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base: VisionCareerMiniapp:master
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VisionCareerMiniapp:master
VisionCareerMiniapp:extract-original-title-from-vacancies
VisionCareerMiniapp:staging
VisionCareerMiniapp:simplified-recommendations
VisionCareerMiniapp:change-recomendation-strategy
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compare: VisionCareerMiniapp:simplified-recommendations
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VisionCareerMiniapp:extract-original-title-from-vacancies
VisionCareerMiniapp:master
VisionCareerMiniapp:staging
VisionCareerMiniapp:simplified-recommendations
VisionCareerMiniapp:change-recomendation-strategy

1 Commits
master ... simplified

Author SHA1 Message Date
estromenko
3a7098f751 Extend structured output 2025-11-09 16:27:41 +03:00
21 changed files with 98 additions and 797 deletions
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7
compose.yaml
View File

@ -9,10 +9,3 @@ services:
- "127.0.0.1:5432:5432"
volumes:
- "/srv/vision-career/postgres:/var/lib/postgresql/data"
qdrant:
image: qdrant/qdrant:latest
restart: always
ports:
- "127.0.0.1:6333:6333"
volumes:
- "/srv/vision-career/qdrant:/qdrant/storage"

2
pyproject.toml
View File

@ -7,7 +7,6 @@ requires-python = ">=3.13"
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@ -16,7 +15,6 @@ dependencies = [
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]

409
uv.lock
View File

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{ name = "whitenoise" },
]
@ -1520,7 +1113,6 @@ dev = [
requires-dist = [
{ name = "clickhouse-connect", specifier = ">=0.9.2" },
{ name = "django", specifier = ">=5.2.7" },
{ name = "flashrank", specifier = ">=0.2.10" },
{ name = "gunicorn", specifier = ">=23.0.0" },
{ name = "langchain", specifier = ">=0.3.27" },
{ name = "langchain-openai", specifier = ">=0.3.35" },
@ -1529,7 +1121,6 @@ requires-dist = [
{ name = "pydantic", specifier = ">=2.0" },
{ name = "pypdf", specifier = ">=6.1.2" },
{ name = "python-telegram-bot", specifier = ">=22.5" },
{ name = "qdrant-client", specifier = ">=1.16.1" },
{ name = "sentry-sdk", specifier = ">=2.43.0" },
{ name = "whitenoise", specifier = ">=6.11.0" },
]

12
vacancies/conf/settings.py
View File

@ -43,7 +43,6 @@ INSTALLED_APPS = [
'django.contrib.messages',
'django.contrib.staticfiles',
'vacancies.main',
'vacancies.hh_parser',
]
MIDDLEWARE = [
@ -90,19 +89,8 @@ DATABASES = {
"PORT": os.getenv("PG_PORT", "5432"),
"CONN_HEALTH_CHECKS": True,
},
"hh_parser": {
"ENGINE": "django.db.backends.postgresql",
"NAME": os.getenv("HH_PARSER_PG_NAME", "postgres"),
"USER": os.getenv("HH_PARSER_PG_USER", "postgres"),
"PASSWORD": os.getenv("HH_PARSER_PG_PASSWORD", "postgres"),
"HOST": os.getenv("HH_PARSER_PG_HOST", "localhost"),
"PORT": os.getenv("HH_PARSER_PG_PORT", "5432"),
"CONN_HEALTH_CHECKS": True,
},
}
DATABASE_ROUTERS = ["vacancies.hh_parser.routers.AppRouter"]
DB_URI = f"postgres://{DATABASES['default']['USER']}:{quote(DATABASES['default']['PASSWORD'])}@{DATABASES['default']['HOST']}:{DATABASES['default']['PORT']}/{DATABASES['default']['NAME']}?sslmode=disable"

0
vacancies/hh_parser/__init__.py
View File

9
vacancies/hh_parser/admin.py
View File

@ -1,9 +0,0 @@
from django.contrib import admin
from vacancies.hh_parser import models
@admin.register(models.Vacancy)
class VacancyAdmin(admin.ModelAdmin):
list_filter = ("source", "created_at")
search_fields = ("title",)
list_display = ("company", "title", "source", "link", "created_at")

6
vacancies/hh_parser/apps.py
View File

@ -1,6 +0,0 @@
from django.apps import AppConfig
class HHParserConfig(AppConfig):
default_auto_field = 'django.db.models.BigAutoField'
name = 'vacancies.hh_parser'

