azure-ai-search-vector-compression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Azure AI Search Vector Index Compression Configurations\n",
    "This notebook demonstrates different compression configurations for vector search indexes and their impact on storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install azure-search-documents==11.6.0b8\n",
    "!pip install datasets\n",
    "!pip install tabulate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import requests\n",
    "from azure.core.credentials import AzureKeyCredential\n",
    "from azure.identity import DefaultAzureCredential\n",
    "from azure.search.documents import SearchClient\n",
    "from azure.search.documents.indexes import SearchIndexClient\n",
    "from azure.search.documents.indexes.models import (\n",
    "    AzureOpenAIModelName,\n",
    "    AzureOpenAIVectorizer,\n",
    "    AzureOpenAIVectorizerParameters,\n",
    "    BinaryQuantizationCompression,\n",
    "    HnswAlgorithmConfiguration,\n",
    "    HnswParameters,\n",
    "    ScalarQuantizationCompression,\n",
    "    ScalarQuantizationParameters,\n",
    "    SearchField,\n",
    "    SearchFieldDataType,\n",
    "    SearchIndex,\n",
    "    SearchableField,\n",
    "    SimpleField,\n",
    "    VectorSearch,\n",
    "    VectorSearchAlgorithmKind,\n",
    "    VectorSearchProfile,\n",
    "    VectorSearchCompressionKind,\n",
    "    VectorSearchCompressionTarget,\n",
    "    VectorSearchCompression,\n",
    "    VectorSearchCompressionRescoreStorageMethod,\n",
    "    RescoringOptions\n",
    ")\n",
    "\n",
    "from azure.search.documents import SearchIndexingBufferedSender\n",
    "\n",
    "from azure.search.documents.models import VectorizableTextQuery, VectorizedQuery\n",
    "from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set Configuration Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load environment variables from .env file\n",
    "load_dotenv()\n",
    "\n",
    "# Azure Search Configuration\n",
    "SERVICE_ENDPOINT = os.getenv(\"AZURE_SEARCH_SERVICE_ENDPOINT\")\n",
    "ADMIN_KEY = os.getenv(\"AZURE_SEARCH_ADMIN_KEY\")\n",
    "INDEX_PREFIX = \"compression-test\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector Search Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vector Search Parameters\n",
    "VECTOR_DIMENSIONS = 3072  # OpenAI text-embedding-3-large embedding dimensions\n",
    "HNSW_CONFIG = {\n",
    "    \"m\": 4,\n",
    "    \"metric\": \"cosine\",\n",
    "    \"ef_construction\": 400,\n",
    "    \"ef_search\": 500\n",
    "}\n",
    "OVERSAMPLE_RATIO = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Index Manager Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AzureSearchIndexManager:\n",
    "    def __init__(self, service_endpoint: str, admin_key: str, index_name_prefix: str):\n",
    "        self.credential = AzureKeyCredential(admin_key)\n",
    "        self.client = SearchIndexClient(endpoint=service_endpoint, credential=self.credential)\n",
    "        self.index_name_prefix = index_name_prefix\n",
    "\n",
    "    def _create_base_fields(self, stored_embedding=True):\n",
    "        return [\n",
    "            SimpleField(name=\"id\", type=SearchFieldDataType.String, key=True),\n",
    "            SearchField(name=\"title\", type=SearchFieldDataType.String, searchable=True),\n",
    "            SearchField(name=\"content\", type=SearchFieldDataType.String, searchable=True),\n",
    "            SearchField(\n",
    "                name=\"embedding\",\n",
    "                type=SearchFieldDataType.Collection(SearchFieldDataType.Single),\n",
    "                searchable=True,\n",
    "                vector_search_dimensions=VECTOR_DIMENSIONS,\n",
    "                vector_search_profile_name=\"default-profile\",\n",
    "                stored=stored_embedding,  # Use the 'stored_embedding' parameter\n",
    "            ),\n",
    "        ]\n",
    "\n",
    "    def _create_compression_config(\n",
    "        self,\n",
    "        config_type: str,\n",
    "        truncate_dims: int = None,\n",
    "        discard_originals: bool = False,\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Creates a compression configuration based on the scenario.\n",
    "        \"\"\"\n",
    "        compression_name = f\"{config_type}-compression\"\n",
    "\n",
    "        # Determine the storage method based on whether originals are discarded\n",
    "        rescore_storage_method = (\n",
    "            VectorSearchCompressionRescoreStorageMethod.DISCARD_ORIGINALS\n",
    "            if discard_originals\n",
    "            else VectorSearchCompressionRescoreStorageMethod.PRESERVE_ORIGINALS\n",
    "        )\n",
    "\n",
    "        # Enable rescoring only if originals are preserved\n",
    "        enable_rescoring = not discard_originals\n",
    "\n",
    "        # Configure rescoring options\n",
    "        rescoring_options = RescoringOptions(\n",
    "            enable_rescoring=enable_rescoring,\n",
    "            default_oversampling=OVERSAMPLE_RATIO if enable_rescoring else None,\n",
    "            rescore_storage_method=rescore_storage_method,\n",
    "        )\n",
    "\n",
    "        # Base parameters for compression\n",
