mirror of
https://github.com/QuivrHQ/quivr.git
synced 2024-12-15 09:32:22 +03:00
288 lines
9.2 KiB
Python
288 lines
9.2 KiB
Python
from typing import List, Optional
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from uuid import UUID
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from venv import logger
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from fastapi import APIRouter, Depends, HTTPException, Query, Request
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from fastapi.responses import StreamingResponse
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from llm.qa_base import QABaseBrainPicking
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from llm.qa_headless import HeadlessQA
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from middlewares.auth import AuthBearer, get_current_user
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from models import Chat, ChatQuestion, UserUsage, get_supabase_db
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from models.databases.supabase.chats import QuestionAndAnswer
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from modules.user.entity.user_identity import UserIdentity
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from repository.brain.get_brain_by_id import get_brain_by_id
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from repository.chat import (
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ChatUpdatableProperties,
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CreateChatProperties,
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GetChatHistoryOutput,
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create_chat,
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get_chat_by_id,
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get_user_chats,
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update_chat,
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)
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from repository.chat.add_question_and_answer import add_question_and_answer
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from repository.chat.get_chat_history_with_notifications import (
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ChatItem,
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get_chat_history_with_notifications,
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)
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from repository.notification.remove_chat_notifications import remove_chat_notifications
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from routes.chat.factory import get_chat_strategy
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from routes.chat.utils import (
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NullableUUID,
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check_user_requests_limit,
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delete_chat_from_db,
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)
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chat_router = APIRouter()
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@chat_router.get("/chat/healthz", tags=["Health"])
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async def healthz():
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return {"status": "ok"}
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# get all chats
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@chat_router.get("/chat", dependencies=[Depends(AuthBearer())], tags=["Chat"])
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async def get_chats(current_user: UserIdentity = Depends(get_current_user)):
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"""
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Retrieve all chats for the current user.
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- `current_user`: The current authenticated user.
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- Returns a list of all chats for the user.
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This endpoint retrieves all the chats associated with the current authenticated user. It returns a list of chat objects
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containing the chat ID and chat name for each chat.
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"""
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chats = get_user_chats(str(current_user.id))
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return {"chats": chats}
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# delete one chat
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@chat_router.delete(
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"/chat/{chat_id}", dependencies=[Depends(AuthBearer())], tags=["Chat"]
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)
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async def delete_chat(chat_id: UUID):
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"""
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Delete a specific chat by chat ID.
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"""
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supabase_db = get_supabase_db()
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remove_chat_notifications(chat_id)
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delete_chat_from_db(supabase_db=supabase_db, chat_id=chat_id)
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return {"message": f"{chat_id} has been deleted."}
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# update existing chat metadata
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@chat_router.put(
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"/chat/{chat_id}/metadata", dependencies=[Depends(AuthBearer())], tags=["Chat"]
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)
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async def update_chat_metadata_handler(
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chat_data: ChatUpdatableProperties,
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chat_id: UUID,
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current_user: UserIdentity = Depends(get_current_user),
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) -> Chat:
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"""
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Update chat attributes
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"""
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chat = get_chat_by_id(chat_id) # pyright: ignore reportPrivateUsage=none
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if str(current_user.id) != chat.user_id:
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raise HTTPException(
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status_code=403, # pyright: ignore reportPrivateUsage=none
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detail="You should be the owner of the chat to update it.", # pyright: ignore reportPrivateUsage=none
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)
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return update_chat(chat_id=chat_id, chat_data=chat_data)
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# create new chat
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@chat_router.post("/chat", dependencies=[Depends(AuthBearer())], tags=["Chat"])
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async def create_chat_handler(
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chat_data: CreateChatProperties,
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current_user: UserIdentity = Depends(get_current_user),
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):
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"""
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Create a new chat with initial chat messages.
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"""
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return create_chat(user_id=current_user.id, chat_data=chat_data)
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# add new question to chat
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@chat_router.post(
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"/chat/{chat_id}/question",
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dependencies=[
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Depends(
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AuthBearer(),
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),
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],
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tags=["Chat"],
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)
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async def create_question_handler(
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request: Request,
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chat_question: ChatQuestion,
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chat_id: UUID,
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brain_id: NullableUUID
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| UUID
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| None = Query(..., description="The ID of the brain"),
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current_user: UserIdentity = Depends(get_current_user),
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) -> GetChatHistoryOutput:
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"""
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Add a new question to the chat.
