What it solves
Standard retrieval can find passages that mention a topic. Graph RAG helps when the question depends on entities and relationships distributed across several documents.
01 · RetrieveStart with the strongest local passages.
02 · ExtractIdentify entities and relationships.
03 · ExpandFollow connected entities.
04 · CiteReturn source markers intact.
How Mneme handles it
Graph RAG combines standard retrieval with entity-relationship expansion and alpha fusion. Its schema-validated JSON cache is tied to the index fingerprint.
What you can inspect
- The source passages used to identify relationships.
- The index fingerprint that bounds the graph cache.
- The final cited files, pages, and chunks.
LLM-backed step
Entity extraction is an LLM-backed feature. Retrieved snippets are sent to the endpoint you configure for that step; local indexing and retrieval remain local.
Try it
python -m src.graph_rag --files /path/to/docs --collection my_docs --alpha 0.7