Simplify vacancy recommendations

README.md

@@ -18,7 +18,6 @@ uv run --env-file .env manage.py migrate
 uv run --env-file .env manage.py createsuperuser --username stromenko_es --email estromenko@mail.ru
 uv run --env-file .env manage.py runserver
 
-uv run --env-file .env manage.py process_vacancies
 uv run --env-file .env manage.py generate_recommended_vacancies
 uv run --env-file .env manage.py collect_vacancies_from_telegram_messages
 uv run --env-file .env manage.py runbot

vacancies/main/features_extractor.py

@@ -1,29 +0,0 @@
-from langchain_openai import ChatOpenAI
-from vacancies.main.models import VacancyFeatures
-
-
-def extract_vacancy_features(content: str) -> VacancyFeatures:
-    """Extract features from vacancy content using structured output."""
-
-    prompt = f"""
-    Extract the following features from the job vacancy description. If a feature is not mentioned, set it to null.
-    Features:
-    - employment_type: Тип занятости (e.g., Полная занятость, Частичная)
-    - work_format: Формат работы (e.g., Офис, Удалённо, Гибрид)
-    - experience: Опыт работы (e.g., 3-5 лет, Нет опыта)
-    - position_level: Уровень позиции (e.g., Junior, Senior)
-    - industry: Отрасль / Сфера деятельности (e.g., IT, Финансы)
-    - tech_stack: Технологический стек / Ключевые навыки (list of strings)
-    - location: География (e.g., Москва, Россия)
-    - salary_range: Зарплатные ожидания / вилка (e.g., 100000-200000 руб)
-    - languages: Языки (list of strings, e.g., ["Русский", "Английский"])
-    - education: Образование (e.g., Высшее, Среднее специальное)
-    - schedule: График работы (e.g., Полный день, Сменный)
-    - additional_requirements: Дополнительные предпочтения / требования (list of strings)
-    Vacancy content:
-    {content}
-    """
-    openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal")
-    structured_llm = openai_client.with_structured_output(VacancyFeatures)
-    response = structured_llm.invoke(prompt)
-    return response

vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py

@@ -1,8 +1,7 @@
 from django.core.management import BaseCommand
 from vacancies.main.models import Vacancy
 import clickhouse_connect
-from vacancies.main.vector_store import add_vacancy_vectors
+from vacancies.main.vector_store import add_vacancy_vectors, extract_vacancy_features
-from vacancies.main.features_extractor import extract_vacancy_features
 
 clickhouse_client = clickhouse_connect.create_client(host="127.0.0.1", port=18123)
 
@@ -29,10 +28,11 @@ class Command(BaseCommand):
             (id, chat_username, telegram_id, message, timestamp) = row
 
             link = f"https://t.me/{chat_username}/{telegram_id}"
+            features = extract_vacancy_features(message)
             vacancy, created = Vacancy.objects.get_or_create(
                 link=link,
-                defaults={'content': message}
+                defaults={'content': message, 'features_json': features.model_dump()}
             )
 
             print(index, link)
-            add_vacancy_vectors(vacancy.id, extract_vacancy_features(message).model_dump(), {"link": link})
+            add_vacancy_vectors(vacancy.id, features.model_dump(), {"link": link})

vacancies/main/management/commands/generate_recommended_vacancies.py

@@ -15,19 +15,14 @@ class Command(BaseCommand):
 
         for customer_cv in customer_cvs:
             features = extract_vacancy_features(customer_cv.content)
-            print(features)
             recommended_vacancy_ids = RecommendedVacancy.objects.filter(
-                customer=customer_cv.customer
+                customer=customer_cv.customer,
             ).values_list('vacancy_id', flat=True)
 
-            query_filter = Filter(
+            query_filter = Filter(must_not=[HasIdCondition(has_id=recommended_vacancy_ids)])
-                must_not = [
-                    HasIdCondition(has_id=recommended_vacancy_ids),
-                ]
-            )
             search_result_id = search_similarities(features.model_dump(), query_filter)
 
