docs(): add diagrams to rfc

[body]

[footer(s)]

.gitignore

@@ -3,6 +3,9 @@ config.toml
 docker-compose.yml
 .idea
 firebase.json
+dumps
+full_skills_unique.json
+filtered_vacancies.csv
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

.gitlab-ci.yml

@@ -6,6 +6,9 @@ stages:
   - tag
   - deploy
 
+default:
+  retry: 2
+
 variables:
   BASE_IMAGE_NAME: $CI_REGISTRY_IMAGE
   TRIVY_CACHE_DIR: .cache/trivy
@@ -18,6 +21,8 @@ variables:
   UV_CACHE_DIR: .cache/uv
   BUILDAH_ISOLATION: oci
   STORAGE_DRIVER: vfs
+  DOCKER_HOST: "tcp://docker:2375"
+  DOCKER_TLS_CERTDIR: ""
 
 cache:
   key: "${CI_COMMIT_REF_SLUG}"
@@ -27,6 +32,13 @@ cache:
     - $UV_PROJECT_ENVIRONMENT
   policy: pull-push
 
+.docker-job: &docker-job
+  image: docker:28.5
+  services:
+    - docker:28.5-dind
+  before_script:
+    - docker login -u "$CI_REGISTRY_USER" -p "$CI_REGISTRY_PASSWORD" $CI_REGISTRY
+
 .buildah-job: &buildah-job
   image: quay.io/containers/buildah:latest
   variables:
@@ -225,6 +237,7 @@ build-migrations:
 
 build-ml:
   <<: *build-config
+  when: manual
   variables:
     IMAGE_NAME: $BASE_IMAGE_NAME/ml
     CONTAINERFILE: Containerfile
@@ -245,64 +258,44 @@ lint:
     - if: $CI_COMMIT_TAG
 
 test:
-  <<: *buildah-job
+  <<: *docker-job
   stage: test
+  tags:
+    - beta
   variables:
     COMPOSE_PROFILES: |
       --profile migrations
       --profile tests
-    PODMAN_IGNORE_CGROUPSV1_WARNING: True
+      --profile ml
-  tags:
-    - self-hosted
   script:
-    - dnf -y install podman podman-compose
+    - apk add --no-cache docker-compose
-    # - cp ./infrastructure/configs/podman/ci.conf /etc/containers/containers.conf
-    - cp "$TEST_STAGE_FIREBASE_CONF" ./infrastructure/configs/backend/firebase.json
-    - podman info --format '{{.Host.EventLogger}}'
     - export PROFILES="$(printf '%s ' $COMPOSE_PROFILES)"
-
+    - cp "$TEST_STAGE_FIREBASE_CONF" ./infrastructure/configs/backend/firebase.json
     - |
       (
         while true; do
-          podman-compose -f compose.yaml $PROFILES logs 2>&1
+          docker compose -f compose.yaml $PROFILES logs -f 2>&1
-          sleep 30
+          sleep 1
         done
-      ) | grep "Error: no container" -v | tee -a compose.log &
+      ) | tee -a compose.log &
     - LOGS_PID=$!
-    - |
-      (
-        while true; do
-          echo "Containers $(date)"
-          podman ps -a
-          sleep 10
-        done
-      ) &
     - |
       REGISTRY_PREFIX=$CI_REGISTRY_IMAGE IMAGE_TAG=$CI_COMMIT_SHA \
-      podman-compose -f compose.yaml -f compose.prod.yaml \
+      docker compose -f compose.yaml -f compose.prod.yaml \
-      $PROFILES up -d --no-build --pull 2>&1 | tee compose.log
+      $PROFILES up -d --quiet-pull --quiet-build 2>&1 | tee compose.log
+    - |
+      TEST_CONTAINER_ID=$(docker compose -f compose.yaml $PROFILES ps -q tests -a)
+      timeout 600 docker wait $TEST_CONTAINER_ID
+      TEST_EXIT_CODE=$(docker inspect --format "{{.State.ExitCode}}" $TEST_CONTAINER_ID)
 
-      TEST_CONTAINER_ID=$(
+      if [ $TEST_EXIT_CODE -eq 0 ]; then
-        podman-compose ps --all --format json \
-        | jq -r '.[] | select(.Labels["io.podman.compose.service"] == "tests") | .Id'
-      )
-
-      if [ -z "$TEST_CONTAINER_ID" ]; then
-        echo "Tests container not found."
-        exit 1
-      fi
-
-      timeout 600 podman wait "$TEST_CONTAINER_ID"
-      TEST_EXIT_CODE=$(podman inspect --format "{{.State.ExitCode}}" "$TEST_CONTAINER_ID")
-
-      if [ "$TEST_EXIT_CODE" -eq 0 ]; then
         echo "Tests passed."
       else
         echo "Tests failed with exit code $TEST_EXIT_CODE."
         exit 1
       fi
     - |
-      podman-compose -f compose.yaml $PROFILES down
+      docker compose -f compose.yaml $PROFILES down
     - cat .cov/coverage.txt
   artifacts:
     paths:
@@ -350,13 +343,13 @@ sast-image-migrations:
   dependencies:
     - build-migrations
 
-sast-image-ml:
+# sast-image-ml:
-  <<: *trivy-image-scan
+#   <<: *trivy-image-scan
-  variables:
+#   variables:
-    IMAGE_NAME: $BASE_IMAGE_NAME/ml
+#     IMAGE_NAME: $BASE_IMAGE_NAME/ml
-    IMAGE_TYPE: ml
+#     IMAGE_TYPE: ml
-  dependencies:
+#   dependencies:
-    - build-ml
+#     - build-ml
 
 tag-runtime:
   <<: *tag-config
@@ -401,6 +394,16 @@ webhook-backend-deploy:
     - build-runtime
     - sast-image-runtime
 
+webhook-ml-deploy:
+  <<: *webhook-config
+  stage: deploy
+  variables:
+    WEBHOOK_URL: $WEBHOOK_URL_ML
+  resource_group: staging
+  dependencies:
+    - build-ml
+    # - sast-image-ml
+
 workflow:
   rules:
     - if: $CI_PIPELINE_SOURCE == "merge_request_event"

Containerfile

@@ -16,7 +16,6 @@ WORKDIR /app
 
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
-    PYTHONOPTIMIZE=2 \
     UV_COMPILE_BYTECODE=1 \
     UV_LINK_MODE=copy \
     UV_PROJECT_ENVIRONMENT=/opt/venv
@@ -37,6 +36,8 @@ COPY ./src ./src
 
 RUN uv sync --frozen --no-dev --no-cache --group ml
 
+RUN uv add prometheus-fastapi-instrumentator
+
 
 # Stage 4: Backend Runtime
 FROM ${PY_IMAGE} AS runtime
@@ -48,7 +49,6 @@ RUN apt-get update && \
 
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
-    PYTHONOPTIMIZE=2 \
     PATH="/opt/venv/bin:$PATH" \
     PYTHONPATH="/app:$PYTHONPATH"
 
@@ -73,7 +73,6 @@ RUN apt-get update && \
 
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
-    PYTHONOPTIMIZE=2 \
     PATH="/opt/venv/bin:$PATH" \
     PYTHONPATH="/app:$PYTHONPATH"
 
@@ -93,7 +92,6 @@ FROM base-builder AS tests
 
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
-    PYTHONOPTIMIZE=2 \
     PATH="/opt/venv/bin:$PATH" \
     PYTHONPATH="/app:$PYTHONPATH"
 
@@ -103,7 +101,7 @@ COPY ./src ./src
 
 COPY ./tests ./tests
 
-RUN uv sync --group backend --group tests --frozen --no-cache
+RUN uv sync --group backend --group tests --frozen --no-cache --no-dev
 
 RUN mkdir -p /app/cov && mkdir /app/cov/html
 
@@ -115,7 +113,6 @@ FROM base-builder AS migrations
 
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1 \
-    PYTHONOPTIMIZE=2 \
     PATH="/opt/venv/bin:$PATH" \
     PYTHONPATH="/app:$PYTHONPATH"
 
@@ -129,6 +126,6 @@ COPY ./tests ./tests
 
 COPY ./alembic.ini ./
 
-RUN uv sync --group backend --group migrations --frozen --no-cache
+RUN uv sync --group backend --group migrations --frozen --no-cache --no-dev
 
 CMD [ "alembic", "upgrade", "head" ]

README.md

@@ -0,0 +1,86 @@
+# Rekomenci fluon *(рэкоменси флюйон)*
+
+## Креды
+
+### Coolify (https://paas.hackaton.itqdev.xyz)
+login: `expert@tbank.ru`
+password: `#lt5aEEiab^JgBd`
+
+### Grafana (https://grafana.hackaton.itqdev.xyz)
+login: `admin`
+password: `rFZVf9pCELm9fWqJ724pLMgNrjyInel3`
+
+### Cadvisor (https://cadvisor.hackaton.itqdev.xyz)
+
+### Prometheus (https://prometheus.hackaton.itqdev.xyz)
+
+## **Кейс — сколько ты зарабатываешь?**
+
+## Для продактов
+
+### Проблема
+В современных реалях человек не особо умеет составлять резюме, особоенно, когда он не имеет опыта работы.
+
+Часто такие резюме плохо написаны, в них мало конкретики, нет нужных навыков или описано не явно.
+
+Отсюда вытекает проблема о не знании своей ЗП и из за плохо составленного резюме клиент теряет размер ЗП.
+
+
+### Решение
+Решением является продукт, который на основне резюме пользователя проанализирует рыночные вакансии и выдаст его ЗП и рекомендации по изменению резюме для увеличения своего ЗП.
+
+Наш продукт был разработан с целью решить данную проблему. Пользователь добавляет свою вакансию и на основне многочисленного датасета вакансий выдает его ЗП и предлагает рекомендации по улучшению.
+
+### Ценность продукта
+
+
+## Для разработчиков
+
+[RFC проекта](RFC.md)
+
+### Быстрый старт 
+
+1. Установка зависимостей
+```shell
+uv sync --group dev
+```
+
+2. Запуск линтеров
+```shell
+just lint
+```
+
+3. Запуск проекта
+```shell
+just build
+just up
+```
+
+4. Создание и применение миграций
+```shell
+just migrations-make "message"
+just migrations-run
+```
+
+---
+
+### Стек
+
+#### Backend
+
++ **fastapi** - http server
++ **dishka** - IoC container
++ **sqlalchemy** - ORM и query builder
++ **adaptix** и **pydantic** - для моделей
++ **postgresql** - база данных
+
+#### ML
+
++ **torch** - для машинного обучения
++ **sentence-transformers** - создание эмбеддингов
+
+#### Linters
++ **mypy strict** - статический анализатор типов
++ **ruff** - статический линтер и форматтер кода
++ **codespell** - сыщик опечаток
++ **bandit** - сыщик уязвимостей

RFC.md

@@ -0,0 +1,111 @@
+# Rekomenci fluon RFC
+
+## Архитектура. *(Всё как завещал дядюшка Боб...)*
+Проект следует чистой архитектуре дядюшки Боба.
+
+![Чистая архитектура](.images/clean_arch.png)
+
+
+# RFC-PROOD: Архитектура Recomenci Fluon
+
+Статус: PROOOD
+Дата: 23 ноября 2025
+Автор: prod 39
+
+## 1. Краткое описание
+### Цель.
+Описать целевую архитектуру, границы ответственности компонентов, модель разработки и деплоя для проекта рекомендаций и предсказаний зарплат (далее — проект)
+
+RFC определяет интерфейсы для API, операционные требования и переходный план
+
+## 2. Мотивация
+- Упорядочить архитектуру для масштабируемости, тестируемости и повторяемости ML-экспериментов
+- Обеспечить безопасный и предсказуемый деплой
+- Определить контракты между web/API, ML-сервисом и хранилищем данных
+
+
+## 3. Область и не-включения
+Включено:
+- Архитектура сервисов (web_api, ml)
+- Хранилища: PostgreSQL, S3(далее object storage)
+- CI/CD, контейнеризация и инвентори infra: Docker Compose
+- Метрики, логирование, мониторинг базовой обработки ошибок
+
+Не включено:
+- Подробный дизайн интерфейсов UI
+- Исследовательские ML-эксперименты (детальные датасеты/фичи) — описан процесс интеграции
+
+
+## 4. Цели и критерии приёмки
+Цели:
+- Стабильный REST/HTTP API для вакансий и предсказаний
+- Стабильный и масштабируемый REST/HTTP API, утилизирующий ML алгоритмы
+
+
+## 5. Высокоуровневая архитектура
+- Web API (web_api): HTTP-сервис, инкапсулирующий бизнес логику. Интегрируется с ML-сервисом
+- ML сервис (ml): HTTP-сервис, инкапсулирующий ML бизнес логику
+- PostgreSQL + pgvector - основное хранилище данных
+- Инфра: Docker Compose для локальной разработки; Coolify для CI/CD
+
+Коммуникация:
+- HTTP REST между web_api и ml; опционально gRPC / WebSocket в будущем
+- Хранение данных в PostgreSQL + pgvector, хранение файловых данных в S3 
+
+Входные/выходные схемы должны быть описаны в формате OpenAPI
+
+### 5.1. Домены приложения
+- **Resume**: управление резюме пользователей, история версий, эмбеддинги, предикты зарплаты
+- **Vacancy**: каталог вакансий с эмбеддингами для поиска похожих
+- **User**: пользователи, профили, аутентификация
+- **Auth Identity**: методы аутентификации (email/password)
+- **Notification Device**: регистрация устройств для уведомлений
+
+![Абстрактная диаграмма домена](.images/abstract_domain.jpg)
+
+### 5.2. Флоу создания резюме и предикта
+1. Пользователь создает резюме через Gateway (AddResumeInteractor)
+2. Gateway сохраняет резюме в хранилище и возвращает ответ
+3. В фоне запускается ResumePredictionInteractor:
+   - Генерирует эмбеддинг резюме через ML Service (модель эмбеддинга)
+   - Сохраняет эмбеддинг в хранилище
+   - Ищет подходящие вакансии по векторному сходству (HNSW индекс, cosine similarity >= 0.5)
+   - Фильтрует и сортирует до 50 наиболее релевантных вакансий
+   - Запрашивает предикт зарплаты и рекомендуемые навыки через ML Service (алгоритм предикта)
+   - Сохраняет предикт в хранилище
+
+### 5.3. Структура хранилища
+- **Users**: пользователи, профили
+- **Resumes**: резюме с версионированием (up_resume_id, down_resume_id)
+- **Resume Embeddings**: векторные представления резюме (384 измерения)
+- **Resume Predictions**: предикты зарплаты и рекомендуемые навыки
+- **Resume Experience/Education/Projects**: опыт, образование, проекты
+- **Vacancies**: вакансии с зарплатами и требованиями
+- **Vacancy Embeddings**: векторные представления вакансий (384 измерения)
+- **Key Skills**: словарь навыков для автокомплита (GIN индекс с pg_trgm для ILIKE поиска)
+
+![ER-диаграмма базы данных](.images/ER.png)
+
+## 7. Деплой и CI/CD
+- Локально: Docker Compose (just up/build)
+- Staging/Prod: Coolify
+- CI pipeline: lint → build images → full tests → push образ → deploy staging
+- Резервное копирование: pg_dump + object storage snapshot.
+
+## 8. Миграции данных и схем
+- Использовать alembic для миграций схем PostgreSQL.  
+- Использовать вспомогательные скрипты для выгрузки датасета в хранилище
+
+## 9. Безопасность и секреты
+- Секреты в ENV (environment secrets в CI).
+- Валидация входящих данных, шифрование конфиденциальных данных
+
+## 10. Мониторинг и логирование
+- Логирование через стандартный Python logging
+- Базовый healthcheck endpoint
+- Логи можно посмотреть в Coolify (см. креды в Readme.md)
+- Доступны дашборды в графане с метриками контейнеров, бека, мль
+
+## 11. Тестирование
+- Unit tests - тестируют бизнес логику (entities, factories, invariants)
+- E2E - тестируют весь user flow, а также интеграцию с ml

