{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9db3b813-22dc-4209-86d2-42e935f5f5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.document_loaders.csv_loader import CSVLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "import pandas as pd\n",
    "import langchain\n",
    "import os\n",
    "import openai\n",
    "import ast\n",
    "from langchain import OpenAI\n",
    "from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain.document_loaders import UnstructuredURLLoader\n",
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "from langchain.vectorstores import FAISS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fb48dccd-37e5-484a-a2fc-c482839b9ed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loader = CSVLoader(file_path=\"trials/brief_summaries.csv\")\n",
    "# data = loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cdcc3107-e6a8-47ca-bd89-e381fbcf9b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df= pd.read_csv(\"trials/brief_summaries.txt\",  delimiter=\"|\")\n",
    "# df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c6a94c14-b197-4eff-9941-1ca52069cd5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95c000ff-bf0c-4489-8643-93a238db41dc",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e2e03936-fcce-4287-bfe6-e31f1b69f693",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/aldan.creo/miniconda3/envs/hackupc/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:119: LangChainDeprecationWarning: The class `OpenAI` was deprecated in LangChain 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAI`.\n",
      "  warn_deprecated(\n"
     ]
    }
   ],
   "source": [
    "llm= OpenAI(\n",
    "    temperature=0.6, max_tokens=500\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aede3d75-2441-44f6-b3cf-2f86f050da24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(440517, 2)\n"
     ]
    },
    {
     "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>desease_condition</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['marijuana abuse', 'substance-related disorde...</td>\n",
       "      <td>nct_id: NCT03055377\\nsummary: This is a 12-wee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['marijuana abuse', 'substance-related disorde...</td>\n",
       "      <td>nct_id: NCT03055377\\nsummary: This is a 12-wee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['tuberculosis', 'latent tuberculosis', 'infec...</td>\n",
       "      <td>nct_id: NCT03042754\\nsummary: Early diagnosis ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['heart failure', 'heart diseases', 'cardiovas...</td>\n",
       "      <td>nct_id: NCT03035123\\nsummary: The EduStra-HF s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['lymphoma', 'neoplasms by histologic type', '...</td>\n",
       "      <td>nct_id: NCT02272751\\nsummary: This study will ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   desease_condition  \\\n",
       "0  ['marijuana abuse', 'substance-related disorde...   \n",
       "1  ['marijuana abuse', 'substance-related disorde...   \n",
       "2  ['tuberculosis', 'latent tuberculosis', 'infec...   \n",
       "3  ['heart failure', 'heart diseases', 'cardiovas...   \n",
       "4  ['lymphoma', 'neoplasms by histologic type', '...   \n",
       "\n",
       "                                                text  \n",
       "0  nct_id: NCT03055377\\nsummary: This is a 12-wee...  \n",
       "1  nct_id: NCT03055377\\nsummary: This is a 12-wee...  \n",
       "2  nct_id: NCT03042754\\nsummary: Early diagnosis ...  \n",
       "3  nct_id: NCT03035123\\nsummary: The EduStra-HF s...  \n",
       "4  nct_id: NCT02272751\\nsummary: This study will ...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trials= pd.read_csv(\"clinical_trials.csv\")\n",
    "print(df_trials.shape)\n",
    "df_trials.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "65589fc4-4f55-4b72-9935-c3b163fde0e2",
   "metadata": {},
   "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>desease_condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['marijuana abuse', 'substance-related disorde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['marijuana abuse', 'substance-related disorde...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['tuberculosis', 'latent tuberculosis', 'infec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['heart failure', 'heart diseases', 'cardiovas...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['lymphoma', 'neoplasms by histologic type', '...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   desease_condition\n",
       "0  ['marijuana abuse', 'substance-related disorde...\n",
       "1  ['marijuana abuse', 'substance-related disorde...\n",
       "2  ['tuberculosis', 'latent tuberculosis', 'infec...\n",
       "3  ['heart failure', 'heart diseases', 'cardiovas...\n",
       "4  ['lymphoma', 'neoplasms by histologic type', '..."
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trials_filtered=df_trials[['desease_condition']]\n",
    "df_trials_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "88e28056-e340-416c-a1a9-4a6c29556dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"['marijuana abuse', 'substance-related disorders', 'chemically-induced disorders', 'mental disorders']\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trials_filtered['desease_condition'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5bd8f876-0480-40a5-a32f-ca7ec137a70f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ariji\\AppData\\Local\\Temp\\ipykernel_22340\\16068817.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_trials_filtered['desease_condition']= df_trials_filtered['desease_condition'].apply(list_to_string)\n"
     ]
    },
    {
     "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>desease_condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>marijuana abuse, substance-related disorders, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>marijuana abuse, substance-related disorders, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tuberculosis, latent tuberculosis, infections,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>heart failure, heart diseases, cardiovascular ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lymphoma, neoplasms by histologic type, neopla...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440512</th>\n",
       "      <td>obesity, overweight, overnutrition, nutrition ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440513</th>\n",
       "      <td>obesity, overweight, overnutrition, nutrition ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440514</th>\n",
       "      <td>obesity, overweight, overnutrition, nutrition ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440515</th>\n",
