diff --git "a/.ipynb_checkpoints/Vision_AI-checkpoint.ipynb" "b/.ipynb_checkpoints/Vision_AI-checkpoint.ipynb"
new file mode 100644--- /dev/null
+++ "b/.ipynb_checkpoints/Vision_AI-checkpoint.ipynb"
@@ -0,0 +1,9882 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a99f0e72-e33b-49a0-82a3-eb7f86851b26",
+   "metadata": {},
+   "source": [
+    "## Import Pacakge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9692ea8a-3dea-4388-a147-84ecad054658",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
+      "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n",
+      "[nltk_data] Downloading package punkt to /home/jupyter/nltk_data...\n",
+      "[nltk_data]   Package punkt is already up-to-date!\n",
+      "[nltk_data] Downloading package stopwords to\n",
+      "[nltk_data]     /home/jupyter/nltk_data...\n",
+      "[nltk_data]   Package stopwords is already up-to-date!\n",
+      "[nltk_data] Downloading package wordnet to /home/jupyter/nltk_data...\n",
+      "[nltk_data]   Package wordnet is already up-to-date!\n",
+      "2024-12-08 14:38:54.669301: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+      "E0000 00:00:1733668735.192032    3196 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "E0000 00:00:1733668735.404067    3196 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "2024-12-08 14:38:56.758396: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO: Pandarallel will run on 15 workers.\n",
+      "INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import gc\n",
+    "import psutil\n",
+    "from google.cloud import aiplatform\n",
+    "from google.cloud import storage\n",
+    "from google.cloud import storage\n",
+    "from google.api_core import exceptions\n",
+    "from google.cloud.aiplatform import matching_engine\n",
+    "from google.cloud.aiplatform.matching_engine import matching_engine_index_config\n",
+    "from google.cloud.aiplatform_v1.types import index_endpoint as index_endpoint_pb2\n",
+    "from google.cloud.aiplatform_v1.types import index as index_pb2\n",
+    "from pandarallel import pandarallel\n",
+    "from tqdm.auto import tqdm\n",
+    "from collections import defaultdict\n",
+    "import pickle\n",
+    "import json\n",
+    "import uuid\n",
+    "import shutil\n",
+    "from datetime import datetime\n",
+    "import time\n",
+    "import io\n",
+    "from io import BytesIO\n",
+    "import os\n",
+    "import re\n",
+    "import functools\n",
+    "from datasets import load_dataset\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from fuzzywuzzy import fuzz\n",
+    "from typing import List, Optional, Tuple, Dict\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('dark_background')\n",
+    "import faiss\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.metrics.pairwise import cosine_similarity\n",
+    "from sklearn.neighbors import NearestNeighbors\n",
+    "from rank_bm25 import BM25Okapi\n",
+    "\n",
+    "from nltk.translate.bleu_score import sentence_bleu\n",
+    "import nltk\n",
+    "from nltk.corpus import stopwords\n",
+    "from nltk.tokenize import word_tokenize, sent_tokenize\n",
+    "from nltk.stem import WordNetLemmatizer\n",
+    "\n",
+    "nltk.download('punkt')\n",
+    "nltk.download('stopwords')\n",
+    "nltk.download('wordnet')\n",
+    "\n",
+    "from PIL import Image\n",
+    "import PIL\n",
+    "from urllib.parse import urlparse, unquote\n",
+    "import logging\n",
+    "logging.basicConfig(\n",
+    "    level=logging.ERROR,\n",
+    "    format='%(asctime)s - %(levelname)s - %(message)s',\n",
+    "    handlers=[\n",
+    "        logging.StreamHandler(),\n",
+    "        logging.FileHandler('image_processing_errors.log')\n",
+    "    ]\n",
+    ")\n",
+    "import requests\n",
+    "from concurrent.futures import ThreadPoolExecutor\n",
+    "import requests.exceptions\n",
+    "\n",
+    "from transformers import CLIPProcessor, CLIPModel, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TrainingArguments, Trainer, get_cosine_schedule_with_warmup, AutoModel, AutoProcessor, pipeline\n",
+    "import open_clip\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torch.optim import AdamW\n",
+    "from torch.optim.lr_scheduler import CosineAnnealingLR\n",
+    "from torch.utils.tensorboard import SummaryWriter\n",
+    "from torchvision import transforms\n",
+    "\n",
+    "from langchain.embeddings import HuggingFaceEmbeddings\n",
+    "from langchain.vectorstores import FAISS\n",
+    "from langchain.docstore.document import Document\n",
+    "from langchain.llms import HuggingFacePipeline\n",
+    "from langchain.chains import RetrievalQA\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "from huggingface_hub import HfApi, login, hf_hub_download\n",
+    "HF_TOKEN = os.getenv('HUGGINGFACE_TOKEN', '')\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
+    "# Initialize parallel processing\n",
+    "pandarallel.initialize(progress_bar=True, nb_workers=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "580a5315-39cf-4307-b408-1f1667798370",
+   "metadata": {},
+   "source": [
+    "## 1. Initialize Google Cloud"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6fb57948-891b-4082-984f-af0f67fd8acd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "aiplatform.init(\n",
+    "    project='adsp-genai-group1',\n",
+    "    location='us-central1'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "796ff1d7-2592-4308-a967-9ddc775e34a1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Using device: cuda\n",
+      "GPU Name: Tesla T4\n",
+      "Memory Usage:\n",
+      "Allocated: 0.0 GB\n",
+      "Cached: 0.0 GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check GPU availability\n",
+    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
+    "print(f'Using device: {device}')\n",
+    "\n",
+    "if device.type == 'cuda':\n",
+    "    print(f'GPU Name: {torch.cuda.get_device_name(0)}')\n",
+    "    print(f'Memory Usage:')\n",
+    "    print(f'Allocated: {round(torch.cuda.memory_allocated(0)/1024**3,1)} GB')\n",
+    "    print(f'Cached: {round(torch.cuda.memory_cached(0)/1024**3,1)} GB')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7eaf829-93c1-4c81-8b4a-e486dd147927",
+   "metadata": {},
+   "source": [
+    "## 2. Load Amazon Product Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "665d9aca-ffc7-490e-9214-57b4c8c423f8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Uniq Id</th>\n",
+       "      <th>Product Name</th>\n",
+       "      <th>Brand Name</th>\n",
+       "      <th>Asin</th>\n",
+       "      <th>Category</th>\n",
+       "      <th>Upc Ean Code</th>\n",
+       "      <th>List Price</th>\n",
+       "      <th>Selling Price</th>\n",
+       "      <th>Quantity</th>\n",
+       "      <th>Model Number</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Product Url</th>\n",
+       "      <th>Stock</th>\n",
+       "      <th>Product Details</th>\n",
+       "      <th>Dimensions</th>\n",
+       "      <th>Color</th>\n",
+       "      <th>Ingredients</th>\n",
+       "      <th>Direction To Use</th>\n",
+       "      <th>Is Amazon Seller</th>\n",
+       "      <th>Size Quantity Variant</th>\n",
+       "      <th>Product Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4c69b61db1fc16e7013b43fc926e502d</td>\n",
+       "      <td>DB Longboards CoreFlex Crossbow 41\" Bamboo Fib...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Sports &amp; Outdoors | Outdoor Recreation | Skate...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>$237.68</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>https://www.amazon.com/DB-Longboards-CoreFlex-...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>66d49bbed043f5be260fa9f7fbff5957</td>\n",
+       "      <td>Electronic Snap Circuits Mini Kits Classpack, ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Toys &amp; Games | Learning &amp; Education | Science ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>$99.95</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>55324</td>\n",
+       "      <td>...</td>\n",
+       "      <td>https://www.amazon.com/Electronic-Circuits-Cla...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Y</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 28 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            Uniq Id  \\\n",
+       "0  4c69b61db1fc16e7013b43fc926e502d   \n",
+       "1  66d49bbed043f5be260fa9f7fbff5957   \n",
+       "\n",
+       "                                        Product Name  Brand Name  Asin  \\\n",
+       "0  DB Longboards CoreFlex Crossbow 41\" Bamboo Fib...         NaN   NaN   \n",
+       "1  Electronic Snap Circuits Mini Kits Classpack, ...         NaN   NaN   \n",
+       "\n",
+       "                                            Category Upc Ean Code  List Price  \\\n",
+       "0  Sports & Outdoors | Outdoor Recreation | Skate...          NaN         NaN   \n",
+       "1  Toys & Games | Learning & Education | Science ...          NaN         NaN   \n",
+       "\n",
+       "  Selling Price  Quantity Model Number  ...  \\\n",
+       "0       $237.68       NaN          NaN  ...   \n",
+       "1        $99.95       NaN        55324  ...   \n",
+       "\n",
+       "                                         Product Url Stock Product Details  \\\n",
+       "0  https://www.amazon.com/DB-Longboards-CoreFlex-...   NaN             NaN   \n",
+       "1  https://www.amazon.com/Electronic-Circuits-Cla...   NaN             NaN   \n",
+       "\n",
+       "  Dimensions Color Ingredients Direction To Use  Is Amazon Seller  \\\n",
+       "0        NaN   NaN         NaN              NaN                 Y   \n",
+       "1        NaN   NaN         NaN              NaN                 Y   \n",
+       "\n",
+       "  Size Quantity Variant  Product Description  \n",
+       "0                   NaN                  NaN  \n",
+       "1                   NaN                  NaN  \n",
+       "\n",
+       "[2 rows x 28 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "amazon_df = pd.read_csv(\"amazon_data/amazon_data.csv\")\n",
+    "amazon_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d8549710-e2b8-456e-a8e7-21e219fa8eee",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Uniq Id', 'Product Name', 'Brand Name', 'Asin', 'Category',\n",
+       "       'Upc Ean Code', 'List Price', 'Selling Price', 'Quantity',\n",
+       "       'Model Number', 'About Product', 'Product Specification',\n",
+       "       'Technical Details', 'Shipping Weight', 'Product Dimensions', 'Image',\n",
+       "       'Variants', 'Sku', 'Product Url', 'Stock', 'Product Details',\n",
+       "       'Dimensions', 'Color', 'Ingredients', 'Direction To Use',\n",
+       "       'Is Amazon Seller', 'Size Quantity Variant', 'Product Description'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "amazon_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "64ba664a-cebd-4362-8988-d6c6cf0b8ac7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Data Loading and Quality Checks\n",
+    "def load_and_validate_data(file_path):\n",
+    "    \"\"\"Load and validate the Amazon product dataset with improved image handling\"\"\"\n",
+    "    df = pd.read_csv(file_path)\n",
+    "    required_columns = [\"Uniq Id\", \"Product Name\", \"Category\", \"Selling Price\", \n",
+    "                       \"Model Number\", \"About Product\", \"Product Specification\", \n",
+    "                       \"Product Url\", \"Image\"]\n",
+    "    \n",
+    "    missing_cols = set(required_columns) - set(df.columns)\n",
+    "    if missing_cols:\n",
+    "        raise ValueError(f\"Missing required columns: {missing_cols}\")\n",
+    "    \n",
+    "    df = df[required_columns]\n",
+    "    \n",
+    "    # Process images and create a mapping of successful images\n",
+    "    processed_images = process_images_parallel(df)\n",
+    "    df['Processed Image'] = processed_images\n",
+    "    \n",
+    "    # Remove rows with invalid images\n",
+    "    valid_mask = df['Processed Image'].notna()\n",
+    "    invalid_count = (~valid_mask).sum()\n",
+    "    \n",
+    "    if invalid_count > 0:\n",
+    "        print(f\"\\nRemoved {invalid_count} records with invalid images\")\n",
+    "        df = df[valid_mask]\n",
+    "    \n",
+    "    print(\"\\nData Quality Report:\")\n",
+    "    print(f\"Total valid records: {len(df)}\")\n",
+    "    print(\"\\nMissing values per column:\")\n",
+    "    print(df.isnull().sum())\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "001932e9-fba6-4dbc-a575-0c0f0315b57a",
+   "metadata": {},
+   "source": [
+    "## 3. Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "7b450ee8-ac2a-4f58-9801-a1c09de126d3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def process_image(image_urls, target_size=(224, 224)):\n",
+    "    \"\"\"Process and validate individual images with improved error handling\"\"\"\n",
+    "    # Split URLs if multiple URLs are provided\n",
+    "    urls = image_urls.split('|') if '|' in image_urls else [image_urls]\n",
+    "    \n",
+    "    # Try each URL until we get a valid image\n",
+    "    for url in urls:\n",
+    "        try:\n",
+    "            # Clean and validate URL\n",
+    "            url = url.strip()\n",
+    "            url = unquote(url)  # URL decode\n",
+    "            \n",
+    "            # Skip transparent pixel images\n",
+    "            if 'transparent-pixel' in url:\n",
+    "                continue\n",
+    "                \n",
+    "            # Skip size chart images\n",
+    "            if any(x in url.lower() for x in ['sizechart', 'chart', 'quarterdeck']):\n",
+    "                continue\n",
+    "            \n",
+    "            # Validate URL format\n",
+    "            parsed_url = urlparse(url)\n",
+    "            if not all([parsed_url.scheme, parsed_url.netloc]):\n",
+    "                continue\n",
+    "            \n",
+    "            # Fetch image with increased timeout and proper headers\n",
+    "            headers = {\n",
+    "                'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'\n",
+    "            }\n",
+    "            response = requests.get(url, timeout=30, headers=headers, allow_redirects=True)\n",
+    "            response.raise_for_status()\n",
+    "            \n",
+    "            # Attempt to open image with PIL\n",
+    "            image = Image.open(io.BytesIO(response.content))\n",
+    "            \n",
+    "            # Convert RGBA images to RGB\n",
+    "            if image.mode == 'RGBA':\n",
+    "                image = image.convert('RGB')\n",
+    "            elif image.mode not in ['RGB', 'L']:\n",
+    "                image = image.convert('RGB')\n",
+    "            \n",
+    "            # Resize image\n",
+    "            image = image.resize(target_size, Image.LANCZOS)\n",
+    "            \n",
+    "            # Validate image size\n",
+    "            if image.size[0] < 100 or image.size[1] < 100:\n",
+    "                continue\n",
+    "            \n",
+    "            return image\n",
+    "            \n",
+    "        except requests.exceptions.RequestException:\n",
+    "            continue\n",
+    "        except ValueError:\n",
+    "            continue\n",
+    "        except Image.UnidentifiedImageError:\n",
+    "            continue\n",
+    "        except Exception as e:\n",
+    "            continue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b02b9e33-0988-40ce-8472-bf85c376e0bd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def process_images_parallel(df, num_workers=15):\n",
+    "    \"\"\"Process images in parallel\"\"\"\n",
+    "    with ThreadPoolExecutor(max_workers=num_workers) as executor:\n",
+    "        processed_images = list(executor.map(process_image, df['Image']))\n",
+    "    return processed_images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3f3c46c7-27ec-41a0-958e-181622dc9325",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def normalize_text(text):\n",
+    "    \"\"\"Normalize text while preserving important information\"\"\"\n",
+    "    # Define fields and phrases to preserve exactly\n",
+    "    preserve_fields = {\n",
+    "        'Uniq Id', 'Price', 'Model', 'Product Url', 'Category', 'ASIN', 'Unavailable Price', \n",
+    "        'Model Number Not Available', 'No Product Description Available',\n",
+    "        'No Specifications Available', 'Unknown Category', 'No URL Available',\n",
+    "        'Item model number', 'Product Dimensions', 'Shipping Weight'\n",
+    "    }\n",
+    "    \n",
+    "    # Define status messages to preserve exactly\n",
+    "    status_messages = {\n",
+    "        'Unavailable Price',\n",
+    "        'Model Number Not Available',\n",
+    "        'No Product Description Available',\n",
+    "        'No Specifications Available',\n",
+    "        'Unknown Category',\n",
+    "        'No URL Available'\n",
+    "    }\n",
+    "    \n",
+    "    # Define product-specific terms to preserve\n",
+    "    product_terms = {\n",
+    "        'FM Radio', 'HD', 'USB', '3D', 'LED', 'LCD', 'DVD', 'TV', 'AC',\n",
+    "        'inches', 'pounds', 'lbs', 'cm', 'mm', 'ml', 'oz', 'MHz', 'GB', \n",
+    "        'TB', 'MP', 'WiFi', 'Bluetooth'\n",
+    "    }\n",
+    "    \n",
+    "    sections = text.split(' | ')\n",
+    "    normalized_sections = []\n",
+    "    \n",
+    "    for section in sections:\n",
+    "        if ':' in section:\n",
+    "            header, content = section.split(':', 1)\n",
+    "            header = header.strip()\n",
+    "            content = content.strip()\n",
+    "            \n",
+    "            # Preserve headers and status messages exactly\n",
+    "            if header in preserve_fields or content in status_messages:\n",
+    "                normalized_sections.append(f\"{header}: {content}\")\n",
+    "                continue\n",
+    "            \n",
+    "            # Preserve product identifiers\n",
+    "            if re.search(r'(ASIN:|#\\d+\\sin\\s|Item model number:)', content):\n",
+    "                normalized_sections.append(f\"{header}: {content}\")\n",
+    "                continue\n",
+    "            \n",
+    "            # Preserve measurements and specifications\n",
+    "            if any(term in content for term in product_terms):\n",
+    "                normalized_sections.append(f\"{header}: {content}\")\n",
+    "                continue\n",
+    "            \n",
+    "            # Preserve prices and numbers\n",
+    "            if re.search(r'[\\d$]', content):\n",
+    "                normalized_sections.append(f\"{header}: {content}\")\n",
+    "                continue\n",
+    "            \n",
+    "            # Normalize other content while preserving structure\n",
+    "            tokens = word_tokenize(content.lower())\n",
+    "            stop_words = set(stopwords.words('english'))\n",
+    "            tokens = [token for token in tokens if (\n",
+    "                token not in stop_words or \n",
+    "                token.isnumeric() or \n",
+    "                any(char.isdigit() for char in token) or\n",
+    "                token in product_terms\n",
+    "            )]\n",
+    "            \n",
+    "            lemmatizer = WordNetLemmatizer()\n",
+    "            lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
+    "            normalized_sections.append(f\"{header}: {' '.join(lemmatized_tokens)}\")\n",
+    "        else:\n",
+    "            normalized_sections.append(section)\n",
+    "    \n",
+    "    return ' | '.join(normalized_sections)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a3114854-f519-4f2a-9c7a-c5c42baea3e8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def clean_product_data(df):\n",
+    "    \"\"\"Clean and standardize product data\"\"\"\n",
+    "    def clean_price(x):\n",
+    "        \"\"\"Clean and standardize price values\"\"\"\n",
+    "        if pd.isna(x):\n",
+    "            return 'Unavailable Price'\n",
+    "        try:\n",
+    "            if isinstance(x, (int, float)):\n",
+    "                return float(x)\n",
+    "            # Remove currency symbols and commas\n",
+    "            cleaned = str(x).replace('$', '').replace(',', '').strip()\n",
+    "            if '-' in cleaned:  # Handle price ranges\n",
+    "                first_price = cleaned.split('-')[0].strip()\n",
+    "                return float(first_price)\n",
+    "            numbers = re.findall(r'\\d+\\.?\\d*', cleaned)\n",
+    "            if not numbers:\n",
+    "                return 'Unavailable Price'\n",
+    "            return float(numbers[0])\n",
+    "        except (ValueError, TypeError):\n",
+    "            return 'Unavailable Price'\n",
+    "    \n",
+    "    def clean_category(x):\n",
+    "        if pd.isna(x):\n",
+    "            return 'Unknown Category'\n",
+    "        categories = [cat.strip().lower() for cat in str(x).split('|')]\n",
+    "        return ' > '.join(filter(None, categories))\n",
+    "    \n",
+    "    def clean_model_number(x):\n",
+    "        if pd.isna(x) or not str(x).strip():\n",
+    "            return 'Model Number Not Available'\n",
+    "        return str(x).strip().upper()\n",
+    "    \n",
+    "    def clean_specification_text(x):\n",
+    "        if pd.isna(x):\n",
+    "            return 'No Specifications Available'\n",
+    "        text = re.sub(r'\\s+', ' ', str(x))\n",
+    "        specs = [s.strip() for s in re.split(r'[|;]', text) if s.strip()]\n",
+    "        specs = list(dict.fromkeys(specs))\n",
+    "        return ' | '.join(specs)\n",
+    "    \n",
+    "    def clean_about_product_text(x):\n",
+    "        if pd.isna(x):\n",
+    "            return 'No Product Description Available'\n",
+    "        text = re.sub(r'Make sure this fits by entering your model number\\.\\s*\\|?\\s*', '', str(x))\n",
+    "        features = [f.strip() for f in text.split('|') if f.strip()]\n",
+    "        features = list(dict.fromkeys(features))\n",
+    "        return ' | '.join(features)\n",
+    "    \n",
+    "    def clean_product_name(x):\n",
+    "        if pd.isna(x):\n",
+    "            return 'Unknown Product'\n",
+    "        return re.sub(r'\\s+', ' ', str(x).strip())\n",
+    "    \n",
+    "    def clean_url(x):\n",
+    "        if pd.isna(x) or not str(x).strip():\n",
+    "            return 'No URL Available'\n",
+    "        return str(x).strip()\n",
+    "    \n",
+    "    # Define status messages to preserve\n",
+    "    status_messages = {\n",
+    "        'Unavailable Price',\n",
+    "        'Model Number Not Available',\n",
+    "        'No Product Description Available',\n",
+    "        'No Specifications Available',\n",
+    "        'Unknown Category',\n",
+    "        'No URL Available'\n",
+    "    }\n",
+    "\n",
+    "    def clean_text(x):\n",
+    "        # Preserve status messages\n",
+    "        if any(status in str(x) for status in status_messages):\n",
+    "            return x\n",
+    "        # Preserve product identifiers\n",
+    "        if re.search(r'(Item model number:|ASIN:|#\\d+\\sin\\s)', str(x)):\n",
+    "            return x\n",
+    "        return re.sub(r'[^\\w\\s|>$&(),.;:\\-+#@/]', '', str(x))\n",
+    "\n",
+    "    # Apply cleaning functions\n",
+    "    df['Product Name'] = df['Product Name'].apply(clean_product_name)\n",
+    "    df['Category'] = df['Category'].apply(clean_category)\n",
+    "    df['Selling Price'] = df['Selling Price'].apply(clean_price)\n",
+    "    df['Model Number'] = df['Model Number'].apply(clean_model_number)\n",
+    "    df['About Product'] = df['About Product'].apply(clean_about_product_text)\n",
+    "    df['Product Specification'] = df['Product Specification'].apply(clean_specification_text)\n",
+    "    df['Product Url'] = df['Product Url'].apply(clean_url)\n",
+    "    \n",
+    "    # Clean text columns while preserving important information\n",
