feat: [#45] llm_agent

README.md

@@ -1,40 +1,92 @@
-# Python Contrib
+# Голосовой Ассистент на Langchain (OpenAI)
 
-Python packages mono-repository
+## Описание проекта
 
-## Development
+Этот проект представляет собой голосового ассистента, разработанного в рамках дипломного проекта для Яндекс.Практикума.
+Ассистент основан на технологиях OpenAI и предназначен для поиска информации о фильмах в нашем сервисе кинотеатра.
 
-### Global dependencies
+## Что удалось реализовать?
 
-- node
+###
-- poetry
+- Организация кодовой базы по [шаблону DDD](https://github.com/yp-middle-python-24/python-service-example/)
+- Speech To Text на базе [Whisper](https://openai.com/research/whisper) от OpenAI
+- LLM:
+  - Получение embeddings для фильмов в векторную базу данных pgvector
+  - Поиск фильмов по embeddings
+  - Получение информации о конкретном фильме по embeddings
+  - Сохранение истории диалогов с ассистентом
+- Text to speech на базе [Yanex SpeechKit](https://cloud.yandex.ru/services/speechkit) и [ElevenLabs](https://elevenlabs.io/)
+- Клиент для работы с сервисом на базе Telegram-бота
 
-### Makefile commands
+## Команда:
 
-#### Root commands
+- [Артем](https://github.com/ijaric/python-monorepo/commits?author=ijaric)
+- [Александр](https://github.com/ksieuk)
+- [Алексей](https://github.com/grucshetskyaleksei)
 
-- `make init` - Initialize repository
+## Как запустить проект?
-- `make lint` - Lint repository
+1. Скачать [файл базы данных](https://disk.yandex.ru/d/ZAKDDg8lP9DHBQ) с `embeddings` и поместить её по пути `src/assistant/data/dump.sql`.
-- `make lint-fix` - Auto-fix repository
-- `make test` - Test repository
-- `make clean` - Clean up repository
-- `make all-init` - Init all packages
-- `make all-lint` - Lint all packages
-- `make all-lint-fix` - Auto-fix all packages
-- `make all-test` - Test all packages
-- `make all-clean` - Clean all packages
-- `make all-dependencies-update` - Update dependencies in all packages
-- `make ci-init` - CI-specific version of init command
 
-#### Common package commands
+2. В директории `src/assistant` файл `.env.example` переименовать в `.env` и заполнить переменные окружения.
+Пример заполнения переменных окружения:
+```
+POSTGRES_DRIVER=postgresql+asyncpg # Драйвер для работы с базой данных
+POSTGRES_HOST=db # Хост базы данных
+POSTGRES_PORT=5432 # Порт базы данных
+POSTGRES_USER=app # Пользователь базы данных
+POSTGRES_PASSWORD=123qwe # Пароль пользователя базы данных
+POSTGRES_DB_NAME=movies_database # Название базы данных
 