32
vacancies/hh_parser/models.py
View File

@ -1,32 +0,0 @@
from django.db import models
class Vacancy(models.Model):
id = models.CharField(max_length=255, primary_key=True)
title = models.CharField(max_length=255, null=True, blank=True)
description = models.TextField(null=True, blank=True)
min_payment = models.IntegerField(null=True, blank=True)
max_payment = models.IntegerField(null=True, blank=True)
payment_currency = models.CharField(max_length=255, null=True, blank=True)
payment_type = models.CharField(max_length=255, null=True, blank=True)
payment_period = models.CharField(max_length=255, null=True, blank=True)
work_experience = models.CharField(max_length=255, null=True, blank=True)
type_of_employment = models.CharField(max_length=255, null=True, blank=True)
work_schedule = models.CharField(max_length=255, null=True, blank=True)
work_format = models.CharField(max_length=255, null=True, blank=True)
link = models.CharField(max_length=255, null=True, blank=True)
address = models.CharField(max_length=255, null=True, blank=True)
created_at = models.DateTimeField(null=True, blank=True)
company = models.CharField(max_length=255, null=True, blank=True)
processed = models.BooleanField()
source = models.CharField()
objects = models.Manager()
def __str__(self):
return f'{self.source}: {self.company} -> {self.id} {self.title}'
class Meta:
managed = False
db_table = "vacancies"
verbose_name_plural = 'Vacancies'

23
vacancies/hh_parser/routers.py
View File

@ -1,23 +0,0 @@
class AppRouter:
route_app_labels = {"hh_parser"}
db_name = "hh_parser"
def db_for_read(self, model, **hints):
if model._meta.app_label in self.route_app_labels:
return self.db_name
return None
def db_for_write(self, model, **hints):
if model._meta.app_label in self.route_app_labels:
return self.db_name
return None
def allow_relation(self, obj1, obj2, **hints):
if obj1._meta.app_label in self.route_app_labels and obj2._meta.app_label in self.route_app_labels:
return True
return None
def allow_migrate(self, db, app_label, model_name=None, **hints):
if app_label in self.route_app_labels:
return db == self.db_name
return None

3
vacancies/main/__init__.py
View File

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

67
vacancies/main/bot.py
View File

@ -1,14 +1,12 @@
import asyncio
import io
import os
import asyncio
import traceback
from typing import Literal
from asgiref.sync import sync_to_async
from langchain.agents import create_agent
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
from pydantic import BaseModel
from pypdf import PdfReader
from telegram import (
InlineKeyboardButton,
@ -25,35 +23,19 @@ from telegram.ext import (
filters,
)
from pydantic import BaseModel
from typing import Literal
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")
async def get_relevant_messages(query: str):
"""Получает релевантные сообщения по запросу пользователя."""
embedding = await openai_client.embeddings.create(
model="qwen/qwen3-embedding-8b",
input=query,
encoding_format="float",
)
response = await qdrant_client.query_points(
collection_name="messages",
query=embedding.data[0].embedding,
limit=20,
)
messages = [hit.payload["message"] for hit in response.points]
return "\n\n".join(messages)
SYSTEM_PROMPT = """
Ты — карьерный копилот для ИТ. Ты можешь отвечать на любые вопросы по тематике карьеры.
У тебя есть доступ к резюме пользователя при необходимости.
Пиши кратко (до 5–6 строк, буллеты приветствуются).
После полезного ответа предложи что-нибудь, чем ты можешь помочь еще.
Отвечай простым текстом, не используй форматирование markdown.
"""
async def get_user_resume(user_id: int):
@ -77,7 +59,7 @@ async def start(update: Update, context: ContextTypes.DEFAULT_TYPE):
async def next_vacancy(update: Update, context: ContextTypes.DEFAULT_TYPE):
await context.bot.send_message(update.effective_chat.id, "⏳ Обрабатываю твой запрос. Пожалуйста, подождите...")
await context.bot.send_message(update.effective_chat.id, "📝 Обрабатываю твой запрос. Пожалуйста, подождите...")
customer_cv = await CustomerCV.objects.filter(customer__telegram_id=update.effective_user.id).afirst()
if not customer_cv:
@ -93,8 +75,7 @@ async def next_vacancy(update: Update, context: ContextTypes.DEFAULT_TYPE):
await context.bot.send_message(
chat_id=update.effective_chat.id,
parse_mode="Markdown",
text=vacancy.get_formatted_response(),
text=vacancy.content,
reply_markup=InlineKeyboardMarkup([[
InlineKeyboardButton("Откликнуться", url=vacancy.link),
]]),
@ -103,18 +84,14 @@ async def next_vacancy(update: Update, context: ContextTypes.DEFAULT_TYPE):
async def prompt(update: Update, context: ContextTypes.DEFAULT_TYPE):
async with AsyncPostgresSaver.from_conn_string(DB_URI) as checkpointer:
chat_model = ChatOpenAI(
model_name="openai/gpt-5-mini:online",
openai_api_base="https://openrouter.ai/api/v1",
)
agent = create_agent(
model=chat_model,
tools=[get_user_resume, get_relevant_messages],
system_prompt=prompts.BOT_SYSTEM_PROMPT,
model=ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal"),
tools=[get_user_resume],
system_prompt=SYSTEM_PROMPT,
checkpointer=checkpointer,
)
message = await context.bot.send_message(update.effective_chat.id, "⏳ Обрабатываю твой запрос. Пожалуйста, подождите...")
message = await context.bot.send_message(update.effective_chat.id, "📝 Обрабатываю твой запрос. Пожалуйста, подождите...")
response = await agent.ainvoke(
input={"messages": [{"role": "user", "content": f'user_id = {update.effective_user.id}\n{update.message.text}'}]},
@ -130,7 +107,7 @@ async def error_handler(update: object, context: ContextTypes.DEFAULT_TYPE) -> N
async def handle_document(update: Update, context: ContextTypes.DEFAULT_TYPE):
message = await context.bot.send_message(update.effective_chat.id, "⏳ Обрабатываю твой запрос. Пожалуйста, подождите...")
message = await context.bot.send_message(update.effective_chat.id, "📝 Обрабатываю твой запрос. Пожалуйста, подождите...")
if not update.message.document:
await context.bot.send_message(chat_id=update.effective_chat.id, text="Не удалось прочитать информацию из файла! Попробуйте другой формат.")
@ -149,18 +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'{prompts.STRUCTURED_OUTPUT_PROMPT} {resume}'
prompt = f"Extract sturcture from following CV. {resume}"
print('1')
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])

