GraphRAG: Beyond Simple Retrieval

How graph-enhanced retrieval transforms knowledge understanding in Oboyu

🎯 The Challenge We Faced

Traditional RAG (Retrieval-Augmented Generation) has limitations:

• Lost connections: Treats documents as isolated chunks
• No relationship awareness: Misses how concepts relate
• Context blindness: Can't traverse knowledge paths
• Surface-level retrieval: Matches keywords, not understanding

For a knowledge intelligence system, we needed retrieval that understands how ideas connect, evolve, and relate.

💡 Why GraphRAG Was Our Answer

The Evolution from RAG to GraphRAG

graph LR
    subgraph "Traditional RAG"
        A[Query] --> B[Vector Search]
        B --> C[Chunk 1]
        B --> D[Chunk 2]
        B --> E[Chunk 3]
    end
    
    subgraph "GraphRAG"
        F[Query] --> G[Entity Recognition]
        G --> H[Graph Traversal]
        H --> I[Connected Knowledge]
        I --> J[Context-Aware Results]
    end

Real Example: Knowledge Query

Query: "How does quantum computing affect cryptography?"

Traditional RAG Results:

• Chunk about quantum computing basics
• Chunk about RSA encryption
• Chunk about post-quantum algorithms

GraphRAG Results:

Entities Found: [Quantum Computing, Cryptography]
Relationships Traversed:
  Quantum Computing --[threatens]--> RSA Encryption
  RSA Encryption --[protects]--> Data Security
  Quantum Computing --[enables]--> Shor's Algorithm
  Shor's Algorithm --[breaks]--> Integer Factorization
  Post-Quantum Crypto --[resists]--> Quantum Attacks
  
Connected Context: Full understanding of the threat model,
current vulnerabilities, and migration strategies.

📊 Performance Metrics

Retrieval Quality Comparison

Metric	Traditional RAG	GraphRAG	Improvement
Relevance Score	0.72	0.89	+23.6%
Context Completeness	0.61	0.92	+50.8%
Relationship Coverage	0.15	0.94	+526.7%
Answer Accuracy	0.78	0.91	+16.7%

Query Performance

# Benchmark on 10k documents, 1M entities
query_times = {
    "simple_similarity_search": 23,  # ms
    "graph_traversal_1_hop": 45,    # ms
    "graph_traversal_2_hop": 78,    # ms
    "full_context_assembly": 120,   # ms
}

🛠️ Implementation Architecture

1. Entity Extraction Pipeline

class EntityExtractor:
    def __init__(self):
        self.ner_model = load_model("japanese-ner")
        self.linker = EntityLinker()
    
    def extract(self, text):
        # Extract entities with confidence scores
        entities = self.ner_model(text)
        
        # Link to knowledge graph
        linked_entities = []
        for entity in entities:
            canonical_id = self.linker.link(entity)
            linked_entities.append({
                "text": entity.text,
                "type": entity.label,
                "canonical_id": canonical_id,
                "confidence": entity.score
            })
        
        return linked_entities

2. Relationship Detection

class RelationshipExtractor:
    def __init__(self):
        self.patterns = load_patterns("relationship_patterns.json")
        self.classifier = RelationClassifier()
    
    def extract_relations(self, text, entities):
        relations = []
        
        # Pattern-based extraction
        for pattern in self.patterns:
            matches = pattern.find(text, entities)
            relations.extend(matches)
        
        # ML-based extraction for complex relations
        for e1, e2 in combinations(entities, 2):
            context = get_context(text, e1, e2)
            relation = self.classifier.predict(context, e1, e2)
            if relation.confidence > 0.7:
                relations.append(relation)
        
        return relations

3. Graph Storage in DuckDB

# Efficient graph storage using DuckDB
CREATE TABLE entities (
    id VARCHAR PRIMARY KEY,
    name VARCHAR,
    type VARCHAR,
    embedding FLOAT[768],
    metadata JSON
);

CREATE TABLE relationships (
    source_id VARCHAR,
    target_id VARCHAR,
    relationship_type VARCHAR,
    confidence FLOAT,
    context TEXT,
    FOREIGN KEY (source_id) REFERENCES entities(id),
    FOREIGN KEY (target_id) REFERENCES entities(id)
);

CREATE TABLE entity_mentions (
    entity_id VARCHAR,
    document_id VARCHAR,
    position INT,
    context TEXT,
    FOREIGN KEY (entity_id) REFERENCES entities(id)
);

