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436e49a5e7
# Description - Chat Module - External Api Secrets Interface, exposed through brain service
63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
from langchain.embeddings.ollama import OllamaEmbeddings
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from langchain.embeddings.openai import OpenAIEmbeddings
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from logger import get_logger
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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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logger = get_logger(__name__)
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class BrainRateLimiting(BaseSettings):
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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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supabase_url: str
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supabase_service_key: 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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ollama_api_base_url: str = None
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class ResendSettings(BaseSettings):
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resend_api_key: str = "null"
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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():
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settings = BrainSettings() # pyright: ignore reportPrivateUsage=none
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if settings.ollama_api_base_url:
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embeddings = OllamaEmbeddings(
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base_url=settings.ollama_api_base_url,
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) # pyright: ignore reportPrivateUsage=none
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else:
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embeddings = OpenAIEmbeddings() # 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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