diff --git a/docs/experimental/Binary Quantization from Scratch.ipynb b/docs/experimental/Binary Quantization from Scratch.ipynb
index 5a3369c..1463c49 100644
--- a/docs/experimental/Binary Quantization from Scratch.ipynb	
+++ b/docs/experimental/Binary Quantization from Scratch.ipynb	
@@ -14,23 +14,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-06T17:00:06.460001Z",
+     "start_time": "2024-06-06T17:00:04.214098Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "!pip install matplotlib tqdm pandas numpy --quiet"
+    "!pip install matplotlib tqdm pandas numpy datasets --quiet --upgrade"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-06T17:00:07.041784Z",
+     "start_time": "2024-06-06T17:00:06.461658Z"
+    },
     "id": "WBVTItUX4yyr"
    },
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
+    "from datasets import load_dataset\n",
     "from tqdm import tqdm"
    ]
   },
@@ -52,8 +62,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-06T17:01:09.343230Z",
+     "start_time": "2024-06-06T17:00:07.042526Z"
+    },
     "colab": {
      "base_uri": "https://localhost:8080/",
      "height": 250
@@ -61,58 +75,24 @@
     "id": "REJpFqkG7EG2",
     "outputId": "7a43c0ae-fbcc-45fe-fd58-bfe691297b22"
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 26/26 [00:10<00:00,  2.45it/s]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(1000000, 1536)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "def get_openai_vectors(force_download: bool = False):\n",
-    "    res = []\n",
-    "    for i in tqdm(range(26)):\n",
-    "        if force_download:\n",
-    "            !wget https://huggingface.co/api/datasets/KShivendu/dbpedia-entities-openai-1M/parquet/KShivendu--dbpedia-entities-openai-1M/train/{i}.parquet\n",
-    "        df = pd.read_parquet(f\"{i}.parquet\", engine=\"pyarrow\")\n",
-    "        res.append(np.stack(df.openai))\n",
-    "        del df\n",
-    "\n",
-    "    openai_vectors = np.concatenate(res)\n",
-    "    del res\n",
-    "    return openai_vectors\n",
-    "\n",
-    "\n",
-    "openai_vectors = get_openai_vectors(force_download=False)\n",
-    "openai_vectors.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## ㆓ Binary Conversion\n",
-    "\n",
-    "Here, we will use 0 as the threshold for the binary conversion. All values greater than 0 will be set to 1, and others will remain 0. This is a simple and effective way to convert continuous values into binary values for OpenAI embeddings."
+    "# Download from Huggingface Hub\n",
+    "ds = load_dataset(\n",
+    "    \"Qdrant/dbpedia-entities-openai3-text-embedding-3-large-3072-100K\", split=\"train\"\n",
+    ")\n",
+    "openai_vectors = np.array(ds[\"text-embedding-3-large-3072-embedding\"])\n",
+    "del ds"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 4,
    "metadata": {
-    "id": "0JM2-Bj2Jkab"
+    "ExecuteTime": {
+     "end_time": "2024-06-06T17:01:10.900963Z",
+     "start_time": "2024-06-06T17:01:09.344842Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -120,6 +100,30 @@
     "openai_bin[openai_vectors > 0] = 1"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-06T17:01:10.906827Z",
+     "start_time": "2024-06-06T17:01:10.901820Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "3072"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_dim = openai_vectors.shape[1]\n",
+    "n_dim"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -131,8 +135,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-06T17:01:10.909730Z",
+     "start_time": "2024-06-06T17:01:10.908166Z"
+    },
     "id": "FqshI-GlIERd"
    },
    "outputs": [],
@@ -141,7 +149,7 @@
     "    scores = np.dot(openai_vectors, openai_vectors[idx])\n",
     "    dot_results = np.argsort(scores)[-limit:][::-1]\n",
     "\n",
-    "    bin_scores = 1536 - np.logical_xor(openai_bin, openai_bin[idx]).sum(axis=1)\n",
+    "    bin_scores = n_dim - np.logical_xor(openai_bin, openai_bin[idx]).sum(axis=1)\n",
     "    bin_results = np.argsort(bin_scores)[-(limit * oversampling) :][::-1]\n",
     "\n",
     "    return len(set(dot_results).intersection(set(bin_results))) / limit"
@@ -156,8 +164,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-06T17:01:25.206592Z",
+     "start_time": "2024-06-06T17:01:10.911971Z"
+    },
     "colab": {
      "base_uri": "https://localhost:8080/"
     },
@@ -169,110 +181,128 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "  0%|          | 0/4 [00:00<?, ?it/s]"
