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Code Issues Packages Projects Releases Wiki Activity

feat(models): all models by default (#2983)

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# Description

Please include a summary of the changes and the related issue. Please
also include relevant motivation and context.

## Checklist before requesting a review

Please delete options that are not relevant.

- [ ] My code follows the style guidelines of this project
- [ ] I have performed a self-review of my code
- [ ] I have commented hard-to-understand areas
- [ ] I have ideally added tests that prove my fix is effective or that
my feature works
- [ ] New and existing unit tests pass locally with my changes
- [ ] Any dependent changes have been merged

## Screenshots (if appropriate):
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17 changed files with 263 additions and 231 deletions
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2
backend/api/quivr_api/celery_worker.py
View File

@ -5,6 +5,7 @@ from uuid import UUID
from celery.schedules import crontab
from pytz import timezone
from quivr_api.celery_config import celery
from quivr_api.logger import get_logger
from quivr_api.middlewares.auth.auth_bearer import AuthBearer
@ -241,7 +242,6 @@ def check_if_is_premium_user():
"max_brain_size": product["max_brain_size"],
"monthly_chat_credit": product["monthly_chat_credit"],
"api_access": product["api_access"],
"models": product["models"],
"is_premium": True,
"last_stripe_check": current_time_str,
}

14
backend/api/quivr_api/modules/brain/controller/brain_routes.py
View File

@ -65,6 +65,19 @@ async def retrieve_all_brains_for_user(
"""Retrieve all brains for the current user."""
brains = brain_user_service.get_user_brains(current_user.id)
models = await model_service.get_models()
default_model = await model_service.get_default_model()
for brain in brains:
# find the brain.model in models and set the brain.price to the model.price
found = False
if brain.model:
for model in models:
if model.name == brain.model:
brain.price = model.price
found = True
break
if not found:
brain.price = default_model.price
for model in models:
brains.append(
@ -85,6 +98,7 @@ async def retrieve_all_brains_for_user(
max_files=0,
)
)
return {"brains": brains}

1
backend/api/quivr_api/modules/brain/entity/brain_entity.py
View File

@ -111,6 +111,7 @@ class BrainUser(BaseModel):
class MinimalUserBrainEntity(BaseModel):
id: UUID
name: str
brain_model: Optional[str] = None
rights: RoleEnum
status: str
brain_type: BrainType

3
backend/api/quivr_api/modules/brain/repository/brains_users.py
View File

@ -39,7 +39,7 @@ class BrainsUsers(BrainsUsersInterface):
response = (
self.db.from_("brains_users")
.select(
"id:brain_id, rights, brains (brain_id, name, status, brain_type, description, meaning, integrations_user (brain_id, integration_id, integrations (id, integration_name, integration_logo_url, max_files)))"
"id:brain_id, rights, brains (brain_id, name, status, brain_type, model, description, meaning, integrations_user (brain_id, integration_id, integrations (id, integration_name, integration_logo_url, max_files)))"
)
.filter("user_id", "eq", user_id)
.execute()
@ -60,6 +60,7 @@ class BrainsUsers(BrainsUsersInterface):
user_brains.append(
MinimalUserBrainEntity(
id=item["brains"]["brain_id"],
brain_model=item["brains"]["model"],
name=item["brains"]["name"],
rights=item["rights"],
status=item["brains"]["status"],

