1
def create_graphrag_test_dataset(size="medium", domain="technical"):
2
    """
3
    Generate realistic test datasets for GraphRAG benchmarking.
4

5
    Args:
6
        size: 'small' (~100 docs), 'medium' (~1K docs), 'large' (~10K docs)
7
        domain: Type of content to generate
8

9
    Returns:
10
        TestDataset with documents, expected entities, and relationships
11
    """
12
    dataset_configs = {
13
        "small": {
14
            "documents": 100,
15
            "avg_doc_length": 2000,
16
            "entity_density": 10,  # entities per doc
17
            "relationship_density": 2.5  # relationships per entity
18
        },
19
        "medium": {
20
            "documents": 1000,
21
            "avg_doc_length": 3000,
22
            "entity_density": 15,
23
            "relationship_density": 3.0
24
        },
25
        "large": {
26
            "documents": 10000,
27
            "avg_doc_length": 3500,
28
            "entity_density": 20,
29
            "relationship_density": 3.5
30
        }
31
    }
32

33
    config = dataset_configs[size]
34
    documents = []
35

36
    # Generate documents with realistic complexity
37
    for i in range(config["documents"]):
38
        doc = generate_document(
39
            length=config["avg_doc_length"],
40
            entity_count=config["entity_density"],
41
            domain=domain
42
        )
43
        documents.append(doc)
44

45
    # Create expected relationships based on entity overlap
46
    expected_relationships = generate_relationship_patterns(
47
        documents,
48
        density=config["relationship_density"]
49
    )
50

51
    return TestDataset(
52
        documents=documents,
53
        expected_entities=extract_ground_truth_entities(documents),
54
        expected_relationships=expected_relationships,
55
        metadata={
56
            "size": size,
57
            "domain": domain,
58
            "total_chunks_estimate": estimate_chunks(documents)
59
        }
60
    )

1
class SemanticAwareChunker:
2
    """
3
    Intelligent document chunking that preserves semantic boundaries
4
    and reduces overall chunk count.
5
    """
6

7
    def __init__(self,
8
                 min_size=200,
9
                 max_size=1000,
10
                 overlap=50):
11
        self.min_size = min_size
12
        self.max_size = max_size
13
        self.overlap = overlap
14

15
    def chunk_document(self, document):
16
        """
17
        Create semantically coherent chunks from a document.
18

19
        Returns:
20
            List of chunks with metadata
21
        """
22
        # First, identify natural boundaries
23
        sections = self._identify_sections(document)
24
        chunks = []
25

26
        for section in sections:
27
            # If section is small enough, keep it whole
28
            if len(section.content) <= self.max_size:
29
                chunks.append(Chunk(
30
                    content=section.content,
31
                    metadata={
32
                        'section': section.title,
33
                        'type': section.type,
34
                        'position': section.position
35
                    }
36
                ))
37
            else:
38
                # Break large sections at paragraph boundaries
39
                sub_chunks = self._chunk_large_section(section)
40
                chunks.extend(sub_chunks)
41

42
        # Add overlap for context continuity
43
        chunks = self._add_overlap(chunks)
44

45
        return chunks
46

47
    def _identify_sections(self, document):
48
        """Identify document structure and natural boundaries."""
49
        # Look for headers, code blocks, lists, etc.
50
        patterns = {
51
            'header': r'^#{1,6}\s+(.+)$',
52
            'code_block': r'```[\s\S]*?```',
53
            'list': r'^\s*[-*+]\s+.+$',
54
            'paragraph': r'\n\n'
55
        }
56

57
        sections = []
58
        current_pos = 0
59

60
        # Parse document structure
61
        for match in re.finditer('|'.join(patterns.values()), document, re.MULTILINE):
62
            section_type = self._identify_match_type(match, patterns)
63
            sections.append(Section(
64
                content=match.group(),
65
                type=section_type,
66
                position=current_pos
67
            ))
68
            current_pos = match.end()
69

70
        return sections

1
class BatchOptimizedProcessor:
2
    """
3
    Batch processing optimization for GraphRAG pipelines.
4
    Reduces overhead and improves throughput significantly.
5
    """
6

