Extend structured output

2025-11-09 16:27:41 +03:00
21 changed files with 98 additions and 797 deletions
--- a/compose.yaml
+++ b/compose.yaml
@ -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"
--- a/pyproject.toml
+++ b/pyproject.toml
@ -7,7 +7,6 @@ requires-python = ">=3.13"
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    "langchain>=0.3.27",
    "langchain-openai>=0.3.35",
@ -16,7 +15,6 @@ dependencies = [
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    "python-telegram-bot>=22.5",
    "qdrant-client>=1.16.1",
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--- a/uv.lock
+++ b/uv.lock
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    { name = "pypdf" },
    { name = "python-telegram-bot" },
    { name = "qdrant-client" },
    { name = "sentry-sdk" },
    { 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" },
 ]
--- a/vacancies/conf/settings.py
+++ b/vacancies/conf/settings.py
@ -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"
--- a/vacancies/hh_parser/init.py
+++ b/vacancies/hh_parser/init.py
--- a/vacancies/hh_parser/admin.py
+++ b/vacancies/hh_parser/admin.py
@ -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")
--- a/vacancies/hh_parser/apps.py
+++ b/vacancies/hh_parser/apps.py
@ -1,6 +0,0 @@
 from django.apps import AppConfig
 class HHParserConfig(AppConfig):
    default_auto_field = 'django.db.models.BigAutoField'
    name = 'vacancies.hh_parser'
--- a/vacancies/hh_parser/models.py
+++ b/vacancies/hh_parser/models.py
@ -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'
--- a/vacancies/hh_parser/routers.py
+++ b/vacancies/hh_parser/routers.py
@ -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
--- a/vacancies/main/init.py
+++ b/vacancies/main/init.py
@ -0,0 +1,3 @@
 import logging
 logging.basicConfig(level=logging.INFO)
--- a/vacancies/main/bot.py
+++ b/vacancies/main/bot.py
@ -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)
+SYSTEM_PROMPT = """
-openai_client = AsyncOpenAI(base_url="https://openrouter.ai/api/v1")
+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).
-async def get_relevant_messages(query: str):
+После полезного ответа предложи что-нибудь, чем ты можешь помочь еще.
-    """Получает релевантные сообщения по запросу пользователя."""
+Отвечай простым текстом, не используй форматирование markdown.
-
+"""
    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)
 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.content,
        text=vacancy.get_formatted_response(),
        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,
+            model=ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal"),
-            tools=[get_user_resume, get_relevant_messages],
+            tools=[get_user_resume],
-            system_prompt=prompts.BOT_SYSTEM_PROMPT,
+            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])
--- a/vacancies/main/management/commands/collect_vacancies_from_hh_parser.py
+++ b/vacancies/main/management/commands/collect_vacancies_from_hh_parser.py
@ -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}")
--- a/vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py
+++ b/vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py
@ -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 django.conf import settings
 from langchain_openai import ChatOpenAI
 from pydantic import BaseModel
 from vacancies.main import prompts
 from vacancies.main.models import JobTitle, Vacancy
 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
+            min_years_of_experience: int
-            requirements: str
+            work_format: Literal["remote", "office", None]
-        openai_client = ChatOpenAI(
+        openai_client = ChatOpenAI(model_name="gpt-5-mini", temperature=0, seed=42, top_p=1)
            model_name="openai/gpt-5-mini",
            openai_api_base="https://openrouter.ai/api/v1",
            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,
+                    min_years_of_experience=response.min_years_of_experience,
-                    requirements=response.requirements,
+                    work_format=response.work_format,
                    content=message,
                    timestamp=timezone.make_aware(timestamp),
                    link=f"https://t.me/{chat_username}/{telegram_id}",
--- a/vacancies/main/management/commands/generate_recommended_vacancies.py
+++ b/vacancies/main/management/commands/generate_recommended_vacancies.py
@ -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(),
+                    text=vacancy.content,
                    parse_mode="Markdown",
                    reply_markup=InlineKeyboardMarkup([[
                        InlineKeyboardButton("Откликнуться", url=vacancy.link),
                    ]]),
--- a/vacancies/main/management/commands/sync_clickhouse_and_qdrant.py
+++ b/vacancies/main/management/commands/sync_clickhouse_and_qdrant.py
@ -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")
--- a/vacancies/main/migrations/0012_customercv_work_format_and_more.py
+++ b/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),
        ),
    ]
--- a/vacancies/main/migrations/0012_vacancy_company_name_vacancy_requirements.py
+++ b/vacancies/main/migrations/0012_vacancy_company_name_vacancy_requirements.py
@ -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,
        ),
    ]
--- a/vacancies/main/migrations/0013_alter_vacancy_options.py
+++ b/vacancies/main/migrations/0013_alter_vacancy_options.py
@ -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'},
        ),
    ]
--- a/vacancies/main/models.py
+++ b/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)
@ -45,8 +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)
+    min_years_of_experience = models.PositiveBigIntegerField()
-    requirements = models.TextField()
+    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")
--- a/vacancies/main/prompts.py
+++ b/vacancies/main/prompts.py
@ -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:
 """
--- a/vacancies/main/recommendations.py
+++ b/vacancies/main/recommendations.py
@ -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(
+        min_years_of_experience__lte=customer_cv.years_of_experience,
-        (Q(min_salary_rub__isnull=True) | Q(min_salary_rub__gt=customer_cv.min_salary_rub)) |
+    ).first()
        (Q(max_salary_rub__isnull=False) & Q(max_salary_rub__gte=customer_cv.min_salary_rub)),
    ).order_by("-timestamp").first()
    if vacancy:
        customer_cv.customer.recommended_vacancies.create(vacancy=vacancy)
		`@ -0,0 +1,3 @@`
							`import logging`

							`logging.basicConfig(level=logging.INFO)`