Understanding Multi-Hop Reasoning
Vector databases work for semantic search. They struggle with multi-step reasoning across people, systems, and events.
Standard RAG retrieves text chunks based on math. It misses the links between data points. GraphRAG solves this. It connects Large Language Models to a live knowledge graph.
A Text-Attributed Graph uses nodes and edges.
- Nodes are entities like users or products.
- Edges are relationships like "IMPACTS" or "CLONED_FROM".
- A "hop" is moving from one node to another via an edge.
Multi-hop reasoning finds connections that no single document contains.
Example: A user asks, "How do I fix the CSV upload error?"
Standard RAG searches for keywords. It might return an old FAQ or a random bug report. It misses the business context.
GraphRAG traces the exact path: (User Bug Report) -> [CLONED_FROM] -> (Master Engineering Ticket) -> [HAS_RESOLUTION] -> (Patch Deployed).
This gives the LLM verified steps instead of a loose guess.
You can control this through two methods:
Depth Traversal Use this to drill down into specific details. It is useful for root-cause analysis. In Cypher, you set a range of hops. A query can look 1 to 5 layers deep to find reproduction steps for a bug.
Breadth Traversal Use this to see the whole ecosystem. It expands horizontally to see all immediate connections. This helps you see every support ticket impacting a single product at once.
To build advanced AI, do not hard-code these queries. Use a multi-agent system:
- The Coordinator Agent: This agent plans the query. It decides if the question needs breadth or depth. It turns the user prompt into a mathematical parameter for the graph query.
- The Recursive Retrieval Agent: This agent evaluates nodes as the database finds them. It keeps important data and removes noise before the LLM sees it.
This architecture builds systems that scale their investigation. You get complete answers that vector search cannot provide.
Optional learning community: https://t.me/GyaanSetuAi