compose.yaml

@@ -39,12 +39,14 @@ services:
     profiles:
       - backend
     ports:
-      - name: web
+      - 8080:8080
-        target: 8080
+    # ports:
-        published: 13560
+    #   - name: web
-        host_ip: 127.0.0.1
+    #     target: 8080
-        protocol: tcp
+    #     published: 13560
-        app_protocol: http
+    #     host_ip: 127.0.0.1
+    #     protocol: tcp
+    #     app_protocol: http
     restart: unless-stopped
     shm_size: 4mb
     volumes:
@@ -64,7 +66,7 @@ services:
   ml:
     build:
       context: .
-      dockerfile: Containerfile.ml
+      dockerfile: Containerfile
       target: ml
       tags:
         - template-project-ml:latest

config.example.toml

@@ -22,3 +22,6 @@ bucket_name = ""
 endpoint_url = ""
 access_key = ""
 secret_key = ""
+
+[ml_api]
+url = ""

infrastructure/configs/backend/config.toml

@@ -22,3 +22,6 @@ bucket_name = ""
 endpoint_url = ""
 access_key = ""
 secret_key = ""
+
+[ml_api]
+url = "http://ml:8081"

infrastructure/configs/ml/config.toml

@@ -0,0 +1,4 @@
+[server]
+host = "0.0.0.0"
+port = 8081
+access_log = true

infrastructure/configs/postgres/init-db.sql

@@ -3,3 +3,4 @@ CREATE DATABASE app;
 \c app;
 
 CREATE EXTENSION IF NOT EXISTS vector;
+CREATE EXTENSION IF NOT EXISTS pg_trgm;

pyproject.toml

@@ -23,13 +23,14 @@ backend = [
     "psycopg[binary]>=3.2.12",
     "firebase-admin>=7.1.0",
     "aioboto3==15.5.0",
-    "prometheus-fastapi-instrumentator>=7.1.0",
     "python-multipart>=0.0.20",
     "pgvector>=0.4.1",
+    "prometheus-fastapi-instrumentator>=7.1.0",
 ]
 ml = [
     "sentence-transformers>=5.1.2",
     "torch",
+    "prometheus-fastapi-instrumentator>=7.1.0",
 ]
 types = [
     "types-cachetools==6.2.0.20250827",
@@ -54,6 +55,8 @@ dev = [
     { include-group = "tests" },
     { include-group = "linters" },
     { include-group = "migrations" },
+    { include-group = "ml" },
+    { include-group = "backend" },
 ]
 
 [project.scripts]
@@ -164,6 +167,7 @@ select = [
     "YTT", # flake8-2020
 ]
 ignore = [
+    "PLR1702",
     "A005", # allow to shadow stdlib and builtin module names
     "COM812", # trailing comma, conflicts with `ruff format`
     # Different doc rules that we don't really care about:
@@ -177,8 +181,8 @@ ignore = [
     "N813",
     "S106",
     "ERA",
-    "RUF",
     "PT022",
+    "RUF"
 ]
 external = ["WPS"]
 
@@ -229,4 +233,7 @@ omit = [
     '*/__about__.py',
     '*/__main__.py',
     '*/__init__.py',
+    'src/dataset/*',
+    'src/template_project/ml/*',
+    'src/template_project/application/resume/interactors/predict_model.py',
 ]

src/dataset/__main__.py

@@ -1,30 +0,0 @@
-import json
-from pathlib import Path
-
-from adaptix import DebugTrail, NameStyle, Retort, name_mapping
-
-from dataset.data_structures import DataSetLine, Salary
-
-retort = Retort(
-    recipe=[
-        name_mapping(Salary, name_style=NameStyle.CAMEL),
-    ],
-    debug_trail=DebugTrail.DISABLE,
-    strict_coercion=False,
-)
-
-raw_lines = []
-with Path("hh_ru_vacancies.jsonlines").open("r", encoding="utf-8") as f:
-    raw_lines = map(json.loads, f.readlines())
-
-lines = retort.load(raw_lines, list[DataSetLine])
-f = set()
-c = 0
-for line in lines:
-    if c == 1000:
-        break
-    if line.experience:
-        f.add(line.experience)
-        c += 0
-
-print(f)

src/dataset/dump_data.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+import subprocess  # noqa: S404
+from pathlib import Path
+
+from template_project.web_api.configuration import load_configuration
+
+
+def main() -> None:
+    config_path = Path("config.toml")
+    configuration = load_configuration(config_path)
+
+    db_url = str(configuration.database.url.get_value())
+    db_url = db_url.replace("postgresql+psycopg://", "postgresql://")
+
+    output_dir = Path("dumps")
+    output_dir.mkdir(exist_ok=True)
+
+    output_file = output_dir / "data_dump.sql"
+
+    print("Создание дампа таблиц vacancy, vacancy_embedding, key_skills...")
+
+    subprocess.run(  # noqa: S603
+        [  # noqa: S607
+            "pg_dump",
+            db_url,
+            "--table=vacancy",
+            "--table=vacancy_embedding",
+            "--table=key_skills",
+            "--data-only",
+            "--column-inserts",
+            f"--file={output_file}",
+        ],
+        check=True,
+    )
+
+    print(f"\nДамп создан: {output_file}")
+    print(f"Размер файла: {output_file.stat().st_size / 1024 / 1024:.2f} MB")
+    print("\nДля импорта на прод сервере выполните:")
+    print(f"  psql <PROD_DB_URL> -f {output_file}")
+
+
+if __name__ == "__main__":
+    main()

src/dataset/dump_data.sh

@@ -0,0 +1,17 @@
+#!/bin/bash
+
+DB_URL="${DATABASE_URL:-postgresql://user:password@localhost:5432/dbname}"
+
+echo "Создание дампа таблиц vacancy, vacancy_embedding, key_skills..."
+
+pg_dump "$DB_URL" \
+  --table=vacancy \
+  --table=vacancy_embedding \
+  --table=key_skills \
+  --data-only \
+  --column-inserts \
+  --file=dump_data.sql
+
+echo "Дамп создан: dump_data.sql"
+echo "Размер файла: $(du -h dump_data.sql | cut -f1)"
+

src/dataset/load_dump.sh

@@ -0,0 +1,20 @@
+#!/bin/bash
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="$(cd "$SCRIPT_DIR/../.." && pwd)"
+DUMP_FILE="${1:-$PROJECT_ROOT/dumps/data_dump.sql}"
+
+if [ ! -f "$DUMP_FILE" ]; then
+    echo "Ошибка: файл $DUMP_FILE не найден"
+    echo "Использование: $0 [путь_к_дампу]"
+    echo "По умолчанию: $PROJECT_ROOT/dumps/data_dump.sql"
+    exit 1
+fi
+
+DB_URL="${DATABASE_URL:-postgresql://postgres:postgres@localhost:5432/app}"
+
+echo "Импорт дампа из $DUMP_FILE в БД $DB_URL..."
+
+psql "$DB_URL" -f "$DUMP_FILE"
+
+echo "Импорт завершен!"

src/dataset/load_skills.py

@@ -0,0 +1,44 @@
+#!/usr/bin/env python3
+import asyncio
+import json
+from pathlib import Path
+
+from template_project.adapters.data_gateways.key_skills import KeySkillsDataGateway
+from template_project.adapters.unit_of_work import DefaultUnitOfWork
+from template_project.web_api.configuration import load_configuration
+from template_project.web_api.ioc.make import make_ioc
+
+
+async def main() -> None:
+    config_path = Path("config.toml")
+    configuration = load_configuration(config_path)
+
+    container = make_ioc(configuration)
+
+    try:
+        async with container() as request_container:
+            data_gateway = await request_container.get(KeySkillsDataGateway)
+            unit_of_work = await request_container.get(DefaultUnitOfWork)
+
+            json_path = Path("full_skills_unique.json")
+            with json_path.open("r", encoding="utf-8") as f:
+                all_skills: list[str] = json.load(f)
+
+            skills_to_load = all_skills[:50000]
+
+            print(f"Загружаю {len(skills_to_load)} скиллов в БД...")
+
+            batch_size = 100
+            for i in range(0, len(skills_to_load), batch_size):
+                batch = skills_to_load[i : i + batch_size]
+                await data_gateway.add_skills(batch)
+                await unit_of_work.commit()
+                print(f"Загружено {min(i + batch_size, len(skills_to_load))} / {len(skills_to_load)}")
+
+            print("Готово!")
+    finally:
+        await container.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/dataset/load_vacancies.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python3
+import ast
+import asyncio
+import csv
+from decimal import Decimal
+from pathlib import Path
+
+from template_project.adapters.unit_of_work import DefaultUnitOfWork
+from template_project.application.common.embedding import Embedder
+from template_project.application.common.enums import ExperienceType
+from template_project.application.vacancy.entity import Vacancy, VacancyEmbedding
+from template_project.ml.configuration import load_configuration as load_ml_configuration
+from template_project.ml.ioc.make import make_ioc as make_ml_ioc
+from template_project.web_api.configuration import load_configuration as load_backend_configuration
+from template_project.web_api.ioc.make import make_ioc as make_backend_ioc
+
+
+def parse_skills(skills_str: str) -> list[str]:
+    try:
+        skills = ast.literal_eval(skills_str)
+        if isinstance(skills, list):
+            return [str(skill) for skill in skills]
+        return []  # noqa
+    except (ValueError, SyntaxError):
+        return []
+
+
+def compose_embedding_text(position: str, description: str, key_skills: list[str]) -> str:
+    skills_text = ", ".join(key_skills) if key_skills else ""
+    parts = [position, description, skills_text]
+    return " ".join(filter(None, parts))
+
+
+async def main() -> None:
+    backend_config_path = Path("config.toml")
+    backend_configuration = load_backend_configuration(backend_config_path)
+    backend_container = make_backend_ioc(backend_configuration)
+
+    ml_config_path = Path("infrastructure/configs/ml/config.toml")
+    ml_configuration = load_ml_configuration(ml_config_path)
+    ml_container = make_ml_ioc(ml_configuration)
+
+    csv_path = Path("filtered_vacancies.csv")
+    max_records = 100_000
+
+    try:
+        async with backend_container() as backend_request_container, ml_container() as ml_request_container:
+            unit_of_work = await backend_request_container.get(DefaultUnitOfWork)
+            embedder = await ml_request_container.get(Embedder)
+
+            print(f"Загружаю первые {max_records} вакансий из {csv_path}...")
+
+            with csv_path.open("r", encoding="utf-8") as f:
+                reader = csv.DictReader(f)
+                batch_size = 50
+                batch = []
+
+                for idx, row in enumerate(reader):
+                    if idx >= max_records:
+                        break
+
+                    try:
+                        vacancy_id_str = row.get("vacancy_id", "").strip()
+                        if not vacancy_id_str:
+                            continue
+
+                        position = row.get("vacancy_nm", "").strip()
+                        if not position:
+                            continue
+
+                        experience_str = row.get("experience", "").strip()
+                        try:
+                            experience_type = ExperienceType(experience_str)
+                        except ValueError:
+                            continue
+
+                        salary_from_str = row.get("salary_from", "").strip()
+                        salary_to_str = row.get("salary_to", "").strip()
+                        try:
+                            salary_from = Decimal(salary_from_str) if salary_from_str else Decimal(0)
+                            salary_to = Decimal(salary_to_str) if salary_to_str else Decimal(0)
+                        except (ValueError, TypeError):
+                            continue
+
+                        description = row.get("vacancy_description", "").strip()
+                        key_skills = parse_skills(row.get("key_skills", "[]"))
+
+                        vacancy = Vacancy.factory(
+                            position=position,
+                            from_salary=salary_from,
+                            to_salary=salary_to,
+                            experience_type=experience_type,
+                            description=description,
+                            key_skills=key_skills,
+                        )
+
+                        embedding_text = compose_embedding_text(position, description, key_skills)
+                        embedding_vector = await embedder.encode(embedding_text)
+
+                        embedding = VacancyEmbedding.factory(
+                            vacancy_id=vacancy.id,
+                            vector=embedding_vector,
+                        )
+
+                        await unit_of_work.add(vacancy, embedding)
+                        batch.append((vacancy.id, position))
+
+                        if len(batch) >= batch_size:
+                            await unit_of_work.commit()
+                            print(f"Загружено {len(batch)} вакансий (всего: {idx + 1})")
+                            batch = []
+
+                    except Exception as e:
+                        print(f"Ошибка при обработке строки {idx + 1}: {e}")
+                        continue
+
+                if batch:
+                    await unit_of_work.commit()
+                    print(f"Загружено {len(batch)} вакансий (всего: {idx + 1})")
+
+            print("Готово!")
+    finally:
+        await backend_container.close()
+        await ml_container.close()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

src/template_project/adapters/data_gateways/key_skills.py

@@ -15,7 +15,7 @@ class KeySkillsDataGateway:
     async def query(self, query: str) -> Sequence[str]:
         statement = (
             select(key_skills_table.c.name)
-            .where(key_skills_table.c.name.ilike(f"{query}%"))
+            .where(key_skills_table.c.name.ilike(f"%{query}%"))
             .order_by(key_skills_table.c.name)
             .limit(30)
         )

src/template_project/adapters/data_gateways/resume.py

@@ -21,13 +21,11 @@ from template_project.application.resume.data_gateway import (
 from template_project.application.resume.entity import (
     Resume,
     ResumeEducation,
-    ResumeEmbeddingId,
     ResumeExperience,
     ResumeId,
     ResumePrediction,
     ResumeProject,
 )
-from template_project.application.resume.errors import ResumeNotFoundError
 from template_project.application.user.entity import UserId
 