       "      <td>autistic disorder, autism spectrum disorder, c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440516</th>\n",
       "      <td>autistic disorder, autism spectrum disorder, c...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440517 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        desease_condition\n",
       "0       marijuana abuse, substance-related disorders, ...\n",
       "1       marijuana abuse, substance-related disorders, ...\n",
       "2       tuberculosis, latent tuberculosis, infections,...\n",
       "3       heart failure, heart diseases, cardiovascular ...\n",
       "4       lymphoma, neoplasms by histologic type, neopla...\n",
       "...                                                   ...\n",
       "440512  obesity, overweight, overnutrition, nutrition ...\n",
       "440513  obesity, overweight, overnutrition, nutrition ...\n",
       "440514  obesity, overweight, overnutrition, nutrition ...\n",
       "440515  autistic disorder, autism spectrum disorder, c...\n",
       "440516  autistic disorder, autism spectrum disorder, c...\n",
       "\n",
       "[440517 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def list_to_string(disease_list):\n",
    "    disease_list= ast.literal_eval(disease_list)\n",
    "    return ', '.join(disease_list)\n",
    "df_trials_filtered['desease_condition']= df_trials_filtered['desease_condition'].apply(list_to_string)\n",
    "df_trials_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bbbb22e6-4883-4869-8ccc-95696bc67b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    marijuana abuse, substance-related disorders, ...\n",
       "1    marijuana abuse, substance-related disorders, ...\n",
       "2    tuberculosis, latent tuberculosis, infections,...\n",
       "3    heart failure, heart diseases, cardiovascular ...\n",
       "4    lymphoma, neoplasms by histologic type, neopla...\n",
       "Name: desease_condition, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trials_filtered['desease_condition'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7f4e7ceb-8bfd-4294-a850-8935f88b6555",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_trials_filtered.to_csv(\"diseases.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "af1c5c2b-24a0-44a1-9e5d-7ee89ca4cccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "440517"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader= CSVLoader(file_path=\"./diseases.csv\", encoding=\"utf-8\")\n",
    "data = loader.load()\n",
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cab89218-41ca-4048-886d-bc2c1c9b30bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings()\n",
    "vectorstore = FAISS.from_documents(data, embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "225ade4a-d004-44cc-a5ff-22ce2bfcac32",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_path= \"vector_index.pkl\"\n",
    "with open(file_path, \"wb\") as f:\n",
    "    pickle.dump(vectorstore, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "11912a93-ad02-41cb-8bce-2750c947fa74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(440517, 2)\n"
     ]
    },
    {
     "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>desease_condition</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>['marijuana abuse', 'substance-related disorde...</td>\n",
       "      <td>nct_id: NCT03055377\\nsummary: This is a 12-wee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>['marijuana abuse', 'substance-related disorde...</td>\n",
       "      <td>nct_id: NCT03055377\\nsummary: This is a 12-wee...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>['tuberculosis', 'latent tuberculosis', 'infec...</td>\n",
       "      <td>nct_id: NCT03042754\\nsummary: Early diagnosis ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>['heart failure', 'heart diseases', 'cardiovas...</td>\n",
       "      <td>nct_id: NCT03035123\\nsummary: The EduStra-HF s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>['lymphoma', 'neoplasms by histologic type', '...</td>\n",
       "      <td>nct_id: NCT02272751\\nsummary: This study will ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   desease_condition  \\\n",
       "0  ['marijuana abuse', 'substance-related disorde...   \n",
       "1  ['marijuana abuse', 'substance-related disorde...   \n",
       "2  ['tuberculosis', 'latent tuberculosis', 'infec...   \n",
       "3  ['heart failure', 'heart diseases', 'cardiovas...   \n",
       "4  ['lymphoma', 'neoplasms by histologic type', '...   \n",
       "\n",
       "                                                text  \n",
       "0  nct_id: NCT03055377\\nsummary: This is a 12-wee...  \n",
       "1  nct_id: NCT03055377\\nsummary: This is a 12-wee...  \n",
       "2  nct_id: NCT03042754\\nsummary: Early diagnosis ...  \n",
       "3  nct_id: NCT03035123\\nsummary: The EduStra-HF s...  \n",
       "4  nct_id: NCT02272751\\nsummary: This study will ...  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trials= pd.read_csv(\"clinical_trials.csv\")\n",
    "print(df_trials.shape)\n",
    "df_trials.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a8113876-f38e-4c7a-891e-17dc51e2bacf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"nct_id: NCT03055377\\nsummary: This is a 12-week randomized, placebo-controlled trial of N-acetylcysteine for cannabis use disorder (CUD) in youth (N=192). Participants will be randomized to double-blind NAC or PBO, yielding two equally-allocated treatment groups. All participants will receive brief weekly cannabis cessation counseling and medication management. The primary efficacy outcome will be the proportion of negative urine cannabinoid tests during the 12-week active treatment, compared between groups.\\nintervention_type: Drug\\nintervention_name: N-acetyl cysteine\\nintervention_description: N-acetylcysteine 1200 mg twice daily for 12 weeks (administered orally)\\nkeywords: ['cannabis', 'marijuana', 'youth', 'adolescent', 'pharmacotherapy', 'medication', 'n-acetylcysteine', 'trial']\""
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trials.iloc[0].text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c9ace03c-cf71-4605-adeb-9c6ee10e158a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 2)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= df_trials[:1000]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85a985c5-845b-464e-9527-e806b6883741",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings= OpenAIEmbeddings()\n",
