Files
seed-mcp/eval/retrievers.py
T
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

201 lines
7.0 KiB
Python

"""Retriever protocol + concrete implementations for seed-mcp eval.
Each retriever returns a ranked list of chunk_ids. The eval harness
in ``run_eval.py`` measures each retriever against the golden
``queries.jsonl`` set across MRR / Recall@K / nDCG@K.
Four named configurations, matching the four switches in
``docs_mcp/server.py``:
dense — Chroma dense retrieval alone
bm25 — SQLite FTS5 BM25 alone
hybrid — dense + bm25 fused via RRF
hybrid_rerank — hybrid pool → cross-encoder rerank
Each retriever takes ``filters`` (the same dict shape
``_build_where`` accepts in server.py) so trial-specific facets
(data_type, state, year, crop) work consistently across the
four configurations.
"""
from __future__ import annotations
import os
import sys
from pathlib import Path
from typing import Protocol
# Add repo root so we can import docs_mcp and rag from here.
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
class Retriever(Protocol):
name: str
def retrieve(self, query: str, k: int, filters: dict | None) -> list[str]:
"""Return up to k chunk_ids in rank order."""
...
def _build_where(filters: dict | None) -> dict | None:
"""Mirror of docs_mcp.server._build_where but accepts the eval's
looser shape."""
if not filters:
return None
conds: list[dict] = []
if filters.get("data_type"):
conds.append({"data_type": filters["data_type"]})
if filters.get("crop"):
conds.append({"crop": filters["crop"].lower()})
if filters.get("brand"):
conds.append({"brand": filters["brand"].upper()})
if filters.get("state"):
s = filters["state"]
conds.append({"state": s.upper() if len(s) <= 3 else s})
if filters.get("year"):
conds.append({"year": int(filters["year"])})
if not conds:
return None
if len(conds) == 1:
return conds[0]
return {"$and": conds}
class DenseRetriever:
name = "dense"
def __init__(self, collection, pool: int = 50):
self.col = collection
self.pool = pool
def retrieve(self, query: str, k: int, filters: dict | None) -> list[str]:
where = _build_where(filters)
try:
r = self.col.query(
query_texts=[query], n_results=max(k, self.pool), where=where,
)
except Exception:
return []
return (r.get("ids") or [[]])[0][:k]
class BM25Retriever:
name = "bm25"
def __init__(self, bm25, pool: int = 50):
self.bm25 = bm25
self.pool = pool
def retrieve(self, query: str, k: int, filters: dict | None) -> list[str]:
where = _build_where(filters)
hits = self.bm25.query(query, n=max(k, self.pool), where=where)
return [cid for cid, _ in hits[:k]]
class HybridRetriever:
"""Dense + BM25 fused via RRF — same fusion the server uses."""
name = "hybrid"
def __init__(self, collection, bm25, pool: int = 50, rrf_k: int = 60):
self.col = collection
self.bm25 = bm25
self.pool = pool
self.rrf_k = rrf_k
def retrieve(self, query: str, k: int, filters: dict | None) -> list[str]:
where = _build_where(filters)
try:
d = self.col.query(query_texts=[query], n_results=self.pool, where=where)
dense_ids = (d.get("ids") or [[]])[0]
except Exception:
dense_ids = []
bm25_ids = [c for c, _ in self.bm25.query(query, n=self.pool, where=where)]
scores: dict[str, float] = {}
for ranking in (dense_ids, bm25_ids):
for rank, cid in enumerate(ranking):
scores[cid] = scores.get(cid, 0.0) + 1.0 / (self.rrf_k + rank + 1)
fused = sorted(scores, key=lambda d: scores[d], reverse=True)
return fused[:k]
class HybridRerankRetriever:
"""Hybrid pool → cross-encoder rerank via the llama.cpp endpoint."""
name = "hybrid+rerank"
def __init__(self, collection, bm25, rerank_url: str,
pool: int = 50, rerank_pool: int = 50,
rrf_k: int = 60, doc_max_chars: int = 2000,
timeout: float = 30.0):
self.col = collection
self.bm25 = bm25
self.rerank_url = rerank_url.rstrip("/")
self.pool = pool
self.rerank_pool = rerank_pool
self.rrf_k = rrf_k
self.doc_max_chars = doc_max_chars
self.timeout = timeout
def retrieve(self, query: str, k: int, filters: dict | None) -> list[str]:
where = _build_where(filters)
try:
d = self.col.query(
query_texts=[query], n_results=self.pool, where=where,
include=["documents"],
)
dense_ids = (d.get("ids") or [[]])[0]
dense_docs = (d.get("documents") or [[]])[0]
id_to_doc = dict(zip(dense_ids, dense_docs))
except Exception:
dense_ids = []
id_to_doc = {}
bm25_ids = [c for c, _ in self.bm25.query(query, n=self.pool, where=where)]
# Fuse to a hybrid pool
scores: dict[str, float] = {}
for ranking in (dense_ids, bm25_ids):
for rank, cid in enumerate(ranking):
scores[cid] = scores.get(cid, 0.0) + 1.0 / (self.rrf_k + rank + 1)
fused = sorted(scores, key=lambda d: scores[d], reverse=True)
# Fetch docs for any BM25-only ids in the rerank pool
missing = [cid for cid in fused[: self.rerank_pool] if cid not in id_to_doc]
if missing:
try:
extra = self.col.get(ids=missing, include=["documents"])
for cid, doc in zip(extra.get("ids") or [], extra.get("documents") or []):
id_to_doc[cid] = doc
except Exception:
pass
# Rerank
pool_ids = fused[: self.rerank_pool]
docs = [(id_to_doc.get(cid, "") or "")[: self.doc_max_chars] for cid in pool_ids]
try:
import httpx
with httpx.Client(timeout=self.timeout) as c:
r = c.post(
f"{self.rerank_url}/v1/rerank",
json={"model": "rerank", "query": query, "documents": docs},
)
r.raise_for_status()
results = r.json().get("results") or []
if not results:
return fused[:k]
ordered = sorted(results, key=lambda x: -x.get("relevance_score", float("-inf")))
reranked = [pool_ids[x["index"]] for x in ordered if 0 <= x.get("index", -1) < len(pool_ids)]
return reranked[:k]
except Exception:
return fused[:k]
def build_all_retrievers(collection, bm25, rerank_url: str | None = None) -> list[Retriever]:
"""Return the four named retrievers ready to evaluate."""
out: list[Retriever] = [
DenseRetriever(collection),
BM25Retriever(bm25),
HybridRetriever(collection, bm25),
]
if rerank_url:
out.append(HybridRerankRetriever(collection, bm25, rerank_url))
return out