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Merge pull request #55 from ijaric/tasks/#45_agent

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Tasks/#45 agent
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Григорич 2023-10-14 23:56:46 +03:00 committed by GitHub
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22 changed files with 926 additions and 54 deletions
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8
src/assistant/alembic/env.py
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@ -6,7 +6,7 @@ from sqlalchemy.engine import Connection
from sqlalchemy.ext.asyncio import async_engine_from_config
import lib.app.settings as app_settings
import lib.models as models
import lib.orm_models as orm_models
from alembic import context
# this is the Alembic Config object, which provides
@ -19,7 +19,11 @@ if config.config_file_name is not None:
config.set_main_option("sqlalchemy.url", app_settings.Settings().postgres.dsn)
target_metadata = models.Base.metadata
print("BASE: ", orm_models.Base.metadata.schema)
for t in orm_models.Base.metadata.sorted_tables:
print(t.name)
target_metadata = orm_models.Base.metadata
def run_migrations_offline() -> None:

37
src/assistant/alembic/versions/2023-10-02_9749b063b095_added_initial_table.py
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@ -1,37 +0,0 @@
"""Added initial table
Revision ID: 9749b063b095
Revises:
Create Date: 2023-10-02 19:46:05.078494
"""
from typing import Sequence, Union
import sqlalchemy as sa
from alembic import op
# revision identifiers, used by Alembic.
revision: str = "9749b063b095"
down_revision: Union[str, None] = None
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.create_table(
"joke",
sa.Column("type", sa.String(), nullable=False),
sa.Column("setup", sa.String(), nullable=False),
sa.Column("punchline", sa.String(), nullable=False),
sa.Column("id", sa.Uuid(), nullable=False),
sa.PrimaryKeyConstraint("id"),
)
# ### end Alembic commands ###
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.drop_table("joke")
# ### end Alembic commands ###

