What RAGthrows away.
RAG retrieves chunks that look similar. A graph keeps the relationships, and hands your assistant a path to walk.
Ten rows, one difference.
It all follows from one thing: RAG keeps the text and drops the structure.
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.
- Code knowledge graph vs RAG: why structure beats similarity: RAG pulls back text that resembles the question. A graph follows the relationships that answer it, and code is the worst case for similarity.
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.