README.md

@@ -1,41 +1,24 @@
-# vision-career
+# vision-career-backend
 
 Sample `.env`:
 
 ```dotenv
+DEEPINFRA_API_TOKEN=your-token-here
 OPENAI_API_KEY=your-token-here
-OPENAI_PROXY=http://user:password@host:port
 
 BOT_TOKEN=your-token-here
 
 SSL_CERT_FILE=/etc/ssl/certs/ca-certificates.crt
-
-SECRET_KEY=secret
-DEBUG=true
 ```
 
 Commands:
 
 ```bash
-docker compose up -d
-
-KUBECONFIG=clickhouse-kubeconfig.yaml kubectl port-forward svc/clickhouse-clickhouse -n clickhouse 18123:8123
-
-uv sync
-
 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 collectstatic
 uv run --env-file .env manage.py runserver
 
 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
 ```
-
-Production port-forwards:
-
-```bash
-KUBECONFIG=production-kubeconfig.yaml kubectl port-forward svc/qdrant -n qdrant 6333:6333
-KUBECONFIG=production-kubeconfig.yaml kubectl port-forward svc/main-cluster-rw -n postgresql-cluster 5432
-```

compose.yaml

@@ -1,4 +1,11 @@
 services:
+  qdrant:
+    image: qdrant/qdrant:latest
+    restart: always
+    ports:
+      - "127.0.0.1:6333:6333"
+    volumes:
+      - "/srv/vision-career/qdrant:/qdrant/storage"
   postgres:
     image: postgres:17-alpine3.20
     restart: always

vacancies/main/admin.py

@@ -14,13 +14,3 @@ class CustomerCVADMIN(admin.ModelAdmin):
 @admin.register(models.RecommendedVacancy)
 class RecommendedVacancyAdmin(admin.ModelAdmin):
     pass
-
-
-@admin.register(models.Vacancy)
-class VacancyAdmin(admin.ModelAdmin):
-    pass
-
-
-@admin.register(models.JobTitle)
-class JobTitleAdmin(admin.ModelAdmin):
-    pass

vacancies/main/bot.py

@@ -1,4 +1,5 @@
 import io
+import asyncio
 import os
 import traceback
 
@@ -21,11 +22,14 @@ from telegram.ext import (
     filters,
 )
 
-from pydantic import BaseModel
-from typing import Literal
 from vacancies.conf.settings import DB_URI
-from vacancies.main.models import Customer, CustomerCV, JobTitle
-from vacancies.main.vector_store import get_next_vacancy
+from vacancies.main.models import Customer, CustomerCV
+from vacancies.main.vector_store import (
+    add_vectors,
+    batch_extract_features,
+    get_next_vacancy,
+    embed_features,
+)
 
 SYSTEM_PROMPT = """
 Ты — карьерный копилот для ИТ. Ты можешь отвечать на любые вопросы по тематике карьеры.
@@ -65,17 +69,19 @@ 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)
-    if not vacancy:
+    result = get_next_vacancy(customer_cv)
+    if not result:
         message = "Вакансии закончились, возвращайтесь позже!"
         await context.bot.send_message(chat_id=update.effective_chat.id, text=message)
         return
 
+    recommendation, vacancy_content, link = result
+
     await context.bot.send_message(
         chat_id=update.effective_chat.id,
-        text=vacancy.content,
+        text=vacancy_content,
         reply_markup=InlineKeyboardMarkup([[
-            InlineKeyboardButton("Откликнуться", url=vacancy.link),
+            InlineKeyboardButton("Откликнуться", url=link),
         ]]),
     )
 
@@ -117,28 +123,23 @@ async def handle_document(update: Update, context: ContextTypes.DEFAULT_TYPE):
     reader = PdfReader(buffer)
     resume = "\n".join(page.extract_text() for page in reader.pages)
 