-- `make init` - Initialize package
+NGINX_PORT=80 # Порт nginx
-- `make lint` - Lint package
+API_HOST=0.0.0.0 # Хост API
-- `make lint-fix` - Auto-fix package
+API_PORT=8000 # Порт API
-- `make test` - Test package
+
-- `make clean` - Clean up package folder
+JWT_SECRET_KEY=secret # Секретный ключ для JWT
-- `make dependencies-update` - Update not restricted dependencies
+JWT_ALGORITHM=HS256 # Алгоритм шифрования JWT
-- `PYPI_WRITER_PASSWORD=... make ci-login-pypi-publish` - Login to personal pypi with publish rights
+
-- `make ci-test` - Test package in CI
+APP_RELOAD=True # Автоматическая перезагрузка приложения
-- `make ci-package-build` - Builds package in CI
+
-- `make ci-package-publish` - Publishes package in CI
+VOICE_AVAILABLE_FORMATS=mp3,ogg,wav,oga # Доступные форматы аудиофайлов
+VOICE_MAX_INPUT_SIZE=5120 # Максимальный размер входного аудиофайла в килобайтах
+VOICE_MAX_INPUT_SECONDS=30 # Максимальная длительность входного аудиофайла в секундах
+
+OPENAI_API_KEY=sk-1234567890 # API-ключ OpenAI
+OPENAI_STT_MODEL=whisper-1 # Модель для распознавания речи
+
+PROXY_HOST=123.123.123.123 # Хост прокси
+PROXY_PORT=1234 # Порт прокси
+PROXY_USER=proxy_user # Пользователь прокси
+PROXY_PASSWORD=proxy_password # Пароль прокси
+PROXY_ENABLE=True # Включить прокси
+
+TTS_YANDEX_API_KEY=1234567890 # API-ключ Yandex SpeechKit
+TTS_YANDEX_AUDIO_FORMAT=oggopus # Формат аудиофайла
+TTS_YANDEX_SAMPLE_RATE_HERTZ=48000 # Частота дискретизации аудиофайла
+
+TTS_ELEVEN_LABS_API_KEY=1234567890 # API-ключ ElevenLabs
+TTS_ELEVEN_LABS_DEFAULT_VOICE_ID=EXAVITQu4vr4xnSDxMaL # ID голоса по умолчанию
+```
+
+3. В директории `src/bot_aiogram` файл `.env.example` переименовать в `.env` и заполнить переменные окружения.
+Пример заполнения переменных окружения:
+
+```
+BOT_CONTAINER_NAME=bot_container_name # Название контейнера
+BOT_IMAGE_NAME=botimage_name # Название образа
+BOT_NAME=mybotname # Название бота
+BOT_TOKEN=1234567890:ABCdefghIJKlmnopQRStuVWXYz # Токен бота
+BOT_ADMINS=1234567890,9876543210 # ID администраторов бота через запятую
+
+API_PROTOCOL=http # Протокол API
+API_URL=api# URL API
+API_PORT=8000 # Порт API
+
+REDIS_HOST=redis # Хост Redis
+REDIS_PORT=6379 # Порт Redis
+```
+
+3. Запустить проект командой `docker-compose up -d`
+
+### Важно!
+Для работы с Telegram-ботом необходимо предварительно начать с ним диалог и отключить в параметрах конфиденциальности
+вашего аккаунта запрет на голосовые сообщения.

docker-compose.yml

@@ -0,0 +1,85 @@
+version: "3"
+
+services:
+  postgres:
+    image: ankane/pgvector:v0.5.0
+    restart: always
+    environment:
+      POSTGRES_USER: ${POSTGRES_USER}
+      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
+      POSTGRES_DB: ${POSTGRES_NAME}
+    env_file:
+      - ./src/assistant/.env
+    expose:
+      - "${POSTGRES_PORT}"
+    volumes:
+      - - ./src/assistant/dump.sql:/docker-entrypoint-initdb.d/dump.sql:ro
+    networks:
+      - backend_network
+
+  api:
+    build:
+      context: .
+    container_name: api
+    image: fastapi_app
+    restart: always
+    entrypoint: ["/opt/app/entrypoint.sh"]
+    env_file:
+      - ./src/assistant/.env
+    expose:
+      - "${API_PORT}"
+    depends_on:
+      - postgres
+    networks:
+      - backend_network
+      - api_network
+
+  nginx:
+    image: nginx:1.25.1
+    env_file:
+      - .env
+    ports:
+      - "${NGINX_PORT}:${NGINX_PORT}"
+    volumes:
+      - ./src/assistant/nginx/nginx.conf:/etc/nginx/nginx.conf:ro
+      - ./src/assistant/nginx/templates:/etc/nginx/templates
+    depends_on:
+      - api
+    networks:
+      - api_network
+
+  bot:
+    image: "${BOT_IMAGE_NAME:-tg_bot-image}"
+    container_name: "${BOT_CONTAINER_NAME:-tg_bot-container}"
+    build:
+      context: ./src/bot_aiogram/Dockerfile
+    restart: always
+    env_file:
+      - ./src/bot_aiogram/.env
+    networks:
+      - backend_network
+      - api_network
+    depends_on: api
+
+  redis:
+    image: redis:7.0.11
+    restart: always
+    env_file:
+      - ./src/bot_aiogram/.env
+    ports:
+      - "127.0.0.1:${REDIS_PORT}:${REDIS_PORT}"
+    command: redis-server --bind 0.0.0.0 --appendonly yes
+    volumes:
+      - redis_data:/data
+    networks:
+      - backend_network
+
+volumes:
+  redis_data:
+  postgres_data:
+
+networks:
+  api_network:
+    driver: bridge
+  backend_network:
+    driver: bridge

src/assistant/entrypoint.sh

@@ -2,4 +2,6 @@
 
 while ! nc -z postgres 5432; do sleep 1; done;
 
