79 lines
3.2 KiB
Python
79 lines
3.2 KiB
Python
from datetime import datetime, timedelta
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import clickhouse_connect
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from django.core.management import BaseCommand
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from qdrant_client.models import OrderBy
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from vacancies.conf.settings import CLICKHOUSE_HOST, CLICKHOUSE_PORT
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from vacancies.main.vector_store import add_vectors, extract_features, qdrant_client
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clickhouse_client = clickhouse_connect.create_client(host=CLICKHOUSE_HOST, port=CLICKHOUSE_PORT)
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query = """
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SELECT id, chat_username, telegram_id, message, timestamp
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FROM telegram_parser_chatmessage
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WHERE timestamp >= %(timestamp)s
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AND length(message) > 150
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AND arrayCount(x -> position(message, x) > 0, [
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'ваканси', 'ищем', 'требуется', 'разработчик', 'будет плюсом',
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'зарплат', 'оклад', 'з/п', 'руб', 'опыт',
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'требовани', 'обязанности', 'условия', 'офис',
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'удаленн', 'гибкий график', 'полный день', 'занятост',
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'резюме', 'собеседовани', 'junior', 'middle', 'senior', 'ждем', 'компани',
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'заниматься', 'формат', 'занятость', 'вилка', 'должност', 'контакт'
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]) >= 5
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AND arrayCount(x -> position(lower(message), x) > 0, [
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'о себе', 'обо мне', 'умею', '#ищу', '#резюме', 'университет', 'колледж'
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]) = 0
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AND id NOT IN %(exist_points)s
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"""
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class Command(BaseCommand):
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help = "Collect vacancies from telegram messages"
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def handle(self, *args, **options):
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next_page_offset = 0
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exist_points_ids = [-1]
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while next_page_offset is not None:
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response = qdrant_client.scroll(
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collection_name="vacancies",
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limit=100_000,
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offset=next_page_offset,
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with_payload=False,
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with_vectors=False,
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timeout=30,
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)
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exist_points_ids.extend([point.id for point in response[0]])
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next_page_offset = response[1]
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exist_points_set = tuple(set(exist_points_ids))
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response = qdrant_client.scroll(
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collection_name="vacancies",
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limit=1,
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order_by=OrderBy(
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key="timestamp",
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direction="desc",
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),
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)
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last_point_timestamp = datetime.now() - timedelta(days=30)
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if response:
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last_point_timestamp = response[0][0].payload["timestamp"]
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result_rows = clickhouse_client.query(
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query,
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parameters={"timestamp": last_point_timestamp, "exist_points": exist_points_set},
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).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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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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