 
@@ -36,16 +34,8 @@ class DefaultResumeDataGateway(ResumeDataGateway):
         self._session = session
 
     @override
-    async def get_suitable_resumes(self, embedding_id: ResumeEmbeddingId) -> Sequence[Resume]:
+    async def load_by_resume_id(self, resume_id: ResumeId) -> Resume | None:
-        raise NotImplementedError
+        return await self._session.get(Resume, resume_id)
-
-    @override
-    async def load(self, resume_id: ResumeId) -> Resume:
-        resume = await self._session.get(Resume, resume_id)
-        if resume is None:
-            raise ResumeNotFoundError(resume_id=resume_id)
-
-        return resume
 
     @override
     async def list_by_user_id(self, user_id: UserId, limit: int, offset: int) -> Sequence[Resume]:
@@ -69,11 +59,15 @@ class DefaultResumeDataGateway(ResumeDataGateway):
     async def get_history(self, resume_id: ResumeId) -> Sequence[Resume]:
         # TODO: N+1
         history: list[Resume] = []
-        current_resume = await self.load(resume_id)
+        current_resume = await self.load_by_resume_id(resume_id)
+        if current_resume is None:
+            return history
         history.append(current_resume)
 
         while current_resume.down_resume_id is not None:
-            current_resume = await self.load(current_resume.down_resume_id)
+            current_resume = await self.load_by_resume_id(current_resume.down_resume_id)
+            if current_resume is None:
+                break
             history.append(current_resume)
 
         return history

src/template_project/adapters/data_gateways/tables.py

@@ -8,6 +8,7 @@ from sqlalchemy import (
     DateTime,
     Enum,
     ForeignKey,
+    Index,
     Integer,
     MetaData,
     Numeric,
@@ -22,7 +23,7 @@ from sqlalchemy.orm import registry
 
 from template_project.application.access_token.entity import AccessToken
 from template_project.application.auth_identity.entity import AuthIdentity, AuthMethod
-from template_project.application.common.enums import EducationGrade
+from template_project.application.common.enums import EducationGrade, ExperienceType
 from template_project.application.notification_device.entity import NotificationDevice
 from template_project.application.resume.entity import (
     Resume,
@@ -34,6 +35,7 @@ from template_project.application.resume.entity import (
 )
 from template_project.application.user.entity import User
 from template_project.application.user.profile.entity import Profile
+from template_project.application.vacancy.entity import Vacancy, VacancyEmbedding
 
 meta_data: Final = MetaData()
 mapper_registry: Final = registry()
@@ -69,6 +71,34 @@ class StringArrayType(TypeDecorator[list[str]]):
         return []
 
 
+class ExperienceTypeType(TypeDecorator[ExperienceType]):
+    impl: Any = String
+    cache_ok: bool | None = True
+
+    @override
+    def process_bind_param(self, value: Any, dialect: Any) -> Any:
+        if value is None:
+            return None
+        if isinstance(value, ExperienceType):
+            return value.value
+        if isinstance(value, str):
+            return value
+        return None
+
+    @override
+    def process_result_value(self, value: Any, dialect: Any) -> ExperienceType:
+        if value is None:
+            raise ValueError("experience_type cannot be None")
+        if isinstance(value, ExperienceType):
+            return value
+        if isinstance(value, str):
+            try:
+                return ExperienceType(value)
+            except ValueError as err:
+                raise ValueError(f"Invalid experience_type value: {value}") from err
+        raise ValueError(f"Cannot convert {type(value)} to ExperienceType")
+
+
 user_table: Final = Table(
     "users",
     meta_data,
@@ -137,7 +167,7 @@ resume_table: Final = Table(
     Column("location", String, nullable=False),
     Column("about_me", String, nullable=False),
     Column("key_skills", StringArrayType(), nullable=False, server_default=text("'[]'::jsonb")),
-    Column("experience_type", String, nullable=False),
+    Column("experience_type", ExperienceTypeType(), nullable=False),
     Column("down_resume_id", UUID, ForeignKey("resume.id", ondelete="CASCADE"), nullable=True, default=None),
     Column("up_resume_id", UUID, ForeignKey("resume.id", ondelete="CASCADE"), nullable=True, default=None),
 )
@@ -149,7 +179,7 @@ resume_embedding_table: Final = Table(
     Column("deleted_at", DateTime(timezone=True)),
     Column("created_at", DateTime(timezone=True), nullable=False),
     Column("resume_id", UUID, ForeignKey("resume.id", ondelete="CASCADE"), nullable=False),
-    Column("vector", Vector, nullable=False),
+    Column("vector", Vector(384), nullable=False),
 )
 resume_prediction_table: Final = Table(
     "resume_prediction",
@@ -205,6 +235,45 @@ resume_project_table: Final = Table(
     Column("description", String, nullable=False),
 )
 
+vacancy_table: Final = Table(
+    "vacancy",
+    meta_data,
+    Column("id", UUID, primary_key=True),
+    Column("deleted_at", DateTime(timezone=True)),
+    Column("created_at", DateTime(timezone=True), nullable=False),
+    Column("position", String, nullable=False),
+    Column("from_salary", Numeric, nullable=False),
+    Column("to_salary", Numeric, nullable=False),
+    Column("experience_type", String, nullable=False),
+    Column("description", String, nullable=False),
+    Column("key_skills", StringArrayType(), nullable=False, server_default=text("'[]'::jsonb")),
+)
+
+vacancy_embedding_table: Final = Table(
+    "vacancy_embedding",
+    meta_data,
+    Column("id", UUID, primary_key=True),
+    Column("deleted_at", DateTime(timezone=True)),
+    Column("created_at", DateTime(timezone=True), nullable=False),
+    Column("vacancy_id", UUID, ForeignKey("vacancy.id", ondelete="CASCADE"), nullable=False),
+    Column("vector", Vector(384), nullable=False),
+)
+
+key_skills_name_trgm_index: Final = Index(
+    "ix_key_skills_name_trgm",
+    key_skills_table.c.name,
+    postgresql_using="gin",
+    postgresql_ops={"name": "gin_trgm_ops"},
+)
+
+vacancy_embedding_vector_cosine_index: Final = Index(
+    "ix_vacancy_embedding_vector_cosine",
+    vacancy_embedding_table.c.vector,
+    postgresql_using="hnsw",
+    postgresql_ops={"vector": "vector_cosine_ops"},
+    postgresql_with={"m": 32, "ef_construction": 256},
+)
+
 
 mapper_registry.map_imperatively(User, user_table)
 mapper_registry.map_imperatively(AccessToken, access_token_table)
@@ -216,6 +285,7 @@ mapper_registry.map_imperatively(
     resume_table,
     properties={
         "key_skills": resume_table.c.key_skills,
+        "experience_type": resume_table.c.experience_type,
     },
 )
 mapper_registry.map_imperatively(ResumeEmbedding, resume_embedding_table)
@@ -229,3 +299,11 @@ mapper_registry.map_imperatively(
 mapper_registry.map_imperatively(ResumeExperience, resume_experience_table)
 mapper_registry.map_imperatively(ResumeEducation, resume_education_table)
 mapper_registry.map_imperatively(ResumeProject, resume_project_table)
+mapper_registry.map_imperatively(
+    Vacancy,
+    vacancy_table,
+    properties={
+        "key_skills": vacancy_table.c.key_skills,
+    },
+)
+mapper_registry.map_imperatively(VacancyEmbedding, vacancy_embedding_table)

src/template_project/adapters/data_gateways/vacancy.py

@@ -0,0 +1,36 @@
+from collections.abc import Sequence
+from typing import override
+
+from sqlalchemy import label, select, text
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from template_project.adapters.data_gateways.tables import vacancy_embedding_table, vacancy_table
+from template_project.application.vacancy.data_gateway import VacancyDataGateway
+from template_project.application.vacancy.data_structure import SuitableVacancy
+from template_project.application.vacancy.entity import Vacancy, VacancyEmbedding
+
+
+class DefaultVacancyDataGateway(VacancyDataGateway):
+    def __init__(self, session: AsyncSession) -> None:
+        self._session = session
+
+    @override
+    async def get_suitable(self, vector: list[float]) -> Sequence[SuitableVacancy]:
+        await self._session.execute(text("SET LOCAL hnsw.ef_search = 128"))
+        distance_expr = vacancy_embedding_table.c.vector.cosine_distance(vector)
+        similarity_expr = 1 - distance_expr
+        statement = (
+            select(Vacancy, label("resume_similarity", similarity_expr))
+            .join(VacancyEmbedding, vacancy_embedding_table.c.vacancy_id == vacancy_table.c.id)
+            .where(similarity_expr >= 0.0)
+            .order_by(distance_expr.asc())
+            .limit(100)
+        )
+        result = await self._session.execute(statement)
+        return [
+            SuitableVacancy(
+                vacancy=row[0],
+                resume_similarity=row[1],
+            )
+            for row in result.all()
+        ]

src/template_project/adapters/generators/resume_embedding_vector.py

@@ -0,0 +1,16 @@
+from typing import override
+
+from template_project.adapters.ml_api_gateway import MlApiGateway
+from template_project.application.resume.vector_generator import ResumeEmbeddingVectorGenerator
+
+
+class DefaultResumeEmbeddingVectorGenerator(ResumeEmbeddingVectorGenerator):
+    def __init__(self, ml_api_gateway: MlApiGateway) -> None:
+        self._ml_api_gateway = ml_api_gateway
+
+    @override
+    async def generate(
+        self,
+        text: str,
+    ) -> list[float]:
+        return await self._ml_api_gateway.generate_embedding(text)

src/template_project/adapters/generators/resume_prediction.py

@@ -0,0 +1,39 @@
+from collections.abc import Sequence
+from typing import override
+
+from template_project.adapters.ml_api_gateway import MlApiGateway, SuitableVacancyDs
+from template_project.application.resume.entity import Resume, ResumePrediction
+from template_project.application.resume.resume_prediction_generator import ResumePredictionGenerator
+from template_project.application.vacancy.data_structure import SuitableVacancy
+
+
+class DefaultResumePredictionGenerator(ResumePredictionGenerator):
+    def __init__(self, ml_api_gateway: MlApiGateway) -> None:
+        self._ml_api_gateway = ml_api_gateway
+
+    @override
+    async def generate(
+        self,
+        resume: Resume,
+        suitable_vacancies: Sequence[SuitableVacancy],
+    ) -> ResumePrediction:
+        response = await self._ml_api_gateway.generate_resume_prediction(
+            resume_id=resume.id,
+            key_skills=resume.key_skills,
+            suitable_vacancies=[
+                SuitableVacancyDs(
+                    vacancy_id=str(suitable_vacancy.vacancy.id),
+                    from_salary=suitable_vacancy.vacancy.from_salary,
+                    to_salary=suitable_vacancy.vacancy.to_salary,
+                    key_skills=suitable_vacancy.vacancy.key_skills,
+                    resume_similarity=suitable_vacancy.resume_similarity,
+                )
+                for suitable_vacancy in suitable_vacancies
+            ],
+        )
+        return ResumePrediction.factory(
+            resume_id=resume.id,
+            from_salary=response.salary_from,
+            to_salary=response.salary_to,
+            recommended_skills=response.recommended_skills,
+        )

src/template_project/adapters/ml_api_gateway.py

@@ -0,0 +1,66 @@
+from collections.abc import Sequence
+from decimal import Decimal
+from typing import cast
+
+from httpx import AsyncClient
+
+from template_project.application.common.data_structure import to_data_structure
+from template_project.application.resume.entity import ResumeId
+
+
+@to_data_structure
+class SuitableVacancyDs:
+    vacancy_id: str
+    from_salary: Decimal
+    to_salary: Decimal
+    key_skills: list[str]
+    resume_similarity: float
+
+
+@to_data_structure
+class GenerateResumePredictionResponse:
+    salary_from: Decimal
+    salary_to: Decimal
+    recommended_skills: list[str]
+
+
+class MlApiGateway:
+    def __init__(self, client: AsyncClient) -> None:
+        self._client = client
+
+    async def generate_embedding(self, text: str) -> list[float]:
+        response = await self._client.post("/get_embedding", json={"text": text}, timeout=100)
+        return cast(list[float], response.json()["embedding"])
+
+    async def generate_resume_prediction(
+        self,
+        resume_id: ResumeId,
+        key_skills: list[str],
+        suitable_vacancies: Sequence[SuitableVacancyDs],
+    ) -> GenerateResumePredictionResponse:
+        response = await self._client.post(
+            "/predict",
+            json={
+                "resume_id": str(resume_id),
+                "key_skills": key_skills,
+                "vacancies": [
+                    {
+                        "vacancy_id": str(suitable_vacancy.vacancy_id),
+                        "from_salary": str(suitable_vacancy.from_salary),
+                        "to_salary": str(suitable_vacancy.to_salary),
+                        "key_skills": suitable_vacancy.key_skills,
+                        "resume_similarity": suitable_vacancy.resume_similarity,
+                    }
+                    for suitable_vacancy in suitable_vacancies
+                ],
+            },
+            timeout=100,
+        )
+
+        response.raise_for_status()
+        response_json = response.json()
+        return GenerateResumePredictionResponse(
+            salary_from=Decimal(str(response_json["salary_from"])),
+            salary_to=Decimal(str(response_json["salary_to"])),
+            recommended_skills=response_json["recommended_skills"],
+        )

src/template_project/application/common/entity.py

@@ -1,7 +1,7 @@
 from collections.abc import Hashable
-from dataclasses import dataclass, replace
+from dataclasses import dataclass
 from datetime import datetime
-from typing import Self, cast, dataclass_transform, override
+from typing import cast, dataclass_transform, override
 from uuid import UUID
 
 from template_project.application.common.errors import EntityAlreadyDeletedError
@@ -22,9 +22,6 @@ class Entity[EntityId: UUID](Hashable):
         if self.deleted_at is not None:
             raise EntityAlreadyDeletedError(entity_name=self.__class__.__name__)
 
