import ast
import logging
import os

import pandas as pd
from dotenv import load_dotenv
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import SupabaseVectorStore
from supabase.client import create_client

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class SupabaseConnector:
    def __init__(self):
        load_dotenv()
        self.supabase = create_client(
            os.environ.get("SUPABASE_URL"), os.environ.get("SUPABASE_SERVICE_KEY")
        )
        self.embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-mpnet-base-v2"
        )
        self.vector_store = SupabaseVectorStore(
            client=self.supabase,
            embedding=self.embeddings,
            table_name="documents",
            query_name="match_documents_langchain",
        )

    def upload_csv(self, file_path: str, batch_size: int = 100):
        """
        Upload documents from supabase_docs.csv to Supabase vector store.
        Only 'content' and parsed 'metadata' are used.
        """
        df = pd.read_csv(file_path)
        logger.info(f"Loaded {len(df)} records from {file_path}")

        # Parse metadata column from string to dict
        df["metadata"] = df["metadata"].apply(
            lambda x: ast.literal_eval(x) if isinstance(x, str) else {}
        )

        for i in range(0, len(df), batch_size):
            batch = df.iloc[i : i + batch_size]
            texts = batch["content"].tolist()
            metadatas = batch["metadata"].tolist()
            self.vector_store.add_texts(texts=texts, metadatas=metadatas)
            logger.info(f"Uploaded batch {i//batch_size + 1}")

        logger.info("CSV upload completed.")


if __name__ == "__main__":
    connector = SupabaseConnector()
    connector.upload_csv("supabase_docs.csv")