File size: 6,442 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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | #!/usr/bin/env python3
"""
load_pubs_qdrant.py — stream the ALREADY-EMBEDDED Copernicus publications corpus
(out/chunks_embedded.jsonl, 768-dim gemini-embedding-2-preview, L2-norm)
into a fresh embedded Qdrant at pubs_rag/qdrant_db, collection `publications`.
NO re-embedding: dense vectors are read straight from the archive.
Sparse BM25 (FastEmbed Qdrant/bm25, IDF modifier) is computed from text_raw
during load. Join metadata (canonical DOI, domains, has_local_md, registry
orphan/linked_products) comes from pubs_join.build_paper_index.
Payload: chunk_id, paper_id, doi (CANONICAL), title, journal, year, domains[],
section, chunk_type, text_raw (<=2500), orphan, linked_products[], has_local_md.
Indexes: doi, paper_id, journal, year, domains, orphan, linked_products,
chunk_type.
Single clean run (embedded Qdrant = single-process lock). ~20-60 min.
"""
import argparse
import json
import shutil
import time
import uuid
from pathlib import Path
from qdrant_client import QdrantClient, models
from fastembed import SparseTextEmbedding
import pubs_join
ROOT = Path(__file__).resolve().parent
OUT = ROOT / "out"
LOG = OUT / "load_archive.log"
COLLECTION = "publications"
DENSE_DIM = 768
LOCAL_DB = ROOT / "qdrant_db"
ARCHIVE = pubs_join.ARCHIVE
BATCH = 400
_bm25 = SparseTextEmbedding(model_name="Qdrant/bm25")
_logf = None
def log(msg: str):
line = f"[{time.strftime('%H:%M:%S')}] {msg}"
print(line, flush=True)
if _logf:
_logf.write(line + "\n")
_logf.flush()
def create_collection(client: QdrantClient):
names = [c.name for c in client.get_collections().collections]
if COLLECTION in names:
client.delete_collection(COLLECTION)
client.create_collection(
collection_name=COLLECTION,
vectors_config={"dense": models.VectorParams(size=DENSE_DIM, distance=models.Distance.COSINE)},
sparse_vectors_config={"sparse": models.SparseVectorParams(modifier=models.Modifier.IDF)},
)
for field, schema in [
("doi", models.PayloadSchemaType.KEYWORD),
("paper_id", models.PayloadSchemaType.KEYWORD),
("journal", models.PayloadSchemaType.KEYWORD),
("year", models.PayloadSchemaType.INTEGER),
("domains", models.PayloadSchemaType.KEYWORD),
("orphan", models.PayloadSchemaType.BOOL),
("linked_products", models.PayloadSchemaType.KEYWORD),
("chunk_type", models.PayloadSchemaType.KEYWORD),
]:
client.create_payload_index(collection_name=COLLECTION, field_name=field, field_schema=schema)
log(f"created '{COLLECTION}' (dense 768 cosine + sparse bm25, 8 payload indexes)")
def sparse_batch(texts):
return list(_bm25.embed(texts))
def flush(client, buf_pts, buf_txt):
sparses = sparse_batch(buf_txt)
for pt, sp in zip(buf_pts, sparses):
pt.vector["sparse"] = models.SparseVector(
indices=sp.indices.tolist(), values=sp.values.tolist())
client.upsert(collection_name=COLLECTION, points=buf_pts)
def load(client, index, limit=None):
buf_pts, buf_txt = [], []
total = skipped = 0
t0 = time.time()
with open(ARCHIVE, encoding="utf-8") as f:
for i, line in enumerate(f):
if limit and i >= limit:
break
c = json.loads(line)
emb = c.get("embedding")
if not emb:
skipped += 1
continue
pid = c["paper_id"]
m = index[pid]
raw = c.get("text_raw") or c.get("text_with_prefix", "")
year = c.get("year")
try:
year = int(year)
except (TypeError, ValueError):
year = None
section = c.get("section") or c.get("section_path") or c.get("section_name") or ""
pt = models.PointStruct(
id=str(uuid.uuid5(uuid.NAMESPACE_DNS, c["chunk_id"])),
vector={"dense": emb}, # sparse filled in flush()
payload={
"chunk_id": c["chunk_id"],
"paper_id": pid,
"doi": m["doi"],
"title": c.get("title", ""),
"journal": c.get("journal", ""),
"year": year,
"domains": m["domains"],
"section": section,
"chunk_type": c.get("chunk_type", "text"),
"text_raw": raw[:2500],
"orphan": m["orphan"],
"linked_products": m["linked_products"],
"has_local_md": m["has_local_md"],
},
)
buf_pts.append(pt)
buf_txt.append(raw)
if len(buf_pts) >= BATCH:
flush(client, buf_pts, buf_txt)
total += len(buf_pts)
buf_pts, buf_txt = [], []
if total % 5000 < BATCH:
rate = total / (time.time() - t0)
log(f" loaded {total:,} chunks {rate:.0f}/s "
f"eta {(430066 - total) / max(rate, 1) / 60:.1f} min")
if buf_pts:
flush(client, buf_pts, buf_txt)
total += len(buf_pts)
dur = time.time() - t0
pc = client.get_collection(COLLECTION).points_count
log(f"DONE: loaded {total:,} chunks (skipped {skipped}) in {dur/60:.1f} min "
f"({total/dur:.0f}/s); collection points_count={pc:,}")
return total, dur
def main():
global _logf
ap = argparse.ArgumentParser()
ap.add_argument("--limit", type=int, default=None)
ap.add_argument("--fresh", action="store_true", help="rm qdrant_db dir first")
a = ap.parse_args()
OUT.mkdir(exist_ok=True)
_logf = open(LOG, "a", encoding="utf-8")
log("=== load_pubs_qdrant start ===")
free_gb = shutil.disk_usage(str(ROOT)).free / 1e9
log(f"disk free: {free_gb:.1f} GB")
if free_gb < 4:
raise SystemExit("need ~4GB free")
log("building paper join index (one pass over archive headers)...")
index, stats = pubs_join.build_paper_index(log=log)
pubs_join.print_report(stats)
if a.fresh and LOCAL_DB.exists():
shutil.rmtree(LOCAL_DB)
log(f"removed {LOCAL_DB}")
client = QdrantClient(path=str(LOCAL_DB))
log(f"Qdrant: local {LOCAL_DB}")
create_collection(client)
total, dur = load(client, index, a.limit)
client.close()
log("client closed")
if __name__ == "__main__":
main()
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