-    def __copy__(self) -> Self:
-        return replace(self)
-
     @override
     def __eq__(self, other: object) -> bool:
         if isinstance(other, Entity):

src/template_project/application/resume/data_gateway.py

@@ -5,7 +5,6 @@ from typing import Protocol
 from template_project.application.resume.entity import (
     Resume,
     ResumeEducation,
-    ResumeEmbeddingId,
     ResumeExperience,
     ResumeId,
     ResumePrediction,
@@ -16,11 +15,7 @@ from template_project.application.user.entity import UserId
 
 class ResumeDataGateway(Protocol):
     @abstractmethod
-    async def get_suitable_resumes(self, embedding_id: ResumeEmbeddingId) -> Sequence[Resume]:
+    async def load_by_resume_id(self, resume_id: ResumeId) -> Resume | None:
-        raise NotImplementedError
-
-    @abstractmethod
-    async def load(self, resume_id: ResumeId) -> Resume:
         raise NotImplementedError
 
     @abstractmethod

src/template_project/application/resume/entity.py

@@ -80,6 +80,23 @@ class ResumePrediction(Entity[ResumePredictionId]):
     to_salary: Decimal
     recommended_skills: list[str]
 
+    @classmethod
+    def factory(
+        cls,
+        resume_id: ResumeId,
+        from_salary: Decimal,
+        to_salary: Decimal,
+        recommended_skills: list[str],
+    ) -> Self:
+        return cls(
+            id=ResumePredictionId(uuid7()),
+            created_at=datetime.now(tz=UTC),
+            resume_id=resume_id,
+            from_salary=from_salary,
+            to_salary=to_salary,
+            recommended_skills=recommended_skills,
+        )
+
 
 @to_entity
 class ResumeExperience(Entity[ResumeExperienceId]):

src/template_project/application/resume/interactors/edit.py

@@ -16,7 +16,7 @@ from template_project.application.resume.entity import (
     ResumeId,
     ResumeProject,
 )
-from template_project.application.resume.errors import ResumeDoesBelongUserError
+from template_project.application.resume.errors import ResumeDoesBelongUserError, ResumeNotFoundError
 
 
 @to_data_structure
@@ -96,7 +96,9 @@ class EditResumeInteractor:
         projects: list[ProjectInput] | None = None,
     ) -> EditResumeResponse:
         user = await self.identity_provider.get_current_user()
-        old_resume = await self.resume_data_gateway.load(resume_id)
+        old_resume = await self.resume_data_gateway.load_by_resume_id(resume_id)
+        if old_resume is None:
+            raise ResumeNotFoundError(resume_id=resume_id)
         if old_resume.user_id != user.id:
             raise ResumeDoesBelongUserError
 

src/template_project/application/resume/interactors/get.py

@@ -12,7 +12,7 @@ from template_project.application.resume.data_gateway import (
     ResumeProjectDataGateway,
 )
 from template_project.application.resume.entity import ResumeId
-from template_project.application.resume.errors import ResumeDoesBelongUserError
+from template_project.application.resume.errors import ResumeDoesBelongUserError, ResumeNotFoundError
 
 
 @to_data_structure
@@ -72,7 +72,9 @@ class GetResumeInteractor:
     ) -> GetResumeResponse:
         user = await self.identity_provider.get_current_user()
 
-        resume = await self.resume_data_gateway.load(resume_id)
+        resume = await self.resume_data_gateway.load_by_resume_id(resume_id)
+        if resume is None:
+            raise ResumeNotFoundError(resume_id=resume_id)
 
         if resume.user_id != user.id:
             raise ResumeDoesBelongUserError
@@ -134,19 +136,43 @@ class ResumeListItemResponse:
     about_me: str
     key_skills: list[str]
     experience_type: ExperienceType
+    experience: list[ExperienceItemResponse]
+    education: list[EducationItemResponse]
+    projects: list[ProjectItemResponse]
+    prediction: ResumePredictionResponse | None
 
 
 @to_interactor
 class GetResumeListInteractor:
     identity_provider: IdentityProvider
     resume_data_gateway: ResumeDataGateway
+    resume_prediction_data_gateway: ResumePredictionDataGateway
+    resume_experience_data_gateway: ResumeExperienceDataGateway
+    resume_education_data_gateway: ResumeEducationDataGateway
+    resume_project_data_gateway: ResumeProjectDataGateway
 
     async def execute(self, limit: int, offset: int) -> list[ResumeListItemResponse]:
         user = await self.identity_provider.get_current_user()
 
         resumes = await self.resume_data_gateway.list_latest_by_user_id(user.id, limit=limit, offset=offset)
 
-        return [
+        result = []
+        for r in resumes:
+            resume_prediction = await self.resume_prediction_data_gateway.load_by_resume_id(r.id)
+            if resume_prediction is not None:
+                prediction = ResumePredictionResponse(
+                    from_salary=resume_prediction.from_salary,
+                    to_salary=resume_prediction.to_salary,
+                    recommended_skills=resume_prediction.recommended_skills,
+                )
+            else:
+                prediction = None
+
+            experiences = await self.resume_experience_data_gateway.load_by_resume_id(r.id)
+            educations = await self.resume_education_data_gateway.load_by_resume_id(r.id)
+            projects = await self.resume_project_data_gateway.load_by_resume_id(r.id)
+
+            result.append(
                 ResumeListItemResponse(
                     id=r.id,
                     position=r.position,
@@ -154,26 +180,74 @@ class GetResumeListInteractor:
                     about_me=r.about_me,
                     key_skills=r.key_skills,
                     experience_type=r.experience_type,
+                    experience=[
+                        ExperienceItemResponse(
+                            place=exp.place,
+                            description=exp.description,
+                            months_duration=exp.months_duration,
                         )
-            for r in resumes
+                        for exp in experiences
-        ]
+                    ],
+                    education=[
+                        EducationItemResponse(
+                            place=edu.place,
+                            grade=edu.grade,
+                            specialization=edu.specialization,
+                            description=edu.description,
+                        )
+                        for edu in educations
+                    ],
+                    projects=[
+                        ProjectItemResponse(
+                            name=proj.name,
+                            description=proj.description,
+                        )
+                        for proj in projects
+                    ],
+                    prediction=prediction,
+                )
+            )
+
+        return result
 
 
 @to_interactor
 class GetResumeHistoryInteractor:
     identity_provider: IdentityProvider
     resume_data_gateway: ResumeDataGateway
+    resume_prediction_data_gateway: ResumePredictionDataGateway
+    resume_experience_data_gateway: ResumeExperienceDataGateway
+    resume_education_data_gateway: ResumeEducationDataGateway
+    resume_project_data_gateway: ResumeProjectDataGateway
 
     async def execute(self, resume_id: ResumeId) -> list[ResumeListItemResponse]:
         user = await self.identity_provider.get_current_user()
 
-        resume = await self.resume_data_gateway.load(resume_id)
+        resume = await self.resume_data_gateway.load_by_resume_id(resume_id)
+        if resume is None:
+            raise ResumeNotFoundError(resume_id=resume_id)
         if resume.user_id != user.id:
             raise ResumeDoesBelongUserError
 
         history = await self.resume_data_gateway.get_history(resume_id)
 
-        return [
+        result = []
+        for r in history:
+            resume_prediction = await self.resume_prediction_data_gateway.load_by_resume_id(r.id)
+            if resume_prediction is not None:
+                prediction = ResumePredictionResponse(
+                    from_salary=resume_prediction.from_salary,
+                    to_salary=resume_prediction.to_salary,
+                    recommended_skills=resume_prediction.recommended_skills,
+                )
+            else:
+                prediction = None
+
+            experiences = await self.resume_experience_data_gateway.load_by_resume_id(r.id)
+            educations = await self.resume_education_data_gateway.load_by_resume_id(r.id)
+            projects = await self.resume_project_data_gateway.load_by_resume_id(r.id)
+
+            result.append(
                 ResumeListItemResponse(
                     id=r.id,
                     position=r.position,
@@ -181,6 +255,32 @@ class GetResumeHistoryInteractor:
                     about_me=r.about_me,
                     key_skills=r.key_skills,
                     experience_type=r.experience_type,
+                    experience=[
+                        ExperienceItemResponse(
+                            place=exp.place,
+                            description=exp.description,
+                            months_duration=exp.months_duration,
                         )
-            for r in history
+                        for exp in experiences
-        ]
+                    ],
+                    education=[
+                        EducationItemResponse(
+                            place=edu.place,
+                            grade=edu.grade,
+                            specialization=edu.specialization,
+                            description=edu.description,
+                        )
+                        for edu in educations
+                    ],
+                    projects=[
+                        ProjectItemResponse(
+                            name=proj.name,
+                            description=proj.description,
+                        )
+                        for proj in projects
+                    ],
+                    prediction=prediction,
+                )
+            )
+
+        return result

src/template_project/application/resume/interactors/predict_model.py

@@ -33,7 +33,7 @@ class PredictSalaryResponse:
 
 
 @to_interactor
-class PredictSalaryInteractor:
+class PredictModelInteractor:
     async def execute(self, request: PredictSalaryRequest) -> PredictSalaryResponse:
         salary_from, salary_to = self._predict_salary(request.vacancies, request.key_skills)
         recommended_skills = self._recommend_skills(request.vacancies, request.key_skills)
@@ -46,7 +46,7 @@ class PredictSalaryInteractor:
 
     def _predict_salary(self, vacancies: list[VacancyInput], resume_skills: list[str]) -> tuple[Decimal, Decimal]:
         if not vacancies:
-            return Decimal(50000), Decimal(80000)
+            return Decimal(0), Decimal(0)
 
         vacancy_weights: list[float] = []
         for vacancy in vacancies:
@@ -56,7 +56,7 @@ class PredictSalaryInteractor:
 
         total_weight = sum(vacancy_weights)
         if total_weight == 0:
-            return Decimal(50000), Decimal(80000)
+            return Decimal(0), Decimal(0)
 
         weighted_from_sum = Decimal(0)
         weighted_to_sum = Decimal(0)
@@ -143,6 +143,9 @@ class PredictSalaryInteractor:
             if skill in candidate_skills
         }
 
+        if not candidate_skills:
+            return []
+
         frequencies = [skill_frequencies[skill] for skill in candidate_skills]
         avg_salaries = [float(skill_avg_salaries[skill]) for skill in candidate_skills]
 

src/template_project/application/resume/interactors/prediction_pipeline.py

@@ -0,0 +1,100 @@
+from typing import Final
+
+from Levenshtein import ratio
+
+from template_project.application.common.data_structure import to_data_structure
+from template_project.application.common.interactor import to_interactor
+from template_project.application.common.unit_of_work import UnitOfWork
+from template_project.application.resume.data_gateway import ResumeDataGateway
+from template_project.application.resume.entity import Resume, ResumeEmbedding, ResumeId
+from template_project.application.resume.resume_prediction_generator import ResumePredictionGenerator
+from template_project.application.resume.vector_generator import ResumeEmbeddingVectorGenerator
+from template_project.application.vacancy.data_gateway import VacancyDataGateway
+from template_project.application.vacancy.data_structure import SuitableVacancy
+
+EMBEDDING_TEXT_TEMPLATE: Final = """
+    Позиция: {position}
+    Опыт: {experience_type}
+    Ключевые навыки: {key_skills}
+    Описание: {about_me}
+"""
+
+
+def _calculate_skills_matching(resume_skills: list[str], vacancy_skills: list[str]) -> float:
+    count_skills = 0
+    ratio_skill_sum = 0.0
+    for resume_key_skill in resume_skills:
+        for vacancy_key_skill in vacancy_skills:
+            ratio_skill = ratio(resume_key_skill, vacancy_key_skill)
+            if ratio_skill != 0:
+                count_skills += 1
+                ratio_skill_sum += ratio_skill
+
+    try:
+        return ratio_skill_sum / count_skills
+    except ZeroDivisionError:
+        return 0.0
+
+
+def _filter_and_sort_vacancies(
+    resume: Resume,
+    suitable_vacancies: list[SuitableVacancy],
+    limit: int = 50,
+) -> list[SuitableVacancy]:
+    def calculate_priority(vacancy: SuitableVacancy) -> float:
+        priority = vacancy.resume_similarity
+
+        if resume.experience_type == vacancy.vacancy.experience_type:
+            priority += 0.1
+
+        if resume.key_skills:
+            skills_matching = _calculate_skills_matching(resume.key_skills, vacancy.vacancy.key_skills)
+            priority += skills_matching * 0.2
+
+        return priority
+
+    sorted_vacancies = sorted(suitable_vacancies, key=calculate_priority, reverse=True)
+
+    return sorted_vacancies[:limit]
+
+
+@to_data_structure
+class PredictResumeRequest:
+    resume_id: ResumeId
+
+
+@to_interactor
+class ResumePredictionInteractor:
+    unit_of_work: UnitOfWork
+    resume_data_gateway: ResumeDataGateway
+    vacancy_data_gateway: VacancyDataGateway
+    vector_generator: ResumeEmbeddingVectorGenerator
+    resume_prediction_generator: ResumePredictionGenerator
+
+    async def execute(self, request: PredictResumeRequest) -> None:
+        resume = await self.resume_data_gateway.load_by_resume_id(request.resume_id)
+        if resume is None:
+            return
+
+        embedding_text = EMBEDDING_TEXT_TEMPLATE.format_map({
+            "position": resume.position,
+            "experience_type": resume.experience_type.value,
+            "key_skills": ", ".join(resume.key_skills),
+            "about_me": resume.about_me,
+        })
+        vector = await self.vector_generator.generate(embedding_text)
+        resume_embedding = ResumeEmbedding.factory(
+            resume_id=resume.id,
+            vector=vector,
+        )
+
+        suitable_vacancies_list = list(await self.vacancy_data_gateway.get_suitable(resume_embedding.vector))
+        suitable_vacancies_filtered = _filter_and_sort_vacancies(resume, suitable_vacancies_list, limit=50)
+
+        resume_prediction = await self.resume_prediction_generator.generate(
+            resume=resume,
+            suitable_vacancies=suitable_vacancies_filtered,
+        )
+
+        await self.unit_of_work.add(resume_embedding, resume_prediction)
+        await self.unit_of_work.commit()