+      "  0%|          | 0/4 [00:00<?, ?it/s]\n",
+      "  0%|          | 0/2 [00:00<?, ?it/s]\u001b[A\n",
+      " 50%|█████     | 1/2 [00:02<00:02,  2.05s/it]\u001b[A"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'sampling_rate': 1, 'limit': 10, 'recall': 0.8}\n"
+      "{'sampling_rate': 1, 'limit': 3, 'mean_acc': 0.9}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2/2 [00:33<00:00, 16.98s/it]\n",
-      " 25%|██▌       | 1/4 [00:33<01:41, 33.96s/it]"
+      "\n",
+      "100%|██████████| 2/2 [00:04<00:00,  2.02s/it]\u001b[A\n",
+      " 25%|██▌       | 1/4 [00:04<00:12,  4.05s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'sampling_rate': 1, 'limit': 100, 'recall': 0.708}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'sampling_rate': 2, 'limit': 10, 'recall': 0.95}\n"
+      "{'sampling_rate': 1, 'limit': 10, 'mean_acc': 0.8300000000000001}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2/2 [00:32<00:00, 16.38s/it]\n",
-      " 50%|█████     | 2/4 [01:06<01:06, 33.26s/it]"
+      "\n",
+      "  0%|          | 0/2 [00:00<?, ?it/s]\u001b[A\n",
+      " 50%|█████     | 1/2 [00:01<00:01,  1.72s/it]\u001b[A"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'sampling_rate': 2, 'limit': 100, 'recall': 0.877}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'sampling_rate': 3, 'limit': 10, 'recall': 0.96}\n"
+      "{'sampling_rate': 2, 'limit': 3, 'mean_acc': 1.0}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2/2 [00:32<00:00, 16.49s/it]\n",
-      " 75%|███████▌  | 3/4 [01:39<00:33, 33.13s/it]"
+      "\n",
+      "100%|██████████| 2/2 [00:03<00:00,  1.76s/it]\u001b[A\n",
+      " 50%|█████     | 2/4 [00:07<00:07,  3.75s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'sampling_rate': 3, 'limit': 100, 'recall': 0.937}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": []
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'sampling_rate': 5, 'limit': 10, 'recall': 0.9800000000000001}\n"
+      "{'sampling_rate': 2, 'limit': 10, 'mean_acc': 0.9700000000000001}\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2/2 [00:32<00:00, 16.47s/it]\n",
-      "100%|██████████| 4/4 [02:12<00:00, 33.17s/it]"
+      "\n",
+      "  0%|          | 0/2 [00:00<?, ?it/s]\u001b[A\n",
+      " 50%|█████     | 1/2 [00:01<00:01,  1.72s/it]\u001b[A"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'sampling_rate': 5, 'limit': 100, 'recall': 0.977}\n"
+      "{'sampling_rate': 3, 'limit': 3, 'mean_acc': 1.0}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "100%|██████████| 2/2 [00:03<00:00,  1.69s/it]\u001b[A\n",
+      " 75%|███████▌  | 3/4 [00:10<00:03,  3.58s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 3, 'limit': 10, 'mean_acc': 0.9800000000000001}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "  0%|          | 0/2 [00:00<?, ?it/s]\u001b[A\n",
+      " 50%|█████     | 1/2 [00:01<00:01,  1.68s/it]\u001b[A"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 5, 'limit': 3, 'mean_acc': 1.0}\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "100%|██████████| 2/2 [00:03<00:00,  1.65s/it]\u001b[A\n",
+      "100%|██████████| 4/4 [00:14<00:00,  3.57s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'sampling_rate': 5, 'limit': 10, 'mean_acc': 0.99}\n"
      ]
     },
     {
@@ -285,119 +315,53 @@
    ],
    "source": [
     "number_of_samples = 10\n",
-    "limits = [10, 100]\n",
+    "limits = [3, 10]\n",
     "sampling_rate = [1, 2, 3, 5]\n",
     "results = []\n",
     "\n",
     "\n",
     "def mean_accuracy(number_of_samples, limit, sampling_rate):\n",
-    "    return np.mean([accuracy(i, limit=limit, oversampling=sampling_rate) for i in range(number_of_samples)])\n",
+    "    return np.mean(\n",
+    "        [accuracy(i, limit=limit, oversampling=sampling_rate) for i in range(number_of_samples)]\n",
+    "    )\n",
     "\n",
     "\n",
     "for i in tqdm(sampling_rate):\n",
     "    for j in tqdm(limits):\n",
-    "        result = {\"sampling_rate\": i, \"limit\": j, \"recall\": mean_accuracy(number_of_samples, j, i)}\n",
+    "        result = {\n",
+    "            \"sampling_rate\": i,\n",
+    "            \"limit\": j,\n",
+    "            \"mean_acc\": mean_accuracy(number_of_samples, j, i),\n",
+    "        }\n",
     "        print(result)\n",
     "        results.append(result)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 19,
