⚙️ Session 6 Advanced: Hybrid Graph-Vector Search¶

⚙️ IMPLEMENTER PATH CONTENT Prerequisites: Complete 🎯 Observer and 📝 Participant paths Time Investment: 3-4 hours Outcome: Master hybrid search combining graph reasoning with vector similarity

Advanced Learning Outcomes¶

After completing this module, you will master:

Hybrid search architecture design and implementation
Fusion algorithms for combining graph and vector results
Adaptive scoring and context processing
Performance optimization for hybrid systems

Understanding Hybrid Graph-Vector Architecture¶

Traditional approaches force a choice between graph reasoning and vector similarity search. Hybrid GraphRAG eliminates this limitation by intelligently combining both approaches, using graph traversal for relationship-based reasoning and vector similarity for semantic matching.

The key insight is that different query types benefit from different retrieval approaches:

Factual queries benefit from precise graph traversal
Semantic queries benefit from vector similarity
Complex queries require both approaches working together

Advanced Hybrid Search System¶

System Architecture Overview¶

class HybridGraphVectorRAG:
    """Advanced hybrid system combining graph reasoning with vector similarity"""

    def __init__(self, graph_store, vector_store, fusion_algorithm='adaptive'):
        self.graph_rag = GraphRAGSystem(graph_store)
        self.vector_rag = VectorRAGSystem(vector_store)
        self.fusion_algorithm = fusion_algorithm
        self.query_analyzer = QueryTypeAnalyzer()

The hybrid architecture maintains both graph and vector systems while adding intelligent fusion capabilities.

        # Advanced fusion components
        self.result_fusion = ResultFusionEngine()
        self.adaptive_router = AdaptiveQueryRouter()
        self.context_synthesizer = ContextSynthesizer()

        # Performance optimization
        self.cache_manager = HybridCacheManager()
        self.query_optimizer = QueryOptimizer()

Intelligent Query Analysis and Routing¶

The hybrid system begins with sophisticated query analysis to determine optimal search strategies:

    def analyze_query_requirements(self, query):
        """Analyze query to determine optimal search strategy"""

        analysis = {
            'query_type': self.classify_query_type(query),
            'complexity': self.assess_query_complexity(query),
            'entity_focus': self.identify_entity_requirements(query),
            'relationship_requirements': self.assess_relationship_needs(query)
        }

Query analysis examines multiple dimensions to understand what type of knowledge retrieval will be most effective.

        # Determine search strategy weights
        strategy_weights = {
            'graph_weight': 0.5,  # Default balanced approach
            'vector_weight': 0.5,
            'hybrid_confidence': 0.8
        }

        # Adjust weights based on query characteristics
        if analysis['query_type'] == 'factual':
            strategy_weights['graph_weight'] = 0.7
            strategy_weights['vector_weight'] = 0.3
        elif analysis['query_type'] == 'semantic':
            strategy_weights['graph_weight'] = 0.3
            strategy_weights['vector_weight'] = 0.7
        elif analysis['query_type'] == 'multi_hop':
            strategy_weights['graph_weight'] = 0.8
            strategy_weights['vector_weight'] = 0.2

Dynamic weight adjustment ensures each query uses the most appropriate combination of search approaches.

Parallel Search Execution¶

Hybrid search executes both graph and vector searches in parallel for optimal performance:

    def execute_hybrid_search(self, query, strategy_weights):
        """Execute parallel graph and vector searches"""

        import asyncio
        from concurrent.futures import ThreadPoolExecutor

        async def parallel_search():
            with ThreadPoolExecutor(max_workers=2) as executor:
                # Execute searches in parallel
                graph_future = executor.submit(
                    self.graph_rag.search,
                    query,
                    max_results=20
                )

                vector_future = executor.submit(
                    self.vector_rag.search,
                    query,
                    max_results=20
                )

Parallel execution minimizes latency while gathering comprehensive results from both search approaches.

