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ae06a295de
There was one cutoff, Qdrant's score_threshold at 0.3, and nomic scores unrelated text well above it, so every question retrieved *something* (nonEmptyRateOnRefusal 1.0) and rag_context wasted four lines asking a 3B model to "silently judge" relevance. That is a classification a number should decide, not a micro model. Adds `RAG_MIN_FINAL_SCORE`, a floor on the post-rerank score, applied before source diversity. The floor judges relevance, the 0.85^n penalty judges redundancy. Nothing clears it and nothing is injected. `score_threshold` stays at 0.3 as the candidate net. Exposed as `rag.minRelevance` with a module- scoped TTL cache invalidated from `updateSetting`. The floor is calibrated and the harness was describing the wrong number: aggregate() bucketed semanticScore, so its percentiles measured raw cosine while the new knob filters the reranked score. Both axes now, and the gap matters: recall falls at 0.60 on the semantic axis, 0.66 on the final one. floor recall@5 precision@5 emptyOnAnswerable nonEmptyOnRefusal 0.00 0.991 0.221 0% 100% 0.62 0.991 0.524 0% 60% 0.66 0.991 0.695 0% 60% 0.67 0.972 0.704 1% 60% Default 0.62, not the last-lossless 0.66: the refusal win is complete at 0.62 and 28 documents is a thin basis for spending margin. 0.60 is the honest floor. Three of five refusal goldens are near-corpus by construction, where declining is the generation tier's job. Both eval tiers pin the floor to the constant and ignore the setting, for the same reason the harness never sets skipRetrieval. 4 metrics improved against baseline, no regressions. |
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| .. | ||
| corpus_source.ts | ||
| generation_metrics.ts | ||
| golden_set.ts | ||
| quiet.ts | ||
| report.ts | ||
| retrieval_metrics.ts | ||