quivr/backend/repository/brain/get_question_context_from_brain.py

from uuid import UUID

from attr import dataclass
from logger import get_logger
from models.settings import get_embeddings, get_supabase_client
from vectorstore.supabase import CustomSupabaseVectorStore

logger = get_logger(__name__)


@dataclass
class DocumentAnswer:
    file_name: str
    file_sha1: str
    file_size: int
    file_url: str = ""
    file_id: str = ""
    file_similarity: float = 0.0


def get_question_context_from_brain(brain_id: UUID, question: str) -> str:
    # TODO: Move to AnswerGenerator service
    supabase_client = get_supabase_client()
    embeddings = get_embeddings()

    vector_store = CustomSupabaseVectorStore(
        supabase_client,
        embeddings,
        table_name="vectors",
        brain_id=str(brain_id),
    )
    documents = vector_store.similarity_search(question, k=20, threshold=0.8)

    ## Create a list of DocumentAnswer objects from the documents but with no duplicates file_sha1
    answers = []
    file_sha1s = []
    for document in documents:
        if document.metadata["file_sha1"] not in file_sha1s:
            file_sha1s.append(document.metadata["file_sha1"])
            answers.append(
                DocumentAnswer(
                    file_name=document.metadata["file_name"],
                    file_sha1=document.metadata["file_sha1"],
                    file_size=document.metadata["file_size"],
                    file_id=document.metadata["id"],
                    file_similarity=document.metadata["similarity"],
                )
            )

    return answers