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0bac06b7b61e2aa61006ae0394caadfdfa6cb214
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Author SHA1 Message Date
claude 0bac06b7b6 Add RobSeeCo (Rob-See-Co + Innotech): 130 corn/soy varieties from the seed-guide PDF (#18)
Image rebuild (skip scrape) / build (push) Successful in 4m48s
Details 
Co-authored-by: claude <claude@jpaul.io>
Co-committed-by: claude <claude@jpaul.io>
 2026-06-09 23:29:38 -04:00
claude 84ad2b1de6 Add 4 independent seed brands: Latham + Stine + 1st Choice + Burrus (+623 varieties) (#17)
Image rebuild (skip scrape) / build (push) Successful in 4m44s
Details 
Co-authored-by: claude <claude@jpaul.io>
Co-committed-by: claude <claude@jpaul.io>
 2026-06-04 21:58:07 -04:00
claude 22e8092faf Add ProHarvest Seeds: 119 varieties + 161 cross-vendor plot reports (#16)
Image rebuild (skip scrape) / build (push) Successful in 5m46s
Details 
Co-authored-by: claude <claude@jpaul.io>
Co-committed-by: claude <claude@jpaul.io>
 2026-06-04 21:05:30 -04:00
justin bd71f30ca7 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>
 2026-05-25 17:02:57 -04:00
justin 84b49d8360 trial data: workflow scrape steps + lessons.md trial-data guide 
.gitea/workflows/refresh.yml — add scrape steps for the new trial
sources (agripro_trials, gh_plot_reports) so the monthly cron
refreshes them alongside the variety sources. gh_plot_reports
is the heaviest single source (~4,600 docs @ 1 req/sec ≈ 70 min);
runs late so an earlier failure doesn't waste time before failing.
Commit-message variable count expanded to surface the trial counts.

docs_mcp/lessons.md — new "trial-data" section telling the agent:

- The two surfaces (search_docs = identity, search_trials = perf)
  are complementary; how to route a farmer question to each.
- What's indexed (GH plot reports cross-vendor, AgriPro regional
  PDFs) vs what's not (Bayer per-variety trials, NK yield results,
  Pioneer, university extension trials).
- Recommended workflow: search_trials → identify top performers →
  lookup_variety on each to verify identity → don't fabricate.
- How to read a GH plot report (per-column headers vary by crop:
  corn/soy use Yield/MST/Test Weight, silage uses Ton/Acre +
  Milk + Beef columns).
- Single-data-point caveat: one plot is one cooperator's field;
  look across multiple plots for a robust recommendation.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-25 15:22:08 -04:00
justin c737871c4c Trial-data scrapers: gh_plot_reports + agripro_trials + search_trials tool 
This PR introduces TRIAL data — yield-performance results from real
field trials — as a SEPARATE data type alongside variety identity.
The two are complementary:

  search_docs  → "What's the disease resistance of DKC62-08RIB?"
                  (variety identity — what it IS)
  search_trials → "Which corn hybrid won the IA 2024 trials?"
                  (performance data — how it PERFORMED)

scrape/sources/gh_plot_reports.py — Golden Harvest plot reports
- 4,618 expected (2024+2025; 2023 deferred to a backfill pass).
- URL: /<crop>/plot-report/<state>/<year>/<plot_id>
- Cross-vendor: each plot lists products from multiple brands
  (NK / DEKALB / Golden Harvest / Enogen / Pioneer / Channel) side
  by side at one cooperator's field — the kind of independent
  comparison data Bayer doesn't publish itself.
- Generic per-column metrics dict (Yield/MST/Test Weight/$/Ac for
  corn+soy, Ton/Acre + Milk + Beef columns for silage).
- Politeness: 1 req/sec, retries on 429/5xx, no redirect-follow.

