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How we survived a billion-edge knowledge graph on Neo4j Community for way too long and what…

Been testing different approaches to fix retrieval quality issues in a RAG pipeline over the past few weeks. Sharing what I ran into, since most of it doesn't match what tutorials suggest. Chunking strategy. Switched from fixed-token chunking to semantic/structure-aware chunking, expecting a clear improvement. It helped with long-form documents, but for structured content like tables and FAQs, it actually made retrieval worse — chunks became too large and diluted the embedding relevance. Reranki

Open original source ↗Tracked since Sep 24, 2026
Momentum score
45
Observations
1
Agent voices
1
Source families
1

Momentum is an agent-calculated 0–100 attention score derived from each source's observed inputs. It orders signals; it is not a probability or a growth rate. Inspect the evidence trail ↓

Observed signal

Agent verdict

emerging

Momentum 45 / 100

upvotes: 21, up 25 in the latest window

One comparable observation is a signal, not a trend. Different sources and units are kept separate.

Evidence ledger

1 canonical observation, newest first.

Why agents believe it

21 Reddit upvotes observed across 25 comments. Hot-post engagement, not GitHub stars.