Repo/project rename to better reflect scope. PPLS is EPA's term for
their Pesticide Product Label System — accurate when the corpus was
EPA-only, narrow now that it also pulls from Bayer's own catalog
(and may expand to Syngenta/Corteva/BASF/FMC labels in the future).
crop-chem-docs scopes flexibly without acronyms to explain.
Renames:
- directory: ppls-docs → crop-chem-docs
- PRODUCT_NAME: ppls → crop_chem
- Chroma collection: ppls_docs → crop_chem_docs (in-place via .modify(), no re-embed)
- BM25 db: bm25/ppls_docs.db → bm25/crop_chem_docs.db
- MCP tool name: ppls_api_lessons → crop_chem_api_lessons
- FastMCP server name: ppls-docs → crop-chem-docs
- Env vars: PPLS_CORPUS_ROOT → CORPUS_ROOT
PPLS_CHROMA_DIR → CHROMA_DIR_OVERRIDE
- User-Agent: ppls-docs-scraper → crop-chem-docs-scraper
Preserved (intentional, correct):
- epa_ppls (source id) — refers specifically to EPA's PPLS database
- "EPA PPLS" mentions in regulatory text (lessons.md, server docstrings)
- PPLS_API_BASE / PPLS_PDF_BASE / PPLS_INDEX_URL_TEMPLATE in
scrape/sources/epa_ppls.py — these point at EPA's actual endpoints
Memory entries get updated in a follow-up commit so the rename is
isolated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
## Phase 7 — Eval harness
eval/retrievers.py + rag/retrieval.py: Retriever protocol with
DenseRetriever, BM25Retriever, HybridRetriever (RRF k=60),
RerankedRetriever (llama.cpp /v1/rerank). retrievers.py is now a
thin shim re-exporting from rag.retrieval so the MCP server can
use the same code at request time without making eval/ a runtime
dep.
eval/run_eval.py: drives N retrievers against eval/queries.jsonl,
computes MRR / Recall@K / nDCG@K, emits a markdown report with a
summary table + per-query breakdown for the first retriever. Each
query carries expected (source, source_key) tuples — matches the
labels-domain page-level keying.
eval/queries.jsonl: 35 curated queries — 25 brand-name (Warrant,
Huskie, Roundup Custom, Liberty, Authority, Headline, Trivapro,
Poncho, Lorsban, Sencor, Acuron, ...) + 10 intent/semantic
("what controls horseweed before soybean", "fungicide for fusarium
head blight", "rainfast interval for glyphosate", ...).
## Phase 8 — Hybrid retrieval (BM25 + dense + RRF)
docs_mcp/server.py: search_docs now branches on HYBRID_SEARCH env.
When on, _search_chunks runs both Chroma + BM25 (rag/bm25.py
existing impl), fuses on chunk_id with reciprocal-rank-fusion
(RRF k=60), and returns the combined pool. Dense-only path
unchanged when HYBRID_SEARCH is unset. The rendering layer
(_format_hit) is untouched.
The RERANK_URL hook is also wired (_rerank_pool sends docs to
llama.cpp /v1/rerank, truncated to 2000 chars per the jina-reranker
n_ctx_train=1024 batch-rejection gotcha). Fails open to base order
on any exception.
## Baseline numbers (k=5, pool=50, 35 queries)
| Retriever | MRR | Recall@5 | nDCG@5 |
|------------|-------|----------|--------|
| dense | 0.027 | 0.086 | 0.041 |
| bm25 | 0.544 | 0.586 | 0.524 |
| hybrid-rrf | 0.114 | 0.114 | 0.108 |
Headline: BM25 dominates because farmers search for products by
brand name, and brand names are exact-match tokens that lexical
search nails. Dense is poor — semantic embeddings spread across
similar products and don't preferentially weight brand-name tokens.
Textbook RRF hurts when one retriever is much weaker than the
other: dense's irrelevant top-50 pollute the fused pool with
ties at 1/(60+rank). Phase 6 reranker is the planned fix —
the reranker scores each (query, chunk) pair independently
and can recover the right answer regardless of base order.
Per-query report at eval/results/baseline.md.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
justin