+   "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "## ㆓ Binary Conversion\n",
+    "\n",
+    "Here, we will use 0 as the threshold for the binary conversion. All values greater than 0 will be set to 1, and others will remain 0. This is a simple and effective way to convert continuous values into binary values for OpenAI embeddings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-06T17:01:25.247495Z",
+     "start_time": "2024-06-06T17:01:25.213508Z"
+    }
+   },
    "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>sampling_rate</th>\n",
-       "      <th>limit</th>\n",
-       "      <th>recall</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0.800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>100</td>\n",
-       "      <td>0.708</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0.950</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2</td>\n",
-       "      <td>100</td>\n",
-       "      <td>0.877</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0.960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>3</td>\n",
-       "      <td>100</td>\n",
-       "      <td>0.937</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0.980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>5</td>\n",
-       "      <td>100</td>\n",
-       "      <td>0.977</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sampling_rate  limit  recall\n",
-       "0              1     10   0.800\n",
-       "1              1    100   0.708\n",
-       "2              2     10   0.950\n",
-       "3              2    100   0.877\n",
-       "4              3     10   0.960\n",
-       "5              3    100   0.937\n",
-       "6              5     10   0.980\n",
-       "7              5    100   0.977"
-      ]
+      "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>sampling_rate</th>\n      <th>limit</th>\n      <th>mean_acc</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>3</td>\n      <td>0.90</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>10</td>\n      <td>0.83</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>3</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2</td>\n      <td>10</td>\n      <td>0.97</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>3</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>3</td>\n      <td>10</td>\n      <td>0.98</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>5</td>\n      <td>3</td>\n      <td>1.00</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>5</td>\n      <td>10</td>\n      <td>0.99</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
+      "text/plain": "   sampling_rate  limit  mean_acc\n0              1      3      0.90\n1              1     10      0.83\n2              2      3      1.00\n3              2     10      0.97\n4              3      3      1.00\n5              3     10      0.98\n6              5      3      1.00\n7              5     10      0.99"
      },
-     "execution_count": 19,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -408,22 +372,13 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "| sampling_rate | limit | accuracy |\n",
-    "|---------------|-------|----------|\n",
-    "| 1             | 10    | 0.800    |\n",
-    "| 1             | 100   | 0.708    |\n",
-    "| 2             | 10    | 0.950    |\n",
-    "| 2             | 100   | 0.877    |\n",
-    "| 4             | 10    | 0.970    |\n",
-    "| 4             | 100   | 0.956    |\n",
-    "| 8             | 10    | 0.990    |\n",
-    "| 8             | 100   | 0.990    |\n",
-    "| 16            | 10    | 1.000    |\n",
-    "| 16            | 100   | 0.998    |"
-   ]
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -432,7 +387,8 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
@@ -445,7 +401,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,
diff --git a/docs/qdrant/Binary_Quantization_with_Qdrant.ipynb b/docs/qdrant/Binary_Quantization_with_Qdrant.ipynb
index b6d8b5c..cf1f5de 100644
--- a/docs/qdrant/Binary_Quantization_with_Qdrant.ipynb
+++ b/docs/qdrant/Binary_Quantization_with_Qdrant.ipynb
@@ -36,8 +36,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:33:03.013948Z",
-     "start_time": "2024-04-01T16:33:01.019043Z"
+     "end_time": "2024-06-06T18:31:54.357040Z",
+     "start_time": "2024-06-06T18:31:52.672431Z"
     }
    },
    "outputs": [],
@@ -50,25 +50,17 @@
    "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:33:03.914729Z",
