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

pyproject.toml

@@ -10,7 +10,6 @@ dependencies = [
     "langchain>=0.3.27",
     "langchain-openai>=0.3.35",
     "langchain-qdrant>=1.1.0",
-    "pydantic>=2.0",
     "pypdf>=6.1.2",
     "python-telegram-bot>=22.5",
 ]

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 +1,8 @@
 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 langchain_core.documents import Document
-from vacancies.main.features_extractor import extract_vacancy_features
+from vacancies.main.vector_store import vector_store
 
 clickhouse_client = clickhouse_connect.create_client(host="127.0.0.1", port=18123)
 
@@ -25,14 +25,21 @@ class Command(BaseCommand):
     help = "Collect vacancies from telegram messages"
 
     def handle(self, *args, **options):
+        ids, documents = [], []
+
         for index, row in enumerate(clickhouse_client.query(query).result_rows):
             (id, chat_username, telegram_id, message, timestamp) = row
 
-            link = f"https://t.me/{chat_username}/{telegram_id}"
+            link = f"https://t.me/c/{chat_username}/{telegram_id}"
             vacancy, created = Vacancy.objects.get_or_create(
                 link=link,
                 defaults={'content': message}
             )
 
+            metadata = {"link": link}
+            ids.append(vacancy.id)
+            documents.append(Document(page_content=message, metadata=metadata))
+
             print(index, link)
-            add_vacancy_vectors(vacancy.id, extract_vacancy_features(message).model_dump(), {"link": link})
+
+        vector_store.add_documents(documents, ids=ids)

vacancies/main/management/commands/generate_recommended_vacancies.py

@@ -1,8 +1,8 @@
 from django.core.management import BaseCommand
-from vacancies.main.vector_store import search_similarities
+import asyncio
+from vacancies.main.vector_store import vector_store
 from vacancies.main.models import CustomerCV, RecommendedVacancy
 from vacancies.main.bot import application
-from vacancies.main.features_extractor import extract_vacancy_features
 from telegram import InlineKeyboardButton, InlineKeyboardMarkup
 from qdrant_client.models import Filter, HasIdCondition
 
@@ -12,10 +12,7 @@ class Command(BaseCommand):
 
     def handle(self, *args, **options):
         customer_cvs = CustomerCV.objects.all()
-
         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
             ).values_list('vacancy_id', flat=True)
@@ -25,17 +22,22 @@ class Command(BaseCommand):
                     HasIdCondition(has_id=recommended_vacancy_ids),
                 ]
             )
-            search_result_id = search_similarities(features.model_dump(), query_filter)
 
+            document = vector_store.similarity_search(
+                customer_cv.content,
+                k=1,
+                filter=query_filter,
+            )[0]
+            print(document.__dict__)
             recommendation, _ = RecommendedVacancy.objects.get_or_create(
                 customer=customer_cv.customer,
-                vacancy_id=search_result_id,
+                vacancy_id=document.metadata["_id"],
             )
 
-            application.bot.send_message(
+            asyncio.run(application.bot.send_message(
                 chat_id=recommendation.customer.chat_id,
                 text=recommendation.vacancy.content,
                 reply_markup=InlineKeyboardMarkup([[
                     InlineKeyboardButton("Откликнуться", url=recommendation.vacancy.link),
                 ]]),
-            )
+            ))

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/0004_vacancy_features_json_vacancy_is_processed.py

@@ -1,23 +0,0 @@
-# Generated by Django 5.2.7 on 2025-10-26 11:36
-
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-
-    dependencies = [
-        ('main', '0003_remove_recommendedvacancy_is_shown_and_more'),
-    ]
-
-    operations = [
-        migrations.AddField(
-            model_name='vacancy',
-            name='features_json',
-            field=models.JSONField(blank=True, null=True),
-        ),
-        migrations.AddField(
-            model_name='vacancy',
-            name='is_processed',
-            field=models.BooleanField(default=False),
-        ),
-    ]

