Building a GraphRAG AI Agent

Learn how to build an intelligent AI agent using TrustGraph's GraphRAG (Graph-based Retrieval-Augmented Generation) architecture. GraphRAG combines Knowledge Graphs with vector search to provide relationship-aware, contextually grounded responses while reducing hallucinations.

Overview

GraphRAG in TrustGraph enhances traditional RAG by combining:

Vector search for semantic similarity (finding relevant entities)
Graph traversal for relationship discovery (connecting related information)
Subgraph context for LLM generation (rich, connected knowledge)

How GraphRAG Works

The GraphRAG pipeline follows seven key steps:

Document Chunking: Documents are split into smaller segments
Entity & Relationship Extraction: Automatic discovery without predefined schemas
Vector Embeddings: Entities receive embeddings stored in vector database
Knowledge Graph Construction: Relationships populate the graph
Semantic Entry Points: Vector search identifies relevant starting nodes
Graph Traversal: Connected information discovered through relationships
LLM Generation: Responses generated using subgraph as context

GraphRAG vs Traditional RAG

Aspect	Traditional RAG	GraphRAG
Retrieval	Vector similarity only	Vector + graph traversal
Context	Isolated text chunks	Connected subgraph
Relationships	Not represented	Explicit graph edges
Multi-hop reasoning	Not supported	Native support
Schema requirements	None	None (ontology-free)

Prerequisites

Before you begin, you'll need:

TrustGraph platform installed (deployment guide)
TrustGraph CLI tools (tg-* commands)
Basic understanding of graph concepts
Your source documents (PDFs, text files, structured data)

Step 1: Set Up Your Collection

First, create a collection to organize your knowledge:

# Create a collection for your documents
tg-set-collection \
  -n "Intelligence" \
  -d "Intelligence documents and reports" \
  intelligence

Collections in TrustGraph are logical groupings of documents that share a common purpose or domain. They help organize your Knowledge Graph.

Step 2: Add Documents

Add documents to your library with metadata:

# Add a document with metadata
tg-add-library-document \
  --name "AI Research Report 2024" \
  --description "Comprehensive analysis of AI trends" \
  --tags 'artificial-intelligence,research,2024' \
  --id doc-ai-research-2024 \
  --kind text/plain \
  documents/ai-research-2024.txt

Key parameters:

--name: Document title
--description: Brief description
--tags: Comma-separated tags for categorization
--id: Unique identifier (URI)
--kind: MIME type (text/plain, application/pdf, etc.)

Step 3: Create GraphRAG Flow

Create a processing flow for GraphRAG:

# Create the GraphRAG flow
tg-start-flow \
  -n graph-rag \
  -i graph-rag \
  -d "Graph RAG processing flow"

Flows define processing pipelines in TrustGraph. The graph-rag flow handles:

Document chunking
Entity extraction (automatic, no schema required)
Relationship discovery
Vector embedding generation
Knowledge Graph construction

Step 4: Process Documents

Submit documents for GraphRAG processing:

# Process a document through the GraphRAG flow
tg-start-library-processing \
  --flow-id graph-rag \
  --document-id doc-ai-research-2024 \
  --collection intelligence \
  --processing-id proc-ai-research-2024-001

What happens during processing:

Document is chunked into segments
Entities are automatically extracted (no schema needed)
Relationships between entities are discovered
Vector embeddings are generated and stored
Knowledge Graph is updated with new nodes and edges

Monitor processing:

Use Grafana dashboards to track queue backlogs
Monitor LLM latency and token throughput
Check for rate-limit events

Step 5: Query with GraphRAG

Now query your Knowledge Graph using GraphRAG:

# Query using GraphRAG
tg-invoke-graph-rag \
  -f graph-rag \
  -C intelligence \
  -q "What are the key AI trends in 2024?"

The query process:

Vector Search: Finds semantically similar entities (entry points)
Graph Traversal: Discovers connected information through relationships
Subgraph Extraction: Builds contextual subgraph around relevant entities
LLM Generation: Generates response using subgraph as grounded context

Query Response

The response includes:

Answer: Generated text based on subgraph context
Citations: Traceable back to source documents
Entity References: Specific nodes used in reasoning
Confidence Score: Based on graph connectivity and source quality

Step 6: Explore with Workbench

TrustGraph Workbench provides visualization and exploration tools:

Vector Search

Search for entities by semantic similarity:

# Access Workbench at http://localhost:8001
# Navigate to Vector Search

Features:

Search entities by text query
View similarity scores for each result
Explore entity properties and metadata

Graph Visualization

Visualize your Knowledge Graph in 3D:

# Access Graph Visualization in Workbench

Features:

3D Interactive Graph: Navigate nodes and relationships in 3D space
Node Inspection: Click nodes to view properties
Edge Inspection: View relationship types (subject-predicate-object triples)
Subgraph Exploration: Zoom into specific areas of the graph

Understanding Graph Structure

Each relationship in the graph follows the triple pattern:

(Subject) --[Predicate]--> (Object)

Examples:
(Alice Johnson) --[worksAt]--> (TechCorp)
(AI Research Report) --[mentions]--> (Machine Learning)
(TechCorp) --[focusesOn]--> (Artificial Intelligence)

This structure enables multi-hop reasoning:

Query: "What topics does Alice Johnson work on?"