7
    def __init__(self, vector_db, graph_db, llm_client):
8
        self.vector_db = vector_db
9
        self.graph_db = graph_db
10
        self.llm_client = llm_client
11

12
        # Adaptive batch sizes
13
        self.vector_batch_size = 1000
14
        self.entity_batch_size = 500
15
        self.relationship_batch_size = 1000
16

17
    def process_documents_batch(self, documents):
18
        """
19
        Process documents with optimized batching at every stage.
20
        """
21
        all_chunks = []
22
        all_entities = []
23
        all_relationships = []
24

25
        # Stage 1: Batch document processing
26
        for doc_batch in self._batch_items(documents, size=10):
27
            chunks = self._process_document_batch(doc_batch)
28
            all_chunks.extend(chunks)
29

30
        # Stage 2: Batch entity extraction
31
        for chunk_batch in self._batch_items(all_chunks, size=20):
32
            entities, relationships = self._extract_entities_batch(chunk_batch)
33
            all_entities.extend(entities)
34
            all_relationships.extend(relationships)
35

36
        # Stage 3: Batch storage operations
37
        self._store_vectors_batch(all_chunks)
38
        self._store_graph_batch(all_entities, all_relationships)
39

40
        return len(all_chunks), len(all_entities), len(all_relationships)
41

42
    def _extract_entities_batch(self, chunks):
43
        """
44
        Extract entities from multiple chunks in a single LLM call.
45
        """
46
        # Combine chunks for batch processing
47
        combined_prompt = self._create_batch_extraction_prompt(chunks)
48

49
        # Single LLM call for multiple chunks
50
        response = self.llm_client.complete(
51
            prompt=combined_prompt,
52
            max_tokens=2000,
53
            temperature=0.1
54
        )
55

56
        # Parse batch response
57
        entities, relationships = self._parse_batch_response(response, chunks)
58

59
        return entities, relationships
60

61
    def _store_graph_batch(self, entities, relationships):
62
        """
63
        Optimized batch storage for graph database.
64
        """
65
        # Create entities in batches
66
        for batch in self._batch_items(entities, self.entity_batch_size):
67
            query = """
68
            UNWIND $batch AS entity
69
            CREATE (n:Entity {id: entity.id, name: entity.name})
70
            SET n += entity.properties
71
            """
72
            self.graph_db.run(query, batch=[e.to_dict() for e in batch])
73

74
        # Create relationships with grouping to prevent conflicts
75
        grouped_rels = self._group_relationships_by_type(relationships)
76
        for rel_type, rels in grouped_rels.items():
77
            for batch in self._batch_items(rels, self.relationship_batch_size):
78
                query = f"""
79
                UNWIND $batch AS rel
80
                MATCH (a:Entity {{id: rel.source}})
81
                MATCH (b:Entity {{id: rel.target}})
82
                CREATE (a)-[r:{rel_type}]->(b)
83
                SET r += rel.properties
84
                """
85
                self.graph_db.run(query, batch=[r.to_dict() for r in batch])

1
class ParallelGraphProcessor:
2
    """
3
    Parallel processing for graph operations with intelligent
4
    conflict resolution and deadlock prevention.
5
    """
6

7
    def __init__(self, graph_db, num_workers=4):
8
        self.graph_db = graph_db
9
        self.num_workers = num_workers
10
        self.conflict_resolver = ConflictResolver()
11

12
    def create_relationships_parallel(self, relationships):
13
        """
14
        Create relationships in parallel while preventing deadlocks.
15
        """
16
        # Group relationships to minimize conflicts
17
        relationship_groups = self.conflict_resolver.partition_relationships(
18
            relationships,
19
            self.num_workers
20
        )
21

22
        with ThreadPoolExecutor(max_workers=self.num_workers) as executor:
23
            futures = []
24

25
            for group_id, group in enumerate(relationship_groups):
26
                future = executor.submit(
27
                    self._process_relationship_group,
28
                    group,
29
                    group_id
30
                )
31
                futures.append(future)
32

33
            # Collect results and handle any conflicts
34
            total_created = 0
35
            conflicts = []
36

