Benchmarking and Optimizing GraphRAG Systems: Performance Insights from Production – 4 of 4

class GraphRAGBenchmark:
    """
    Comprehensive benchmarking framework for GraphRAG systems.
    Captures both component-level and end-to-end metrics.
    """
    
    def __init__(self, vector_db, graph_db, doc_processor, query_engine):
        self.vector_db = vector_db
        self.graph_db = graph_db
        self.doc_processor = doc_processor
        self.query_engine = query_engine
        self.metrics = MetricsCollector()
        
    def benchmark_ingestion(self, documents, optimization_config=None):
        """
        Benchmark the complete document ingestion pipeline.
        
        Args:
            documents: List of documents to process
            optimization_config: Configuration for optimizations to test
        
        Returns:
            Detailed performance metrics
        """
        optimization_config = optimization_config or {}
        
        # Start comprehensive monitoring
        with self.metrics.capture() as capture:
            # Phase 1: Document Processing
            with capture.phase("document_processing"):
                chunks = []
                for doc in documents:
                    doc_chunks = self.doc_processor.process(
                        doc, 
                        chunking_strategy=optimization_config.get('chunking', 'fixed')
                    )
                    chunks.extend(doc_chunks)
                    
                    # Capture intermediate metrics
                    capture.record("chunks_per_doc", len(doc_chunks))
            
            # Phase 2: Entity Extraction
            with capture.phase("entity_extraction"):
                entities = []
                relationships = []
                
                extraction_batch_size = optimization_config.get('extraction_batch', 1)
                for i in range(0, len(chunks), extraction_batch_size):
                    batch = chunks[i:i + extraction_batch_size]
                    batch_entities, batch_rels = self.doc_processor.extract_entities(batch)
                    entities.extend(batch_entities)
                    relationships.extend(batch_rels)
                
                capture.record("total_entities", len(entities))
                capture.record("total_relationships", len(relationships))
            
            # Phase 3: Vector Embedding and Storage
            with capture.phase("vector_storage"):
                embeddings = self._generate_embeddings(chunks)
                
                vector_batch_size = optimization_config.get('vector_batch', 100)
                for i in range(0, len(embeddings), vector_batch_size):
                    batch = embeddings[i:i + vector_batch_size]
                    self.vector_db.insert_batch(batch)
            
            # Phase 4: Graph Construction
            with capture.phase("graph_construction"):
                # Entity creation
                entity_batch_size = optimization_config.get('entity_batch', 1000)
                for i in range(0, len(entities), entity_batch_size):
                    batch = entities[i:i + entity_batch_size]
                    self.graph_db.create_entities(batch)
                
                # Relationship creation with optional grouping
                if optimization_config.get('relationship_grouping', False):
                    rel_groups = self._group_relationships(relationships)
                    for group in rel_groups:
                        self.graph_db.create_relationships(group)
                else:
                    rel_batch_size = optimization_config.get('rel_batch', 500)
                    for i in range(0, len(relationships), rel_batch_size):
                        batch = relationships[i:i + rel_batch_size]
                        self.graph_db.create_relationships(batch)
        
        return capture.get_results()

def create_graphrag_test_dataset(size="medium", domain="technical"):
    """
    Generate realistic test datasets for GraphRAG benchmarking.
    
    Args:
        size: 'small' (~100 docs), 'medium' (~1K docs), 'large' (~10K docs)
        domain: Type of content to generate
    
    Returns:
        TestDataset with documents, expected entities, and relationships
    """
    dataset_configs = {
        "small": {
            "documents": 100,
            "avg_doc_length": 2000,
            "entity_density": 10,  # entities per doc
            "relationship_density": 2.5  # relationships per entity
        },
        "medium": {
            "documents": 1000,
            "avg_doc_length": 3000,
            "entity_density": 15,
            "relationship_density": 3.0
        },
        "large": {
            "documents": 10000,
            "avg_doc_length": 3500,
            "entity_density": 20,
            "relationship_density": 3.5
        }
    }
    
    config = dataset_configs[size]
    documents = []
    
    # Generate documents with realistic complexity
    for i in range(config["documents"]):
        doc = generate_document(
            length=config["avg_doc_length"],
            entity_count=config["entity_density"],
            domain=domain
        )
        documents.append(doc)
    
    # Create expected relationships based on entity overlap
    expected_relationships = generate_relationship_patterns(
        documents,
        density=config["relationship_density"]
    )
    
    return TestDataset(
        documents=documents,
        expected_entities=extract_ground_truth_entities(documents),
        expected_relationships=expected_relationships,
        metadata={
            "size": size,
            "domain": domain,
            "total_chunks_estimate": estimate_chunks(documents)
        }
    )

class SemanticAwareChunker:
    """
    Intelligent document chunking that preserves semantic boundaries
    and reduces overall chunk count.
    """
    
    def __init__(self, 
                 min_size=200,
                 max_size=1000,
                 overlap=50):
        self.min_size = min_size
        self.max_size = max_size
        self.overlap = overlap
        
    def chunk_document(self, document):
        """
        Create semantically coherent chunks from a document.
        