    "vectorindex_openapi= FAISS.from"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25b31e55-2961-474d-92d8-5963f2c6bf84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "918c078c-46fe-4d7b-9748-88c52a5b004a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36e83202-97ad-425d-95ae-075a1e26a34e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d705875-5dd7-4c71-8d94-99c101020ac0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50c15b4d-9f65-4385-9aa6-3f782bf57775",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2818abe-1a43-4d7d-92a7-7562812bf43d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>desease_condition</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>tuberculosis, latent tuberculosis, infections,...</td>\n",
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       "      <td>heart failure, heart diseases, cardiovascular ...</td>\n",
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       "      <th>4</th>\n",
       "      <td>lymphoma, neoplasms by histologic type, neopla...</td>\n",
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       "    <tr>\n",
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       "      <th>440512</th>\n",
       "      <td>obesity, overweight, overnutrition, nutrition ...</td>\n",
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       "      <td>obesity, overweight, overnutrition, nutrition ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440515</th>\n",
       "      <td>autistic disorder, autism spectrum disorder, c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440516</th>\n",
       "      <td>autistic disorder, autism spectrum disorder, c...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>440517 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        desease_condition\n",
       "0       marijuana abuse, substance-related disorders, ...\n",
       "1       marijuana abuse, substance-related disorders, ...\n",
       "2       tuberculosis, latent tuberculosis, infections,...\n",
       "3       heart failure, heart diseases, cardiovascular ...\n",
       "4       lymphoma, neoplasms by histologic type, neopla...\n",
       "...                                                   ...\n",
       "440512  obesity, overweight, overnutrition, nutrition ...\n",
       "440513  obesity, overweight, overnutrition, nutrition ...\n",
       "440514  obesity, overweight, overnutrition, nutrition ...\n",
       "440515  autistic disorder, autism spectrum disorder, c...\n",
       "440516  autistic disorder, autism spectrum disorder, c...\n",
       "\n",
       "[440517 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_trials_filtered = pd.read_csv(\"diseases.csv\")\n",
    "df_trials_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c89e3cf6-a376-4029-9c04-0f5e664a2237",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 1)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2= df_trials_filtered[:100]\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c5012bcf-3e25-4f21-a29c-6bdbdafbb8c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "client= OpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "40a480bd-6754-40b6-870c-42d10ce9a960",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_embeddings(text):\n",
    "    response= client.embeddings.create(\n",
    "        input= text,\n",
    "        dimensions= 128,\n",
    "        model= \"text-embedding-3-small\"\n",
    "    )\n",
    "    return response.data[0].embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef6d6b62-de0b-4bc6-a6eb-847ab8e99da5",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'sentence_transformers'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:1\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sentence_transformers'"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from  sentence_transformers import SentenceTransformer\n",
    "\n",
    "encoder= SentenceTransformer(\"all-mpnet-base-v2\")\n",
    "vectors= encoder.encode(df2.desease_condition)\n",
    "vectors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7966d754-56d7-4555-a6c6-6a13772fb000",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 62.5 ms\n",
      "Wall time: 24.7 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<timed exec>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>desease_condition</th>\n",
       "      <th>embeddings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>marijuana abuse, substance-related disorders, ...</td>\n",
       "      <td>[-0.05811865255236626, -0.023393018171191216, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>marijuana abuse, substance-related disorders, ...</td>\n",
       "      <td>[-0.05811865255236626, -0.023393018171191216, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tuberculosis, latent tuberculosis, infections,...</td>\n",
       "      <td>[-0.03460180386900902, -0.084668830037117, 0.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>heart failure, heart diseases, cardiovascular ...</td>\n",
       "      <td>[-0.08236236125230789, -0.1235777735710144, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>lymphoma, neoplasms by histologic type, neopla...</td>\n",
       "      <td>[-0.1227850392460823, 0.07155642658472061, 0.1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   desease_condition  \\\n",
       "0  marijuana abuse, substance-related disorders, ...   \n",
       "1  marijuana abuse, substance-related disorders, ...   \n",
       "2  tuberculosis, latent tuberculosis, infections,...   \n",
       "3  heart failure, heart diseases, cardiovascular ...   \n",
       "4  lymphoma, neoplasms by histologic type, neopla...   \n",
       "\n",
       "                                          embeddings  \n",
       "0  [-0.05811865255236626, -0.023393018171191216, ...  \n",
       "1  [-0.05811865255236626, -0.023393018171191216, ...  \n",
       "2  [-0.03460180386900902, -0.084668830037117, 0.2...  \n",
       "3  [-0.08236236125230789, -0.1235777735710144, 0....  \n",