+    "    text_columns = ['Product Name', 'Category','Selling Price', 'Model Number', \n",
+    "                    'About Product', 'Product Specification']\n",
+    "    for col in text_columns:\n",
+    "        df[col] = df[col].apply(clean_text)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "3ac3ee04-066b-457c-ae4b-49f058a660f8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Enhanced product description creation\n",
+    "def create_product_description(row):\n",
+    "    \"\"\"Create comprehensive product description\"\"\"\n",
+    "    desc_parts = []\n",
+    "    \n",
+    "    desc_parts.append(f\"Uniq Id: {row['Uniq Id']}\")\n",
+    "    desc_parts.append(f\"Product: {row['Product Name']}\")\n",
+    "    \n",
+    "    if row['Category'] != 'Unknown Category':\n",
+    "        desc_parts.append(f\"Category: {row['Category']}\")\n",
+    "    \n",
+    "    # Handle price formatting\n",
+    "    if isinstance(row['Selling Price'], (int, float)):\n",
+    "        desc_parts.append(f\"Price: ${row['Selling Price']:.2f}\")\n",
+    "    else:\n",
+    "        desc_parts.append(f\"Price: {row['Selling Price']}\")\n",
+    "    \n",
+    "    if row['Model Number'] != 'Model Number Not Available':\n",
+    "        desc_parts.append(f\"Model: {row['Model Number']}\")\n",
+    "    \n",
+    "    if row['About Product'] != 'No Product Description Available':\n",
+    "        desc_parts.append(f\"Description: {row['About Product']}\")\n",
+    "    \n",
+    "    if row['Product Specification'] != 'No Specifications Available':\n",
+    "        desc_parts.append(f\"Specifications: {row['Product Specification']}\")\n",
+    "    \n",
+    "    desc_parts.append(f\"Product Url: {row['Product Url']}\")\n",
+    "    \n",
+    "    return ' | '.join(desc_parts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "655f47cb-0f92-4c24-8856-0f3da86d3c37",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Removed 22 records with invalid images\n",
+      "\n",
+      "Data Quality Report:\n",
+      "Total valid records: 9980\n",
+      "\n",
+      "Missing values per column:\n",
+      "Uniq Id                     0\n",
+      "Product Name                0\n",
+      "Category                  829\n",
+      "Selling Price             107\n",
+      "Model Number             1751\n",
+      "About Product             273\n",
+      "Product Specification    1611\n",
+      "Product Url                 0\n",
+      "Image                       0\n",
+      "Processed Image             0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "amazon_df = load_and_validate_data(\"amazon_data/amazon_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6a1008ef-c848-4c85-8871-a196c044a8e5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "amazon_df = clean_product_data(amazon_df)\n",
+    "amazon_df['Product Description'] = amazon_df.apply(create_product_description, axis=1)\n",
+    "amazon_df['Normalized Description'] = amazon_df['Product Description'].apply(normalize_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c1ae51eb-0281-49d5-a032-c0d50ff2b689",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Uniq Id', 'Product Name', 'Category', 'Selling Price', 'Model Number',\n",
+       "       'About Product', 'Product Specification', 'Product Url', 'Image',\n",
+       "       'Processed Image', 'Product Description', 'Normalized Description'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "amazon_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "dabd32b8-b80c-49e0-bc73-914eba1be125",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Category</th>\n",
+       "      <th>Model Number</th>\n",
+       "      <th>Product Description</th>\n",
+       "      <th>Normalized Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>sports &amp; outdoors &gt; outdoor recreation &gt; skate...</td>\n",
+       "      <td>Model Number Not Available</td>\n",
+       "      <td>Uniq Id: 4c69b61db1fc16e7013b43fc926e502d | Pr...</td>\n",
+       "      <td>Uniq Id: 4c69b61db1fc16e7013b43fc926e502d | Pr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>toys &amp; games &gt; learning &amp; education &gt; science ...</td>\n",
+       "      <td>55324</td>\n",
+       "      <td>Uniq Id: 66d49bbed043f5be260fa9f7fbff5957 | Pr...</td>\n",
+       "      <td>Uniq Id: 66d49bbed043f5be260fa9f7fbff5957 | Pr...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                            Category  \\\n",
+       "0  sports & outdoors > outdoor recreation > skate...   \n",
+       "1  toys & games > learning & education > science ...   \n",
+       "\n",
+       "                 Model Number  \\\n",
+       "0  Model Number Not Available   \n",
+       "1                       55324   \n",
+       "\n",
+       "                                 Product Description  \\\n",
+       "0  Uniq Id: 4c69b61db1fc16e7013b43fc926e502d | Pr...   \n",
+       "1  Uniq Id: 66d49bbed043f5be260fa9f7fbff5957 | Pr...   \n",
+       "\n",
+       "                              Normalized Description  \n",
+       "0  Uniq Id: 4c69b61db1fc16e7013b43fc926e502d | Pr...  \n",
+       "1  Uniq Id: 66d49bbed043f5be260fa9f7fbff5957 | Pr...  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "amazon_df[['Category', \"Model Number\", 'Product Description', 'Normalized Description']].head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "700f17df-df04-4d82-b997-c8e7a83386bf",
+   "metadata": {},
+   "source": [
+    "##### Save the cleaned data on Hugging Face"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1076e001-3858-42e7-9a71-1283d226453a",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def save_cleaned_data_to_hf(df, repo_id, token):\n",
+    "    \"\"\"Save cleaned data to HuggingFace as parquet\"\"\"\n",
+    "    # Create a copy of the dataframe\n",
+    "    df_to_save = df.copy()\n",
+    "    \n",
+    "    # Convert PIL images to bytes\n",
+    "    def image_to_bytes(img):\n",
+    "        if isinstance(img, PIL.Image.Image):\n",
+    "            buffer = io.BytesIO()\n",
+    "            img.save(buffer, format='PNG')\n",
+    "            return buffer.getvalue()\n",
+    "        return None\n",
+    "    \n",
+    "    # Convert the Processed Image column\n",
+    "    df_to_save['Processed Image'] = df_to_save['Processed Image'].apply(image_to_bytes)\n",
+    "    \n",
+    "    # Login to Hugging Face\n",
+    "    login(token=token)\n",
+    "    api = HfApi()\n",
+    "    \n",
+    "    # Create temporary directory\n",
+    "    os.makedirs('cleaned_data', exist_ok=True)\n",
+    "    \n",
+    "    # Save as parquet with compression\n",
+    "    parquet_path = 'cleaned_data/amazon_cleaned.parquet'\n",
+    "    df_to_save.to_parquet(\n",
+    "        parquet_path,\n",
+    "        compression='snappy',\n",
+    "        index=False\n",
+    "    )\n",
+    "    \n",
+    "    # Create README\n",
+    "    readme_content = \"\"\"\n",
+    "    # Amazon Product 2020 Cleaned Dataset\n",
+    "\n",
+    "    This dataset contains cleaned and preprocessed Amazon product data with:\n",
+    "    - Normalized text descriptions\n",
+    "    - Processed images (stored as PNG bytes)\n",
+    "    - Standardized categories and prices\n",
+    "    - Cleaned product specifications\n",
+    "    \n",
+    "    ## Columns\n",
+    "    - Uniq Id: Unique identifier for each product\n",
+    "    - Product Name: Cleaned product names\n",
+    "    - Category: Standardized category hierarchy\n",
+    "    - Selling Price: Normalized price values\n",
+    "    - Model Number: Standardized model numbers\n",
+    "    - About Product: Cleaned product descriptions\n",
+    "    - Product Specification: Normalized specifications\n",
+    "    - Product Url: Valid product URLs\n",
+    "    - Processed Image: PNG image bytes\n",
+    "    - Product Description: Combined product information\n",
+    "    - Normalized Description: Lemmatized and normalized text\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    with open(\"cleaned_data/README.md\", \"w\") as f:\n",
+    "        f.write(readme_content)\n",
+    "    \n",
+    "    # Upload to HuggingFace\n",
+    "    api.upload_folder(\n",
+    "        folder_path=\"cleaned_data\",\n",
+    "        repo_id=repo_id,\n",
+    "        repo_type=\"dataset\"\n",
+    "    )\n",
+    "    \n",
+    "    # Cleanup\n",
+    "    shutil.rmtree('cleaned_data')\n",
+    "    print(f\"Cleaned data pushed to: https://huggingface.co/datasets/{repo_id}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "e17876b5-10d5-495d-bbe5-30eba7958c0c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "13661471263348868711b55782ac1942",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "amazon_cleaned.parquet:   0%|          | 0.00/580M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cleaned data pushed to: https://huggingface.co/datasets/chen196473/amazon_product_2020_cleaned\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Save the cleaned data\n",
+    "save_cleaned_data_to_hf(\n",
+    "    df=amazon_df,\n",
+    "    repo_id=\"chen196473/amazon_product_2020_cleaned\",\n",
+    "    token=HF_TOKEN\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "35d902bd-7c71-48d8-b98e-50020d6b301e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_colwidth', None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "2c3ea62e-7d63-4548-909e-43c4019c1664",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Repo card metadata block was not found. Setting CardData to empty.\n",
+      "2024-12-04 23:33:58,760 - WARNING - Repo card metadata block was not found. Setting CardData to empty.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "41eb654596c440d1b3332bf973393f03",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "amazon_cleaned.parquet:   0%|          | 0.00/580M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eec753bef97a40b9bb13487f2487c05d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Product Description</th>\n",
+       "      <th>Normalized Description</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Uniq Id: 4c69b61db1fc16e7013b43fc926e502d | Product: DB Longboards CoreFlex Crossbow 41 Bamboo Fiberglass Longboard Complete | Category: sports &amp; outdoors &gt; outdoor recreation &gt; skates, skateboards &amp; scooters &gt; skateboarding &gt; standard skateboards &amp; longboards &gt; longboards | Price: 237.68 | Description: RESPONSIVE FLEX: The Crossbow features a bamboo core encased in triaxial fiberglass and HD plastic for a responsive flex pattern thats second to none. Pumping &amp; carving have never been so satisfying Flex 2 is recommended for people 120 to 170 pounds. | COREFLEX TECH: CoreFlex construction is water resistant, impact resistant, scratch resistant and has a flex like you wont believe. These boards combine fiberglass, epoxy, HD plastic and bamboo to create a perfect blend of performance and strength. | INSPIRED BY THE NORTHWEST: Our founding ideal is chasing adventure &amp; riding the best boards possible, inspired by the hills, waves, beaches &amp; mountains all around our headquarters in the Northwest | BEST IN THE WORLD: DB was founded out of sheer love of longboarding with a mission to create the best custom longboards in the world, to do it sustainably, &amp; to treat customers &amp; employees like family | BEYOND COMPARE: Try our skateboards &amp; accessories if youve tried similar products by Sector 9, Landyachtz, Arbor, Loaded, Globe, Orangatang, Hawgs, Powell-Peralta, Blood Orange, Caliber or Gullwing | Specifications: Shipping Weight: 10.7 pounds (View shipping rates and policies) | ASIN: B07KMVJJK7 | #474 in Longboards Skateboard | Product Url: https://www.amazon.com/DB-Longboards-CoreFlex-Fiberglass-Longboard/dp/B07KMVJJK7</td>\n",
+       "      <td>Uniq Id: 4c69b61db1fc16e7013b43fc926e502d | Product: DB Longboards CoreFlex Crossbow 41 Bamboo Fiberglass Longboard Complete | Category: sports &amp; outdoors &gt; outdoor recreation &gt; skates, skateboards &amp; scooters &gt; skateboarding &gt; standard skateboards &amp; longboards &gt; longboards | Price: 237.68 | Description: RESPONSIVE FLEX: The Crossbow features a bamboo core encased in triaxial fiberglass and HD plastic for a responsive flex pattern thats second to none. Pumping &amp; carving have never been so satisfying Flex 2 is recommended for people 120 to 170 pounds. | COREFLEX TECH: CoreFlex construction is water resistant, impact resistant, scratch resistant and has a flex like you wont believe. These boards combine fiberglass, epoxy, HD plastic and bamboo to create a perfect blend of performance and strength. | INSPIRED BY THE NORTHWEST: founding ideal chasing adventure &amp; riding best board possible , inspired hill , wave , beach &amp; mountain around headquarters northwest | BEST IN THE WORLD: db founded sheer love longboarding mission create best custom longboards world , sustainably , &amp; treat customer &amp; employee like family | BEYOND COMPARE: Try our skateboards &amp; accessories if youve tried similar products by Sector 9, Landyachtz, Arbor, Loaded, Globe, Orangatang, Hawgs, Powell-Peralta, Blood Orange, Caliber or Gullwing | Specifications: Shipping Weight: 10.7 pounds (View shipping rates and policies) | ASIN: B07KMVJJK7 | #474 in Longboards Skateboard | Product Url: https://www.amazon.com/DB-Longboards-CoreFlex-Fiberglass-Longboard/dp/B07KMVJJK7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Uniq Id: 66d49bbed043f5be260fa9f7fbff5957 | Product: Electronic Snap Circuits Mini Kits Classpack, FM Radio, Motion Detector, Music Box (Set of 5) | Category: toys &amp; games &gt; learning &amp; education &gt; science kits &amp; toys | Price: 99.95 | Model: 55324 | Description: Snap circuits mini kits classpack provides basic electronic circuitry activities for students in grades 2-6 | Includes 5 separate mini building kits- an FM radio, a motion detector, music box, space battle sound effects, and a flying saucer | Each kit includes separate components and instructions to build | Each component represents one function in a circuit; components snap together to create working models of everyday electronic devices | Activity guide provides additional projects to teach students how circuitry works | Specifications: Product Dimensions: 14.7 x 11.1 x 10.2 inches | 4.06 pounds | Shipping Weight: 4 pounds (View shipping rates and policies) | Domestic Shipping: Item can be shipped within U.S. | International Shipping: This item can be shipped to select countries outside of the U.S. Learn More | ASIN: B008AK6DAS | Item model number: 55324 | #3032 in Science Kits &amp; Toys | Product Url: https://www.amazon.com/Electronic-Circuits-Classpack-Motion-Detector/dp/B008AK6DAS</td>\n",
+       "      <td>Uniq Id: 66d49bbed043f5be260fa9f7fbff5957 | Product: Electronic Snap Circuits Mini Kits Classpack, FM Radio, Motion Detector, Music Box (Set of 5) | Category: toys &amp; games &gt; learning &amp; education &gt; science kits &amp; toys | Price: 99.95 | Model: 55324 | Description: Snap circuits mini kits classpack provides basic electronic circuitry activities for students in grades 2-6 | Includes 5 separate mini building kits- an FM radio, a motion detector, music box, space battle sound effects, and a flying saucer | Each kit includes separate components and instructions to build | Each component represents one function in a circuit; components snap together to create working models of everyday electronic devices | Activity guide provides additional projects to teach students how circuitry works | Specifications: Product Dimensions: 14.7 x 11.1 x 10.2 inches | 4.06 pounds | Shipping Weight: 4 pounds (View shipping rates and policies) | Domestic Shipping: item shipped within u.s . | International Shipping: item shipped select country outside u.s. learn | ASIN: B008AK6DAS | Item model number: 55324 | #3032 in Science Kits &amp; Toys | Product Url: https://www.amazon.com/Electronic-Circuits-Classpack-Motion-Detector/dp/B008AK6DAS</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Product Description  \\\n",
+       "0  Uniq Id: 4c69b61db1fc16e7013b43fc926e502d | Product: DB Longboards CoreFlex Crossbow 41 Bamboo Fiberglass Longboard Complete | Category: sports & outdoors > outdoor recreation > skates, skateboards & scooters > skateboarding > standard skateboards & longboards > longboards | Price: 237.68 | Description: RESPONSIVE FLEX: The Crossbow features a bamboo core encased in triaxial fiberglass and HD plastic for a responsive flex pattern thats second to none. Pumping & carving have never been so satisfying Flex 2 is recommended for people 120 to 170 pounds. | COREFLEX TECH: CoreFlex construction is water resistant, impact resistant, scratch resistant and has a flex like you wont believe. These boards combine fiberglass, epoxy, HD plastic and bamboo to create a perfect blend of performance and strength. | INSPIRED BY THE NORTHWEST: Our founding ideal is chasing adventure & riding the best boards possible, inspired by the hills, waves, beaches & mountains all around our headquarters in the Northwest | BEST IN THE WORLD: DB was founded out of sheer love of longboarding with a mission to create the best custom longboards in the world, to do it sustainably, & to treat customers & employees like family | BEYOND COMPARE: Try our skateboards & accessories if youve tried similar products by Sector 9, Landyachtz, Arbor, Loaded, Globe, Orangatang, Hawgs, Powell-Peralta, Blood Orange, Caliber or Gullwing | Specifications: Shipping Weight: 10.7 pounds (View shipping rates and policies) | ASIN: B07KMVJJK7 | #474 in Longboards Skateboard | Product Url: https://www.amazon.com/DB-Longboards-CoreFlex-Fiberglass-Longboard/dp/B07KMVJJK7   \n",
+       "1                                                                                                                                                                                                                                                                                                                                                                                           Uniq Id: 66d49bbed043f5be260fa9f7fbff5957 | Product: Electronic Snap Circuits Mini Kits Classpack, FM Radio, Motion Detector, Music Box (Set of 5) | Category: toys & games > learning & education > science kits & toys | Price: 99.95 | Model: 55324 | Description: Snap circuits mini kits classpack provides basic electronic circuitry activities for students in grades 2-6 | Includes 5 separate mini building kits- an FM radio, a motion detector, music box, space battle sound effects, and a flying saucer | Each kit includes separate components and instructions to build | Each component represents one function in a circuit; components snap together to create working models of everyday electronic devices | Activity guide provides additional projects to teach students how circuitry works | Specifications: Product Dimensions: 14.7 x 11.1 x 10.2 inches | 4.06 pounds | Shipping Weight: 4 pounds (View shipping rates and policies) | Domestic Shipping: Item can be shipped within U.S. | International Shipping: This item can be shipped to select countries outside of the U.S. Learn More | ASIN: B008AK6DAS | Item model number: 55324 | #3032 in Science Kits & Toys | Product Url: https://www.amazon.com/Electronic-Circuits-Classpack-Motion-Detector/dp/B008AK6DAS   \n",
+       "\n",
+       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Normalized Description  \n",
+       "0  Uniq Id: 4c69b61db1fc16e7013b43fc926e502d | Product: DB Longboards CoreFlex Crossbow 41 Bamboo Fiberglass Longboard Complete | Category: sports & outdoors > outdoor recreation > skates, skateboards & scooters > skateboarding > standard skateboards & longboards > longboards | Price: 237.68 | Description: RESPONSIVE FLEX: The Crossbow features a bamboo core encased in triaxial fiberglass and HD plastic for a responsive flex pattern thats second to none. Pumping & carving have never been so satisfying Flex 2 is recommended for people 120 to 170 pounds. | COREFLEX TECH: CoreFlex construction is water resistant, impact resistant, scratch resistant and has a flex like you wont believe. These boards combine fiberglass, epoxy, HD plastic and bamboo to create a perfect blend of performance and strength. | INSPIRED BY THE NORTHWEST: founding ideal chasing adventure & riding best board possible , inspired hill , wave , beach & mountain around headquarters northwest | BEST IN THE WORLD: db founded sheer love longboarding mission create best custom longboards world , sustainably , & treat customer & employee like family | BEYOND COMPARE: Try our skateboards & accessories if youve tried similar products by Sector 9, Landyachtz, Arbor, Loaded, Globe, Orangatang, Hawgs, Powell-Peralta, Blood Orange, Caliber or Gullwing | Specifications: Shipping Weight: 10.7 pounds (View shipping rates and policies) | ASIN: B07KMVJJK7 | #474 in Longboards Skateboard | Product Url: https://www.amazon.com/DB-Longboards-CoreFlex-Fiberglass-Longboard/dp/B07KMVJJK7  \n",
+       "1                                                                                                                                                                                                                                                                                                                                           Uniq Id: 66d49bbed043f5be260fa9f7fbff5957 | Product: Electronic Snap Circuits Mini Kits Classpack, FM Radio, Motion Detector, Music Box (Set of 5) | Category: toys & games > learning & education > science kits & toys | Price: 99.95 | Model: 55324 | Description: Snap circuits mini kits classpack provides basic electronic circuitry activities for students in grades 2-6 | Includes 5 separate mini building kits- an FM radio, a motion detector, music box, space battle sound effects, and a flying saucer | Each kit includes separate components and instructions to build | Each component represents one function in a circuit; components snap together to create working models of everyday electronic devices | Activity guide provides additional projects to teach students how circuitry works | Specifications: Product Dimensions: 14.7 x 11.1 x 10.2 inches | 4.06 pounds | Shipping Weight: 4 pounds (View shipping rates and policies) | Domestic Shipping: item shipped within u.s . | International Shipping: item shipped select country outside u.s. learn | ASIN: B008AK6DAS | Item model number: 55324 | #3032 in Science Kits & Toys | Product Url: https://www.amazon.com/Electronic-Circuits-Classpack-Motion-Detector/dp/B008AK6DAS  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the dataset\n",
+    "dataset = load_dataset(\"chen196473/amazon_product_2020_cleaned\")\n",
+    "\n",
+    "# Convert to pandas DataFrame if needed\n",
+    "cleaned_df = dataset['train'].to_pandas()\n",
+    "\n",
+    "cleaned_df[['Product Description', 'Normalized Description']].head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3cbdcd18-b881-4dd2-bd71-8909ea4357bf",
+   "metadata": {},
+   "source": [
+    "#### Create the Metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "f8c802a7-d45e-4c79-bae1-3864cc9edf10",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def parse_price(price):\n",
+    "    \"\"\"Parse and normalize price values\"\"\"\n",
+    "    if pd.isna(price):\n",
+    "        return 0.0\n",
+    "    if price == 'Unavailable Price':\n",
+    "        return 0.0\n",
+    "    try:\n",
+    "        if isinstance(price, (int, float)):\n",
+    "            return float(price)\n",
+    "        cleaned = str(price).replace('$', '').replace(',', '').strip()\n",
+    "        if '-' in cleaned:\n",
+    "            first_price = cleaned.split('-')[0].strip()\n",
+    "            return float(first_price)\n",
+    "        numbers = re.findall(r'\\d+\\.?\\d*', cleaned)\n",
+    "        if not numbers:\n",
+    "            return 0.0\n",
+    "        return float(numbers[0])\n",
