File size: 3,996 Bytes
657d287 | 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 | """Create the 6 Qdrant collections (one per module × strategy).
Each collection:
- 5 named dense vectors (mxbai Matryoshka dims)
- 2 named sparse vectors (SPLADE, BM25)
- HNSW indexing on dense vectors (m=16, ef_construct=128)
- Payload indexes for common filters
Idempotent: if a collection already exists, skip it (use --recreate to drop+create).
"""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from qdrant_client.http import models as rest
from pipelines.shared.qdrant_client import (
DENSE_DIMENSIONS,
SPARSE_VECTOR_NAMES,
_dense_name,
all_collection_specs,
get_client,
)
# Payload fields we want indexed for fast filtered search.
# Most are keyword (string equality); a couple are integer for range filters.
PAYLOAD_INDEXES = [
("doc_id", "keyword"),
("doc_type", "keyword"),
("module", "keyword"),
("regulatory_body", "keyword"),
("jurisdiction", "keyword"),
("company_ticker", "keyword"),
("section_type", "keyword"),
("chunk_level", "keyword"),
("contains_table", "bool"),
("fiscal_year", "integer"),
("fiscal_quarter", "integer"),
]
def vector_config() -> dict:
"""Named dense vector config: 5 entries, all cosine, all HNSW."""
return {
_dense_name(dim): rest.VectorParams(
size=dim,
distance=rest.Distance.COSINE,
hnsw_config=rest.HnswConfigDiff(m=16, ef_construct=128),
)
for dim in DENSE_DIMENSIONS
}
def sparse_config() -> dict:
"""Named sparse vector config: SPLADE + BM25, both with default IDF modifier off
(we compute weights ourselves; let Qdrant just dot-product)."""
return {
name: rest.SparseVectorParams(
index=rest.SparseIndexParams(on_disk=False),
)
for name in SPARSE_VECTOR_NAMES
}
def ensure_collection(client, name: str, *, recreate: bool) -> str:
"""Create the collection if missing. Returns 'created' | 'skipped' | 'recreated'."""
exists = client.collection_exists(name)
if exists and not recreate:
return "skipped"
if exists and recreate:
client.delete_collection(name)
client.create_collection(
collection_name=name,
vectors_config=vector_config(),
sparse_vectors_config=sparse_config(),
)
# Payload indexes
for field_name, field_type in PAYLOAD_INDEXES:
client.create_payload_index(
collection_name=name,
field_name=field_name,
field_schema=field_type,
)
return "recreated" if exists else "created"
def main() -> int:
ap = argparse.ArgumentParser()
ap.add_argument("--recreate", action="store_true",
help="Drop and recreate collections that already exist (DESTRUCTIVE).")
args = ap.parse_args()
client = get_client()
specs = all_collection_specs()
print(f"Setting up {len(specs)} Qdrant collections "
f"({'recreate=True' if args.recreate else 'idempotent mode'})\n")
statuses: list[tuple[str, str, str]] = []
for module, strategy, name in specs:
status = ensure_collection(client, name, recreate=args.recreate)
statuses.append((name, module, strategy))
print(f" {status:>10s} {name} (module={module}, strategy={strategy})")
# Verify
print("\n=== Verification ===")
server_collections = {c.name for c in client.get_collections().collections}
for name, module, strategy in statuses:
if name not in server_collections:
print(f" ! MISSING: {name}")
return 1
info = client.get_collection(name)
print(f" {name}: vectors={list(info.config.params.vectors.keys())}, "
f"sparse={list(info.config.params.sparse_vectors.keys())}, "
f"points={info.points_count or 0}")
return 0
if __name__ == "__main__":
sys.exit(main())
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