# Graph RAG

Graph RAG (Retrieval-Augmented Generation) components combine graph databases with vector search to provide advanced knowledge representation and retrieval capabilities.

## Graph RAG

Graph RAG integrates graph database structures with vector embeddings to enable sophisticated knowledge graphs and enhanced retrieval-augmented generation.

### Usage

Graph RAG capabilities:

* Knowledge graph integration
* Relationship-aware retrieval
* Multi-hop reasoning
* Entity recognition
* Contextual understanding

### Inputs

| Name            | Display Name   | Info                                  |
| --------------- | -------------- | ------------------------------------- |
| graph\_database | Graph Database | Connected graph database instance     |
| vector\_store   | Vector Store   | Connected vector database             |
| query           | Query          | Natural language query                |
| max\_hops       | Max Hops       | Maximum relationship hops to traverse |
| entity\_types   | Entity Types   | Types of entities to consider         |

### Outputs

| Name              | Display Name     | Info                                  |
| ----------------- | ---------------- | ------------------------------------- |
| graph\_results    | Graph Results    | Results from graph traversal          |
| vector\_results   | Vector Results   | Results from vector similarity search |
| combined\_context | Combined Context | Merged context from both sources      |

## Graph Database Integration

### Neo4j Integration

* **Cypher Queries**: Native graph query language
* **Relationship Traversal**: Multi-hop entity relationships
* **Pattern Matching**: Complex graph pattern discovery
* **Performance**: Optimized graph operations

### ArangoDB Integration

* **Multi-model**: Document, graph, and key-value
* **AQL Queries**: ArangoDB Query Language
* **Flexible Schema**: Dynamic graph structures
* **Distributed**: Multi-server deployments

### Amazon Neptune Integration

* **Managed Service**: Fully managed graph database
* **Property Graphs**: Rich entity and relationship properties
* **RDF Support**: Semantic web standards
* **High Availability**: Multi-AZ deployments

## Knowledge Graph Features

### Entity Recognition

* **Named Entities**: Person, organization, location extraction
* **Custom Entities**: Domain-specific entity types
* **Entity Linking**: Connect entities to knowledge bases
* **Disambiguation**: Resolve entity ambiguities

### Relationship Extraction

* **Semantic Relations**: Extract meaningful relationships
* **Temporal Relations**: Time-based relationships
* **Causal Relations**: Cause-and-effect connections
* **Hierarchical Relations**: Parent-child structures

### Graph Construction

* **Automatic**: AI-powered graph construction
* **Manual**: Human-curated knowledge graphs
* **Hybrid**: Combination of automatic and manual
* **Incremental**: Continuous graph updates

## Advanced Retrieval Strategies

### Multi-hop Reasoning

* **Path Finding**: Shortest path algorithms
* **Relationship Chains**: Multi-step reasoning paths
* **Weighted Paths**: Importance-based path scoring
* **Circular Detection**: Avoid infinite loops

### Hybrid Search

* **Graph + Vector**: Combine structural and semantic search
* **Weighted Fusion**: Balance graph and vector results
* **Re-ranking**: Post-process combined results
* **Filtering**: Apply graph constraints to vector results

### Contextual Expansion

* **Neighbor Context**: Include related entities
* **Temporal Context**: Time-sensitive information
* **Hierarchical Context**: Parent-child relationships
* **Thematic Context**: Topic-based expansion

## Use Cases

### Knowledge Management

* **Enterprise Knowledge**: Internal knowledge bases
* **Research Databases**: Scientific literature graphs
* **Legal Documents**: Case law and statute relationships
* **Medical Knowledge**: Disease-drug-symptom graphs

### Question Answering

* **Complex Queries**: Multi-entity questions
* **Factual QA**: Fact verification and retrieval
* **Reasoning Tasks**: Logical inference questions
* **Exploratory Search**: Open-ended investigation

### Recommendation Systems

* **Content Discovery**: Related content suggestions
* **Expert Finding**: Subject matter expert identification
* **Research Recommendations**: Paper and patent suggestions
* **Learning Paths**: Educational content sequencing

## Technical Architecture

### Data Flow

1. **Ingestion**: Documents processed for entities and relationships
2. **Graph Construction**: Build knowledge graph structure
3. **Vector Embedding**: Generate embeddings for entities and text
4. **Dual Storage**: Store in both graph and vector databases
5. **Query Processing**: Parallel graph and vector queries
6. **Result Fusion**: Combine and rank results

### Performance Optimization

* **Caching**: Cache frequent graph patterns
* **Indexing**: Optimize graph and vector indexes
* **Parallelization**: Concurrent query execution
* **Pruning**: Eliminate irrelevant paths early

### Scalability

* **Distributed Graphs**: Scale across multiple nodes
* **Sharding**: Partition large graphs
* **Federation**: Connect multiple knowledge sources
* **Edge Computing**: Deploy at network edge

## Implementation Patterns

### Graph-First Approach

1. Start with graph query
2. Expand with vector similarity
3. Filter by graph constraints
4. Rank by combined scores

### Vector-First Approach

1. Start with vector similarity
2. Enhance with graph context
3. Apply relationship filters
4. Re-rank with graph signals

### Parallel Approach

1. Execute graph and vector queries simultaneously
2. Merge results based on relevance
3. Apply post-processing filters
4. Generate final ranked results

## Usage Notes

* **Complexity**: More sophisticated than simple vector search
* **Performance**: May require optimization for large graphs
* **Setup**: Requires both graph and vector database infrastructure
* **Maintenance**: Keep graph and vector stores synchronized
* **Quality**: Results depend on knowledge graph quality
* **Scalability**: Plan for growth in both dimensions
