Intermediate LevelAI, LLMs, Embeddings & MCP
What is GraphRAG and how do Knowledge Graphs complement vector search?
❓ Interview Prompt: Explain GraphRAG, entity-relationship extraction, Knowledge Graph indexing (Neo4j), and multi-hop reasoning.
Standard vector search handles isolated chunk similarity well, but struggles with multi-hop relational queries (e.g. "How does Company A's supplier affect Company C's product line?"). GraphRAG solves this.
GraphRAG Architecture:
1
Graph Extraction: Uses an LLM to extract Entities (Nodes) and Relationships (Edges) from documents.
2
Knowledge Graph Storage: Stores graph structures inside graph databases like Neo4j or Memgraph.
3
Graph Traversal Retrieval: Combines Cypher graph traversal with vector search to aggregate context across multi-hop relationships.
Code Implementation Example
cypher
// Neo4j Cypher query traversing GraphRAG entity relationships
MATCH (c:Company {name: "TechCorp"})-[r:SUPPLIES]->(p:Product)-[:USED_BY]->(client:Client)
RETURN c.name, p.name, client.name;💡Neo4j Cypher query traversing multi-hop GraphRAG relationships.
🎯 Interview Key Takeaway
GraphRAG extracts entity-relationship graphs to execute multi-hop relational queries that vector similarity search misses.
#GraphRAG#Knowledge Graph#Neo4j#Multi-Hop#Intermediate
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