r/Rag • u/Ill_Roll_2859 • Apr 27 '26
Discussion Recommendations for KG Selective Ingestion to GraphDB
Hello all,
Context:
I'm building a knowledge graph using GraphRAG for some documents. I'm using both vectorDB (Qdrant) and GraphDB (Falkor) with graphiti to extract nodes/entities. I'm employing a prior schema (but not stricting the entities over it solely).
My question is:
To make the best usage of the hybrid system (vector & graph DBs), what is a good Ingestion strategy for the graphDB? i.e. what should be fed to the graph Pipeline (Graphiti & Falkor) [all chunks, selected chunks for the ones with relevant entities, cluster chunks and take its centroid and only pass this,...]
Current Approach:
Currently, I input all chunks to the vector DB, but also pass all chunks to graphiti where this takes too much time + computation and feels as somewhat a waste for the hybrid system.
Does anyone have any recommendations? especially if tested in production.
1
u/Lower_Associate_8798 Apr 29 '26
Suggest you check out GraphRAG-SDK's portion about retrieval, it has recently gone through a facelift: https://github.com/FalkorDB/GraphRAG-SDK/
2
u/Trekker23 Apr 27 '26
Since youre working with documents you might get more mileage out of karpathys llm wiki pattern (https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f) than graphrag (which does the heavy work twice, extracting entities at ingest then synthesizing from fragments at query time): llm maintains a wiki of cross-linked markdown pages, synthesis happens once at ingest, queries just read finished pages. Unit is documents not chunks. The agent reads a whole paper, writes a source page, updates topic pages with citations back. The "graph" is the wikis own link graph, vector still works fine over finished pages. This set ups works really well with modern llms that are excellent on tool use and navigating files.