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# VerifyEmbeddings.py
import os
import glob
import pandas as pd
import numpy as np
from tqdm import tqdm
EMBEDDING_KEY = "text_sentences_sonar_emb"
EXPECTED_DIM = 1024
def verify_embedding_array(arr, expected_dim=EXPECTED_DIM):
try:
arr = np.stack([np.array(vec, dtype=np.float32) for vec in arr])
if arr.ndim != 2:
return False, f"Wrong ndim: {arr.ndim}"
if arr.shape[1] != expected_dim:
return False, f"Wrong dim: {arr.shape[1]}"
return True, None
except Exception as e:
return False, str(e)
def scan_parquet_dir(directory):
all_files = sorted(glob.glob(os.path.join(directory, "**", "*.parquet"), recursive=True))
total_checked = 0
total_failed = 0
file_failures = {}
for file in tqdm(all_files, desc="Scanning Parquet Files"):
try:
df = pd.read_parquet(file, columns=[EMBEDDING_KEY])
except Exception as e:
print(f"❌ Failed to load {file}: {e}")
continue
for i, row in enumerate(df[EMBEDDING_KEY]):
total_checked += 1
ok, reason = verify_embedding_array(row)
if not ok:
total_failed += 1
file_failures.setdefault(file, []).append((i, reason))
print("\n=== Scan Report ===")
print(f"Files scanned: {len(all_files)}")
print(f"Rows checked: {total_checked}")
print(f"Broken rows: {total_failed}")
if total_failed:
print("\n🚨 Failures by file:")
for file, failures in file_failures.items():
print(f"{file}: {len(failures)} failures")
for idx, reason in failures[:5]: # only print first 5 per file
print(f" - Row {idx}: {reason}")
else:
print("✅ All embeddings look valid!")
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--data-dir", type=str, required=True, help="Path to directory with parquet files")
args = parser.parse_args()
scan_parquet_dir(args.data_dir)
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