138
src/assistant/alembic/versions/2023-10-12_3d448c6327cd_init_commit.py Normal file
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@ -0,0 +1,138 @@
"""init commit
Revision ID: 3d448c6327cd
Revises:
Create Date: 2023-10-12 00:01:42.248941
"""
from typing import Sequence, Union
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql
from alembic import op
# revision identifiers, used by Alembic.
revision: str = "3d448c6327cd"
down_revision: Union[str, None] = None
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.create_table(
"chat_history",
sa.Column("id", sa.Uuid(), nullable=False),
sa.Column("session_id", sa.Uuid(), nullable=False),
sa.Column("channel", sa.String(), nullable=False),
sa.Column("user_id", sa.String(), nullable=False),
sa.Column("content", sa.JSON(), nullable=False),
sa.Column("created", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.Column("modified", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=False),
sa.PrimaryKeyConstraint("id"),
schema="content",
)
def downgrade() -> None:
# ### commands auto generated by Alembic - please adjust! ###
op.add_column("person_film_work", sa.Column("id", sa.UUID(), autoincrement=False, nullable=False))
op.drop_constraint(None, "person_film_work", type_="foreignkey")
op.drop_constraint(None, "person_film_work", type_="foreignkey")
op.alter_column(
"person_film_work", "role", existing_type=sa.String(length=50), type_=sa.TEXT(), existing_nullable=False
)
op.alter_column("person", "modified", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=True)
op.alter_column("person", "created", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=True)
op.alter_column("person", "full_name", existing_type=sa.String(), type_=sa.TEXT(), existing_nullable=False)
op.drop_constraint(None, "genre_film_work", type_="foreignkey")
op.drop_constraint(None, "genre_film_work", type_="foreignkey")
op.alter_column("genre", "modified", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=True)
op.alter_column("genre", "created", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=True)
op.alter_column("genre", "description", existing_type=sa.String(), type_=sa.TEXT(), existing_nullable=True)
op.alter_column("genre", "name", existing_type=sa.String(), type_=sa.TEXT(), existing_nullable=False)
op.alter_column("film_work", "modified", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=True)
op.alter_column("film_work", "created", existing_type=postgresql.TIMESTAMP(timezone=True), nullable=True)
op.alter_column("film_work", "type", existing_type=sa.String(), type_=sa.TEXT(), existing_nullable=False)
op.alter_column("film_work", "file_path", existing_type=sa.String(), type_=sa.TEXT(), existing_nullable=True)
op.alter_column("film_work", "creation_date", existing_type=sa.DateTime(), type_=sa.DATE(), existing_nullable=True)
op.alter_column("film_work", "description", existing_type=sa.String(), type_=sa.TEXT(), existing_nullable=True)
op.alter_column("film_work", "title", existing_type=sa.String(), type_=sa.TEXT(), existing_nullable=False)
op.create_table(
"django_migrations",
sa.Column("id", sa.BIGINT(), autoincrement=True, nullable=False),
sa.Column("app", sa.VARCHAR(length=255), autoincrement=False, nullable=False),
sa.Column("name", sa.VARCHAR(length=255), autoincrement=False, nullable=False),
sa.Column("applied", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=False),
)
op.create_table(
"django_admin_log",
sa.Column("id", sa.INTEGER(), autoincrement=True, nullable=False),
sa.Column("action_time", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=False),
sa.Column("object_id", sa.TEXT(), autoincrement=False, nullable=True),
sa.Column("object_repr", sa.VARCHAR(length=200), autoincrement=False, nullable=False),
sa.Column("action_flag", sa.SMALLINT(), autoincrement=False, nullable=False),
sa.Column("change_message", sa.TEXT(), autoincrement=False, nullable=False),
sa.Column("content_type_id", sa.INTEGER(), autoincrement=False, nullable=True),
sa.Column("user_id", sa.INTEGER(), autoincrement=False, nullable=False),
sa.CheckConstraint("action_flag >= 0", name="django_admin_log_action_flag_check"),
)
op.create_table(
"django_session",
sa.Column("session_key", sa.VARCHAR(length=40), autoincrement=False, nullable=False),
sa.Column("session_data", sa.TEXT(), autoincrement=False, nullable=False),
sa.Column("expire_date", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=False),
)
op.create_table(
"auth_permission",
sa.Column("id", sa.INTEGER(), autoincrement=True, nullable=False),
sa.Column("name", sa.VARCHAR(length=255), autoincrement=False, nullable=False),
sa.Column("content_type_id", sa.INTEGER(), autoincrement=False, nullable=False),
sa.Column("codename", sa.VARCHAR(length=100), autoincrement=False, nullable=False),
)
op.create_table(
"django_content_type",
sa.Column("id", sa.INTEGER(), autoincrement=True, nullable=False),
sa.Column("app_label", sa.VARCHAR(length=100), autoincrement=False, nullable=False),
sa.Column("model", sa.VARCHAR(length=100), autoincrement=False, nullable=False),
)
op.create_table(
"auth_user",
sa.Column("id", sa.INTEGER(), autoincrement=True, nullable=False),
sa.Column("password", sa.VARCHAR(length=128), autoincrement=False, nullable=False),
sa.Column("last_login", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=True),
sa.Column("is_superuser", sa.BOOLEAN(), autoincrement=False, nullable=False),
sa.Column("username", sa.VARCHAR(length=150), autoincrement=False, nullable=False),
sa.Column("first_name", sa.VARCHAR(length=150), autoincrement=False, nullable=False),
sa.Column("last_name", sa.VARCHAR(length=150), autoincrement=False, nullable=False),
sa.Column("email", sa.VARCHAR(length=254), autoincrement=False, nullable=False),
sa.Column("is_staff", sa.BOOLEAN(), autoincrement=False, nullable=False),
sa.Column("is_active", sa.BOOLEAN(), autoincrement=False, nullable=False),
sa.Column("date_joined", postgresql.TIMESTAMP(timezone=True), autoincrement=False, nullable=False),
)
op.create_table(
"auth_user_user_permissions",
sa.Column("id", sa.BIGINT(), autoincrement=True, nullable=False),
sa.Column("user_id", sa.INTEGER(), autoincrement=False, nullable=False),
sa.Column("permission_id", sa.INTEGER(), autoincrement=False, nullable=False),
)
op.create_table(
"auth_group_permissions",
sa.Column("id", sa.BIGINT(), autoincrement=True, nullable=False),
sa.Column("group_id", sa.INTEGER(), autoincrement=False, nullable=False),
sa.Column("permission_id", sa.INTEGER(), autoincrement=False, nullable=False),
)
op.create_table(
"auth_user_groups",
sa.Column("id", sa.BIGINT(), autoincrement=True, nullable=False),
sa.Column("user_id", sa.INTEGER(), autoincrement=False, nullable=False),
sa.Column("group_id", sa.INTEGER(), autoincrement=False, nullable=False),
)
op.create_table(
"auth_group",
sa.Column("id", sa.INTEGER(), autoincrement=True, nullable=False),
sa.Column("name", sa.VARCHAR(length=150), autoincrement=False, nullable=False),
)
op.drop_table("chat_history", schema="content")
# ### end Alembic commands ###

