Add context from vector search to RAG pipeline

compose.yaml

@@ -9,3 +9,10 @@ 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"

pyproject.toml

@@ -7,6 +7,7 @@ requires-python = ">=3.13"
 dependencies = [
     "clickhouse-connect>=0.9.2",
     "django>=5.2.7",
+    "flashrank>=0.2.10",
     "gunicorn>=23.0.0",
     "langchain>=0.3.27",
     "langchain-openai>=0.3.35",
@@ -15,6 +16,7 @@ dependencies = [
     "pydantic>=2.0",
     "pypdf>=6.1.2",
     "python-telegram-bot>=22.5",
+    "qdrant-client>=1.16.1",
     "sentry-sdk>=2.43.0",
     "whitenoise>=6.11.0",
 ]

uv.lock

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 version = "0.9.2"
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 version = "5.2.1"
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+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5e/dd/5cbf31f402f1cc0ab087c94d4669cfa55bd1e818688b910631e131d74e75/typer_slim-0.20.0-py3-none-any.whl", hash = "sha256:f42a9b7571a12b97dddf364745d29f12221865acef7a2680065f9bb29c7dc89d", size = 47087, upload-time = "2025-10-20T17:03:44.546Z" },
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.15.0"
@@ -1092,6 +1497,7 @@ source = { virtual = "." }
 dependencies = [
     { name = "clickhouse-connect" },
     { name = "django" },
+    { name = "flashrank" },
     { name = "gunicorn" },
     { name = "langchain" },
     { name = "langchain-openai" },
@@ -1100,6 +1506,7 @@ dependencies = [
     { name = "pydantic" },
     { name = "pypdf" },
     { name = "python-telegram-bot" },
+    { name = "qdrant-client" },
     { name = "sentry-sdk" },
     { name = "whitenoise" },
 ]
@@ -1113,6 +1520,7 @@ 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" },
@@ -1121,6 +1529,7 @@ 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" },
 ]

vacancies/conf/settings.py

@@ -43,6 +43,7 @@ INSTALLED_APPS = [
     'django.contrib.messages',
     'django.contrib.staticfiles',
     'vacancies.main',
+    'vacancies.hh_parser',
 ]
 
 MIDDLEWARE = [
@@ -89,8 +90,19 @@ 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"
 
 

vacancies/hh_parser/admin.py

@@ -0,0 +1,9 @@
+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")

vacancies/hh_parser/apps.py

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

vacancies/hh_parser/models.py

@@ -0,0 +1,32 @@
+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'

vacancies/hh_parser/routers.py

@@ -0,0 +1,23 @@
+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

vacancies/main/__init__.py

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

vacancies/main/bot.py

@@ -1,12 +1,14 @@
+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,
@@ -23,19 +25,35 @@ 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
 
-SYSTEM_PROMPT = """
+qdrant_client = AsyncQdrantClient(url=settings.QDRANT_URL)
-Ты — карьерный копилот для ИТ. Ты можешь отвечать на любые вопросы по тематике карьеры.
+openai_client = AsyncOpenAI(base_url="https://openrouter.ai/api/v1")
-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.
+async def get_relevant_messages(query: str):
-Отвечай простым текстом, не используй форматирование markdown.
+    """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)
 
 
 async def get_user_resume(user_id: int):
@@ -59,7 +77,7 @@ 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:
@@ -75,7 +93,8 @@ async def next_vacancy(update: Update, context: ContextTypes.DEFAULT_TYPE):
 
     await context.bot.send_message(
         chat_id=update.effective_chat.id,
-        text=vacancy.content,
+        parse_mode="Markdown",
+        text=vacancy.get_formatted_response(),
         reply_markup=InlineKeyboardMarkup([[
             InlineKeyboardButton("Откликнуться", url=vacancy.link),
         ]]),
@@ -84,14 +103,18 @@ 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=ChatOpenAI(model_name="gpt-5-mini", reasoning_effort="minimal"),
+            model=chat_model,
-            tools=[get_user_resume],
+            tools=[get_user_resume, get_relevant_messages],
-            system_prompt=SYSTEM_PROMPT,
+            system_prompt=prompts.BOT_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}'}]},
@@ -107,7 +130,7 @@ 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.")
@@ -126,24 +149,18 @@ 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")
+    openai_client = ChatOpenAI(model_name="gpt-5-mini", temperature=0, seed=42, top_p=1)
     structured_llm = openai_client.with_structured_output(Structure)
 
