Extend structured output

vacancies/main/__init__.py

@@ -0,0 +1,3 @@
+import logging
+
+logging.basicConfig(level=logging.INFO)

vacancies/main/bot.py

@@ -1,5 +1,6 @@
 import io
 import os
+import asyncio
 import traceback
 
 from asgiref.sync import sync_to_async
@@ -66,7 +67,7 @@ async def next_vacancy(update: Update, context: ContextTypes.DEFAULT_TYPE):
         await context.bot.send_message(chat_id=update.effective_chat.id, text=message)
         return
 
-    vacancy = get_next_vacancy(customer_cv)
+    vacancy = await asyncio.to_thread(get_next_vacancy, customer_cv)
     if not vacancy:
         message = "Вакансии закончились, возвращайтесь позже!"
         await context.bot.send_message(chat_id=update.effective_chat.id, text=message)
@@ -125,25 +126,24 @@ async def handle_document(update: Update, context: ContextTypes.DEFAULT_TYPE):
         job_titles: list[Literal[tuple(db_job_titles)]]
         min_salary_rub: int | None
         max_salary_rub: int | None
+        years_of_experience: int | None
+        work_format: Literal["Удаленный", "Офис", "Гибрид", None]
 
-    openai_client = ChatOpenAI(model_name="gpt-5-mini", temperature=0, seed=42, top_p=1)
+    openai_client = ChatOpenAI(model_name="gpt-5-mini")
     structured_llm = openai_client.with_structured_output(Structure)
 
-    prompt = f"""
+    prompt = f"Extract sturcture from following CV. {resume}"
-        Ты — HR-классификатор. Ниже приведён список допустимых профессий.
+    print('1')
-        Твоя задача — выбрать наиболее подходящие по смыслу.
-        Качество классификации - самое важное.
-        Игнорируй орфографические и стилистические различия.
-        Резюме:
-        {resume}
-    """
     response = await structured_llm.ainvoke(prompt)
+    print('2')
 
     customer = await Customer.objects.aget(telegram_id=update.effective_user.id)
     customer_cv, _ = await CustomerCV.objects.aupdate_or_create(customer=customer, defaults=dict(
         content=resume,
         min_salary_rub=response.min_salary_rub,
         max_salary_rub=response.max_salary_rub,
+        years_of_experience=response.years_of_experience,
+        work_format=response.work_format,
     ))
     await customer_cv.job_titles.aset([job_title_map[job_title] for job_title in response.job_titles])
 

vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py

@@ -24,7 +24,8 @@ WHERE timestamp >= %(timestamp)s
     'заниматься', 'формат', 'занятость', 'вилка', 'должност', 'контакт'
   ]) >= 5
   AND arrayCount(x -> position(lower(message), x) > 0, [
-    'о себе', 'обо мне', 'умею', '#ищу', '#резюме', 'университет', 'колледж'
+    'о себе', 'обо мне', 'умею', '#ищу', '#резюме', 'университет', 'колледж',
+    'не будет опубликовано'
   ]) = 0
 ORDER BY timestamp ASC
 """
@@ -41,8 +42,10 @@ class Command(BaseCommand):
             job_title: Literal[tuple(job_titles)]
             min_salary_rub: int | None
             max_salary_rub: int | None
+            min_years_of_experience: int
+            work_format: Literal["remote", "office", None]
 
-        openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", 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)
 
         last_timestamp = timezone.now() - timedelta(days=30)
@@ -59,6 +62,7 @@ class Command(BaseCommand):
                 Ты — HR-классификатор. Ниже приведён список допустимых профессий.
                 Твоя задача — выбрать наиболее подходящую по смыслу.
                 Качество классификации - самое важное.
+                Если не уверен, то лучше укажи "Другое", ошибки недопустимы.
                 Игнорируй орфографические и стилистические различия.
                 Вакансия:
                 {row[3]}
@@ -74,6 +78,8 @@ class Command(BaseCommand):
                     job_title_id=job_title_map[response.job_title],
                     min_salary_rub=response.min_salary_rub,
                     max_salary_rub=response.max_salary_rub,
+                    min_years_of_experience=response.min_years_of_experience,
+                    work_format=response.work_format,
                     content=message,
                     timestamp=timezone.make_aware(timestamp),
                     link=f"https://t.me/{chat_username}/{telegram_id}",

vacancies/main/migrations/0012_customercv_work_format_and_more.py

@@ -0,0 +1,35 @@
+# Generated by Django 5.2.7 on 2025-11-09 12:54
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('main', '0011_remove_customercv_job_title_customercv_job_titles_and_more'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='customercv',
+            name='work_format',
+            field=models.CharField(blank=True, max_length=64, null=True),
+        ),
+        migrations.AddField(
+            model_name='customercv',
+            name='years_of_experience',
+            field=models.PositiveBigIntegerField(default=0),
+            preserve_default=False,
+        ),
+        migrations.AddField(
+            model_name='vacancy',
+            name='min_years_of_experience',
+            field=models.PositiveBigIntegerField(default=0),
+            preserve_default=False,
+        ),
+        migrations.AddField(
+            model_name='vacancy',
+            name='work_format',
+            field=models.CharField(blank=True, max_length=64, null=True),
+        ),
+    ]

vacancies/main/models.py

@@ -28,6 +28,8 @@ class CustomerCV(models.Model):
     job_titles = models.ManyToManyField(JobTitle, related_name="vacancies")
     min_salary_rub = models.PositiveIntegerField(null=True, blank=True, default=None)
     max_salary_rub = models.PositiveIntegerField(null=True, blank=True, default=None)
+    years_of_experience = models.PositiveBigIntegerField()
+    work_format = models.CharField(max_length=64, null=True, blank=True)
     content = models.TextField()
     created_at = models.DateTimeField(auto_now_add=True)
 
@@ -45,6 +47,8 @@ class Vacancy(models.Model):
     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)
+    min_years_of_experience = models.PositiveBigIntegerField()
+    work_format = models.CharField(max_length=64, null=True, blank=True)
     content = models.TextField()
     timestamp = models.DateTimeField()
     link = models.URLField()

vacancies/main/recommendations.py

@@ -1,12 +1,14 @@
 from vacancies.main.models import Vacancy
+from django.db.models import Q
 
 
 def get_next_vacancy(customer_cv):
-    vacancy = Vacancy.objects.exclude(
+    vacancy = Vacancy.objects.filter(
-        id__in=customer_cv.customer.recommended_vacancies.values_list("vacancy_id", flat=True),
+        ~Q(id__in=customer_cv.customer.recommended_vacancies.values_list("vacancy_id", flat=True)),
-    ).filter(
+        Q(min_salary_rub__isnull=True) | Q(min_salary_rub__gt=customer_cv.min_salary_rub),
+        Q(work_format__isnull=True) | Q(work_format=customer_cv.work_format),
         job_title__title__in=customer_cv.job_titles.values_list("title", flat=True),
-        min_salary_rub__gt=customer_cv.min_salary_rub,
+        min_years_of_experience__lte=customer_cv.years_of_experience,
     ).first()
     if vacancy:
         customer_cv.customer.recommended_vacancies.create(vacancy=vacancy)