3
src/assistant/lib/agent/__init__.py Normal file
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@ -0,0 +1,3 @@
from .chat_repository import ChatHistoryRepository
__all__ = ["ChatHistoryRepository"]

83
src/assistant/lib/agent/chat_repository.py Normal file
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@ -0,0 +1,83 @@
import logging
import uuid
import sqlalchemy as sa
import sqlalchemy.exc
import sqlalchemy.ext.asyncio as sa_asyncio
import lib.models as models
import lib.orm_models as orm_models
class ChatHistoryRepository:
def __init__(self, pg_async_session: sa_asyncio.async_sessionmaker[sa_asyncio.AsyncSession]) -> None:
self.pg_async_session = pg_async_session
self.logger = logging.getLogger(__name__)
async def get_last_session_id(self, request: models.RequestLastSessionId) -> uuid.UUID | None:
"""Get a current session ID if exists."""
self.logger.debug("get_last_session_id: %s", request)
try:
async with self.pg_async_session() as session:
statement = (
sa.select(orm_models.ChatHistory)
.filter_by(channel=request.channel, user_id=request.user_id)
.filter(
(
sa.func.extract("epoch", sa.text("NOW()"))
- sa.func.extract("epoch", orm_models.ChatHistory.created)
)
/ 60
<= request.minutes_ago
)
.order_by(orm_models.ChatHistory.created.desc())
.limit(1)
)
result = await session.execute(statement)
chat_session = result.scalars().first()
if chat_session:
return chat_session.session_id
except sqlalchemy.exc.SQLAlchemyError as error:
self.logger.exception("Error: %s", error)
async def get_messages_by_sid(self, request: models.RequestChatHistory) -> list[models.Message] | None:
"""Get all messages of a chat by session ID."""
self.logger.debug("get_messages_by_sid: %s", request)
try:
async with self.pg_async_session() as session:
messages: list[models.Message] = []
statement = (
sa.select(orm_models.ChatHistory)
.filter_by(session_id=request.session_id)
.order_by(orm_models.ChatHistory.created.asc())
)
print("get_messages_by_sid:", statement)
result = await session.execute(statement)
for row in result.scalars().all():
# TODO: Было бы интересно понять почему pyright ругается ниже и как правильно вызывать компоненты
messages.append(models.Message(role=row.content["role"], content=row.content["content"])) # type: ignore[reportGeneralTypeIssues]
return messages
except sqlalchemy.exc.SQLAlchemyError as error:
self.logger.exception("Error: %s", error)
async def add_message(self, request: models.RequestChatMessage) -> None:
"""Add a message to the chat history."""
self.logger.debug("add_message: %s", request)
try:
async with self.pg_async_session() as session:
chat_history = orm_models.ChatHistory(
id=uuid.uuid4(),
session_id=request.session_id,
user_id=request.user_id,
channel=request.channel,
content=request.message,
)
session.add(chat_history)
await session.commit()
# TODO: Add refresh to session and return added object
except sqlalchemy.exc.SQLAlchemyError as error:
self.logger.exception("Error: %s", error)

