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@ -1,3 +1,7 @@
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import traceback
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from itertools import batched
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from concurrent.futures import ThreadPoolExecutor
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from django.core.management import BaseCommand
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import clickhouse_connect
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from vacancies.main.vector_store import add_vectors, extract_features, qdrant_client
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@ -45,17 +49,24 @@ class Command(BaseCommand):
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exist_points_set = tuple(set(exist_points_ids))
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result_rows = clickhouse_client.query(query, parameters={"exist_points": exist_points_set}).result_rows
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result_rows_len = len(result_rows)
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for index, row in enumerate(result_rows):
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(id, chat_username, telegram_id, message, timestamp) = row
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batch_size = 10
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with ThreadPoolExecutor(max_workers=batch_size) as pool:
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pool.map(self._process_batch, batched(result_rows, batch_size))
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link = f"https://t.me/{chat_username}/{telegram_id}"
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print(f"Processing {index+1}/{result_rows_len} link: {link}")
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features = extract_features(message)
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def _process_batch(self, result_rows):
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try:
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for index, row in enumerate(result_rows):
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(id, chat_username, telegram_id, message, timestamp) = row
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add_vectors(
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"vacancies",
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id,
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features.model_dump(),
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{'content': message, 'features_json': features.model_dump(), "link": link, "timestamp": timestamp},
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)
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link = f"https://t.me/{chat_username}/{telegram_id}"
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print(f"Processing {index+1}/{len(result_rows)} link: {link}")
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features = extract_features(message)
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add_vectors(
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"vacancies",
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id,
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features.model_dump(),
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{'content': message, 'features_json': features.model_dump(), "link": link, "timestamp": timestamp},
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)
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except Exception as exc:
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traceback.print_exception(exc)
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