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d650360 320004c d650360 320004c d650360 320004c d650360 320004c d650360 320004c d650360 320004c d650360 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | #!/usr/bin/env python3
"""Migrate the prebuilt copernicus-rag-core Qdrant indexes into a Qdrant SERVER.
Downloads indexes/*.tar.gz from the HF dataset (or uses --source-dir if you
already have them), opens each embedded index locally, and streams every
collection into the target server: identical vectors (dense 768 + sparse BM25),
identical payloads (incl. the relinked publication<->dataset fields), identical
payload indexes. No embedding model or API key of any kind is needed.
Usage:
python load_all.py --url http://localhost:6333
python load_all.py --url http://localhost:6333 --collections publications
python load_all.py --url https://<cluster>.cloud.qdrant.io --api-key <key>
python load_all.py --url ... --source-dir ./indexes_untarred # skip download
Resumable: a collection already on the server with the full point count is
skipped; pass --recreate to force a clean re-copy.
"""
import argparse
import os
import sys
import tarfile
import tempfile
import time
from pathlib import Path
from qdrant_client import QdrantClient, models
REPO = "dmpantiu/copernicus-rag-core"
TARBALLS = {
"qdrant_marine_and_cards.tar.gz": ["marine_docs", "copernicus_docs"],
"qdrant_cds_docs.tar.gz": ["cds_docs"],
"qdrant_eqc_qa.tar.gz": ["eqc_qa"],
"qdrant_publications.tar.gz": ["publications"],
}
BATCH = 512
# The embedded (local-mode) indexes cannot persist payload indexes, so they are
# re-created here exactly as the original loaders defined them.
K, I, B = "keyword", "integer", "bool"
PAYLOAD_INDEXES = {
"marine_docs": {"product_id": K, "doc_type": K, "chunk_type": K, "section_path": K},
"copernicus_docs": {"product_id": K, "doc_type": K, "store": K},
"cds_docs": {"dataset_ids": K, "store": K, "doc_type": K, "doc_url": K},
"eqc_qa": {"dataset_id": K, "store": K, "doc_type": K, "aspect": K},
"publications": {"doi": K, "paper_id": K, "journal": K, "year": I,
"domains": K, "orphan": B, "linked_products": K, "chunk_type": K},
}
def log(msg):
print(f"[load_all] {msg}", flush=True)
def fetch_and_untar(work: Path, only: set[str] | None) -> dict[str, Path]:
"""Download needed tarballs from HF and untar. Returns {tarball: qdrant_db dir}."""
from huggingface_hub import hf_hub_download
token = os.environ.get("HF_TOKEN")
if not token:
sys.exit("HF_TOKEN env var required to download the private dataset "
"(or pre-download and use --source-dir).")
out = {}
for tb, colls in TARBALLS.items():
if only and not (set(colls) & only):
continue
dest = work / tb.replace(".tar.gz", "")
if (dest / "qdrant_db").exists():
log(f"{tb}: already untarred, reusing")
else:
log(f"downloading {tb} ...")
p = hf_hub_download(REPO, f"indexes/{tb}", repo_type="dataset",
token=token, local_dir=work / "_dl")
dest.mkdir(parents=True, exist_ok=True)
log(f"untarring {tb} ...")
with tarfile.open(p) as t:
t.extractall(dest)
out[tb] = dest / "qdrant_db"
return out
def source_dirs(src: Path, only: set[str] | None) -> dict[str, Path]:
"""Use pre-untarred dirs: <src>/<tarball-stem>/qdrant_db."""
out = {}
for tb, colls in TARBALLS.items():
if only and not (set(colls) & only):
continue
d = src / tb.replace(".tar.gz", "") / "qdrant_db"
if not d.exists():
sys.exit(f"missing {d} — untar indexes/{tb} there, "
f"or drop --source-dir to auto-download")
out[tb] = d
return out
def migrate_collection(local: QdrantClient, remote: QdrantClient,
coll: str, recreate: bool):
info = local.get_collection(coll)
total = local.count(coll).count
skip_first = 0
if remote.collection_exists(coll):
have = remote.count(coll).count
if have == total and not recreate:
log(f"{coll}: server already has {have}/{total} points — skip "
f"(--recreate to force)")
return
if recreate or have == 0 or have > total:
log(f"{coll}: server has {have}/{total} — recreating")
remote.delete_collection(coll)
else:
# Interrupted copy: local scroll order is deterministic and
# upserts are idempotent, so resume with an overlap margin.
skip_first = max(0, have - 8 * BATCH)
log(f"{coll}: server has {have}/{total} — resuming from "
f"~{skip_first} (with overlap)")
if not remote.collection_exists(coll):
remote.create_collection(
collection_name=coll,
vectors_config=info.config.params.vectors,
sparse_vectors_config=info.config.params.sparse_vectors,
on_disk_payload=True,
)
indexes = {f: s.data_type for f, s in (info.payload_schema or {}).items()}
if not indexes:
indexes = PAYLOAD_INDEXES.get(coll, {})
for field, schema in indexes.items():
remote.create_payload_index(collection_name=coll, field_name=field,
field_schema=schema)
log(f"{coll}: created (vectors={list(info.config.params.vectors)}, "
f"sparse={list(info.config.params.sparse_vectors or {})}, "
f"payload indexes={list(indexes)})")
done, seen, offset, t0 = 0, 0, None, time.time()
while True:
points, offset = local.scroll(coll, limit=BATCH, offset=offset,
with_payload=True, with_vectors=True)
if not points:
break
seen += len(points)
if seen > skip_first:
batch = points if seen - len(points) >= skip_first else \
points[-(seen - skip_first):]
remote.upsert(coll, wait=False, points=[
models.PointStruct(id=p.id, vector=p.vector, payload=p.payload)
for p in batch])
done += len(batch)
if seen % (BATCH * 40) == 0 or offset is None:
rate = done / max(time.time() - t0, 1e-9)
log(f"{coll}: {skip_first + done}/{total} ({rate:.0f} pts/s live)")
if offset is None:
break
time.sleep(2) # let async upserts settle before the count check
got = remote.count(coll).count
status = "OK" if got == total else "MISMATCH"
log(f"{coll}: {status} — server {got} / source {total}")
if got != total:
sys.exit(f"{coll}: point count mismatch, aborting")
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--url", required=True, help="Qdrant server URL, e.g. http://localhost:6333")
ap.add_argument("--api-key", default=None)
ap.add_argument("--source-dir", default=None,
help="dir with pre-untarred indexes (skips HF download)")
ap.add_argument("--collections", nargs="*", default=None,
help="subset, e.g. --collections publications eqc_qa")
ap.add_argument("--recreate", action="store_true")
a = ap.parse_args()
only = set(a.collections) if a.collections else None
remote = QdrantClient(url=a.url, api_key=a.api_key, timeout=120)
remote.get_collections() # fail fast if unreachable
if a.source_dir:
dirs = source_dirs(Path(a.source_dir), only)
else:
work = Path(tempfile.gettempdir()) / "copernicus_rag_indexes"
work.mkdir(parents=True, exist_ok=True)
log(f"workdir: {work}")
dirs = fetch_and_untar(work, only)
for tb, db_dir in dirs.items():
log(f"opening embedded index {db_dir}")
local = QdrantClient(path=str(db_dir))
try:
for coll in TARBALLS[tb]:
if only and coll not in only:
continue
migrate_collection(local, remote, coll, a.recreate)
finally:
local.close()
log("ALL DONE. Server collections:")
for c in remote.get_collections().collections:
log(f" {c.name}: {remote.count(c.name).count} points")
if __name__ == "__main__":
main()
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