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Graph vs RAG

What RAGthrows away.

RAG retrieves chunks that look similar. A graph keeps the relationships, and hands your assistant a path to walk.

Side by side

Ten rows, one difference.

It all follows from one thing: RAG keeps the text and drops the structure.

Vector / RAG
Graphify
Returns
Fuzzy top-k text chunks
Connected entities and relationships
Reasoning
The model guesses from snippets
Paths the model follows
Multi-hop
Breaks past one or two hops
Native multi-hop traversal
Freshness
Re-embed on every change
Update a node, the links stay
Explainability
An opaque similarity score
Every answer traces to a path
Trust
No provenance; you judge each answer
A confidence tag on every edge
Privacy
Chunks shipped to an embedding API
On-device, no telemetry
Cost at scale
Re-embedding and storage grow fast
Incremental; edges are cheap
Debugging
Guess why a chunk matched
Inspect the exact traversal
Setup
Chunk, embed, tune top-k
Connect a repo at app.graphify.com, or run it yourself
The argument

Why relationships beat similarity.

Retrieval-augmented generation, introduced by Lewis et al. (2020), splits your documents into chunks, embeds them, and pulls back the top-k most similar. It works for lookup, and it throws away the one thing an agent needs in order to reason: how things connect.

The multi-hop failure is architectural, not a tuning problem: a two-hop question needs the first answer before the second can be phrased, hard enough that benchmarks like HotpotQA (Yang et al., 2018) exist to measure it. A graph sidesteps it: each hop is one edge, so the third is as reliable as the first. GraphRAG (Edge et al., 2024) builds a knowledge graph over the corpus for exactly this reason. Graphify starts from the graph.

Freshness without re-embedding.

When a source changes, RAG re-embeds: every stale chunk goes back through a model. Graphify’s code edges come from a parser, so a changed file means re-parsing that one file and nothing else.

Answers you can trace.

Every result is a walk, so every answer traces to a path through your real code, and every relationship on it carries a tag: EXTRACTED, INFERRED or AMBIGUOUS. The difference between probably the right chunk and here is the exact route.

FAQ

Questions about the swap

What teams ask before retiring the pipeline.

  • For coding-assistant memory, usually. Teams that adopt Graphify tend to retire the chunk, embed and retrieve pipeline entirely. Keep a vector index for fuzzy search over large volumes of prose.

  • Yes. Some teams keep vectors for pure semantic recall and use Graphify for anything involving relationships.

  • It can store embeddings as one signal on a node. Retrieval itself is a traversal, so results come back connected and explainable.

  • Point it at the same sources you were embedding and it extracts the entities itself. Most of the work is deleting the old pipeline.

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