-    job_titles = JobTitle.objects.values_list('title', flat=True)
-    job_title_map = dict(JobTitle.objects.values_list('title', 'id'))
-
-    class Structure(BaseModel):
-        job_title: Literal[tuple(job_titles)]
-        min_salary_rub: int | None
-        max_salary_rub: int | None
-
-    openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", temperature=0, seed=42, top_p=1)
-    structured_llm = openai_client.with_structured_output(Structure)
-
-    prompt = f"Extract fields from following CV: {resume}"
-    response = await structured_llm.ainvoke(prompt)
-
     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,
-        job_title_id=job_title_map[response.job_title],
-        min_salary_rub=response.min_salary_rub,
-        max_salary_rub=response.max_salary_rub,
     ))
 
+    def upload_vectors():
+        features = batch_extract_features([customer_cv.content])[0]
+        add_vectors(
+            "cvs",
+            customer_cv.id,
+            features.model_dump(),
+            {'content': customer_cv.content, 'features_json': features.model_dump()},
+            embed_features(features.model_dump()),
+        )
+
+    await asyncio.to_thread(upload_vectors)
+
     await context.bot.editMessageText("Отлично! Запомнил Ваше резюме.", update.effective_chat.id, message.id)
 
 

vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py

@@ -1,14 +1,18 @@
+from concurrent.futures import ThreadPoolExecutor
+from datetime import datetime, timedelta
 from itertools import batched
-from datetime import timedelta
-from django.utils import timezone
-from pydantic import BaseModel
-from typing import Literal
-from vacancies.main.models import Vacancy, JobTitle
-from langchain_openai import ChatOpenAI
 
 import clickhouse_connect
 from django.core.management import BaseCommand
 from django.conf import settings
+from qdrant_client.models import OrderBy
+
+from vacancies.main.vector_store import (
+    add_vectors,
+    batch_extract_features,
+    embed_features,
+    qdrant_client,
+)
 
 query = """
 SELECT DISTINCT ON (message) id, chat_username, telegram_id, message, timestamp
@@ -34,38 +38,23 @@ class Command(BaseCommand):
     help = "Collect vacancies from telegram messages"
 
     def handle(self, *args, **options):
-        job_titles = JobTitle.objects.values_list('title', flat=True)
-        job_title_map = dict(JobTitle.objects.values_list('title', 'id'))
-
-        class Structure(BaseModel):
-            job_title: Literal[tuple(job_titles)]
-            min_salary_rub: int | None
-            max_salary_rub: int | None
-
-        openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", temperature=0, seed=42, top_p=1)
-        structured_llm = openai_client.with_structured_output(Structure)
-
-        last_timestamp = timezone.now() - timedelta(days=30)
-        if last_vacancy := Vacancy.objects.order_by("-timestamp").first():
-            last_timestamp = last_vacancy.timestamp
+        response = qdrant_client.scroll(collection_name="vacancies", limit=1, order_by=OrderBy(key="timestamp", direction="desc"))
+        last_point_timestamp = datetime.now() - timedelta(days=30)
+        if response[0]:
+            last_point_timestamp = response[0][0].payload["timestamp"]
 
         clickhouse_client = clickhouse_connect.create_client(host=settings.CLICKHOUSE_HOST, port=settings.CLICKHOUSE_PORT)
-        result_rows = clickhouse_client.query(query, parameters={"timestamp": last_timestamp}).result_rows
+        result_rows = clickhouse_client.query(query, parameters={"timestamp": last_point_timestamp}).result_rows
 
