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711e9fb8c9
* use function for get_documents_vector_store * use function for get_embeddings * use function for get_supabase_client * use function for get_supabase_db * delete lasts common_dependencies
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
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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def get_supabase_client() -> Client:
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settings = BrainSettings() # 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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return supabase_client
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def get_supabase_db() -> SupabaseDB:
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supabase_client = get_supabase_client()
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return SupabaseDB(supabase_client)
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def get_embeddings() -> OpenAIEmbeddings:
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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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return embeddings
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def get_documents_vector_store() -> SupabaseVectorStore:
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settings = BrainSettings() # pyright: ignore reportPrivateUsage=none
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embeddings = get_embeddings()
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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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return documents_vector_store
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