+    "    except (ValueError, TypeError):\n",
+    "        return 0.0\n",
+    "\n",
+    "def extract_key_terms(text):\n",
+    "    \"\"\"Extract key terms from product description\"\"\"\n",
+    "    if pd.isna(text):\n",
+    "        return []\n",
+    "    \n",
+    "    # Remove special characters and split\n",
+    "    cleaned_text = re.sub(r'[^\\w\\s|]', ' ', str(text))\n",
+    "    terms = cleaned_text.lower().split()\n",
+    "    \n",
+    "    # Remove common words and numbers\n",
+    "    stop_words = set(['the', 'and', 'or', 'in', 'at', 'on', 'with', 'by', \n",
+    "                     'from', 'to', 'for', 'of', 'this', 'that', 'these', \n",
+    "                     'those', 'is', 'are', 'was', 'were'])\n",
+    "    terms = [term for term in terms if term not in stop_words and not term.isdigit()]\n",
+    "    \n",
+    "    return terms\n",
+    "\n",
+    "def extract_brand(product_name):\n",
+    "    \"\"\"Extract brand name from product name\"\"\"\n",
+    "    if pd.isna(product_name):\n",
+    "        return None\n",
+    "        \n",
+    "    # Known brands dictionary with aliases\n",
+    "    known_brands = {\n",
+    "        'google': ['nest', 'google home', 'google nest'],\n",
+    "        'amazon': ['echo', 'alexa', 'amazon echo', 'kindle'],\n",
+    "        'apple': ['iphone', 'ipad', 'macbook', 'airpods'],\n",
+    "        'samsung': ['galaxy', 'samsung gear'],\n",
+    "        'microsoft': ['xbox', 'surface'],\n",
+    "        'sony': ['playstation', 'ps4', 'ps5'],\n",
+    "        'db': ['db longboards'],\n",
+    "        '3doodler': ['3d printing']\n",
+    "    }\n",
+    "    \n",
+    "    name_lower = product_name.lower()\n",
+    "    \n",
+    "    # Check for known brands and their aliases\n",
+    "    for brand, aliases in known_brands.items():\n",
+    "        if brand in name_lower or any(alias in name_lower for alias in aliases):\n",
+    "            return brand\n",
+    "    \n",
+    "    # Remove common prefixes\n",
+    "    brand_prefixes = ['by ', 'from ', 'made by ']\n",
+    "    name = name_lower\n",
+    "    for prefix in brand_prefixes:\n",
+    "        if prefix in name:\n",
+    "            name = name.split(prefix)[1]\n",
+    "    \n",
+    "    # Take first word unless it's a number or common word\n",
+    "    words = name.split()\n",
+    "    if words:\n",
+    "        first_word = words[0]\n",
+    "        if not first_word.isdigit() and first_word not in {'the', 'a', 'an'}:\n",
+    "            return first_word\n",
+    "    \n",
+    "    return None\n",
+    "\n",
+    "def create_category_aliases():\n",
+    "    \"\"\"Create comprehensive category aliases mapping\"\"\"\n",
+    "    return {\n",
+    "        'smart speaker': ['smart home', 'speaker', 'voice assistant', 'home automation', \n",
+    "                         'alexa', 'google home', 'nest', 'echo'],\n",
+    "        'wireless earbuds': ['headphones', 'earphones', 'audio', 'airpods', 'buds', \n",
+    "                            'wireless audio'],\n",
+    "        'smart display': ['smart home', 'display', 'screen', 'echo show', 'nest hub', \n",
+    "                         'smart screen'],\n",
+    "        'electronics': ['tech', 'gadget', 'device', 'electronic', 'accessories', \n",
+    "                       'charger', 'cable'],\n",
+    "        'longboard': ['skateboard', 'board', 'outdoor', 'skate', 'cruiser', \n",
+    "                     'skateboarding'],\n",
+    "        'toys': ['game', 'play', 'educational', 'kids', 'children', 'learning', \n",
+    "                'toy', 'games'],\n",
+    "        'computer': ['laptop', 'desktop', 'pc', 'computing', 'monitor', 'keyboard', \n",
+    "                    'mouse'],\n",
+    "        'camera': ['digital camera', 'dslr', 'mirrorless', 'photography', 'lens', \n",
+    "                  'photo'],\n",
+    "        'phone': ['smartphone', 'mobile', 'cell phone', 'iphone', 'android', \n",
+    "                 'mobile phone']\n",
+    "    }\n",
+    "\n",
+    "def create_product_metadata(row):\n",
+    "    \"\"\"Enhanced metadata creation with advanced data validation and normalization\"\"\"\n",
+    "    category_aliases = create_category_aliases()\n",
+    "    \n",
+    "    def normalize_metadata_text(text, category_aliases):\n",
+    "        if pd.isna(text):\n",
+    "            return ''\n",
+    "        \n",
+    "        normalized_text = str(text).lower()\n",
+    "        # Add category context\n",
+    "        for main_category, aliases in category_aliases.items():\n",
+    "            if any(alias in normalized_text for alias in aliases):\n",
+    "                normalized_text = f\"{normalized_text} {main_category}\"\n",
+    "        \n",
+    "        return re.sub(r'[^\\w\\s|>$&(),.;:\\-+#@/]', '', normalized_text).strip()\n",
+    "    \n",
+    "    # Generate comprehensive keywords\n",
+    "    keywords = set()\n",
+    "    keywords.update(row['Product Name'].lower().split())\n",
+    "    if not pd.isna(row['Category']):\n",
+    "        categories = row['Category'].lower().split(' > ')\n",
+    "        for i, category in enumerate(categories):\n",
+    "            keywords.update(category.split())\n",
+    "            keywords.add(' > '.join(categories[:i+1]))\n",
+    "    \n",
+    "    # Add model number variations\n",
+    "    if row['Model Number'] != 'Model Number Not Available':\n",
+    "        model = row['Model Number'].lower()\n",
+    "        keywords.add(model)\n",
+    "        keywords.add(re.sub(r'[^\\w]', '', model))\n",
+    "    \n",
+    "    # Add price range indicators\n",
+    "    price = parse_price(row['Selling Price'])\n",
+    "    if price > 0:\n",
+    "        price_range = f\"price_{(price // 50) * 50}_{((price // 50) + 1) * 50}\"\n",
+    "        keywords.add(price_range)\n",
+    "    \n",
+    "    # Create optimized search text\n",
+    "    search_text = [\n",
+    "        row['Product Name'].lower(),\n",
+    "        row['Category'].lower() if not pd.isna(row['Category']) else '',\n",
+    "        row['Model Number'].lower() if row['Model Number'] != 'Model Number Not Available' else ''\n",
+    "    ]\n",
+    "    \n",
+    "    if 'About Product' in row and not pd.isna(row['About Product']):\n",
+    "        search_text.extend(extract_key_terms(row['About Product']))\n",
+    "    \n",
+    "    metadata = {\n",
+    "        'Uniq_Id': row['Uniq Id'],\n",
+    "        'Product_Name': normalize_metadata_text(row['Product Name'], category_aliases),\n",
+    "        'Category': normalize_metadata_text(row['Category'], category_aliases),\n",
+    "        'Selling_Price': parse_price(row['Selling Price']),\n",
+    "        'Model_Number': row['Model Number'],\n",
+    "        'Keywords': list(keywords),\n",
+    "        'Search_Text': ' '.join(filter(None, search_text)),\n",
+    "        'Image': row['Image'],\n",
+    "        'Has_Processed_Image': row['Processed Image'] is not None,\n",
+    "        'Image_Status': 'valid' if row['Processed Image'] is not None else 'invalid',\n",
+    "        'Has_Processed_Image': True if 'Processed Image' in row and row['Processed Image'] is not None else False,\n",
+    "        'Normalized_Description': row['Normalized Description'] if 'Normalized Description' in row else '',\n",
+    "        'Has_Valid_Image': row['Processed Image'] is not None,\n",
+    "        'Image_Status': 'valid' if row['Processed Image'] is not None else 'invalid'\n",
+    "    }\n",
+    "    \n",
+    "    return metadata\n",
+    "\n",
+    "def create_metadata_index(df):\n",
+    "    \"\"\"Create a comprehensive searchable metadata index\"\"\"\n",
+    "    from collections import defaultdict\n",
+    "    \n",
+    "    base_metadata = df.apply(create_product_metadata, axis=1).tolist()\n",
+    "    \n",
+    "    indices = {\n",
+    "        'base_metadata': base_metadata,\n",
+    "        'category_index': defaultdict(list),\n",
+    "        'price_range_index': defaultdict(list),\n",
+    "        'keyword_index': defaultdict(list),\n",
+    "        'brand_index': defaultdict(list),\n",
+    "        'product_name_index': defaultdict(list)  # Add product name index\n",
+    "    }\n",
+    "    \n",
+    "    for meta in base_metadata:\n",
+    "        # Index all keywords\n",
+    "        for keyword in meta['Keywords']:\n",
+    "            indices['keyword_index'][keyword].append(meta['Uniq_Id'])\n",
+    "        \n",
+    "        # Index categories with hierarchy\n",
+    "        categories = meta['Category'].split(' > ')\n",
+    "        for i, category in enumerate(categories):\n",
+    "            indices['category_index'][category].append(meta['Uniq_Id'])\n",
+    "            indices['category_index'][' > '.join(categories[:i+1])].append(meta['Uniq_Id'])\n",
+    "        \n",
+    "        # Index price ranges with finer granularity\n",
+    "        price = meta['Selling_Price']\n",
+    "        for granularity in [50, 100, 500]:\n",
+    "            price_range = f\"{(price // granularity) * granularity}-{((price // granularity) + 1) * granularity}\"\n",
+    "            indices['price_range_index'][price_range].append(meta['Uniq_Id'])\n",
+    "        \n",
+    "        # Index brands with aliases\n",
+    "        brand = extract_brand(meta['Product_Name'])\n",
+    "        if brand:\n",
+    "            indices['brand_index'][brand].append(meta['Uniq_Id'])\n",
+    "            \n",
+    "        # Index product names\n",
+    "        product_name_words = meta['Product_Name'].lower().split()\n",
+    "        for word in product_name_words:\n",
+    "            indices['product_name_index'][word].append(meta['Uniq_Id'])\n",
+    "    \n",
+    "    return indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "8873ccec-7a2d-4e99-9aaf-6c3d2a4ab569",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "## Base Metadata Sample (First 3 Items)\n",
+      "\n",
+      "Product ID: 4c69b61db1fc16e7013b43fc926e502d\n",
+      "Name: db longboards coreflex crossbow 41 bamboo fiberglass longboard complete longboard\n",
+      "Category: sports & outdoors > outdoor recreation > skates, skateboards & scooters > skateboarding > standard skateboards & longboards > longboards longboard\n",
+      "Price: $237.68\n",
+      "Keywords: &, sports, longboards, bamboo, crossbow...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Product ID: 66d49bbed043f5be260fa9f7fbff5957\n",
+      "Name: electronic snap circuits mini kits classpack, fm radio, motion detector, music box (set of 5) electronics\n",
+      "Category: toys & games > learning & education > science kits & toys toys\n",
+      "Price: $99.95\n",
+      "Keywords: &, (set, kits, fm, box...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Product ID: 2c55cae269aebf53838484b0d7dd931a\n",
+      "Name: 3doodler create flexy 3d printing filament refill bundle (x5 pack, over 1000. of extruded plastics - innovate\n",
+      "Category: toys & games > arts & crafts > craft kits toys\n",
+      "Price: $34.99\n",
+      "Keywords: &, price_0.0_50.0, -, kits, flexy...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "## Category Index Sample (First 3 Categories)\n",
+      "\n",
+      "Category: sports & outdoors\n",
+      "Product Count: 1080\n",
+      "Sample Products: 4c69b61db1fc16e7013b43fc926e502d, 4c69b61db1fc16e7013b43fc926e502d, 5bb4a9aa52085ada20006d166b1e2f87...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Category: outdoor recreation\n",
+      "Product Count: 352\n",
+      "Sample Products: 4c69b61db1fc16e7013b43fc926e502d, a6737b0c04b96b728bc14669376d3ebf, 22bccbee6c18e566f04592da316e3c34...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Category: sports & outdoors > outdoor recreation\n",
+      "Product Count: 352\n",
+      "Sample Products: 4c69b61db1fc16e7013b43fc926e502d, a6737b0c04b96b728bc14669376d3ebf, 22bccbee6c18e566f04592da316e3c34...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "## Keyword Index Sample (First 3 Keywords)\n",
+      "\n",
+      "Keyword: &\n",
+      "Product Count: 9082\n",
+      "Sample Products: 4c69b61db1fc16e7013b43fc926e502d, 66d49bbed043f5be260fa9f7fbff5957, 2c55cae269aebf53838484b0d7dd931a...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Keyword: sports\n",
+      "Product Count: 975\n",
+      "Sample Products: 4c69b61db1fc16e7013b43fc926e502d, a84f14dd0114d78f178b16f093cab0e0, 5bb4a9aa52085ada20006d166b1e2f87...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Keyword: longboards\n",
+      "Product Count: 31\n",
+      "Sample Products: 4c69b61db1fc16e7013b43fc926e502d, 22bccbee6c18e566f04592da316e3c34, 612ee4086381c0390869d2544d6153d3...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "## Brand Index Sample (First 3 Brands)\n",
+      "\n",
+      "Brand: db\n",
+      "Product Count: 65\n",
+      "Sample Products: 4c69b61db1fc16e7013b43fc926e502d, 9952053dcd6dc43d321a85127afc4051, b47ca8eae8e6379f5a5104574833bd66...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Brand: electronic\n",
+      "Product Count: 1\n",
+      "Sample Products: 66d49bbed043f5be260fa9f7fbff5957...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Brand: 3doodler\n",
+      "Product Count: 3\n",
+      "Sample Products: 2c55cae269aebf53838484b0d7dd931a, 8b1fa7cbd70657196c4c848e8b3ad029, 8fe20cbf4ecffbe1f69e04137a95feef...\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "## Image Status Statistics\n",
+      "Valid Images: 9980/9980 (100.00%)\n",
+      "\n",
+      "## Metadata Statistics\n",
+      "Total Products: 9980\n",
+      "Total Categories: 2749\n",
+      "Total Keywords: 29375\n",
+      "Total Brands: 2465\n",
+      "Total Product Name Indexs: 18603\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_metadata_preview(metadata_index):\n",
+    "    \"\"\"Print a preview of the metadata structure before uploading\"\"\"\n",
+    "    print(\"\\n## Base Metadata Sample (First 3 Items)\")\n",
+    "    for item in metadata_index['base_metadata'][:3]:\n",
+    "        print(f\"\\nProduct ID: {item['Uniq_Id']}\")\n",
+    "        print(f\"Name: {item['Product_Name']}\")\n",
+    "        print(f\"Category: {item['Category']}\")\n",
+    "        print(f\"Price: ${item['Selling_Price']:.2f}\")\n",
+    "        print(f\"Keywords: {', '.join(item['Keywords'][:5])}...\")\n",
+    "        print(\"-\" * 50)\n",
+    "    \n",
+    "    print(\"\\n## Category Index Sample (First 3 Categories)\")\n",
+    "    for i, (category, products) in enumerate(metadata_index['category_index'].items()):\n",
+    "        if i >= 3: break\n",
+    "        print(f\"\\nCategory: {category}\")\n",
+    "        print(f\"Product Count: {len(products)}\")\n",
+    "        print(f\"Sample Products: {', '.join(products[:3])}...\")\n",
+    "        print(\"-\" * 50)\n",
+    "    \n",
+    "    print(\"\\n## Keyword Index Sample (First 3 Keywords)\")\n",
+    "    for i, (keyword, products) in enumerate(metadata_index['keyword_index'].items()):\n",
+    "        if i >= 3: break\n",
+    "        print(f\"\\nKeyword: {keyword}\")\n",
+    "        print(f\"Product Count: {len(products)}\")\n",
+    "        print(f\"Sample Products: {', '.join(products[:3])}...\")\n",
+    "        print(\"-\" * 50)\n",
+    "    \n",
+    "    print(\"\\n## Brand Index Sample (First 3 Brands)\")\n",
+    "    for i, (brand, products) in enumerate(metadata_index['brand_index'].items()):\n",
+    "        if i >= 3: break\n",
+    "        print(f\"\\nBrand: {brand}\")\n",
+    "        print(f\"Product Count: {len(products)}\")\n",
+    "        print(f\"Sample Products: {', '.join(products[:3])}...\")\n",
+    "        print(\"-\" * 50)\n",
+    "        \n",
+    "    print(\"\\n## Image Status Statistics\")\n",
+    "    valid_images = sum(1 for item in metadata_index['base_metadata'] \n",
+    "                      if item.get('Has_Valid_Image', False))\n",
+    "    total_items = len(metadata_index['base_metadata'])\n",
+    "    print(f\"Valid Images: {valid_images}/{total_items} ({valid_images/total_items*100:.2f}%)\")\n",
+    "    \n",
+    "    # Print statistics\n",
+    "    print(\"\\n## Metadata Statistics\")\n",
+    "    print(f\"Total Products: {len(metadata_index['base_metadata'])}\")\n",
+    "    print(f\"Total Categories: {len(metadata_index['category_index'])}\")\n",
+    "    print(f\"Total Keywords: {len(metadata_index['keyword_index'])}\")\n",
+    "    print(f\"Total Brands: {len(metadata_index['brand_index'])}\")\n",
+    "    print(f\"Total Product Name Indexs: {len(metadata_index['product_name_index'])}\")\n",
+    "    \n",
+    "\n",
+    "def validate_metadata(metadata_index):\n",
+    "    \"\"\"Enhanced metadata validation\"\"\"\n",
+    "    validation_results = {\n",
+    "        'errors': [],\n",
+    "        'warnings': []\n",
+    "    }\n",
+    "    \n",
+    "    # Check image alignment\n",
+    "    image_count = sum(1 for item in metadata_index['base_metadata'] \n",
+    "                     if item.get('Has_Valid_Image', False))\n",
+    "    if image_count != len(metadata_index['base_metadata']):\n",
+    "        validation_results['warnings'].append(\n",
+    "            f\"Image misalignment: {image_count} valid images for \"\n",
+    "            f\"{len(metadata_index['base_metadata'])} products\"\n",
+    "        )\n",
+    "    \n",
+    "    # Check required fields\n",
+    "    required_fields = ['base_metadata', 'category_index', 'keyword_index', 'brand_index', 'product_name_index']\n",
+    "    for field in required_fields:\n",
+    "        if field not in metadata_index:\n",
+    "            validation_results['errors'].append(f\"Missing required field: {field}\")\n",
+    "    \n",
+    "    # Validate base metadata\n",
+    "    for item in metadata_index['base_metadata']:\n",
+    "        if 'Uniq_Id' not in item:\n",
+    "            validation_results['errors'].append(f\"Missing Uniq_Id in product metadata\")\n",
+    "        if 'Keywords' not in item:\n",
+    "            validation_results['warnings'].append(f\"Missing Keywords for product {item.get('Uniq_Id', 'Unknown')}\")\n",
+    "    \n",
+    "    return validation_results\n",
+    "\n",
+    "# Usage example:\n",
+    "if __name__ == \"__main__\":\n",
+    "    # Create metadata index\n",
+    "    metadata_index = create_metadata_index(amazon_df)\n",
+    "    \n",
+    "    # Validate metadata\n",
+    "    validation_results = validate_metadata(metadata_index)\n",
+    "    \n",
+    "    # Print validation results\n",
+    "    if validation_results['errors']:\n",
+    "        print(\"\\n## Validation Errors\")\n",
+    "        for error in validation_results['errors']:\n",
+    "            print(f\"- {error}\")\n",
+    "    \n",
+    "    if validation_results['warnings']:\n",
+    "        print(\"\\n## Validation Warnings\")\n",
+    "        for warning in validation_results['warnings']:\n",
+    "            print(f\"- {warning}\")\n",
+    "    \n",
+    "    # Print metadata preview\n",
+    "    print_metadata_preview(metadata_index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "20442dd6-209c-4b07-bbd3-e02347055c7c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def save_metadata_index_to_hf(metadata_index, repo_id, token):\n",
+    "    \"\"\"Save enhanced metadata index to HuggingFace\"\"\"\n",
+    "    # Login to Hugging Face\n",
+    "    login(token=token)\n",
+    "    api = HfApi()\n",
+    "    \n",
+    "    # Create the repository if it doesn't exist\n",
+    "    try:\n",
+    "        api.create_repo(\n",
+    "            repo_id=repo_id,\n",
+    "            repo_type=\"dataset\",\n",
+    "            private=False,\n",
+    "            exist_ok=True\n",
+    "        )\n",
+    "    except Exception as e:\n",
+    "        print(f\"Repository creation error: {e}\")\n",
+    "        return\n",
+    "    \n",
+    "    # Create temporary directory\n",
+    "    os.makedirs('metadata_index', exist_ok=True)\n",
+    "    \n",
+    "    # Save each index component with proper handling of defaultdict\n",
+    "    for index_name, index_data in metadata_index.items():\n",
+    "        file_path = f'metadata_index/{index_name}.json'\n",
+    "        \n",
+    "        # Convert defaultdict to regular dict for JSON serialization\n",
+    "        if isinstance(index_data, defaultdict):\n",
+    "            index_data = dict(index_data)\n",
+    "        \n",
+    "        # Handle special cases for base_metadata\n",
+    "        if index_name == 'base_metadata':\n",
+    "            # Convert sets to lists in keywords\n",
+    "            for item in index_data:\n",
+    "                if 'Keywords' in item and isinstance(item['Keywords'], set):\n",
+    "                    item['Keywords'] = list(item['Keywords'])\n",
+    "        \n",
+    "        with open(file_path, 'w') as f:\n",
+    "            json.dump(index_data, f, indent=2)\n",
+    "    \n",
+    "    # Create enhanced README\n",
+    "    readme_content = \"\"\"\n",
+    "    # Enhanced Amazon Product Metadata Index\n",
+    "    \n",
+    "    Comprehensive searchable index containing:\n",
+    "    - Base metadata for all products\n",
+    "    - Category hierarchy index with aliases\n",
+    "    - Price range index with multiple granularities\n",
+    "    - Keyword index with enhanced product terms\n",
+    "    - Brand index with known brand aliases\n",
+    "    \n",
+    "    Features:\n",
+    "    - Improved category matching\n",
+    "    - Better brand recognition\n",
+    "    - Enhanced keyword generation\n",
+    "    - Multiple price range granularities\n",
+    "    - Normalized text descriptions\n",
+    "    \n",
+    "    Use for advanced product search and filtering capabilities.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    with open(\"metadata_index/README.md\", \"w\") as f:\n",
+    "        f.write(readme_content)\n",
+    "    \n",
+    "    # Create metadata schema documentation\n",
+    "    schema_content = \"\"\"\n",
+    "    # Metadata Schema\n",
+    "    \n",
+    "    ## Base Metadata\n",
+    "    - Uniq_Id: Unique product identifier\n",
+    "    - Product_Name: Normalized product name\n",
+    "    - Category: Hierarchical category path\n",
+    "    - Selling_Price: Normalized price value\n",
+    "    - Model_Number: Product model number\n",
+    "    - Keywords: Enhanced keyword list\n",
+    "    - Search_Text: Optimized search text\n",
+    "    - Image: Product image URL\n",
+    "    - Has_Processed_Image: Image processing status\n",
+    "    - Normalized_Description: Cleaned product description\n",
+    "    \n",
+    "    ## Indices\n",
+    "    - category_index: Category-based lookup\n",
+    "    - price_range_index: Multi-granular price ranges\n",
+    "    - keyword_index: Enhanced keyword mapping\n",
+    "    - brand_index: Brand recognition with aliases\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    with open(\"metadata_index/SCHEMA.md\", \"w\") as f:\n",
+    "        f.write(schema_content)\n",
+    "    \n",
+    "    try:\n",
+    "        # Upload to HuggingFace\n",
+    "        api.upload_folder(\n",
+    "            folder_path=\"metadata_index\",\n",
+    "            repo_id=repo_id,\n",
+    "            repo_type=\"dataset\"\n",
+    "        )\n",
+    "        print(f\"Enhanced metadata index pushed to: https://huggingface.co/datasets/{repo_id}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Upload error: {e}\")\n",
+    "    finally:\n",
+    "        # Cleanup\n",
+    "        shutil.rmtree('metadata_index')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "12eb2466-372b-4dc6-ac32-ee1f2e705b84",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No files have been modified since last commit. Skipping to prevent empty commit.\n",