-     "start_time": "2024-04-01T16:33:03.015394Z"
+     "end_time": "2024-06-06T18:31:55.482034Z",
+     "start_time": "2024-06-06T18:31:54.357645Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/joein/work/qdrant/fastembed/venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "import random\n",
     "import time\n",
     "\n",
+    "import datasets\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "from qdrant_client import QdrantClient, models\n",
@@ -91,8 +83,8 @@
    "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:33:09.085853Z",
-     "start_time": "2024-04-01T16:33:03.912688Z"
+     "end_time": "2024-06-06T18:31:58.405581Z",
+     "start_time": "2024-06-06T18:31:55.471027Z"
     }
    },
    "outputs": [
@@ -106,8 +98,6 @@
     }
    ],
    "source": [
-    "import datasets\n",
-    "\n",
     "dataset = datasets.load_dataset(\n",
     "    \"Qdrant/dbpedia-entities-openai3-text-embedding-3-small-1536-100K\", split=\"train\"\n",
     ")\n",
@@ -119,14 +109,14 @@
    "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:33:09.176212Z",
-     "start_time": "2024-04-01T16:33:09.084550Z"
+     "end_time": "2024-06-06T18:32:24.456211Z",
+     "start_time": "2024-06-06T18:31:58.396924Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "True"
+      "text/plain": "1536"
      },
      "execution_count": 4,
      "metadata": {},
@@ -134,15 +124,40 @@
     }
    ],
    "source": [
-    "client = QdrantClient(\n",
+    "n_dim = len(dataset[\"text-embedding-3-small-1536-embedding\"][0])\n",
+    "n_dim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-06T18:32:24.536891Z",
+     "start_time": "2024-06-06T18:32:24.455087Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "True"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "client = QdrantClient(  # assumes Qdrant is launched at localhost:6333\n",
     "    prefer_grpc=True,\n",
     ")\n",
     "\n",
     "collection_name = \"binary-quantization\"\n",
-    "client.recreate_collection(\n",
+    "\n",
+    "client.create_collection(\n",
     "    collection_name=collection_name,\n",
     "    vectors_config=models.VectorParams(\n",
-    "        size=1536,\n",
+    "        size=n_dim,\n",
     "        distance=models.Distance.DOT,\n",
     "        on_disk=True,\n",
     "    ),\n",
@@ -154,11 +169,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:34:13.383986Z",
-     "start_time": "2024-04-01T16:33:09.175725Z"
+     "end_time": "2024-06-06T18:33:22.583349Z",
+     "start_time": "2024-06-06T18:32:24.815155Z"
     }
    },
    "outputs": [],
@@ -179,19 +194,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:34:13.390886Z",
-     "start_time": "2024-04-01T16:34:13.385961Z"
+     "end_time": "2024-06-06T18:35:47.748898Z",
+     "start_time": "2024-06-06T18:35:47.740719Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "text/plain": "{'status': <CollectionStatus.YELLOW: 'yellow'>,\n 'optimizer_status': <OptimizersStatusOneOf.OK: 'ok'>,\n 'vectors_count': 116640,\n 'indexed_vectors_count': 43520,\n 'points_count': 116640,\n 'segments_count': 6,\n 'config': {'params': {'vectors': {'size': 1536,\n    'distance': <Distance.DOT: 'Dot'>,\n    'hnsw_config': None,\n    'quantization_config': None,\n    'on_disk': True},\n   'shard_number': 1,\n   'sharding_method': None,\n   'replication_factor': 1,\n   'write_consistency_factor': 1,\n   'read_fan_out_factor': None,\n   'on_disk_payload': True,\n   'sparse_vectors': None},\n  'hnsw_config': {'m': 16,\n   'ef_construct': 100,\n   'full_scan_threshold': 10000,\n   'max_indexing_threads': 0,\n   'on_disk': False,\n   'payload_m': None},\n  'optimizer_config': {'deleted_threshold': 0.2,\n   'vacuum_min_vector_number': 1000,\n   'default_segment_number': 0,\n   'max_segment_size': None,\n   'memmap_threshold': None,\n   'indexing_threshold': 20000,\n   'flush_interval_sec': 5,\n   'max_optimization_threads': None},\n  'wal_config': {'wal_capacity_mb': 32, 'wal_segments_ahead': 0},\n  'quantization_config': {'binary': {'always_ram': True}}},\n 'payload_schema': {}}"