-exec .venv/bin/python -m bin
+alembic upgrade head
+
+exec .venv/bin/python -m bin

src/assistant/lib/agent/openai_functions.py

@@ -2,13 +2,13 @@ import logging
 import uuid
 
 import langchain.agents
-import orm_models
 import sqlalchemy as sa
 import sqlalchemy.exc
 import sqlalchemy.ext.asyncio as sa_asyncio
 
 import lib.agent.repositories as repositories
 import lib.models as models
+import lib.orm_models as orm_models
 
 
 class OpenAIFunctions:
@@ -23,7 +23,6 @@ class OpenAIFunctions:
         self.pg_async_session = pg_async_session
         self.repository = repository
 
-    @langchain.agents.tool
     async def get_movie_by_description(self, description: str) -> list[models.Movie] | None:
         """Provide a movie data by description."""
 
@@ -34,7 +33,7 @@ class OpenAIFunctions:
                 result: list[models.Movie] = []
                 stmt = (
                     sa.select(orm_models.FilmWork)
-                    .order_by(orm_models.FilmWork.embedding.cosine_distance(embedded_description))
+                    .order_by(orm_models.FilmWork.embeddings.cosine_distance(embedded_description.root))
                     .limit(5)
                 )
                 neighbours = session.scalars(stmt)

src/assistant/lib/agent/repositories.py

@@ -1,6 +1,7 @@
 import logging
 import typing
 
+import langchain.chat_models
 import openai
 import openai.error
 
@@ -13,7 +14,7 @@ class EmbeddingRepository:
 
     def __init__(self, settings: app_settings.Settings) -> None:
         """Initialize the service with an OpenAI API key."""
-        self.llm = openai.api_key = settings.openai.api_key
+        self.llm = openai.api_key = settings.openai.api_key.get_secret_value()
         self.logger = logging.getLogger(__name__)
 
     def get_embedding(self, text: str, model: str = "text-embedding-ada-002") -> models.Embedding | None:
@@ -28,13 +29,36 @@ class EmbeddingRepository:
             self.logger.exception("Failed to get async embedding for: %s", text)
 
     async def aget_embedding(self, text: str, model: str = "text-embedding-ada-002") -> models.Embedding | None:
-        """Get the embedding for a given text."""
+        """Get the embedding for a given text.[Async]"""
         try:
             response: dict[str, typing.Any] = await openai.Embedding.acreate(
                 input=text,
                 model=model,
             )  # type: ignore[reportGeneralTypeIssues]
-            return models.Embedding(**response["data"][0]["embedding"])
+            # print(response["data"][0]["embedding"])
+            return models.Embedding(root=response["data"][0]["embedding"])
 
         except openai.error.OpenAIError:
             self.logger.exception("Failed to get async embedding for: %s", text)
+
+
+class LlmRepository:
+    """A service for getting embeddings from OpenAI."""
+
+    def __init__(self, settings: app_settings.Settings) -> None:
+        """Initialize the service with an OpenAI API key."""
+        self.llm = langchain.chat_models.ChatOpenAI(
+            temperature=0.7,
+            openai_api_key=self.settings.openai.api_key.get_secret_value()
+        )
+
+    async def get_chat_response(self, request: str, prompt: str) -> str:
+        """Get the embedding for a given text."""
+        prompt = langchain.prompts.ChatPromptTemplate.from_messages(
+            [
+                ("system", prompt),
+            ]
+        )
+        chain = prompt | self.llm
+        response = await chain.ainvoke({"input": request})
+        return response.content

src/assistant/lib/agent/services.py

@@ -2,13 +2,13 @@ import asyncio
 import logging
 import uuid
 
+import fastapi
 import langchain.agents
 import langchain.agents.format_scratchpad
 import langchain.agents.output_parsers
 import langchain.chat_models
 import langchain.prompts
-import langchain.schema.agent
+import langchain.schema
-import langchain.schema.messages
 import langchain.tools.render
 
 import assistant.lib.models.movies as movies
@@ -24,44 +24,36 @@ class AgentService:
         self.settings = settings
         self.tools = tools
 
