File size: 3,728 Bytes
0ec8fd6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | #!/usr/bin/env python3
"""
verify_pubs.py — sanity-check the `publications` Qdrant collection.
NO Gemini API calls (quota exhausted).
(a) point count + per-domain + orphan/linked/local counts
(b) BM25 (sparse) probes — top-5 (title, year, score)
(c) dense sanity via a STORED vector: fetch a chunk on a distinctive topic,
query dense neighbours, confirm same-topic chunks rank top.
"""
from pathlib import Path
from qdrant_client import QdrantClient, models
from fastembed import SparseTextEmbedding
ROOT = Path(__file__).resolve().parent
COLLECTION = "publications"
LOCAL_DB = ROOT / "qdrant_db"
PROBES = [
"ocean heat content trend",
"sea ice albedo feedback",
"ERA5 reanalysis evaluation",
"flood forecasting skill",
]
DOMAINS = ["ocean/marine", "atmosphere", "cryosphere", "land",
"climate-modeling", "climate-general", "emergency"]
_bm25 = SparseTextEmbedding(model_name="Qdrant/bm25")
def sparse_vec(text):
r = list(_bm25.query_embed(text))[0]
return models.SparseVector(indices=r.indices.tolist(), values=r.values.tolist())
def show(hits, n=5):
for h in hits[:n]:
p = h.payload
dom = ",".join(p.get("domains", []))
title = (p.get("title") or "")[:70]
print(f" [{h.score:.3f}] ({p.get('year')}) {title} <{p.get('chunk_type')}> [{dom}]")
def main():
client = QdrantClient(path=str(LOCAL_DB))
info = client.get_collection(COLLECTION)
print(f"collection '{COLLECTION}': {info.points_count:,} points\n")
def count(flt):
return client.count(collection_name=COLLECTION, exact=True, count_filter=flt).count
print("per-domain counts:")
for d in DOMAINS:
c = count(models.Filter(must=[models.FieldCondition(
key="domains", match=models.MatchValue(value=d))]))
print(f" {d:18s} {c:,}")
orphan = count(models.Filter(must=[models.FieldCondition(
key="orphan", match=models.MatchValue(value=True))]))
linked = count(models.Filter(must=[models.FieldCondition(
key="orphan", match=models.MatchValue(value=False))]))
local = count(models.Filter(must=[models.FieldCondition(
key="has_local_md", match=models.MatchValue(value=True))]))
print(f"\n orphan=true {orphan:,}")
print(f" orphan=false {linked:,} (registry-linked)")
print(f" has_local_md {local:,}\n")
print("=== (b) BM25 sparse probes (top-5) ===")
for q in PROBES:
print(f"\nQUERY: {q}")
hits = client.query_points(collection_name=COLLECTION, query=sparse_vec(q),
using="sparse", limit=5, with_payload=True).points
show(hits, 5)
print("\n=== (c) dense sanity (stored vector, no API) ===")
# pick a distinctive-topic chunk via BM25, use its stored dense vector as query
seed_hits = client.query_points(collection_name=COLLECTION,
query=sparse_vec("sea ice albedo feedback arctic"),
using="sparse", limit=1, with_payload=True).points
seed = seed_hits[0]
rec = client.retrieve(collection_name=COLLECTION, ids=[seed.id],
with_vectors=True, with_payload=True)[0]
dvec = rec.vector["dense"]
print(f"seed chunk: ({rec.payload.get('year')}) "
f"{(rec.payload.get('title') or '')[:70]} [{','.join(rec.payload.get('domains', []))}]")
print(f" seed text: {(rec.payload.get('text_raw') or '')[:110].replace(chr(10),' ')}")
nn = client.query_points(collection_name=COLLECTION, query=dvec, using="dense",
limit=6, with_payload=True).points
print(" dense nearest neighbours:")
show(nn, 6)
if __name__ == "__main__":
main()
|