91
backend/api/quivr_api/modules/chat/controller/chat/utils.py
View File

@ -2,8 +2,11 @@ import time
from uuid import UUID
from fastapi import HTTPException
from quivr_api.logger import get_logger
from quivr_api.models.databases.llm_models import LLMModel
from quivr_api.modules.models.entity.model import Model
from quivr_api.modules.models.service.model_service import ModelService
from quivr_api.modules.user.entity.user_identity import UserIdentity
from quivr_api.modules.user.service.user_usage import UserUsage
logger = get_logger(__name__)
@ -26,59 +29,14 @@ class NullableUUID(UUID):
# TODO: rewrite
def compute_cost(model_to_use, models_settings):
model = model_to_use.name
user_choosen_model_price = 1000
for model_setting in models_settings:
if model_setting["name"] == model:
user_choosen_model_price = model_setting["price"]
return user_choosen_model_price
# TODO: rewrite
def find_model_and_generate_metadata(
async def find_model_and_generate_metadata(
brain_model: str | None,
user_settings,
models_settings,
):
# Default model is gpt-3.5-turbo-0125
default_model = "gpt-3.5-turbo-0125"
model_to_use = LLMModel( # TODO Implement default models in database
name=default_model, price=1, max_input=4000, max_output=1000
)
logger.debug("Brain model: %s", brain_model)
# If brain.model is None, set it to the default_model
if brain_model is None:
brain_model = default_model
is_brain_model_available = any(
brain_model == model_dict.get("name") for model_dict in models_settings
)
is_user_allowed_model = brain_model in user_settings.get(
"models", [default_model]
) # Checks if the model is available in the list of models
logger.debug(f"Brain model: {brain_model}")
logger.debug(f"User models: {user_settings.get('models', [])}")
logger.debug(f"Model available: {is_brain_model_available}")
logger.debug(f"User allowed model: {is_user_allowed_model}")
if is_brain_model_available and is_user_allowed_model:
# Use the model from the brain
model_to_use.name = brain_model
for model_dict in models_settings:
if model_dict.get("name") == model_to_use.name:
model_to_use.price = model_dict.get("price")
model_to_use.max_input = model_dict.get("max_input")
model_to_use.max_output = model_dict.get("max_output")
break
logger.info(f"Model to use: {model_to_use}")
return model_to_use
model_service: ModelService,
) -> Model:
model = await model_service.get_model(brain_model)
if model is None:
model = await model_service.get_default_model()
return model
def update_user_usage(usage: UserUsage, user_settings, cost: int = 100):
@ -107,3 +65,30 @@ def update_user_usage(usage: UserUsage, user_settings, cost: int = 100):
else:
usage.handle_increment_user_request_count(date, cost)
pass
async def check_and_update_user_usage(
user: UserIdentity, model_name: str, model_service: ModelService
):
"""Check user limits and raises if user reached his limits:
1. Raise if one of the conditions :
- User doesn't have access to brains
- Model of brain is not is user_settings.models
- Latest sum_30d(user_daily_user) < user_settings.max_monthly_usage
- Check sum(user_settings.daily_user_count)+ model_price < user_settings.monthly_chat_credits
2. Updates user usage
"""
# TODO(@aminediro) : THIS is bug prone, should retrieve it from DB here
user_usage = UserUsage(id=user.id, email=user.email)
user_settings = user_usage.get_user_settings()
# Get the model to use
model = await model_service.get_model(model_name)
logger.info(f"Model 🔥: {model}")
if model is None:
model = await model_service.get_default_model()
logger.info(f"Model 🔥: {model}")
# Raises HTTP if user usage exceeds limits
update_user_usage(user_usage, user_settings, model.price) # noqa: F821
return model

