Phase 6/7: wire rerank + eval harness — 100% pass on 21 golden queries
Phase 6 — Reranker integration
- New _rerank(query, [(cid, doc), ...]) helper in server.py calls
llama.cpp's /v1/rerank endpoint, returns reranker-ordered ids
or None on failure (graceful fallback — search never blocks
on the sidecar).
- search_docs + search_trials both call _rerank() on the post-
hybrid pool BEFORE truncating to k. The variety-code prefilter
still pins exact matches on top.
- Per-doc truncation to 2000 chars to fit jina-reranker-v2-base's
per-pair token budget. Full chunk text still returned to the
caller — truncation is rerank-input-only.
- Telemetry adds `reranked: true|false` so usage logs distinguish
reranked calls.
Phase 7 — Eval harness
- eval/queries.jsonl: 21 golden queries spanning:
* variety-code lookups (DKC62-08RIB, AG29XF4, WB6430, E085Z5,
AP Iliad)
* semantic variety queries (drought-tolerant corn, SCN MG-3
soy, Rps3a, XtendFlex, HRS stripe rust, SWW PNW, Goss's Wilt)
* trial queries (IA/IN/MN regional, AP Iliad ID, NK1701 head-
to-head, silage Ton/Acre, product=DKC65-95)
* anti-hallucination (Pioneer P1142 fallback, DKC65-20 not-in-
corpus expected_empty)
- eval/retrievers.py: 4 named retrievers — dense, bm25, hybrid
(dense+bm25+RRF), hybrid+rerank — all sharing the same filter
shape as docs_mcp/server.py._build_where.
- eval/run_eval.py: runs each retriever against each query,
reports Recall / Precision@1 / MRR / avg latency. Markdown
output in eval/results/baseline.md.
Baseline results (k=5, 21 queries):
| Retriever | Pass | Recall | P@1 | MRR | Avg ms |
|-----------------|-------|--------|-------|-------|--------|
| hybrid+rerank | 21/21 | 100% | 90% | 0.905 | 2064 |
| bm25 | 20/21 | 95% | 81% | 0.833 | 5 |
| hybrid | 15/21 | 71% | 62% | 0.619 | 73 |
| dense | 14/21 | 67% | 38% | 0.440 | 79 |
Key findings:
1. hybrid+rerank wins on quality — 100% pass, 90% P@1.
2. BM25 alone is surprisingly competitive (95% pass) at 5 ms —
excellent fallback when rerank is down. The variety-code
prefilter in search_docs is doing a lot of work here.
3. Dense embedding alone is the WEAKEST configuration on this
corpus — variety identity tokens (DKC62-08RIB, AP Iliad,
Rps3a) have no semantic neighbors, so nomic-embed-text returns
noise. The hybrid (no rerank) layer actively hurts because
RRF dilutes the BM25 ranking with dense noise.
4. Anti-hallucination queries (Pioneer fallback, DKC65-20 not-
in-corpus) pass on ALL retrievers including dense-only —
the must_not_contain + expected_empty design holds.
Deploy decision: HYBRID_SEARCH=true + RERANK_URL set
(production env already has both — refresh.yml + image-only.yml
+ deploy/docker-compose.yml all configured).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
justin
bd71f30ca7