37
            for future in as_completed(futures):
38
                try:
39
                    created, group_conflicts = future.result()
40
                    total_created += created
41
                    conflicts.extend(group_conflicts)
42
                except Exception as e:
43
                    self._handle_processing_error(e)
44

45
        # Retry conflicts serially
46
        if conflicts:
47
            conflict_created = self._process_conflicts(conflicts)
48
            total_created += conflict_created
49

50
        return total_created
51

52
    def _process_relationship_group(self, relationships, group_id):
53
        """
54
        Process a group of non-conflicting relationships.
55
        """
56
        created = 0
57
        conflicts = []
58

59
        # Use a dedicated session for this group
60
        with self.graph_db.session() as session:
61
            for rel in relationships:
62
                try:
63
                    session.run("""
64
                        MATCH (a:Entity {id: $source_id})
65
                        MATCH (b:Entity {id: $target_id})
66
                        CREATE (a)-[r:RELATES_TO]->(b)
67
                        SET r += $properties
68
                    """, source_id=rel.source, target_id=rel.target,
69
                        properties=rel.properties)
70
                    created += 1
71
                except TransactionError:
72
                    conflicts.append(rel)
73

74
        return created, conflicts

1
class OptimizedGraphRAGRetriever:
2
    """
3
    Optimized retrieval for GraphRAG queries with intelligent
4
    traversal strategies and caching.
5
    """
6

7
    def __init__(self, vector_db, graph_db, cache_size=1000):
8
        self.vector_db = vector_db
9
        self.graph_db = graph_db
10
        self.cache = LRUCache(cache_size)
11

12
    def retrieve(self, query, max_results=10):
13
        """
14
        Retrieve relevant context using optimized dual retrieval.
15
        """
16
        # Check cache first
17
        cache_key = self._generate_cache_key(query)
18
        if cache_key in self.cache:
19
            return self.cache[cache_key]
20

21
        # Phase 1: Vector search with pre-filtering
22
        vector_results = self._optimized_vector_search(query, max_results * 2)
23

24
        # Phase 2: Targeted graph expansion
25
        graph_context = self._bounded_graph_expansion(
26
            vector_results,
27
            max_depth=2,
28
            max_nodes=50
29
        )
30

31
        # Phase 3: Intelligent merging
32
        merged_context = self._merge_contexts(vector_results, graph_context)
33

34
        # Cache the result
35
        self.cache[cache_key] = merged_context
36

37
        return merged_context
38

39
    def _bounded_graph_expansion(self, seed_nodes, max_depth, max_nodes):
40
        """
41
        Perform bounded graph traversal to prevent explosion.
42
        """
43
        expanded_nodes = set()
44
        current_layer = seed_nodes
45
        nodes_added = len(seed_nodes)
46

47
        for depth in range(max_depth):
48
            if nodes_added >= max_nodes:
49
                break
50

51
            next_layer = []
52

53
            # Batch graph queries for efficiency
54
            query = """
55
            UNWIND $nodes AS node_id
56
            MATCH (n:Entity {id: node_id})-[r]-(connected)
57
            WHERE NOT connected.id IN $excluded
58
            RETURN connected, r, node_id
59
            LIMIT $limit
60
            """
61

62
            results = self.graph_db.run(
63
                query,
64
                nodes=[n.id for n in current_layer],
65
                excluded=list(expanded_nodes),
66
                limit=max_nodes - nodes_added
67
            )
68

69
            for record in results:
70
                connected_node = record['connected']
71
                if connected_node.id not in expanded_nodes:
72
                    next_layer.append(connected_node)
73
                    expanded_nodes.add(connected_node.id)
74
                    nodes_added += 1
75

76
            current_layer = next_layer
77

78
        return expanded_nodes

1
class MemoryAwareGraphRAGProcessor:
2
    """
3
    Memory-conscious processing for large-scale GraphRAG.
4
    """
5

6
    def __init__(self, memory_limit_gb=8):
7
        self.memory_limit_bytes = memory_limit_gb * 1024 * 1024 * 1024
8
        self.current_usage = 0
9

10
    def process_with_memory_management(self, documents):
11
        """
12
        Process documents while respecting memory constraints.
13
        """
14
        processed = 0
15
        buffer = []
16