                # Collect results
                graph_results = graph_future.result()
                vector_results = vector_future.result()

                return {
                    'graph_results': graph_results,
                    'vector_results': vector_results,
                    'strategy_weights': strategy_weights
                }

        return asyncio.run(parallel_search())

Advanced Result Fusion Algorithms¶

Result fusion intelligently combines results from both search approaches:

class ResultFusionEngine:
    """Advanced fusion algorithms for hybrid search results"""

    def __init__(self):
        self.fusion_strategies = {
            'weighted_combination': self.weighted_fusion,
            'rank_fusion': self.rank_based_fusion,
            'adaptive_fusion': self.adaptive_fusion
        }

Multiple fusion strategies enable optimization for different query types and result characteristics.

    def adaptive_fusion(self, graph_results, vector_results,
                       strategy_weights, query_context):
        """Adaptive fusion based on result quality and query context"""

        # Analyze result quality
        graph_quality = self.assess_result_quality(graph_results, 'graph')
        vector_quality = self.assess_result_quality(vector_results, 'vector')

        # Adjust fusion weights based on quality
        quality_adjusted_weights = self.adjust_weights_for_quality(
            strategy_weights, graph_quality, vector_quality
        )

Quality-based adjustment ensures poor results from one approach don't negatively impact the final output.

        # Apply sophisticated fusion algorithm
        fused_results = []

        # Phase 1: Direct high-confidence matches
        for result in graph_results:
            if result.confidence > 0.9:
                fused_results.append({
                    'content': result.content,
                    'score': result.confidence * quality_adjusted_weights['graph_weight'],
                    'source': 'graph_high_confidence',
                    'reasoning_path': result.path
                })

High-confidence results from graph traversal receive priority due to their logical reasoning foundation.

        # Phase 2: Semantic similarity matches
        for result in vector_results:
            if result.similarity > 0.85:
                fused_results.append({
                    'content': result.content,
                    'score': result.similarity * quality_adjusted_weights['vector_weight'],
                    'source': 'vector_high_similarity',
                    'metadata': result.metadata
                })

        return self.deduplicate_and_rank(fused_results)

Context Synthesis and Response Generation¶

Context synthesis creates coherent responses from diverse hybrid search results:

class ContextSynthesizer:
    """Synthesize contexts from hybrid search results"""

    def synthesize_hybrid_context(self, fused_results, query):
        """Create coherent context from hybrid results"""

        # Separate results by source type
        graph_contexts = [r for r in fused_results if 'graph' in r['source']]
        vector_contexts = [r for r in fused_results if 'vector' in r['source']]

        synthesis = {
            'primary_context': self.select_primary_context(fused_results),
            'supporting_contexts': self.organize_supporting_contexts(fused_results),
            'reasoning_chains': self.extract_reasoning_chains(graph_contexts),
            'semantic_matches': self.organize_semantic_matches(vector_contexts)
        }

Context organization enables the LLM to understand both the factual information and the reasoning pathways.

        # Generate synthesis prompt
        synthesis_prompt = f"""
        Based on hybrid search combining graph reasoning and vector similarity:

        Question: {query}

        REASONING-BASED INFORMATION:
        {self._format_reasoning_contexts(graph_contexts)}

        SIMILARITY-BASED INFORMATION:
        {self._format_similarity_contexts(vector_contexts)}

        Provide a comprehensive answer that leverages both logical reasoning
        and semantic similarity, clearly distinguishing between facts derived
        from explicit relationships versus semantic associations.
        """

        return synthesis_prompt

Performance Optimization for Hybrid Systems¶

Intelligent Caching Strategies¶

Hybrid systems require sophisticated caching due to the complexity of fusion operations:

class HybridCacheManager:
    """Advanced caching for hybrid graph-vector systems"""

    def __init__(self, max_cache_size=50000):
        # Multi-level cache architecture
        self.query_analysis_cache = LRUCache(max_cache_size // 10)
        self.graph_results_cache = LRUCache(max_cache_size // 3)
        self.vector_results_cache = LRUCache(max_cache_size // 3)
        self.fusion_results_cache = LRUCache(max_cache_size // 3)

Multi-level caching optimizes different stages of the hybrid search pipeline.

    def get_cached_hybrid_results(self, query, strategy_weights):
        """Retrieve cached results with fallback strategy"""

        cache_key = self.generate_cache_key(query, strategy_weights)

        # Try full fusion cache first
        if cache_key in self.fusion_results_cache:
            return self.fusion_results_cache[cache_key]

