GraphRAG

GraphRAG (Graph-based Retrieval Augmented Generation) combines knowledge graphs with intelligent search to provide context-aware information that enhances agent reasoning and decision-making.

Overview

GraphRAG improves agent intelligence by:

Knowledge Graphs: Understanding how information connects
Semantic Search: Finding information by meaning, not just keywords
Contextual Retrieval: Providing relevant background for decisions
Relationship Discovery: Uncovering hidden connections

Core Concepts

What is GraphRAG?

Think of GraphRAG as a smart research assistant that:

Remembers Relationships: Knows how entities connect
Finds Context: Provides relevant background information
Discovers Patterns: Identifies trends across data
Answers Questions: Responds to natural language queries

Knowledge Graphs

Information is stored as connected nodes:

Customer "Acme Corp" ──[placed_order]──> Order "Order-123"
                                           │
                                      [contains_product]
                                           │
                                           ▼
                                    Product "Widget-X"

Smart Search

Three search modes work together:

Mode	How It Works	Best For
Keyword	Exact word matching	Finding specific terms
Semantic	Meaning-based search	Conceptual queries
Graph	Follows relationships	Multi-hop connections

How GraphRAG Works

Information Storage

1. Extract Entities
   - Identify people, organizations, concepts
   - Extract properties and attributes

2. Find Relationships
   - Discover how entities connect
   - Classify relationship types

3. Create Embeddings
   - Convert text to mathematical vectors
   - Enable semantic search

4. Store in Knowledge Graph
   - Nodes: Entities with properties
   - Edges: Relationships between entities
   - Vectors: Semantic representations

Query Processing

User Question
    ↓
Understand Intent
    ↓
Search Strategy
    ├─ Keyword Search (exact matches)
    ├─ Semantic Search (similar meanings)
    └─ Graph Traversal (follow relationships)
    ↓
Combine Results
    ↓
Rank by Relevance
    ↓
Return Contextual Answer

Relationship Traversal

Follow connections to discover insights:

Question: "What products do enterprise customers buy?"

Traversal:
1. Find enterprise customers
2. Follow "placed_order" relationships
3. Follow "contains_product" relationships
4. Aggregate products
5. Rank by frequency

Answer: "Enterprise customers primarily buy:
- Product A (45% of orders)
- Product B (32% of orders)
- Product C (23% of orders)"

Query Types

Local Retrieval

Find directly relevant information:

Use Case: "Tell me about Acme Corp"
Returns: Acme Corp entity + directly related information
Speed: Fast, focused results

Global Retrieval

Traverse multiple relationship levels:

Use Case: "Show the complete order chain"
Returns: Customer → Orders → Products → Suppliers
Speed: Slower, but comprehensive

Hybrid Retrieval (Recommended)

Combines both approaches:

Use Case: Most queries
Returns: Direct matches + related context + patterns
Speed: Balanced, rich results

Automatic Learning

Entity Extraction

GraphRAG automatically learns from documents:

Document Uploaded
    ↓
AI Analyzes Content
    ↓
Extracts Entities
    - Names, dates, amounts
    - Organizations, locations
    - Concepts, topics
    ↓
Finds Relationships
    - "works for"
    - "located in"
    - "purchased from"
    ↓
Adds to Knowledge Graph

Continuous Improvement

The system gets smarter over time:

Learn from Queries: Understand what information is important
User Feedback: Improve result relevance
Pattern Recognition: Identify recurring relationships
Knowledge Expansion: Grow the graph organically

Usage Patterns

Customer 360 View

Query: "Tell me everything about Acme Corp"

GraphRAG Returns:
- Company profile
- Order history
- Support tickets
- Communications
- Related companies
- Market insights
- Recommendations

Benefits:
- Complete picture in one query
- Discover hidden connections
- Make informed decisions

Impact Analysis

Query: "What happens if we discontinue Product X?"

GraphRAG Traverses:
1. Find customers who bought Product X
2. Calculate revenue impact
3. Identify related products
4. Find alternative products
5. Assess customer satisfaction risk

Returns:
- Revenue at risk: $250K/year
- Affected customers: 15
- Recommended alternatives: Product Y, Product Z
- Retention strategy: Proactive outreach

Recommendation Engine

Query: "What should we recommend to Acme Corp?"

GraphRAG Analyzes:
1. Acme Corp's purchase history
2. Similar customers' purchases
3. Product relationships
4. Current trends
5. Seasonal patterns

Returns:
- Product recommendations with confidence scores
- Bundling opportunities
- Upsell suggestions
- Timing recommendations

Integration with Agent Intelligence

Enhanced Reasoning

GraphRAG provides context for agent decisions:

Agent wants to send promotional email
    ↓
GraphRAG checks:
    - Has customer been contacted recently?
    - What are their interests?
    - What have they purchased?
    - What are their preferences?
    ↓
Agent makes informed decision:
    - Personalize content
    - Choose right timing
    - Select relevant offers
    - Avoid over-messaging

Memory and Learning

GraphRAG integrates with agent memory:

Agent completes task
    ↓
GraphRAG records:
    - What entities were involved
    - What actions were taken
    - What was the outcome
    ↓
Future queries benefit:
    - Agent remembers context
    - Applies past learnings
    - Makes better decisions

API Overview

Query Operations

Natural language query
Keyword search
Semantic search
Graph traversal
Hybrid retrieval

Entity Operations

Extract entities from text
Create entities manually
Update entity properties
Delete entities

Relationship Operations

Create relationships
Find related entities
Traverse connections
Analyze relationship patterns

Analytics

Entity statistics
Relationship insights
Trend analysis
Pattern discovery

Best Practices

Start with Hybrid: Use hybrid retrieval for most queries
Provide Context: Include relevant details in queries
Iterate: Refine queries based on results
Explore Relationships: Follow connections to discover insights
Give Feedback: Help the system learn what's relevant

Common Scenarios

Due Diligence

Query: "What should we know about Acme Corp before partnering?"

GraphRAG Returns:
- Company background
- Financial health
- Market position
- Related companies
- News and events
- Risk factors
- Partnership history

Decision: Make informed partnership decision

Root Cause Analysis

Query: "Why are customers churning?"

GraphRAG Analyzes:
- Churned customer profiles
- Common patterns
- Related events
- Support tickets
- Product usage
- Market changes

Returns:
- Top churn reasons
- At-risk customers
- Recommended actions
- Prevention strategies

Opportunity Discovery

Query: "Are there cross-sell opportunities?"

GraphRAG Finds:
- Products frequently bought together
- Customers missing related products
- Complementary product patterns
- Timing trends
- Customer segments

Returns:
- Cross-sell recommendations
- Target customer lists
- Expected revenue impact
- Implementation strategy

ATOM Documentation