+      "text/plain": "{'status': <CollectionStatus.GREEN: 'green'>,\n 'optimizer_status': <OptimizersStatusOneOf.OK: 'ok'>,\n 'vectors_count': None,\n 'indexed_vectors_count': 97760,\n 'points_count': 100000,\n 'segments_count': 7,\n 'config': {'params': {'vectors': {'size': 1536,\n    'distance': <Distance.DOT: 'Dot'>,\n    'hnsw_config': None,\n    'quantization_config': None,\n    'on_disk': True,\n    'datatype': None},\n   'shard_number': 1,\n   'sharding_method': None,\n   'replication_factor': 1,\n   'write_consistency_factor': 1,\n   'read_fan_out_factor': None,\n   'on_disk_payload': True,\n   'sparse_vectors': None},\n  'hnsw_config': {'m': 16,\n   'ef_construct': 100,\n   'full_scan_threshold': 10000,\n   'max_indexing_threads': 0,\n   'on_disk': False,\n   'payload_m': None},\n  'optimizer_config': {'deleted_threshold': 0.2,\n   'vacuum_min_vector_number': 1000,\n   'default_segment_number': 0,\n   'max_segment_size': None,\n   'memmap_threshold': None,\n   'indexing_threshold': 20000,\n   'flush_interval_sec': 5,\n   'max_optimization_threads': None},\n  'wal_config': {'wal_capacity_mb': 32, 'wal_segments_ahead': 0},\n  'quantization_config': {'binary': {'always_ram': True}}},\n 'payload_schema': {}}"
      },
-     "execution_count": 6,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,11 +229,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:34:13.453626Z",
-     "start_time": "2024-04-01T16:34:13.391567Z"
+     "end_time": "2024-06-06T18:33:22.643845Z",
+     "start_time": "2024-06-06T18:33:22.589346Z"
     }
    },
    "outputs": [
@@ -226,7 +241,7 @@
      "data": {
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      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -239,11 +254,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:34:13.453928Z",
-     "start_time": "2024-04-01T16:34:13.452405Z"
+     "end_time": "2024-06-06T18:33:22.644389Z",
+     "start_time": "2024-06-06T18:33:22.642909Z"
     }
    },
    "outputs": [],
@@ -257,11 +272,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:34:13.457839Z",
-     "start_time": "2024-04-01T16:34:13.455431Z"
+     "end_time": "2024-06-06T18:33:22.647634Z",
+     "start_time": "2024-06-06T18:33:22.646107Z"
     }
    },
    "outputs": [],
@@ -290,11 +305,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:40:48.347002Z",
-     "start_time": "2024-04-01T16:40:42.228551Z"
+     "end_time": "2024-06-06T18:36:09.037565Z",
+     "start_time": "2024-06-06T18:35:58.069484Z"
     },
     "collapsed": false
    },
@@ -302,7 +317,7 @@
    "source": [
     "limit_grid = [1, 3, 10, 20, 50]\n",
     "oversampling_grid = [1.0, 3.0, 5.0]\n",
-    "rescore_grid = [False, True]\n",
+    "rescore_grid = [True, False]\n",
     "results = []\n",
     "\n",
     "for limit in limit_grid:\n",
@@ -317,7 +332,7 @@
     "                {\n",
     "                    \"limit\": limit,\n",
     "                    \"oversampling\": oversampling,\n",
-    "                    \"candidates\": int(oversampling * limit),\n",
+    "                    \"bq_candidates\": int(oversampling * limit),\n",
     "                    \"rescore\": rescore,\n",
     "                    \"accuracy\": correct_results / 100,\n",
     "                    \"total queries\": len(query_dataset[\"text\"]),\n",
@@ -328,29 +343,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 15,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2024-04-01T16:41:55.445405Z",
-     "start_time": "2024-04-01T16:41:55.442687Z"
+     "end_time": "2024-06-06T18:36:09.054593Z",
+     "start_time": "2024-06-06T18:36:09.048053Z"
     }
    },
    "outputs": [
     {
      "data": {
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-      "text/plain": "    candidates  rescore  accuracy      time\n0            1    False      0.90  0.221826\n1            1     True      0.91  0.134167\n2            3    False      0.88  0.115299\n3            3     True      0.97  0.209320\n4            5    False      0.84  0.154485\n5            5     True      0.91  0.124424\n6            3    False      0.99  0.121695\n7            3     True      0.96  0.123257\n8            9    False      0.94  0.119629\n9            9     True      0.98  0.119372\n10          15    False      0.90  0.121621\n11          15     True      0.97  0.125466\n12          10    False      0.93  0.135910\n13          10     True      0.95  0.138135\n14          30    False      0.94  0.177928\n15          30     True      0.98  0.254588\n16          50    False      0.94  0.268659\n17          50     True      0.96  0.269792\n18          20    False      0.96  0.249941\n19          20     True      0.96  0.247138\n20          60    False      0.97  0.251301\n21          60     True      0.98  0.256504\n22         100    False      0.98  0.270049\n23         100     True      0.97  0.248972\n24          50    False      0.97  0.306356\n25          50     True      0.98  0.257544\n26         150    False      0.98  0.238811\n27         150     True      0.99  0.263939\n28         250    False      0.99  0.256558\n29         250     True      1.00  0.335823"