17
        for doc in documents:
18
            estimated_size = self._estimate_memory_usage(doc)
19

20
            # Check if we need to flush
21
            if self.current_usage + estimated_size > self.memory_limit_bytes:
22
                self._flush_buffer(buffer)
23
                buffer = []
24
                self.current_usage = 0
25
                gc.collect()  # Force garbage collection
26

27
            # Process document
28
            processed_doc = self._process_document(doc)
29
            buffer.append(processed_doc)
30
            self.current_usage += estimated_size
31
            processed += 1
32

33
            # Periodic memory health check
34
            if processed % 100 == 0:
35
                self._check_memory_health()
36

37
        # Don't forget the last batch
38
        if buffer:
39
            self._flush_buffer(buffer)
40

41
        return processed

1
def handle_supernode_relationships(graph_db, supernode_threshold=1000):
2
    """
3
    Special handling for highly connected nodes that can cause
4
    performance degradation.
5
    """
6
    # Identify supernodes
7
    supernode_query = """
8
    MATCH (n)
9
    WITH n, size((n)--()) as degree
10
    WHERE degree > $threshold
11
    RETURN n.id as node_id, degree
12
    ORDER BY degree DESC
13
    """
14

15
    supernodes = graph_db.run(supernode_query, threshold=supernode_threshold)
16

17
    # Process supernode relationships differently
18
    for node in supernodes:
19
        # Create a virtual node to distribute load
20
        virtual_node_query = """
21
        MATCH (super:Entity {id: $node_id})
22
        CREATE (virtual:VirtualNode {
23
            original_id: $node_id,
24
            partition: $partition
25
        })
26
        CREATE (super)-[:HAS_PARTITION]->(virtual)
27
        """
28

29
        # Distribute relationships across virtual nodes
30
        partition_count = max(1, node['degree'] // supernode_threshold)
31
        for partition in range(partition_count):
32
            graph_db.run(virtual_node_query,
33
                        node_id=node['node_id'],
34
                        partition=partition)

1
class IncrementalGraphRAGUpdater:
2
    """
3
    Handle real-time updates to GraphRAG systems without
4
    full reprocessing.
5
    """
6

7
    def __init__(self, vector_db, graph_db):
8
        self.vector_db = vector_db
9
        self.graph_db = graph_db
10
        self.update_queue = Queue()
11
        self.processing = True
12

13
    def add_document(self, document):
14
        """
15
        Add a new document to the GraphRAG system incrementally.
16
        """
17
        # Process the document
18
        chunks = self._chunk_document(document)
19
        entities, relationships = self._extract_entities(chunks)
20

21
        # Update vector store
22
        embeddings = self._generate_embeddings(chunks)
23
        self.vector_db.add_vectors(embeddings)
24

25
        # Update graph with conflict detection
26
        existing_entities = self._check_existing_entities(entities)
27
        new_entities = [e for e in entities if e.id not in existing_entities]
28

29
        # Add new entities
30
        if new_entities:
31
            self.graph_db.create_entities(new_entities)
32

33
        # Add relationships with deduplication
34
        self._add_relationships_incremental(relationships)
35

36
        # Update indices
37
        self._refresh_indices()

1
# Their custom caching strategy
2
class ComplianceGraphCache:
3
    def __init__(self, ttl_seconds=3600):
4
        self.cache = {}
5
        self.ttl = ttl_seconds
6
        self.access_log = []  # For audit trails
7

8
    def get_regulatory_context(self, query, user_id):
9
        cache_key = f"{query}:{user_id}"
10

11
        # Log access for compliance
12
        self.access_log.append({
13
            'user': user_id,
14
            'query': query,
15
            'timestamp': datetime.now()
16
        })
17

18
        # Check cache with TTL
19
        if cache_key in self.cache:
20
            entry = self.cache[cache_key]
21
            if time.time() - entry['timestamp'] < self.ttl:
22
                return entry['data']
23

24
        # Cache miss - fetch and cache
25
        context = self._fetch_context(query)
26
        self.cache[cache_key] = {
27
            'data': context,
28
            'timestamp': time.time()
29
        }
30

31
        return context