-    async def process_request(self, request: str, chat_history: list[langchain.schema.messages.Message]) -> str:
+    async def process_request(self, request: models.AgentCreateRequestModel) -> models.AgentCreateResponseModel:
-        llm = langchain.chat_models.ChatOpenAI(temperature=0.7, openai_api_key=self.settings.openai.api_key)
+
-        tools = [self.tools.get_movie_by_description, self.tools.get_movie_by_id, self.tools.get_similar_movies]
+        result = await self.tools.get_movie_by_description(request.text)
+
+        if len(result) == 0:
+            raise fastapi.HTTPException(status_code=404, detail="Movies not found")
+
+        # llm = langchain.chat_models.ChatOpenAI(
+        #     temperature=self.settings.openai.agent_temperature,
+        #     openai_api_key=self.settings.openai.api_key.get_secret_value()
+        # )
+
+        content_films = "\n".join(film.get_movie_info_line() for film in result)
+
+        system_prompt = (
+            "You are a cinema expert. "
+            f"Here are the movies I found for you: {content_films}"
+            "Listen to the question and answer it based on the information above."
+        )
 
-        chat_history = []
-        chat_history_name = f"{chat_history=}".partition("=")[0]
         prompt = langchain.prompts.ChatPromptTemplate.from_messages(
             [
-                (
+                ("system", system_prompt),
-                    "system",
-                    "You are very powerful assistant. If you are asked about movies you will you provided functions.",
-                ),
-                langchain.prompts.MessagesPlaceholder(variable_name=chat_history_name),
-                ("user", "{input}"),
-                langchain.prompts.MessagesPlaceholder(variable_name="agent_scratchpad"),
             ]
         )
+        chain = prompt | self.llm
+        response = await chain.ainvoke({"input": request.text})
+        response_model = models.AgentCreateResponseModel(text=response.content)
+        return response_model
 
-        llm_with_tools = llm.bind(
-            functions=[langchain.tools.render.format_tool_to_openai_function(tool) for tool in tools]
-        )
-
-        chat_history = []
-
-        agent = (
-            {
-                "input": lambda _: _["input"],
-                "agent_scratchpad": lambda _: langchain.agents.format_scratchpad.format_to_openai_functions(
-                    _["intermediate_steps"]
-                ),
-                "chat_history": lambda _: _["chat_history"],
-            }
-            | prompt
-            | llm_with_tools
-            | langchain.agents.output_parsers.OpenAIFunctionsAgentOutputParser()
-        )
-
-        agent_executor = langchain.agents.AgentExecutor(agent=agent, tools=tools, verbose=True)
 
         return await agent_executor.ainvoke({"input": first_question, "chat_history": chat_history})
 

src/assistant/lib/app/split_settings/postgres.py

@@ -37,7 +37,7 @@ class PostgresSettings(pydantic_settings.BaseSettings):
     @property
     def dsn(self) -> str:
         password = self.password.get_secret_value()
-        return f"{self.driver}://{self.user}:{password}@{self.host}:{self.port}"
+        return f"{self.driver}://{self.user}:{password}@{self.host}:{self.port}/{self.db_name}"
 
     @property
     def dsn_as_safe_url(self) -> str:

src/assistant/lib/models/movies.py

@@ -8,7 +8,30 @@ class Movie(pydantic.BaseModel):
     id: uuid.UUID
     title: str
     description: str | None = None
-    rating: float
+    rating: float | None = None
-    type: str
+    type: str | None = None
     created: datetime.datetime
     modified: datetime.datetime
+    creation_date: datetime.datetime | None = None
+    runtime: int | None = None
+    budget: int | None = None
+    imdb_id: str | None = None
+
+    @pydantic.computed_field
+    @property
+    def imdb_url(self) -> str:
+        return f"https://www.imdb.com/title/{self.imdb_id}"
+
+    def get_movie_info_line(self):
+        not_provided_value = "not provided"
+        content_film_info = {
+            "Title": self.title,
+            "Description": self.description or not_provided_value,
+            "Rating": self.rating or not_provided_value,
+            "Imdb_id": self.imdb_url or not_provided_value,
+            "Creation_date": self.creation_date or not_provided_value,
+            "Runtime": self.runtime or not_provided_value,
+            "Budget": self.budget or not_provided_value,
+        }
+        content_film_info_line = ", ".join(f"{k}: {v}" for k, v in content_film_info.items())
+        return content_film_info_line