+      "2024-12-04 23:58:57,444 - WARNING - No files have been modified since last commit. Skipping to prevent empty commit.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enhanced metadata index pushed to: https://huggingface.co/datasets/chen196473/amazon_product_2020_metadata\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create and save metadata index\n",
+    "metadata_index = create_metadata_index(amazon_df)\n",
+    "save_metadata_index_to_hf(\n",
+    "    metadata_index=metadata_index,\n",
+    "    repo_id=\"chen196473/amazon_product_2020_metadata\",\n",
+    "    token=HF_TOKEN\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37ee513a-81f7-4539-b117-0a5484163e1a",
+   "metadata": {},
+   "source": [
+    "## 4. Generate Embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "0c324e1e-8ff5-442f-9370-79782f6a60db",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    model, _, preprocess = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionCLIP')\n",
+    "    model = model.to(device)\n",
+    "    tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionCLIP')\n",
+    "except Exception as e:\n",
+    "    print(f\"Error loading FashionCLIP model: {e}\")\n",
+    "    raise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "7ce7c414-16dd-46ba-9bc1-de2a7ca8eae5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def generate_embeddings_batch(texts, images, batch_size=16):\n",
+    "    \"\"\"Generate embeddings with improved error handling and alignment\"\"\"\n",
+    "    text_embeddings = []\n",
+    "    image_embeddings = []\n",
+    "    valid_indices = []\n",
+    "    \n",
+    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "    \n",
+    "    for i in tqdm(range(0, len(texts), batch_size)):\n",
+    "        batch_texts = texts[i:i + batch_size]\n",
+    "        batch_images = images[i:i + batch_size]\n",
+    "        \n",
+    "        # Process text embeddings\n",
+    "        try:\n",
+    "            text_tokens = tokenizer(batch_texts).to(device)\n",
+    "            with torch.no_grad():\n",
+    "                batch_text_features = model.encode_text(text_tokens)\n",
+    "                batch_text_features = F.normalize(batch_text_features, dim=-1)\n",
+    "                \n",
+    "            valid_batch_images = []\n",
+    "            batch_valid_indices = []\n",
+    "            \n",
+    "            for idx, img in enumerate(batch_images):\n",
+    "                if isinstance(img, PIL.Image.Image):\n",
+    "                    image_tensor = preprocess(img).unsqueeze(0).to(device)\n",
+    "                    valid_batch_images.append(image_tensor)\n",
+    "                    batch_valid_indices.append(i + idx)\n",
+    "                    text_embeddings.append(batch_text_features[idx].cpu().numpy())\n",
+    "            \n",
+    "            # Process valid images\n",
+    "            if valid_batch_images:\n",
+    "                batch_image_tensor = torch.cat(valid_batch_images)\n",
+    "                with torch.no_grad():\n",
+    "                    batch_image_features = model.encode_image(batch_image_tensor)\n",
+    "                    batch_image_features = F.normalize(batch_image_features, dim=-1)\n",
+    "                image_embeddings.extend(batch_image_features.cpu().numpy())\n",
+    "                valid_indices.extend(batch_valid_indices)\n",
+    "                \n",
+    "        except Exception as e:\n",
+    "            print(f\"Error processing batch {i}: {e}\")\n",
+    "            continue\n",
+    "    \n",
+    "    # Ensure alignment\n",
+    "    assert len(text_embeddings) == len(image_embeddings) == len(valid_indices), \\\n",
+    "           \"Mismatch in number of text and image embeddings\"\n",
+    "    \n",
+    "    return np.array(text_embeddings), np.array(image_embeddings), np.array(valid_indices)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "729e3661-beab-4e9e-9e87-bc75f590cdeb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d4986a0e827e44ccb1b3bd2edbb2eca0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/312 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate embeddings using the Processed Image column\n",
+    "text_embeddings, image_embeddings, valid_indices = generate_embeddings_batch(\n",
+    "    amazon_df['Normalized Description'].tolist(),\n",
+    "    amazon_df['Processed Image'].tolist(),  # Use Processed Image instead of Image URLs\n",
+    "    batch_size=32\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "b91751b0-fb66-4e6e-beb9-9eda77bb8db8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.13392106 -0.00923525  0.06332949 ...  0.02971128  0.02477443\n",
+      "   0.01240829]\n",
+      " [ 0.03311984 -0.00722326  0.04126031 ...  0.03464625  0.00985251\n",
+      "  -0.04208111]\n",
+      " [ 0.05380866  0.01543138  0.0044403  ...  0.01067015 -0.0101916\n",
+      "  -0.08373534]\n",
+      " ...\n",
+      " [ 0.02664584  0.03543622  0.05537754 ... -0.04643651  0.05243566\n",
+      "  -0.02503548]\n",
+      " [-0.01901179  0.02495399  0.1121289  ...  0.03234312  0.00708733\n",
+      "  -0.02304489]\n",
+      " [ 0.03919033  0.01869911  0.11646345 ...  0.09256022 -0.00384819\n",
+      "   0.00085049]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(text_embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "66f0aa29-21a4-4e81-8dc6-d6b3bd003e9f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.0088947   0.01645885 -0.01755457 ... -0.01515883  0.01191969\n",
+      "   0.07177787]\n",
+      " [-0.00751104  0.02890389  0.03630358 ... -0.02090657  0.02068301\n",
+      "  -0.02182541]\n",
+      " [ 0.05240997  0.06508177  0.00281847 ...  0.02108709 -0.01663866\n",
+      "  -0.02079704]\n",
+      " ...\n",
+      " [-0.02106668  0.01587815  0.04554193 ... -0.07888434  0.04167601\n",
+      "  -0.03748502]\n",
+      " [-0.01308739 -0.00201654  0.11671228 ... -0.01773771  0.01193193\n",
+      "  -0.08870839]\n",
+      " [-0.0298837   0.03185922  0.05529908 ...  0.03888161  0.01258473\n",
+      "   0.02966855]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(image_embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "61fdfa36-7b62-4789-994a-54c58a8f59f6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(9980, 512)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(text_embeddings.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "5a2a000b-ad0c-4796-91fb-3931d2d43854",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(9980, 512)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(image_embeddings.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "5ca135dc-b5cc-4fc4-bd9f-4a4793dddcc7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[   0    1    2 ... 9977 9978 9979]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(valid_indices)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09ed0aa4-4e25-4dda-bef9-5f7cc8acbd1c",
+   "metadata": {},
+   "source": [
+    "## 5. Store Embeddings in Vertex AI Vector Search"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "79978160-b97d-4670-bc91-4c2f4a440096",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Initialize Vertex AI\n",
+    "aiplatform.init(project='adsp-genai-group1', location='us-central1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "f3b6d91b-175f-48a5-ae26-e68e25212bd2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def store_embeddings_in_vertex_ai(embeddings, metadata, embedding_type):\n",
+    "    print(f\"Number of {embedding_type} embeddings: {len(embeddings)}\")\n",
+    "    print(f\"Number of metadata entries: {len(metadata)}\")\n",
+    "\n",
+    "    unique_id = str(uuid.uuid4())[:8]\n",
+    "\n",
+    "    index = aiplatform.MatchingEngineIndex.create_tree_ah_index(\n",
+    "        display_name=f\"product_{embedding_type}_embeddings_{unique_id}\",\n",
+    "        contents_delta_uri=None,\n",
+    "        dimensions=embeddings.shape[1],\n",
+    "        approximate_neighbors_count=150,\n",
+    "        distance_measure_type=\"DOT_PRODUCT_DISTANCE\",\n",
+    "        leaf_node_embedding_count=500,\n",
+    "        leaf_nodes_to_search_percent=10,\n",
+    "        description=f\"Vector index for {embedding_type} embeddings\",\n",
+    "        index_update_method=\"STREAM_UPDATE\"\n",
+    "    )\n",
+    "\n",
+    "    index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(\n",
+    "        display_name=f\"product_{embedding_type}_embeddings_endpoint_{unique_id}\",\n",
+    "        public_endpoint_enabled=True\n",
+    "    )\n",
+    "    \n",
+    "    try:\n",
+    "        deployed_index = index_endpoint.deploy_index(\n",
+    "            index=index,\n",
+    "            deployed_index_id=f\"{embedding_type}_deployed_index_{unique_id}\"\n",
+    "        )\n",
+    "    except Exception as e:\n",
+    "        print(f\"Failed to deploy index: {e}\")\n",
+    "        print(\"Attempting to undeploy existing index and retry...\")\n",
+    "        try:\n",
+    "            index_endpoint.undeploy_index(deployed_index_id=f\"{embedding_type}_deployed_index_{unique_id}\")\n",
+    "            deployed_index = index_endpoint.deploy_index(\n",
+    "                index=index,\n",
+    "                deployed_index_id=f\"{embedding_type}_deployed_index_{unique_id}\"\n",
+    "            )\n",
+    "        except Exception as undeploy_error:\n",
+    "            print(f\"Failed to undeploy and redeploy index: {undeploy_error}\")\n",
+    "            return None, None\n",
+    "\n",
+    "    datapoints = []\n",
+    "    for i, embedding in enumerate(embeddings):\n",
+    "        try:\n",
+    "            datapoint = index_pb2.IndexDatapoint(\n",
+    "                datapoint_id=str(i),\n",
+    "                feature_vector=embedding.tolist(),\n",
+    "                restricts=[\n",
+    "                    index_pb2.IndexDatapoint.Restriction(\n",
+    "                        namespace=k,\n",
+    "                        allow_list=[str(v)]\n",
+    "                    ) for k, v in metadata[i].items() if isinstance(v, str)\n",
+    "                ],\n",
+    "                numeric_restricts=[\n",
+    "                    index_pb2.IndexDatapoint.NumericRestriction(\n",
+    "                        namespace=k,\n",
+    "                        value_double=float(v)\n",
+    "                    ) for k, v in metadata[i].items() if isinstance(v, (int, float))\n",
+    "                ]\n",
+    "            )\n",
+    "            datapoints.append(datapoint)\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error processing {embedding_type} datapoint {i}: {e}\")\n",
+    "\n",
+    "    batch_size = 100\n",
+    "    for i in range(0, len(datapoints), batch_size):\n",
+    "        batch = datapoints[i:i+batch_size]\n",
+    "        try:\n",
+    "            index.upsert_datapoints(datapoints=batch)\n",
+    "            print(f\"Successfully upserted {embedding_type} datapoints batch {i//batch_size + 1}\")\n",
+    "        except Exception as upsert_error:\n",
+    "            print(f\"Failed to upsert {embedding_type} datapoints batch {i//batch_size + 1}: {upsert_error}\")\n",
+    "            print(f\"Error details: {str(upsert_error)}\")\n",
+    "\n",
+    "    print(f\"Stored {len(datapoints)} {embedding_type} embeddings in Vertex AI Vector Search\")\n",
+    "    return index, index_endpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "5b816a17-7394-4298-bfe1-87757d51284c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of text embeddings: 9980\n",
+      "Number of metadata entries: 9980\n",
+      "Creating MatchingEngineIndex\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:02:57,863 - INFO - Creating MatchingEngineIndex\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Create MatchingEngineIndex backing LRO: projects/193585189707/locations/us-central1/indexes/4390618210546745344/operations/370009199167406080\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:02:57,865 - INFO - Create MatchingEngineIndex backing LRO: projects/193585189707/locations/us-central1/indexes/4390618210546745344/operations/370009199167406080\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex created. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:03:30,819 - INFO - MatchingEngineIndex created. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "To use this MatchingEngineIndex in another session:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:03:30,820 - INFO - To use this MatchingEngineIndex in another session:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "index = aiplatform.MatchingEngineIndex('projects/193585189707/locations/us-central1/indexes/4390618210546745344')\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:03:30,821 - INFO - index = aiplatform.MatchingEngineIndex('projects/193585189707/locations/us-central1/indexes/4390618210546745344')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating MatchingEngineIndexEndpoint\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:03:31,043 - INFO - Creating MatchingEngineIndexEndpoint\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Create MatchingEngineIndexEndpoint backing LRO: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616/operations/8407245684163477504\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:03:31,044 - INFO - Create MatchingEngineIndexEndpoint backing LRO: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616/operations/8407245684163477504\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndexEndpoint created. Resource name: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:03:32,931 - INFO - MatchingEngineIndexEndpoint created. Resource name: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "To use this MatchingEngineIndexEndpoint in another session:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:03:32,933 - INFO - To use this MatchingEngineIndexEndpoint in another session:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "index_endpoint = aiplatform.MatchingEngineIndexEndpoint('projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616')\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:03:32,934 - INFO - index_endpoint = aiplatform.MatchingEngineIndexEndpoint('projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Deploying index MatchingEngineIndexEndpoint index_endpoint: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:03:33,006 - INFO - Deploying index MatchingEngineIndexEndpoint index_endpoint: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Deploy index MatchingEngineIndexEndpoint index_endpoint backing LRO: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616/operations/6517985640481554432\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:03:33,108 - INFO - Deploy index MatchingEngineIndexEndpoint index_endpoint backing LRO: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616/operations/6517985640481554432\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndexEndpoint index_endpoint Deployed index. Resource name: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:29:54,493 - INFO - MatchingEngineIndexEndpoint index_endpoint Deployed index. Resource name: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:13,248 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:13,402 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 1\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:13,404 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:13,564 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 2\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:13,566 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:13,724 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 3\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:13,726 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:13,872 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 4\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:13,874 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:14,017 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 5\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:14,018 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:14,154 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 6\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:14,155 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:14,307 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 7\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:14,309 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:14,444 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 8\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:14,446 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:14,582 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 9\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:14,583 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:15,742 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 10\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:15,744 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:15,883 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 11\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:15,885 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,018 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 12\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,019 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,122 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 13\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,124 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,232 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 14\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,234 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,334 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 15\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,335 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,452 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 16\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,454 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,576 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 17\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:16,577 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:17,689 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 18\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:17,691 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:17,805 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 19\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:17,807 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:17,953 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 20\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:17,954 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,112 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 21\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,114 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,219 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 22\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,220 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,362 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 23\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,363 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,514 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 24\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,516 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,664 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 25\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,665 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,834 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 26\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,835 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,979 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 27\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:18,980 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:19,148 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 28\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:19,150 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,260 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 29\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,262 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,372 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 30\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,374 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,476 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 31\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,478 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,592 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 32\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,593 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,750 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 33\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,752 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,853 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 34\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,855 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,985 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 35\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:20,987 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,098 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 36\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,099 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,197 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 37\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,199 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,287 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 38\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,289 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,408 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 39\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,410 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,537 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 40\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,539 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,638 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 41\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,640 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,754 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 42\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,756 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,860 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 43\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,861 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,954 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 44\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:21,955 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,065 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 45\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,067 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,198 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 46\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,200 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,317 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 47\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,318 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,424 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 48\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,425 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,568 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 49\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,570 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,669 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 50\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,671 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,783 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 51\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,784 