic.assess_context_quality(
                    query, context
                )

                if quality_assessment['quality'] < self.confidence_thresholds[ConfidenceLevel.MEDIUM]:
                    # Corrective retrieval needed
                    context = await self.corrective_retrieve(
                        query, context, quality_assessment
                    )

                context_history.append(context)
            else:
                context = None  # Use parametric knowledge only

            # Step 4: Generate with self-reflection
            response = await self.generate_with_reflection(query, context)
            response_history.append(response)

            # Step 5: Self-evaluation
            evaluation = await self.critic.evaluate_response(
                query, context, response
            )

            if evaluation['confidence'] >= self.confidence_thresholds[ConfidenceLevel.HIGH]:
                # High confidence - return result
                return {
                    'answer': response,
                    'confidence': evaluation['confidence'],
                    'iterations': iteration + 1,
                    'context_used': context,
                    'self_correction_applied': iteration > 0
                }

            iteration += 1

        # Max iterations reached - return best attempt
        return {
            'answer': response_history[-1],
            'confidence': evaluation['confidence'],
            'iterations': max_iterations,
            'context_used': context_history[-1] if context_history else None,
            'max_iterations_reached': True
        }

    async def corrective_retrieve(self, query: str, initial_context: List[Dict],
                                  quality_assessment: Dict) -> List[Dict]:
        """Implement corrective retrieval based on quality issues"""
        correction_strategies = {
            'insufficient_coverage': self.expand_retrieval_scope,
            'poor_relevance': self.refine_query_focus,
            'incomplete_information': self.retrieve_complementary_context,
            'temporal_mismatch': self.retrieve_recent_information
        }

        issues = quality_assessment.get('issues', [])
        corrected_context = initial_context.copy()

        for issue in issues:
            if issue in correction_strategies:
                additional_context = await correction_strategies[issue](
                    query, initial_context, quality_assessment
                )
                corrected_context.extend(additional_context)

        return corrected_context

# Advanced Multi-Agent RAG Orchestration
class AgenticRAGOrchestrator:
    def __init__(self, knowledge_graph, vector_store, llm_pool):
        self.kg = knowledge_graph
        self.vector_store = vector_store
        self.llm_pool = llm_pool

        # Specialized agent architecture
        self.agents = {
            'planner': QueryPlanningAgent(llm_pool['reasoning']),
            'retriever': AdaptiveRetrievalAgent(vector_store, kg),
            'validator': ContextValidationAgent(llm_pool['critic']),
            'reasoner': MultiHopReasoningAgent(kg, llm_pool['reasoning']),
            'synthesizer': ResponseSynthesisAgent(llm_pool['generation']),
            'coordinator': OrchestrationCoordinator()
        }

        # Inter-agent communication system
        self.message_bus = AgentMessageBus()
        self.workflow_executor = WorkflowExecutor()

    async def process_complex_query(self, user_question: str) -> Dict[str, Any]:
        """Orchestrate multi-agent processing for complex queries"""

        # Phase 1: Query Analysis and Planning
        planning_result = await self.agents['planner'].analyze_and_plan(
            user_question
        )

        if planning_result['complexity'] == 'simple':
            # Use streamlined single-agent processing
            return await self.single_agent_processing(user_question)

        # Phase 2: Multi-Agent Workflow Execution
        workflow = self.agents['coordinator'].design_workflow(
            user_question, planning_result
        )

        execution_context = {
            'original_query': user_question,
            'planning_result': planning_result,
            'intermediate_results': {},
            'agent_communications': []
        }

        # Execute workflow with agent coordination
        final_result = await self.workflow_executor.execute(
            workflow, execution_context, self.agents
        )

        return final_result

    async def execute_parallel_reasoning(self, sub_queries: List[Dict],
                                         execution_context: Dict) -> Dict[str, Any]:
        """Execute parallel reasoning across multiple agents"""

        # Create parallel reasoning tasks
        reasoning_tasks = []
        for sub_query in sub_queries:
            task = self.create_reasoning_task(sub_query, execution_context)
            reasoning_tasks.append(task)

        # Execute all reasoning tasks concurrently
        parallel_results = await asyncio.gather(*reasoning_tasks,
                                                 return_exceptions=True)

        # Filter successful results and handle errors
        successful_results = []
        failed_tasks = []

        for i, result in enumerate(parallel_results):
            if isinstance(result, Exception):
                failed_tasks.append((sub_queries[i], result))
            else:
                successful_results.append(result)