         for index, rows in enumerate(batched(result_rows, settings.COLLECT_VACANCIES_BATCH_SIZE)):
-            prompts = [f"Extract fields from following vacancies: {row[3]}" for row in rows]
-            responses = structured_llm.batch(prompts)
-            vacancies = []
-            for row, response in zip(rows, responses):
-                print(response)
+            vacancies_features = batch_extract_features([row[3] for row in rows])
+
+            print(f"Processing {index+1}/{len(result_rows)//settings.COLLECT_VACANCIES_BATCH_SIZE}")
+            with ThreadPoolExecutor() as pool:
+                vacancies_vectors = pool.map(embed_features, [vacancy_features.model_dump() for vacancy_features in vacancies_features])
+
+            for row, vacancy_features, vacancy_vectors in zip(rows, vacancies_features, vacancies_vectors):
                 (id, chat_username, telegram_id, message, timestamp) = row
-                vacancies.append(Vacancy(
-                    external_id=id,
-                    job_title_id=job_title_map[response.job_title],
-                    min_salary_rub=response.min_salary_rub,
-                    max_salary_rub=response.max_salary_rub,
-                    content=message,
-                    timestamp=timestamp,
-                    link=f"https://t.me/{chat_username}/{telegram_id}",
-                ))
-            print(Vacancy.objects.bulk_create(vacancies, ignore_conflicts=True))
+                link = f"https://t.me/{chat_username}/{telegram_id}"
+                payload = {'content': message, 'features_json': vacancy_features.model_dump(), "link": link, "timestamp": timestamp}
+                add_vectors("vacancies", id, vacancy_features.model_dump(), payload, vacancy_vectors)

vacancies/main/management/commands/generate_recommended_vacancies.py

@@ -15,14 +15,16 @@ class Command(BaseCommand):
 
     async def ahandle(self, *args, **options):
         for customer_cv in CustomerCV.objects.all():
-            vacancy = get_next_vacancy(customer_cv)
-            if not vacancy:
+            result = get_next_vacancy(customer_cv)
+            if not result:
                 continue
 
+            recommendation, vacancy_content, link = result
+
             await application.bot.send_message(
-                chat_id=customer_cv.customer.chat_id,
-                text=vacancy.content,
+                chat_id=recommendation.customer.chat_id,
+                text=vacancy_content,
                 reply_markup=InlineKeyboardMarkup([[
-                    InlineKeyboardButton("Откликнуться", url=vacancy.link),
+                    InlineKeyboardButton("Откликнуться", url=link),
                 ]]),
             )

vacancies/main/migrations/0009_jobtitle_customercv_max_salary_rub_and_more.py

@@ -1,55 +0,0 @@
-# Generated by Django 5.2.7 on 2025-11-08 19:11
-
-import django.db.models.deletion
-from django.db import migrations, models
-
-
-class Migration(migrations.Migration):
-
-    dependencies = [
-        ('main', '0008_alter_recommendedvacancy_vacancy_id'),
-    ]
-
-    operations = [
-        migrations.CreateModel(
-            name='JobTitle',
-            fields=[
-                ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
-                ('title', models.CharField(max_length=255, unique=True)),
-            ],
-        ),
-        migrations.AddField(
-            model_name='customercv',
-            name='max_salary_rub',
-            field=models.PositiveIntegerField(blank=True, default=None, null=True),
-        ),
-        migrations.AddField(
-            model_name='customercv',
-            name='min_salary_rub',
-            field=models.PositiveIntegerField(blank=True, default=None, null=True),
-        ),
-        migrations.AddField(
-            model_name='customercv',
-            name='job_title',
-            field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, to='main.jobtitle'),
-            preserve_default=False,
-        ),
-        migrations.CreateModel(
-            name='Vacancy',
-            fields=[
-                ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
-                ('external_id', models.CharField(max_length=255, unique=True)),
-                ('min_salary_rub', models.PositiveIntegerField(blank=True, default=None, null=True)),
-                ('max_salary_rub', models.PositiveIntegerField(blank=True, default=None, null=True)),
-                ('content', models.TextField()),
-                ('timestamp', models.DateTimeField()),
-                ('link', models.URLField()),
-                ('job_title', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.jobtitle')),
-            ],
-        ),
-        migrations.AlterField(
-            model_name='recommendedvacancy',
-            name='vacancy_id',
-            field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.vacancy'),
-        ),
-    ]

vacancies/main/models.py

@@ -1,4 +1,5 @@
 from django.db import models
+from pydantic import BaseModel
 
 
 class Customer(models.Model):
@@ -16,18 +17,8 @@ class Customer(models.Model):
         db_table = "customers"
 