vacancies/main/models.py

@@ -1,5 +1,4 @@
 from django.db import models
-from pydantic import BaseModel
 
 
 class Customer(models.Model):
@@ -34,8 +33,6 @@ class CustomerCV(models.Model):
 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()
@@ -61,18 +58,3 @@ class RecommendedVacancy(models.Model):
     class Meta:
         verbose_name_plural = 'Recommended Vacancies'
         db_table = "recommended_vacancies"
-
-
-class VacancyFeatures(BaseModel):
-    employment_type: str | None = None  # Тип занятости
-    work_format: str | None = None  # Формат работы
-    experience: str | None = None  # Опыт работы
-    position_level: str | None = None  # Уровень позиции
-    industry: str | None = None  # Отрасль / Сфера деятельности
-    tech_stack: list[str] | None = None  # Технологический стек / Ключевые навыки
-    location: str | None = None  # География
-    salary_range: str | None = None  # Зарплатные ожидания / вилка
-    languages: list[str] | None = None  # Языки
-    education: str | None = None  # Образование
-    schedule: str | None = None  # График работы
-    additional_requirements: list[str] | None = None  # Дополнительные предпочтения / требования

vacancies/main/vector_store.py

@@ -1,97 +1,18 @@
-from qdrant_client import models
+from qdrant_client.models import Distance, VectorParams
+from langchain_qdrant import QdrantVectorStore
 from langchain_openai import OpenAIEmbeddings
 from qdrant_client import QdrantClient
-from qdrant_client.models import Filter
 
-# client = QdrantClient(path="./embeddings")
+client = QdrantClient(path="./embeddings")
-client = QdrantClient(url="http://localhost:6333")
 
-FEATURE_NAMES = [
+if not client.collection_exists("vacancies"):
-    "employment_type", "work_format", "experience", "position_level", "industry", "tech_stack",
-    "location", "salary_range", "languages", "education", "schedule", "additional_requirements"
-]
-
-vectors_config = {
-    name: models.VectorParams(size=3072, distance=models.Distance.COSINE) for name in FEATURE_NAMES
-}
-
-collection_name = "vacancies"
-if not client.collection_exists(collection_name):
     client.create_collection(
-        collection_name=collection_name,
+        collection_name="vacancies",
-        vectors_config=vectors_config
+        vectors_config=VectorParams(size=3072, distance=Distance.COSINE)
     )
 
-embedding = OpenAIEmbeddings(model="text-embedding-3-large")
+vector_store = QdrantVectorStore(
-
+    client=client,
-def _prepare_texts(features):
+    collection_name="vacancies",
-    """Prepare texts for each feature from features dict."""
+    embedding=OpenAIEmbeddings(model="text-embedding-3-large"),
-    texts = {}
-    for name in FEATURE_NAMES:
-        value = features.get(name)
-        if isinstance(value, list):
-            text = " ".join(value) if value else ""
-        else:
-            text = str(value) if value else ""
-        texts[name] = text
-    return texts
-
-
-def add_vacancy_vectors(vacancy_id: int, features: dict, payload: dict):
-    """Add vectors for a vacancy based on its features."""
-    texts = _prepare_texts(features)
-    vectors = {}
-    for name, text in texts.items():
-        vectors[name] = [0.0] * 3072
-        if text:
-            vec = embedding.embed_query(text)
-            vectors[name] = vec
-    client.upsert(
-        collection_name=collection_name,
-        points=[
-            models.PointStruct(
-                id=vacancy_id,
-                vector=vectors,
-                payload=payload,
 )
-        ]
-    )
-
-
-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():
-        vectors[name] = [0.0] * 3072
-        if text:
-            vec = embedding.embed_query(text)
-            vectors[name] = vec
-
-    max_similarities = {}
-    for name, vec in vectors.items():
-        if any(v != 0 for v in vec):
-            results = client.search(
-                collection_name=collection_name,
-                query_vector=(name, vec),
-                limit=1000,
-                with_payload=True,
-                query_filter=query_filter,
-            )
-            for res in results:
-                vid = res.id
-                sim = res.score
-                if vid not in max_similarities:
-                    max_similarities[vid] = {}
-                max_similarities[vid][name] = sim
-
-    scored = []
-    for vid, feature_sims in max_similarities.items():
-        total = sum(feature_sims.values())
-        scored.append({"id": vid, "score": total})
-
-    scored.sort(key=lambda x: x["score"], reverse=True)
-    return scored[0]["id"]