    "        base_params = {\n",
    "            \"compression_name\": compression_name,\n",
    "            \"rescoring_options\": rescoring_options,\n",
    "            # Explicitly set deprecated parameters to None\n",
    "            \"rerank_with_original_vectors\": None,\n",
    "            \"default_oversampling\": None,\n",
    "        }\n",
    "\n",
    "        # Add truncation dimension if specified\n",
    "        if truncate_dims:\n",
    "            base_params[\"truncation_dimension\"] = truncate_dims\n",
    "\n",
    "        # Create the appropriate compression object\n",
    "        if config_type == \"scalar\":\n",
    "            compression = ScalarQuantizationCompression(\n",
    "                parameters=ScalarQuantizationParameters(quantized_data_type=\"int8\"),\n",
    "                **base_params,\n",
    "            )\n",
    "        elif config_type == \"binary\":\n",
    "            compression = BinaryQuantizationCompression(\n",
    "                **base_params,\n",
    "            )\n",
    "        else:\n",
    "            compression = None\n",
    "\n",
    "        return compression\n",
    "\n",
    "    def _create_vector_search_config(self, compression_config=None):\n",
    "        \"\"\"\n",
    "        Creates the VectorSearch configuration, including algorithm and compression settings.\n",
    "        \"\"\"\n",
    "        # Define the HNSW algorithm configuration\n",
    "        algorithm_config = HnswAlgorithmConfiguration(\n",
    "            name=\"hnsw-config\",\n",
    "            kind=\"hnsw\",\n",
    "            parameters=HnswParameters(**HNSW_CONFIG),\n",
    "        )\n",
    "\n",
    "        # Define the VectorSearchProfile\n",
    "        profiles = [\n",
    "            VectorSearchProfile(\n",
    "                name=\"default-profile\",\n",
    "                algorithm_configuration_name=algorithm_config.name,\n",
    "                compression_name=compression_config.compression_name if compression_config else None,\n",
    "            )\n",
    "        ]\n",
    "\n",
    "        # Assemble the VectorSearch configuration\n",
    "        vector_search = VectorSearch(\n",
    "            profiles=profiles,\n",
    "            algorithms=[algorithm_config],\n",
    "            compressions=[compression_config] if compression_config else None,\n",
    "        )\n",
    "\n",
    "        return vector_search\n",
    "\n",
    "    def create_index(self, scenario: dict):\n",
    "        \"\"\"\n",
    "        Creates or updates an index based on the provided scenario.\n",
    "        \"\"\"\n",
    "        index_name = f\"{self.index_name_prefix}-{scenario['name']}\"\n",
    "\n",
    "        # Use the 'stored_embedding' value from the scenario\n",
    "        stored_embedding = scenario.get('stored_embedding', True)\n",
    "\n",
    "        # Create base fields with the stored_embedding flag\n",
    "        fields = self._create_base_fields(stored_embedding=stored_embedding)\n",
    "\n",
    "        # Create compression configuration if specified\n",
    "        compression_config = None\n",
    "        if scenario[\"compression_type\"]:\n",
    "            compression_config = self._create_compression_config(\n",
    "                config_type=scenario[\"compression_type\"],\n",
    "                truncate_dims=scenario.get(\"truncate_dims\"),\n",
    "                discard_originals=scenario.get(\"discard_originals\", False),\n",
    "            )\n",
    "\n",
    "        # Create vector search configuration\n",
    "        vector_search = self._create_vector_search_config(compression_config)\n",
    "\n",
    "        # Define the SearchIndex\n",
    "        index = SearchIndex(\n",
    "            name=index_name,\n",
    "            fields=fields,\n",
    "            vector_search=vector_search,\n",
    "        )\n",
    "\n",
    "        # Create or update the index\n",
    "        self.client.create_or_update_index(index)\n",
    "        print(f\"Successfully created index: {index_name}\")\n",
    "\n",
    "        # Return the index name\n",
    "        return index_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.search.documents.indexes.models import (\n",
    "    HnswAlgorithmConfiguration,\n",
    "    HnswParameters,\n",
    "    SearchField,\n",
    "    SearchFieldDataType,\n",
    "    SearchIndex,\n",
    "    SimpleField,\n",
    "    VectorSearch,\n",
    "    VectorSearchAlgorithmKind,\n",
    "    VectorSearchProfile,\n",
    "    VectorSearchCompression,\n",
    "    BinaryQuantizationCompression,\n",
    ")\n",
    "\n",
    "AZURE_SEARCH_TINY_INDEX = \"teenytinyindex\"\n",
    "azure_credential = AzureKeyCredential(ADMIN_KEY)\n",
    "index_client = SearchIndexClient(endpoint=SERVICE_ENDPOINT, credential=azure_credential)\n",
    "\n",
    "\n",
    "index = SearchIndex(\n",
    "    name=AZURE_SEARCH_TINY_INDEX,\n",
    "    fields=[\n",
    "        SimpleField(name=\"id\", type=SearchFieldDataType.String, key=True),\n",
    "        SearchField(\n",
    "            name=\"embedding\",\n",
    "            type=SearchFieldDataType.Collection(SearchFieldDataType.Single),\n",
    "            searchable=True,\n",
    "            vector_search_dimensions=3,\n",