 
-class JobTitle(models.Model):
-    title = models.CharField(max_length=255, unique=True)
-
-    def __str__(self):
-        return self.title
-
-
 class CustomerCV(models.Model):
     customer = models.OneToOneField(Customer, on_delete=models.CASCADE)
-    job_title = models.ForeignKey(JobTitle, on_delete=models.CASCADE)
-    min_salary_rub = models.PositiveIntegerField(null=True, blank=True, default=None)
-    max_salary_rub = models.PositiveIntegerField(null=True, blank=True, default=None)
     content = models.TextField()
     created_at = models.DateTimeField(auto_now_add=True)
 
@@ -40,21 +31,9 @@ class CustomerCV(models.Model):
         db_table = "customer_vcs"
 
 
-class Vacancy(models.Model):
-    job_title = models.ForeignKey(JobTitle, on_delete=models.CASCADE)
-    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)
-    content = models.TextField()
-    timestamp = models.DateTimeField()
-    link = models.URLField()
-
-    def __str__(self):
-        return self.job_title.title
-
 class RecommendedVacancy(models.Model):
     customer = models.ForeignKey(Customer, on_delete=models.CASCADE)
-    vacancy_id = models.ForeignKey(Vacancy, on_delete=models.CASCADE)
+    vacancy_id = models.BigIntegerField()
     created_at = models.DateTimeField(auto_now_add=True)
 
     objects = models.Manager()
@@ -65,3 +44,19 @@ class RecommendedVacancy(models.Model):
     class Meta:
         verbose_name_plural = 'Recommended Vacancies'
         db_table = "recommended_vacancies"
+
+
+class VacancyFeatures(BaseModel):
+    job_title: str | None = None # Должность
+    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