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,920 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 52\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:22,922 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,021 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 53\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,023 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,123 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 54\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,125 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,226 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 55\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,227 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,333 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 56\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,335 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,442 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 57\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,443 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,574 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 58\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,576 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,709 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 59\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,711 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,812 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 60\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,813 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,917 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 61\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:23,919 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,027 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 62\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,028 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,144 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 63\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,145 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,238 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 64\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,240 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,353 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 65\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,355 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,455 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 66\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,456 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,557 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 67\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,559 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,694 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 68\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,695 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,803 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 69\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,804 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,908 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 70\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:24,909 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,046 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 71\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,048 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,198 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 72\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,200 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,303 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 73\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,305 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,416 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 74\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,418 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,519 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 75\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,520 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,625 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 76\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,626 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,719 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 77\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,721 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,826 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 78\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,827 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,917 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 79\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:25,919 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,020 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 80\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,021 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,122 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 81\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,123 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,243 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 82\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,244 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,365 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 83\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,366 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,484 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 84\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,485 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,576 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 85\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,578 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,678 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 86\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,679 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,790 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 87\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,792 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,903 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 88\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:26,904 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,006 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 89\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,007 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,094 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 90\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,095 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,202 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 91\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,204 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,310 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 92\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,311 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,402 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 93\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,403 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,538 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 94\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:27,540 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:28,668 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 95\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:28,670 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:28,767 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 96\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:28,769 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:28,862 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 97\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:28,863 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:28,952 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 98\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:28,954 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:29,042 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 99\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:29,044 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:29,142 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted text datapoints batch 100\n",
+      "Stored 9980 text embeddings in Vertex AI Vector Search\n",
+      "Number of image embeddings: 9980\n",
+      "Number of metadata entries: 9980\n",
+      "Creating MatchingEngineIndex\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:29,328 - INFO - Creating MatchingEngineIndex\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Create MatchingEngineIndex backing LRO: projects/193585189707/locations/us-central1/indexes/3833016281682935808/operations/3552928235811504128\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:29,330 - INFO - Create MatchingEngineIndex backing LRO: projects/193585189707/locations/us-central1/indexes/3833016281682935808/operations/3552928235811504128\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex created. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:56,843 - INFO - MatchingEngineIndex created. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "To use this MatchingEngineIndex in another session:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:56,845 - INFO - To use this MatchingEngineIndex in another session:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "index = aiplatform.MatchingEngineIndex('projects/193585189707/locations/us-central1/indexes/3833016281682935808')\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:56,846 - INFO - index = aiplatform.MatchingEngineIndex('projects/193585189707/locations/us-central1/indexes/3833016281682935808')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating MatchingEngineIndexEndpoint\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:57,051 - INFO - Creating MatchingEngineIndexEndpoint\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Create MatchingEngineIndexEndpoint backing LRO: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584/operations/2430406028689408000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:57,053 - INFO - Create MatchingEngineIndexEndpoint backing LRO: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584/operations/2430406028689408000\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndexEndpoint created. Resource name: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:58,453 - INFO - MatchingEngineIndexEndpoint created. Resource name: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "To use this MatchingEngineIndexEndpoint in another session:\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:58,455 - INFO - To use this MatchingEngineIndexEndpoint in another session:\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "index_endpoint = aiplatform.MatchingEngineIndexEndpoint('projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584')\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:58,456 - INFO - index_endpoint = aiplatform.MatchingEngineIndexEndpoint('projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584')\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Deploying index MatchingEngineIndexEndpoint index_endpoint: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:58,535 - INFO - Deploying index MatchingEngineIndexEndpoint index_endpoint: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Deploy index MatchingEngineIndexEndpoint index_endpoint backing LRO: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584/operations/7905657275665088512\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:30:58,624 - INFO - Deploy index MatchingEngineIndexEndpoint index_endpoint backing LRO: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584/operations/7905657275665088512\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndexEndpoint index_endpoint Deployed index. Resource name: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:38,719 - INFO - MatchingEngineIndexEndpoint index_endpoint Deployed index. Resource name: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:57,450 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:57,583 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 1\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:57,584 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:57,717 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 2\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:57,719 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:57,844 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 3\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:57,845 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:57,986 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 4\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:57,988 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,084 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 5\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,085 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,216 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 6\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,217 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,338 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 7\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,340 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,438 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 8\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,439 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,541 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 9\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,542 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,665 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 10\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,666 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,802 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 11\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,803 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,921 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 12\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:58,923 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,046 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 13\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,048 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,178 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 14\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,179 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,281 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 15\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,283 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,402 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 16\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,404 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,495 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 17\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,497 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,635 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 18\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,637 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,736 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 19\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,737 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,836 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 20\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,837 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,932 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 21\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:56:59,934 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,025 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 22\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,026 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,158 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 23\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,160 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,254 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 24\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,256 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,345 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 25\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,346 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,447 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 26\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,449 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,575 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 27\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,577 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,677 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 28\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,679 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,816 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 29\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,817 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,940 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 30\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:00,941 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,043 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 31\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,045 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,190 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 32\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,191 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,290 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 33\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,292 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,384 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 34\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,386 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,484 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 35\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,485 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,572 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 36\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,574 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,668 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 37\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,669 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,778 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 38\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,779 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,880 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 39\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:01,881 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,015 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 40\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,016 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,113 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 41\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,115 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,214 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 42\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,216 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,316 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 43\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,318 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,413 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 44\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,414 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,500 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 45\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,502 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,589 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 46\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,590 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,717 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 47\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,718 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,817 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 48\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,819 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,907 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 49\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:02,909 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,002 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 50\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,003 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,124 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 51\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,126 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,219 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 52\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,221 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,307 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 53\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,309 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,405 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 54\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,406 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,492 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 55\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,494 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,589 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 56\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,591 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,694 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 57\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,695 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,788 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 58\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,789 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,885 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 59\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,886 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,996 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 60\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:03,998 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,125 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 61\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,126 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,220 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 62\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,222 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,317 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 63\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,319 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,414 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 64\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,415 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,507 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 65\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,509 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,604 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 66\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,606 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,704 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 67\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,705 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,802 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 68\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,803 