        # Attempt recovery for failed tasks
        if failed_tasks:
            recovery_results = await self.recover_failed_reasoning(
                failed_tasks, execution_context
            )
            successful_results.extend(recovery_results)

        return {
            'parallel_results': successful_results,
            'failed_tasks': len(failed_tasks),
            'recovery_applied': len(failed_tasks) > 0
        }

# Advanced Graph-Enhanced RAG System
class GraphEnhancedRAGSystem:
    def __init__(self, knowledge_graph, vector_store, graph_embedder, llm):
        self.kg = knowledge_graph
        self.vector_store = vector_store
        self.graph_embedder = graph_embedder
        self.llm = llm

        # Graph-aware components
        self.entity_extractor = EntityExtractor(llm)
        self.relationship_analyzer = RelationshipAnalyzer(kg)
        self.path_finder = GraphPathFinder(kg)
        self.hybrid_retriever = HybridGraphVectorRetriever(kg, vector_store)

    async def graph_aware_query(self, user_question: str) -> Dict[str, Any]:
        """Process queries with graph-enhanced reasoning"""

        # Step 1: Extract entities and relationships from query
        entity_analysis = await self.entity_extractor.extract_entities(
            user_question
        )

        # Step 2: Identify relevant graph subgraphs
        relevant_subgraphs = await self.identify_relevant_subgraphs(
            entity_analysis['entities'], user_question
        )

        # Step 3: Multi-hop reasoning across graph paths
        reasoning_paths = []
        for subgraph in relevant_subgraphs:
            paths = await self.path_finder.find_reasoning_paths(
                subgraph, entity_analysis, user_question
            )
            reasoning_paths.extend(paths)

        # Step 4: Hybrid retrieval (graph + vector)
        hybrid_context = await self.hybrid_retriever.retrieve_hybrid(
            user_question, reasoning_paths, entity_analysis
        )

        # Step 5: Graph-informed generation
        response = await self.generate_with_graph_context(
            user_question, hybrid_context, reasoning_paths
        )

        return {
            'answer': response,
            'entities_identified': entity_analysis['entities'],
            'reasoning_paths': reasoning_paths,
            'hybrid_context': hybrid_context,
            'graph_subgraphs_used': len(relevant_subgraphs)
        }

    async def identify_relevant_subgraphs(self, entities: List[str],
                                          query: str) -> List[Dict]:
        """Identify graph subgraphs relevant to the query"""
        subgraphs = []

        for entity in entities:
            # Find k-hop neighborhoods around each entity
            neighborhood = await self.kg.get_k_hop_neighborhood(
                entity, k=2, max_nodes=50
            )

            # Score subgraph relevance to query
            relevance_score = await self.score_subgraph_relevance(
                neighborhood, query, entities
            )

            if relevance_score > 0.6:  # Threshold for inclusion
                subgraphs.append({
                    'center_entity': entity,
                    'nodes': neighborhood['nodes'],
                    'edges': neighborhood['edges'],
                    'relevance_score': relevance_score
                })

        # Remove overlapping subgraphs
        filtered_subgraphs = self.merge_overlapping_subgraphs(subgraphs)

        return filtered_subgraphs

# Enterprise Hybrid RAG Architecture
class EnterpriseHybridRAGSystem:
    def __init__(self, rag_system, fine_tuned_models, function_registry,
                 knowledge_graph, monitoring_system):
        self.rag = rag_system
        self.specialists = fine_tuned_models  # Domain-specific models
        self.functions = function_registry    # Computational tools
        self.kg = knowledge_graph
        self.monitor = monitoring_system

        # Advanced routing and orchestration
        self.intelligent_router = IntelligentQueryRouter()
        self.capability_matcher = CapabilityMatcher()
        self.result_synthesizer = HybridResultSynthesizer()
        self.quality_assurance = QualityAssuranceSystem()

    async def process_enterprise_query(self, query: str,
                                       context: Dict = None) -> Dict[str, Any]:
        """Process queries using optimal combination of techniques"""

        # Step 1: Comprehensive query analysis
        query_analysis = await self.intelligent_router.analyze_comprehensive(
            query, context
        )

        # Step 2: Capability matching and orchestration planning
        execution_plan = await self.capability_matcher.create_execution_plan(
            query, query_analysis
        )