src/assistant/lib/orm_models/movies.py

@@ -43,8 +43,10 @@ class FilmWork(base_models.Base):
     title: sa_orm.Mapped[str] = sa_orm.mapped_column()
     description: sa_orm.Mapped[str] = sa_orm.mapped_column(nullable=True)
     creation_date: sa_orm.Mapped[datetime.datetime] = sa_orm.mapped_column(nullable=True)
-    file_path: sa_orm.Mapped[str] = sa_orm.mapped_column(nullable=True)
     rating: sa_orm.Mapped[float] = sa_orm.mapped_column(nullable=True)
+    runtime: sa_orm.Mapped[int] = sa_orm.mapped_column(nullable=False)
+    budget: sa_orm.Mapped[int] = sa_orm.mapped_column(default=0)
+    imdb_id: sa_orm.Mapped[str] = sa_orm.mapped_column(nullable=False)
     type: sa_orm.Mapped[str] = sa_orm.mapped_column()
     created: sa_orm.Mapped[datetime.datetime] = sa_orm.mapped_column(
         sa.DateTime(timezone=True), server_default=sa_sql.func.now()
@@ -52,7 +54,7 @@ class FilmWork(base_models.Base):
     modified: sa_orm.Mapped[datetime.datetime] = sa_orm.mapped_column(
         sa.DateTime(timezone=True), server_default=sa_sql.func.now(), onupdate=sa_sql.func.now()
     )
-    embedding: sa_orm.Mapped[list[float]] = sa_orm.mapped_column(pgvector.sqlalchemy.Vector(1536))
+    embeddings: sa_orm.Mapped[list[float]] = sa_orm.mapped_column(pgvector.sqlalchemy.Vector(1536))
     genres: sa_orm.Mapped[list[Genre]] = sa_orm.relationship(secondary="genre_film_work")
 
 

src/assistant/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohttp"
@@ -448,21 +448,6 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
-[[package]]
-name = "dataclasses-json"
-version = "0.6.1"
-description = "Easily serialize dataclasses to and from JSON."
-optional = false
-python-versions = ">=3.7,<4.0"
-files = [
-    {file = "dataclasses_json-0.6.1-py3-none-any.whl", hash = "sha256:1bd8418a61fe3d588bb0079214d7fb71d44937da40742b787256fd53b26b6c80"},
-    {file = "dataclasses_json-0.6.1.tar.gz", hash = "sha256:a53c220c35134ce08211a1057fd0e5bf76dc5331627c6b241cacbc570a89faae"},
-]
-
-[package.dependencies]
-marshmallow = ">=3.18.0,<4.0.0"
-typing-inspect = ">=0.4.0,<1"
-
 [[package]]
 name = "dill"
 version = "0.3.7"
@@ -529,20 +514,6 @@ files = [
 dnspython = ">=2.0.0"
 idna = ">=2.0.0"
 
-[[package]]
-name = "faker"
-version = "19.10.0"
-description = "Faker is a Python package that generates fake data for you."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "Faker-19.10.0-py3-none-any.whl", hash = "sha256:f321e657ed61616fbfe14dbb9ccc6b2e8282652bbcfcb503c1bd0231ff834df6"},
-    {file = "Faker-19.10.0.tar.gz", hash = "sha256:63da90512d0cb3acdb71bd833bb3071cb8a196020d08b8567a01d232954f1820"},
-]
-
-[package.dependencies]
-python-dateutil = ">=2.4"
-
 [[package]]
 name = "fastapi"
 version = "0.103.1"
@@ -645,6 +616,7 @@ files = [
     {file = "greenlet-2.0.2-cp27-cp27m-win32.whl", hash = "sha256:6c3acb79b0bfd4fe733dff8bc62695283b57949ebcca05ae5c129eb606ff2d74"},
     {file = "greenlet-2.0.2-cp27-cp27m-win_amd64.whl", hash = "sha256:283737e0da3f08bd637b5ad058507e578dd462db259f7f6e4c5c365ba4ee9343"},
     {file = "greenlet-2.0.2-cp27-cp27mu-manylinux2010_x86_64.whl", hash = "sha256:d27ec7509b9c18b6d73f2f5ede2622441de812e7b1a80bbd446cb0633bd3d5ae"},
+    {file = "greenlet-2.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:d967650d3f56af314b72df7089d96cda1083a7fc2da05b375d2bc48c82ab3f3c"},
     {file = "greenlet-2.0.2-cp310-cp310-macosx_11_0_x86_64.whl", hash = "sha256:30bcf80dda7f15ac77ba5af2b961bdd9dbc77fd4ac6105cee85b0d0a5fcf74df"},
     {file = "greenlet-2.0.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:26fbfce90728d82bc9e6c38ea4d038cba20b7faf8a0ca53a9c07b67318d46088"},
     {file = "greenlet-2.0.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9190f09060ea4debddd24665d6804b995a9c122ef5917ab26e1566dcc712ceeb"},
@@ -653,6 +625,7 @@ files = [
     {file = "greenlet-2.0.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:76ae285c8104046b3a7f06b42f29c7b73f77683df18c49ab5af7983994c2dd91"},
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     {file = "SQLAlchemy-2.0.22-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0b0b3f2686c3f162123adba3cb8b626ed7e9b8433ab528e36ed270b4f70d1cdb"},
-    {file = "SQLAlchemy-2.0.22-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:0c1fea8c0abcb070ffe15311853abfda4e55bf7dc1d4889497b3403629f3bf00"},
     {file = "SQLAlchemy-2.0.22-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:4bb062784f37b2d75fd9b074c8ec360ad5df71f933f927e9e95c50eb8e05323c"},
     {file = "SQLAlchemy-2.0.22-cp39-cp39-win32.whl", hash = "sha256:58a3aba1bfb32ae7af68da3f277ed91d9f57620cf7ce651db96636790a78b736"},
     {file = "SQLAlchemy-2.0.22-cp39-cp39-win_amd64.whl", hash = "sha256:92e512a6af769e4725fa5b25981ba790335d42c5977e94ded07db7d641490a85"},
@@ -2003,7 +1810,7 @@ files = [
 ]
 