@@ -1,4 +1,154 @@
-from vacancies.main.models import RecommendedVacancy, Vacancy
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from qdrant_client import QdrantClient, models
+from qdrant_client.models import Filter, HasIdCondition
+
+from vacancies.conf.settings import QDRANT_URL
+from vacancies.main.models import RecommendedVacancy, VacancyFeatures
+
+qdrant_client = QdrantClient(url=QDRANT_URL)
+
+FEATURE_NAMES = [
+    "job_title", "employment_type", "work_format", "experience", "position_level", "industry", "tech_stack",
+    "location", "salary_range", "languages", "education", "schedule", "additional_requirements"
+]
+
+weights = {
+    "job_title": 70,
+    "tech_stack": 10,
+    "salary_range": 10,
+}
+
+vectors_config = {
+    name: models.VectorParams(size=3072, distance=models.Distance.COSINE) for name in FEATURE_NAMES
+}
+
+if not qdrant_client.collection_exists("vacancies"):
+    qdrant_client.create_collection(
+        collection_name="vacancies",
+        vectors_config=vectors_config,
+    )
+    qdrant_client.create_payload_index(
+        collection_name="vacancies",
+        field_name="timestamp",
+        field_schema="datetime",
+    )
+if not qdrant_client.collection_exists("cvs"):
+    qdrant_client.create_collection(
+        collection_name="cvs",
+        vectors_config=vectors_config,
+    )
+
+embedding = OpenAIEmbeddings(model="text-embedding-3-large")
+
+def _prepare_texts(features):
+    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 embed_features(features):
+    features = {key: value for key, value in features.items() if value}
+    features_texts = _prepare_texts(features)
+    names, texts = features_texts.keys(), features_texts.values()
+    vectors = dict(zip(names, embedding.embed_documents(texts)))
+    return vectors
+
+
+def add_vectors(collection_name: str, _id: int, features: dict, payload: dict, vectors):
+    max_similarities = {}
+    for name, vec in vectors.items():
+        results = qdrant_client.query_points(collection_name="vacancies", query=vec, using=name, limit=100)
+        for res in results.points:
+            max_similarities.setdefault(res.id, {})
+            max_similarities[res.id][name] = res.score
+
+    scored = []
+    for vid, feature_sims in max_similarities.items():
+        total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
+        scored.append({"id": vid, "score": total})
+
+    scored.sort(key=lambda x: x["score"], reverse=True)
+    if scored and scored[0]["score"] > 80:  # threshold
+        return
+
+    qdrant_client.upsert(
+        collection_name=collection_name,
+        points=[models.PointStruct(id=_id, vector=vectors, payload=payload)]
+    )
+
+
+def search_similarities(query_filter: Filter, cv_id: int):
+    cv = qdrant_client.retrieve(collection_name="cvs", ids=[cv_id], with_vectors=True)[0]
+
+    max_similarities, vacancies_content = {}, {}
+    for name, vec in cv.vector.items():
+        results = qdrant_client.query_points(
+            collection_name="vacancies",
+            query=vec,
+            using=name,
+            limit=100000,
+            with_payload=True,
+            query_filter=query_filter,
+        )
+        for res in results.points:
+            max_similarities.setdefault(res.id, {})
+            vacancies_content.setdefault(res.id, {})
+
+            max_similarities[res.id][name] = res.score
+            vacancies_content[res.id]["content"] = res.payload["content"]
+            vacancies_content[res.id]["features_json"] = res.payload["features_json"]
+            vacancies_content[res.id]["link"] = res.payload["link"]
+
+    scored = []
+    for vid, feature_sims in max_similarities.items():
+        total = sum(feature_sims[feature] * weights.get(feature, 1) for feature in feature_sims)
+        scored.append({
+            "id": vid,
+            "score": total,
+            "content": vacancies_content[vid]["content"],
+            "features_json": vacancies_content[vid]["features_json"],
+            "link": vacancies_content[vid]["link"],
+            "sims": feature_sims,
+        })
+
+    scored.sort(key=lambda x: x["score"], reverse=True)
+
+    return scored[0]["id"], scored[0]["content"], scored[0]["link"]
+
+
+def batch_extract_features(contents: list[str]) -> list[VacancyFeatures]:
+    prompts = [
+    f"""
+    Extract the following features from the job vacancy description. If a feature is not mentioned, set it to null.
+    Features:
+    - job_title: Должность (e.g., DevOps, Python программист)
+    - 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}
+    """
+    for content in contents
+    ]
+    openai_client = ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal", temperature=0, seed=42, top_p=1)
+    structured_llm = openai_client.with_structured_output(VacancyFeatures)
+    response = structured_llm.batch(prompts)
+    return response
 
 
 def get_next_vacancy(customer_cv):
@@ -6,14 +156,16 @@ def get_next_vacancy(customer_cv):
         customer=customer_cv.customer,
     ).values_list('vacancy_id', flat=True)
 
-    vacancy = Vacancy.objects.exclude(id__in=recommended_vacancy_ids).filter(
-        job_title=customer_cv.job_title,
-        min_salary_rub__gt=customer_cv.min_salary_rub,
-    ).first()
+    query_filter = Filter(must_not=[HasIdCondition(has_id=recommended_vacancy_ids)])
+    result = search_similarities(query_filter, customer_cv.id)
+    if not result:
+        return None
 
-    RecommendedVacancy.objects.create(
+    search_result_id, vacancy_content, link = result
+
+    recommendation = RecommendedVacancy.objects.create(
         customer=customer_cv.customer,
-        vacancy=vacancy,
+        vacancy_id=search_result_id,
     )
 
-    return vacancy
+    return recommendation, vacancy_content, link