Files
seed-mcp/rag/index.py
T
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

122 lines
3.8 KiB
Python

"""Build Chroma (and BM25) indexes from the seed corpus on disk.
Reads ``corpus/<source>/<source_key>.json`` sidecars, chunks each
variety via ``rag.chunk.chunks_from_variety``, upserts into Chroma.
With ``--rebuild``, drops + recreates the collection (clean state).
With ``--bm25-only``, skips Chroma and rebuilds only the FTS5 index
— useful for fast iteration when the chunker didn't change.
Collection name is ``<PRODUCT_NAME>_docs`` (default: ``crop_seed_docs``).
Override via the PRODUCT_NAME env var.
"""
from __future__ import annotations
import argparse
import logging
import os
import time
from pathlib import Path
from typing import Iterator
import chromadb
from chromadb.config import Settings
from .chunk import chunks_from_variety
from .embeddings import embedding_function
log = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(message)s")
ROOT = Path(__file__).resolve().parent.parent
CORPUS = ROOT / "corpus"
CHROMA_DIR = ROOT / "chroma"
PRODUCT_NAME = os.environ.get("PRODUCT_NAME", "crop_seed")
COLLECTION = f"{PRODUCT_NAME}_docs"
def variety_records() -> Iterator[dict]:
"""Walk ``corpus/<source>/<source_key>.json``, yield one chunk per
variety."""
if not CORPUS.exists():
log.error("corpus/ doesn't exist; run a scraper first")
return
for source_dir in sorted(CORPUS.iterdir()):
if not source_dir.is_dir() or source_dir.name.startswith("."):
continue
for sidecar_path in sorted(source_dir.glob("*.json")):
yield from chunks_from_variety(sidecar_path)
def upsert_to_chroma(records: list[dict]) -> int:
client = chromadb.PersistentClient(
path=str(CHROMA_DIR),
settings=Settings(anonymized_telemetry=False),
)
# Drop + recreate for --rebuild semantics.
try:
client.delete_collection(COLLECTION)
except Exception:
pass
col = client.create_collection(COLLECTION, embedding_function=embedding_function())
BATCH = 64
total = 0
for i in range(0, len(records), BATCH):
chunk = records[i:i + BATCH]
col.upsert(
ids=[r["id"] for r in chunk],
documents=[r["text"] for r in chunk],
metadatas=[r["metadata"] for r in chunk],
)
total += len(chunk)
log.info("upserted %d / %d chunks", total, len(records))
return total
def main() -> int:
p = argparse.ArgumentParser()
p.add_argument("--rebuild", action="store_true",
help="Drop and recreate the Chroma collection.")
p.add_argument("--bm25-only", action="store_true",
help="Rebuild only the BM25 index, skip Chroma.")
p.add_argument("--bm25-db", type=Path,
default=ROOT / "bm25" / f"{PRODUCT_NAME}_docs.db",
help="Path to the BM25 sqlite db.")
args = p.parse_args()
log.info("reading corpus from %s", CORPUS)
t0 = time.time()
records = list(variety_records())
log.info("loaded %d chunks in %.1fs", len(records), time.time() - t0)
if not records:
log.error("no chunks — is corpus/ populated?")
return 1
if args.bm25_only:
from .bm25 import BM25Index
log.info("--bm25-only: building FTS5 only")
BM25Index(args.bm25_db).build(records)
return 0
if not args.rebuild:
log.info("no --rebuild; nothing to do. (Use --rebuild to upsert.)")
return 0
t_c = time.time()
n = upsert_to_chroma(records)
log.info("chroma: %d chunks in %.1fs", n, time.time() - t_c)
try:
from .bm25 import BM25Index
t_b = time.time()
BM25Index(args.bm25_db).build(records)
log.info("bm25 done in %.1fs", time.time() - t_b)
except ImportError:
log.info("rag.bm25 not available — skipping BM25 build")
return 0
if __name__ == "__main__":
raise SystemExit(main())