Keeping GraphRAG Fresh: Incremental Knowledge Graph Indexing Without Full Rebuilds

Keeping a GraphRAG knowledge graph current requires incremental indexing rather than costly full rebuilds, which become slow, expensive, disruptive, and potentially unreliable as document collections grow. While append-only approaches are simple, they leave outdated entities and relationships behind after source edits or deletions; true incremental pipelines instead detect changes through document hashes or event-driven change-data-capture, then apply targeted upserts and retractions. Effective implementations depend on provenance linking every node and edge to its source documents, stable canonical identifiers that merge entity aliases, and idempotent writes using operations such as MERGE to prevent duplicates during retries. For edited documents, diff-based reconciliation can preserve unchanged graph elements, while deletion logic must remove only data exclusively supported by the deleted source so shared entities remain intact. The FalkorDB and CocoIndex example illustrates how memoized per-file extraction, source-state tracking, and declarative graph reconciliation can automate these updates, allowing changed files to be reprocessed while unchanged graph data is retained.

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