 [package.dependencies]
-greenlet = {version = "!=0.4.17", markers = "platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_machine == \"AMD64\" or platform_machine == \"amd64\" or platform_machine == \"x86_64\" or platform_machine == \"ppc64le\" or platform_machine == \"aarch64\""}
+greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""}
 typing-extensions = ">=4.2.0"
 
 [package.extras]
@@ -2047,20 +1854,6 @@ anyio = ">=3.4.0,<5"
 [package.extras]
 full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart", "pyyaml"]
 
-[[package]]
-name = "tenacity"
-version = "8.2.3"
-description = "Retry code until it succeeds"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "tenacity-8.2.3-py3-none-any.whl", hash = "sha256:ce510e327a630c9e1beaf17d42e6ffacc88185044ad85cf74c0a8887c6a0f88c"},
-    {file = "tenacity-8.2.3.tar.gz", hash = "sha256:5398ef0d78e63f40007c1fb4c0bff96e1911394d2fa8d194f77619c05ff6cc8a"},
-]
-
-[package.extras]
-doc = ["reno", "sphinx", "tornado (>=4.5)"]
-
 [[package]]
 name = "tokenize-rt"
 version = "5.2.0"
@@ -2128,21 +1921,6 @@ files = [
     {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
 ]
 
-[[package]]
-name = "typing-inspect"
-version = "0.9.0"
-description = "Runtime inspection utilities for typing module."
-optional = false
-python-versions = "*"
-files = [
-    {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
-    {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
-]
-
-[package.dependencies]
-mypy-extensions = ">=0.3.0"
-typing-extensions = ">=3.7.4"
-
 [[package]]
 name = "urllib3"
 version = "2.0.6"
@@ -2352,4 +2130,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "cf7c2e88dd377d6929d87da3553dabdc48acaa30d58f7de2d8303159180b0c09"
+content-hash = "f11c55e41071ec0ef9662649522f774a7d01df83b439d27bd711fb2d1e137d82"

src/bot_aiogram/.env.example

@@ -8,3 +8,6 @@ BOT_ADMINS=123456,654321
 API_PROTOCOL=http
 API_URL=api
 API_PORT=8000
+
+REDIS_HOST=redis
+REDIS_PORT=6379

src/bot_aiogram/docker-compose.yml

@@ -12,6 +12,22 @@ services:
     networks:
       - tg_bot_network
 
+  redis:
+    image: redis:7.0.11
+    restart: always
+    env_file:
+      - .env
+    ports:
+      - "127.0.0.1:${REDIS_PORT}:${REDIS_PORT}"
+    command: redis-server --bind 0.0.0.0 --appendonly yes
+    volumes:
+      - redis_data:/data
+    networks:
+      - tg_bot_network
+
+volumes:
+    redis_data:
+
 networks:
   tg_bot_network:
     driver: bridge