[Question]:Cross-chunk relations & multi-hop retrieval design choices in LightRAG

[SangwookBaek]

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Hi LightRAG team 👋,

First, thanks for releasing such a clean and practical RAG framework—its dual-level retrieval has been working great for us!

While reviewing the indexing flow I noticed that

• entity / relation extraction is performed within each chunk only;
• during graph construction, entities deduplicated by D(·) become shared nodes, but no new edges are created between entities that never co-occur in the same chunk.

I’d love to understand the design rationale and its impact on deeper reasoning:

1. Why omit explicit cross-chunk edge creation?

   • Is the main goal to avoid graph explosion, reduce LLM calls, minimise hallucination risk, or something else?

2. Multi-hop coverage

   • In practice, does the current dual-level retrieval (low-level 1-hop + high-level keyword expansion) capture >2-hop relations reliably when the entities never appeared together?
   • Have you benchmarked long-range queries that depend on such paths (e.g. linking characters introduced in different chapters of a novel)?

3. Possible extensions

   • Would the project welcome an option to add lightweight cross-chunk edges, such as:

     • coreference-resolved “same entity” links
     • “same paragraph / same section” contextual edges
     • LLM-inferred relations between high-frequency entity pairs across chunks

   • Prompting an LLM on a vector‑based top‑k chunk set to infer cross‑chunk relations
     Any insights, papers, or code pointers would be greatly appreciated.
     Thanks again for the excellent work!

Best regards,
SangwookBaek

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[eric2323223]

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[Eco-nom]

Reducing the chunk size and increasing the overlap size may help, but it won't be enough on its own.