    "            vector_search_profile_name=\"embedding_profile\",\n",
    "            stored=False\n",
    "        ),\n",
    "    ],\n",
    "    vector_search=VectorSearch(\n",
    "        algorithms=[\n",
    "            HnswAlgorithmConfiguration(\n",
    "                name=\"hnsw_config\",\n",
    "                kind=VectorSearchAlgorithmKind.HNSW,\n",
    "                parameters=HnswParameters(metric=\"cosine\"),\n",
    "            )\n",
    "        ],\n",
    "        profiles=[\n",
    "            VectorSearchProfile(\n",
    "                name=\"embedding_profile\", algorithm_configuration_name=\"hnsw_config\", compression_name=\"binary_compression\"\n",
    "            )\n",
    "        ],\n",
    "        compressions=[\n",
    "            BinaryQuantizationCompression(\n",
    "                compression_name=\"binary_compression\",\n",
    "                rescoring_options=RescoringOptions(\n",
    "                    enable_rescoring=False,\n",
    "                    rescore_storage_method=VectorSearchCompressionRescoreStorageMethod.DISCARD_ORIGINALS,\n",
    "                ),\n",
    "            )\n",
    "        ],\n",
    "    ),\n",
    ")\n",
    "\n",
    "index_client.create_index(index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Test Scenarios\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenarios = [\n",
    "    {\n",
    "        \"name\": \"baseline\",\n",
    "        \"compression_type\": None,\n",
    "        \"truncate_dims\": None,\n",
    "        \"discard_originals\": False,\n",
    "        \"stored_embedding\": True,\n",
    "        \"description\": \"Baseline configuration without compression\"\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"baseline-s\",\n",
    "        \"compression_type\": None,\n",
    "        \"truncate_dims\": None,\n",
    "        \"discard_originals\": False,\n",
    "        \"stored_embedding\": False,\n",
    "        \"description\": \"Baseline configuration with stored=False without compression\"\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"scalar-full\",\n",
    "        \"compression_type\": \"scalar\",\n",
    "        \"truncate_dims\": None,\n",
    "        \"discard_originals\": False,\n",
    "        \"stored_embedding\": False,\n",
    "        \"description\": \"Scalar quantization (int8) with preserved originals\"\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"scalar-truncated-1024\",\n",
    "        \"compression_type\": \"scalar\",\n",
    "        \"truncate_dims\": 1024,\n",
    "        \"discard_originals\": False,\n",
    "        \"stored_embedding\": False,\n",
    "        \"description\": \"Scalar quantization (int8) with 1024 dimensions\"\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"scalar-truncated-1024-discard\",\n",
    "        \"compression_type\": \"scalar\",\n",
    "        \"truncate_dims\": 1024,\n",
    "        \"discard_originals\": True,\n",
    "        \"stored_embedding\": False,\n",
    "        \"description\": \"Scalar quantization (int8) with 1024 dimensions, discarded originals\"\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"binary-full\",\n",
    "        \"compression_type\": \"binary\",\n",
    "        \"truncate_dims\": None,\n",
    "        \"discard_originals\": False,\n",
    "        \"stored_embedding\": False,\n",
    "        \"description\": \"Binary quantization with preserved originals\"\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"binary-truncated-1024\",\n",
    "        \"compression_type\": \"binary\",\n",
    "        \"truncate_dims\": 1024,\n",
    "        \"discard_originals\": False,\n",
    "        \"stored_embedding\": False,\n",
    "        \"description\": \"Binary quantization with 1024 dimensions, preserved originals\"\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"binary-truncated-1024-discard\",\n",
    "        \"compression_type\": \"binary\",\n",
    "        \"truncate_dims\": 1024,\n",
    "        \"discard_originals\": True,\n",
    "        \"stored_embedding\": False,\n",
    "        \"description\": \"Binary quantization with 1024 dimensions, discarded originals\"\n",
    "    }\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute Index Creation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully created index: compression-test-baseline-s\n",
      "\n",
      "Created Indexes:\n",
      "                 index_name                              configuration\n",
      "compression-test-baseline-s Baseline configuration without compression\n"
     ]
    }
   ],
   "source": [
    "manager = AzureSearchIndexManager(SERVICE_ENDPOINT, ADMIN_KEY, INDEX_PREFIX)\n",
    "\n",
    "# Create all index configurations\n",
    "created_indexes = []\n",
    "for scenario in scenarios:\n",
    "    try:\n",
    "        index_name = manager.create_index(scenario)\n",
    "        created_indexes.append({\n",
    "            \"index_name\": index_name,\n",
    "            \"configuration\": scenario[\"description\"]\n",
    "        })\n",
    "    except Exception as e:\n",
    "        print(f\"Error creating index for scenario {scenario['name']}: {str(e)}\")\n",
    "\n",
    "# Display created indexes\n",
    "results_df = pd.DataFrame(created_indexes)\n",
    "if not results_df.empty:\n",
    "    print(\"\\nCreated Indexes:\")\n",
    "    print(results_df.to_string(index=False))\n",
    "else:\n",
    "    print(\"\\nNo indexes were created successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading dataset in streaming mode...\n"
     ]
    },
    {
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      },
      "text/plain": [
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      ]
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     "metadata": {},
     "output_type": "display_data"
    },
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taking first 100K examples...\n",
      "Processed 0 examples...\n",
      "Converting to parquet...\n",
      "Dataset saved to dbpedia_100k.parquet\n",
      "\n",
      "Saved dataset shape: 100000 rows × 4 columns\n",
      "Columns: ['_id', 'title', 'text', 'text-embedding-3-large-3072-embedding']\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "import pyarrow.parquet as pq\n",
    "import pyarrow as pa  # Added this import for Table\n",
    "\n",
    "# Load dataset in streaming mode and select only needed columns\n",
    "print(\"Loading dataset in streaming mode...\")\n",
    "ds = load_dataset(\n",
    "    \"Qdrant/dbpedia-entities-openai3-text-embedding-3-large-3072-1M\",\n",
    "    streaming=True,\n",
    "    split='train'\n",
    ")\n",
    "\n",
    "# Select only needed columns\n",
    "ds = ds.select_columns([\"_id\", \"title\", \"text\", \"text-embedding-3-large-3072-embedding\"])\n",
    "\n",
    "# Take first 100K examples\n",
    "print(\"Taking first 100K examples...\")\n",
    "data = []\n",
    "for i, example in enumerate(ds):\n",
    "    if i >= 100000:\n",
    "        break\n",
    "    data.append(example)\n",
    "    if i % 100000 == 0:  # Progress indicator\n",
    "        print(f\"Processed {i} examples...\")\n",
    "\n",
    "# Convert to Arrow table and save to parquet\n",
    "print(\"Converting to parquet...\")\n",
    "output_file = \"dbpedia_100k.parquet\"\n",
    "table = pa.Table.from_pylist(data)  # Using pa.Table instead of pq.Table\n",
    "pq.write_table(\n",
    "    table,\n",
    "    output_file,\n",
    "    compression='snappy'  # Good balance of compression and speed\n",
    ")\n",
    "\n",
    "print(f\"Dataset saved to {output_file}\")\n",
    "\n",
    "# Verify the saved file\n",
    "table = pq.read_table(output_file)\n",
    "print(f\"\\nSaved dataset shape: {table.num_rows} rows × {table.num_columns} columns\")\n",
    "print(\"Columns:\", table.column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset Information:\n",
      "Total number of rows: 100000\n",
      "Columns: ['_id', 'title', 'text', 'text-embedding-3-large-3072-embedding']\n",
      "\n",
      "First document structure:\n",
      "{\n",
      "  \"id\": \"<dbpedia:Parabolic_reflector>\",\n",
      "  \"title\": \"Parabolic reflector\",\n",
      "  \"content\": \"A parabolic (or paraboloid or paraboloidal) reflector (or dish or mirror) is a reflective surface used to collect or project energy such as light, sound, or radio waves. Its shape is part of a circular paraboloid, that is, the surface generated by a parabola revolving around its axis. The parabolic reflector transforms an incoming plane wave traveling along the axis into a spherical wave converging toward the focus.\",\n",
      "  \"embedding\": [\n",
      "    -0.022388368844985962,\n",
      "    0.028537113219499588,\n",
      "    -0.017491688951849937,\n",
      "    -0.01533466950058937,\n",
      "    -0.03451230376958847,\n",
      "    0.0162644200026989,\n",
      "    -0.020231351256370544,\n",
      "    -0.012743767350912094,\n",
      "    -0.032082557678222656,\n",
      "    0.0037468906957656145,\n",
      "    -0.003864658996462822,\n",
      "    0.004292343743145466,\n",
      "    0.007035105023533106,\n",
      "    -0.03314867243170738,\n",
      "    0.0015302128158509731,\n",
      "    0.027074307203292847,\n",
      "    0.02011978067457676,\n",
      "    -0.0034772634971886873,\n",
      "    0.02200407348573208,\n",
      "    -0.008039234206080437,\n",
      "    -0.0002686588850338012,\n",
      "    -0.037809815257787704,\n",
      "    -0.06664445251226425,\n",
      "    -0.017181772738695145,\n",
      "    -0.01554541289806366,\n",
      "    0.015322272665798664,\n",
      "    0.026256127282977104,\n",
      "    -0.03215693682432175,\n",
      "    -0.0459420271217823,\n",
      "    -0.02085118368268013,\n",
      "    0.05697505548596382,\n",
      "    0.00940906535834074,\n",
      "    0.008659067563712597,\n",
      "    -0.02898339368402958,\n",
      "    -0.03329743072390556,\n",
      "    -0.013140460476279259,\n",
      "    -0.025834642350673676,\n",
      "    -0.04946267604827881,\n",
      "    -0.026206541806459427,\n",
      "    0.0009258756181225181,\n",
      "    -0.012285090982913971,\n",
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      "    0.028636286035180092,\n",
      "    0.013822277076542377,\n",
      "    -0.007035105023533106,\n",
      "    0.008981380611658096,\n",
      "    0.00926650408655405,\n",
      "    -0.004360525403171778,\n",
      "    0.005411142483353615,\n",