58
src/assistant/lib/agent/openai_functions.py Normal file
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@ -0,0 +1,58 @@
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
class OpenAIFunctions:
"""OpenAI Functions for langchain agents."""
def __init__(
self,
repository: repositories.EmbeddingRepository,
pg_async_session: sa_asyncio.async_sessionmaker[sa_asyncio.AsyncSession],
) -> None:
self.logger = logging.getLogger(__name__)
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."""
self.logger.info("Request to get movie by description: %s", description)
embedded_description = await self.repository.aget_embedding(description)
try:
async with self.pg_async_session() as session:
result: list[models.Movie] = []
stmt = (
sa.select(orm_models.FilmWork)
.order_by(orm_models.FilmWork.embedding.cosine_distance(embedded_description))
.limit(5)
)
neighbours = session.scalars(stmt)
for neighbour in await neighbours:
result.append(models.Movie(**neighbour.__dict__))
return result
except sqlalchemy.exc.SQLAlchemyError as error:
self.logger.exception("Error: %s", error)
@langchain.agents.tool
def get_movie_by_id(self, id: uuid.UUID) -> models.Movie | None:
"""Provide a movie data by movie id."""
self.logger.info("Request to get movie by id: %s", id)
return None
@langchain.agents.tool
def get_similar_movies(self, id: uuid.UUID) -> list[models.Movie] | None:
"""Provide similar movies for the given movie ID."""
self.logger.info("Request to get movie by id: %s", id)
return None

40
src/assistant/lib/agent/repositories.py Normal file
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@ -0,0 +1,40 @@
import logging
import typing
import openai
import openai.error
import lib.app.settings as app_settings
import lib.models as models
class EmbeddingRepository:
"""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 = openai.api_key = settings.openai.api_key
self.logger = logging.getLogger(__name__)
def get_embedding(self, text: str, model: str = "text-embedding-ada-002") -> models.Embedding | None:
"""Get the embedding for a given text."""
try:
response: dict[str, typing.Any] = openai.Embedding.create(
input=text,
model=model,
) # type: ignore[reportGeneralTypeIssues]
return models.Embedding(**response["data"][0]["embedding"])
except openai.error.OpenAIError:
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."""
try:
response: dict[str, typing.Any] = await openai.Embedding.acreate(
input=text,
model=model,
) # type: ignore[reportGeneralTypeIssues]
return models.Embedding(**response["data"][0]["embedding"])
except openai.error.OpenAIError:
self.logger.exception("Failed to get async embedding for: %s", text)

88
src/assistant/lib/agent/services.py Normal file
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@ -0,0 +1,88 @@
import asyncio
import logging
import uuid
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.tools.render
import assistant.lib.models.movies as movies
import lib.agent.openai_functions as openai_functions
import lib.app.settings as app_settings
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class AgentService:
def __init__(self, settings: app_settings.Settings, tools: openai_functions.OpenAIFunctions) -> None:
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]
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"),
]
)
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})
# async def main():
# agent_executor = langchain.agents.AgentExecutor(agent=agent, tools=tools, verbose=True)
# # first_question = "What is the movie where halfling bring the ring to the volcano?"
# first_question = (
# "What is the movie about a famous country singer meet a talented singer and songwriter who works as a waitress?"
# )
# second_question = "So what is the rating of the movie? Do you recommend it?"
# third_question = "What are the similar movies?"
# first_result = await agent_executor.ainvoke({"input": first_question, "chat_history": chat_history})
# chat_history.append(langchain.schema.messages.HumanMessage(content=first_question))
# chat_history.append(langchain.schema.messages.AIMessage(content=first_result["output"]))
# second_result = await agent_executor.ainvoke({"input": second_question, "chat_history": chat_history})
# chat_history.append(langchain.schema.messages.HumanMessage(content=second_question))
# chat_history.append(langchain.schema.messages.AIMessage(content=second_result["output"]))
# final_result = await agent_executor.ainvoke({"input": third_question, "chat_history": chat_history})
# if __name__ == "__main__":
# asyncio.run(main())