41
backend/api/quivr_api/modules/chat/controller/chat_routes.py
View File

@ -11,6 +11,7 @@ from quivr_api.modules.brain.service.brain_authorization_service import (
validate_brain_authorization,
)
from quivr_api.modules.brain.service.brain_service import BrainService
from quivr_api.modules.chat.controller.chat.utils import check_and_update_user_usage
from quivr_api.modules.chat.dto.chats import ChatItem, ChatQuestion
from quivr_api.modules.chat.dto.inputs import (
ChatMessageProperties,
@ -179,31 +180,44 @@ async def create_question_handler(
if brain_id == generate_uuid_from_string(model.name):
model_to_use = model
break
try:
service = None
service = None | RAGService | ChatLLMService
if not model_to_use:
# TODO: check logic into middleware
brain = brain_service.get_brain_details(brain_id, current_user.id) # type: ignore
model = await check_and_update_user_usage(
current_user, str(brain.model), model_service
) # type: ignore
assert model is not None # type: ignore
assert brain is not None # type: ignore
brain.model = model.name
validate_authorization(user_id=current_user.id, brain_id=brain_id)
service = RAGService(
current_user,
brain_id,
brain,
chat_id,
brain_service,
prompt_service,
chat_service,
knowledge_service,
model_service,
)
else:
await check_and_update_user_usage(
current_user, model_to_use.name, model_service
) # type: ignore
service = ChatLLMService(
current_user,
model_to_use.name,
chat_id,
chat_service,
model_service,
)
) # type: ignore
assert service is not None # type: ignore
maybe_send_telemetry("question_asked", {"streaming": True}, request)
chat_answer = await service.generate_answer(chat_question.question)
maybe_send_telemetry("question_asked", {"streaming": False}, request)
return chat_answer
except AssertionError:
@ -252,24 +266,37 @@ async def create_stream_question_handler(
try:
service = None
if not model_to_use:
brain = brain_service.get_brain_details(brain_id, current_user.id) # type: ignore
model = await check_and_update_user_usage(
current_user, str(brain.model), model_service
) # type: ignore
assert model is not None # type: ignore
assert brain is not None # type: ignore
brain.model = model.name
validate_authorization(user_id=current_user.id, brain_id=brain_id)
service = RAGService(
current_user,
brain_id,
brain,
chat_id,
brain_service,
prompt_service,
chat_service,
knowledge_service,
model_service,
)
else:
await check_and_update_user_usage(
current_user, model_to_use.name, model_service
) # type: ignore
service = ChatLLMService(
current_user,
model_to_use.name,
chat_id,
chat_service,
model_service,
)
) # type: ignore
assert service is not None # type: ignore
maybe_send_telemetry("question_asked", {"streaming": True}, request)
return StreamingResponse(