45
vacancies/main/management/commands/collect_vacancies_from_hh_parser.py
View File

@ -1,45 +0,0 @@
from django.core.management import BaseCommand
from django.utils import timezone
from vacancies.hh_parser.models import Vacancy as ExternalVacancy
from vacancies.main.models import Vacancy, JobTitle
from flashrank import Ranker, RerankRequest
import re
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)
def handle(self, *args, **options):
job_titles = list(JobTitle.objects.values("id", "title"))
passages = [{"text": job_title["title"], **job_title} for job_title in job_titles]
queryset = ExternalVacancy.objects.filter(title__isnull=False, description__isnull=False)
total_vacancies = queryset.count()
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)
job_title_id = ordered_results[0]["id"]
vacancy, created = Vacancy.objects.get_or_create(
external_id=vacancy.id,
defaults=dict(
job_title_id=job_title_id,
min_salary_rub=vacancy.min_payment,
max_salary_rub=vacancy.max_payment,
company_name=vacancy.company,
requirements=self._remove_tags(vacancy.description),
content=self._remove_tags(vacancy.description),
timestamp=timezone.make_aware(vacancy.created_at),
link=vacancy.link,
),
)
print(f"{index+1}/{total_vacancies} Vacancy: {vacancy}, created: {created}")

43
vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py
View File

@ -1,16 +1,14 @@
from datetime import 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.conf import settings
from django.core.management import BaseCommand
from django.utils import timezone
from langchain_openai import ChatOpenAI
from pydantic import BaseModel
from vacancies.main import prompts
from vacancies.main.models import JobTitle, Vacancy
from django.conf import settings
query = """
SELECT DISTINCT ON (message) id, chat_username, telegram_id, message, timestamp
@ -44,16 +42,10 @@ class Command(BaseCommand):
job_title: Literal[tuple(job_titles)]
min_salary_rub: int | None
max_salary_rub: int | None
company_name: str
requirements: str
min_years_of_experience: int
work_format: Literal["remote", "office", None]
openai_client = ChatOpenAI(
model_name="openai/gpt-5-mini",
openai_api_base="https://openrouter.ai/api/v1",
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)
@ -65,7 +57,18 @@ class Command(BaseCommand):
batches = list(batched(result_rows, settings.COLLECT_VACANCIES_BATCH_SIZE))
for index, rows in enumerate(batches):
prompts = [f"{prompts.STRUCTURED_OUTPUT_PROMPT} {row[3]}" for row in rows]
prompts = [
f"""
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]}
"""
for row in rows
]
responses = structured_llm.batch(prompts)
vacancies = []
for row, response in zip(rows, responses):
@ -75,8 +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,
company_name=response.company_name,
requirements=response.requirements,
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}",

