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