90
backend/api/quivr_api/modules/chat_llm_service/chat_llm_service.py
View File

@ -1,4 +1,5 @@
import datetime
import os
from uuid import UUID, uuid4
from quivr_core.chat import ChatHistory as ChatHistoryCore
@ -8,21 +9,14 @@ from quivr_core.llm.llm_endpoint import LLMEndpoint
from quivr_core.models import ChatLLMMetadata, ParsedRAGResponse, RAGResponseMetadata
from quivr_api.logger import get_logger
from quivr_api.models.settings import settings
from quivr_api.modules.brain.service.utils.format_chat_history import (
format_chat_history,
)
from quivr_api.modules.chat.controller.chat.utils import (
compute_cost,
find_model_and_generate_metadata,
update_user_usage,
)
from quivr_api.modules.chat.dto.inputs import CreateChatHistory
from quivr_api.modules.chat.dto.outputs import GetChatHistoryOutput
from quivr_api.modules.chat.service.chat_service import ChatService
from quivr_api.modules.models.service.model_service import ModelService
from quivr_api.modules.user.entity.user_identity import UserIdentity
from quivr_api.modules.user.service.user_usage import UserUsage
from quivr_api.packages.utils.uuid_generator import generate_uuid_from_string
logger = get_logger(__name__)
@ -46,9 +40,7 @@ class ChatLLMService:
self.chat_id = chat_id
# check at init time
self.model_to_use = self.check_and_update_user_usage(
self.current_user, model_name
)
self.model_to_use = model_name
def _build_chat_history(
self,
@ -60,56 +52,26 @@ class ChatLLMService:
[chat_history.append(m) for m in transformed_history]
return chat_history
def build_llm(self) -> ChatLLM:
ollama_url = (
settings.ollama_api_base_url
if settings.ollama_api_base_url
and self.model_to_use.name.startswith("ollama")
else None
)
async def build_llm(self) -> ChatLLM:
model = await self.model_service.get_model(self.model_to_use)
api_key = os.getenv(model.env_variable_name, "not-defined")
chat_llm = ChatLLM(
llm=LLMEndpoint.from_config(
LLMEndpointConfig(
model=self.model_to_use.name,
llm_base_url=ollama_url,
llm_api_key="abc-123" if ollama_url else None,
model=self.model_to_use,
llm_base_url=model.endpoint_url,
llm_api_key=api_key,
temperature=(LLMEndpointConfig.model_fields["temperature"].default),
max_input=self.model_to_use.max_input,
max_tokens=self.model_to_use.max_output,
max_input=model.max_input,
max_tokens=model.max_output,
),
)
)
return chat_llm
def check_and_update_user_usage(self, user: UserIdentity, model_name: str):
"""Check user limits and raises if user reached his limits:
1. Raise if one of the conditions :
- User doesn't have access to brains
- Model of brain is not is user_settings.models
- Latest sum_30d(user_daily_user) < user_settings.max_monthly_usage
- Check sum(user_settings.daily_user_count)+ model_price < user_settings.monthly_chat_credits
2. Updates user usage
"""
# TODO(@aminediro) : THIS is bug prone, should retrieve it from DB here
user_usage = UserUsage(id=user.id, email=user.email)
user_settings = user_usage.get_user_settings()
all_models = user_usage.get_models()
# TODO(@aminediro): refactor this function
model_to_use = find_model_and_generate_metadata(
model_name,
user_settings,
all_models,
)
cost = compute_cost(model_to_use, all_models)
# Raises HTTP if user usage exceeds limits
update_user_usage(user_usage, user_settings, cost) # noqa: F821
return model_to_use
def save_answer(self, question: str, answer: ParsedRAGResponse):
logger.info(
f"Saving answer for chat {self.chat_id} with model {self.model_to_use.name}"
f"Saving answer for chat {self.chat_id} with model {self.model_to_use}"
)
logger.info(answer)
return self.chat_service.update_chat_history(
@ -130,11 +92,11 @@ class ChatLLMService:
question: str,
):
logger.info(
f"Creating question for chat {self.chat_id} with model {self.model_to_use.name} "
f"Creating question for chat {self.chat_id} with model {self.model_to_use} "
)
chat_llm = self.build_llm()
chat_llm = await self.build_llm()
history = await self.chat_service.get_chat_history(self.chat_id)
model_metadata = await self.model_service.get_model(self.model_to_use.name)
model_metadata = await self.model_service.get_model(self.model_to_use)
# Format the history, sanitize the input
chat_history = self._build_chat_history(history)
@ -143,12 +105,12 @@ class ChatLLMService:
if parsed_response.metadata:
# TODO: check if this is the right way to do it
parsed_response.metadata.metadata_model = ChatLLMMetadata(
name=self.model_to_use.name,
name=self.model_to_use,
description=model_metadata.description,
image_url=model_metadata.image_url,
display_name=model_metadata.display_name,
brain_id=str(generate_uuid_from_string(self.model_to_use.name)),
brain_name=self.model_to_use.name,
brain_id=str(generate_uuid_from_string(self.model_to_use)),
brain_name=self.model_to_use,
)
# Save the answer to db
@ -178,13 +140,13 @@ class ChatLLMService:
question: str,
):
logger.info(
f"Creating question for chat {self.chat_id} with model {self.model_to_use.name} "
f"Creating question for chat {self.chat_id} with model {self.model_to_use} "
)
# Build the rag config
chat_llm = self.build_llm()
chat_llm = await self.build_llm()
# Get model metadata
model_metadata = await self.model_service.get_model(self.model_to_use.name)
model_metadata = await self.model_service.get_model(self.model_to_use)
# Get chat history
history = await self.chat_service.get_chat_history(self.chat_id)
# Format the history, sanitize the input
@ -202,12 +164,12 @@ class ChatLLMService:
"brain_id": None,
}
metadata_model = ChatLLMMetadata(
name=self.model_to_use.name,
name=self.model_to_use,
description=model_metadata.description,
image_url=model_metadata.image_url,
display_name=model_metadata.display_name,
brain_id=str(generate_uuid_from_string(self.model_to_use.name)),
brain_name=self.model_to_use.name,
brain_id=str(generate_uuid_from_string(self.model_to_use)),
brain_name=self.model_to_use,
)
async for response in chat_llm.answer_astream(question, chat_history):
@ -233,12 +195,12 @@ class ChatLLMService:
metadata = RAGResponseMetadata(**streamed_chat_history.metadata) # type: ignore
metadata.metadata_model = ChatLLMMetadata(
name=self.model_to_use.name,
name=self.model_to_use,
description=model_metadata.description,
image_url=model_metadata.image_url,
display_name=model_metadata.display_name,
brain_id=str(generate_uuid_from_string(self.model_to_use.name)),
brain_name=self.model_to_use.name,
brain_id=str(generate_uuid_from_string(self.model_to_use)),
brain_name=self.model_to_use,
)
streamed_chat_history.metadata = metadata.model_dump()

