quivr/backend/utils/vectors.py

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.schema import Document
from llm.brainpicking import BrainPicking, BrainSettings
from llm.summarization import llm_evaluate_summaries, llm_summerize
from logger import get_logger
from models.chats import ChatMessage
from models.settings import BrainSettings, CommonsDep
from pydantic import BaseModel

logger = get_logger(__name__)

class Neurons(BaseModel):

    commons: CommonsDep
    settings = BrainSettings()
    
    def create_vector(self, user_id, doc, user_openai_api_key=None):
        logger.info(f"Creating vector for document")
        logger.info(f"Document: {doc}")
        if user_openai_api_key:
            self.commons['documents_vector_store']._embedding = OpenAIEmbeddings(openai_api_key=user_openai_api_key)
        try:
            sids = self.commons['documents_vector_store'].add_documents([doc])
            if sids and len(sids) > 0:
                self.commons['supabase'].table("vectors").update({"user_id": user_id}).match({"id": sids[0]}).execute()
        except Exception as e:
            logger.error(f"Error creating vector for document {e}")

    def create_embedding(self, content):
        return self.commons['embeddings'].embed_query(content)

    def similarity_search(self, query, table='match_summaries', top_k=5, threshold=0.5):
        query_embedding = self.create_embedding(query)
        summaries = self.commons['supabase'].rpc(
            table, {'query_embedding': query_embedding,
                    'match_count': top_k, 'match_threshold': threshold}
        ).execute()
        return summaries.data


def create_summary(commons: CommonsDep, document_id, content, metadata):
    logger.info(f"Summarizing document {content[:100]}")
    summary = llm_summerize(content)
    logger.info(f"Summary: {summary}")
    metadata['document_id'] = document_id
    summary_doc_with_metadata = Document(
        page_content=summary, metadata=metadata)
    sids = commons['summaries_vector_store'].add_documents(
        [summary_doc_with_metadata])
    if sids and len(sids) > 0:
        commons['supabase'].table("summaries").update(
            {"document_id": document_id}).match({"id": sids[0]}).execute()