{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SCRIPT TO EXTRACT EXISTING TEXT EMBEDDINGS INTO A NEW WORKFLOW WITH NEW LOOKUP TABLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright (c) 2024 Microsoft Corporation.\n",
    "# Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.insert(1, \"../../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SET VALUES FOR THE INDEX FOLDER TO BE EXTRACTED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set local folder where the index data is located\n",
    "LOCAL_ROOT = \"<local-path-to-data-folder>\"\n",
    "\n",
    "# value to decide if the original file should maintain or remove the embedding column\n",
    "REMOVE_ORIGINAL_EMBEDDING_COLUMN_IN_SOURCE_FILE = True\n",
    "\n",
    "# identifier field\n",
    "STANDARD_IDENTIFIER_FIELD = \"id\"\n",
    "\n",
    "# new embedding field name\n",
    "NEW_STANDARD_EMBEDDING_FIELD = \"embedding\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GENERIC METHOD TO EXTRACT EMBEDDING COLUMNS FROM A FILE AND CREATE A NEW EMBEDDINGS SPECIFIC FILE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_text_embedding_from_table(\n",
    "    input_path: str, original_embedding_field: str, embeddings_parquet_output_field: str\n",
    "):\n",
    "    \"\"\"Migrate table for embeddings.\"\"\"\n",
    "    original_df = pd.read_parquet(input_path)\n",
    "    no_embeddings_df = original_df.drop(columns=[original_embedding_field])\n",
    "\n",
    "    embeddings_df = original_df[[STANDARD_IDENTIFIER_FIELD, original_embedding_field]]\n",
    "    embeddings_df = embeddings_df.rename(\n",
    "        columns={original_embedding_field: NEW_STANDARD_EMBEDDING_FIELD}\n",
    "    )  # type: ignore\n",
    "    embeddings_df.to_parquet(embeddings_parquet_output_field, index=False)\n",
    "\n",
    "    if REMOVE_ORIGINAL_EMBEDDING_COLUMN_IN_SOURCE_FILE is True:\n",
    "        no_embeddings_df.to_parquet(input_path, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ITERATES PER ALL PARQUET FILES INSIDE  THE FOLDER AND DETECTS ALL EMBEDDINGS COLUMNS IN ALL OF THEM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# READ ENTIRE DATA FOLDER LOOKING FOR EMBEDDING COLUMNS IN EACH FILE\n",
    "folder_path = Path(LOCAL_ROOT)\n",
    "pattern = r\"^(.*?)(_embedding)$\"\n",
    "\n",
    "for file_path in folder_path.iterdir():\n",
    "    if file_path.is_file() and file_path.suffix == \".parquet\":\n",
    "        original_df = pd.read_parquet(str(file_path))\n",
    "        columns = original_df.columns.tolist()\n",
    "\n",
    "        for column in columns:\n",
    "            match = re.match(pattern, column)\n",
    "            if match:\n",
    "                print(f\"Reading {file_path}\")\n",
    "                filename_without_extension = str(file_path.with_suffix(\"\").as_posix())\n",
    "                embedding_file_name = (\n",
    "                    f\"{filename_without_extension}_{column}s{file_path.suffix}\"\n",
    "                )\n",
    "                extract_text_embedding_from_table(\n",
    "                    str(file_path), column, embedding_file_name\n",
    "                )"
   ]
  }
 ],
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