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33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
from uuid import UUID
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from logger import get_logger
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from models.settings import get_embeddings, get_supabase_client
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from vectorstore.supabase import CustomSupabaseVectorStore
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logger = get_logger(__name__)
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def get_question_context_from_brain(brain_id: UUID, question: str) -> str:
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supabase_client = get_supabase_client()
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embeddings = get_embeddings()
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vector_store = CustomSupabaseVectorStore(
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supabase_client,
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embeddings,
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table_name="vectors",
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brain_id=str(brain_id),
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)
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documents = vector_store.similarity_search(question)
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## I can't pass more than 2500 tokens to as return value in my array. So i need to remove the docs after i reach 2000 tokens. A token equals 1.5 characters. So 2000 tokens is 3000 characters.
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tokens = 0
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for doc in documents:
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tokens += len(doc.page_content) * 1.5
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if tokens > 3000:
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documents.remove(doc)
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logger.info("documents", documents)
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logger.info("tokens", tokens)
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logger.info("🔥🔥🔥🔥🔥🔥")
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# aggregate all the documents into one string
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return "\n".join([doc.page_content for doc in documents])
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