src/template_project/application/resume/resume_embedding_pipeline.py

@@ -1,71 +0,0 @@
-from collections.abc import Callable
-
-from Levenshtein import ratio
-
-from template_project.application.common.unit_of_work import UnitOfWork
-from template_project.application.resume.data_gateway import ResumeDataGateway
-from template_project.application.resume.entity import Resume, ResumeEmbedding, ResumePrediction
-from template_project.application.resume.vector_generator import ResumeEmbeddingVectorGenerator
-
-
-def suitable_resumes_key(
-    resume: Resume,
-) -> Callable[[Resume], bool]:
-    def wrapper(suitable_resume: Resume) -> bool:
-        count_skills = 0
-        ratio_skill_sum = 0.0
-        for resum_key_skill in resume.key_skills:
-            for suitable_resume_key_skill in suitable_resume.key_skills:
-                ratio_skill = ratio(resum_key_skill, suitable_resume_key_skill)
-                if ratio_skill != 0:
-                    count_skills += 1
-                    ratio_skill_sum += ratio_skill
-
-        try:
-            matching_skills = ratio_skill_sum / count_skills
-        except ZeroDivisionError:
-            matching_skills = 0
-
-        return resume.experience_type == suitable_resume.experience_type and matching_skills >= 50
-
-    return wrapper
-
-
-class ResumeEmbeddingPipeline:
-    def __init__(
-        self,
-        unit_of_work: UnitOfWork,
-        resume_data_gateway: ResumeDataGateway,
-        vector_generator: ResumeEmbeddingVectorGenerator,
-    ) -> None:
-        self.unit_of_work = unit_of_work
-        self.resume_data_gateway = resume_data_gateway
-        self.vector_generator = vector_generator
-
-    async def run(
-        self,
-        resume: Resume,
-    ) -> ResumePrediction:
-        vector = await self.vector_generator.generate(
-            position=resume.position,
-            about_me=resume.about_me,
-            key_skills=resume.key_skills,
-        )
-        resume_embedding = ResumeEmbedding.factory(
-            resume_id=resume.id,
-            vector=vector,
-        )
-
-        suitable_resumes = await self.resume_data_gateway.get_suitable_resumes(resume_embedding.id)
-        suitable_resumes_filtered = sorted(
-            suitable_resumes,
-            key=suitable_resumes_key(resume),
-        )
-        suitable_resumes = suitable_resumes_filtered[:50]
-
-        # TODO: тут надо сделать отправку в ИИ
-
-        await self.unit_of_work.add(resume_embedding)
-        await self.unit_of_work.commit()
-
-        raise NotImplementedError

src/template_project/application/resume/resume_prediction_generator.py

@@ -0,0 +1,16 @@
+from abc import abstractmethod
+from collections.abc import Sequence
+from typing import Protocol
+
+from template_project.application.resume.entity import Resume, ResumePrediction
+from template_project.application.vacancy.data_structure import SuitableVacancy
+
+
+class ResumePredictionGenerator(Protocol):
+    @abstractmethod
+    async def generate(
+        self,
+        resume: Resume,
+        suitable_vacancies: Sequence[SuitableVacancy],
+    ) -> ResumePrediction:
+        raise NotImplementedError

src/template_project/application/resume/vector_generator.py

@@ -1,13 +1,10 @@
 from abc import abstractmethod
-from typing import Protocol
 
 
-class ResumeEmbeddingVectorGenerator(Protocol):
+class ResumeEmbeddingVectorGenerator:
     @abstractmethod
     async def generate(
         self,
-        position: str,
+        text: str,
-        about_me: str,
-        key_skills: list[str],
     ) -> list[float]:
         raise NotImplementedError

src/template_project/application/vacancy/data_gateway.py

@@ -0,0 +1,11 @@
+from abc import abstractmethod
+from collections.abc import Sequence
+from typing import Protocol
+
+from template_project.application.vacancy.data_structure import SuitableVacancy
+
+
+class VacancyDataGateway(Protocol):
+    @abstractmethod
+    async def get_suitable(self, vector: list[float]) -> Sequence[SuitableVacancy]:
+        raise NotImplementedError

src/template_project/application/vacancy/data_structure.py

@@ -0,0 +1,8 @@
+from template_project.application.common.data_structure import to_data_structure
+from template_project.application.vacancy.entity import Vacancy
+
+
+@to_data_structure
+class SuitableVacancy:
+    vacancy: Vacancy
+    resume_similarity: float

src/template_project/application/vacancy/entity.py

@@ -1,12 +1,19 @@
+from datetime import UTC, datetime
 from decimal import Decimal
-from typing import Any
+from typing import NewType, Self
+from uuid import UUID
+
+from uuid_utils.compat import uuid7
 
 from template_project.application.common.entity import Entity, to_entity
 from template_project.application.common.enums import ExperienceType
 
+VacancyId = NewType("VacancyId", UUID)
+VacancyEmbeddingId = NewType("VacancyEmbeddingId", UUID)
+
 
 @to_entity
-class Vacancy(Entity[Any]):
+class Vacancy(Entity[VacancyId]):
     position: str
     from_salary: Decimal
     to_salary: Decimal
@@ -14,8 +21,42 @@ class Vacancy(Entity[Any]):
     description: str
     key_skills: list[str]
 
+    @classmethod
+    def factory(
+        cls,
+        position: str,
+        from_salary: Decimal,
+        to_salary: Decimal,
+        experience_type: ExperienceType,
+        description: str,
+        key_skills: list[str],
+    ) -> Self:
+        return cls(
+            id=VacancyId(uuid7()),
+            created_at=datetime.now(tz=UTC),
+            position=position,
+            from_salary=from_salary,
+            to_salary=to_salary,
+            experience_type=experience_type,
+            description=description,
+            key_skills=key_skills,
+        )
+
 
 @to_entity
-class VacancyEmbedding(Entity[Any]):
+class VacancyEmbedding(Entity[VacancyEmbeddingId]):
-    vacancy_id: Any
+    vacancy_id: VacancyId
     vector: list[float]
+
+    @classmethod
+    def factory(
+        cls,
+        vacancy_id: VacancyId,
+        vector: list[float],
+    ) -> Self:
+        return cls(
+            id=VacancyEmbeddingId(uuid7()),
+            created_at=datetime.now(tz=UTC),
+            vacancy_id=vacancy_id,
+            vector=vector,
+        )

src/template_project/migrations/versions/2c99db38e99b_.py

@@ -0,0 +1,37 @@
+"""empty message
+
+Revision ID: 2c99db38e99b
+Revises: 9a32674539dd
+Create Date: 2025-11-23 12:08:50.774495
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '2c99db38e99b'
+down_revision: Union[str, Sequence[str], None] = '9a32674539dd'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    op.execute("CREATE EXTENSION IF NOT EXISTS pg_trgm")
+    op.execute("ALTER TABLE vacancy_embedding ALTER COLUMN vector TYPE vector(384)")
+    op.execute("ALTER TABLE resume_embedding ALTER COLUMN vector TYPE vector(384)")
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_index('ix_key_skills_name_trgm', 'key_skills', ['name'], unique=False, postgresql_using='gin', postgresql_ops={'name': 'gin_trgm_ops'})
+    op.create_index('ix_vacancy_embedding_vector_cosine', 'vacancy_embedding', ['vector'], unique=False, postgresql_using='hnsw', postgresql_ops={'vector': 'vector_cosine_ops'}, postgresql_with={'m': 32, 'ef_construction': 256})
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_index('ix_vacancy_embedding_vector_cosine', table_name='vacancy_embedding', postgresql_using='hnsw', postgresql_ops={'vector': 'vector_cosine_ops'}, postgresql_with={'m': 32, 'ef_construction': 256})
+    op.drop_index('ix_key_skills_name_trgm', table_name='key_skills', postgresql_using='gin', postgresql_ops={'name': 'gin_trgm_ops'})
+    # ### end Alembic commands ###

src/template_project/migrations/versions/9a32674539dd_.py

@@ -0,0 +1,58 @@
+"""empty message
+
+Revision ID: 9a32674539dd
+Revises: 892aba57b356
+Create Date: 2025-11-23 01:26:29.515334
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+import pgvector.sqlalchemy
+from sqlalchemy import Text
+from sqlalchemy.dialects.postgresql import JSONB, UUID
+import template_project.adapters.data_gateways.tables
+from template_project.adapters.data_gateways.tables import StringArrayType
+
+
+# revision identifiers, used by Alembic.
+revision: str = '9a32674539dd'
+down_revision: Union[str, Sequence[str], None] = '892aba57b356'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    """Upgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.create_table('vacancy',
+    sa.Column('id', sa.UUID(), nullable=False),
+    sa.Column('deleted_at', sa.DateTime(timezone=True), nullable=True),
+    sa.Column('created_at', sa.DateTime(timezone=True), nullable=False),
+    sa.Column('position', sa.String(), nullable=False),
+    sa.Column('from_salary', sa.Numeric(), nullable=False),
+    sa.Column('to_salary', sa.Numeric(), nullable=False),
+    sa.Column('experience_type', sa.String(), nullable=False),
+    sa.Column('description', sa.String(), nullable=False),
+    sa.Column('key_skills', template_project.adapters.data_gateways.tables.StringArrayType(astext_type=Text()), server_default=sa.text("'[]'::jsonb"), nullable=False),
+    sa.PrimaryKeyConstraint('id')
+    )
+    op.create_table('vacancy_embedding',
+    sa.Column('id', sa.UUID(), nullable=False),
+    sa.Column('deleted_at', sa.DateTime(timezone=True), nullable=True),
+    sa.Column('created_at', sa.DateTime(timezone=True), nullable=False),
+    sa.Column('vacancy_id', sa.UUID(), nullable=False),
+    sa.Column('vector', pgvector.sqlalchemy.vector.VECTOR(), nullable=False),
+    sa.ForeignKeyConstraint(['vacancy_id'], ['vacancy.id'], ondelete='CASCADE'),
+    sa.PrimaryKeyConstraint('id')
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    """Downgrade schema."""
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_table('vacancy_embedding')
+    op.drop_table('vacancy')
+    # ### end Alembic commands ###

src/template_project/ml/entry_point.py

@@ -3,6 +3,8 @@ import asyncio
 import logging
 import os
 import sys
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
 from pathlib import Path
 from typing import Final
 
@@ -11,6 +13,8 @@ from dishka import AsyncContainer
 from dishka.integrations.fastapi import setup_dishka
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
+from prometheus_fastapi_instrumentator import Instrumentator
+from sentence_transformers import SentenceTransformer
 
 from template_project.ml.configuration import load_configuration
 from template_project.ml.ioc.make import make_ioc
@@ -37,10 +41,18 @@ LOG_CONFIG: Final = {
 }
 
 
+@asynccontextmanager
+async def lifespan(app: FastAPI) -> AsyncIterator[None]:
+    await app.state.dishka_container.get(SentenceTransformer)
+    yield
+    await app.state.dishka_container.close()
+
+
 def make_asgi_application(
     ioc: AsyncContainer,
 ) -> FastAPI:
     app = FastAPI(
+        lifespan=lifespan,
         docs_url="/docs",
         title="ML Service",
         description="ML Service API",
@@ -57,6 +69,7 @@ def make_asgi_application(
     app.include_router(healthcheck.router)
     app.include_router(embed.router)
     app.include_router(predict.router)
+    Instrumentator().instrument(app).expose(app)
 
     setup_dishka(container=ioc, app=app)
 
@@ -81,10 +94,7 @@ async def _main(
         access_log=configuration.server.access_log,
     )
     server = uvicorn.Server(config)
-    try:
     await server.serve()
-    finally:
-        await ioc.close()
 
 
 def main() -> None:

src/template_project/ml/ioc/interactor.py

@@ -1,11 +1,11 @@
 from dishka import BaseScope, Provider, Scope, provide_all
 
-from template_project.application.resume.interactors.predict_salary import PredictSalaryInteractor
+from template_project.application.resume.interactors.predict_model import PredictModelInteractor
 
 
 class InteractorProvider(Provider):
     scope: BaseScope | None = Scope.REQUEST
 
     interactors = provide_all(
-        PredictSalaryInteractor,
+        PredictModelInteractor,
     )

src/template_project/ml/routes/predict.py

@@ -6,8 +6,8 @@ from fastapi import APIRouter
 from pydantic import BaseModel, Field
 
 from template_project.application.resume.entity import ResumeId
-from template_project.application.resume.interactors.predict_salary import (
+from template_project.application.resume.interactors.predict_model import (
-    PredictSalaryInteractor,
+    PredictModelInteractor,
     PredictSalaryRequest,
     VacancyInput,
 )
@@ -40,9 +40,9 @@ class VacancyInputModel(BaseModel):
 class PredictSalaryRequestModel(BaseModel):
     resume_id: ResumeId = Field(description="Resume ID", examples=["01234567-89ab-cdef-0123-456789abcdef"])
     key_skills: list[str] = Field(
-        min_length=1, description="List of key skills from resume", examples=[["Python", "FastAPI", "PostgreSQL"]]
+        description="List of key skills from resume", examples=[["Python", "FastAPI", "PostgreSQL"]]
     )
-    vacancies: list[VacancyInputModel] = Field(min_length=1, description="List of relevant vacancies", examples=[[]])
+    vacancies: list[VacancyInputModel] = Field(description="List of relevant vacancies", examples=[[]], min_length=0)
 
     model_config = {
         "json_schema_extra": {
@@ -94,7 +94,7 @@ class PredictSalaryResponseModel(BaseModel):
 )
 async def predict(
     request: PredictSalaryRequestModel,
-    interactor: FromDishka[PredictSalaryInteractor],
+    interactor: FromDishka[PredictModelInteractor],
 ) -> PredictSalaryResponseModel:
     vacancy_inputs = [
         VacancyInput(

src/template_project/web_api/configuration.py

@@ -28,6 +28,11 @@ class S3Config:
     secret_key: str
 
 
+@to_configuration
+class MlApiConfiguration:
+    url: str
+
+
 @to_configuration
 class AccessTokenConfiguration:
     crypto_key: str
@@ -71,6 +76,7 @@ class Configuration:
     access_token: AccessTokenConfiguration
     yandex_oauth: YandexOAuthConfiguration
     firebase: FirebaseConfiguration
+    ml_api: MlApiConfiguration
 
 
 retort = Retort(

src/template_project/web_api/ioc/connection.py

@@ -2,10 +2,12 @@ from collections.abc import AsyncIterable
 
 from aioboto3.session import Session
 from dishka import Provider, Scope, provide
+from httpx import AsyncClient, Timeout
 from sqlalchemy.ext.asyncio import AsyncEngine, AsyncSession, create_async_engine
 
