vacancies/main/bot.py

@@ -5,14 +5,11 @@ import traceback
 from typing import Literal
 
 from asgiref.sync import sync_to_async
-from django.conf import settings
 from langchain.agents import create_agent
 from langchain_openai import ChatOpenAI
 from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
-from openai import AsyncOpenAI
 from pydantic import BaseModel
 from pypdf import PdfReader
-from qdrant_client import AsyncQdrantClient
 from telegram import (
     InlineKeyboardButton,
     InlineKeyboardMarkup,
@@ -32,6 +29,9 @@ from vacancies.conf.settings import DB_URI
 from vacancies.main import prompts
 from vacancies.main.models import Customer, CustomerCV, JobTitle
 from vacancies.main.recommendations import get_next_vacancy
+from django.conf import settings
+from qdrant_client import AsyncQdrantClient
+from openai import AsyncOpenAI
 
 qdrant_client = AsyncQdrantClient(url=settings.QDRANT_URL)
 openai_client = AsyncOpenAI(base_url="https://openrouter.ai/api/v1")
@@ -150,7 +150,7 @@ async def handle_document(update: Update, context: ContextTypes.DEFAULT_TYPE):
         min_salary_rub: int | None
         max_salary_rub: int | None
 
-    openai_client = ChatOpenAI(base_url="https://openrouter.ai/api/v1", model_name="gpt-5-mini", temperature=0, seed=42, top_p=1)
+    openai_client = ChatOpenAI(model_name="gpt-5-mini", temperature=0, seed=42, top_p=1)
     structured_llm = openai_client.with_structured_output(Structure)
 
     prompt = f'{prompts.STRUCTURED_OUTPUT_PROMPT} {resume}'

vacancies/main/management/commands/collect_vacancies_from_hh_parser.py

@@ -1,24 +1,18 @@
-import re
-from typing import Literal
-
 from django.core.management import BaseCommand
 from django.utils import timezone
-from flashrank import Ranker, RerankRequest
-from langchain_openai import ChatOpenAI
-from pydantic import BaseModel
-
 from vacancies.hh_parser.models import Vacancy as ExternalVacancy
-from vacancies.main import prompts
-from vacancies.main.models import JobTitle, Vacancy
+from vacancies.main.models import Vacancy, JobTitle
+from flashrank import Ranker, RerankRequest
+import re
 
-tags_regex = re.compile('<.*?>')
+tags_regex = re.compile('<.*?>') 
 
 reranker = Ranker("ms-marco-TinyBERT-L-2-v2")
 
 
 class Command(BaseCommand):
     help = "Collect vacancies from hh.ru parser"
-
+    
     def _remove_tags(self, text):
         return re.sub(tags_regex, "", text)
 
@@ -29,26 +23,6 @@ class Command(BaseCommand):
         queryset = ExternalVacancy.objects.filter(title__isnull=False, description__isnull=False)
         total_vacancies = queryset.count()
 
-        # job_titles = JobTitle.objects.values_list('title', flat=True)
-        class Structure(BaseModel):
-            # job_title: Literal[tuple(job_titles)]
-            # original_title: str
-            # min_salary_rub: int | None
-            # max_salary_rub: int | None
-            # company_name: str
-            requirements: str
-
-        openai_client = ChatOpenAI(
-            model_name="openai/gpt-5-mini",
-            openai_api_base="https://openrouter.ai/api/v1",
-            temperature=0,
-            seed=42,
-            top_p=1,
-        )
-        structured_llm = openai_client.with_structured_output(Structure)
-        prompt = prompts.STRUCTURED_OUTPUT_PROMPT
-        response = structured_llm.invoke(prompt)
-
         for index, vacancy in enumerate(queryset):
             results = reranker.rerank(RerankRequest(query=vacancy.title, passages=passages))
             ordered_results = sorted(results, key=lambda i: i["score"], reverse=True)
@@ -58,11 +32,10 @@ class Command(BaseCommand):
                 external_id=vacancy.id,
                 defaults=dict(
                     job_title_id=job_title_id,
-                    original_title=vacancy.title,
                     min_salary_rub=vacancy.min_payment,
                     max_salary_rub=vacancy.max_payment,
                     company_name=vacancy.company,
-                    requirements=response.requirements,
+                    requirements=self._remove_tags(vacancy.description),
                     content=self._remove_tags(vacancy.description),
                     timestamp=timezone.make_aware(vacancy.created_at),
                     link=vacancy.link,

vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py

@@ -42,7 +42,6 @@ class Command(BaseCommand):
 
         class Structure(BaseModel):
             job_title: Literal[tuple(job_titles)]
-            original_title: str
             min_salary_rub: int | None
             max_salary_rub: int | None
             company_name: str
@@ -74,7 +73,6 @@ class Command(BaseCommand):
                 vacancies.append(Vacancy(
                     external_id=id,
                     job_title_id=job_title_map[response.job_title],
-                    original_title=response.original_title,
                     min_salary_rub=response.min_salary_rub,
                     max_salary_rub=response.max_salary_rub,
                     company_name=response.company_name,

vacancies/main/migrations/0014_vacancy_original_title.py

@@ -1,18 +0,0 @@
-# Generated by Django 5.2.7 on 2025-12-03 19:19
-
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-
-    dependencies = [
-        ('main', '0013_alter_vacancy_options'),
-    ]
-
-    operations = [
-        migrations.AddField(
-            model_name='vacancy',
-            name='original_title',
-            field=models.CharField(blank=True, max_length=255, null=True),
-        ),
-    ]

vacancies/main/models.py

@@ -42,7 +42,6 @@ class CustomerCV(models.Model):
 
 class Vacancy(models.Model):
     job_title = models.ForeignKey(JobTitle, on_delete=models.CASCADE)
-    original_title = models.CharField(max_length=255, null=True, blank=True)
     external_id = models.CharField(max_length=255, unique=True)
     min_salary_rub = models.PositiveIntegerField(null=True, blank=True, default=None)
     max_salary_rub = models.PositiveIntegerField(null=True, blank=True, default=None)

vacancies/main/prompts.py

@@ -16,7 +16,7 @@ BOT_SYSTEM_PROMPT = """
 
 
 STRUCTURED_OUTPUT_PROMPT = """
-You are an HR specialist. Your task is to review vacansies and independently select a suitable topic (e.g., DevSecOps, Java Developer, Information Security Specialist, etc.).
+You are an HR specialist. Your task is to review vacansies and independently select a suitable topic (e.g., DevSecOps, Java Developer, Information Security Specialist, etc.). 
 You also need to analyze vacansies and structure the information from them according to the scheme.
 
 You don't need to change or invent anything in the job posting below. You only need to structure the information provided.
@@ -49,16 +49,15 @@ Example vacancy:
 
 🔥 Что мы предлагаем:
 • Полная удаленка или свободное посещение офисов в Москве и Санкт-Петербурге
-• IT-ипотека и оформление в аккредитованную IT-компанию
-• Бесплатное питание в офисах, ДМС со стоматологией (после испытательного срока)
+• IT-ипотека и оформление в аккредитованную IT-компанию  
+• Бесплатное питание в офисах, ДМС со стоматологией (после испытательного срока)  
 • Оплачиваемые Day Off, корпоративное обучение и IT-мероприятия
 
 💘 Контакты: @Alens_HR'
 
-Structured output of the example vacancy:
+Structured output of the example vacansy:
 {
-    job_title: "Network Security lead",
-    original_title: "Network Security Team lead - Infrastructure Security",
+    job_title: "Network Security Team lead - Infrastructure Security",
     company_name: "Wildberries",
     min_salary_rub: None,
     max_salary_rub: 500000,