        Returns:
            List of chunks with metadata
        """
        # First, identify natural boundaries
        sections = self._identify_sections(document)
        chunks = []
        
        for section in sections:
            # If section is small enough, keep it whole
            if len(section.content) <= self.max_size:
                chunks.append(Chunk(
                    content=section.content,
                    metadata={
                        'section': section.title,
                        'type': section.type,
                        'position': section.position
                    }
                ))
            else:
                # Break large sections at paragraph boundaries
                sub_chunks = self._chunk_large_section(section)
                chunks.extend(sub_chunks)
        
        # Add overlap for context continuity
        chunks = self._add_overlap(chunks)
        
        return chunks
    
    def _identify_sections(self, document):
        """Identify document structure and natural boundaries."""
        # Look for headers, code blocks, lists, etc.
        patterns = {
            'header': r'^#{1,6}\s+(.+)$',
            'code_block': r'```[\s\S]*?```',
            'list': r'^\s*[-*+]\s+.+$',
            'paragraph': r'\n\n'
        }
        
        sections = []
        current_pos = 0
        
        # Parse document structure
        for match in re.finditer('|'.join(patterns.values()), document, re.MULTILINE):
            section_type = self._identify_match_type(match, patterns)
            sections.append(Section(
                content=match.group(),
                type=section_type,
                position=current_pos
            ))
            current_pos = match.end()
        
        return sections

class BatchOptimizedProcessor:
    """
    Batch processing optimization for GraphRAG pipelines.
    Reduces overhead and improves throughput significantly.
    """
    
    def __init__(self, vector_db, graph_db, llm_client):
        self.vector_db = vector_db
        self.graph_db = graph_db
        self.llm_client = llm_client
        
        # Adaptive batch sizes
        self.vector_batch_size = 1000
        self.entity_batch_size = 500
        self.relationship_batch_size = 1000
        
    def process_documents_batch(self, documents):
        """
        Process documents with optimized batching at every stage.
        """
        all_chunks = []
        all_entities = []
        all_relationships = []
        
        # Stage 1: Batch document processing
        for doc_batch in self._batch_items(documents, size=10):
            chunks = self._process_document_batch(doc_batch)
            all_chunks.extend(chunks)
        
        # Stage 2: Batch entity extraction
        for chunk_batch in self._batch_items(all_chunks, size=20):
            entities, relationships = self._extract_entities_batch(chunk_batch)
            all_entities.extend(entities)
            all_relationships.extend(relationships)
        
        # Stage 3: Batch storage operations
        self._store_vectors_batch(all_chunks)
        self._store_graph_batch(all_entities, all_relationships)
        
        return len(all_chunks), len(all_entities), len(all_relationships)
    
    def _extract_entities_batch(self, chunks):
        """
        Extract entities from multiple chunks in a single LLM call.
        """
        # Combine chunks for batch processing
        combined_prompt = self._create_batch_extraction_prompt(chunks)
        
        # Single LLM call for multiple chunks
        response = self.llm_client.complete(
            prompt=combined_prompt,
            max_tokens=2000,
            temperature=0.1
        )
        
        # Parse batch response
        entities, relationships = self._parse_batch_response(response, chunks)
        
        return entities, relationships
    
    def _store_graph_batch(self, entities, relationships):
        """
        Optimized batch storage for graph database.
        """
        # Create entities in batches
        for batch in self._batch_items(entities, self.entity_batch_size):
            query = """
            UNWIND $batch AS entity
            CREATE (n:Entity {id: entity.id, name: entity.name})
            SET n += entity.properties
            """
            self.graph_db.run(query, batch=[e.to_dict() for e in batch])
        
