GraphRAG

GraphRAG (Graph-based Retrieval-Augmented Generation) is an advanced approach to RAG that leverages Knowledge Graphs to provide more accurate, contextual, and relationship-aware information retrieval for Large Language Models.

How GraphRAG Works

Traditional RAG uses vector similarity search to find relevant documents. GraphRAG extends this by:

1. Building a Knowledge Graph from source documents
2. Extracting entities and their relationships
3. Combining vector search with graph traversal
4. Following relationship chains to gather comprehensive context
5. Providing structured context to the LLM

// GraphRAG query flow
const context = await trustgraph.retrieve({
  query: "Impact of AI on healthcare industry",
  strategy: "graph-rag",

  // Vector component
  vectorTopK: 10,
  vectorThreshold: 0.7,

  // Graph component
  graphDepth: 3,
  relationshipTypes: ["impacts", "enables", "requires"],

  // Fusion
  fusion: "weighted",
  weights: { vector: 0.4, graph: 0.6 }
});

// Returns rich, structured context
{
  entities: [
    { id: "ai", type: "Technology", properties: {...} },
    { id: "healthcare", type: "Industry", properties: {...} }
  ],
  relationships: [
    { from: "ai", type: "transforms", to: "healthcare", confidence: 0.92 }
  ],
  paths: [
    ["ai", "enables", "diagnosis_tools", "improve", "patient_outcomes"]
  ],
  vectorMatches: [...]
}

Traditional RAG vs GraphRAG

Traditional RAG (Vector-Only)

// Vector similarity search only
const chunks = await vectorSearch(query, topK: 5);
// Returns: ["chunk1 text", "chunk2 text", ...]

const response = await llm.generate({
  context: chunks.join("\n"),
  query: query
});

// Limitations:
// - No understanding of relationships
// - May miss connected information
// - Cannot reason over multiple hops
// - Limited context assembly

GraphRAG (Graph + Vector)

// Hybrid retrieval
const context = await graphrag.retrieve(query);

// Returns structured knowledge
{
  entities: [...],        // Identified entities
  relationships: [...],   // How they connect
  paths: [...],          // Reasoning chains
  vectorChunks: [...],   // Similar text
  fusedContext: "..."    // Optimally combined
}

const response = await llm.generate({
  context: context.fusedContext,
  query: query
});

// Advantages:
// ✅ Understands relationships
// ✅ Multi-hop reasoning
// ✅ Comprehensive context
// ✅ Reduced hallucinations

Key Benefits

1. Relationship-Aware Context

GraphRAG understands how entities relate:

// Query: "How does company X's acquisition affect market Y?"

// Vector RAG might return:
// - Document mentioning company X
// - Document mentioning market Y
// - Document about acquisitions
// (No connection between them)

// GraphRAG returns:
{
  entities: ["Company X", "Startup Z", "Market Y"],
  relationships: [
    "Company X" → "acquired" → "Startup Z",
    "Startup Z" → "operates_in" → "Market Y",
    "Company X" → "expands_to" → "Market Y" (inferred)
  ],
  reasoning: "Company X acquired Startup Z, which operates in Market Y,
              therefore Company X now has presence in Market Y"
}

2. Multi-Hop Reasoning

Follow relationship chains:

// Question: "What technologies might impact Company A's competitor B?"

// GraphRAG traverses:
// Technology → adopted_by → Company A
// Company A → competes_with → Company B
// Therefore: Technology → (indirect impact) → Company B

const analysis = await graphrag.reason({
  startEntity: "Technology X",
  targetEntity: "Company B",
  maxHops: 3,
  includeInferences: true
});

3. Reduced Hallucinations

Graph structure grounds LLM responses:

// GraphRAG validation
const response = await graphrag.generate({
  query: "Describe relationship between X and Y",
  groundingMode: "strict"  // Only reference graph entities
});

// Automatic validation
if (response.containsHallucination) {
  console.warn("Entity not in graph:", response.hallucinated);
  // Can reject or flag response
}

4. Better Context Assembly

Intelligently combine graph and vector results:

// Fusion strategies
const context = await graphrag.retrieve({
  query: "...",
  fusion: "rrf"  // Reciprocal Rank Fusion
});

// Or weighted combination
const context = await graphrag.retrieve({
  query: "...",
  fusion: "weighted",
  weights: {
    vector: 0.3,      // 30% from vector similarity
    graph: 0.5,       // 50% from graph relationships
    pagerank: 0.2     // 20% from entity importance
  }
});

Implementation Approaches

TrustGraph Native GraphRAG

import { TrustGraph } from "@trustgraph/sdk";

const tg = new TrustGraph({ endpoint: "..." });

// Ingest documents - automatically builds graph
await tg.ingest({
  sources: ["documents/"],
  graphConfig: {
    extractEntities: true,
    buildRelationships: true,
    linkingStrategy: "semantic"
  }
});

// Query with GraphRAG
const result = await tg.query({
  query: "Your question here",
  strategy: "graph-rag",  // Enables GraphRAG
  vectorTopK: 20,
  graphDepth: 3
});