       "4  [-0.1227850392460823, 0.07155642658472061, 0.1...  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "df2['embeddings']= df2['desease_condition'].apply(get_embeddings)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2711980a-d1c0-441e-ae9a-531500b7b7cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128,)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.array(df2.iloc[0].embeddings).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "13e40184-999d-4363-be44-18ba9fb6745a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ariji\\Anaconda3\\envs\\python310\\lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[1;32mIn [23]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m  \u001b[38;5;21;01msentence_transformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SentenceTransformer\n\u001b[0;32m      3\u001b[0m encoder\u001b[38;5;241m=\u001b[39m SentenceTransformer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mall-mpnet-base-v2\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m vectors\u001b[38;5;241m=\u001b[39m \u001b[43mencoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_trials_filtered\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdesease_condition\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      5\u001b[0m vectors\u001b[38;5;241m.\u001b[39mshape\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\sentence_transformers\\SentenceTransformer.py:371\u001b[0m, in \u001b[0;36mSentenceTransformer.encode\u001b[1;34m(self, sentences, prompt_name, prompt, batch_size, show_progress_bar, output_value, precision, convert_to_numpy, convert_to_tensor, device, normalize_embeddings)\u001b[0m\n\u001b[0;32m    368\u001b[0m features\u001b[38;5;241m.\u001b[39mupdate(extra_features)\n\u001b[0;32m    370\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[1;32m--> 371\u001b[0m     out_features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeatures\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    372\u001b[0m     out_features[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msentence_embedding\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m truncate_embeddings(\n\u001b[0;32m    373\u001b[0m         out_features[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msentence_embedding\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtruncate_dim\n\u001b[0;32m    374\u001b[0m     )\n\u001b[0;32m    376\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m output_value \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_embeddings\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\torch\\nn\\modules\\container.py:139\u001b[0m, in \u001b[0;36mSequential.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    137\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[0;32m    138\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 139\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    140\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\sentence_transformers\\models\\Transformer.py:98\u001b[0m, in \u001b[0;36mTransformer.forward\u001b[1;34m(self, features)\u001b[0m\n\u001b[0;32m     95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_type_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m features:\n\u001b[0;32m     96\u001b[0m     trans_features[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_type_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m features[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_type_ids\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m---> 98\u001b[0m output_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_model(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mtrans_features, return_dict\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m     99\u001b[0m output_tokens \u001b[38;5;241m=\u001b[39m output_states[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m    101\u001b[0m features\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken_embeddings\u001b[39m\u001b[38;5;124m\"\u001b[39m: output_tokens, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mattention_mask\u001b[39m\u001b[38;5;124m\"\u001b[39m: features[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mattention_mask\u001b[39m\u001b[38;5;124m\"\u001b[39m]})\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\transformers\\models\\mpnet\\modeling_mpnet.py:548\u001b[0m, in \u001b[0;36mMPNetModel.forward\u001b[1;34m(self, input_ids, attention_mask, position_ids, head_mask, inputs_embeds, output_attentions, output_hidden_states, return_dict, **kwargs)\u001b[0m\n\u001b[0;32m    546\u001b[0m head_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_head_mask(head_mask, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mnum_hidden_layers)\n\u001b[0;32m    547\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(input_ids\u001b[38;5;241m=\u001b[39minput_ids, position_ids\u001b[38;5;241m=\u001b[39mposition_ids, inputs_embeds\u001b[38;5;241m=\u001b[39minputs_embeds)\n\u001b[1;32m--> 548\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    549\u001b[0m \u001b[43m    \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    550\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    551\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    552\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    553\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    554\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    555\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    556\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m    557\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(sequence_output) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\transformers\\models\\mpnet\\modeling_mpnet.py:338\u001b[0m, in \u001b[0;36mMPNetEncoder.forward\u001b[1;34m(self, hidden_states, attention_mask, head_mask, output_attentions, output_hidden_states, return_dict, **kwargs)\u001b[0m\n\u001b[0;32m    335\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_hidden_states:\n\u001b[0;32m    336\u001b[0m     all_hidden_states \u001b[38;5;241m=\u001b[39m all_hidden_states \u001b[38;5;241m+\u001b[39m (hidden_states,)\n\u001b[1;32m--> 338\u001b[0m layer_outputs \u001b[38;5;241m=\u001b[39m layer_module(\n\u001b[0;32m    339\u001b[0m     hidden_states,\n\u001b[0;32m    340\u001b[0m     attention_mask,\n\u001b[0;32m    341\u001b[0m     head_mask[i],\n\u001b[0;32m    342\u001b[0m     position_bias,\n\u001b[0;32m    343\u001b[0m     output_attentions\u001b[38;5;241m=\u001b[39moutput_attentions,\n\u001b[0;32m    344\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    345\u001b[0m )\n\u001b[0;32m    346\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m    348\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\transformers\\models\\mpnet\\modeling_mpnet.py:308\u001b[0m, in \u001b[0;36mMPNetLayer.forward\u001b[1;34m(self, hidden_states, attention_mask, head_mask, position_bias, output_attentions, **kwargs)\u001b[0m\n\u001b[0;32m    305\u001b[0m outputs \u001b[38;5;241m=\u001b[39m self_attention_outputs[\u001b[38;5;241m1\u001b[39m:]  \u001b[38;5;66;03m# add self attentions if we output attention weights\u001b[39;00m\n\u001b[0;32m    307\u001b[0m intermediate_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintermediate(attention_output)\n\u001b[1;32m--> 308\u001b[0m layer_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutput\u001b[49m\u001b[43m(\u001b[49m\u001b[43mintermediate_output\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattention_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    309\u001b[0m outputs \u001b[38;5;241m=\u001b[39m (layer_output,) \u001b[38;5;241m+\u001b[39m outputs\n\u001b[0;32m    310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m outputs\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\transformers\\models\\mpnet\\modeling_mpnet.py:275\u001b[0m, in \u001b[0;36mMPNetOutput.forward\u001b[1;34m(self, hidden_states, input_tensor)\u001b[0m\n\u001b[0;32m    274\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, hidden_states: torch\u001b[38;5;241m.