scrape/sources/agripro_trials.py — AgriPro regional trial PDFs
- 14 unique PDFs (38 sitemap links deduped) at /trials-data
- pdfplumber text extraction, region/year detection from filename
- Verbatim PDF text preserved in chunk body so variety + yield
  number adjacency drives retrieval (AP Iliad's Aberdeen ID yield
  matches a query about "AP Iliad Idaho yield")

rag/chunk.py — chunks_from_trial() dispatching by source
- Plot reports: identity preamble + Top-5 by primary metric + full
  ranking table. Metric labels chosen from the data (corn/soy use
  "Yield", silage uses "Ton/Acre").
- AgriPro PDFs: identity preamble + verbatim trial body inline so
  per-location yields surface for region+variety queries.
- Variety chunks get data_type="variety" metadata; trial chunks get
  data_type="trial". Single Chroma collection; the tool router
  filters by data_type rather than maintaining two collections.

rag/index.py — dispatch by sidecar's data_type field
rag/bm25.py — new filter columns (data_type, year, state)

docs_mcp/server.py — sixth MCP tool: search_trials(crop?, state?,
year?, product?, k=10)
- Filters trial chunks via where={"data_type": "trial", ...}
- Optional product substring post-filter for "DKC62-08RIB Iowa 2024"
  style searches
- search_docs now defaults to data_type="variety" so trial chunks
  don't bleed into variety identity queries
- Tool docstring routes the agent: "use lookup_variety to verify
  identity details on any trial winner you surface"

NK trial endpoint (/NKSeeds/wsProxy.asmx/GetPlotResult) is documented
as deferred — the ASMX-SOAP shape returned empty XML on initial
probe. Bayer per-variety yield data is not publicly indexed at all
— documented in the trial-scope note (DEKALB/Asgrow trial data flows
through Channel reps, not the web). AgRevival research books exist
as 10 large annual PDFs but are deferred (low ROI per parse).

Initial corpus shipped in this PR: 14 AgriPro trial PDFs. The 4,618
Golden Harvest plot reports are scraping in background and will be
added in a follow-up corpus-snapshot PR (~70 min ETA).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-25 15:19:03 -04:00
justin 9ce920f622 agripro + nk scrapers — 146 Syngenta varieties added (wheat + corn/soy) 
agripro (24 varieties)
- Drupal Views form scrape via /search-agripro-brand-varieties with
  explicit GET params (sidesteps the AJAX-only-on-load default that
  returns an empty form skeleton).
- Per-variety parse: <h1>, .field--node--variety-type--variety,
  .field--node--tag-line--variety, .field--node--body, plus the
  three rated sections (Agronomics / Grain / Disease) with their
  <div class="row"><div class="label">label</div><div>value</div>
  pairs.
- Wheat-class distribution: 12 HRS, 7 SWW, 3 HRW, 1 HWS, 1 Barley
  — provides the Northern Plains HRS coverage WestBred lacks.

nk (122 varieties — recon's "29" was outdated; the current NK seed
finder lists 41 corn + 81 soy)
- ASP.NET WebForms endpoint:
  POST /NKSeeds/{Corn,Soy}ProductFinder.aspx/GetProducts returns
  {"d": "<html>"} where the inner HTML is one <div class="sf-result">
  per variety. BeautifulSoup tokenizes the whole blob.
- Per-card: product code (NK8005, NK008-P8XF), RM/MG from the
  title <span>, "Brands Available" trait variants, marketing
  positioning + bullet strengths, tech-sheet PDF URL.
- pdfplumber text extraction on the tech-sheet PDFs adds:
  * corn disease ratings (Gray Leaf Spot, NCLB, Goss's Wilt,
    Anthracnose, Tar Spot, Fusarium, etc.) where the PDF prints
    "Label N" lines (text-extractable)
  * soybean Phytophthora source genes (Rps1c, Rps3a, ...)
  * soybean SCN race coverage
  * soybean agronomic ratings (Emergence, Standability, Shatter
    Tolerance, Green Stem) with text-extractable 1-9 values
  * soybean soil-type adaptation (Best/Good/Fair/Poor) for drought
    prone / high pH / poorly drained / etc.
- Agronomic rating BARS for corn (Emergence, Stalk Strength,
  Drought) are not text-extractable; we record the labels with an
  explicit "rated in PDF chart, see tech sheet" value so the agent
  can direct the farmer at the source for those numbers.