      "    0.0018409041222184896,\n",
      "    0.00040521586197428405,\n",
      "    0.0044937897473573685,\n",
      "    0.009694188833236694,\n",
      "    -0.018855322152376175,\n",
      "    -0.017528878524899483,\n",
      "    -0.024966875091195107,\n",
      "    0.03314867243170738,\n",
      "    -0.028313973918557167,\n",
      "    -0.013797483406960964\n",
      "  ]\n",
      "}\n",
      "\n",
      "Data Statistics:\n",
      "Embedding dimension: 3072\n",
      "Average title length: 18.4 characters\n",
      "Average text length: 326.0 characters\n",
      "\n",
      "Checking for null values:\n",
      "_id: 0 null values\n",
      "title: 0 null values\n",
      "text: 0 null values\n",
      "text-embedding-3-large-3072-embedding: 0 null values\n"
     ]
    }
   ],
   "source": [
    "import pyarrow.parquet as pq\n",
    "import json\n",
    "\n",
    "# Read the parquet file\n",
    "table = pq.read_table(\"dbpedia_100k.parquet\")\n",
    "\n",
    "# Convert first row to the exact format we need\n",
    "first_doc = {\n",
    "    \"id\": str(table['_id'][0].as_py()),\n",
    "    \"title\": table['title'][0].as_py(),\n",
    "    \"content\": table['text'][0].as_py(),\n",
    "    \"embedding\": table['text-embedding-3-large-3072-embedding'][0].as_py()\n",
    "}\n",
    "\n",
    "# Print info about the dataset\n",
    "print(\"Dataset Information:\")\n",
    "print(f\"Total number of rows: {table.num_rows}\")\n",
    "print(f\"Columns: {table.column_names}\")\n",
    "print(\"\\nFirst document structure:\")\n",
    "print(json.dumps(first_doc, indent=2, default=str))\n",
    "\n",
    "# Print some basic stats about the data\n",
    "print(\"\\nData Statistics:\")\n",
    "print(f\"Embedding dimension: {len(first_doc['embedding'])}\")\n",
    "print(f\"Average title length: {sum(len(str(title)) for title in table['title']) / table.num_rows:.1f} characters\")\n",
    "print(f\"Average text length: {sum(len(str(text)) for text in table['text']) / table.num_rows:.1f} characters\")\n",
    "\n",
    "# Verify there are no null values\n",
    "print(\"\\nChecking for null values:\")\n",
    "for column in table.column_names:\n",
    "    null_count = table[column].null_count\n",
    "    print(f\"{column}: {null_count} null values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azure.search.documents import SearchClient\n",
    "from azure.core.credentials import AzureKeyCredential\n",
    "import pyarrow.parquet as pq\n",
    "from datetime import datetime\n",
    "from typing import List, Dict, Any\n",
    "import time\n",
    "import json\n",
    "import base64\n",
    "import pyarrow.parquet as pq\n",
    "from datetime import datetime\n",
    "from typing import List, Dict, Any\n",
    "import json\n",
    "\n",
    "\n",
    "def chunk_list(lst: List[Any], chunk_size: int) -> List[List[Any]]:\n",
    "    \"\"\"Split a list into chunks of specified size.\"\"\"\n",
    "    return [lst[i : i + chunk_size] for i in range(0, len(lst), chunk_size)]\n",
    "\n",
    "\n",
    "def encode_key(key: str) -> str:\n",
    "    \"\"\"Encode key to be Azure Search compatible using URL-safe base64.\"\"\"\n",
    "    return base64.urlsafe_b64encode(key.encode()).decode()\n",
    "\n",
    "\n",
    "def prepare_documents(table) -> List[Dict]:\n",
    "    \"\"\"\n",
    "    Convert Arrow table to list of documents with base64 encoded IDs.\n",
    "    \"\"\"\n",
    "    documents = []\n",
    "    total_rows = table.num_rows\n",
    "\n",
    "    print(f\"Converting {total_rows} rows to documents...\")\n",
    "    for i in range(total_rows):\n",
    "        # Encode the ID to make it Azure Search compatible\n",
    "        original_id = str(table[\"_id\"][i].as_py())\n",
    "        encoded_id = encode_key(original_id)\n",
    "\n",
    "        document = {\n",
    "            \"id\": encoded_id,\n",
    "            \"title\": table[\"title\"][i].as_py(),\n",
    "            \"content\": table[\"text\"][i].as_py(),\n",
    "            \"embedding\": table[\"text-embedding-3-large-3072-embedding\"][i].as_py(),\n",
    "        }\n",
    "        documents.append(document)\n",
    "\n",
    "        if i % 1000 == 0:  # Progress indicator\n",
    "            print(f\"Processed {i}/{total_rows} documents...\")\n",
    "\n",
    "    print(\"Document conversion complete\")\n",
    "\n",
    "    # Print first document as sample (including both original and encoded ID)\n",
    "    print(\"\\nSample document format:\")\n",
    "    sample_doc = documents[0].copy()\n",
    "    print(\"Original ID:\", original_id)\n",
    "    print(\"Encoded ID:\", sample_doc[\"id\"])\n",
    "    print(json.dumps(sample_doc, indent=2, default=str))\n",
    "\n",
    "    return documents\n",
    "\n",
    "\n",
    "def upload_to_search(\n",
    "    documents: List[Dict],\n",
    "    endpoint: str,\n",
    "    index_name: str,\n",
    "    credential: AzureKeyCredential,\n",
    "    batch_size: int = 100,\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Upload documents to Azure Search index using manual batching.\n",
    "    \"\"\"\n",
    "    # Create search client\n",
    "    search_client = SearchClient(\n",
    "        endpoint=endpoint, index_name=index_name, credential=credential\n",