3
src/assistant/lib/api/v1/handlers/__init__.py
View File

@ -1,7 +1,10 @@
from .agent import AgentHandler
from .health import basic_router
from .voice_responce_handler import VoiceResponseHandler
__all__ = [
"AgentHandler",
"VoiceResponseHandler",
"basic_router",
]

59
src/assistant/lib/api/v1/handlers/agent.py Normal file
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@ -0,0 +1,59 @@
import uuid
import fastapi
import lib.agent as agent
import lib.models as models
class AgentHandler:
def __init__(self, chat_history_repository: agent.ChatHistoryRepository):
self.chat_history_repository = chat_history_repository
self.router = fastapi.APIRouter()
self.router.add_api_route(
"/",
self.get_agent,
methods=["GET"],
summary="Статус работоспособности",
description="Проверяет доступность сервиса FastAPI.",
)
self.router.add_api_route(
"/add",
self.add_message,
methods=["GET"],
summary="Статус работоспособности",
description="Проверяет доступность сервиса FastAPI.",
)
self.router.add_api_route(
"/messages",
self.get_messages,
methods=["GET"],
summary="Статус работоспособности",
description="Проверяет доступность сервиса FastAPI.",
)
async def get_agent(self):
request = models.RequestLastSessionId(channel="test", user_id="user_id_1", minutes_ago=3)
response = await self.chat_history_repository.get_last_session_id(request=request)
print("RESPONSE: ", response)
return {"response": response}
async def add_message(self):
sid: uuid.UUID = uuid.UUID("0cd3c882-affd-4929-aff1-e1724f5b54f2")
import faker
fake = faker.Faker()
message = models.RequestChatMessage(
session_id=sid, user_id="user_id_1", channel="test", message={"role": "system", "content": fake.sentence()}
)
await self.chat_history_repository.add_message(request=message)
return {"response": "ok"}
async def get_messages(self):
sid: uuid.UUID = uuid.UUID("0cd3c882-affd-4929-aff1-e1724f5b54f2")
request = models.RequestChatHistory(session_id=sid)
response = await self.chat_history_repository.get_messages_by_sid(request=request)
print("RESPONSE: ", response)
return {"response": response}

4
src/assistant/lib/app/app.py
View File

@ -6,6 +6,7 @@ import typing
import fastapi
import uvicorn
import lib.agent as agent
import lib.api.v1.handlers as api_v1_handlers
import lib.app.errors as app_errors
import lib.app.settings as app_settings
@ -89,6 +90,7 @@ class Application:
logger.info("Initializing repositories")
stt_repository: stt.STTProtocol = stt.OpenaiSpeechRepository(settings=settings)
chat_history_repository = agent.ChatHistoryRepository(pg_async_session=postgres_client.get_async_session())
tts_yandex_repository = tts.TTSYandexRepository(
tts_settings=app_split_settings.TTSYandexSettings(),
@ -120,6 +122,7 @@ class Application:
logger.info("Initializing handlers")
liveness_probe_handler = api_v1_handlers.basic_router
agent_handler = api_v1_handlers.AgentHandler(chat_history_repository=chat_history_repository).router
# TODO: объявить сервисы tts и openai и добавить их в voice_response_handler
voice_response_handler = api_v1_handlers.VoiceResponseHandler(
@ -139,6 +142,7 @@ class Application:
# Routes
fastapi_app.include_router(liveness_probe_handler, prefix="/api/v1/health", tags=["health"])
fastapi_app.include_router(agent_handler, prefix="/api/v1/agent", tags=["testing"])
fastapi_app.include_router(voice_response_handler, prefix="/api/v1/voice", tags=["voice"])
application = Application(

