From 36089de163a49af60edde83907dccc962d673ec5 Mon Sep 17 00:00:00 2001 From: jsdio Date: Sun, 15 Oct 2023 07:49:19 +0300 Subject: [PATCH] feat: [#45] llm_agent --- README.md | 114 ++++-- docker-compose.yml | 85 +++++ {.husky => husky}/.huskyrc | 0 {.husky => husky}/commit-msg | 0 {.husky => husky}/pre-commit | 0 src/assistant/entrypoint.sh | 4 +- src/assistant/lib/agent/openai_functions.py | 5 +- src/assistant/lib/agent/repositories.py | 30 +- src/assistant/lib/agent/services.py | 60 ++- .../lib/app/split_settings/postgres.py | 2 +- src/assistant/lib/models/movies.py | 27 +- src/assistant/lib/orm_models/movies.py | 6 +- src/assistant/poetry.lock | 360 ++++-------------- src/bot_aiogram/.env.example | 3 + src/bot_aiogram/docker-compose.yml | 16 + 15 files changed, 344 insertions(+), 368 deletions(-) create mode 100644 docker-compose.yml rename {.husky => husky}/.huskyrc (100%) rename {.husky => husky}/commit-msg (100%) rename {.husky => husky}/pre-commit (100%) diff --git a/README.md b/README.md index a1976f3..ab2d73f 100644 --- a/README.md +++ b/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 -- `make lint` - Lint repository -- `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 +## Как запустить проект? +1. Скачать [файл базы данных](https://disk.yandex.ru/d/ZAKDDg8lP9DHBQ) с `embeddings` и поместить её по пути `src/assistant/data/dump.sql`. -#### 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 -- `make lint` - Lint package -- `make lint-fix` - Auto-fix package -- `make test` - Test package -- `make clean` - Clean up package folder -- `make dependencies-update` - Update not restricted dependencies -- `PYPI_WRITER_PASSWORD=... make ci-login-pypi-publish` - Login to personal pypi with publish rights -- `make ci-test` - Test package in CI -- `make ci-package-build` - Builds package in CI -- `make ci-package-publish` - Publishes package in CI +NGINX_PORT=80 # Порт nginx +API_HOST=0.0.0.0 # Хост API +API_PORT=8000 # Порт API + +JWT_SECRET_KEY=secret # Секретный ключ для JWT +JWT_ALGORITHM=HS256 # Алгоритм шифрования JWT + +APP_RELOAD=True # Автоматическая перезагрузка приложения + +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. diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000..ed9d75a --- /dev/null +++ b/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 diff --git a/.husky/.huskyrc b/husky/.huskyrc similarity index 100% rename from .husky/.huskyrc rename to husky/.huskyrc diff --git a/.husky/commit-msg b/husky/commit-msg similarity index 100% rename from .husky/commit-msg rename to husky/commit-msg diff --git a/.husky/pre-commit b/husky/pre-commit similarity index 100% rename from .husky/pre-commit rename to husky/pre-commit diff --git a/src/assistant/entrypoint.sh b/src/assistant/entrypoint.sh index fe288b7..e48a4fb 100644 --- a/src/assistant/entrypoint.sh +++ b/src/assistant/entrypoint.sh @@ -2,4 +2,6 @@ while ! nc -z postgres 5432; do sleep 1; done; -exec .venv/bin/python -m bin \ No newline at end of file +alembic upgrade head + +exec .venv/bin/python -m bin diff --git a/src/assistant/lib/agent/openai_functions.py b/src/assistant/lib/agent/openai_functions.py index 673a1ba..e1d99a4 100644 --- a/src/assistant/lib/agent/openai_functions.py +++ b/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) diff --git a/src/assistant/lib/agent/repositories.py b/src/assistant/lib/agent/repositories.py index f8fe6f1..c119a89 100644 --- a/src/assistant/lib/agent/repositories.py +++ b/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 diff --git a/src/assistant/lib/agent/services.py b/src/assistant/lib/agent/services.py index 83f89a4..046e35d 100644 --- a/src/assistant/lib/agent/services.py +++ b/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.messages +import langchain.schema 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: - 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] + async def process_request(self, request: models.AgentCreateRequestModel) -> models.AgentCreateResponseModel: + + 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", - "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"), + ("system", system_prompt), ] ) + 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}) diff --git a/src/assistant/lib/app/split_settings/postgres.py b/src/assistant/lib/app/split_settings/postgres.py index b5a2eb9..e055c19 100644 --- a/src/assistant/lib/app/split_settings/postgres.py +++ b/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: diff --git a/src/assistant/lib/models/movies.py b/src/assistant/lib/models/movies.py index e432111..1d7feed 100644 --- a/src/assistant/lib/models/movies.py +++ b/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 - type: str + rating: float | None = None + 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 diff --git a/src/assistant/lib/orm_models/movies.py b/src/assistant/lib/orm_models/movies.py index 88082e2..524c8ec 100644 --- a/src/assistant/lib/orm_models/movies.py +++ b/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") diff --git a/src/assistant/poetry.lock b/src/assistant/poetry.lock index 48b0fb8..4c71746 100644 --- a/src/assistant/poetry.lock +++ b/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. 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