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"""
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chat_instance = get_chat_strategy(brain_id)
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chat_instance.validate_authorization(user_id=current_user.id, brain_id=brain_id)
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fallback_model = "gpt-3.5-turbo"
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fallback_temperature = 0.1
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fallback_max_tokens = 512
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user_daily_usage = UserUsage(
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id=current_user.id,
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email=current_user.email,
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)
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user_settings = user_daily_usage.get_user_settings()
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is_model_ok = (chat_question).model in user_settings.get("models", ["gpt-3.5-turbo"]) # type: ignore
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# Retrieve chat model (temperature, max_tokens, model)
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if (
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not chat_question.model
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or not chat_question.temperature
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or not chat_question.max_tokens
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):
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if brain_id:
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brain = get_brain_by_id(brain_id)
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if brain:
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fallback_model = brain.model or fallback_model
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fallback_temperature = brain.temperature or fallback_temperature
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fallback_max_tokens = brain.max_tokens or fallback_max_tokens
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chat_question.model = chat_question.model or fallback_model
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chat_question.temperature = chat_question.temperature or fallback_temperature
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chat_question.max_tokens = chat_question.max_tokens or fallback_max_tokens
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try:
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check_user_requests_limit(current_user)
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is_model_ok = (chat_question).model in user_settings.get("models", ["gpt-3.5-turbo"]) # type: ignore
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gpt_answer_generator = chat_instance.get_answer_generator(
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chat_id=str(chat_id),
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model=chat_question.model if is_model_ok else "gpt-3.5-turbo", # type: ignore
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max_tokens=chat_question.max_tokens,
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temperature=chat_question.temperature,
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brain_id=str(brain_id),
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streaming=False,
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prompt_id=chat_question.prompt_id,
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user_id=current_user.id,
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)
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chat_answer = gpt_answer_generator.generate_answer(chat_id, chat_question)
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return chat_answer
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except HTTPException as e:
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raise e
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# stream new question response from chat
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@chat_router.post(
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"/chat/{chat_id}/question/stream",
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dependencies=[
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Depends(
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AuthBearer(),
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),
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],
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tags=["Chat"],
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)
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async def create_stream_question_handler(
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request: Request,
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chat_question: ChatQuestion,
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chat_id: UUID,
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brain_id: NullableUUID
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| UUID
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| None = Query(..., description="The ID of the brain"),
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current_user: UserIdentity = Depends(get_current_user),
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) -> StreamingResponse:
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chat_instance = get_chat_strategy(brain_id)
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chat_instance.validate_authorization(user_id=current_user.id, brain_id=brain_id)
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user_daily_usage = UserUsage(
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id=current_user.id,
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email=current_user.email,
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)
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user_settings = user_daily_usage.get_user_settings()
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# Retrieve chat model (temperature, max_tokens, model)
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if (
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not chat_question.model
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or chat_question.temperature is None
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or not chat_question.max_tokens
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):
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fallback_model = "gpt-3.5-turbo"
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fallback_temperature = 0
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fallback_max_tokens = 256
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if brain_id:
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brain = get_brain_by_id(brain_id)
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if brain:
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fallback_model = brain.model or fallback_model
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fallback_temperature = brain.temperature or fallback_temperature
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fallback_max_tokens = brain.max_tokens or fallback_max_tokens
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chat_question.model = chat_question.model or fallback_model
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chat_question.temperature = chat_question.temperature or fallback_temperature
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chat_question.max_tokens = chat_question.max_tokens or fallback_max_tokens
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try:
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logger.info(f"Streaming request for {chat_question.model}")
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check_user_requests_limit(current_user)
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gpt_answer_generator: HeadlessQA | QABaseBrainPicking
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# TODO check if model is in the list of models available for the user
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is_model_ok = chat_question.model in user_settings.get("models", ["gpt-3.5-turbo"]) # type: ignore
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gpt_answer_generator = chat_instance.get_answer_generator(
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chat_id=str(chat_id),
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model=chat_question.model if is_model_ok else "gpt-3.5-turbo", # type: ignore
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max_tokens=chat_question.max_tokens,
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temperature=chat_question.temperature, # type: ignore
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streaming=True,
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prompt_id=chat_question.prompt_id,
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brain_id=str(brain_id),
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user_id=current_user.id,
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)
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return StreamingResponse(
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gpt_answer_generator.generate_stream(chat_id, chat_question),
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media_type="text/event-stream",
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)
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except HTTPException as e:
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raise e
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# get chat history
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@chat_router.get(
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"/chat/{chat_id}/history", dependencies=[Depends(AuthBearer())], tags=["Chat"]
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)
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async def get_chat_history_handler(
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chat_id: UUID,
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) -> List[ChatItem]:
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# TODO: RBAC with current_user
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return get_chat_history_with_notifications(chat_id)
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@chat_router.post(
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"/chat/{chat_id}/question/answer",
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dependencies=[Depends(AuthBearer())],
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tags=["Chat"],
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)
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async def add_question_and_answer_handler(
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chat_id: UUID,
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question_and_answer: QuestionAndAnswer,
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) -> Optional[Chat]:
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"""
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Add a new question and anwser to the chat.
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"""
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return add_question_and_answer(chat_id, question_and_answer)
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