6
src/assistant/lib/models/__init__.py
View File

@ -1,7 +1,11 @@
from .orm import Base, IdCreatedUpdatedBaseMixin
from .chat_history import Message, RequestChatHistory, RequestChatMessage, RequestLastSessionId
from .embedding import Embedding
from .movies import Movie
from .token import Token
from .tts import *
__all__ = ["Embedding", "Message", "Movie", "RequestChatHistory", "RequestChatMessage", "RequestLastSessionId", "Token"]
__all__ = [
"AVAILABLE_MODELS_TYPE",
"Base",

33
src/assistant/lib/models/chat_history.py Normal file
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@ -0,0 +1,33 @@
import uuid
import pydantic
class RequestLastSessionId(pydantic.BaseModel):
"""Request for a new session ID."""
channel: str
user_id: str
minutes_ago: int
class RequestChatMessage(pydantic.BaseModel):
"""A chat message."""
session_id: uuid.UUID
user_id: str
channel: str
message: dict[str, str]
class RequestChatHistory(pydantic.BaseModel):
"""Request for chat history."""
session_id: uuid.UUID
class Message(pydantic.BaseModel):
"""A chat message."""
role: str
content: str

5
src/assistant/lib/models/embedding.py Normal file
View File

@ -0,0 +1,5 @@
import pydantic
class Embedding(pydantic.RootModel[list[float]]):
root: list[float]

14
src/assistant/lib/models/movies.py Normal file
View File

@ -0,0 +1,14 @@
import datetime
import uuid
import pydantic
class Movie(pydantic.BaseModel):
id: uuid.UUID
title: str
description: str | None = None
rating: float
type: str
created: datetime.datetime
modified: datetime.datetime

3
src/assistant/lib/models/orm/__init__.py
View File

@ -1,3 +0,0 @@
from .base import Base, IdCreatedUpdatedBaseMixin
__all__ = ["Base", "IdCreatedUpdatedBaseMixin"]

14
src/assistant/lib/orm_models/__init__.py Normal file
View File

@ -0,0 +1,14 @@
from .base import Base, IdCreatedUpdatedBaseMixin
from .chat_history import ChatHistory
from .movies import FilmWork, Genre, GenreFilmWork, Person, PersonFilmWork
__all__ = [
"Base",
"ChatHistory",
"FilmWork",
"Genre",
"GenreFilmWork",
"IdCreatedUpdatedBaseMixin",
"Person",
"PersonFilmWork",
]

12
src/assistant/lib/models/orm/base.py → src/assistant/lib/orm_models/base.py
View File

@ -16,20 +16,12 @@ class Base(sa_orm.DeclarativeBase):
return cls.__name__.lower()
__mapper_args__ = {"eager_defaults": True}
id: sa_orm.Mapped[uuid.UUID] = sa_orm.mapped_column(primary_key=True, default=uuid.uuid4)
__table_args__ = {"schema": "content"}
class IdCreatedUpdatedBaseMixin:
# id: sa_orm.Mapped[int] = sa_orm.mapped_column(primary_key=True)
# id_field: sa_orm.Mapped[uuid.UUID] = sa_orm.mapped_column(name="uuid", primary_key=True, unique=True, default=uuid.uuid4, nullable=False)
id: sa_orm.Mapped[uuid.UUID] = sa_orm.mapped_column(primary_key=True, default=uuid.uuid4)
created: sa_orm.Mapped[datetime.datetime] = sa_orm.mapped_column(server_default=sa_sql.func.now())
updated: sa_orm.Mapped[datetime.datetime] = sa_orm.mapped_column(
server_default=sa_sql.func.now(), onupdate=sa_sql.func.now()
)
# __mapper_args__ = {"eager_defaults": True}
# @sqlalchemy.ext.declarative.declared_attr.directive
# def __tablename__(cls) -> str:
# return cls.__name__.lower()