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,893 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 69\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,894 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,982 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 70\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:04,983 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,093 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 71\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,095 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,189 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 72\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,190 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,282 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 73\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,283 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,385 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 74\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,387 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,476 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 75\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,477 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,574 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 76\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,576 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,682 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 77\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,683 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,787 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 78\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,788 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,912 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 79\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:05,913 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,009 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 80\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,010 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,106 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 81\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,108 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,209 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 82\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,210 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,309 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 83\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,311 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,407 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 84\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,409 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,490 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 85\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,491 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,573 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 86\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,574 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,686 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 87\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,687 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,781 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 88\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,782 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,873 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 89\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,874 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,976 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 90\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:06,977 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,174 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 91\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,175 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,273 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 92\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,275 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,381 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 93\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,382 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,474 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 94\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,475 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,563 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 95\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,564 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,661 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 96\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,662 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,755 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 97\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,756 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,846 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 98\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,848 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,943 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 99\n",
+      "Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:07,945 - INFO - Upserting datapoints MatchingEngineIndex index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2024-12-05 00:57:08,032 - INFO - MatchingEngineIndex index Upserted datapoints. Resource name: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Successfully upserted image datapoints batch 100\n",
+      "Stored 9980 image embeddings in Vertex AI Vector Search\n",
+      "Embeddings storage process completed.\n",
+      "Text Index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n",
+      "Text Endpoint: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616\n",
+      "Image Index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n",
+      "Image Endpoint: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create metadata for valid indices only\n",
+    "valid_metadata = [create_product_metadata(amazon_df.iloc[i]) for i in valid_indices]\n",
+    "\n",
+    "# Filter text embeddings to match valid indices\n",
+    "valid_text_embeddings = text_embeddings[valid_indices]\n",
+    "\n",
+    "# Store text embeddings\n",
+    "text_index, text_endpoint = store_embeddings_in_vertex_ai(valid_text_embeddings, valid_metadata, \"text\")\n",
+    "\n",
+    "# Store image embeddings (assuming all image embeddings are valid)\n",
+    "image_index, image_endpoint = store_embeddings_in_vertex_ai(image_embeddings, valid_metadata, \"image\")\n",
+    "\n",
+    "print(\"Embeddings storage process completed.\")\n",
+    "\n",
+    "# Print information about the created indexes and endpoints\n",
+    "if text_index and text_endpoint:\n",
+    "    print(f\"Text Index: {text_index.resource_name}\")\n",
+    "    print(f\"Text Endpoint: {text_endpoint.resource_name}\")\n",
+    "\n",
+    "if image_index and image_endpoint:\n",
+    "    print(f\"Image Index: {image_index.resource_name}\")\n",
+    "    print(f\"Image Endpoint: {image_endpoint.resource_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "9bd83954-ee55-429a-a0e1-8ebfac0099d5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Text Index: projects/193585189707/locations/us-central1/indexes/4390618210546745344\n",
+      "Text Endpoint: projects/193585189707/locations/us-central1/indexEndpoints/1955718924511215616\n",
+      "Image Index: projects/193585189707/locations/us-central1/indexes/3833016281682935808\n",
+      "Image Endpoint: projects/193585189707/locations/us-central1/indexEndpoints/6636084837256003584\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print information about the created indexes and endpoints\n",
+    "if text_index and text_endpoint:\n",
+    "    print(f\"Text Index: {text_index.resource_name}\")\n",
+    "    print(f\"Text Endpoint: {text_endpoint.resource_name}\")\n",
+    "\n",
+    "if image_index and image_endpoint:\n",
+    "    print(f\"Image Index: {image_index.resource_name}\")\n",
+    "    print(f\"Image Endpoint: {image_endpoint.resource_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "8d2407e0-11e8-4db0-a873-318d31ad7eec",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def push_to_huggingface(text_embeddings, image_embeddings, metadata, repo_id, token):\n",
+    "    login(token=token)\n",
+    "    api = HfApi()\n",
+    "    \n",
+    "    # Verify dimensions and align data\n",
+    "    n_samples = len(metadata)\n",
+    "    \n",
+    "    # Trim text embeddings to match metadata size\n",
+    "    if text_embeddings.shape[0] > n_samples:\n",
+    "        text_embeddings = text_embeddings[:n_samples]\n",
+    "    \n",
+    "    # Verify final dimensions\n",
+    "    if text_embeddings.shape[0] != n_samples or image_embeddings.shape[0] != n_samples:\n",
+    "        raise ValueError(\n",
+    "            f\"Dimension mismatch after alignment: metadata({n_samples}), \"\n",
+    "            f\"text_embeddings({text_embeddings.shape[0]}), \"\n",
+    "            f\"image_embeddings({image_embeddings.shape[0]})\"\n",
+    "        )\n",
+    "    \n",
+    "    # Create a single DataFrame containing all data\n",
+    "    data_dict = {}\n",
+    "    \n",
+    "    # Add text embeddings\n",
+    "    for i in range(text_embeddings.shape[1]):\n",
+    "        data_dict[f'text_embedding_{i}'] = text_embeddings[:, i].astype(np.float32)\n",
+    "    \n",
+    "    # Add image embeddings\n",
+    "    for i in range(image_embeddings.shape[1]):\n",
+    "        data_dict[f'image_embedding_{i}'] = image_embeddings[:, i].astype(np.float32)\n",
+    "    \n",
+    "    # Add metadata columns as lists of equal length\n",
+    "    for key in metadata[0].keys():\n",
+    "        data_dict[key] = [str(m[key]) for m in metadata]\n",
+    "    \n",
+    "    # Convert to DataFrame\n",
+    "    df = pd.DataFrame(data_dict)\n",
+    "    \n",
+    "    # Create temporary directory\n",
+    "    os.makedirs('embeddings_data', exist_ok=True)\n",
+    "    \n",
+    "    # Save as a single parquet file with compression\n",
+    "    print(\"Creating parquet file...\")\n",
+    "    df.to_parquet(\n",
+    "        'embeddings_data/embeddings.parquet',\n",
+    "        compression='snappy',\n",
+    "        index=False\n",
+    "    )\n",
+    "    \n",
+    "    # Create README\n",
+    "    readme_content = \"\"\"\n",
+    "    # Amazon Product Vector Database\n",
+    "\n",
+    "    This dataset contains vector embeddings for Amazon products, including both text and image embeddings.\n",
+    "\n",
+    "    ## Contents\n",
+    "    - `embeddings.parquet`: Contains text embeddings, image embeddings, and metadata for all products\n",
+    "\n",
+    "    ## Usage\n",
+    "    ```python\n",
+    "    import pandas as pd\n",
+    "    from datasets import load_dataset\n",
+    "\n",
+    "    # Load the dataset\n",
+    "    dataset = load_dataset(\"chen196473/amazon_vector_database\")\n",
+    "    \n",
+    "    # Read the data\n",
+    "    df = pd.read_parquet(\"embeddings.parquet\")\n",
+    "    \n",
+    "    # Extract embeddings\n",
+    "    text_embeddings = df[[col for col in df.columns if col.startswith('text_embedding_')]].values\n",
+    "    image_embeddings = df[[col for col in df.columns if col.startswith('image_embedding_')]].values\n",
+    "    ```\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    with open(\"embeddings_data/README.md\", \"w\") as f:\n",
+    "        f.write(readme_content)\n",
+    "    \n",
+    "    print(\"Uploading files to Hugging Face...\")\n",
+    "    api.upload_folder(\n",
+    "        folder_path=\"embeddings_data\",\n",
+    "        repo_id=repo_id,\n",
+    "        repo_type=\"dataset\"\n",
+    "    )\n",
+    "    \n",
+    "    shutil.rmtree('embeddings_data')\n",
+    "    print(f\"Dataset pushed to Hugging Face: https://huggingface.co/datasets/{repo_id}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "ce17d96d-1d82-4368-acc1-1fd771c17e9c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating parquet file...\n",
+      "Uploading files to Hugging Face...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "610400fc93aa4b4482a64903e2b0cbe7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "embeddings.parquet:   0%|          | 0.00/69.4M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset pushed to Hugging Face: https://huggingface.co/datasets/chen196473/amazon_vector_database\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Push embeddings to Hugging Face\n",
+    "push_to_huggingface(\n",
+    "    text_embeddings=text_embeddings,\n",
+    "    image_embeddings=image_embeddings,\n",
+    "    metadata=valid_metadata,\n",
+    "    repo_id=\"chen196473/amazon_vector_database\",\n",
+    "    token=HF_TOKEN\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cf301e8-3376-4de6-81f2-27f6602ae4a5",
+   "metadata": {},
+   "source": [
+    "## 6. Accuracy of Retrieval Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "358babb9-9236-4b05-804f-d527939d079c",
+   "metadata": {},
+   "source": [
+    "#### a. Load the Embedding from Vertex AI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "8ff3ce7a-7712-498e-af8e-f864a8025e83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def load_embeddings_from_vertex_ai(text_index_endpoint_name, image_index_endpoint_name):\n",
+    "#     \"\"\"Load embeddings from Vertex AI Vector Search\"\"\"\n",
+    "#     print(\"Loading embeddings from Vertex AI...\")\n",
+    "    \n",
+    "#     # Initialize dictionaries\n",
+    "#     text_embeddings_dict = {}\n",
+    "#     image_embeddings_dict = {}\n",
+    "    \n",
+    "#     # Load text index endpoint\n",
+    "#     text_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(text_index_endpoint_name)\n",
+    "    \n",
+    "#     # Load image index endpoint\n",
+    "#     image_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(image_index_endpoint_name)\n",
+    "    \n",
+    "#     # Function to retrieve embeddings from an endpoint\n",
+    "#     def retrieve_embeddings(index_endpoint, embeddings_dict):\n",
+    "#         # Get the deployed index\n",
+    "#         deployed_index = index_endpoint.deployed_indexes[0]\n",
+    "        \n",
+    "#         # Retrieve datapoints in batches\n",
+    "#         batch_size = 100\n",
+    "#         total_neighbors = 0\n",
+    "        \n",
+    "#         # Use a normalized dummy vector for better retrieval\n",
+    "#         dummy_vector = [0.1] * 512 \n",
+    "        \n",
+    "#         try:\n",
+    "#             # Query the index endpoint\n",
+    "#             response = index_endpoint.find_neighbors(\n",
+    "#                 deployed_index_id=deployed_index.id,\n",
+    "#                 queries=[dummy_vector],\n",
+    "#                 num_neighbors=batch_size,\n",
+    "#                 return_full_datapoint=True\n",
+    "#             )\n",
+    "            \n",
+    "#             # Process the response\n",
+    "#             if response and isinstance(response, list) and len(response) > 0:\n",
+    "#                 nearest_neighbors = response[0]\n",
+    "#                 if hasattr(nearest_neighbors, 'neighbors'):\n",
+    "#                     neighbors = nearest_neighbors.neighbors\n",
+    "#                 else:\n",
+    "#                     neighbors = nearest_neighbors\n",
+    "                \n",
+    "#                 for neighbor in neighbors:\n",
+    "#                     if hasattr(neighbor, 'datapoint'):\n",
+    "#                         datapoint = neighbor.datapoint\n",
+    "#                         if hasattr(datapoint, 'datapoint_id') and hasattr(datapoint, 'feature_vector'):\n",
+    "#                             embeddings_dict[datapoint.datapoint_id] = np.array(datapoint.feature_vector)\n",
+    "#                             total_neighbors += 1\n",
+    "#                     elif hasattr(neighbor, 'id') and hasattr(neighbor, 'feature_vector'):\n",
+    "#                         embeddings_dict[str(neighbor.id)] = np.array(neighbor.feature_vector)\n",
+    "#                         total_neighbors += 1\n",
+    "                        \n",
+    "#             print(f\"Retrieved {total_neighbors} neighbors with embeddings\")\n",
+    "#             if embeddings_dict:\n",
+    "#                 first_key = next(iter(embeddings_dict))\n",
+    "#                 print(f\"First embedding shape: {embeddings_dict[first_key].shape}\")\n",
+    "#                 print(f\"First embedding sample: {embeddings_dict[first_key][:5]}\")\n",
+    "            \n",
+    "#         except Exception as e:\n",
+    "#             print(f\"Error retrieving neighbors: {e}\")\n",
+    "#             print(f\"Deployed index ID: {deployed_index.id}\")\n",
+    "#             print(f\"Index endpoint: {index_endpoint.resource_name}\")\n",
+    "    \n",
+    "#     print(\"Processing text embeddings...\")\n",
+    "#     retrieve_embeddings(text_index_endpoint, text_embeddings_dict)\n",
+    "    \n",
+    "#     print(\"Processing image embeddings...\")\n",
+    "#     retrieve_embeddings(image_index_endpoint, image_embeddings_dict)\n",
+    "    \n",
+    "#     return text_embeddings_dict, image_embeddings_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "714b3283-c10a-4809-bb98-b5a04d79ba01",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading embeddings from Vertex AI...\n",
+      "Processing text embeddings...\n",
+      "Retrieved 100 neighbors with embeddings\n",
+      "First embedding shape: (512,)\n",
+      "First embedding sample: [ 0.12956573 -0.25549966  0.05005869  0.15066057 -0.00649694]\n",
+      "Processing image embeddings...\n",
+      "Retrieved 100 neighbors with embeddings\n",
+      "First embedding shape: (512,)\n",
+      "First embedding sample: [0.1638764  0.03753049 0.04466695 0.10117859 0.28408772]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Load from Vertex AI\n",
+    "# text_embeddings_dict, image_embeddings_dict = load_embeddings_from_vertex_ai(\n",
+    "#     text_index_endpoint_name=\"projects/193585189707/locations/us-central1/indexEndpoints/8330511420496019456\",\n",
+    "#     image_index_endpoint_name=\"projects/193585189707/locations/us-central1/indexEndpoints/478485470175559680\"\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85bd151a-eac5-4174-9253-c0c048114f5a",
+   "metadata": {},
+   "source": [
+    "#### b. Load the Embedding from Hugging Face"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "ddfdcbb8-4d94-4af2-9e48-de3149252306",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_embeddings_from_huggingface(repo_id):\n",
+    "    \"\"\"Load embeddings from Hugging Face dataset\"\"\"\n",
+    "    print(\"Loading embeddings from Hugging Face...\")\n",
+    "    \n",
+    "    try:\n",
+    "        # Download the parquet file directly\n",
+    "        from huggingface_hub import hf_hub_download\n",
+    "        \n",
+    "        # Download the embeddings file\n",
+    "        file_path = hf_hub_download(\n",
+    "            repo_id=repo_id,\n",
+    "            filename=\"embeddings.parquet\",\n",
+    "            repo_type=\"dataset\"\n",
+    "        )\n",
+    "        \n",
+    "        # Read the parquet file\n",
+    "        df = pd.read_parquet(file_path)\n",
+    "        \n",
+    "        # Initialize dictionaries\n",
+    "        text_embeddings_dict = {}\n",
+    "        image_embeddings_dict = {}\n",
+    "        \n",
+    "        print(\"Processing embeddings...\")\n",
+    "        \n",
+    "        # Get text and image embedding column names\n",
+    "        text_cols = [col for col in df.columns if col.startswith('text_embedding_')]\n",
+    "        image_cols = [col for col in df.columns if col.startswith('image_embedding_')]\n",
+    "        \n",
+    "        # Extract embeddings for each product\n",
+    "        for _, row in df.iterrows():\n",
+    "            uniq_id = row['Uniq_Id']\n",
+    "            text_embeddings_dict[uniq_id] = row[text_cols].values.astype(np.float32)\n",
+    "            image_embeddings_dict[uniq_id] = row[image_cols].values.astype(np.float32)\n",
+    "        \n",
+    "        print(f\"Successfully loaded {len(text_embeddings_dict)} embeddings\")\n",
+    "        return text_embeddings_dict, image_embeddings_dict\n",
+    "        \n",
+    "    except Exception as e:\n",
+    "        print(f\"Error loading embeddings: {e}\")\n",
+    "        return {}, {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "793f760c-0c6a-4887-a1c9-b0e6e0747415",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading embeddings from Hugging Face...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ebf64eb1cae040cca854f591e37a5a34",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "embeddings.parquet:   0%|          | 0.00/69.4M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing embeddings...\n",
+      "Successfully loaded 9980 embeddings\n"
+     ]
+    }
+   ],
+   "source": [
+    "# # Load embeddings from Hugging Face\n",
+    "text_embeddings_dict, image_embeddings_dict = load_embeddings_from_huggingface(\n",
+    "    repo_id=\"chen196473/amazon_vector_database\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "894e6ee5-eee4-45e4-b264-b60e2ae200cc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of products: 9980\n",
+      "Number of products: 9980\n",
+      "Text embedding shape: (512,)\n",
+      "Image embedding shape: (512,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verify the loaded embeddings\n",
+    "print(f\"Number of products: {len(text_embeddings_dict)}\")\n",
+    "print(f\"Number of products: {len(image_embeddings_dict)}\")\n",
+    "first_key = next(iter(text_embeddings_dict))\n",
+    "print(f\"Text embedding shape: {text_embeddings_dict[first_key].shape}\")\n",
+    "print(f\"Image embedding shape: {image_embeddings_dict[first_key].shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e9926e5-a3bb-4d0d-8f26-d4176bcdb09d",
+   "metadata": {},
+   "source": [
+    "#### c. Accuracy of Retrieval"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "5e5bb202-01dc-42fb-b474-ff6ad005ecad",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class FAISSRetrievalEvaluator:\n",
+    "    def __init__(self, device='cuda' if torch.cuda.is_available() else 'cpu', n_workers=15):\n",
+    "        self.device = device\n",
+    "        self.n_workers = n_workers\n",
+    "        self.text_index = None\n",
+    "        self.image_index = None\n",
+    "        \n",
+    "        # Initialize FAISS GPU resources if available\n",
+    "        if self.device == 'cuda':\n",
+    "            self.res = faiss.StandardGpuResources()\n",
+    "        \n",
+    "    def build_faiss_index(self, embeddings, dimension):\n",
+    "        \"\"\"Build FAISS index for fast similarity search\"\"\"\n",
+    "        embeddings = embeddings.astype(np.float32)  # Ensure correct data type\n",
+    "        index = faiss.IndexFlatIP(dimension)\n",
+    "        \n",
+    "        if self.device == 'cuda':\n",
+    "            index = faiss.index_cpu_to_gpu(self.res, 0, index)\n",
+    "            \n",
+    "        faiss.normalize_L2(embeddings)\n",
+    "        index.add(embeddings)\n",
+    "        return index\n",
+    "    \n",
+    "    def initialize_indices(self, text_embeddings, image_embeddings):\n",
+    "        \"\"\"Initialize FAISS indices for both text and image embeddings\"\"\"\n",
+    "        dimension = text_embeddings.shape[1]\n",
+    "        self.text_index = self.build_faiss_index(text_embeddings, dimension)\n",
+    "        self.image_index = self.build_faiss_index(image_embeddings, dimension)\n",
+    "    \n",
+    "    def search_index(self, query_embeddings, index, k):\n",
+    "        \"\"\"Search FAISS index and return distances and indices\"\"\"\n",