    "    )\n",
    "\n",
    "    total_docs = len(documents)\n",
    "    print(f\"\\nStarting upload to index: {index_name} at {datetime.now()}\")\n",
    "    print(f\"Total documents to upload: {total_docs}\")\n",
    "    start_time = datetime.now()\n",
    "\n",
    "    # Split documents into batches\n",
    "    batches = chunk_list(documents, batch_size)\n",
    "    total_batches = len(batches)\n",
    "\n",
    "    successful_docs = 0\n",
    "    failed_docs = 0\n",
    "\n",
    "    try:\n",
    "        for batch_num, batch in enumerate(batches, 1):\n",
    "            max_retries = 3\n",
    "            retry_count = 0\n",
    "\n",
    "            while retry_count < max_retries:\n",
    "                try:\n",
    "                    results = search_client.upload_documents(documents=batch)\n",
    "\n",
    "                    # Count successes and failures\n",
    "                    for result in results:\n",
    "                        if result.succeeded:\n",
    "                            successful_docs += 1\n",
    "                        else:\n",
    "                            failed_docs += 1\n",
    "                            print(\n",
    "                                f\"Failed to upload document {result.key}: {result.error}\"\n",
    "                            )\n",
    "\n",
    "                    elapsed_time = datetime.now() - start_time\n",
    "                    print(\n",
    "                        f\"Index {index_name}: Processed batch {batch_num}/{total_batches} \"\n",
    "                        f\"({successful_docs}/{total_docs} docs). \"\n",
    "                        f\"Elapsed time: {elapsed_time}\"\n",
    "                    )\n",
    "\n",
    "                    # Short pause between batches to prevent throttling\n",
    "                    time.sleep(0.25)\n",
    "                    break  # Success - exit retry loop\n",
    "\n",
    "                except Exception as e:\n",
    "                    retry_count += 1\n",
    "                    if retry_count == max_retries:\n",
    "                        print(\n",
    "                            f\"Failed to upload batch after {max_retries} attempts: {str(e)}\"\n",
    "                        )\n",
    "                        failed_docs += len(batch)\n",
    "                    else:\n",
    "                        print(\n",
    "                            f\"Retry {retry_count}/{max_retries} after error: {str(e)}\"\n",
    "                        )\n",
    "                        time.sleep(2**retry_count)  # Exponential backoff\n",
    "\n",
    "        total_time = datetime.now() - start_time\n",
    "        print(f\"\\nUpload to {index_name} completed:\")\n",
    "        print(f\"Successfully uploaded: {successful_docs} documents\")\n",
    "        print(f\"Failed to upload: {failed_docs} documents\")\n",
    "        print(f\"Total time: {total_time}\")\n",
    "\n",
    "        # Verify final count\n",
    "        result = search_client.search(\"*\", top=0)\n",
    "        final_count = result.get_count()\n",
    "        print(f\"Final document count in index: {final_count}\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"Fatal error during upload to {index_name}: {str(e)}\")\n",
    "        raise\n",
    "\n",
    "\n",
    "def upload_to_all_indexes(\n",
    "    documents: List[Dict],\n",
    "    endpoint: str,\n",
    "    index_prefix: str,\n",
    "    scenarios: list,\n",
    "    credential: AzureKeyCredential,\n",
    "    batch_size: int = 100,\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Upload documents to all indexes sequentially.\n",
    "    \"\"\"\n",
    "    total_start_time = datetime.now()\n",
    "\n",
    "    for i, scenario in enumerate(scenarios, 1):\n",
    "        index_name = f\"{index_prefix}-{scenario['name']}\"\n",
    "        print(f\"\\nProcessing index {i} of {len(scenarios)}: {index_name}\")\n",
    "        upload_to_search(\n",
    "            documents=documents,\n",
    "            endpoint=endpoint,\n",
    "            index_name=index_name,\n",
    "            credential=credential,\n",
    "            batch_size=batch_size,\n",
    "        )\n",
    "\n",
    "    total_time = datetime.now() - total_start_time\n",
    "    print(f\"\\nCompleted all uploads. Total time: {total_time}\")\n",
    "\n",
    "\n",
    "# Usage:\n",
    "print(\"Loading parquet file...\")\n",
    "table = pq.read_table(\"dbpedia_100k.parquet\")\n",
    "print(f\"Loaded {table.num_rows} rows\")\n",
    "\n",
    "# Prepare documents once\n",
    "documents = prepare_documents(table)\n",
    "\n",
    "# Create credential and upload to all indexes\n",
    "credential = AzureKeyCredential(ADMIN_KEY)\n",
    "upload_to_all_indexes(\n",
    "    documents=documents,\n",
    "    endpoint=SERVICE_ENDPOINT,\n",
    "    index_prefix=INDEX_PREFIX,\n",
    "    scenarios=scenarios,\n",
    "    credential=credential,\n",
    "    batch_size=100,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting 30 seconds for index statistics to update...\n",
      "\n",
      "Gathering index statistics...\n",
      "Note: There may be delays in finding index statistics after document upload\n",
      "Index statistics is not a real-time API\n",
      "\n",
      "\n",
      "Index Storage Statistics:\n",
      "\n",
      "+------------------------------------------------+-------------------------------+---------------------+-------------------------+--------------------+------------------------+\n",
      "| Index Name                                     | Scenario                      |   Storage Size (MB) |   Storage Reduction (%) |   Vector Size (MB) |   Vector Reduction (%) |\n",
      "+================================================+===============================+=====================+=========================+====================+========================+\n",
      "| compression-test-baseline                      | baseline                      |            3813.29  |                    0    |          1177.15   |                   0    |\n",
      "+------------------------------------------------+-------------------------------+---------------------+-------------------------+--------------------+------------------------+\n",
      "| compression-test-baseline-s                    | baseline-s                    |            1305.12  |                   65.77 |          1177.12   |                   0    |\n",
      "+------------------------------------------------+-------------------------------+---------------------+-------------------------+--------------------+------------------------+\n",
      "| compression-test-scalar-full                   | scalar-full                   |            1606.84  |                   57.86 |           300.603  |                  74.46 |\n",
      "+------------------------------------------------+-------------------------------+---------------------+-------------------------+--------------------+------------------------+\n",
      "| compression-test-scalar-truncated-1024         | scalar-truncated-1024         |            1409.21  |                   63.04 |           103.769  |                  91.18 |\n",
      "+------------------------------------------------+-------------------------------+---------------------+-------------------------+--------------------+------------------------+\n",
      "| compression-test-binary-full                   | binary-full                   |            1347.59  |                   64.66 |            41.8804 |                  96.44 |\n",
      "+------------------------------------------------+-------------------------------+---------------------+-------------------------+--------------------+------------------------+\n",
      "| compression-test-binary-truncated-1024         | binary-truncated-1024         |            1323.33  |                   65.3  |            17.4668 |                  98.52 |\n",
      "+------------------------------------------------+-------------------------------+---------------------+-------------------------+--------------------+------------------------+\n",
      "| compression-test-scalar-truncated-1024-discard | scalar-truncated-1024-discard |             232.592 |                   93.9  |           102.906  |                  91.26 |\n",
      "+------------------------------------------------+-------------------------------+---------------------+-------------------------+--------------------+------------------------+\n",
      "| compression-test-binary-truncated-1024-discard | binary-truncated-1024-discard |             145.436 |                   96.19 |            17.4491 |                  98.52 |\n",
      "+------------------------------------------------+-------------------------------+---------------------+-------------------------+--------------------+------------------------+\n"
     ]
    }
   ],
   "source": [
    "from azure.search.documents.indexes import SearchIndexClient\n",
    "from azure.core.credentials import AzureKeyCredential\n",
    "import time\n",
    "from tabulate import tabulate \n",
    "\n",
    "def bytes_to_mb(bytes):\n",
    "    \"\"\"Convert bytes to megabytes with 4 decimal places\"\"\"\n",
    "    return round(bytes / (1024 * 1024), 4)\n",
    "\n",
    "def get_index_sizes(\n",
    "    endpoint: str,\n",
    "    index_prefix: str,\n",
    "    scenarios: list,\n",
    "    credential: AzureKeyCredential,\n",
    "    retry_attempts: int = 3\n",
    ") -> None:\n",
    "    \"\"\"\n",
    "    Get and print storage sizes for all indexes, with retry logic for eventual consistency.\n",
    "    \"\"\"\n",
    "    # Create search index client\n",
    "    search_index_client = SearchIndexClient(endpoint=endpoint, credential=credential)\n",
    "    \n",
    "    print(\"\\nGathering index statistics...\")\n",
    "    print(\"Note: There may be delays in finding index statistics after document upload\")\n",
    "    print(\"Index statistics is not a real-time API\\n\")\n",
    "    \n",
    "    # Collect all index sizes with retries\n",
    "    index_data = []\n",
    "    for scenario in scenarios:\n",
    "        index_name = f\"{index_prefix}-{scenario['name']}\"\n",
    "        \n",
    "        for attempt in range(retry_attempts):\n",
    "            try:\n",
    "                stats = search_index_client.get_index_statistics(index_name)\n",
    "                storage_size = bytes_to_mb(stats[\"storage_size\"])\n",
