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
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
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
Ingestion: Documents processed for entities and relationships
Graph Construction: Build knowledge graph structure
Vector Embedding: Generate embeddings for entities and text
Dual Storage: Store in both graph and vector databases
Query Processing: Parallel graph and vector queries
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
Start with graph query
Expand with vector similarity
Filter by graph constraints
Rank by combined scores
Vector-First Approach
Start with vector similarity
Enhance with graph context
Apply relationship filters
Re-rank with graph signals
Parallel Approach
Execute graph and vector queries simultaneously
Merge results based on relevance
Apply post-processing filters
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
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