72 lines
3.3 KiB
Python
72 lines
3.3 KiB
Python
from itertools import batched
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from datetime import timedelta
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from django.utils import timezone
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from pydantic import BaseModel
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from typing import Literal
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from vacancies.main.models import Vacancy, JobTitle
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from langchain_openai import ChatOpenAI
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import clickhouse_connect
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from django.core.management import BaseCommand
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from django.conf import settings
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query = """
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SELECT DISTINCT ON (message) 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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ORDER BY timestamp ASC
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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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job_titles = JobTitle.objects.values_list('title', flat=True)
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job_title_map = dict(JobTitle.objects.values_list('title', 'id'))
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class Structure(BaseModel):
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job_title: Literal[tuple(job_titles)]
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min_salary_rub: int | None
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max_salary_rub: int | None
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openai_client = ChatOpenAI(model_name="gpt-5-mini", temperature=0, seed=42, top_p=1)
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structured_llm = openai_client.with_structured_output(Structure)
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last_timestamp = timezone.now() - timedelta(days=30)
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if last_vacancy := Vacancy.objects.order_by("-timestamp").first():
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last_timestamp = last_vacancy.timestamp
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clickhouse_client = clickhouse_connect.create_client(host=settings.CLICKHOUSE_HOST, port=settings.CLICKHOUSE_PORT)
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result_rows = clickhouse_client.query(query, parameters={"timestamp": last_timestamp}).result_rows
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for index, rows in enumerate(batched(result_rows, settings.COLLECT_VACANCIES_BATCH_SIZE)):
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prompts = [f"Extract fields from vacancy. Carefully choice job title.\nVacancy: {row[3]}" for row in rows]
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responses = structured_llm.batch(prompts)
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vacancies = []
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for row, response in zip(rows, responses):
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print(response)
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(id, chat_username, telegram_id, message, timestamp) = row
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vacancies.append(Vacancy(
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external_id=id,
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job_title_id=job_title_map[response.job_title],
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min_salary_rub=response.min_salary_rub,
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max_salary_rub=response.max_salary_rub,
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content=message,
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timestamp=timestamp,
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link=f"https://t.me/{chat_username}/{telegram_id}",
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))
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print(Vacancy.objects.bulk_create(vacancies, ignore_conflicts=True))
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