8
vacancies/main/management/commands/generate_recommended_vacancies.py
View File

@ -1,11 +1,10 @@
import asyncio
from django.core.management import BaseCommand
from telegram import InlineKeyboardButton, InlineKeyboardMarkup
from vacancies.main.bot import application
from vacancies.main.models import CustomerCV
from vacancies.main.bot import application
from vacancies.main.recommendations import get_next_vacancy
from telegram import InlineKeyboardButton, InlineKeyboardMarkup
class Command(BaseCommand):
@ -19,8 +18,7 @@ class Command(BaseCommand):
if vacancy := get_next_vacancy(customer_cv):
await application.bot.send_message(
chat_id=customer_cv.customer.chat_id,
text=vacancy.get_formatted_response(),
parse_mode="Markdown",
text=vacancy.content,
reply_markup=InlineKeyboardMarkup([[
InlineKeyboardButton("Откликнуться", url=vacancy.link),
]]),

48
vacancies/main/management/commands/sync_clickhouse_and_qdrant.py
View File

@ -1,48 +0,0 @@
import clickhouse_connect
from django.conf import settings
from django.core.management import BaseCommand
from qdrant_client import QdrantClient
from qdrant_client.models import VectorParams, Distance, PointStruct
from openai import OpenAI
from itertools import batched
query = """
SELECT DISTINCT ON (message) id, message
FROM telegram_parser_chatmessage
WHERE timestamp >= now() - INTERVAL 30 DAYS AND length(message) > 200
AND position(message, '?') = 0 AND position(message, 'spam') = 0
ORDER BY timestamp ASC
"""
class Command(BaseCommand):
help = "Sync clickhouse and qdrant"
def handle(self, *args, **options):
clickhouse_client = clickhouse_connect.create_client(host=settings.CLICKHOUSE_HOST, port=settings.CLICKHOUSE_PORT)
qdrant_client = QdrantClient(url=settings.QDRANT_URL)
if not qdrant_client.collection_exists("messages"):
qdrant_client.create_collection(
collection_name="messages",
vectors_config=VectorParams(size=4096, distance=Distance.COSINE),
)
openai_client = OpenAI(base_url="https://openrouter.ai/api/v1")
result_rows = clickhouse_client.query(query).result_rows
batches = list(batched(result_rows, 100))
batches_quantity = len(batches)
for index, batch in enumerate(batches):
ids, messages = list(zip(*batch))
embedding = openai_client.embeddings.create(model="qwen/qwen3-embedding-8b", input=messages, encoding_format="float")
embeddings = [row.embedding for row in embedding.data]
qdrant_client.upsert(
collection_name="messages",
points=[
PointStruct(id=idx, vector=vector, payload={"message": message})
for idx, message, vector in zip(ids, messages, embeddings)
]
)
print(f"{index+1}/{batches_quantity} processed")

35
vacancies/main/migrations/0012_customercv_work_format_and_more.py Normal file
View File

@ -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),
),
]

25
vacancies/main/migrations/0012_vacancy_company_name_vacancy_requirements.py
View File

@ -1,25 +0,0 @@
# Generated by Django 5.2.7 on 2025-11-09 19:56
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='vacancy',
name='company_name',
field=models.CharField(default='test', max_length=255),
preserve_default=False,
),
migrations.AddField(
model_name='vacancy',
name='requirements',
field=models.TextField(default='test'),
preserve_default=False,
),
]

17
vacancies/main/migrations/0013_alter_vacancy_options.py
View File

@ -1,17 +0,0 @@
# Generated by Django 5.2.7 on 2025-11-30 11:32
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('main', '0012_vacancy_company_name_vacancy_requirements'),
]
operations = [
migrations.AlterModelOptions(
name='vacancy',
options={'verbose_name_plural': 'Vacancies'},
),
]

25
vacancies/main/models.py
View File

@ -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)
@ -45,8 +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)
company_name = models.CharField(max_length=255)
requirements = models.TextField()
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()
@ -54,25 +56,6 @@ class Vacancy(models.Model):
def __str__(self):
return self.job_title.title
def get_formatted_response(self):
response = f"""
💼 **Вакансия**: {self.job_title}
\n🏢 **Компания**: {self.company_name}
\n📝 **Требования**: {self.requirements}
"""
if self.min_salary_rub:
if self.max_salary_rub:
response += f"\n💸 **ЗП**: от {self.min_salary_rub} т.р."
else:
response += f"\n💸 **ЗП**: {self.min_salary_rub} т.р. - {self.max_salary_rub} т.р."
elif self.max_salary_rub:
response += f"\n💸 **ЗП**: до {self.max_salary_rub} т.р."
return response
class Meta:
verbose_name_plural = 'Vacancies'
class RecommendedVacancy(models.Model):
customer = models.ForeignKey(Customer, on_delete=models.CASCADE, related_name="recommended_vacancies")