+    "        query_embeddings = query_embeddings.astype(np.float32)  # Ensure correct data type\n",
+    "        faiss.normalize_L2(query_embeddings)\n",
+    "        return index.search(query_embeddings, k)\n",
+    "    \n",
+    "    def calculate_recall_at_k(self, query_embeddings, index, true_indices, k):\n",
+    "        \"\"\"Calculate Recall@K using FAISS\"\"\"\n",
+    "        distances, retrieved_indices = self.search_index(query_embeddings, index, k)\n",
+    "        correct = 0\n",
+    "        for i, true_idx in enumerate(true_indices):\n",
+    "            if true_idx in retrieved_indices[i]:\n",
+    "                correct += 1\n",
+    "        return correct / len(true_indices)\n",
+    "    \n",
+    "    def calculate_precision_at_k(self, query_embeddings, index, true_indices, k):\n",
+    "        \"\"\"Calculate Precision@K using FAISS\"\"\"\n",
+    "        distances, retrieved_indices = self.search_index(query_embeddings, index, k)\n",
+    "        precision_scores = []\n",
+    "        for i, true_idx in enumerate(true_indices):\n",
+    "            relevant = sum(1 for idx in retrieved_indices[i] if idx == true_idx)\n",
+    "            precision_scores.append(relevant / k)\n",
+    "        return np.mean(precision_scores)\n",
+    "    \n",
+    "    def calculate_ndcg_at_k(self, query_embeddings, index, true_indices, k):\n",
+    "        \"\"\"Calculate NDCG@K using FAISS\"\"\"\n",
+    "        distances, retrieved_indices = self.search_index(query_embeddings, index, k)\n",
+    "        ndcg_scores = []\n",
+    "        for i, true_idx in enumerate(true_indices):\n",
+    "            relevance = [1 if idx == true_idx else 0 for idx in retrieved_indices[i]]\n",
+    "            dcg = sum(rel / np.log2(pos + 2) for pos, rel in enumerate(relevance))\n",
+    "            idcg = 1  # Ideal DCG for single relevant document\n",
+    "            ndcg_scores.append(dcg / idcg)\n",
+    "        return np.mean(ndcg_scores)\n",
+    "    \n",
+    "    def evaluate(self, text_embeddings, image_embeddings, valid_indices, batch_size=1000):\n",
+    "        \"\"\"Evaluate retrieval performance using FAISS\"\"\"\n",
+    "        self.initialize_indices(text_embeddings, image_embeddings)\n",
+    "        \n",
+    "        metrics = {}\n",
+    "        for k in [1, 5, 10]:\n",
+    "            metrics[f'Recall@{k}'] = self.calculate_recall_at_k(\n",
+    "                text_embeddings, self.image_index, valid_indices, k)\n",
+    "            metrics[f'Precision@{k}'] = self.calculate_precision_at_k(\n",
+    "                text_embeddings, self.image_index, valid_indices, k)\n",
+    "            if k > 1:\n",
+    "                metrics[f'NDCG@{k}'] = self.calculate_ndcg_at_k(\n",
+    "                    text_embeddings, self.image_index, valid_indices, k)\n",
+    "        \n",
+    "        return metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "ae8fde81-3234-4c81-b09b-70f0eb2316fd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def save_evaluation_results(metrics, model_name, dataset_name, output_dir=\"evaluation_results\"):\n",
+    "    \"\"\"Save evaluation results to CSV with detailed information\"\"\"\n",
+    "    os.makedirs(output_dir, exist_ok=True)\n",
+    "    timestamp = datetime.now().strftime(\"%Y%m%d\")\n",
+    "    \n",
+    "    results_dict = {\n",
+    "        'Timestamp': timestamp,\n",
+    "        'Model': model_name,\n",
+    "        'Dataset': dataset_name\n",
+    "    }\n",
+    "    results_dict.update(metrics)\n",
+    "    \n",
+    "    results_df = pd.DataFrame([results_dict])\n",
+    "    \n",
+    "    # Round all numeric columns to 3 decimal places\n",
+    "    numeric_columns = results_df.select_dtypes(include=[np.number]).columns\n",
+    "    results_df[numeric_columns] = results_df[numeric_columns].round(3)\n",
+    "    \n",
+    "    filename = f'{output_dir}/evaluation_metrics.csv'\n",
+    "    results_df.to_csv(filename, index=False)\n",
+    "    \n",
+    "    return filename"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "38bb073b-44e2-4e28-bf87-a378378fe324",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def run_faiss_evaluation(text_embeddings, image_embeddings, valid_indices):\n",
+    "    \"\"\"Run evaluation using FAISS-based retrieval\"\"\"\n",
+    "    try:\n",
+    "        evaluator = FAISSRetrievalEvaluator()\n",
+    "        print(\"Starting FAISS-based evaluation...\")\n",
+    "        metrics = evaluator.evaluate(text_embeddings, image_embeddings, valid_indices)\n",
+    "        \n",
+    "        # Save results\n",
+    "        detailed_file = save_evaluation_results(\n",
+    "            metrics,\n",
+    "            model_name=\"FashionCLIP-FAISS\",\n",
+    "            dataset_name=\"Amazon Product Dataset\"\n",
+    "        )\n",
+    "        \n",
+    "        print(\"\\nFAISS Evaluation Results:\")\n",
+    "        for metric, value in metrics.items():\n",
+    "            print(f\"{metric}: {value:.4f}\")\n",
+    "        \n",
+    "        print(f\"\\nResults saved to: {detailed_file}\")\n",
+    "        return metrics\n",
+    "        \n",
+    "    except Exception as e:\n",
+    "        print(f\"Error during FAISS evaluation: {str(e)}\")\n",
+    "        raise"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "81ad40b9-9911-48e3-9ad8-93f379da7fd3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting FAISS-based evaluation...\n",
+      "\n",
+      "FAISS Evaluation Results:\n",
+      "Recall@1: 0.6385\n",
+      "Precision@1: 0.6385\n",
+      "Recall@5: 0.8513\n",
+      "Precision@5: 0.1703\n",
+      "NDCG@5: 0.7563\n",
+      "Recall@10: 0.9008\n",
+      "Precision@10: 0.0901\n",
+      "NDCG@10: 0.7725\n",
+      "\n",
+      "Results saved to: evaluation_results/evaluation_metrics.csv\n",
+      "\n",
+      "Evaluation metrics: {'Recall@1': 0.6384769539078157, 'Precision@1': 0.6384769539078157, 'Recall@5': 0.8513026052104209, 'Precision@5': 0.17026052104208417, 'NDCG@5': 0.7563030930898683, 'Recall@10': 0.9008016032064128, 'Precision@10': 0.09008016032064128, 'NDCG@10': 0.7724883596439684}\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    # Execute evaluation with parallel processing\n",
+    "    metrics = run_faiss_evaluation(text_embeddings, image_embeddings, valid_indices)\n",
+    "    print(\"\\nEvaluation metrics:\", metrics)\n",
+    "except Exception as e:\n",
+    "    print(f\"Error in main execution: {str(e)}\")\n",
+    "    raise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b65307e-2407-40e9-b163-cd9d451cf175",
+   "metadata": {},
+   "source": [
+    "## 7. Large Language Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4f81d84e-d75c-470f-956b-f1d4ae9ec50f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up global variables\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "clip_model, clip_preprocess, clip_tokenizer = None, None, None\n",
+    "llm_tokenizer, llm_model = None, None\n",
+    "product_df, metadata, embeddings_df = None, None, None\n",
+    "text_faiss, image_faiss = None, None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ef2283d-67ee-4180-9c37-bb0942eb47b4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Define utility functions\n",
+    "def load_embeddings_from_huggingface(repo_id):\n",
+    "    print(\"Loading embeddings from Hugging Face...\")\n",
+    "    try:\n",
+    "        file_path = hf_hub_download(\n",
+    "            repo_id=repo_id,\n",
+    "            filename=\"embeddings.parquet\",\n",
+    "            repo_type=\"dataset\"\n",
+    "        )\n",
+    "        df = pd.read_parquet(file_path)\n",
+    "        text_cols = [col for col in df.columns if col.startswith('text_embedding_')]\n",
+    "        image_cols = [col for col in df.columns if col.startswith('image_embedding_')]\n",
+    "        text_embeddings_dict = {row['Uniq_Id']: row[text_cols].values.astype(np.float32) for _, row in df.iterrows()}\n",
+    "        image_embeddings_dict = {row['Uniq_Id']: row[image_cols].values.astype(np.float32) for _, row in df.iterrows()}\n",
+    "        print(f\"Successfully loaded {len(text_embeddings_dict)} embeddings\")\n",
+    "        return text_embeddings_dict, image_embeddings_dict\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error loading embeddings: {e}\")\n",
+    "        return {}, {}\n",
+    "\n",
+    "# Model initialization\n",
+    "\n",
+    "def initialize_models():\n",
+    "    global clip_model, clip_preprocess, clip_tokenizer, llm_tokenizer, llm_model, device\n",
+    "    \n",
+    "    print(f\"Using device: {device}\")\n",
+    "    \n",
+    "    # Initialize CLIP model\n",
+    "    clip_model, _, clip_preprocess = open_clip.create_model_and_transforms('hf-hub:Marqo/marqo-fashionCLIP')\n",
+    "    clip_model = clip_model.to(device)\n",
+    "    clip_model.eval()\n",
+    "    clip_tokenizer = open_clip.get_tokenizer('hf-hub:Marqo/marqo-fashionCLIP')\n",
+    "\n",
+    "    # Initialize LLM\n",
+    "    model_name = \"mistralai/Mistral-7B-v0.1\"\n",
+    "    quantization_config = BitsAndBytesConfig(\n",
+    "        load_in_4bit=True,\n",
+    "        bnb_4bit_compute_dtype=torch.float16,\n",
+    "        bnb_4bit_use_double_quant=True,\n",
+    "        bnb_4bit_quant_type=\"nf4\"\n",
+    "    )\n",
+    "\n",
+    "    llm_tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side=\"left\", truncation_side=\"left\")\n",
+    "    llm_tokenizer.pad_token = llm_tokenizer.eos_token\n",
+    "\n",
+    "    llm_model = AutoModelForCausalLM.from_pretrained(\n",
+    "        model_name,\n",
+    "        quantization_config=quantization_config,\n",
+    "        device_map=\"auto\",\n",
+    "        torch_dtype=torch.float16\n",
+    "    )\n",
+    "    llm_model.eval()\n",
+    "\n",
+    "# Data loading\n",
+    "def load_data():\n",
+    "    \"\"\"Load and initialize all required data with enhanced metadata support\"\"\"\n",
+    "    global product_df, metadata, embeddings_df\n",
+    "\n",
+    "    try:\n",
+    "        # Load cleaned product data\n",
+    "        cleaned_data_path = hf_hub_download(\n",
+    "            repo_id=\"chen196473/amazon_product_2020_cleaned\",\n",
+    "            filename=\"amazon_cleaned.parquet\",\n",
+    "            repo_type=\"dataset\"\n",
+    "        )\n",
+    "        product_df = pd.read_parquet(cleaned_data_path)\n",
+    "\n",
+    "        # Add image validation columns\n",
+    "        product_df['Has_Valid_Image'] = product_df['Processed Image'].notna()\n",
+    "        product_df['Image_Status'] = product_df['Has_Valid_Image'].map({True: 'valid', False: 'invalid'})\n",
+    "\n",
+    "        # Load enhanced metadata with new structure\n",
+    "        metadata = {}\n",
+    "        metadata_files = [\n",
+    "            'base_metadata.json',\n",
+    "            'category_index.json',\n",
+    "            'price_range_index.json',\n",
+    "            'keyword_index.json',\n",
+    "            'brand_index.json',\n",
+    "            'product_name_index.json'\n",
+    "        ]\n",
+    "        \n",
+    "        for file in metadata_files:\n",
+    "            file_path = hf_hub_download(\n",
+    "                repo_id=\"chen196473/amazon_product_2020_metadata\",\n",
+    "                filename=file,\n",
+    "                repo_type=\"dataset\"\n",
+    "            )\n",
+    "            with open(file_path, 'r') as f:\n",
+    "                index_name = file.replace('.json', '')\n",
+    "                data = json.load(f)\n",
+    "                \n",
+    "                # Convert lists back to sets for keywords if needed\n",
+    "                if index_name == 'base_metadata':\n",
+    "                    # Convert list to dictionary for easier lookup\n",
+    "                    data = {item['Uniq_Id']: item for item in data}\n",
+    "                    for item in data.values():\n",
+    "                        if 'Keywords' in item:\n",
+    "                            item['Keywords'] = set(item['Keywords'])\n",
+    "                \n",
+    "                metadata[index_name] = data\n",
+    "\n",
+    "        # Load embeddings\n",
+    "        text_embeddings_dict, image_embeddings_dict = load_embeddings_from_huggingface(\n",
+    "            \"chen196473/amazon_vector_database\"\n",
+    "        )\n",
+    "\n",
+    "        # Create embeddings DataFrame with enhanced structure\n",
+    "        embeddings_df = pd.DataFrame({\n",
+    "            'text_embeddings': list(text_embeddings_dict.values()),\n",
+    "            'image_embeddings': list(image_embeddings_dict.values()),\n",
+    "            'Uniq_Id': list(text_embeddings_dict.keys())\n",
+    "        })\n",
+    "\n",
+    "        # Merge with product data\n",
+    "        product_df = product_df.merge(\n",
+    "            embeddings_df, \n",
+    "            left_on='Uniq Id', \n",
+    "            right_on='Uniq_Id', \n",
+    "            how='inner'\n",
+    "        )\n",
+    "        \n",
+    "        # Validate required columns\n",
+    "        required_columns = [\n",
+    "            'Uniq Id', 'Product Name', 'Category', 'Selling Price',\n",
+    "            'Model Number', 'Image', 'Normalized Description'\n",
+    "        ]\n",
+    "        missing_cols = set(required_columns) - set(product_df.columns)\n",
+    "        if missing_cols:\n",
+    "            raise ValueError(f\"Missing required columns: {missing_cols}\")\n",
+    "\n",
+    "        # Add enhanced metadata fields if not present\n",
+    "        if 'Search_Text' not in product_df.columns:\n",
+    "            product_df['Search_Text'] = product_df.apply(\n",
+    "                lambda x: metadata['base_metadata'].get(x['Uniq Id'], {}).get('Search_Text', ''),\n",
+    "                axis=1\n",
+    "            )\n",
+    "\n",
+    "        return True\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        raise RuntimeError(f\"Failed to load data: {str(e)}\")\n",
+    "\n",
+    "# FAISS index creation\n",
+    "class MultiModalFAISSIndex:\n",
+    "    def __init__(self, dimension, index_type='L2'):\n",
+    "        import faiss\n",
+    "        self.dimension = dimension\n",
+    "        self.index = faiss.IndexFlatL2(dimension) if index_type == 'L2' else faiss.IndexFlatIP(dimension)\n",
+    "        self.id_to_metadata = {}\n",
+    "        \n",
+    "    def add_embeddings(self, embeddings, metadata_list):\n",
+    "        import numpy as np\n",
+    "        embeddings = np.array(embeddings).astype('float32')\n",
+    "        self.index.add(embeddings)\n",
+    "        for i, metadata in enumerate(metadata_list):\n",
+    "            self.id_to_metadata[i] = metadata\n",
+    "            \n",
+    "    def search(self, query_embedding, k=5):\n",
+    "        import numpy as np\n",
+    "        query_embedding = np.array([query_embedding]).astype('float32')\n",
+    "        distances, indices = self.index.search(query_embedding, k)\n",
+    "        results = []\n",
+    "        for idx in indices[0]:\n",
+    "            if idx in self.id_to_metadata:\n",
+    "                results.append(self.id_to_metadata[idx])\n",
+    "        return results\n",
+    "\n",
+    "def create_faiss_indexes(text_embeddings_dict, image_embeddings_dict):\n",
+    "    global text_faiss, image_faiss\n",
+    "    \n",
+    "    # Get embedding dimension\n",
+    "    text_dim = next(iter(text_embeddings_dict.values())).shape[0]\n",
+    "    image_dim = next(iter(image_embeddings_dict.values())).shape[0]\n",
+    "    \n",
+    "    # Create indexes\n",
+    "    text_faiss = MultiModalFAISSIndex(text_dim)\n",
+    "    image_faiss = MultiModalFAISSIndex(image_dim)\n",
+    "    \n",
+    "    # Prepare text embeddings and metadata\n",
+    "    text_embeddings = []\n",
+    "    text_metadata = []\n",
+    "    for text_id, embedding in text_embeddings_dict.items():\n",
+    "        product = product_df[product_df['Uniq Id'] == text_id].iloc[0]\n",
+    "        text_embeddings.append(embedding)\n",
+    "        text_metadata.append({\n",
+    "            'id': text_id,\n",
+    "            'description': product['Normalized Description'],\n",
+    "            'product_name': product['Product Name']\n",
+    "        })\n",
+    "    \n",
+    "    # Add text embeddings\n",
+    "    text_faiss.add_embeddings(text_embeddings, text_metadata)\n",
+    "    \n",
+    "    # Prepare image embeddings and metadata\n",
+    "    image_embeddings = []\n",
+    "    image_metadata = []\n",
+    "    for image_id, embedding in image_embeddings_dict.items():\n",
+    "        product = product_df[product_df['Uniq Id'] == image_id].iloc[0]\n",
+    "        image_embeddings.append(embedding)\n",
+    "        image_metadata.append({\n",
+    "            'id': image_id,\n",
+    "            'image_url': product['Image'],\n",
+    "            'product_name': product['Product Name']\n",
+    "        })\n",
+    "    \n",
+    "    # Add image embeddings\n",
+    "    image_faiss.add_embeddings(image_embeddings, image_metadata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fb0c2c34-ade2-4c69-9692-1f565a5294a7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_few_shot_product_comparison_template():\n",
+    "    return \"\"\"Compare these specific products based on their actual features and specifications:\n",
+    "\n",
+    "Example 1:\n",
+    "Question: Compare iPhone 13 and Samsung Galaxy S21\n",
+    "Answer: The iPhone 13 features a 6.1-inch Super Retina XDR display and dual 12MP cameras, while the Galaxy S21 has a 6.2-inch Dynamic AMOLED display and triple camera setup. Both phones offer 5G connectivity, but the iPhone uses A15 Bionic chip while S21 uses Snapdragon 888.\n",
+    "\n",
+    "Example 2:\n",
+    "Question: Compare Amazon Echo Dot and Google Nest Mini\n",
+    "Answer: The Amazon Echo Dot features Alexa voice assistant and a 1.6-inch speaker, while the Google Nest Mini comes with Google Assistant and a 40mm driver. Both devices offer smart home control and music playback, but differ in their ecosystem integration.\n",
+    "\n",
+    "Current Question: {query}\n",
+    "Context: {context}\n",
+    "\n",
+    "Guidelines:\n",
+    "- Only compare the specific products mentioned in the query\n",
+    "- Focus on actual product features and specifications\n",
+    "- Keep response to 2-3 clear sentences\n",
+    "- Ensure factual accuracy based on the context provided\n",
+    "\n",
+    "Answer:\"\"\"\n",
+    "\n",
+    "def get_zero_shot_product_template():\n",
+    "    return \"\"\"You are a product information specialist. Describe only the specific product's actual features based on the provided context.\n",
+    "\n",
+    "Context: {context}\n",
+    "\n",
+    "Question: {query}\n",
+    "\n",
+    "Guidelines:\n",
+    "- Only describe the specific product mentioned in the query\n",
+    "- Focus on actual features and specifications from the context\n",
+    "- Keep response to 2-3 factual sentences\n",
+    "- Ensure information accuracy\n",
+    "\n",
+    "Answer:\"\"\"\n",
+    "\n",
+    "def get_zero_shot_image_template():\n",
+    "    return \"\"\"Analyze this product image and provide a concise description:\n",
+    "\n",
+    "Product Information:\n",
+    "{context}\n",
+    "\n",
+    "Guidelines:\n",
+    "- Describe the main product features and intended use\n",
+    "- Highlight key specifications and materials\n",
+    "- Keep response to 2-3 sentences\n",
+    "- Focus on practical information\n",
+    "\n",
+    "Answer:\"\"\"\n",
+    "# Image processing functions\n",
+    "def process_image(image):\n",
+    "    try:\n",
+    "        if isinstance(image, str):\n",
+    "            response = requests.get(image)\n",
+    "            image = Image.open(io.BytesIO(response.content))\n",
+    "        \n",
+    "        processed_image = clip_preprocess(image).unsqueeze(0).to(device)\n",
+    "        \n",
+    "        with torch.no_grad():\n",
+    "            image_features = clip_model.encode_image(processed_image)\n",
+    "            image_features = image_features / image_features.norm(dim=-1, keepdim=True)\n",
+    "        \n",
+    "        return image_features.cpu().numpy()\n",
+    "    except Exception as e:\n",
+    "        raise Exception(f\"Error processing image: {str(e)}\")\n",
+    "\n",
+    "def load_image_from_url(url):\n",
+    "    response = requests.get(url)\n",
+    "    if response.status_code == 200:\n",
+    "        return Image.open(io.BytesIO(response.content))\n",
+    "    else:\n",
+    "        raise Exception(f\"Failed to fetch image from URL: {url}, Status Code: {response.status_code}\")\n",
+    "\n",
+    "# Context retrieval and enhancement\n",
+    "def filter_by_metadata(query, metadata_index):\n",
+    "    relevant_products = set()\n",
+    "    \n",
+    "    # Check category index\n",
+    "    if 'category_index' in metadata_index:\n",
+    "        categories = metadata_index['category_index']\n",
+    "        for category in categories:\n",
+    "            if any(term.lower() in category.lower() for term in query.split()):\n",
+    "                relevant_products.update(categories[category])\n",
+    "    \n",
+    "    # Check product name index\n",
+    "    if 'product_name_index' in metadata_index:\n",
+    "        product_names = metadata_index['product_name_index']\n",
+    "        for term in query.split():\n",
+    "            if term.lower() in product_names:\n",
+    "                relevant_products.update(product_names[term.lower()])\n",
+    "    \n",
+    "    # Check price ranges\n",
+    "    price_terms = {'cheap', 'expensive', 'price', 'cost', 'affordable'}\n",
+    "    if any(term in query.lower() for term in price_terms) and 'price_range_index' in metadata_index:\n",
+    "        price_ranges = metadata_index['price_range_index']\n",
+    "        for price_range in price_ranges:\n",
+    "            relevant_products.update(price_ranges[price_range])\n",
+    "    \n",
+    "    return relevant_products if relevant_products else None\n",
+    "\n",
+    "def enhance_context_with_metadata(product, metadata_index):\n",
+    "    \"\"\"Enhanced context building using new metadata structure\"\"\"\n",
+    "    # Access base_metadata using product ID directly since it's now a dictionary\n",
+    "    base_metadata = metadata_index['base_metadata'].get(product['Uniq Id'])\n",
+    "    \n",
+    "    if base_metadata:\n",
+    "        # Get keywords and search text from enhanced metadata\n",
+    "        keywords = base_metadata.get('Keywords', [])\n",
+    "        search_text = base_metadata.get('Search_Text', '')\n",
+    "        \n",
+    "        # Build enhanced description\n",
+    "        description = []\n",
+    "        description.append(f\"Product Name: {base_metadata['Product_Name']}\")\n",
+    "        description.append(f\"Category: {base_metadata['Category']}\")\n",
+    "        description.append(f\"Price: ${base_metadata['Selling_Price']:.2f}\")\n",
+    "        \n",
+    "        # Add key features from normalized description\n",
+    "        if 'Normalized_Description' in base_metadata:\n",
+    "            features = []\n",
+    "            for feature in base_metadata['Normalized_Description'].split('|'):\n",
+    "                if ':' in feature:\n",
+    "                    key, value = feature.split(':', 1)\n",
+    "                    if not any(skip in key.lower() for skip in \n",
+    "                        ['uniq id', 'product url', 'specifications', 'asin']):\n",
+    "                        features.append(f\"{key.strip()}: {value.strip()}\")\n",
+    "            if features:\n",
+    "                description.append(\"Key Features:\")\n",
+    "                description.extend(features[:3])\n",
+    "        \n",
+    "        # Add relevant keywords\n",