+      "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>limit</th>\n      <th>oversampling</th>\n      <th>rescore</th>\n      <th>accuracy</th>\n      <th>bq_candidates</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>True</td>\n      <td>0.95</td>\n      <td>1</td>\n      <td>0.300152</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>False</td>\n      <td>0.85</td>\n      <td>1</td>\n      <td>0.244668</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>3.0</td>\n      <td>True</td>\n      <td>0.95</td>\n      <td>3</td>\n      <td>0.124406</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>3.0</td>\n      <td>False</td>\n      <td>0.83</td>\n      <td>3</td>\n      <td>0.171471</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>5.0</td>\n      <td>True</td>\n      <td>0.98</td>\n      <td>5</td>\n      <td>0.118219</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1</td>\n      <td>5.0</td>\n      <td>False</td>\n      <td>0.87</td>\n      <td>5</td>\n      <td>0.111914</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>3</td>\n      <td>1.0</td>\n      <td>True</td>\n      <td>0.95</td>\n      <td>3</td>\n      <td>0.121328</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>3</td>\n      <td>1.0</td>\n      <td>False</td>\n      <td>0.92</td>\n      <td>3</td>\n      <td>0.267725</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>3</td>\n      <td>3.0</td>\n      <td>True</td>\n      <td>0.96</td>\n      <td>9</td>\n      <td>0.416834</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>3</td>\n      <td>3.0</td>\n      <td>False</td>\n      <td>0.90</td>\n      <td>9</td>\n      <td>0.410730</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>3</td>\n      <td>5.0</td>\n      <td>True</td>\n      <td>0.97</td>\n      <td>15</td>\n      <td>0.231671</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>3</td>\n      <td>5.0</td>\n      <td>False</td>\n      <td>0.93</td>\n      <td>15</td>\n      <td>0.252269</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>10</td>\n      <td>1.0</td>\n      <td>True</td>\n      <td>0.96</td>\n      <td>10</td>\n      <td>0.133462</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>10</td>\n      <td>1.0</td>\n      <td>False</td>\n      <td>0.92</td>\n      <td>10</td>\n      <td>0.285158</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>10</td>\n      <td>3.0</td>\n      <td>True</td>\n      <td>0.95</td>\n      <td>30</td>\n      <td>0.320695</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>10</td>\n      <td>3.0</td>\n      <td>False</td>\n      <td>0.98</td>\n      <td>30</td>\n      <td>0.457904</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>10</td>\n      <td>5.0</td>\n      <td>True</td>\n      <td>0.96</td>\n      <td>50</td>\n      <td>0.453204</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>10</td>\n      <td>5.0</td>\n      <td>False</td>\n      <td>0.94</td>\n      <td>50</td>\n      <td>0.450944</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>20</td>\n      <td>1.0</td>\n      <td>True</td>\n      <td>0.97</td>\n      <td>20</td>\n      <td>0.361066</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>20</td>\n      <td>1.0</td>\n      <td>False</td>\n      <td>0.95</td>\n      <td>20</td>\n      <td>0.585992</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>20</td>\n      <td>3.0</td>\n      <td>True</td>\n      <td>0.96</td>\n      <td>60</td>\n      <td>0.550389</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>20</td>\n      <td>3.0</td>\n      <td>False</td>\n      <td>0.96</td>\n      <td>60</td>\n      <td>0.618630</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>20</td>\n      <td>5.0</td>\n      <td>True</td>\n      <td>1.00</td>\n      <td>100</td>\n      <td>0.458241</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>20</td>\n      <td>5.0</td>\n      <td>False</td>\n      <td>0.95</td>\n      <td>100</td>\n      <td>0.441106</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>50</td>\n      <td>1.0</td>\n      <td>True</td>\n      <td>0.98</td>\n      <td>50</td>\n      <td>0.603967</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>50</td>\n      <td>1.0</td>\n      <td>False</td>\n      <td>0.96</td>\n      <td>50</td>\n      <td>0.514531</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>50</td>\n      <td>3.0</td>\n      <td>True</td>\n      <td>1.00</td>\n      <td>150</td>\n      <td>0.548153</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>50</td>\n      <td>3.0</td>\n      <td>False</td>\n      <td>0.98</td>\n      <td>150</td>\n      <td>0.608930</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>50</td>\n      <td>5.0</td>\n      <td>True</td>\n      <td>1.00</td>\n      <td>250</td>\n      <td>0.487522</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>50</td>\n      <td>5.0</td>\n      <td>False</td>\n      <td>0.99</td>\n      <td>250</td>\n      <td>0.313810</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
+      "text/plain": "    limit  oversampling  rescore  accuracy  bq_candidates      time\n0       1           1.0     True      0.95              1  0.300152\n1       1           1.0    False      0.85              1  0.244668\n2       1           3.0     True      0.95              3  0.124406\n3       1           3.0    False      0.83              3  0.171471\n4       1           5.0     True      0.98              5  0.118219\n5       1           5.0    False      0.87              5  0.111914\n6       3           1.0     True      0.95              3  0.121328\n7       3           1.0    False      0.92              3  0.267725\n8       3           3.0     True      0.96              9  0.416834\n9       3           3.0    False      0.90              9  0.410730\n10      3           5.0     True      0.97             15  0.231671\n11      3           5.0    False      0.93             15  0.252269\n12     10           1.0     True      0.96             10  0.133462\n13     10           1.0    False      0.92             10  0.285158\n14     10           3.0     True      0.95             30  0.320695\n15     10           3.0    False      0.98             30  0.457904\n16     10           5.0     True      0.96             50  0.453204\n17     10           5.0    False      0.94             50  0.450944\n18     20           1.0     True      0.97             20  0.361066\n19     20           1.0    False      0.95             20  0.585992\n20     20           3.0     True      0.96             60  0.550389\n21     20           3.0    False      0.96             60  0.618630\n22     20           5.0     True      1.00            100  0.458241\n23     20           5.0    False      0.95            100  0.441106\n24     50           1.0     True      0.98             50  0.603967\n25     50           1.0    False      0.96             50  0.514531\n26     50           3.0     True      1.00            150  0.548153\n27     50           3.0    False      0.98            150  0.608930\n28     50           5.0     True      1.00            250  0.487522\n29     50           5.0    False      0.99            250  0.313810"
      },
-     "execution_count": 22,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "df = pd.DataFrame(results)\n",
-    "df[[\"candidates\", \"rescore\", \"accuracy\", \"time\"]]\n",
+    "\n",
+    "df[[\"limit\", \"oversampling\", \"rescore\", \"accuracy\", \"bq_candidates\", \"time\"]]\n",
     "# df.to_csv(\"candidates-rescore-time.csv\", index=False)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "#### Why results for oversampling=1.0 and limit=1 with rescore=True are better than with rescore=False? \n",
+    "\n",
+    "It might seem that with oversampling=1.0 and limit=1 Qdrant retrieves only 1 point, and it does not matter whether we rescore it or not, it should stay the same, but with a different score (from original vectors).\n",
+    "\n",
+    "But in fact, there are 2 reasons why results are different:\n",
+    "1) HNSW is an approximate algorithm, and it might return different results for the same query. \n",
+    "2) Qdrant stores points in segments. When we do a query for 1 point, Qdrant looks for this one point in each segment, and then chooses the best match. \n",
+    "3) In this example we had 8 segments, Qdrant found 8 points with binary scores, replaced their scores with original vectors scores, and selected the best one from them, which led to a better accuracy. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {