End-to-end RAG pipeline for the pesticide-labels corpus. From the
4,066 labels on USB, the indexer produces 216,467 chunks, embeds
them via N parallel Ollama endpoints, upserts to Chroma, and builds
a BM25 lexical index.
## Files
- rag/index.py: adapted to labels schema (source / source_key /
epa_reg_no / product_name / product_class / registrant /
signal_word / active_ingredients flattened for Chroma where-filter);
honors PPLS_CORPUS_ROOT (corpus on USB) and PPLS_CHROMA_DIR;
upsert batch size auto-tuned to 64 * N URLs; --limit + --source
flags for incremental work.
- rag/chunk.py: label-aware. ALL-CAPS section heading detector
(heuristic) for EPA labels alongside markdown `#` headings.
TARGET_CHARS=2000 (~500 tokens), MAX_CHUNK_CHARS=4000 (~1000
tokens) hard cap with _force_split sentence/char fallback to
defend against monolithic crop+rate tables. Chunk 0 is a synthetic
anchor with product name, EPA Reg No, registrant, signal word,
product class, active ingredients + keyword bag for joint
dense/BM25 retrieval.
- rag/embeddings.py: parallel-dispatch across N Ollama URLs via
ThreadPoolExecutor. Each __call__ stride-slices input into N
shards, fires N concurrent HTTP requests, joins in original order.
Bisect-resilient on 400 (context-length): recursively splits the
failing shard down to single doc, logs+drops single bad doc with
zero-vector placeholder so Chroma upsert never sees a gap. Real
HTTP/connection errors still propagate.
- requirements.txt: chromadb already pinned via template.
## Run
PPLS_CORPUS_ROOT=/run/media/justin/USB/ppls-corpus \
OLLAMA_URL=http://host1:11434,http://host2:11434,... \
PRODUCT_NAME=ppls \
python -m rag.index --rebuild
## Build stats
- 216,467 chunks across 4,066 labels (~53 chunks/label avg)
- Wall time: 75.7 min on 4 parallel GPU-backed Ollama endpoints
(Bayer-Crop / BASF / Corteva / FMC / Nufarm / Syngenta / etc.
chemistry; production Ollama on trashpanda + 2× 192.168.0.2 +
1× Windows 192.168.0.125)
- 473 bisect-drops (0.22%) — all from monolithic-table sections
in 1970s-90s scanned PDFs whose pypdf extracts tokenized past
the model's context. Acceptable; the dropped chunks were
garbled OCR with no useful content.
- Chroma: 2.2 GB persistent SQLite at ./chroma/
- BM25: 416 MB SQLite FTS5 at ./bm25/ppls_docs.db
## Smoke-test queries (top-3 dense-only)
"what can I spray on soybeans to control waterhemp"
→ Rage (glyphosate+carfentrazone), Sencor (metribuzin)
"REI for dicamba on corn"
→ Nufarm Credit (DICAMBA tank-mix restrictions section)
"fungicide for wheat head scab"
→ MCW 710 SC (azoxystrobin+tebuconazole), Sercadis (fluxapyroxad)
Distances 0.16-0.23. Dense-only quality is OK-not-great in spots
(exactly the failure mode Phase 6 reranker + Phase 8 hybrid BM25
fusion address).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
justin
38141c362e