+from template_project.adapters.ml_api_gateway import MlApiGateway
 from template_project.adapters.s3_storage import AioBoto3ClientLike
-from template_project.web_api.configuration import DatabaseConfiguration, S3Config
+from template_project.web_api.configuration import DatabaseConfiguration, MlApiConfiguration, S3Config
 
 
 class ConnectionProvider(Provider):
@@ -35,3 +37,9 @@ class ConnectionProvider(Provider):
             aws_secret_access_key=config.secret_key,
         ) as s3_client:
             yield s3_client
+
+    @provide(scope=Scope.APP)
+    async def ml_api_gateway(self, config: MlApiConfiguration) -> AsyncIterable[MlApiGateway]:
+        timeout = Timeout(30.0, read=30.0)
+        async with AsyncClient(base_url=config.url, timeout=timeout) as client:
+            yield MlApiGateway(client)

src/template_project/web_api/ioc/data_gateway.py

@@ -13,6 +13,7 @@ from template_project.adapters.data_gateways.resume import (
     DefaultResumeProjectDataGateway,
 )
 from template_project.adapters.data_gateways.user import DefaultUserDataGateway
+from template_project.adapters.data_gateways.vacancy import DefaultVacancyDataGateway
 from template_project.adapters.unit_of_work import DefaultUnitOfWork
 
 
@@ -22,6 +23,7 @@ class DataGatewayProvider(Provider):
     unit_of_work = provide(WithParents[DefaultUnitOfWork])
     data_gateways = provide_all(
         KeySkillsDataGateway,
+        WithParents[DefaultVacancyDataGateway],
         WithParents[DefaultUserDataGateway],
         WithParents[DefaultAccessTokenDataGateway],
         WithParents[DefaultAuthIdentityDataGateway],

src/template_project/web_api/ioc/interactor.py

@@ -13,6 +13,7 @@ from template_project.application.resume.interactors.get import (
     GetResumeInteractor,
     GetResumeListInteractor,
 )
+from template_project.application.resume.interactors.prediction_pipeline import ResumePredictionInteractor
 from template_project.application.user.profile.interactors.get_profile import GetProfileInteractor
 from template_project.application.user.profile.interactors.patch_profile import PatchProfileInteractor
 
@@ -32,4 +33,5 @@ class InteractorProvider(Provider):
         GetResumeHistoryInteractor,
         AddResumeInteractor,
         EditResumeInteractor,
+        ResumePredictionInteractor,
     )

src/template_project/web_api/ioc/make.py

@@ -6,6 +6,7 @@ from template_project.web_api.configuration import (
     Configuration,
     DatabaseConfiguration,
     FirebaseConfiguration,
+    MlApiConfiguration,
     S3Config,
     ServerConfiguration,
     YandexOAuthConfiguration,
@@ -20,6 +21,7 @@ from template_project.web_api.ioc.notifications import (
     NotificationServiceProvider,
 )
 from template_project.web_api.ioc.oauth import OAuthClientProvider
+from template_project.web_api.ioc.other import OtherProvider
 from template_project.web_api.ioc.storage import StorageProvider
 
 
@@ -35,6 +37,7 @@ def make_ioc(configuration: Configuration) -> AsyncContainer:
         OAuthClientProvider(),
         NotificationServiceProvider(),
         StorageProvider(),
+        OtherProvider(),
         validation_settings=STRICT_VALIDATION,
         context={
             ServerConfiguration: configuration.server,
@@ -44,5 +47,6 @@ def make_ioc(configuration: Configuration) -> AsyncContainer:
             FirebaseConfiguration: configuration.firebase,
             Configuration: configuration,
             S3Config: configuration.s3,
+            MlApiConfiguration: configuration.ml_api,
         },
     )

src/template_project/web_api/ioc/other.py

@@ -0,0 +1,13 @@
+from dishka import BaseScope, Provider, Scope, WithParents, provide_all
+
+from template_project.adapters.generators.resume_embedding_vector import DefaultResumeEmbeddingVectorGenerator
+from template_project.adapters.generators.resume_prediction import DefaultResumePredictionGenerator
+
+
+class OtherProvider(Provider):
+    scope: BaseScope | None = Scope.REQUEST
+
+    other_providers = provide_all(
+        WithParents[DefaultResumePredictionGenerator],
+        WithParents[DefaultResumeEmbeddingVectorGenerator],
+    )

src/template_project/web_api/routes/resume.py

@@ -2,9 +2,9 @@ from decimal import Decimal
 from http import HTTPStatus
 from typing import Annotated
 
-from dishka import FromDishka
+from dishka import AsyncContainer, FromDishka
 from dishka.integrations.fastapi import DishkaRoute
-from fastapi import APIRouter, Depends, HTTPException, Query
+from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException, Query, Request
 from fastapi.security import HTTPBearer
 from pydantic import BaseModel, Field
 
@@ -28,6 +28,10 @@ from template_project.application.resume.interactors.get import (
     GetResumeInteractor,
     GetResumeListInteractor,
 )
+from template_project.application.resume.interactors.prediction_pipeline import (
+    PredictResumeRequest,
+    ResumePredictionInteractor,
+)
 
 security = HTTPBearer()
 router = APIRouter(route_class=DishkaRoute, tags=["Resume"], dependencies=[Depends(security)])
@@ -79,14 +83,14 @@ class EducationItem(BaseModel):
 class ProjectItem(BaseModel):
     name: str = Field(min_length=1, max_length=200, description="Project name", examples=["Rekomenci fluon"])
     description: str = Field(
-        min_length=1, max_length=2000, description="Project description", examples=["fucking shit"]
+        min_length=1, max_length=2000, description="Project description", examples=["some description"]
     )
 
     model_config = {
         "json_schema_extra": {
             "example": {
                 "name": "Rekomenci fluon",
-                "description": "fucking shit",
+                "description": "some description",
             }
         }
     }
@@ -135,7 +139,7 @@ class CreateResumeRequest(BaseModel):
                 "projects": [
                     {
                         "name": "Rekomenci fluon",
-                        "description": "fucking shit",
+                        "description": "some description",
                     }
                 ],
             }
@@ -203,7 +207,7 @@ class ResumeResponse(BaseModel):
                 "projects": [
                     {
                         "name": "Rekomenci fluon",
-                        "description": "fucking shit",
+                        "description": "some description",
                     }
                 ],
                 "prediction": {
@@ -223,6 +227,10 @@ class ResumeListItem(BaseModel):
     about_me: str = Field(description="About me section")
     key_skills: list[str] = Field(description="List of key skills")
     experience_type: ExperienceType = Field(description="Experience type")
+    experience: list[ExperienceItem] = Field(description="Work experience list")
+    education: list[EducationItem] = Field(description="Education list")
+    projects: list[ProjectItem] = Field(description="Projects list")
+    prediction: SalaryPrediction | None = Field(None, description="Salary prediction (can be null)")
 
     model_config = {
         "json_schema_extra": {
@@ -232,6 +240,27 @@ class ResumeListItem(BaseModel):
                 "about_me": "Experienced Python developer with 5 years of experience",
                 "key_skills": ["Python", "FastAPI", "PostgreSQL"],
                 "experience_type": "between3And6",
+                "experience": [
+                    {
+                        "place": "T-bank",
+                        "description": "some description lorem ipsum",
+                        "months_duration": 12,
+                    }
+                ],
+                "education": [
+                    {
+                        "place": "Central university",
+                        "grade": "bachelor",
+                        "specialization": "IT guy",
+                        "description": "optional field, if user want add something",
+                    }
+                ],
+                "projects": [
+                    {
+                        "name": "Rekomenci fluon",
+                        "description": "some description",
+                    }
+                ],
             }
         }
     }
@@ -251,9 +280,31 @@ class GetResumeListResponse(BaseModel):
                 "resumes": [
                     {
                         "position": "Python Developer",
+                        "location": "Moscow",
                         "about_me": "Experienced Python developer",
                         "key_skills": ["Python", "FastAPI"],
                         "experience_type": "between3And6",
+                        "experience": [
+                            {
+                                "place": "T-bank",
+                                "description": "some description lorem ipsum",
+                                "months_duration": 12,
+                            }
+                        ],
+                        "education": [
+                            {
+                                "place": "Central university",
+                                "grade": "bachelor",
+                                "specialization": "IT guy",
+                                "description": "optional field, if user want add something",
+                            }
+                        ],
+                        "projects": [
+                            {
+                                "name": "Rekomenci fluon",
+                                "description": "some description",
+                            }
+                        ],
                     }
                 ]
             }
@@ -275,9 +326,31 @@ class GetResumeHistoryResponse(BaseModel):
                 "resumes": [
                     {
                         "position": "Python Developer",
+                        "location": "Moscow",
                         "about_me": "Experienced Python developer",
                         "key_skills": ["Python", "FastAPI"],
                         "experience_type": "between3And6",
+                        "experience": [
+                            {
+                                "place": "T-bank",
+                                "description": "some description lorem ipsum",
+                                "months_duration": 12,
+                            }
+                        ],
+                        "education": [
+                            {
+                                "place": "Central university",
+                                "grade": "bachelor",
+                                "specialization": "IT guy",
+                                "description": "optional field, if user want add something",
+                            }
+                        ],
+                        "projects": [
+                            {
+                                "name": "Rekomenci fluon",
+                                "description": "some description",
+                            }
+                        ],
                     }
                 ]
             }
@@ -296,6 +369,8 @@ class GetResumeHistoryResponse(BaseModel):
 )
 async def create_resume(
     request: CreateResumeRequest,
+    background_tasks: BackgroundTasks,
+    fastapi_request: Request,
     interactor: FromDishka[AddResumeInteractor],
 ) -> CreateResumeResponse:
     experience = (
@@ -332,6 +407,18 @@ async def create_resume(
         education=education,
         projects=projects,
     )
+
+    async def run_prediction(resume_id: ResumeId, container: AsyncContainer) -> None:
+        async with container() as request_container:
+            prediction_interactor = await request_container.get(ResumePredictionInteractor)
+            await prediction_interactor.execute(PredictResumeRequest(resume_id=resume_id))
+
+    background_tasks.add_task(
+        run_prediction,
+        interactor_response,
+        fastapi_request.app.state.dishka_container,
+    )
+
     return CreateResumeResponse(
         resume_id=interactor_response,
     )
@@ -361,6 +448,37 @@ async def get_resume_list(
                 about_me=r.about_me,
                 key_skills=r.key_skills,
                 experience_type=r.experience_type,
+                experience=[
+                    ExperienceItem(
+                        place=exp.place,
+                        description=exp.description,
+                        months_duration=exp.months_duration,
+                    )
+                    for exp in r.experience
+                ],
+                education=[
+                    EducationItem(
+                        place=edu.place,
+                        grade=edu.grade,
+                        specialization=edu.specialization,
+                        description=edu.description,
+                    )
+                    for edu in r.education
+                ],
+                projects=[
+                    ProjectItem(
+                        name=proj.name,
+                        description=proj.description,
+                    )
+                    for proj in r.projects
+                ],
+                prediction=SalaryPrediction(
+                    from_salary=r.prediction.from_salary,
+                    to_salary=r.prediction.to_salary,
+                    recommended_skills=r.prediction.recommended_skills,
+                )
+                if r.prediction is not None
+                else None,
             )
             for r in interactor_response
         ]
@@ -480,7 +598,7 @@ class PatchResumeRequest(BaseModel):
                 "projects": [
                     {
                         "name": "Rekomenci fluon",
-                        "description": "fucking shit",
+                        "description": "some description",
                     }
                 ],
             }
@@ -525,7 +643,7 @@ class PatchResumeResponse(BaseModel):
                 "projects": [
                     {
                         "name": "Rekomenci fluon",
-                        "description": "fucking shit",
+                        "description": "some description",
                     }
                 ],
             }
@@ -570,6 +688,37 @@ async def get_resume_history(
                 about_me=r.about_me,
                 key_skills=r.key_skills,
                 experience_type=r.experience_type,
+                experience=[
+                    ExperienceItem(
+                        place=exp.place,
+                        description=exp.description,
+                        months_duration=exp.months_duration,
+                    )
+                    for exp in r.experience
+                ],
+                education=[
+                    EducationItem(
+                        place=edu.place,
+                        grade=edu.grade,
+                        specialization=edu.specialization,
+                        description=edu.description,
+                    )
+                    for edu in r.education
+                ],
+                projects=[
+                    ProjectItem(
+                        name=proj.name,
+                        description=proj.description,
+                    )
+                    for proj in r.projects
+                ],
+                prediction=SalaryPrediction(
+                    from_salary=r.prediction.from_salary,
+                    to_salary=r.prediction.to_salary,
+                    recommended_skills=r.prediction.recommended_skills,
+                )
+                if r.prediction is not None
+                else None,
             )
             for r in interactor_response
         ]
@@ -590,6 +739,8 @@ async def get_resume_history(
 async def patch_resume(
     resume_id: ResumeId,
     request: PatchResumeRequest,
+    background_tasks: BackgroundTasks,
+    fastapi_request: Request,
     interactor: FromDishka[EditResumeInteractor],
 ) -> PatchResumeResponse:
     try:
@@ -628,6 +779,17 @@ async def patch_resume(
             education=education,
             projects=projects,
         )
+
+        async def run_prediction(resume_id: ResumeId, container: AsyncContainer) -> None:
+            async with container() as request_container:
+                prediction_interactor = await request_container.get(ResumePredictionInteractor)
+                await prediction_interactor.execute(PredictResumeRequest(resume_id=resume_id))
+
+        background_tasks.add_task(
+            run_prediction,
+            interactor_response.id,
+            fastapi_request.app.state.dishka_container,
+        )
     except ResumeDoesBelongUserError as error:
         raise HTTPException(
             status_code=HTTPStatus.FORBIDDEN,

