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62 lines
1.7 KiB
Python
62 lines
1.7 KiB
Python
from typing import Annotated, TypedDict
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from fastapi import Depends
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from langchain.embeddings.openai import OpenAIEmbeddings
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from models.databases.supabase.supabase import SupabaseDB
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from pydantic import BaseSettings
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from supabase.client import Client, create_client
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from vectorstore.supabase import SupabaseVectorStore
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class BrainRateLimiting(BaseSettings):
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max_brain_size: int = 52428800
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max_brain_per_user: int = 5
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class BrainSettings(BaseSettings):
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openai_api_key: str
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anthropic_api_key: str
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supabase_url: str
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supabase_service_key: str
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pg_database_url: str
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resend_api_key: str = "null"
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resend_email_address: str = "brain@mail.quivr.app"
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class LLMSettings(BaseSettings):
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private: bool = False
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model_path: str = "./local_models/ggml-gpt4all-j-v1.3-groovy.bin"
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class CommonDependencies(TypedDict):
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supabase: Client
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db: SupabaseDB
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embeddings: OpenAIEmbeddings
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documents_vector_store: SupabaseVectorStore
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def common_dependencies() -> CommonDependencies:
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settings = BrainSettings() # pyright: ignore reportPrivateUsage=none
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embeddings = OpenAIEmbeddings(
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openai_api_key=settings.openai_api_key
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) # pyright: ignore reportPrivateUsage=none
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supabase_client: Client = create_client(
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settings.supabase_url, settings.supabase_service_key
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)
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documents_vector_store = SupabaseVectorStore(
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supabase_client, embeddings, table_name="vectors"
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)
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db = None
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db = SupabaseDB(supabase_client)
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return {
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"supabase": supabase_client,
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"db": db,
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"embeddings": embeddings,
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"documents_vector_store": documents_vector_store,
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}
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CommonsDep = Annotated[dict, Depends(common_dependencies)]
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