24
src/assistant/lib/orm_models/chat_history.py Normal file
View File

@ -0,0 +1,24 @@
import datetime
import uuid
import sqlalchemy as sa
import sqlalchemy.orm as sa_orm
import sqlalchemy.sql as sa_sql
import lib.orm_models.base as base_models
class ChatHistory(base_models.Base):
__tablename__: str = "chat_history" # type: ignore[reportIncompatibleVariableOverride]
id: sa_orm.Mapped[uuid.UUID] = sa_orm.mapped_column(primary_key=True, default=uuid.uuid4)
session_id: sa_orm.Mapped[uuid.UUID] = sa_orm.mapped_column(nullable=False, unique=True)
channel: sa_orm.Mapped[str] = sa_orm.mapped_column()
user_id: sa_orm.Mapped[str] = sa_orm.mapped_column()
content: sa_orm.Mapped[sa.JSON] = sa_orm.mapped_column(sa.JSON)
created: sa_orm.Mapped[datetime.datetime] = sa_orm.mapped_column(
sa.DateTime(timezone=True), server_default=sa_sql.func.now()
)
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()
)

76
src/assistant/lib/orm_models/movies.py Normal file
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@ -0,0 +1,76 @@
import datetime
import uuid
import pgvector.sqlalchemy
import sqlalchemy as sa
import sqlalchemy.orm as sa_orm
import sqlalchemy.sql as sa_sql
import lib.orm_models.base as base_models
class Genre(base_models.Base):
__tablename__: str = "genre" # type: ignore[reportIncompatibleVariableOverride]
id: sa_orm.Mapped[uuid.UUID] = sa_orm.mapped_column(primary_key=True, default=uuid.uuid4)
name: sa_orm.Mapped[str] = sa_orm.mapped_column()
description: sa_orm.Mapped[str] = sa_orm.mapped_column(nullable=True)
created: sa_orm.Mapped[datetime.datetime] = sa_orm.mapped_column(
sa.DateTime(timezone=True), server_default=sa_sql.func.now()
)
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()
)
class Person(base_models.Base):
__tablename__: str = "person" # type: ignore[reportIncompatibleVariableOverride]
id: sa_orm.Mapped[uuid.UUID] = sa_orm.mapped_column(primary_key=True, default=uuid.uuid4)
full_name: 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()
)
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()
)
class FilmWork(base_models.Base):
__tablename__: str = "film_work" # type: ignore[reportIncompatibleVariableOverride]
id: sa_orm.Mapped[uuid.UUID] = sa_orm.mapped_column(primary_key=True, default=uuid.uuid4)
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)
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()
)
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))
genres: sa_orm.Mapped[list[Genre]] = sa_orm.relationship(secondary="genre_film_work")
GenreFilmWork = sa.Table(
"genre_film_work",
base_models.Base.metadata,
sa.Column("id", sa.UUID, primary_key=True), # type: ignore[reportUnknownVariableType]
sa.Column("genre_id", sa.ForeignKey(Genre.id), primary_key=True), # type: ignore[reportUnknownVariableType]
sa.Column("film_work_id", sa.ForeignKey(FilmWork.id), primary_key=True), # type: ignore[reportUnknownVariableType]
sa.Column("created", sa.DateTime(timezone=True), server_default=sa_sql.func.now()),
)
PersonFilmWork = sa.Table(
"person_film_work",
base_models.Base.metadata,
sa.Column("person_id", sa.ForeignKey(Person.id), primary_key=True), # type: ignore[reportUnknownVariableType]
sa.Column("film_work_id", sa.ForeignKey(FilmWork.id), primary_key=True), # type: ignore[reportUnknownVariableType]
sa.Column("role", sa.String(50), nullable=False),
sa.Column("created", sa.DateTime(timezone=True), server_default=sa_sql.func.now()),
)