\u001b[39mTensor, input_tensor: torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[1;32m--> 275\u001b[0m     hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdense\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    276\u001b[0m     hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout(hidden_states)\n\u001b[0;32m    277\u001b[0m     hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mLayerNorm(hidden_states \u001b[38;5;241m+\u001b[39m input_tensor)\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1129\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m   1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[1;32m~\\Anaconda3\\envs\\python310\\lib\\site-packages\\torch\\nn\\modules\\linear.py:114\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 114\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from  sentence_transformers import SentenceTransformer\n",
    "\n",
    "encoder= SentenceTransformer(\"all-mpnet-base-v2\")\n",
    "vectors= encoder.encode(df2.desease_condition)\n",
    "vectors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f626382-75f1-4864-8574-bbcd183b926a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e064857-2480-4ac2-911a-1c5a2ffb62d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c0a719a6-ee06-4f6d-a64b-8fc74c7afbca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.01245328  0.07695839  0.01802037 ...  0.0093326  -0.03474615\n",
      "  -0.02757339]\n",
      " [ 0.01846956  0.08282936  0.01921537 ...  0.01068991 -0.03402989\n",
      "  -0.02075185]\n",
      " [ 0.01822413 -0.05593034 -0.00288358 ...  0.03525289 -0.05427228\n",
      "  -0.03371295]\n",
      " ...\n",
      " [ 0.04031958  0.0040107   0.02156032 ...  0.01568209 -0.04320977\n",
      "  -0.02990234]\n",
      " [ 0.0399131   0.00027251  0.02207735 ...  0.01440835 -0.04246744\n",
      "  -0.02869584]\n",
      " [ 0.03773859 -0.00315346  0.0207725  ...  0.01205995 -0.04628598\n",
      "  -0.02870333]]\n"
     ]
    }
   ],
   "source": [
    "print(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f21c08d5-596a-4fc4-ba72-2de8624e6c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "768"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dim= vectors.shape[1]\n",
    "dim "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "8ed985f4-9402-431f-bfba-1236ba16b895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<faiss.swigfaiss_avx2.IndexFlatL2; proxy of <Swig Object of type 'faiss::IndexFlatL2 *' at 0x0000023493347480> >"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import faiss\n",
    "\n",
    "index= faiss.IndexFlatL2(dim)\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c0f1dde8-c173-45ba-957b-f4622fe6f0ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "index.add(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "71dad860-1f19-4166-a309-c9ce15f24792",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(768,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-8.82369652e-03,  8.50650743e-02,  2.08267733e-03,  6.77651772e-03,\n",
       "       -2.86661759e-02, -8.71188380e-03,  6.99447095e-02,  5.04214764e-02,\n",
       "        3.58386151e-02,  5.29594952e-03, -1.40875215e-02,  1.99297220e-02,\n",
       "        2.27009598e-03,  2.10810862e-02,  2.66138893e-02,  1.90623086e-02,\n",
       "        4.44708914e-02,  2.96202525e-02,  5.42085357e-02, -2.34859088e-03,\n",
       "       -9.87798795e-02, -5.00183590e-02, -3.42465192e-02,  2.08440255e-02,\n",
       "        5.31156994e-02, -1.37044629e-02,  2.92537250e-02, -2.61334293e-02,\n",
       "       -1.21854078e-04, -2.36813519e-02, -3.81283499e-02, -1.79494768e-02,\n",
       "       -6.29265187e-03,  1.27150817e-02,  1.19849676e-06, -7.78729608e-03,\n",
       "       -1.28973828e-04,  4.01791967e-02,  4.21229303e-02, -8.72302521e-03,\n",
       "        7.44823692e-03,  7.68032745e-02,  6.50246907e-03,  3.40298638e-02,\n",
       "       -1.80711355e-02, -2.71878559e-02,  5.74751608e-02,  3.67745496e-02,\n",
       "       -3.34868580e-02,  1.05205458e-02,  2.08975170e-02,  4.36686277e-02,\n",
       "        3.47612537e-02, -4.99080680e-02,  4.44446988e-02,  5.57280704e-03,\n",
       "       -2.31200755e-02, -4.60692644e-02, -1.39789237e-02, -3.79957110e-02,\n",
       "        4.67903316e-02,  1.91651955e-02, -5.12171052e-02,  2.46807020e-02,\n",
       "       -5.52081019e-02,  3.50596346e-02, -7.01438356e-03, -3.36519890e-02,\n",
       "       -1.41502097e-02, -1.37693482e-02,  4.11427952e-02,  6.94046309e-03,\n",
       "       -1.30138136e-02,  5.91567121e-02,  3.37168351e-02,  3.01467292e-02,\n",
       "       -4.59552221e-02,  1.37365120e-03,  1.00179566e-02,  6.98126853e-04,\n",
       "        3.58139984e-02,  1.18174301e-02,  1.33722462e-02, -1.35893077e-02,\n",
       "        4.75908853e-02,  5.48331346e-03, -6.41460950e-03, -1.23906611e-02,\n",
       "        5.82688041e-02, -1.60842277e-02, -2.95833423e-04,  6.97355811e-03,\n",
       "        2.48331465e-02, -2.35959496e-02, -1.24989869e-02, -1.36585534e-02,\n",
       "        1.52637456e-02,  7.01832073e-03,  5.50601333e-02,  4.35096538e-03,\n",
       "        2.36319732e-02, -1.38118947e-02, -7.24233836e-02,  9.39742289e-03,\n",
       "       -2.66901590e-02,  2.96042152e-02,  1.28761679e-02,  2.23339219e-02,\n",
       "        3.08373477e-03,  7.12765753e-02,  7.13613164e-03,  3.62721197e-02,\n",
       "       -4.53250594e-02,  2.54001115e-02, -2.54253373e-02, -1.23151275e-03,\n",
       "       -1.34750446e-02, -2.70653702e-02, -1.02220355e-02,  2.07683407e-02,\n",
       "       -7.31003610e-03,  2.65329964e-02, -2.79857730e-03,  4.20840643e-02,\n",
       "        3.20205763e-02, -1.19518824e-02, -5.77116087e-02,  9.88688134e-03,\n",
       "        1.86814573e-02, -5.10204993e-02, -6.77110278e-04,  9.40234493e-03,\n",
       "       -3.33383717e-02, -5.52933291e-02,  5.64148054e-02,  4.92153503e-02,\n",
       "        3.33690383e-02, -3.92963700e-02, -6.91099390e-02,  3.79911740e-03,\n",
       "       -1.74410697e-02, -1.60171147e-02,  4.89675067e-02,  2.67119659e-03,\n",
       "        2.61192098e-02, -2.74193864e-02,  6.92490395e-03, -4.64810384e-03,\n",
       "       -8.99862905e-04,  1.02159111e-02, -4.81114909e-02,  1.22787328e-02,\n",
       "       -9.32844076e-03, -2.00431682e-02, -1.36102587e-02, -3.67914373e-03,\n",