DIY GraphRAG Stack

# Build your own GraphRAG
from langchain import VectorStore, GraphStore, LLM

# 1. Extract entities and relationships
entities = extract_entities(documents)
relationships = extract_relationships(documents)

# 2. Build Knowledge Graph
graph = GraphStore()
graph.add_entities(entities)
graph.add_relationships(relationships)

# 3. Create embeddings
vector_store = VectorStore()
vector_store.add_documents(documents)

# 4. Retrieve with both
def graphrag_retrieve(query):
    # Vector search
    vector_results = vector_store.similarity_search(query, k=10)

    # Graph traversal
    relevant_entities = graph.find_entities(query)
    subgraph = graph.expand(relevant_entities, depth=3)

    # Combine
    context = combine_results(vector_results, subgraph)
    return context

# 5. Generate response
context = graphrag_retrieve(query)
response = llm.generate(context=context, query=query)

Use Cases

1. Complex Q&A Systems

// Financial analysis
const analysis = await graphrag.query({
  query: "How do rising interest rates affect Company X's suppliers?",
  strategy: "graph-rag"
});

// Returns path:
// Interest Rates → affects → Banking Sector
// Banking Sector → impacts → Company X (loans)
// Company X → supplied_by → Supplier Y, Z
// Therefore: Interest Rates → (indirect) → Suppliers

2. Research Literature Review

// Academic research
const literature = await graphrag.query({
  query: "Papers connecting quantum computing to drug discovery",
  strategy: "graph-rag",
  graphDepth: 4  // Follow longer chains
});

// Finds connections:
// Quantum Computing → enables → Molecular Simulation
// Molecular Simulation → used_in → Drug Discovery

3. Customer Intelligence

// Business intelligence
const insights = await graphrag.query({
  query: "Which customers might be interested in Product X?",
  strategy: "graph-rag"
});

// Traverses:
// Product X → similar_to → Product Y
// Product Y → purchased_by → Customer A, B, C
// Customer A → has_need → Feature Z
// Product X → provides → Feature Z

4. Compliance and Risk

// Regulatory analysis
const compliance = await graphrag.query({
  query: "How does new regulation R affect our operations?",
  strategy: "graph-rag"
});

// Maps:
// Regulation R → applies_to → Industry Sector
// Industry Sector → includes → Company Operations
// Operations → uses → Process P
// Process P → requires_compliance → Action A

Performance Considerations

Latency

Traditional RAG:
  - Vector search: 10-50ms
  - Total: 10-50ms

GraphRAG:
  - Vector search: 10-50ms
  - Graph traversal: 20-100ms
  - Fusion: 5-10ms
  - Total: 35-160ms

Trade-off: Slightly higher latency for significantly better accuracy

Accuracy Improvements

Benchmarks show GraphRAG improvements:
  - Multi-hop questions: +40-60% accuracy
  - Relationship queries: +50-70% accuracy
  - Complex reasoning: +30-50% accuracy
  - Hallucination reduction: -30-50%

Best Practices

1. Graph Quality Matters

// Invest in good entity extraction and linking
await trustgraph.ingest({
  sources: ["docs/"],
  graphConfig: {
    extractEntities: true,
    linkingStrategy: "semantic",
    deduplication: "strict",
    validation: "enabled",
    ontology: "domain-schema.ttl"  // Use domain ontology
  }
});

2. Tune Fusion Parameters

// Experiment with fusion weights
const results_a = await graphrag.retrieve({
  fusion: "weighted",
  weights: { vector: 0.4, graph: 0.6 }  // Graph-heavy
});

const results_b = await graphrag.retrieve({
  fusion: "weighted",
  weights: { vector: 0.7, graph: 0.3 }  // Vector-heavy
});

// A/B test to find optimal balance

3. Control Graph Depth

// Balance depth vs. performance
const shallow = await graphrag.retrieve({
  graphDepth: 1  // Fast, local context
});

const deep = await graphrag.retrieve({
  graphDepth: 4  // Comprehensive, slower
});

// Typical: depth 2-3 for most queries

4. Use Graph Constraints

// Constrain traversal for focused results
const result = await graphrag.retrieve({
  query: "...",
  graphConstraints: {
    relationshipTypes: ["employs", "manages", "reports_to"],
    entityTypes: ["Person", "Department"],
    excludeRelations: ["located_in"]  // Ignore irrelevant
  }
});

Comparison with Other Approaches

Limitations

1. Graph Construction Overhead: Initial graph building takes time
2. Graph Quality Dependency: Poor entity extraction = poor results
3. Higher Latency: Graph traversal adds latency
4. Storage Requirements: Graph + vectors requires more storage
5. Complexity: More complex to implement and tune

Future Directions

• Dynamic Graphs: Real-time graph updates
• Multi-modal GraphRAG: Images, audio, video in graphs
• Federated GraphRAG: Distributed graph query
• Learning Graphs: Graphs that improve from feedback
• Probabilistic Edges: Uncertainty in relationships

See Also

• Knowledge Graphs
• RAG (Retrieval-Augmented Generation)
• Vector Databases
• Property Graphs