68
vacancies/main/prompts.py
View File

@ -1,68 +0,0 @@
BOT_SYSTEM_PROMPT = """
Ты — IT Career Copilot, высококвалифицированный HR-советчик и эксперт по рынку труда в ИТ. Твоя главная задача — предоставлять пользователю практические и стратегические рекомендации по любым вопросам, связанным с карьерой в технологической сфере (поиск работы, развитие навыков, переговоры по зарплате, адаптация, увольнение).
Твой стиль общения: Фокус на действии (Action-Oriented): Отвечай максимально конкретно и применимо, предлагая шаги, которые пользователь может предпринять немедленно.
Консультация: Используй инсайты и данные о текущих трендах ИТ-рынка.
Краткость: Пиши лаконично, не более 4-5 содержательных пунктов или предложений. Используй маркированные списки (буллеты) для структурирования информации.
Формат: Отвечай простым текстом, не используя форматирование Markdown (заголовки, жирный шрифт, курсив, кодблоки).
Использование данных: У тебя есть дополнительный доступ к резюме пользователя (или фрагментам его карьерной истории) для персонализации ответов, а также ты можешь обратиться к базе данных с релевантными сообщениями, если тебе требуются дополнительные данные, например реальные данные о зарплатах или мнения людей.
Завершение: После ответа обязательно предложи один-два релевантных следующих шага, чтобы продолжить карьерное планирование.
"""
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 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.
Example vacancy:
'Network Security Team lead - Infrastructure Security, Wildberries 💜
ЗП: до 500 000 рублей net
Уровень: Lead
Формат работы: удалёнка или гибрид
🚀 Вместе с масштабным развитием IT направления Wildberries и Russ развивает информационную безопасность. Мы решаем сложные и разнообразные задачи: от повышения защищенности каждого сервиса до развития безопасности в рамках всей нашей инфраструктуры.
Мы ищем эксперта, который вместе с командой займется разработкой сложных технических решений и внедрением практик безопасности для повышения защищенности нашей сетевой инфраструктуры.
📝 Основные задачи:
• Составление проектных планов и управление командой
• Взаимодействие с сетевыми командами для сбора потребностей и согласования технических решений
• Построение плана развития безопасности сетевой инфраструктуры
• Внедрение практик ИБ и контроль состояния защищенности внешнего и внутреннего сетевого периметра
• Разработка и внедрение мер по повышению прозрачности и контролируемости сетевых доступов в компании
💫 Необходимый опыт и навыки:
• Опыт в организации командной работы
• Опыт в построении сложных кросс-командных процессов
• Умение разрабатывать комплексные решения по безопасности для серверной инфраструктуры на базе Linux
• Хорошее знание сетевых технологий
• Опыт решения проблем ИБ в сетевой инфраструктуре
• Знание сетевых атак и способов защиты от них
• Опыт работы с NGFW
🔥 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)
• Оплачиваемые Day Off, корпоративное обучение и IT-мероприятия
💘 Контакты: @Alens_HR'
Structured output of the example vacansy:
{
job_title: "Network Security Team lead - Infrastructure Security",
company_name: "Wildberries",
min_salary_rub: None,
max_salary_rub: 500000,
requirements: "Опыт в организации командной работы. Опыт в построении сложных кросс-командных процессов. Умение разрабатывать комплексные решения по безопасности для серверной инфраструктуры на базе Linux, Хорошее знание сетевых технологий. Опыт решения проблем ИБ в сетевой инфраструктуре. Знание сетевых атак и способов защиты от них. Опыт работы с NGFW]
}
Vacancy:
"""

11
vacancies/main/recommendations.py
View File

@ -1,16 +1,15 @@
from django.db.models import Q
from vacancies.main.models import Vacancy
from django.db.models import Q
def get_next_vacancy(customer_cv):
vacancy = Vacancy.objects.filter(
~Q(id__in=customer_cv.customer.recommended_vacancies.values_list("vacancy_id", flat=True)),
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),
).filter(
(Q(min_salary_rub__isnull=True) | Q(min_salary_rub__gt=customer_cv.min_salary_rub)) |
(Q(max_salary_rub__isnull=False) & Q(max_salary_rub__gte=customer_cv.min_salary_rub)),
).order_by("-timestamp").first()
min_years_of_experience__lte=customer_cv.years_of_experience,
).first()
if vacancy:
customer_cv.customer.recommended_vacancies.create(vacancy=vacancy)