File size: 1,900 Bytes
0a50834 | 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 | import json
from pathlib import Path
import pyarrow.parquet as pq
from ._state import fingerprint
from ._vectors import validate_vectors
from .config import PRETRAINED_DIMENSION, PRETRAINED_MODEL, TARGETS
def load_vectors(graph, targets, directory, batch_size, limit):
root = Path(directory)
manifest = json.loads((root / 'manifest.json').read_text())
source = fingerprint({'release': manifest['source_id'], 'model': PRETRAINED_MODEL, 'dimension': PRETRAINED_DIMENSION, 'storage': 'float16'})
counts = {}
for name in targets:
target = TARGETS[name]
graph.check_source(target, target.property, source)
paths = sorted((root / 'parquet').glob(target.files))
if not paths:
raise FileNotFoundError(target.files)
written = 0
for path in paths:
for batch in pq.ParquetFile(path).iter_batches(batch_size=batch_size, columns=[target.key, target.column]):
items = [row for row in batch.to_pylist() if row[target.column] is not None]
missing = graph.missing(target, [row[target.key] for row in items], target.property)
pending = [row for row in items if row[target.key] in missing]
if limit:
pending = pending[:limit - written]
if pending:
vectors = validate_vectors([row[target.column] for row in pending], PRETRAINED_DIMENSION)
graph.write(target, target.property, source, [{'key': row[target.key], 'vector': vector} for row, vector in zip(pending, vectors)])
written += len(pending)
if limit and written >= limit:
break
if limit and written >= limit:
break
counts[name] = written
print(json.dumps({'target': name, 'vectors_added': written}), flush=True)
return counts
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