+    "        if keywords:\n",
+    "            description.append(\"Related Terms: \" + \", \".join(list(keywords)[:5]))\n",
+    "        \n",
+    "        return \"\\n\".join(description)\n",
+    "    \n",
+    "    return None\n",
+    "\n",
+    "\n",
+    "def retrieve_context(query, image=None, top_k=5):\n",
+    "    \"\"\"Enhanced context retrieval using both FAISS and metadata\"\"\"\n",
+    "    # Initialize context lists\n",
+    "    similar_items = []\n",
+    "    context = []\n",
+    "    \n",
+    "    if image is not None:\n",
+    "        # Process image query\n",
+    "        image_embedding = process_image(image)\n",
+    "        image_embedding = image_embedding.reshape(1, -1)\n",
+    "        similar_items = image_faiss.search(image_embedding[0], k=top_k)\n",
+    "    else:\n",
+    "        # Process text query with enhanced metadata filtering\n",
+    "        relevant_products = filter_by_metadata(query, metadata)\n",
+    "        \n",
+    "        tokens = clip_tokenizer(query).to(device)\n",
+    "        with torch.no_grad():\n",
+    "            text_embedding = clip_model.encode_text(tokens)\n",
+    "            text_embedding = text_embedding / text_embedding.norm(dim=-1, keepdim=True)\n",
+    "            text_embedding = text_embedding.cpu().numpy()\n",
+    "        \n",
+    "        # Get FAISS results\n",
+    "        similar_items = text_faiss.search(text_embedding[0], k=top_k*2)  # Get more results for filtering\n",
+    "        \n",
+    "        # Filter results using metadata if available\n",
+    "        if relevant_products:\n",
+    "            similar_items = [item for item in similar_items if item['id'] in relevant_products][:top_k]\n",
+    "    \n",
+    "    # Build enhanced context\n",
+    "    for item in similar_items:\n",
+    "        product = product_df[product_df['Uniq Id'] == item['id']].iloc[0]\n",
+    "        enhanced_context = enhance_context_with_metadata(product, metadata)\n",
+    "        if enhanced_context:\n",
+    "            context.append(enhanced_context)\n",
+    "    \n",
+    "    return \"\\n\\n\".join(context), similar_items\n",
+    "\n",
+    "def display_product_images(similar_items, max_images=1):\n",
+    "    \"\"\"Display product images from search results\"\"\"\n",
+    "    displayed_images = []\n",
+    "    \n",
+    "    for item in similar_items[:max_images]:\n",
+    "        try:\n",
+    "            # Access the image URL directly from the item dictionary\n",
+    "            image_url = item['Image']\n",
+    "            \n",
+    "            # Load and display image\n",
+    "            image = load_image_from_url(image_url)\n",
+    "            plt.figure(figsize=(4, 4))\n",
+    "            plt.imshow(image)\n",
+    "            plt.axis('off')\n",
+    "            plt.title(f\"Found Product {len(displayed_images) + 1}\")\n",
+    "            plt.tight_layout()\n",
+    "            plt.show()\n",
+    "            \n",
+    "            # Convert price to float if it's a string\n",
+    "            try:\n",
+    "                price = float(item['Selling Price'])\n",
+    "            except (ValueError, TypeError):\n",
+    "                price = \"N/A\"\n",
+    "            \n",
+    "            displayed_images.append({\n",
+    "                'url': image_url,\n",
+    "                'product_name': item['Product Name'],\n",
+    "                'price': price\n",
+    "            })\n",
+    "            \n",
+    "        except Exception as e:\n",
+    "            print(f\"Error displaying image: {str(e)}\")\n",
+    "            continue\n",
+    "    \n",
+    "    return displayed_images\n",
+    "\n",
+    "def classify_query(query):\n",
+    "    \"\"\"Classify the type of query to determine the retrieval strategy.\"\"\"\n",
+    "    query_lower = query.lower()\n",
+    "    if any(keyword in query_lower for keyword in ['compare', 'difference between']):\n",
+    "        return 'comparison'\n",
+    "    elif any(keyword in query_lower for keyword in ['show', 'picture', 'image', 'photo']):\n",
+    "        return 'image_search'\n",
+    "    else:\n",
+    "        return 'product_info'\n",
+    "\n",
+    "    \n",
+    "def boost_category_relevance(query, product, similarity_score):\n",
+    "    query_terms = set(query.lower().split())\n",
+    "    category_terms = set(product['Category'].lower().split())\n",
+    "    category_overlap = len(query_terms & category_terms)\n",
+    "    category_boost = 1 + (category_overlap * 0.2)  # 20% boost per matching term\n",
+    "    return similarity_score * category_boost\n",
+    "\n",
+    "def hybrid_retrieval(query, top_k=5):\n",
+    "    query_type = classify_query(query)\n",
+    "    \n",
+    "    tokens = clip_tokenizer(query).to(device)\n",
+    "    with torch.no_grad():\n",
+    "        text_embedding = clip_model.encode_text(tokens)\n",
+    "        text_embedding = text_embedding / text_embedding.norm(dim=-1, keepdim=True)\n",
+    "        text_embedding = text_embedding.cpu().numpy()\n",
+    "\n",
+    "    # First get text matches\n",
+    "    text_results = text_faiss.search(text_embedding[0], k=top_k*2)\n",
+    "    \n",
+    "    if query_type == 'image_search':\n",
+    "        image_results = []\n",
+    "        for item in text_results:\n",
+    "            # Get original product with embeddings intact\n",
+    "            product = product_df[product_df['Uniq Id'] == item['id']].iloc[0]\n",
+    "            # Get image embeddings from embeddings_df instead\n",
+    "            image_embedding = embeddings_df[embeddings_df['Uniq_Id'] == item['id']]['image_embeddings'].iloc[0]\n",
+    "            similarity = np.dot(text_embedding.flatten(), image_embedding.flatten())\n",
+    "            boosted_similarity = boost_category_relevance(query, product, similarity)\n",
+    "            image_results.append((product, boosted_similarity))\n",
+    "        \n",
+    "        image_results.sort(key=lambda x: x[1], reverse=True)\n",
+    "        results = [item for item, _ in image_results[:top_k]]\n",
+    "    else:\n",
+    "        results = [product_df[product_df['Uniq Id'] == item['id']].iloc[0] for item in text_results[:top_k]]\n",
+    "\n",
+    "    return results, query_type\n",
+    "\n",
+    "\n",
+    "def fallback_text_search(query, top_k=10):\n",
+    "    relevant_products = filter_by_metadata(query, metadata)\n",
+    "    if not relevant_products:\n",
+    "        # Check brand index specifically\n",
+    "        if 'brand_index' in metadata:\n",
+    "            query_terms = query.lower().split()\n",
+    "            for term in query_terms:\n",
+    "                if term in metadata['brand_index']:\n",
+    "                    relevant_products = set(metadata['brand_index'][term])\n",
+    "                    break\n",
+    "    \n",
+    "    if relevant_products:\n",
+    "        results = [product_df[product_df['Uniq Id'] == pid].iloc[0] for pid in list(relevant_products)[:top_k]]\n",
+    "    else:\n",
+    "        query_lower = query.lower()\n",
+    "        results = product_df[\n",
+    "            (product_df['Product Name'].str.lower().str.contains(query_lower)) |\n",
+    "            (product_df['Category'].str.lower().str.contains(query_lower)) |\n",
+    "            (product_df['Normalized Description'].str.lower().str.contains(query_lower))\n",
+    "        ].head(top_k)\n",
+    "\n",
+    "    return results\n",
+    "\n",
+    "def generate_rag_response(query, context, image=None):\n",
+    "    \"\"\"Enhanced RAG response generation\"\"\"\n",
+    "    # Select template based on query type and metadata\n",
+    "    if \"compare\" in query.lower() or \"difference between\" in query.lower() or \"vs.\" in query.lower():\n",
+    "        template = get_few_shot_product_comparison_template()\n",
+    "    elif image is not None:\n",
+    "        template = get_zero_shot_image_template()\n",
+    "    else:\n",
+    "        template = get_zero_shot_product_template()\n",
+    "    \n",
+    "    # Create enhanced prompt with metadata context\n",
+    "    prompt = PromptTemplate(\n",
+    "        template=template,\n",
+    "        input_variables=[\"query\", \"context\"]\n",
+    "    )\n",
+    "    \n",
+    "    # Configure generation parameters\n",
+    "    pipe = pipeline(\n",
+    "        \"text-generation\",\n",
+    "        model=llm_model,\n",
+    "        tokenizer=llm_tokenizer,\n",
+    "        max_new_tokens=300,\n",
+    "        temperature=0.1,\n",
+    "        do_sample=False,\n",
+    "        repetition_penalty=1.2,\n",
+    "        early_stopping=True,\n",
+    "        truncation=True,\n",
+    "        padding=True\n",
+    "    )\n",
+    "    \n",
+    "    # Generate and clean response\n",
+    "    formatted_prompt = prompt.format(query=query, context=context)\n",
+    "    response = pipe(formatted_prompt)[0]['generated_text']\n",
+    "    \n",
+    "    # Clean response\n",
+    "    for section in [\"Answer:\", \"Question:\", \"Guidelines:\", \"Context:\"]:\n",
+    "        if section in response:\n",
+    "            response = response.split(section)[-1].strip()\n",
+    "    \n",
+    "    return response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8a6dc851-6c36-4566-948b-5bbf668b4784",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def chatbot(query, image_urls=None):\n",
+    "    \"\"\"Main chatbot function to handle queries and provide responses.\"\"\"\n",
+    "    if image_urls:\n",
+    "        if isinstance(image_urls, str):\n",
+    "            image_urls = [image_urls]\n",
+    "\n",
+    "        all_responses = []\n",
+    "        for i, url in enumerate(image_urls):\n",
+    "            try:\n",
+    "                image = load_image_from_url(url)\n",
+    "                context, _ = retrieve_context(query, image)\n",
+    "                response = generate_rag_response(query, context, image)\n",
+    "\n",
+    "                plt.figure(figsize=(4, 4))\n",
+    "                plt.imshow(image)\n",
+    "                plt.axis('off')\n",
+    "                plt.title(f\"Image {i+1}\")\n",
+    "                plt.tight_layout()\n",
+    "                plt.show()\n",
+    "\n",
+    "                all_responses.append(response)\n",
+    "            except Exception as e:\n",
+    "                print(f\"Error processing image {i+1}: {str(e)}\")\n",
+    "                all_responses.append(\"Failed to process image.\")\n",
+    "\n",
+    "        return all_responses\n",
+    "    else:\n",
+    "        print(f\"Processing query: {query}\")\n",
+    "        results, query_type = hybrid_retrieval(query)\n",
+    "        print(f\"Query type: {query_type}\")\n",
+    "\n",
+    "        if not results and query_type == 'image_search':\n",
+    "            print(\"No relevant images found. Falling back to text search.\")\n",
+    "            results = fallback_text_search(query)\n",
+    "\n",
+    "        context = \"\\n\\n\".join([enhance_context_with_metadata(item, metadata) for item in results])\n",
+    "        response = generate_rag_response(query, context)\n",
+    "\n",
+    "        if query_type == 'image_search':\n",
+    "            print(\"\\nFound matching products:\")\n",
+    "            displayed_images = display_product_images(results)\n",
+    "            return response, displayed_images\n",
+    "\n",
+    "        return response\n",
+    "\n",
+    "def cleanup_resources():\n",
+    "    if torch.cuda.is_available():\n",
+    "        torch.cuda.empty_cache()\n",
+    "        print(\"GPU memory cleared\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "566e0d3d-ba00-4e0a-a93d-26acdf3ec05f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initializing models...\n",
+      "Using device: cuda\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "81e01934d98d4408a0de9fe778fe78d4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading data...\n",
+      "Loading embeddings from Hugging Face...\n",
+      "Successfully loaded 9980 embeddings\n",
+      "Creating FAISS index...\n",
+      "Loading embeddings from Hugging Face...\n",
+      "Successfully loaded 9980 embeddings\n",
+      "Running test queries...\n",
+      "Processing query: What are the features of the Samsung Galaxy S21?\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Query type: product_info\n",
+      "Text Query: What are the features of the Samsung Galaxy S21?\n",
+      "Response: The Samsung Galaxy S21 is a high-end Android smartphone that offers advanced features such as a powerful processor, large display, long battery life, and excellent camera capabilities. It also has an IP68 rating for water resistance and supports wireless charging.\n",
+      "\n",
+      "Processing query: What are the features of the DB Longboards CoreFlex Crossbow?\n",
+      "Query type: product_info\n",
+      "Text Query: What are the features of the DB Longboards CoreFlex Crossbow?\n",
+      "Response: The DB Longboards CoreFlex Crossbow is a high-quality longboard that offers excellent performance for riders looking for an affordable option. It has a durable construction with a lightweight design, making it easy to maneuver and control while riding. Additionally, its unique shape provides stability when turning or carving at higher speeds. With its wide range of colors available, this board can be customized according to individual preferences as well!\n",
+      "\n",
+      "Processing query: Can you compare the Amazon Echo Dot with the Google Nest Mini?\n",
+      "Query type: comparison\n",
+      "Text Query: Can you compare the Amazon Echo Dot with the Google Nest Mini?\n",
+      "Response: The Amazon Echo Dot is a compact smart speaker that offers hands-free voice control through its built-in Alexa virtual assistant. It can perform various tasks such as playing music, controlling smart home devices, setting alarms or timers, providing weather updates, answering questions, etc. The device also supports far-field voice recognition technology for better understanding of commands even from across the room. Additionally, it allows users to make calls using just their voice without having to pick up their phone. On the other hand, the Google Nest Mini is another popular smart speaker which works similarly to the Echo Dot by offering similar functionalities via its own Google Assistant platform. However unlike Echo Dot's circular design; Nest Mini takes an oval shape making it more aesthetically pleasing than its competitor. Furthermore; both speakers come equipped with Bluetooth capabilities allowing them to stream audio wirelessly from compatible devices like laptops or tablets.\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image Query 1: Can you identify the product in this image and describe its usage?\n",
+      "\n",
+      "Image 1 Response:\n",
+      "The db longboards coreflex crossbow 41 bamboo fiberglass longboard is designed for both beginners and experienced riders alike. It has an adjustable height of up to 4 inches, making it suitable for various riding styles. The board also comes equipped with high-quality components such as aluminum trucks, grip tape, and bearings that ensure smooth performance while providing durability over time. Additionally, its lightweight construction makes transport easy when not in use or during travel. Finally, this model offers excellent value at just under $200 USD – perfect if you’re looking for something reliable yet affordable!\n",
+      "\n",
+      "Image 2 Response:\n",
+      "The Snap Circuits Stem Electronics Discovery Kit is an innovative STEM toy that allows children to explore the world of electronics through hands-on experimentation. This kit includes over 70 components such as resistors, capacitors, transistors, diodes, LED lights, switches, and more. With these pieces, users can create various electrical circuits using simple snaps instead of soldering or wiring. The included instruction booklet provides step-by-step instructions for building different types of circuits like flashlights, radios, doorbells, buzzers, fans, and much more! Additionally, there are also online resources available with additional projects for advanced learners who want to take their knowledge further. Overall, this kit offers endless possibilities when it comes to exploring how electricity works while having fun at the same time!\n",
+      "Processing query: Can you show me a picture of the Apple AirPods Pro?\n",
+      "Query type: image_search\n",
+      "\n",
+      "Found matching products:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Image Search Query: Can you show me a picture of the Apple AirPods Pro?\n",
+      "Response: The Apple AirPods Pro is a wireless earbud that offers active noise cancellation (ANC) technology for an immersive listening experience. It has three sizes of silicone tips to ensure a comfortable fit and secure seal for optimal sound quality. With its H1 chip, it provides faster connectivity and hands-free \"Hey Siri\" voice control. Additionally, it has sweat and water resistance, making it suitable for workouts or outdoor activities.\n",
+      "\n",
+      "Displayed Products:\n",
+      "Product 1:\n",
+      "- Name: DJI Smart Controller\n",
+      "- Price: $718.98\n",
+      "\n",
+      "Cleaning up resources...\n",
+      "GPU memory cleared\n",
+      "Testing completed\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Main execution\n",
+    "if __name__ == \"__main__\":\n",
+    "    try:\n",
+    "        print(\"Initializing models...\")\n",
+    "        initialize_models()\n",
+    "        \n",
+    "        print(\"Loading data...\")\n",
+    "        load_data()\n",
+    "        \n",
+    "        print(\"Creating FAISS index...\")\n",
+    "        text_embeddings_dict, image_embeddings_dict = load_embeddings_from_huggingface(\"chen196473/amazon_vector_database\")\n",
+    "        create_faiss_indexes(text_embeddings_dict, image_embeddings_dict)\n",
+    "        \n",
+    "        print(\"Running test queries...\")\n",
+    "        # Existing test queries remain the same\n",
+    "        text_query_1 = \"What are the features of the Samsung Galaxy S21?\"\n",
+    "        text_response_1 = chatbot(text_query_1)\n",
+    "        print(f\"Text Query: {text_query_1}\")\n",
+    "        print(f\"Response: {text_response_1}\\n\")\n",
+    "\n",
+    "        text_query_2 = \"What are the features of the DB Longboards CoreFlex Crossbow?\"\n",
+    "        text_response_2 = chatbot(text_query_2)\n",
+    "        print(f\"Text Query: {text_query_2}\")\n",
+    "        print(f\"Response: {text_response_2}\\n\")\n",
+    "\n",
+    "        text_query_3 = \"Can you compare the Amazon Echo Dot with the Google Nest Mini?\"\n",
+    "        text_response_3 = chatbot(text_query_3)\n",
+    "        print(f\"Text Query: {text_query_3}\")\n",
+    "        print(f\"Response: {text_response_3}\\n\")\n",
+    "\n",
+    "        # First image test with provided URLs\n",
+    "        image_urls_1 = [\n",
+    "            \"https://images-na.ssl-images-amazon.com/images/I/51j3fPQTQkL.jpg\",\n",
+    "            \"https://images-na.ssl-images-amazon.com/images/I/5166GD8OkXL.jpg\"\n",
+    "        ]\n",
+    "        image_query_1 = \"Can you identify the product in this image and describe its usage?\"\n",
+    "        image_responses_1 = chatbot(image_query_1, image_urls_1)\n",
+    "        print(f\"Image Query 1: {image_query_1}\")\n",
+    "        for i, response in enumerate(image_responses_1):\n",
+    "            print(f\"\\nImage {i+1} Response:\")\n",
+    "            print(response)\n",
+    "\n",
+    "        # Test image search query\n",
+    "        image_search_query = \"Can you show me a picture of the Apple AirPods Pro?\"\n",
+    "        image_search_response = chatbot(image_search_query)\n",
+    "        print(f\"\\nImage Search Query: {image_search_query}\")\n",
+    "        if isinstance(image_search_response, tuple):\n",
+    "            response, displayed_images = image_search_response\n",
+    "            print(f\"Response: {response}\")\n",
+    "            print(\"\\nDisplayed Products:\")\n",
+    "            for i, img_info in enumerate(displayed_images, 1):\n",
+    "                print(f\"Product {i}:\")\n",
+    "                print(f\"- Name: {img_info['product_name']}\")\n",
+    "                # Handle price formatting based on type\n",
+    "                if isinstance(img_info['price'], (int, float)):\n",
+    "                    print(f\"- Price: ${img_info['price']:.2f}\")\n",
+    "                else:\n",
+    "                    print(f\"- Price: {img_info['price']}\")\n",
+    "        else:\n",
+    "            print(f\"Response: {image_search_response}\")\n",
+    "\n",
+    "    finally:\n",
+    "        # Clean up resources\n",
+    "        print(\"\\nCleaning up resources...\")\n",
+    "        cleanup_resources()\n",
+    "        print(\"Testing completed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9448cac8-188b-4a0a-9294-4a2b7c366d38",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing query: Can you show me a picture of the db longboards?\n",
+      "Query type: image_search\n",
+      "\n",
+      "Found matching products:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Image Search Query: Can you show me a picture of the db longboards?\n",
+      "Response: The db longboards is a type of longboard that has been designed for both beginners and experienced riders alike. It comes with an adjustable height so it can be customized according to your needs. This board also includes a kick tail which allows you to perform tricks such as ollies or flips easily without having any prior experience in doing them beforehand! Additionally, this model boasts high quality construction materials like aluminum alloy trucks along with polyurethane wheels making sure they last longer than other brands available today while still providing excellent performance when riding down hills at speedy rates too!\n",
+      "\n",
+      "Displayed Products:\n",
+      "Product 1:\n",
+      "- Name: DB Longboards Contra Drop Deck Maple Longboard Complete\n",
+      "- Price: $80.96\n"
+     ]
+    }
+   ],
+   "source": [
+    "        # Test image search query\n",
+    "        image_search_query = \"Can you show me a picture of the db longboards?\"\n",
+    "        image_search_response = chatbot(image_search_query)\n",
+    "        print(f\"\\nImage Search Query: {image_search_query}\")\n",
+    "        if isinstance(image_search_response, tuple):\n",
+    "            response, displayed_images = image_search_response\n",
+    "            print(f\"Response: {response}\")\n",
+    "            print(\"\\nDisplayed Products:\")\n",
+    "            for i, img_info in enumerate(displayed_images, 1):\n",
+    "                print(f\"Product {i}:\")\n",
+    "                print(f\"- Name: {img_info['product_name']}\")\n",
+    "                # Handle price formatting based on type\n",
+    "                if isinstance(img_info['price'], (int, float)):\n",
+    "                    print(f\"- Price: ${img_info['price']:.2f}\")\n",
+    "                else:\n",
+    "                    print(f\"- Price: {img_info['price']}\")\n",
+    "        else:\n",
+    "            print(f\"Response: {image_search_response}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c6fed8a-5c27-47e5-bec8-0a8cab54f833",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d22ca15-756f-43d2-bb75-5385adae356f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "environment": {
+   "kernel": "python3",
+   "name": ".m125",
+   "type": "gcloud",
+   "uri": "us-docker.pkg.dev/deeplearning-platform-release/gcr.io/:m125"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (Local)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}