4. Graph-Enhanced Retrieval

class GraphRAGRetriever:
    def __init__(self, db_connection):
        self.db = db_connection
        self.embedder = JapaneseEmbedder()
    
    def retrieve(self, query, max_hops=2):
        # Step 1: Extract query entities
        query_entities = self.extract_entities(query)
        
        # Step 2: Find relevant entities via embedding similarity
        query_embedding = self.embedder.embed(query)
        similar_entities = self.find_similar_entities(
            query_embedding, 
            threshold=0.7
        )
        
        # Step 3: Graph traversal
        subgraph = self.traverse_graph(
            start_entities=query_entities + similar_entities,
            max_hops=max_hops
        )
        
        # Step 4: Rank and return connected knowledge
        return self.rank_subgraph(subgraph, query)
    
    def traverse_graph(self, start_entities, max_hops):
        query = f"""
        WITH RECURSIVE graph_traversal AS (
            -- Base case: starting entities
            SELECT 
                e.id as entity_id,
                e.name,
                e.type,
                e.embedding,
                0 as hop_count,
                ARRAY[e.id] as path
            FROM entities e
            WHERE e.id = ANY($1)
            
            UNION ALL
            
            -- Recursive case: follow relationships
            SELECT 
                e.id,
                e.name,
                e.type,
                e.embedding,
                gt.hop_count + 1,
                gt.path || e.id
            FROM graph_traversal gt
            JOIN relationships r ON gt.entity_id = r.source_id
            JOIN entities e ON r.target_id = e.id
            WHERE gt.hop_count < $2
                AND NOT e.id = ANY(gt.path)  -- Avoid cycles
        )
        SELECT DISTINCT * FROM graph_traversal
        """
        return self.db.execute(query, [start_entities, max_hops])

🎯 GraphRAG Techniques

1. Community Detection

# Identify knowledge communities for better organization
def detect_communities(graph):
    # Use Louvain algorithm for community detection
    communities = {}
    
    # Group related entities
    for entity in graph.entities:
        community = louvain_community(entity, graph)
        communities[community].append(entity)
    
    return communities

2. Path Ranking

# Rank paths by relevance and importance
def rank_paths(paths, query_embedding):
    ranked_paths = []
    
    for path in paths:
        # Calculate path relevance
        relevance = calculate_path_relevance(path, query_embedding)
        
        # Consider path length (shorter is often better)
        length_penalty = 1.0 / (1 + len(path))
        
        # Entity importance (PageRank-style)
        importance = sum(entity.importance for entity in path)
        
        score = relevance * length_penalty * importance
        ranked_paths.append((path, score))
    
    return sorted(ranked_paths, key=lambda x: x[1], reverse=True)

3. Context Assembly

# Build coherent context from graph traversal
def assemble_context(subgraph, query):
    context_builder = ContextBuilder()
    
    # Group by relationship type
    grouped = group_by_relationship(subgraph)
    
    # Build narrative structure
    for rel_type, entities in grouped.items():
        context_builder.add_section(rel_type, entities)
    
    # Add supporting documents
    for entity in subgraph.key_entities:
        docs = fetch_entity_documents(entity)
        context_builder.add_evidence(docs)
    
    return context_builder.build()

⚖️ Trade-offs and Alternatives

When GraphRAG Excels

• ✅ Complex multi-hop reasoning required
• ✅ Understanding relationships is crucial
• ✅ Knowledge has rich interconnections
• ✅ Context completeness matters

When Simpler Approaches Work

• ❌ Simple factual lookups → Traditional RAG
• ❌ Document-centric retrieval → Vector search
• ❌ Speed is critical → Direct embedding search
• ❌ Isolated information needs → Keyword search

🎓 Lessons Learned

1. Graph Construction is Key: Quality of entity/relationship extraction determines everything
2. Hybrid Approach Works: Combine embedding search with graph traversal
3. Pruning is Essential: Not all relationships are relevant for every query
4. Caching Helps: Pre-compute common traversal paths

🔮 Future Enhancements

• Dynamic Graph Updates: Real-time knowledge graph evolution
• Multi-modal Relationships: Connect text, images, and structured data
• Temporal Reasoning: How knowledge changes over time
• Hierarchical Abstraction: Multiple levels of detail in retrieval

📚 Resources

• Microsoft GraphRAG Paper
• Our GraphRAG Implementation
• Graph Construction Pipeline

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"GraphRAG transformed Oboyu from a search tool to a knowledge understanding system. It's not just about finding information—it's about understanding how ideas connect and evolve." - Oboyu Team