Knowledge Graphs (GraphRAG)

RAG Strategy (Source: Ottomator Agents)

What It Is

Combines Vector Search with Knowledge Graphs (stored in databases like Neo4j or FalkorDB) to capture complex entity relationships that pure vector embeddings might miss.

Reference Video

Implementations

Several libraries facilitate GraphRAG:

1. Microsoft GraphRAG
2. LightRAG

#TODO: Deep dive into specific implementation differences.

Pros & Cons

• ✅ Pros: Captures deep relationships missed by vectors; excellent for highly interconnected data.
• ❌ Cons: Requires setup (e.g., Neo4j), entity extraction, and graph maintenance. Slower and more expensive than pure vector retrieval.