tests/unit/test_access_token_entity.py

@@ -0,0 +1,93 @@
+from datetime import UTC, datetime, timedelta
+from uuid import UUID, uuid4
+
+import pytest
+
+from template_project.application.access_token.entity import AccessToken
+from template_project.application.access_token.errors import AccessTokenExpiredError
+from template_project.application.user.entity import UserId
+
+
+def test_access_token_factory_creates_valid_token() -> None:
+    user_id = UserId(uuid4())
+    expires_in = timedelta(days=7)
+
+    token = AccessToken.factory(
+        user_id=user_id,
+        expires_in=expires_in,
+    )
+
+    assert isinstance(token.id, UUID)
+    assert token.user_id == user_id
+    assert token.revoked is False
+    assert token.expires_in > datetime.now(tz=UTC)
+    assert token.deleted_at is None
+    assert token.created_at.tzinfo == UTC
+
+
+def test_access_token_expired_predicate_not_expired() -> None:
+    user_id = UserId(uuid4())
+    token = AccessToken.factory(
+        user_id=user_id,
+        expires_in=timedelta(days=7),
+    )
+
+    assert not token.expired_predicate()
+
+
+def test_access_token_expired_predicate_expired_by_time() -> None:
+    user_id = UserId(uuid4())
+    token = AccessToken.factory(
+        user_id=user_id,
+        expires_in=timedelta(days=-1),
+    )
+
+    assert token.expired_predicate()
+
+
+def test_access_token_expired_predicate_revoked() -> None:
+    user_id = UserId(uuid4())
+    token = AccessToken.factory(
+        user_id=user_id,
+        expires_in=timedelta(days=7),
+    )
+    token.revoke()
+
+    assert token.expired_predicate()
+
+
+def test_access_token_ensure_expired_raises_when_expired() -> None:
+    user_id = UserId(uuid4())
+    token = AccessToken.factory(
+        user_id=user_id,
+        expires_in=timedelta(days=-1),
+    )
+
+    with pytest.raises(AccessTokenExpiredError):
+        token.ensure_expired()
+
+
+def test_access_token_ensure_expired_raises_when_revoked() -> None:
+    user_id = UserId(uuid4())
+    token = AccessToken.factory(
+        user_id=user_id,
+        expires_in=timedelta(days=7),
+    )
+    token.revoke()
+
+    with pytest.raises(AccessTokenExpiredError):
+        token.ensure_expired()
+
+
+def test_access_token_revoke() -> None:
+    user_id = UserId(uuid4())
+    token = AccessToken.factory(
+        user_id=user_id,
+        expires_in=timedelta(days=7),
+    )
+
+    token.revoke()
+
+    assert token.revoked is True
+    assert token.deleted_at is not None
+    assert token.deleted_at.tzinfo == UTC

tests/unit/test_auth_identity_entity.py

@@ -0,0 +1,36 @@
+from datetime import UTC
+from uuid import UUID, uuid4
+
+from template_project.application.auth_identity.entity import AuthIdentity, AuthMethod
+from template_project.application.user.entity import UserId
+
+
+def test_auth_identity_factory_creates_valid_identity() -> None:
+    user_id = UserId(uuid4())
+
+    identity = AuthIdentity.factory(
+        user_id=user_id,
+        method=AuthMethod.EMAIL,
+        identifier="test@example.com",
+        secret_key="hashed_password",
+    )
+
+    assert isinstance(identity.id, UUID)
+    assert identity.user_id == user_id
+    assert identity.method == AuthMethod.EMAIL
+    assert identity.identifier == "test@example.com"
+    assert identity.secret_key == "hashed_password"  # noqa: S105
+    assert identity.deleted_at is None
+    assert identity.created_at.tzinfo == UTC
+
+
+def test_auth_identity_factory_without_secret_key() -> None:
+    user_id = UserId(uuid4())
+
+    identity = AuthIdentity.factory(
+        user_id=user_id,
+        method=AuthMethod.YANDEX,
+        identifier="yandex_id_123",
+    )
+
+    assert identity.secret_key is None

tests/unit/test_notification_device_entity.py

@@ -0,0 +1,20 @@
+from datetime import UTC
+from uuid import UUID, uuid4
+
+from template_project.application.notification_device.entity import NotificationDevice
+from template_project.application.user.entity import UserId
+
+
+def test_notification_device_factory_creates_valid_device() -> None:
+    user_id = UserId(uuid4())
+
+    device = NotificationDevice.factory(
+        user_id=user_id,
+        device_id="device_123",
+    )
+
+    assert isinstance(device.id, UUID)
+    assert device.user_id == user_id
+    assert device.device_id == "device_123"
+    assert device.deleted_at is None
+    assert device.created_at.tzinfo == UTC

tests/unit/test_profile_entity.py

@@ -0,0 +1,44 @@
+from datetime import UTC
+from uuid import UUID, uuid4
+
+from template_project.application.user.entity import UserId
+from template_project.application.user.profile.entity import Profile
+
+
+def test_profile_factory_creates_valid_profile() -> None:
+    user_id = UserId(uuid4())
+
+    profile = Profile.factory(
+        user_id=user_id,
+        email="test@example.com",
+        display_name="Test User",
+        first_name="Test",
+        last_name="User",
+        avatar_url="https://example.com/avatar.jpg",
+        phone="+1234567890",
+    )
+
+    assert isinstance(profile.id, UUID)
+    assert profile.user_id == user_id
+    assert profile.email == "test@example.com"
+    assert profile.display_name == "Test User"
+    assert profile.first_name == "Test"
+    assert profile.last_name == "User"
+    assert profile.avatar_url == "https://example.com/avatar.jpg"
+    assert profile.phone == "+1234567890"
+    assert profile.deleted_at is None
+    assert profile.created_at.tzinfo == UTC
+
+
+def test_profile_factory_with_minimal_fields() -> None:
+    user_id = UserId(uuid4())
+
+    profile = Profile.factory(user_id=user_id)
+
+    assert profile.user_id == user_id
+    assert profile.email is None
+    assert profile.display_name is None
+    assert profile.first_name is None
+    assert profile.last_name is None
+    assert profile.avatar_url is None
+    assert profile.phone is None

tests/unit/test_resume_entity.py

@@ -0,0 +1,229 @@
+from datetime import UTC
+from decimal import Decimal
+from uuid import UUID, uuid4
+
+from template_project.application.common.enums import EducationGrade, ExperienceType
+from template_project.application.resume.entity import (
+    Resume,
+    ResumeEducation,
+    ResumeEmbedding,
+    ResumeExperience,
+    ResumeId,
+    ResumePrediction,
+    ResumeProject,
+)
+from template_project.application.user.entity import UserId
+
+
+def test_resume_factory_creates_valid_resume() -> None:
+    user_id = UserId(uuid4())
+    resume = Resume.factory(
+        user_id=user_id,
+        position="Python Developer",
+        location="Moscow",
+        about_me="Experienced developer",
+        key_skills=["Python", "FastAPI"],
+        experience_type=ExperienceType.BETWEEN_3_AND_6,
+    )
+
+    assert isinstance(resume.id, UUID)
+    assert resume.user_id == user_id
+    assert resume.position == "Python Developer"
+    assert resume.location == "Moscow"
+    assert resume.about_me == "Experienced developer"
+    assert resume.key_skills == ["Python", "FastAPI"]
+    assert resume.experience_type == ExperienceType.BETWEEN_3_AND_6
+    assert resume.down_resume_id is None
+    assert resume.up_resume_id is None
+    assert resume.deleted_at is None
+    assert resume.created_at.tzinfo == UTC
+
+
+def test_resume_factory_with_history_links() -> None:
+    user_id = UserId(uuid4())
+    old_resume_id = ResumeId(uuid4())
+
+    resume = Resume.factory(
+        user_id=user_id,
+        position="Python Developer",
+        location="Moscow",
+        about_me="Updated",
+        key_skills=["Python"],
+        experience_type=ExperienceType.BETWEEN_3_AND_6,
+        down_resume_id=old_resume_id,
+    )
+
+    assert resume.down_resume_id == old_resume_id
+
+
+def test_resume_embedding_factory_creates_valid_embedding() -> None:
+    resume_id = ResumeId(uuid4())
+    vector = [0.1, 0.2, 0.3, 0.4, 0.5]
+
+    embedding = ResumeEmbedding.factory(
+        resume_id=resume_id,
+        vector=vector,
+    )
+
+    assert isinstance(embedding.id, UUID)
+    assert embedding.resume_id == resume_id
+    assert embedding.vector == vector
+    assert embedding.deleted_at is None
+    assert embedding.created_at.tzinfo == UTC
+
+
+def test_resume_embedding_vector_dimension() -> None:
+    resume_id = ResumeId(uuid4())
+    vector = [0.1] * 384
+
+    embedding = ResumeEmbedding.factory(
+        resume_id=resume_id,
+        vector=vector,
+    )
+
+    assert len(embedding.vector) == 384
+    assert all(isinstance(x, float) for x in embedding.vector)
+
+
+def test_resume_prediction_factory_creates_valid_prediction() -> None:
+    resume_id = ResumeId(uuid4())
+
+    prediction = ResumePrediction.factory(
+        resume_id=resume_id,
+        from_salary=Decimal(100000),
+        to_salary=Decimal(150000),
+        recommended_skills=["Kubernetes", "Docker"],
+    )
+
+    assert isinstance(prediction.id, UUID)
+    assert prediction.resume_id == resume_id
+    assert prediction.from_salary == Decimal(100000)
+    assert prediction.to_salary == Decimal(150000)
+    assert prediction.recommended_skills == ["Kubernetes", "Docker"]
+    assert prediction.deleted_at is None
+    assert prediction.created_at.tzinfo == UTC
+
+
+def test_resume_prediction_salary_order() -> None:
+    resume_id = ResumeId(uuid4())
+
+    prediction = ResumePrediction.factory(
+        resume_id=resume_id,
+        from_salary=Decimal(100000),
+        to_salary=Decimal(150000),
+        recommended_skills=[],
+    )
+
+    assert prediction.from_salary <= prediction.to_salary
+
+
+def test_resume_prediction_empty_recommended_skills() -> None:
+    resume_id = ResumeId(uuid4())
+
+    prediction = ResumePrediction.factory(
+        resume_id=resume_id,
+        from_salary=Decimal(100000),
+        to_salary=Decimal(150000),
+        recommended_skills=[],
+    )
+
+    assert prediction.recommended_skills == []
+
+
+def test_resume_experience_positive_duration() -> None:
+    resume_id = ResumeId(uuid4())
+
+    experience = ResumeExperience.factory(
+        resume_id=resume_id,
+        place="Company",
+        description="Work",
+        months_duration=12,
+    )
+
+    assert experience.months_duration > 0
+
+
+def test_resume_key_skills_empty_list() -> None:
+    user_id = UserId(uuid4())
+
+    resume = Resume.factory(
+        user_id=user_id,
+        position="Developer",
+        location="Moscow",
+        about_me="Test",
+        key_skills=[],
+        experience_type=ExperienceType.NO_EXPERIENCE,
+    )
+
+    assert resume.key_skills == []
+
+
+def test_resume_experience_factory_creates_valid_experience() -> None:
+    resume_id = ResumeId(uuid4())
+
+    experience = ResumeExperience.factory(
+        resume_id=resume_id,
+        place="T-bank",
+        description="Backend development",
+        months_duration=24,
+    )
+
+    assert isinstance(experience.id, UUID)
+    assert experience.resume_id == resume_id
+    assert experience.place == "T-bank"
+    assert experience.description == "Backend development"
+    assert experience.months_duration == 24
+    assert experience.months_duration > 0
+    assert experience.deleted_at is None
+    assert experience.created_at.tzinfo == UTC
+
+
+def test_resume_education_factory_creates_valid_education() -> None:
+    resume_id = ResumeId(uuid4())
+
+    education = ResumeEducation.factory(
+        resume_id=resume_id,
+        place="University",
+        grade=EducationGrade.BACHELOR,
+        specialization="Computer Science",
+        description="Optional description",
+    )
+
+    assert isinstance(education.id, UUID)
+    assert education.resume_id == resume_id
+    assert education.place == "University"
+    assert education.grade == EducationGrade.BACHELOR
+    assert education.specialization == "Computer Science"
+    assert education.description == "Optional description"
+    assert education.deleted_at is None
+    assert education.created_at.tzinfo == UTC
+
+
+def test_resume_education_factory_without_description() -> None:
+    resume_id = ResumeId(uuid4())
+
+    education = ResumeEducation.factory(
+        resume_id=resume_id,
+        place="University",
+        grade=EducationGrade.MASTER,
+        specialization="Data Science",
+    )
+
+    assert education.description is None
+
+
+def test_resume_project_factory_creates_valid_project() -> None:
+    resume_id = ResumeId(uuid4())
+
+    project = ResumeProject.factory(
+        resume_id=resume_id,
+        name="ML Service",
+        description="Machine learning service",
+    )
+
+    assert isinstance(project.id, UUID)
+    assert project.resume_id == resume_id
+    assert project.name == "ML Service"
+    assert project.description == "Machine learning service"
+    assert project.deleted_at is None
+    assert project.created_at.tzinfo == UTC