       "       -1.60810221e-02, -2.20200215e-02,  2.32051890e-02, -5.07331975e-02,\n",
       "       -1.01248249e-02,  5.62567115e-02, -2.60966737e-03,  9.27545596e-03,\n",
       "        5.32410555e-02,  4.81746234e-02, -9.83138476e-03,  1.81230865e-02,\n",
       "       -2.12969314e-02,  9.82244611e-02, -2.47648880e-02,  7.06253499e-02,\n",
       "        8.71159416e-03, -2.73140483e-02,  5.59884915e-03, -2.14829091e-02,\n",
       "       -6.67077005e-02,  2.48677693e-02, -8.29503238e-02, -7.96182230e-02,\n",
       "        3.77488993e-02, -1.37352264e-02, -2.85069812e-02,  1.81708820e-02,\n",
       "       -4.07746173e-02, -4.71230270e-03, -1.59605164e-02, -1.25815195e-03,\n",
       "       -6.59954594e-03, -1.51611334e-02,  7.87123516e-02, -4.09705602e-02,\n",
       "        3.07933297e-02,  1.27626080e-02, -4.34489138e-02,  9.91576444e-03,\n",
       "        1.25470785e-02, -8.67356583e-02,  1.26097840e-03,  3.24709825e-02,\n",
       "       -6.92409948e-02, -4.35011238e-02, -2.79313605e-02, -3.37213017e-02,\n",
       "       -2.35359464e-02, -2.95022167e-02,  2.88009271e-02, -3.26618887e-02,\n",
       "        7.09307985e-03,  3.09435464e-03, -5.09097055e-02,  3.54242921e-02,\n",
       "        5.37336655e-02,  1.55867739e-02,  2.09988486e-02, -4.38529663e-02,\n",
       "        2.93767708e-03,  2.27999203e-02,  1.02668423e-02,  3.35033536e-02,\n",
       "       -8.28316063e-02, -4.17127199e-02, -1.23034064e-02,  2.38543525e-02,\n",
       "       -3.72257493e-02,  2.97443867e-02,  3.35034318e-02, -5.21336049e-02,\n",
       "        5.74519299e-03,  2.89844945e-02, -2.21337453e-02,  2.34603398e-02,\n",
       "        6.33142609e-03, -2.24104542e-02,  1.47326495e-02,  1.98041964e-02,\n",
       "        3.05697713e-02, -9.37094465e-02, -6.84579164e-02,  4.63523576e-03,\n",
       "        3.88860740e-02, -3.97440195e-02, -4.70216498e-02,  1.02172708e-02,\n",
       "       -3.37972888e-03, -8.54947045e-03,  4.81354557e-02,  4.99849804e-02,\n",
       "        7.11378129e-03, -2.54327375e-02, -1.14872465e-02, -3.54485810e-02,\n",
       "        5.24284095e-02,  2.16708388e-02, -4.00698110e-02,  5.15380092e-02,\n",
       "       -6.03203699e-02, -6.50304696e-03, -1.03860423e-02, -7.47132823e-02,\n",
       "        3.59848235e-03, -4.68364358e-02, -4.23019789e-02, -1.86387468e-02,\n",
       "       -2.88047381e-02, -2.81904116e-02,  1.52729014e-02, -1.55570190e-02,\n",
       "        1.34619148e-02,  2.34364290e-02,  3.10326237e-02, -4.70464528e-02,\n",
       "       -2.43550166e-02, -7.20657408e-03, -1.16065536e-02, -3.42444591e-02,\n",
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       "       -3.45575088e-03,  5.12249060e-02, -8.63745401e-04,  5.59820198e-02,\n",
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       "       -3.24050151e-02,  7.39779174e-02,  2.67242044e-02,  1.16365692e-02,\n",
       "        8.20766483e-03, -6.27530292e-02, -1.30661400e-02, -3.52081768e-02,\n",
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       "       -4.79784003e-03,  6.93615247e-03,  3.59064825e-02,  2.08517518e-02,\n",
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       "       -5.06135784e-02, -3.05179805e-02,  7.06539825e-02, -3.55644710e-02,\n",
       "       -4.92612133e-03,  9.91953164e-02,  1.00235650e-02, -2.22671125e-02,\n",
       "       -1.86746120e-02,  2.49281265e-02, -4.92450967e-03,  1.66887734e-02,\n",
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       "       -2.78001893e-02, -1.69764105e-02,  1.79186705e-02,  1.09842177e-02,\n",
       "        1.09969089e-02,  1.69700030e-02, -8.59475043e-03,  4.70476560e-02,\n",
       "        3.64770554e-02,  2.09835749e-02,  1.01236468e-02,  2.75151283e-02,\n",
       "        4.33402918e-02, -4.30559181e-02, -3.53547297e-02,  7.77268112e-02,\n",
       "       -6.10819347e-02, -2.86280159e-02,  4.68054451e-02,  1.29892454e-02,\n",
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       "       -5.27431667e-02,  6.45831088e-03,  2.96409409e-02,  5.97442053e-02,\n",
       "        3.23252901e-02,  5.03172688e-02, -4.45654802e-02,  2.90075876e-02,\n",
       "       -1.35373492e-02,  6.78209821e-03, -5.89249916e-02,  4.28890549e-02,\n",
       "       -2.36034058e-02, -5.30969724e-03,  3.85405980e-02, -1.82616734e-03,\n",
       "        1.45543357e-02,  1.07806427e-02, -6.06855676e-02, -4.95252907e-02,\n",
       "        1.02004781e-02,  4.60227691e-02, -1.08090881e-02,  4.42408510e-02,\n",
       "        4.15152796e-02,  1.23609398e-02,  5.11957100e-03,  1.17597533e-02,\n",
       "       -2.70090066e-02,  2.68773828e-02, -1.97812133e-02,  2.25932393e-02,\n",
       "       -1.33560598e-02, -1.50896851e-02, -3.14053567e-03,  1.54051669e-02,\n",
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       "        4.44293395e-02, -1.11264118e-03, -4.79441285e-02,  3.46464328e-02,\n",
       "       -2.53370814e-02, -3.26901935e-02, -2.28975322e-02, -1.96164921e-02,\n",
       "       -4.38152434e-04,  4.08602282e-02, -2.29470823e-02, -1.89938806e-02,\n",
       "       -1.52037974e-04,  1.05516789e-02,  2.08601039e-02, -6.98119551e-02,\n",
       "        3.66246551e-02, -1.26779894e-03, -4.03217562e-02, -5.35424761e-02,\n",
       "        6.51817098e-02,  4.29646857e-02,  2.56071109e-02, -3.28080021e-02,\n",
       "        1.20534413e-02,  3.56224040e-03, -1.01593453e-02, -1.96505673e-04,\n",
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       "       -1.40319867e-02, -3.63940969e-02, -3.09983976e-02, -4.19548260e-33,\n",
       "        7.11604580e-02,  4.78382297e-02,  1.89297704e-03, -1.60731785e-02,\n",
       "        2.53787991e-02, -3.15741785e-02, -4.27713171e-02, -7.53164338e-03,\n",
       "        1.68679946e-03,  1.92391127e-02, -2.20667192e-04,  1.32907527e-02,\n",
       "        5.99487219e-03,  2.75156219e-02, -5.06000873e-03, -3.58465910e-02,\n",
       "        8.20948277e-03, -2.11624149e-02, -7.07996823e-03, -4.23992332e-03,\n",
       "       -1.09853260e-01, -3.66037302e-02,  3.55480015e-02,  4.23291475e-02,\n",
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       "       -4.00471613e-02,  1.01988176e-02,  2.83056553e-02, -1.55737845e-03,\n",
       "        1.24238459e-02,  1.20237898e-02, -7.69484974e-03, -3.30727436e-02,\n",
       "       -1.45808076e-02,  3.43246050e-02,  3.21143419e-02, -4.96741422e-02,\n",
       "       -5.27968369e-02,  2.51889303e-02, -1.11904610e-02,  5.64832352e-02,\n",
       "        2.77636852e-02,  5.90689071e-02, -2.61273161e-02, -6.95008039e-02,\n",
       "       -3.15576978e-02, -5.62214339e-03, -7.93884136e-03, -3.62196900e-02,\n",
       "       -8.26047733e-03,  8.05249214e-02, -4.16241921e-02, -2.01846119e-02,\n",
       "       -2.52235290e-02, -3.88054736e-02, -2.00710595e-02,  1.50789914e-03,\n",