265
src/assistant/poetry.lock generated
View File

@ -448,6 +448,21 @@ files = [
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]
[[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"
@ -514,6 +529,20 @@ 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"
@ -771,6 +800,85 @@ pipfile-deprecated-finder = ["pip-shims (>=0.5.2)", "pipreqs", "requirementslib"
plugins = ["setuptools"]
requirements-deprecated-finder = ["pip-api", "pipreqs"]
[[package]]
name = "jsonpatch"
version = "1.33"
description = "Apply JSON-Patches (RFC 6902)"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
files = [
{file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"},
{file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"},
]
[package.dependencies]
jsonpointer = ">=1.9"
[[package]]
name = "jsonpointer"
version = "2.4"
description = "Identify specific nodes in a JSON document (RFC 6901)"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
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]
[[package]]
name = "langchain"
version = "0.0.312"
description = "Building applications with LLMs through composability"
optional = false
python-versions = ">=3.8.1,<4.0"
files = [
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aiohttp = ">=3.8.3,<4.0.0"
anyio = "<4.0"
dataclasses-json = ">=0.5.7,<0.7"
jsonpatch = ">=1.33,<2.0"
langsmith = ">=0.0.43,<0.1.0"
numpy = ">=1,<2"
pydantic = ">=1,<3"
PyYAML = ">=5.3"
requests = ">=2,<3"
SQLAlchemy = ">=1.4,<3"
tenacity = ">=8.1.0,<9.0.0"
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all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "amadeus (>=8.1.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "awadb (>=0.3.9,<0.4.0)", "azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clarifai (>=9.1.0)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=4,<5)", "deeplake (>=3.6.8,<4.0.0)", "docarray[hnswlib] (>=0.32.0,<0.33.0)", "duckduckgo-search (>=3.8.3,<4.0.0)", "elasticsearch (>=8,<9)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-auth (>=2.18.1,<3.0.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lancedb (>=0.1,<0.2)", "langkit (>=0.0.6,<0.1.0)", "lark (>=1.1.5,<2.0.0)", "libdeeplake (>=0.0.60,<0.0.61)", "librosa (>=0.10.0.post2,<0.11.0)", "lxml (>=4.9.2,<5.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "marqo (>=1.2.4,<2.0.0)", "momento (>=1.10.1,<2.0.0)", "nebula3-python (>=3.4.0,<4.0.0)", "neo4j (>=5.8.1,<6.0.0)", "networkx (>=2.6.3,<4)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "opensearch-py (>=2.0.0,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pymongo (>=4.3.3,<5.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "python-arango (>=7.5.9,<8.0.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.3.1,<2.0.0)", "rdflib (>=6.3.2,<7.0.0)", "redis (>=4,<5)", "requests-toolbelt (>=1.0.0,<2.0.0)", "sentence-transformers (>=2,<3)", "singlestoredb (>=0.7.1,<0.8.0)", "tensorflow-text (>=2.11.0,<3.0.0)", "tigrisdb (>=1.0.0b6,<2.0.0)", "tiktoken (>=0.3.2,<0.6.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
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clarifai = ["clarifai (>=9.1.0)"]
cli = ["typer (>=0.9.0,<0.10.0)"]
cohere = ["cohere (>=4,<5)"]
docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
embeddings = ["sentence-transformers (>=2,<3)"]
extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "amazon-textract-caller (<2)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "dashvector (>=1.0.1,<2.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (>=0,<1)", "openapi-schema-pydantic (>=1.2,<2.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
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[[package]]
name = "langsmith"
version = "0.0.43"
description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
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python-versions = ">=3.8.1,<4.0"
files = [
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tests = ["pytest", "pytz", "simplejson"]
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setuptools = "*"
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7
src/assistant/pyproject.toml
View File

@ -23,9 +23,13 @@ version = "0.1.0"
alembic = "^1.12.0"
asyncpg = "^0.28.0"
dill = "^0.3.7"
faker = "^19.10.0"
fastapi = "0.103.1"
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pgvector = "^0.2.3"
multidict = "^6.0.4"
openai = "^0.28.1"
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[tool.pyright]
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".pytest_cache",
".venv"
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pythonVersion = "3.11"