        # Create relationships with grouping to prevent conflicts
        grouped_rels = self._group_relationships_by_type(relationships)
        for rel_type, rels in grouped_rels.items():
            for batch in self._batch_items(rels, self.relationship_batch_size):
                query = f"""
                UNWIND $batch AS rel
                MATCH (a:Entity {{id: rel.source}})
                MATCH (b:Entity {{id: rel.target}})
                CREATE (a)-[r:{rel_type}]->(b)
                SET r += rel.properties
                """
                self.graph_db.run(query, batch=[r.to_dict() for r in batch])

class ParallelGraphProcessor:
    """
    Parallel processing for graph operations with intelligent
    conflict resolution and deadlock prevention.
    """
    
    def __init__(self, graph_db, num_workers=4):
        self.graph_db = graph_db
        self.num_workers = num_workers
        self.conflict_resolver = ConflictResolver()
        
    def create_relationships_parallel(self, relationships):
        """
        Create relationships in parallel while preventing deadlocks.
        """
        # Group relationships to minimize conflicts
        relationship_groups = self.conflict_resolver.partition_relationships(
            relationships,
            self.num_workers
        )
        
        with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
            futures = []
            
            for group_id, group in enumerate(relationship_groups):
                future = executor.submit(
                    self._process_relationship_group,
                    group,
                    group_id
                )
                futures.append(future)
            
            # Collect results and handle any conflicts
            total_created = 0
            conflicts = []
            
            for future in as_completed(futures):
                try:
                    created, group_conflicts = future.result()
                    total_created += created
                    conflicts.extend(group_conflicts)
                except Exception as e:
                    self._handle_processing_error(e)
        
        # Retry conflicts serially
        if conflicts:
            conflict_created = self._process_conflicts(conflicts)
            total_created += conflict_created
        
        return total_created
    
    def _process_relationship_group(self, relationships, group_id):
        """
        Process a group of non-conflicting relationships.
        """
        created = 0
        conflicts = []
        
        # Use a dedicated session for this group
        with self.graph_db.session() as session:
            for rel in relationships:
                try:
                    session.run("""
                        MATCH (a:Entity {id: $source_id})
                        MATCH (b:Entity {id: $target_id})
                        CREATE (a)-[r:RELATES_TO]->(b)
                        SET r += $properties
                    """, source_id=rel.source, target_id=rel.target, 
                        properties=rel.properties)
                    created += 1
                except TransactionError:
                    conflicts.append(rel)
        
        return created, conflicts

class OptimizedGraphRAGRetriever:
    """
    Optimized retrieval for GraphRAG queries with intelligent
    traversal strategies and caching.
    """
    
    def __init__(self, vector_db, graph_db, cache_size=1000):
        self.vector_db = vector_db
        self.graph_db = graph_db
        self.cache = LRUCache(cache_size)
        
    def retrieve(self, query, max_results=10):
        """
        Retrieve relevant context using optimized dual retrieval.
        """
        # Check cache first
        cache_key = self._generate_cache_key(query)
        if cache_key in self.cache:
            return self.cache[cache_key]
        
        # Phase 1: Vector search with pre-filtering
        vector_results = self._optimized_vector_search(query, max_results * 2)
        
        # Phase 2: Targeted graph expansion
        graph_context = self._bounded_graph_expansion(
            vector_results,
            max_depth=2,
            max_nodes=50
        )
        
        # Phase 3: Intelligent merging
        merged_context = self._merge_contexts(vector_results, graph_context)
        
        # Cache the result
        self.cache[cache_key] = merged_context
        
        return merged_context
    
    def _bounded_graph_expansion(self, seed_nodes, max_depth, max_nodes):
        """
        Perform bounded graph traversal to prevent explosion.
        """
        expanded_nodes = set()
        current_layer = seed_nodes
        nodes_added = len(seed_nodes)
        
        for depth in range(max_depth):
            if nodes_added >= max_nodes:
                break
                
            next_layer = []
            
            # Batch graph queries for efficiency
            query = """
            UNWIND $nodes AS node_id
            MATCH (n:Entity {id: node_id})-[r]-(connected)
            WHERE NOT connected.id IN $excluded
            RETURN connected, r, node_id
            LIMIT $limit
            """
            
            results = self.graph_db.run(
                query,
                nodes=[n.id for n in current_layer],
                excluded=list(expanded_nodes),
                limit=max_nodes - nodes_added
            )
            
            for record in results:
                connected_node = record['connected']
                if connected_node.id not in expanded_nodes:
                    next_layer.append(connected_node)
                    expanded_nodes.add(connected_node.id)
                    nodes_added += 1
            
            current_layer = next_layer
        
        return expanded_nodes

class MemoryAwareGraphRAGProcessor:
    """
    Memory-conscious processing for large-scale GraphRAG.
    """
    
    def __init__(self, memory_limit_gb=8):
        self.memory_limit_bytes = memory_limit_gb * 1024 * 1024 * 1024
        self.current_usage = 0
        
    def process_with_memory_management(self, documents):
        """
        Process documents while respecting memory constraints.
        """
        processed = 0
        buffer = []
        
        for doc in documents:
            estimated_size = self._estimate_memory_usage(doc)
            