       "       -5.51338419e-02, -8.35673045e-03, -1.61523875e-02, -8.79513845e-02,\n",
       "       -5.28004877e-02, -2.88654189e-03, -1.11697149e-02,  7.10910782e-02,\n",
       "        4.44932319e-02,  8.69598426e-03, -1.14432694e-02,  4.47212979e-02,\n",
       "        2.70624813e-02, -3.86100151e-02, -3.07358261e-02,  2.75634117e-02,\n",
       "        1.48464069e-02, -1.00845508e-02,  6.45884350e-02,  4.28387662e-03,\n",
       "        8.05836394e-02, -1.69498641e-02,  4.44465503e-02, -2.09145956e-02,\n",
       "       -3.37407738e-02,  3.85780074e-02, -7.44559616e-02,  1.17512364e-02,\n",
       "        1.01964204e-02, -3.02421930e-03,  4.80608828e-02, -1.49494391e-02,\n",
       "        2.54592765e-02, -1.46158040e-02,  5.46646416e-02,  1.43051194e-03,\n",
       "        2.99116820e-02,  2.24273186e-02, -5.79927117e-03, -1.33864526e-02,\n",
       "       -2.52460372e-02, -2.69225910e-02,  1.64003875e-02,  1.20901112e-02,\n",
       "        3.38429734e-02, -2.11539529e-02,  7.17787817e-02, -7.78904185e-02,\n",
       "       -4.04084288e-02,  4.90567498e-02, -2.61603445e-02,  1.97753590e-02,\n",
       "        4.97209951e-02, -4.88655381e-02, -4.52128090e-02,  3.63065898e-02,\n",
       "        2.68440694e-02,  3.29160057e-02, -8.24410375e-03, -1.33646047e-02,\n",
       "       -6.22822754e-02, -1.13362661e-02, -3.79339382e-02, -6.56360280e-05,\n",
       "       -1.08087100e-02,  2.67575700e-02,  1.33866509e-02,  5.89998253e-02,\n",
       "       -2.54666172e-02, -3.05371322e-02, -1.53249800e-02, -9.87035502e-03,\n",
       "        1.95337094e-07, -1.76476724e-02,  5.71432859e-02, -2.49180794e-02,\n",
       "        5.85253723e-02,  4.49808314e-02, -5.99673577e-02, -9.97425616e-03,\n",
       "        4.07801419e-02,  4.13940698e-02,  2.55707726e-02,  2.18985360e-02,\n",
       "       -3.04434425e-03, -3.77355106e-02, -6.24866784e-02, -1.17468778e-02,\n",
       "       -4.82194684e-02, -7.78659210e-02, -1.48841189e-02, -1.75396129e-02,\n",
       "       -2.48471629e-02,  8.05181568e-04, -4.85844910e-03, -5.16015477e-03,\n",
       "        7.53483502e-03, -9.46175400e-03, -2.39896346e-02, -3.14654633e-02,\n",
       "        1.50111094e-02, -1.22348899e-02,  3.00448518e-02,  3.55701670e-02,\n",
       "        3.08971256e-02,  1.72299352e-02,  5.93419448e-02, -5.74274361e-02,\n",
       "       -8.16087723e-02, -4.80572283e-02, -2.68838424e-02, -1.96331330e-02,\n",
       "       -9.15831141e-03,  1.07509056e-02,  2.35639680e-02, -2.62569580e-02,\n",
       "        9.21937004e-02,  1.37132118e-02, -1.19096776e-02, -4.09874134e-02,\n",
       "        3.37628126e-02, -4.64820908e-03, -2.50304434e-02,  6.25852346e-02,\n",
       "       -1.24449311e-02,  3.82654071e-02, -2.35330854e-02,  8.68125912e-03,\n",
       "        5.08641489e-02,  2.53822445e-03,  5.25634140e-02,  1.14882430e-02,\n",
       "        5.01894541e-02, -3.55215147e-02, -3.31749097e-02, -3.02003417e-03,\n",
       "       -5.36288768e-02, -2.80938316e-02, -7.51279444e-02, -4.71623316e-02,\n",
       "        9.56887701e-35,  2.55127084e-02, -1.44770980e-04,  1.96710341e-02,\n",
       "       -1.33620016e-02, -1.51910949e-02, -3.28495577e-02, -1.52465852e-03,\n",
       "       -2.65272055e-02, -4.35708016e-02, -1.75950192e-02, -2.20594816e-02],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_query= \"clinical trials related to alzheimers\"\n",
    "vec= encoder.encode(search_query)\n",
    "print(vec.shape)\n",
    "vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "613fd415-4194-45e6-b9f3-9a7707845ad5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 768)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-8.82369652e-03,  8.50650743e-02,  2.08267733e-03,\n",
       "         6.77651772e-03, -2.86661759e-02, -8.71188380e-03,\n",
       "         6.99447095e-02,  5.04214764e-02,  3.58386151e-02,\n",
       "         5.29594952e-03, -1.40875215e-02,  1.99297220e-02,\n",
       "         2.27009598e-03,  2.10810862e-02,  2.66138893e-02,\n",
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       "        -7.31003610e-03,  2.65329964e-02, -2.79857730e-03,\n",
       "         4.20840643e-02,  3.20205763e-02, -1.19518824e-02,\n",
       "        -5.77116087e-02,  9.88688134e-03,  1.86814573e-02,\n",
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       "        -1.60171147e-02,  4.89675067e-02,  2.67119659e-03,\n",
       "         2.61192098e-02, -2.74193864e-02,  6.92490395e-03,\n",
       "        -4.64810384e-03, -8.99862905e-04,  1.02159111e-02,\n",
       "        -4.81114909e-02,  1.22787328e-02, -9.32844076e-03,\n",
       "        -2.00431682e-02, -1.36102587e-02, -3.67914373e-03,\n",
       "        -1.60810221e-02, -2.20200215e-02,  2.32051890e-02,\n",
       "        -5.07331975e-02, -1.01248249e-02,  5.62567115e-02,\n",
       "        -2.60966737e-03,  9.27545596e-03,  5.32410555e-02,\n",
       "         4.81746234e-02, -9.83138476e-03,  1.81230865e-02,\n",
       "        -2.12969314e-02,  9.82244611e-02, -2.47648880e-02,\n",
       "         7.06253499e-02,  8.71159416e-03, -2.73140483e-02,\n",
       "         5.59884915e-03, -2.14829091e-02, -6.67077005e-02,\n",
       "         2.48677693e-02, -8.29503238e-02, -7.96182230e-02,\n",
       "         3.77488993e-02, -1.37352264e-02, -2.85069812e-02,\n",
       "         1.81708820e-02, -4.07746173e-02, -4.71230270e-03,\n",
       "        -1.59605164e-02, -1.25815195e-03, -6.59954594e-03,\n",
       "        -1.51611334e-02,  7.87123516e-02, -4.09705602e-02,\n",
       "         3.07933297e-02,  1.27626080e-02, -4.34489138e-02,\n",
       "         9.91576444e-03,  1.25470785e-02, -8.67356583e-02,\n",
       "         1.26097840e-03,  3.24709825e-02, -6.92409948e-02,\n",
       "        -4.35011238e-02, -2.79313605e-02, -3.37213017e-02,\n",
       "        -2.35359464e-02, -2.95022167e-02,  2.88009271e-02,\n",
       "        -3.26618887e-02,  7.09307985e-03,  3.09435464e-03,\n",
       "        -5.09097055e-02,  3.54242921e-02,  5.37336655e-02,\n",
       "         1.55867739e-02,  2.09988486e-02, -4.38529663e-02,\n",
       "         2.93767708e-03,  2.27999203e-02,  1.02668423e-02,\n",
       "         3.35033536e-02, -8.28316063e-02, -4.17127199e-02,\n",
       "        -1.23034064e-02,  2.38543525e-02, -3.72257493e-02,\n",
       "         2.97443867e-02,  3.35034318e-02, -5.21336049e-02,\n",
       "         5.74519299e-03,  2.89844945e-02, -2.21337453e-02,\n",
       "         2.34603398e-02,  6.33142609e-03, -2.24104542e-02,\n",
       "         1.47326495e-02,  1.98041964e-02,  3.05697713e-02,\n",
       "        -9.37094465e-02, -6.84579164e-02,  4.63523576e-03,\n",
       "         3.88860740e-02, -3.97440195e-02, -4.70216498e-02,\n",