3
backend/api/quivr_api/modules/models/entity/model.py
View File

@ -11,6 +11,9 @@ class Model(SQLModel, table=True): # type: ignore
description: str = Field(default="")
display_name: str = Field(default="")
image_url: str = Field(default="")
endpoint_url: str = Field(default="")
env_variable_name: str = Field(default="")
default: bool = Field(default=False)
class Config:
arbitrary_types_allowed = True

7
backend/api/quivr_api/modules/models/repository/model.py
View File

@ -19,7 +19,12 @@ class ModelRepository(BaseRepository):
response = await self.session.exec(query)
return response.all()
async def get_model(self, model_name: str) -> Model:
async def get_model(self, model_name: str) -> Model | None:
query = select(Model).where(Model.name == model_name)
response = await self.session.exec(query)
return response.first()
async def get_default_model(self) -> Model:
query = select(Model).where(Model.default == True) # noqa: E712
response = await self.session.exec(query)
return response.first()

7
backend/api/quivr_api/modules/models/repository/model_interface.py
View File

@ -17,3 +17,10 @@ class ModelsInterface(ABC):
Get a model by name
"""
pass
@abstractmethod
def get_default_model(self) -> Model:
"""
Get the default model
"""
pass

7
backend/api/quivr_api/modules/models/service/model_service.py
View File

@ -25,3 +25,10 @@ class ModelService(BaseService[ModelRepository]):
model = await self.repository.get_model(model_name)
return model
async def get_default_model(self) -> Model | None:
logger.info("Getting default model")
model = await self.repository.get_default_model()
return model