-            recommendation, _ = RecommendedVacancy.objects.get_or_create(
+            recommendation = RecommendedVacancy.objects.create(
                 customer=customer_cv.customer,
                 vacancy_id=search_result_id,
             )

vacancies/main/management/commands/process_vacancies.py

@@ -1,20 +0,0 @@
-from django.core.management import BaseCommand
-from vacancies.main.models import Vacancy
-from vacancies.main.features_extractor import extract_vacancy_features
-from vacancies.main.vector_store import add_vacancy_vectors
-
-
-class Command(BaseCommand):
-    help = "Process vacancies: extract features and index in vector store"
-
-    def handle(self, *args, **options):
-        vacancies = Vacancy.objects.filter(is_processed=False)
-
-        len_vacancies = len(vacancies)
-        for index, vacancy in enumerate(vacancies):
-            print(f"Processing {index}/{len_vacancies} {vacancy}")
-            features = extract_vacancy_features(vacancy.content)
-            vacancy.features_json = features.model_dump()
-            vacancy.is_processed = True
-            vacancy.save()
-            add_vacancy_vectors(vacancy.id, features.model_dump(), {"link": vacancy.link})

vacancies/main/migrations/0005_remove_vacancy_is_processed.py

@@ -0,0 +1,17 @@
+# Generated by Django 5.2.7 on 2025-10-26 15:06
+
+from django.db import migrations
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('main', '0004_vacancy_features_json_vacancy_is_processed'),
+    ]
+
+    operations = [
+        migrations.RemoveField(
+            model_name='vacancy',
+            name='is_processed',
+        ),
+    ]

vacancies/main/models.py

@@ -35,7 +35,6 @@ class Vacancy(models.Model):
     content = models.TextField()
     link = models.URLField(unique=True)
     features_json = models.JSONField(null=True, blank=True)
-    is_processed = models.BooleanField(default=False)
     created_at = models.DateTimeField(auto_now_add=True)
 
     objects = models.Manager()

vacancies/main/vector_store.py

@@ -1,10 +1,12 @@
 from qdrant_client import models
 from langchain_openai import OpenAIEmbeddings
+from langchain_openai import ChatOpenAI
 from qdrant_client import QdrantClient
 from qdrant_client.models import Filter
+from vacancies.main.models import VacancyFeatures
 
-# client = QdrantClient(path="./embeddings")
+client = QdrantClient(path="./embeddings")
-client = QdrantClient(url="http://localhost:6333")
+#client = QdrantClient(url="http://localhost:6333")
 
 FEATURE_NAMES = [
     "employment_type", "work_format", "experience", "position_level", "industry", "tech_stack",
@@ -59,10 +61,6 @@ def add_vacancy_vectors(vacancy_id: int, features: dict, payload: dict):
 
 
 def search_similarities(query_features: dict, query_filter: Filter) -> list[dict]:
-    """Search vacancies using sum of max similarities.
-    For each feature, compute similarities, then for each vacancy, take max per feature, sum.
-    Return top vacancies.
-    """
     texts = _prepare_texts(query_features)
     vectors = {}
     for name, text in texts.items():
@@ -95,3 +93,28 @@ def search_similarities(query_features: dict, query_filter: Filter) -> list[dict
 
     scored.sort(key=lambda x: x["score"], reverse=True)
     return scored[0]["id"]
+
+
+def extract_vacancy_features(content: str) -> VacancyFeatures:
+    prompt = f"""
+    Extract the following features from the job vacancy description. If a feature is not mentioned, set it to null.
+    Features:
+    - employment_type: Тип занятости (e.g., Полная занятость, Частичная)
+    - work_format: Формат работы (e.g., Офис, Удалённо, Гибрид)
+    - experience: Опыт работы (e.g., 3-5 лет, Нет опыта)
+    - position_level: Уровень позиции (e.g., Junior, Senior)
+    - industry: Отрасль / Сфера деятельности (e.g., IT, Финансы)
+    - tech_stack: Технологический стек / Ключевые навыки (list of strings)
+    - location: География (e.g., Москва, Россия)
+    - salary_range: Зарплатные ожидания / вилка (e.g., 100000-200000 руб)
+    - languages: Языки (list of strings, e.g., ["Русский", "Английский"])
+    - education: Образование (e.g., Высшее, Среднее специальное)
+    - schedule: График работы (e.g., Полный день, Сменный)
+    - additional_requirements: Дополнительные предпочтения / требования (list of strings)
+    Vacancy content:
+    {content}
+    """
+    openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal")
+    structured_llm = openai_client.with_structured_output(VacancyFeatures)
+    response = structured_llm.invoke(prompt)
+    return response