        # Step 3: Parallel execution of different approaches
        execution_tasks = []

        if execution_plan['use_rag']:
            rag_task = self.execute_rag_processing(query, execution_plan['rag_config'])
            execution_tasks.append(('rag', rag_task))

        if execution_plan['use_specialist']:
            specialist_task = self.execute_specialist_processing(
                query, execution_plan['specialist_config']
            )
            execution_tasks.append(('specialist', specialist_task))

        if execution_plan['use_functions']:
            function_task = self.execute_function_processing(
                query, execution_plan['function_config']
            )
            execution_tasks.append(('functions', function_task))

        if execution_plan['use_graph']:
            graph_task = self.execute_graph_processing(
                query, execution_plan['graph_config']
            )
            execution_tasks.append(('graph', graph_task))

        # Execute all approaches in parallel
        approach_results = {}
        for approach_name, task in execution_tasks:
            try:
                result = await task
                approach_results[approach_name] = result
            except Exception as e:
                # Log error and continue with other approaches
                await self.monitor.log_approach_error(approach_name, query, e)
                approach_results[approach_name] = {'error': str(e)}

        # Step 4: Intelligent result synthesis
        synthesized_result = await self.result_synthesizer.synthesize_results(
            query, approach_results, execution_plan
        )

        # Step 5: Quality assurance and validation
        qa_result = await self.quality_assurance.validate_result(
            query, synthesized_result, approach_results
        )

        # Step 6: Monitoring and feedback
        await self.monitor.record_enterprise_query(
            query, execution_plan, approach_results, qa_result
        )

        return qa_result

    async def execute_rag_processing(self, query: str, rag_config: Dict) -> Dict[str, Any]:
        """Execute RAG processing with advanced configuration"""
        if rag_config.get('use_adaptive'):
            return await self.rag.adaptive_generate(query)
        elif rag_config.get('use_graph_enhanced'):
            return await self.rag.graph_aware_query(query)
        else:
            return await self.rag.query_with_enhancement(query)

    async def execute_specialist_processing(self, query: str,
                                            specialist_config: Dict) -> Dict[str, Any]:
        """Execute domain specialist processing"""
        domain = specialist_config.get('domain', 'general')
        specialist_model = self.specialists.get(domain)

        if specialist_model:
            return await specialist_model.generate_domain_response(query)
        else:
            raise ValueError(f"No specialist model available for domain: {domain}")

    async def execute_function_processing(self, query: str,
                                          function_config: Dict) -> Dict[str, Any]:
        """Execute computational function processing"""
        required_functions = function_config.get('functions', [])

        function_results = {}
        for func_name in required_functions:
            if func_name in self.functions:
                result = await self.functions[func_name].execute(query)
                function_results[func_name] = result

        return {'function_results': function_results}

# Advanced RAG Monitoring and QA System
class AdvancedRAGMonitoring:
    def __init__(self, metrics_store, alert_system, quality_assessor):
        self.metrics = metrics_store
        self.alerts = alert_system
        self.qa = quality_assessor

        # Monitoring dimensions
        self.performance_monitors = {
            'latency': LatencyMonitor(),
            'throughput': ThroughputMonitor(),
            'accuracy': AccuracyMonitor(),
            'relevance': RelevanceMonitor()
        }

        # Quality thresholds
        self.quality_thresholds = {
            'response_relevance': 0.75,
            'context_quality': 0.70,
            'answer_completeness': 0.80,
            'source_attribution': 0.85
        }

    async def monitor_query_execution(self, query: str, execution_context: Dict,
                                      result: Dict) -> Dict[str, Any]:
        """Comprehensive monitoring of query execution"""

        monitoring_results = {
            'query_id': execution_context.get('query_id'),
            'timestamp': datetime.utcnow(),
            'performance_metrics': {},
            'quality_metrics': {},
            'alerts_triggered': []
        }

        # Performance monitoring
        for metric_name, monitor in self.performance_monitors.items():
            metric_value = await monitor.measure(execution_context, result)
            monitoring_results['performance_metrics'][metric_name] = metric_value

            # Check for performance alerts
            if await self.check_performance_threshold(metric_name, metric_value):
                alert = await self.alerts.create_performance_alert(
                    metric_name, metric_value, query
                )
                monitoring_results['alerts_triggered'].append(alert)

        # Quality assessment
        quality_assessment = await self.qa.assess_response_quality(
            query, result, execution_context
        )
        monitoring_results['quality_metrics'] = quality_assessment

        # Quality alerts
        for quality_dimension, score in quality_assessment.items():
            threshold = self.quality_thresholds.get(quality_dimension)
            if threshold and score < threshold:
                alert = await self.alerts.create_quality_alert(
                    quality_dimension, score, threshold, query
                )
                monitoring_results['alerts_triggered'].append(alert)