    "                vector_size = bytes_to_mb(stats[\"vector_index_size\"])\n",
    "                total_size = storage_size + vector_size\n",
    "                index_data.append({\n",
    "                    'Index Name': index_name,\n",
    "                    'Scenario': scenario['name'],\n",
    "                    'Storage Size (MB)': storage_size,\n",
    "                    'Vector Size (MB)': vector_size,\n",
    "                    'Total Size (MB)': total_size,  # Added for sorting\n",
    "                })\n",
    "                break\n",
    "            except Exception as e:\n",
    "                if attempt == retry_attempts - 1:\n",
    "                    print(f\"Failed to get statistics for {index_name} after {retry_attempts} attempts: {str(e)}\")\n",
    "                else:\n",
    "                    print(f\"Retry {attempt + 1}/{retry_attempts} for {index_name}\")\n",
    "                    time.sleep(2 ** attempt)  # Exponential backoff\n",
    "    \n",
    "    # Find baseline storage and vector sizes\n",
    "    baseline_entry = next((entry for entry in index_data if entry['Scenario'] == 'baseline'), None)\n",
    "    if not baseline_entry:\n",
    "        print(\"Baseline scenario not found.\")\n",
    "        return\n",
    "    baseline_storage_size = baseline_entry['Storage Size (MB)']\n",
    "    baseline_vector_size = baseline_entry['Vector Size (MB)']\n",
    "    \n",
    "    # Compute reduction percentages compared to baseline\n",
    "    for entry in index_data:\n",
    "        storage_reduction_pct = ((baseline_storage_size - entry['Storage Size (MB)']) / baseline_storage_size) * 100\n",
    "        vector_reduction_pct = ((baseline_vector_size - entry['Vector Size (MB)']) / baseline_vector_size) * 100\n",
    "        entry['Storage Reduction (%)'] = f\"{storage_reduction_pct:.2f}\"\n",
    "        entry['Vector Reduction (%)'] = f\"{vector_reduction_pct:.2f}\"\n",
    "    \n",
    "    # Sort by total size\n",
    "    index_data.sort(key=lambda x: x['Total Size (MB)'], reverse=True)\n",
    "    \n",
    "    # Prepare table headers and rows\n",
    "    headers = [\n",
    "        'Index Name', 'Scenario', 'Storage Size (MB)', 'Storage Reduction (%)',\n",
    "        'Vector Size (MB)', 'Vector Reduction (%)'\n",
    "    ]\n",
    "    table_rows = [\n",
    "        [\n",
    "            entry['Index Name'],\n",
    "            entry['Scenario'],\n",
    "            f\"{entry['Storage Size (MB)']:.4f}\",\n",
    "            entry['Storage Reduction (%)'],\n",
    "            f\"{entry['Vector Size (MB)']:.4f}\",\n",
    "            entry['Vector Reduction (%)']\n",
    "        ]\n",
    "        for entry in index_data\n",
    "    ]\n",
    "    \n",
    "    # Print the table using tabulate\n",
    "    print(\"\\nIndex Storage Statistics:\\n\")\n",
    "    print(tabulate(table_rows, headers=headers, tablefmt=\"grid\"))\n",
    "    \n",
    "\n",
    "# Usage:\n",
    "# Add a delay after uploading documents to allow for index statistics to update\n",
    "print(\"Waiting 30 seconds for index statistics to update...\")\n",
    "time.sleep(30)\n",
    "\n",
    "# Get and print index sizes\n",
    "get_index_sizes(\n",
    "    endpoint=SERVICE_ENDPOINT,\n",
    "    index_prefix=INDEX_PREFIX,\n",
    "    scenarios=scenarios,\n",
    "    credential=credential\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Index Name                                     | Scenario                      | Storage Size (MB) | Storage Reduction (%) | Vector Size (MB) | Vector Reduction (%) |\n",
    "|------------------------------------------------|-------------------------------|-------------------|-----------------------|------------------|----------------------|\n",
    "| compression-test-baseline                      | baseline                      | 3813.29           | 0                     | 1177.15          | 0                    |\n",
    "| compression-test-baseline-s                    | baseline-s                    | 1305.12           | 65.77                 | 1177.12          | 0                    |\n",
    "| compression-test-scalar-full                   | scalar-full                   | 1606.84           | 57.86                 | 300.603          | 74.46                |\n",
    "| compression-test-scalar-truncated-1024         | scalar-truncated-1024         | 1409.21           | 63.04                 | 103.769          | 91.18                |\n",
    "| compression-test-binary-full                   | binary-full                   | 1347.59           | 64.66                 | 41.8804          | 96.44                |\n",
    "| compression-test-binary-truncated-1024         | binary-truncated-1024         | 1323.33           | 65.3                  | 17.4668          | 98.52                |\n",
    "| compression-test-scalar-truncated-1024-discard | scalar-truncated-1024-discard | 232.592           | 93.9                  | 102.906          | 91.26                |\n",
    "| compression-test-binary-truncated-1024-discard | binary-truncated-1024-discard | 145.436           | 96.19                 | 17.4491          | 98.52                |\n"
   ]
  }
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