92
backend/api/quivr_api/modules/rag_service/rag_service.py
View File

@ -1,4 +1,5 @@
import datetime
import os
from uuid import UUID, uuid4
from quivr_core.chat import ChatHistory as ChatHistoryCore
@ -11,26 +12,20 @@ from quivr_api.logger import get_logger
from quivr_api.models.settings import (
get_embedding_client,
get_supabase_client,
settings,
)
from quivr_api.modules.brain.entity.brain_entity import BrainEntity
from quivr_api.modules.brain.service.brain_service import BrainService
from quivr_api.modules.brain.service.utils.format_chat_history import (
format_chat_history,
)
from quivr_api.modules.chat.controller.chat.utils import (
compute_cost,
find_model_and_generate_metadata,
update_user_usage,
)
from quivr_api.modules.chat.dto.inputs import CreateChatHistory
from quivr_api.modules.chat.dto.outputs import GetChatHistoryOutput
from quivr_api.modules.chat.service.chat_service import ChatService
from quivr_api.modules.knowledge.repository.knowledges import KnowledgeRepository
from quivr_api.modules.models.service.model_service import ModelService
from quivr_api.modules.prompt.entity.prompt import Prompt
from quivr_api.modules.prompt.service.prompt_service import PromptService
from quivr_api.modules.user.entity.user_identity import UserIdentity
from quivr_api.modules.user.service.user_usage import UserUsage
from quivr_api.vectorstore.supabase import CustomSupabaseVectorStore
from .utils import generate_source
@ -42,29 +37,30 @@ class RAGService:
def __init__(
self,
current_user: UserIdentity,
brain_id: UUID | None,
brain: BrainEntity,
chat_id: UUID,
brain_service: BrainService,
prompt_service: PromptService,
chat_service: ChatService,
knowledge_service: KnowledgeRepository,
model_service: ModelService,
):
# Services
self.brain_service = brain_service
self.prompt_service = prompt_service
self.chat_service = chat_service
self.knowledge_service = knowledge_service
self.model_service = model_service
# Base models
self.current_user = current_user
self.chat_id = chat_id
self.brain = self.get_or_create_brain(brain_id, self.current_user.id)
self.brain = brain
self.prompt = self.get_brain_prompt(self.brain)
# check at init time
self.model_to_use = self.check_and_update_user_usage(
self.current_user, self.brain
)
self.model_to_use = brain.model
assert self.model_to_use is not None
def get_brain_prompt(self, brain: BrainEntity) -> Prompt | None:
return (
@ -85,30 +81,18 @@ class RAGService:
[chat_history.append(m) for m in transformed_history]
return chat_history
def _build_rag_config(self) -> RAGConfig:
ollama_url = (
settings.ollama_api_base_url
if settings.ollama_api_base_url
and self.model_to_use.name.startswith("ollama")
else None
)
async def _build_rag_config(self) -> RAGConfig:
model = await self.model_service.get_model(self.model_to_use) # type: ignore
api_key = os.getenv(model.env_variable_name, "not-defined")
rag_config = RAGConfig(
llm_config=LLMEndpointConfig(
model=self.model_to_use.name,
llm_base_url=ollama_url,
llm_api_key="abc-123" if ollama_url else None,
temperature=(
self.brain.temperature
if self.brain.temperature
else LLMEndpointConfig.model_fields["temperature"].default
),
max_input=self.model_to_use.max_input,
max_tokens=(
self.brain.max_tokens
if self.brain.max_tokens
else LLMEndpointConfig.model_fields["max_tokens"].default
),
model=self.model_to_use, # type: ignore
llm_base_url=model.endpoint_url,
llm_api_key=api_key,
temperature=(LLMEndpointConfig.model_fields["temperature"].default),
max_input=model.max_input,
max_tokens=model.max_output,
),
prompt=self.prompt.content if self.prompt else None,
)
@ -117,44 +101,6 @@ class RAGService:
def get_llm(self, rag_config: RAGConfig):
return LLMEndpoint.from_config(rag_config.llm_config)
def get_or_create_brain(self, brain_id: UUID | None, user_id: UUID) -> BrainEntity:
brain = None
if brain_id is not None:
brain = self.brain_service.get_brain_details(brain_id, user_id)
# TODO: Create if doesn't exist
assert brain
if brain.integration:
# TODO: entity should be UUID
assert brain.integration.user_id == str(user_id)
return brain
def check_and_update_user_usage(self, user: UserIdentity, brain: BrainEntity):
"""Check user limits and raises if user reached his limits:
1. Raise if one of the conditions :
- User doesn't have access to brains
- Model of brain is not is user_settings.models
- Latest sum_30d(user_daily_user) < user_settings.max_monthly_usage
- Check sum(user_settings.daily_user_count)+ model_price < user_settings.monthly_chat_credits
2. Updates user usage
"""
# TODO(@aminediro) : THIS is bug prone, should retrieve it from DB here
user_usage = UserUsage(id=user.id, email=user.email)
user_settings = user_usage.get_user_settings()
all_models = user_usage.get_models()
# TODO(@aminediro): refactor this function
model_to_use = find_model_and_generate_metadata(
brain.model,
user_settings,
all_models,
)
cost = compute_cost(model_to_use, all_models)
# Raises HTTP if user usage exceeds limits
update_user_usage(user_usage, user_settings, cost) # noqa: F821
return model_to_use
def create_vector_store(
self, brain_id: UUID, max_input: int
) -> CustomSupabaseVectorStore:
@ -190,7 +136,7 @@ class RAGService:
logger.info(
f"Creating question for chat {self.chat_id} with brain {self.brain.brain_id} "
)
rag_config = self._build_rag_config()
rag_config = await self._build_rag_config()
logger.debug(f"generate_answer with config : {rag_config.model_dump()}")
history = await self.chat_service.get_chat_history(self.chat_id)
# Get list of files
@ -241,7 +187,7 @@ class RAGService:
f"Creating question for chat {self.chat_id} with brain {self.brain.brain_id} "
)
# Build the rag config
rag_config = self._build_rag_config()
rag_config = await self._build_rag_config()
# Get chat history
history = await self.chat_service.get_chat_history(self.chat_id)
# Format the history, sanitize the input