            # Check if we need to flush
            if self.current_usage + estimated_size > self.memory_limit_bytes:
                self._flush_buffer(buffer)
                buffer = []
                self.current_usage = 0
                gc.collect()  # Force garbage collection
            
            # Process document
            processed_doc = self._process_document(doc)
            buffer.append(processed_doc)
            self.current_usage += estimated_size
            processed += 1
            
            # Periodic memory health check
            if processed % 100 == 0:
                self._check_memory_health()
        
        # Don't forget the last batch
        if buffer:
            self._flush_buffer(buffer)
        
        return processed

def handle_supernode_relationships(graph_db, supernode_threshold=1000):
    """
    Special handling for highly connected nodes that can cause
    performance degradation.
    """
    # Identify supernodes
    supernode_query = """
    MATCH (n)
    WITH n, size((n)--()) as degree
    WHERE degree > $threshold
    RETURN n.id as node_id, degree
    ORDER BY degree DESC
    """
    
    supernodes = graph_db.run(supernode_query, threshold=supernode_threshold)
    
    # Process supernode relationships differently
    for node in supernodes:
        # Create a virtual node to distribute load
        virtual_node_query = """
        MATCH (super:Entity {id: $node_id})
        CREATE (virtual:VirtualNode {
            original_id: $node_id,
            partition: $partition
        })
        CREATE (super)-[:HAS_PARTITION]->(virtual)
        """
        
        # Distribute relationships across virtual nodes
        partition_count = max(1, node['degree'] // supernode_threshold)
        for partition in range(partition_count):
            graph_db.run(virtual_node_query, 
                        node_id=node['node_id'], 
                        partition=partition)

class IncrementalGraphRAGUpdater:
    """
    Handle real-time updates to GraphRAG systems without
    full reprocessing.
    """
    
    def __init__(self, vector_db, graph_db):
        self.vector_db = vector_db
        self.graph_db = graph_db
        self.update_queue = Queue()
        self.processing = True
        
    def add_document(self, document):
        """
        Add a new document to the GraphRAG system incrementally.
        """
        # Process the document
        chunks = self._chunk_document(document)
        entities, relationships = self._extract_entities(chunks)
        
        # Update vector store
        embeddings = self._generate_embeddings(chunks)
        self.vector_db.add_vectors(embeddings)
        
        # Update graph with conflict detection
        existing_entities = self._check_existing_entities(entities)
        new_entities = [e for e in entities if e.id not in existing_entities]
        
        # Add new entities
        if new_entities:
            self.graph_db.create_entities(new_entities)
        
        # Add relationships with deduplication
        self._add_relationships_incremental(relationships)
        
        # Update indices
        self._refresh_indices()

# Their custom caching strategy
class ComplianceGraphCache:
    def __init__(self, ttl_seconds=3600):
        self.cache = {}
        self.ttl = ttl_seconds
        self.access_log = []  # For audit trails
        
    def get_regulatory_context(self, query, user_id):
        cache_key = f"{query}:{user_id}"
        
        # Log access for compliance
        self.access_log.append({
            'user': user_id,
            'query': query,
            'timestamp': datetime.now()
        })
        
        # Check cache with TTL
        if cache_key in self.cache:
            entry = self.cache[cache_key]
            if time.time() - entry['timestamp'] < self.ttl:
                return entry['data']
        
        # Cache miss - fetch and cache
        context = self._fetch_context(query)
        self.cache[cache_key] = {
            'data': context,
            'timestamp': time.time()
        }
        
        return context

class AdaptiveGraphRAGOptimizer:
    """
    Self-tuning optimization system for GraphRAG.
    """
    
    def analyze_workload(self, sample_documents):
        """
        Analyze workload characteristics to recommend optimizations.
        """
        metrics = {
            'avg_doc_length': np.mean([len(d) for d in sample_documents]),
            'entity_density': self._estimate_entity_density(sample_documents),
            'relationship_complexity': self._analyze_relationships(sample_documents),
            'query_patterns': self._analyze_query_log()
        }
        
        # Recommend optimizations based on analysis
        recommendations = []
        
        if metrics['avg_doc_length'] > 5000:
            recommendations.append('semantic_chunking')
        
        if metrics['entity_density'] > 20:
            recommendations.append('extraction_batching')
        
        if metrics['relationship_complexity'] > 3.5:
            recommendations.append('mix_and_batch')
        
        return recommendations