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 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Autofaiss Index selection demo slider"
   ],
   "metadata": {
    "id": "HE9ihpV2p6CH"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "# Install autofaiss\n",
    "!pip install autofaiss &> /dev/null"
   ],
   "outputs": [],
   "metadata": {
    "id": "Ln9cJfKZp5dl"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "## Import automatic index selection function\n",
    "from autofaiss.external.optimize import get_optimal_index_keys_v2\n",
    "from autofaiss.external.metadata import IndexMetadata"
   ],
   "outputs": [],
   "metadata": {
    "id": "KXn8v2hbp-7K"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "#@title Demo\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from ipywidgets import HBox, Label\n",
    "\n",
    "continuous_update = True\n",
    "\n",
    "output = widgets.Output()\n",
    "nb_vectors = widgets.FloatLogSlider(value = 10000, min=1, max=9, description=\"\", readout_format=\".0f\", continuous_update=continuous_update)\n",
    "dim_vectors = widgets.FloatLogSlider(base=2, value = 128, min=0, max=12, description=\"\", readout_format=\".0f\", continuous_update=continuous_update)\n",
    "max_index_memory_usage = widgets.FloatLogSlider(value = 100, min=0, max=5, description=\"\", readout_format=\".0f\", continuous_update=continuous_update)\n",
    "\n",
    "def get_text():\n",
    "    _nb_vectors = round(nb_vectors.value)\n",
    "    _dim_vectors = round(dim_vectors.value)\n",
    "    m = round(max_index_memory_usage.value)\n",
    "    _max_index_memory_usage = f\"{m}MB\"# if m < 1000 else f\"{m/1000}GB\"\n",
    "\n",
    "    s = \"\"\n",
    "    #s += f\"nb_vectors={_nb_vectors}, dim_vectors={_dim_vectors}, max_index_memory_usage={_max_index_memory_usage}\\n\"\n",
    "    opti_keys = get_optimal_index_keys_v2(_nb_vectors, _dim_vectors, _max_index_memory_usage)\n",
    "\n",
    "    if not opti_keys:\n",
    "        s += 'Impossible to find index parameters for such a large dataset and low memory'\n",
    "    else:\n",
    "        index_metadada = IndexMetadata(opti_keys[0], _nb_vectors, _dim_vectors)\n",
    "        \n",
    "        s += f\"The optimal index would be: {opti_keys[0]}\"\n",
    "        s+= \"\\n\\n\"\n",
    "        s+= index_metadada.get_index_description(tunable_parameters_infos=True)\n",
    "\n",
    "    return s\n",
    "\n",
    "text = widgets.Textarea(\n",
    "    value=str(get_text()),\n",
    "    placeholder='...',\n",
    "    description='Result:',\n",
    "    disabled=False,\n",
    "    rows = 50,\n",
    "    continuous_update=False,\n",
    "    layout={'width': '100%'}\n",
    ")\n",
    "\n",
    "def update(b):\n",
    "  # Display the message within the output widget.\n",
    "  with output:\n",
    "    text.value = get_text()\n",
    "\n",
    "nb_vectors.observe(update)\n",
    "dim_vectors.observe(update)\n",
    "max_index_memory_usage.observe(update)\n",
    "\n",
    "display(HBox([Label('Number of vectors in the dataset'), nb_vectors]))\n",
    "display(HBox([Label('Dimension of vectors in the dataset'), dim_vectors]))\n",
    "display(HBox([Label('Max size of index in MB'), max_index_memory_usage]))\n",
    "display(output)\n",
    "display(text)"
   ],
   "outputs": [
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     "output_type": "display_data",
     "data": {
      "text/plain": [
       "HBox(children=(Label(value='Number of vectors in the dataset'), FloatLogSlider(value=10000.0, max=9.0, min=1.0…"
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       "Output()"
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       "Textarea(value='The optimal index would be: HNSW32\\n\\nAn HNSW index.\\nThe size of the index should be around 7…"
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