        # Try component caches with partial fusion
        graph_key = self.generate_graph_cache_key(query)
        vector_key = self.generate_vector_cache_key(query)

        if graph_key in self.graph_results_cache and vector_key in self.vector_results_cache:
            # Perform fusion on cached component results
            graph_results = self.graph_results_cache[graph_key]
            vector_results = self.vector_results_cache[vector_key]

            fused_results = self.fusion_engine.adaptive_fusion(
                graph_results, vector_results, strategy_weights, query
            )

            # Cache the fusion result
            self.fusion_results_cache[cache_key] = fused_results
            return fused_results

        return None  # No cached results available

Query Optimization Techniques¶

Query optimization improves performance by analyzing and transforming queries:

class QueryOptimizer:
    """Optimize queries for hybrid graph-vector search"""

    def optimize_for_hybrid_search(self, original_query):
        """Optimize query for both graph and vector components"""

        optimization = {
            'graph_optimized_query': self.optimize_for_graph_search(original_query),
            'vector_optimized_query': self.optimize_for_vector_search(original_query),
            'entity_extraction': self.extract_key_entities(original_query),
            'relationship_hints': self.identify_relationship_patterns(original_query)
        }

Query optimization creates specialized versions for each search component while maintaining query intent.

        # Apply query expansion strategies
        if self.should_expand_query(original_query):
            optimization['expanded_queries'] = {
                'graph_expanded': self.expand_for_graph_reasoning(original_query),
                'vector_expanded': self.expand_for_semantic_search(original_query)
            }

        return optimization

Advanced Evaluation and Monitoring¶

Hybrid System Metrics¶

Evaluating hybrid systems requires metrics that capture both individual component performance and fusion effectiveness:

class HybridSystemEvaluator:
    """Comprehensive evaluation for hybrid GraphRAG systems"""

    def evaluate_hybrid_performance(self, test_queries, ground_truth):
        """Evaluate hybrid system across multiple dimensions"""

        evaluation_results = {
            'component_performance': {
                'graph_only': self.evaluate_graph_component(test_queries, ground_truth),
                'vector_only': self.evaluate_vector_component(test_queries, ground_truth),
            },
            'fusion_effectiveness': self.evaluate_fusion_quality(test_queries, ground_truth),
            'hybrid_performance': self.evaluate_hybrid_system(test_queries, ground_truth)
        }

Component-level evaluation helps identify which parts of the hybrid system are working well and which need improvement.

        # Calculate hybrid-specific metrics
        evaluation_results['hybrid_metrics'] = {
            'fusion_improvement': self.calculate_fusion_improvement(evaluation_results),
            'query_type_performance': self.analyze_performance_by_query_type(
                test_queries, ground_truth
            ),
            'latency_analysis': self.analyze_hybrid_latency(),
            'cost_effectiveness': self.calculate_cost_per_query()
        }

        return evaluation_results

Real-time Performance Monitoring¶

Production hybrid systems require continuous monitoring:

class HybridSystemMonitor:
    """Real-time monitoring for hybrid GraphRAG systems"""

    def __init__(self):
        self.metrics_collector = MetricsCollector()
        self.performance_tracker = PerformanceTracker()
        self.quality_monitor = QualityMonitor()

Multi-faceted monitoring ensures the system maintains high performance and quality over time.

    def monitor_hybrid_query(self, query, results, execution_time):
        """Monitor individual query execution"""

        monitoring_data = {
            'query_characteristics': self.analyze_query_characteristics(query),
            'component_performance': {
                'graph_latency': execution_time.get('graph_search_time', 0),
                'vector_latency': execution_time.get('vector_search_time', 0),
                'fusion_latency': execution_time.get('fusion_time', 0)
            },
            'result_quality': {
                'graph_result_count': len(results.get('graph_results', [])),
                'vector_result_count': len(results.get('vector_results', [])),
                'fusion_score': results.get('fusion_confidence', 0)
            }
        }

        # Store monitoring data for analysis
        self.metrics_collector.record_query_execution(monitoring_data)

        # Check for performance anomalies
        self.performance_tracker.check_performance_thresholds(monitoring_data)

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