Traversal:
Alice Johnson --[worksAt]--> TechCorp --[focusesOn]--> AI
Result: Alice works on AI-related topics

When to Use GraphRAG

GraphRAG is optimal for:

✅ Complex Relationships: Questions requiring understanding of how entities relate ✅ Multi-document Context: Answers needing information from multiple sources ✅ Connection Discovery: Finding how disparate information connects ✅ Exploratory Questions: "How are X and Y related?" type queries ✅ Hallucination Reduction: Graph grounding reduces fabricated information

Consider alternatives for:

❌ Simple Keyword Search: Small datasets with straightforward lookups ❌ Structured Data Only: When you need strict typed schemas (use Ontology RAG) ❌ Real-time Speed Critical: Initial graph construction has processing cost

Best Practices

1. Ontology-Free Extraction

GraphRAG automatically discovers relationships without predefined schemas:

# No schema definition needed
# Entities and relationships are discovered automatically
tg-start-library-processing \
  --flow-id graph-rag \
  --document-id doc-id \
  --collection intelligence

Advantages:

Works with diverse, unstructured data
Flexible knowledge discovery
No upfront schema design required

Tradeoff: Processing incurs LLM token costs for entity/relationship extraction

2. Monitor Processing

Track processing via Grafana dashboards:

Queue Backlogs: Number of pending processing jobs
LLM Latency: Time for LLM calls during extraction
Token Throughput: Tokens processed per second
Rate Limit Events: Track API rate limiting

3. Retrieval Precision

GraphRAG provides significantly improved retrieval precision compared to traditional RAG:

Vector search finds semantically similar starting points
Graph traversal discovers related information through relationships
Subgraph context provides richer, more connected information to LLM

4. Batch Processing

Process multiple documents efficiently:

# Process multiple documents in batch
for doc in documents/*.txt; do
  doc_id=$(basename "$doc" .txt)
  tg-start-library-processing \
    --flow-id graph-rag \
    --document-id "$doc_id" \
    --collection intelligence \
    --processing-id "proc-${doc_id}-$(date +%s)"
done

Advanced Use Cases

Multi-hop Question Answering

GraphRAG excels at questions requiring multiple reasoning steps:

# Multi-hop question
tg-invoke-graph-rag \
  -f graph-rag \
  -C intelligence \
  -q "How does climate change impact AI research funding?"

Graph traversal might discover:

Climate Change --[affects]--> Government Priorities
Government Priorities --[influences]--> Research Funding
Research Funding --[supports]--> AI Research

Relationship Discovery

Find connections between seemingly unrelated entities:

tg-invoke-graph-rag \
  -f graph-rag \
  -C intelligence \
  -q "What connects renewable energy companies and AI startups?"

GraphRAG can discover:

Shared investors
Technology partnerships
Supply chain relationships
Research collaborations

Combining GraphRAG with Other RAG Approaches

For comprehensive coverage, combine GraphRAG with complementary approaches:

Document RAG + GraphRAG

Document RAG: Simple keyword/semantic search on small datasets
GraphRAG: Complex relationship discovery and multi-hop reasoning
Together: Broad coverage with relationship-aware depth

Ontology RAG + GraphRAG

Ontology RAG: Structured, typed data with formal schemas
GraphRAG: Flexible, automatic relationship discovery
Together: Formal structure where needed, flexible discovery elsewhere

Monitoring and Optimization

Performance Metrics

Track these key metrics:

Processing Performance
- Queue backlog size
- Average processing time per document
- Token consumption rate
Query Performance
- Query latency (vector search + graph traversal)
- Subgraph size distribution
- LLM generation time
Quality Metrics
- Entity extraction accuracy
- Relationship discovery completeness
- Query result relevance

Optimization Tips

# Monitor via Grafana (typically at http://localhost:3000)
# Watch for:
# - High queue backlogs (increase processing workers)
# - High LLM latency (optimize prompts or use faster models)
# - Rate limit events (adjust request throttling)

Next Steps

Explore Other RAG Types
- Document RAG for simple retrieval
- Ontology RAG for typed schemas
Advanced Features
- Enable Model Context Protocol (MCP) integration
- Implement streaming responses
- Set up monitoring dashboards
Scale Your Deployment
- Configure multi-node clusters
- Optimize vector search performance
- Set up backup and recovery

Conclusion

You now have a production-ready GraphRAG AI agent that:

✅ Combines vector search with graph traversal for superior context ✅ Automatically discovers entities and relationships without schemas ✅ Enables multi-hop reasoning across connected information ✅ Reduces hallucinations through graph-grounded responses ✅ Provides traceable citations back to source documents

GraphRAG represents a middle ground between simple semantic search and rigidly-structured ontology approaches, offering automated knowledge discovery with relationship-aware retrieval.

Related Concepts

GraphRAG Key Concepts - Deep dive into GraphRAG architecture
Context Engineering - Optimizing context for LLMs
Knowledge Graphs - Understanding graph structures
Collections - Organizing documents in TrustGraph
Flows - Processing pipelines