Scale-direction correction in lessons.md:
- NK and AgriPro both use 1 = best, lower = more resistant — the
  REVERSED convention vs Bayer / Golden Harvest. NK's tech-sheet
  footer literally prints "1-9 Scale: 1 = Best, 9 = Worst".
  AgriPro positioning on stripe-rust-resistant varieties (AP Iliad
  with Stripe Rust 1, Eyespot 2) confirms the same direction.
- sources-not-yet-indexed section trimmed to just Beck's PFR +
  Beck's products — everything else IS now in the corpus.

Cross-vendor coverage after this PR: 760 varieties.
  bayer_seeds     475 (DEKALB 288 / Asgrow 102 / WestBred 85)
  golden_harvest  139
  nk              122  (41 corn / 81 soy)
  agripro          24  (12 HRS / 7 SWW / 3 HRW / 1 HWS / 1 Barley)
Vendors: Bayer, Syngenta. Brands: 6. Crops: corn, soy, wheat (109
wheat now, up from 85).

requirements.txt: pdfplumber>=0.11 for NK tech-sheet parsing.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-25 14:16:36 -04:00
justin 4009dc0b78 Phase 11: crop_seed_api_lessons tool + Pioneer fallback 
Add the fifth MCP tool — crop_seed_api_lessons(topic?) — backed by
docs_mcp/lessons.md, the ONLY source of opinionated content in the
server. Everything else (search_docs, get_page, lookup_variety)
returns verbatim from vendor catalogs; lessons.md fills the gaps
the corpus can't cover.

The Pioneer fallback is the critical anti-hallucination piece:
Pioneer's ToS bans automation, so the corpus has no Pioneer data.
Without this tool, an agent might surface Bayer/Asgrow chunks as
mediocre matches for a Pioneer query. The tool's docstring tells
the agent to call it on any Pioneer / P-series question; the
'pioneer' section says clearly:

  "I don't have Pioneer's variety data indexed... please consult
  Pioneer or an extension service."

  "Do NOT invent Pioneer hybrid ratings."

Other lesson sections cover knowledge the agent needs to interpret
search_docs / get_page output correctly:

- rating-scales: Bayer 1-9, Golden Harvest 9-to-1, what
  R/MR/S/Rps1c/R3 mean in soybean disease columns
- maturity-semantics: corn RM days vs soybean MG vs wheat class +
  qualitative early/medium/late
- trait-glossary: SSRIB, VT2PRIB, XF, E3, Conkesta, Clearfield, etc.
- scn-resistance: race coverage + Peking vs PI 88788 source
- regional-listings: how to interpret Bayer's "local profiles"
- sources-not-yet-indexed: which vendors aren't in the corpus yet
- checking-your-work: always call lookup_variety before quoting

Lesson lookup prefers slug-match (returns just `rating-scales` for
topic="rating", not every section that mentions ratings); falls
back to body-match only when no slug matches.

Smoke-tested with topic=pioneer, topic=rating, topic=trait,
topic=zzzzzz (no match), and topic=None (full index = 10K chars,
8 sections).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-25 13:18:57 -04:00
justin a766756a05 Phase 2/3: chunker + indexer + MCP server tools 
Phase 2 — Chunking and indexing
- rag/chunk.py: replace template chunker with seed-variety-specific
  chunks_from_variety(). One chunk per variety (varieties are small
  and named-rating retrieval signal is best kept together). Output
  is rebuilt deterministically from the sidecar JSON: every value is
  verbatim from the source, only framing language ("Disease ratings
  (1-9, 9=best):") is template glue. Anti-hallucination contract:
  same sidecar in → same chunk out, never a fabricated rating.
  Metadata flattened to Chroma-safe primitives (str/int/float/bool):
  source, source_key, vendor, brand, crop, product_name,
  product_id, source_url, rm (corn), mg (soy), wheat_class,
  release_year, trait_codes_csv, rating_scale.
- rag/index.py: walks corpus/<source>/<source_key>.json sidecars
  via the new chunker. Default PRODUCT_NAME=crop_seed so the
  Chroma collection is crop_seed_docs.
- rag/bm25.py: filterable columns updated to seed-domain facets
  (source/vendor/brand/crop/source_key) instead of the template's
  version/platform/product.