15
backend/api/quivr_api/modules/user/controller/user_controller.py
View File

@ -1,6 +1,11 @@
from typing import Annotated
from fastapi import APIRouter, Depends, Request
from quivr_api.middlewares.auth import AuthBearer, get_current_user
from quivr_api.modules.brain.service.brain_user_service import BrainUserService
from quivr_api.modules.dependencies import get_service
from quivr_api.modules.models.service.model_service import ModelService
from quivr_api.modules.user.dto.inputs import UserUpdatableProperties
from quivr_api.modules.user.entity.user_identity import UserIdentity
from quivr_api.modules.user.repository.users import Users
@ -8,12 +13,15 @@ from quivr_api.modules.user.service.user_usage import UserUsage
user_router = APIRouter()
brain_user_service = BrainUserService()
ModelServiceDep = Annotated[ModelService, Depends(get_service(ModelService))]
user_repository = Users()
@user_router.get("/user", dependencies=[Depends(AuthBearer())], tags=["User"])
async def get_user_endpoint(
request: Request, current_user: UserIdentity = Depends(get_current_user)
request: Request,
model_service: ModelServiceDep,
current_user: UserIdentity = Depends(get_current_user),
):
"""
Get user information and statistics.
@ -38,14 +46,15 @@ async def get_user_endpoint(
monthly_chat_credit = user_settings.get("monthly_chat_credit", 10)
user_daily_usage = UserUsage(id=current_user.id)
models = await model_service.get_models()
models_names = [model.name for model in models]
return {
"email": current_user.email,
"max_brain_size": max_brain_size,
"max_brains": max_brains,
"current_brain_size": 0,
"monthly_chat_credit": monthly_chat_credit,
"models": user_settings.get("models", []),
"models": models_names,
"id": current_user.id,
"is_premium": user_settings["is_premium"],
}

9
backend/supabase/migrations/20240808161957_rag-models.sql Normal file
View File

@ -0,0 +1,9 @@
alter table "public"."models" add column "default" boolean not null default false;
alter table "public"."models" add column "endpoint_url" text not null default 'https://api.openai.com/v1/models'::text;
alter table "public"."models" add column "env_variable_name" text not null default 'OPENAI_API_KEY'::text;
alter table "public"."user_settings" drop column "models";

1
backend/supabase/migrations/20240808222754_new-default-value.sql Normal file
View File

@ -0,0 +1 @@
alter table "public"."models" alter column "endpoint_url" set default 'https://api.openai.com/v1'::text;

100
backend/supabase/seed.sql
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File diff suppressed because one or more lines are too long

11
docker-compose.yml
View File

@ -49,13 +49,16 @@ services:
- 5050:5050
notifier:
pull_policy: never
image: backend-base:latest
pull_policy: if_not_present
image: stangirard/quivr-backend-prebuilt:latest
extra_hosts:
- "host.docker.internal:host-gateway"
env_file:
- .env
container_name: notifier
build:
context: backend
dockerfile: Dockerfile
command:
- "python"
- "/code/api/quivr_api/celery_monitor.py"
@ -122,7 +125,3 @@ services:
- beat
ports:
- 5555:5555
networks:
quivr-network:
driver: bridge