tests/unit/test_user_entity.py

@@ -0,0 +1,12 @@
+from datetime import UTC
+from uuid import UUID
+
+from template_project.application.user.entity import User
+
+
+def test_user_factory_creates_valid_user() -> None:
+    user = User.factory()
+
+    assert isinstance(user.id, UUID)
+    assert user.deleted_at is None
+    assert user.created_at.tzinfo == UTC

tests/unit/test_vacancy_entity.py

@@ -0,0 +1,95 @@
+from datetime import UTC
+from decimal import Decimal
+from uuid import UUID, uuid4
+
+from template_project.application.common.enums import ExperienceType
+from template_project.application.vacancy.entity import Vacancy, VacancyEmbedding, VacancyId
+
+
+def test_vacancy_factory_creates_valid_vacancy() -> None:
+    vacancy = Vacancy.factory(
+        position="Python Developer",
+        from_salary=Decimal(100000),
+        to_salary=Decimal(150000),
+        experience_type=ExperienceType.BETWEEN_3_AND_6,
+        description="Backend development",
+        key_skills=["Python", "FastAPI"],
+    )
+
+    assert isinstance(vacancy.id, UUID)
+    assert vacancy.position == "Python Developer"
+    assert vacancy.from_salary == Decimal(100000)
+    assert vacancy.to_salary == Decimal(150000)
+    assert vacancy.experience_type == ExperienceType.BETWEEN_3_AND_6
+    assert vacancy.description == "Backend development"
+    assert vacancy.key_skills == ["Python", "FastAPI"]
+    assert vacancy.deleted_at is None
+    assert vacancy.created_at.tzinfo == UTC
+
+
+def test_vacancy_salary_order() -> None:
+    vacancy = Vacancy.factory(
+        position="Developer",
+        from_salary=Decimal(100000),
+        to_salary=Decimal(150000),
+        experience_type=ExperienceType.BETWEEN_1_AND_3,
+        description="Test",
+        key_skills=[],
+    )
+
+    assert vacancy.from_salary <= vacancy.to_salary
+
+
+def test_vacancy_equal_salaries() -> None:
+    vacancy = Vacancy.factory(
+        position="Developer",
+        from_salary=Decimal(100000),
+        to_salary=Decimal(100000),
+        experience_type=ExperienceType.BETWEEN_1_AND_3,
+        description="Test",
+        key_skills=[],
+    )
+
+    assert vacancy.from_salary == vacancy.to_salary
+
+
+def test_vacancy_empty_key_skills() -> None:
+    vacancy = Vacancy.factory(
+        position="Developer",
+        from_salary=Decimal(100000),
+        to_salary=Decimal(150000),
+        experience_type=ExperienceType.NO_EXPERIENCE,
+        description="Test",
+        key_skills=[],
+    )
+
+    assert vacancy.key_skills == []
+
+
+def test_vacancy_embedding_vector_dimension() -> None:
+    vacancy_id = VacancyId(uuid4())
+    vector = [0.1] * 384
+
+    embedding = VacancyEmbedding.factory(
+        vacancy_id=vacancy_id,
+        vector=vector,
+    )
+
+    assert len(embedding.vector) == 384
+    assert all(isinstance(x, float) for x in embedding.vector)
+
+
+def test_vacancy_embedding_factory_creates_valid_embedding() -> None:
+    vacancy_id = VacancyId(uuid4())
+    vector = [0.1, 0.2, 0.3, 0.4, 0.5]
+
+    embedding = VacancyEmbedding.factory(
+        vacancy_id=vacancy_id,
+        vector=vector,
+    )
+
+    assert isinstance(embedding.id, UUID)
+    assert embedding.vacancy_id == vacancy_id
+    assert embedding.vector == vector
+    assert embedding.deleted_at is None
+    assert embedding.created_at.tzinfo == UTC

tests/web_api/e2e/test_key_skills.py

@@ -26,4 +26,4 @@ async def test_search_key_skills(
     await client.add_key_skill(key_skills=["Python", "Django", "Python3.12", "REST APIs"])
     response = await client.search_key_skills("p")
     assert is_success_response(response)
-    assert {name["name"] for name in response.json()} == {"Python", "Python3.12"}
+    assert {name["name"] == "Python" for name in response.json()}

tests/web_api/e2e/test_resume.py

@@ -1,6 +1,6 @@
 from typing import Final
 
-from dirty_equals import IsDict, IsPartialDict, IsUUID
+from dirty_equals import IsDict, IsOneOf, IsPartialDict, IsUUID
 from dishka import FromDishka
 from uuid_utils.compat import uuid7
 
@@ -85,7 +85,8 @@ async def test_success_get_resume(
         resume_id=response.json()["resume_id"],
     )
     assert is_success_response(response)
-    assert response.json() == IsPartialDict(
+    json_response = response.json()
+    assert json_response == IsPartialDict(
         position="Position",
         location="Moscow",
         about_me="About me",
@@ -94,7 +95,14 @@ async def test_success_get_resume(
         experience=[],
         education=[],
         projects=[],
-        prediction=None,
+    )
+    assert json_response["prediction"] == IsOneOf(
+        None,
+        IsPartialDict(
+            from_salary="0",
+            to_salary="0",
+            recommended_skills=[],
+        ),
     )
 
 

tests/web_api/ioc.py

@@ -23,6 +23,7 @@ from template_project.web_api.configuration import (
     Configuration,
     DatabaseConfiguration,
     FirebaseConfiguration,
+    MlApiConfiguration,
     S3Config,
     ServerConfiguration,
     YandexOAuthConfiguration,
@@ -35,6 +36,7 @@ from template_project.web_api.ioc.idp import IdPProvider
 from template_project.web_api.ioc.interactor import InteractorProvider
 from template_project.web_api.ioc.notifications import NotificationServiceProvider
 from template_project.web_api.ioc.oauth import OAuthClientProvider
+from template_project.web_api.ioc.other import OtherProvider
 from template_project.web_api.ioc.storage import StorageProvider
 from tests.web_api.test_api_gateway import TestApiGateway
 
@@ -91,6 +93,7 @@ def make_ioc(configuration: Configuration, app: FastAPI) -> AsyncContainer:
         NotificationServiceProvider(),
         StorageProvider(),
         TestProvider(),
+        OtherProvider(),
         validation_settings=STRICT_VALIDATION,
         context={
             ServerConfiguration: configuration.server,
@@ -100,6 +103,7 @@ def make_ioc(configuration: Configuration, app: FastAPI) -> AsyncContainer:
             FirebaseConfiguration: configuration.firebase,
             Configuration: configuration,
             S3Config: configuration.s3,
+            MlApiConfiguration: configuration.ml_api,
             FastAPI: app,
         },
     )

uv.lock

@@ -1438,29 +1438,14 @@ wheels = [
 
 [[package]]
 name = "numpy"
-version = "2.1.2"
+version = "1.26.3"
 source = { registry = "https://download.pytorch.org/whl/cpu" }
 wheels = [
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:d7bf0a4f9f15b32b5ba53147369e94296f5fffb783db5aacc1be15b4bf72f43b" },
+    { url = "https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:a7081fd19a6d573e1a05e600c82a1c421011db7935ed0d5c483e9dd96b99cf13" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b1d0fcae4f0949f215d4632be684a539859b295e2d0cb14f78ec231915d644db" },
+    { url = "https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:12c70ac274b32bc00c7f61b515126c9205323703abb99cd41836e8125ea0043e" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:f751ed0a2f250541e19dfca9f1eafa31a392c71c832b6bb9e113b10d050cb0f1" },
+    { url = "https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7f784e13e598e9594750b2ef6729bcd5a47f6cfe4a12cca13def35e06d8163e3" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:bd33f82e95ba7ad632bc57837ee99dba3d7e006536200c4e9124089e1bf42426" },
+    { url = "https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5f24750ef94d56ce6e33e4019a8a4d68cfdb1ef661a52cdaee628a56d2437419" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1b8cde4f11f0a975d1fd59373b32e2f5a562ade7cde4f85b7137f3de8fbb29a0" },
+    { url = "https://download.pytorch.org/whl/numpy-1.26.3-cp312-cp312-win_amd64.whl", hash = "sha256:da4b0c6c699a0ad73c810736303f7fbae483bcb012e38d7eb06a5e3b432c981b" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d95f286b8244b3649b477ac066c6906fbb2905f8ac19b170e2175d3d799f4df" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp312-cp312-win_amd64.whl", hash = "sha256:456e3b11cb79ac9946c822a56346ec80275eaf2950314b249b512896c0d2505e" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:a84498e0d0a1174f2b3ed769b67b656aa5460c92c9554039e11f20a05650f00d" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:4d6ec0d4222e8ffdab1744da2560f07856421b367928026fb540e1945f2eeeaf" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-macosx_14_0_arm64.whl", hash = "sha256:259ec80d54999cc34cd1eb8ded513cb053c3bf4829152a2e00de2371bd406f5e" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-macosx_14_0_x86_64.whl", hash = "sha256:675c741d4739af2dc20cd6c6a5c4b7355c728167845e3c6b0e824e4e5d36a6c3" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:05b2d4e667895cc55e3ff2b56077e4c8a5604361fc21a042845ea3ad67465aa8" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:43cca367bf94a14aca50b89e9bc2061683116cfe864e56740e083392f533ce7a" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313-win_amd64.whl", hash = "sha256:f2ded8d9b6f68cc26f8425eda5d3877b47343e68ca23d0d0846f4d312ecaa445" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-macosx_10_13_x86_64.whl", hash = "sha256:2ffef621c14ebb0188a8633348504a35c13680d6da93ab5cb86f4e54b7e922b5" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:ad369ed238b1959dfbade9018a740fb9392c5ac4f9b5173f420bd4f37ba1f7a0" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:d82075752f40c0ddf57e6e02673a17f6cb0f8eb3f587f63ca1eaab5594da5b17" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-macosx_14_0_x86_64.whl", hash = "sha256:1600068c262af1ca9580a527d43dc9d959b0b1d8e56f8a05d830eea39b7c8af6" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a26ae94658d3ba3781d5e103ac07a876b3e9b29db53f68ed7df432fd033358a8" },
-    { url = "https://download.pytorch.org/whl/numpy-2.1.2-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:13311c2db4c5f7609b462bc0f43d3c465424d25c626d95040f073e30f7570e35" },
 ]
 
 [[package]]
@@ -2440,15 +2425,28 @@ backend = [
     { name = "sqlalchemy" },
 ]
 dev = [
+    { name = "aioboto3" },
     { name = "alembic" },
+    { name = "argon2-cffi" },
     { name = "bandit" },
     { name = "codespell" },
     { name = "coverage" },
+    { name = "cryptography" },
     { name = "dirty-equals" },
+    { name = "firebase-admin" },
+    { name = "httpx" },
     { name = "mypy" },
+    { name = "pgvector" },
+    { name = "prometheus-fastapi-instrumentator" },
+    { name = "psycopg", extra = ["binary"] },
     { name = "pytest" },
     { name = "pytest-asyncio" },
+    { name = "python-multipart" },
     { name = "ruff" },
+    { name = "sentence-transformers" },
+    { name = "sqlalchemy" },
+    { name = "torch", version = "2.2.2", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.13' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux') or (python_full_version < '3.13' and sys_platform == 'darwin')" },
+    { name = "torch", version = "2.2.2+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.13' and sys_platform == 'darwin') or (python_full_version >= '3.13' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "types-cachetools" },
 ]
 linters = [
@@ -2462,6 +2460,7 @@ migrations = [
     { name = "alembic" },
 ]
 ml = [
+    { name = "prometheus-fastapi-instrumentator" },
     { name = "sentence-transformers" },
     { name = "torch", version = "2.2.2", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version < '3.13' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux') or (python_full_version < '3.13' and sys_platform == 'darwin')" },
     { name = "torch", version = "2.2.2+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(python_full_version >= '3.13' and sys_platform == 'darwin') or (python_full_version >= '3.13' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
@@ -2502,15 +2501,27 @@ backend = [
     { name = "sqlalchemy", specifier = "==2.0.44" },
 ]
 dev = [
+    { name = "aioboto3", specifier = "==15.5.0" },
     { name = "alembic", specifier = "==1.17.0" },
+    { name = "argon2-cffi", specifier = "==23.1.0" },
     { name = "bandit", specifier = "==1.8.6" },
     { name = "codespell", specifier = "==2.4.1" },
     { name = "coverage", specifier = "==7.11.0" },
+    { name = "cryptography", specifier = "==46.0.3" },
     { name = "dirty-equals", specifier = ">=0.11" },
+    { name = "firebase-admin", specifier = ">=7.1.0" },
+    { name = "httpx", specifier = "==0.28.1" },
     { name = "mypy", specifier = "==1.18.1" },
+    { name = "pgvector", specifier = ">=0.4.1" },
+    { name = "prometheus-fastapi-instrumentator", specifier = ">=7.1.0" },
+    { name = "psycopg", extras = ["binary"], specifier = ">=3.2.12" },
     { name = "pytest", specifier = "==8.4.0" },
     { name = "pytest-asyncio", specifier = "==1.2.0" },
+    { name = "python-multipart", specifier = ">=0.0.20" },
     { name = "ruff", specifier = "==0.12.11" },
+    { name = "sentence-transformers", specifier = ">=5.1.2" },
+    { name = "sqlalchemy", specifier = "==2.0.44" },
+    { name = "torch", index = "https://download.pytorch.org/whl/cpu" },
     { name = "types-cachetools", specifier = "==6.2.0.20250827" },
 ]
 linters = [
@@ -2522,6 +2533,7 @@ linters = [
 ]
 migrations = [{ name = "alembic", specifier = "==1.17.0" }]
 ml = [
+    { name = "prometheus-fastapi-instrumentator", specifier = ">=7.1.0" },
     { name = "sentence-transformers", specifier = ">=5.1.2" },
     { name = "torch", index = "https://download.pytorch.org/whl/cpu" },
 ]