        # Store metrics for analysis
        await self.metrics.store_query_metrics(monitoring_results)

        return monitoring_results

# Advanced RAG Architecture Decision Framework
class RAGArchitectureSelector:
    def __init__(self):
        self.architecture_patterns = {
            'basic_rag': {
                'complexity': 'low',
                'latency': 'fast',
                'accuracy': 'good',
                'use_cases': ['simple_qa', 'document_search']
            },
            'adaptive_rag': {
                'complexity': 'medium',
                'latency': 'medium',
                'accuracy': 'high',
                'use_cases': ['complex_qa', 'research_assistance']
            },
            'agentic_rag': {
                'complexity': 'high',
                'latency': 'slow',
                'accuracy': 'very_high',
                'use_cases': ['multi_step_reasoning', 'research_synthesis']
            },
            'hybrid_rag': {
                'complexity': 'high',
                'latency': 'variable',
                'accuracy': 'very_high',
                'use_cases': ['enterprise_qa', 'multi_domain_systems']
            }
        }

    def recommend_architecture(self, requirements: Dict[str, Any]) -> Dict[str, Any]:
        """Recommend optimal RAG architecture based on requirements"""

        # Scoring matrix for different requirements
        scores = {}
        for arch_name, arch_spec in self.architecture_patterns.items():
            score = 0

            # Accuracy requirements
            if requirements.get('accuracy_critical', False):
                if arch_spec['accuracy'] in ['high', 'very_high']:
                    score += 3

            # Latency requirements
            if requirements.get('low_latency', False):
                if arch_spec['latency'] == 'fast':
                    score += 3
                elif arch_spec['latency'] == 'medium':
                    score += 1

            # Complexity tolerance
            complexity_tolerance = requirements.get('complexity_tolerance', 'medium')
            if complexity_tolerance == 'high' or arch_spec['complexity'] == 'low':
                score += 2
            elif complexity_tolerance == 'medium' and arch_spec['complexity'] == 'medium':
                score += 1

            # Use case alignment
            use_case = requirements.get('primary_use_case')
            if use_case in arch_spec['use_cases']:
                score += 4

            scores[arch_name] = score

        # Select highest scoring architecture
        recommended_arch = max(scores, key=scores.get)

        return {
            'recommended_architecture': recommended_arch,
            'architecture_details': self.architecture_patterns[recommended_arch],
            'all_scores': scores,
            'rationale': self.generate_recommendation_rationale(
                recommended_arch, requirements, scores
            )
        }

# Microservices RAG Architecture
class MicroservicesRAGSystem:
    def __init__(self, service_registry, message_broker):
        self.services = service_registry
        self.broker = message_broker

        # Core RAG microservices
        self.service_endpoints = {
            'query_processor': 'http://query-processor-service:8080',
            'document_indexer': 'http://indexer-service:8080',
            'vector_retriever': 'http://retriever-service:8080',
            'context_optimizer': 'http://optimizer-service:8080',
            'response_generator': 'http://generator-service:8080',
            'quality_assessor': 'http://qa-service:8080'
        }

    async def process_distributed_query(self, query: str) -> Dict[str, Any]:
        """Process query across distributed microservices"""

        # Service orchestration workflow
        workflow_id = self.generate_workflow_id()

        # Step 1: Query processing service
        query_analysis = await self.call_service(
            'query_processor', {'query': query, 'workflow_id': workflow_id}
        )

        # Step 2: Parallel retrieval services
        retrieval_tasks = [
            self.call_service('vector_retriever', {
                'query': query,
                'analysis': query_analysis,
                'workflow_id': workflow_id
            }),
            # Additional specialized retrievers as needed
        ]

        retrieval_results = await asyncio.gather(*retrieval_tasks)

        # Step 3: Context optimization service
        optimized_context = await self.call_service(
            'context_optimizer', {
                'query': query,
                'raw_context': retrieval_results,
                'workflow_id': workflow_id
            }
        )

        # Step 4: Response generation service
        response = await self.call_service(
            'response_generator', {
                'query': query,
                'context': optimized_context,
                'workflow_id': workflow_id
            }
        )

        # Step 5: Quality assessment service
        quality_report = await self.call_service(
            'quality_assessor', {
                'query': query,
                'response': response,
                'context': optimized_context,
                'workflow_id': workflow_id
            }
        )

        return {
            'answer': response['answer'],
            'quality_score': quality_report['overall_score'],
            'workflow_id': workflow_id,
            'services_used': list(self.service_endpoints.keys())
        }