-    prompt = f"Extract sturcture from following CV. {resume}"
+    prompt = f'{prompts.STRUCTURED_OUTPUT_PROMPT} {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])
 

vacancies/main/management/commands/collect_vacancies_from_hh_parser.py

@@ -0,0 +1,45 @@
+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}")

vacancies/main/management/commands/collect_vacancies_from_telegram_messages.py

@@ -1,14 +1,16 @@
-from itertools import batched
 from datetime import timedelta
-from django.utils import timezone
+from itertools import batched
-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 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
 
 query = """
 SELECT DISTINCT ON (message) id, chat_username, telegram_id, message, timestamp
@@ -42,10 +44,16 @@ class Command(BaseCommand):
             job_title: Literal[tuple(job_titles)]
             min_salary_rub: int | None
             max_salary_rub: int | None
-            min_years_of_experience: int
+            company_name: str
-            work_format: Literal["remote", "office", None]
+            requirements: str
 
-        openai_client = ChatOpenAI(model_name="gpt-5-mini", temperature=0, seed=42, top_p=1)
+        openai_client = ChatOpenAI(
+            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)
@@ -57,18 +65,7 @@ class Command(BaseCommand):
 
         batches = list(batched(result_rows, settings.COLLECT_VACANCIES_BATCH_SIZE))
         for index, rows in enumerate(batches):
-            prompts = [
+            prompts = [f"{prompts.STRUCTURED_OUTPUT_PROMPT} {row[3]}" for row in rows]
-                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):
@@ -78,8 +75,8 @@ 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,
-                    min_years_of_experience=response.min_years_of_experience,
+                    company_name=response.company_name,
-                    work_format=response.work_format,
+                    requirements=response.requirements,
                     content=message,
                     timestamp=timezone.make_aware(timestamp),
                     link=f"https://t.me/{chat_username}/{telegram_id}",

vacancies/main/management/commands/generate_recommended_vacancies.py

@@ -1,11 +1,12 @@
 import asyncio
 
 from django.core.management import BaseCommand
-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
 
+from vacancies.main.bot import application
+from vacancies.main.models import CustomerCV
+from vacancies.main.recommendations import get_next_vacancy
+
 
 class Command(BaseCommand):
     help = "Generates new recommended vacancies"
@@ -18,7 +19,8 @@ class Command(BaseCommand):
             if vacancy := get_next_vacancy(customer_cv):
                 await application.bot.send_message(
                     chat_id=customer_cv.customer.chat_id,
-                    text=vacancy.content,
+                    text=vacancy.get_formatted_response(),
+                    parse_mode="Markdown",
                     reply_markup=InlineKeyboardMarkup([[
                         InlineKeyboardButton("Откликнуться", url=vacancy.link),
                     ]]),

vacancies/main/management/commands/sync_clickhouse_and_qdrant.py

@@ -0,0 +1,48 @@
+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")

vacancies/main/migrations/0012_customercv_work_format_and_more.py

@@ -1,35 +0,0 @@
-# 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),
-        ),
-    ]

vacancies/main/migrations/0012_vacancy_company_name_vacancy_requirements.py

@@ -0,0 +1,25 @@
+# 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,
+        ),
+    ]

vacancies/main/migrations/0013_alter_vacancy_options.py

@@ -0,0 +1,17 @@
+# 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'},
+        ),
+    ]

vacancies/main/models.py

@@ -28,8 +28,6 @@ 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)
 
@@ -47,8 +45,8 @@ 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)
-    min_years_of_experience = models.PositiveBigIntegerField()
+    company_name = models.CharField(max_length=255)
-    work_format = models.CharField(max_length=64, null=True, blank=True)
+    requirements = models.TextField()
     content = models.TextField()
     timestamp = models.DateTimeField()
     link = models.URLField()
@@ -56,6 +54,25 @@ 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")

vacancies/main/prompts.py

@@ -0,0 +1,68 @@
+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:
+"""

vacancies/main/recommendations.py

@@ -1,15 +1,16 @@
-from vacancies.main.models import Vacancy
 from django.db.models import Q
 
+from vacancies.main.models import Vacancy
+
 
 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),
-        min_years_of_experience__lte=customer_cv.years_of_experience,
+    ).filter(
-    ).first()
+        (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()
     if vacancy:
         customer_cv.customer.recommended_vacancies.create(vacancy=vacancy)