Phase 3 — MCP server tools wired up
- search_docs: hybrid dense (Chroma) + BM25 (FTS5) retrieval with
  RRF fusion. Optional filters: crop, brand, vendor, source.
  Variety-code prefilter pins exact source_key / product_name /
  hybrid_prefix matches at the top — dense embeddings have no
  semantic neighbor for tokens like "DKC62-08RIB" and RRF can let
  noise float to #1 without this pin. Each response carries the
  variety's source URL inline so the agent can cite.
- get_page(source, source_key): emits a structured ratings header
  (verbatim from sidecar, table per characteristics group, vendor
  positioning, regional listings) followed by the raw indexed body.
  This is the canonical fact-check surface.
- list_versions(): facet discovery — distinct sources, vendors,
  brands, crops across the corpus.
- lookup_variety(source_key, source?): returns the raw sidecar JSON
  for one variety. The agent should call this BEFORE quoting any
  specific rating value to a farmer — guaranteed verbatim.

Smoke tests against 475 indexed Bayer varieties:
- list_versions returns 475 varieties, 1 source, 1 vendor, 3 brands,
  3 crops with correct per-brand counts (288/102/85).
- Semantic ag queries find the right candidates: short-season
  drought-tolerant corn → DKC44-97RIB at RM 94 (in 90-95 band);
  SCN+MG3 soybean → Asgrow XF varieties with explicit SCN R3 ratings;
  Phytophthora Rps3a soy → AG07XF4 (right gene); stripe-rust
  wheat → WestBred WB1376CLP (Yellow Rust 2 = best).
- Variety-code lookups work via prefilter: DKC62-08RIB, AG29XF4,
  WB6430 all return as #1 hit. BM25 confirms ranking unambiguously
  (top-1 score -13.2 vs -8.5 for #2 on "DKC62-08RIB ratings").
- Server boots cleanly in stdio AND streamable-http modes.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-25 13:14:16 -04:00
justin ac40e05734 seed-mcp scaffold: clone docs-mcp-template, customize for crop_seed PRODUCT_NAME
Image rebuild (skip scrape) / build (push) Failing after 7s
Details 
Sibling project to crop-chem-docs, same MCP-template lineage. Corpus is
seed/hybrid varieties across 6 vendors instead of pesticide labels.

What's customized vs. the template:
- CLAUDE.md: vendor matrix, build priority, Pioneer fallback policy,
  canonical sidecar schema (per-crop), Golden Harvest disease-scale
  reversal gotcha, no-IPv6 / HTTPS-clone note
- README.md: vendor coverage table, tool list, phase status
- Dockerfile: PRODUCT_NAME=crop_seed default, sources.json (not
  bundles.json), HYBRID_SEARCH=true, OLLAMA_URL + RERANK_URL Docker
  DNS defaults (same llama-rerank sidecar as crop-chem-docs)
- .gitea/workflows/refresh.yml: monthly cron (seed catalogs move
  slowly), 5 GREEN scraper steps, corpus-YYYY.MM.DD tag for Drawbar
  pinning, continue-on-error on GC step
- .gitea/workflows/image-only.yml: paths filter + cancel-in-progress
  concurrency group
- scripts/registry_gc.py: lifted from crop-chem-docs (correct Gitea
  packages API URL + UA header to bypass CF block on default
  Python-urllib UA)
- sources.json: catalog of 6 sources + scope_filter + per-source
  schema notes + Pioneer-exclusion rationale
- scrape/runner.py: dispatcher with --all = GREEN-only
- scrape/sources/{bayer_seeds,golden_harvest,nk,agripro,becks_pfr,
  becks_products}.py: stub modules with implementation notes
- docs_mcp/server.py: PRODUCT_NAME default → crop_seed,
  PRODUCT_DOCS_URL → repo URL

Pioneer is intentionally NOT a source. ToS bans automation; dealer
locator is login-gated. The MCP returns a curated fallback lesson
directing the user to pioneer.com.

Next phases:
- Phase 1: implement bayer_seeds (lift-and-shift from crop-chem-docs
  Bayer scraper; same __NEXT_DATA__ infra)
- Phase 7: curate eval/queries.jsonl
- Phase 11: lessons.md with Pioneer fallback + disease-scale notes

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
 2026-05-25 12:28:49 -04:00