       "         1.02172708e-02, -3.37972888e-03, -8.54947045e-03,\n",
       "         4.81354557e-02,  4.99849804e-02,  7.11378129e-03,\n",
       "        -2.54327375e-02, -1.14872465e-02, -3.54485810e-02,\n",
       "         5.24284095e-02,  2.16708388e-02, -4.00698110e-02,\n",
       "         5.15380092e-02, -6.03203699e-02, -6.50304696e-03,\n",
       "        -1.03860423e-02, -7.47132823e-02,  3.59848235e-03,\n",
       "        -4.68364358e-02, -4.23019789e-02, -1.86387468e-02,\n",
       "        -2.88047381e-02, -2.81904116e-02,  1.52729014e-02,\n",
       "        -1.55570190e-02,  1.34619148e-02,  2.34364290e-02,\n",
       "         3.10326237e-02, -4.70464528e-02, -2.43550166e-02,\n",
       "        -7.20657408e-03, -1.16065536e-02, -3.42444591e-02,\n",
       "        -5.30204549e-03,  5.52049950e-02,  4.50828709e-02,\n",
       "        -7.30262510e-03,  5.56289777e-02, -9.46066808e-03,\n",
       "        -3.37345451e-02, -1.87659152e-02,  3.57284099e-02,\n",
       "         4.20488343e-02,  1.66770478e-03, -5.27675785e-02,\n",
       "         2.96422077e-04,  4.22447585e-02,  4.97253910e-02,\n",
       "         6.03130311e-02,  1.32281650e-02,  2.35939436e-02,\n",
       "        -1.59284715e-02,  4.46444489e-02, -1.68315917e-02,\n",
       "         1.34740606e-01, -3.54593806e-02,  4.79029641e-02,\n",
       "         8.99049267e-03,  4.74606343e-02,  6.70041004e-03,\n",
       "        -1.15184486e-03,  2.69540539e-03, -2.77549177e-02,\n",
       "        -1.33260442e-02,  2.60788556e-02,  4.35438640e-02,\n",
       "        -2.55859867e-02,  2.76670083e-02,  3.37177999e-02,\n",
       "         2.93240137e-02,  1.82274636e-03, -1.40310880e-02,\n",
       "        -1.91633645e-02,  1.18790809e-02, -4.65121269e-02,\n",
       "        -4.19883654e-02, -2.69681774e-02, -3.23035605e-02,\n",
       "        -6.84630498e-02,  6.26784265e-02,  1.37511576e-02,\n",
       "        -2.55833156e-02, -5.73152229e-02,  3.30126472e-02,\n",
       "        -7.90146552e-03, -1.08651863e-02,  1.10474667e-02,\n",
       "         3.03509296e-03,  1.55274626e-02,  1.05599947e-02,\n",
       "        -7.16960803e-03, -5.01419827e-02, -3.34469602e-02,\n",
       "         3.77239436e-02,  9.44003314e-02, -4.80610691e-02,\n",
       "         4.73537892e-02,  3.40655483e-02,  7.88806472e-03,\n",
       "        -2.84915343e-02,  7.96849206e-02,  1.57442074e-02,\n",
       "        -4.15650755e-02,  7.51048513e-03,  3.66957486e-02,\n",
       "        -1.72730908e-01, -8.72075930e-02,  2.86346450e-02,\n",
       "         2.16962174e-02, -4.80199270e-02,  6.49317261e-03,\n",
       "         1.67240556e-02, -2.56227311e-02,  2.19670162e-02,\n",
       "        -6.10647202e-02, -2.65449155e-02,  6.17929082e-03,\n",
       "        -2.89566331e-02,  1.19498251e-02, -2.33849231e-02,\n",
       "        -2.69133616e-02, -1.46602485e-02,  1.18886270e-02,\n",
       "         1.64973717e-02, -3.90495770e-02, -3.45575088e-03,\n",
       "         5.12249060e-02, -8.63745401e-04,  5.59820198e-02,\n",
       "         2.10017413e-02,  2.74998210e-02,  3.03551817e-04,\n",
       "        -1.15796946e-01, -4.66962112e-03, -4.80118394e-02,\n",
       "        -3.55160870e-02, -4.72528581e-03, -4.29739058e-02,\n",
       "        -1.07347388e-02, -1.32423071e-02, -2.34632343e-02,\n",
       "         1.98413953e-02, -7.27679394e-03,  2.27117930e-02,\n",
       "        -2.59338003e-02,  4.31442596e-02,  1.07885078e-02,\n",
       "        -2.47129947e-02, -4.14506458e-02,  4.40958813e-02,\n",
       "         6.65106403e-04, -2.26945560e-02, -4.76796739e-02,\n",
       "         1.13289580e-02, -5.57265691e-02,  1.71151303e-03,\n",
       "        -1.24145029e-02, -3.57853901e-03, -4.86295968e-02,\n",
       "        -5.14956787e-02,  4.79425713e-02, -3.24050151e-02,\n",
       "         7.39779174e-02,  2.67242044e-02,  1.16365692e-02,\n",
       "         8.20766483e-03, -6.27530292e-02, -1.30661400e-02,\n",
       "        -3.52081768e-02,  4.83807474e-02,  9.81860235e-03,\n",
       "         1.14539362e-01, -1.88471414e-02,  6.07751869e-02,\n",
       "        -1.75345445e-03,  3.13236266e-02, -1.94595556e-03,\n",
       "         2.64345529e-03,  3.07400171e-02, -4.31060083e-02,\n",
       "        -6.19985871e-02,  5.50477020e-03,  1.62547994e-02,\n",
       "        -8.26352183e-03,  7.56437238e-03, -4.79784003e-03,\n",
       "         6.93615247e-03,  3.59064825e-02,  2.08517518e-02,\n",
       "         1.41595434e-02,  5.31185642e-02,  6.78585656e-03,\n",
       "         6.56357184e-02, -5.06135784e-02, -3.05179805e-02,\n",
       "         7.06539825e-02, -3.55644710e-02, -4.92612133e-03,\n",
       "         9.91953164e-02,  1.00235650e-02, -2.22671125e-02,\n",
       "        -1.86746120e-02,  2.49281265e-02, -4.92450967e-03,\n",
       "         1.66887734e-02,  4.62210961e-02,  4.07794118e-02,\n",
       "         2.52511259e-02, -2.83305068e-02, -2.78001893e-02,\n",
       "        -1.69764105e-02,  1.79186705e-02,  1.09842177e-02,\n",
       "         1.09969089e-02,  1.69700030e-02, -8.59475043e-03,\n",
       "         4.70476560e-02,  3.64770554e-02,  2.09835749e-02,\n",
       "         1.01236468e-02,  2.75151283e-02,  4.33402918e-02,\n",
       "        -4.30559181e-02, -3.53547297e-02,  7.77268112e-02,\n",
       "        -6.10819347e-02, -2.86280159e-02,  4.68054451e-02,\n",
       "         1.29892454e-02, -1.71940885e-02, -2.52429228e-02,\n",
       "         3.86423096e-02, -1.35919163e-02, -5.27431667e-02,\n",
       "         6.45831088e-03,  2.96409409e-02,  5.97442053e-02,\n",
       "         3.23252901e-02,  5.03172688e-02, -4.45654802e-02,\n",
       "         2.90075876e-02, -1.35373492e-02,  6.78209821e-03,\n",
       "        -5.89249916e-02,  4.28890549e-02, -2.36034058e-02,\n",
       "        -5.30969724e-03,  3.85405980e-02, -1.82616734e-03,\n",
       "         1.45543357e-02,  1.07806427e-02, -6.06855676e-02,\n",
       "        -4.95252907e-02,  1.02004781e-02,  4.60227691e-02,\n",
       "        -1.08090881e-02,  4.42408510e-02,  4.15152796e-02,\n",
       "         1.23609398e-02,  5.11957100e-03,  1.17597533e-02,\n",
       "        -2.70090066e-02,  2.68773828e-02, -1.97812133e-02,\n",
       "         2.25932393e-02, -1.33560598e-02, -1.50896851e-02,\n",
       "        -3.14053567e-03,  1.54051669e-02,  1.86488125e-02,\n",
       "        -1.71708278e-02, -3.95283476e-03,  7.68053811e-04,\n",
       "        -2.37891261e-04,  1.84722953e-02,  3.60381305e-02,\n",
       "        -5.85213909e-03,  4.44293395e-02, -1.11264118e-03,\n",
       "        -4.79441285e-02,  3.46464328e-02, -2.53370814e-02,\n",
       "        -3.26901935e-02, -2.28975322e-02, -1.96164921e-02,\n",
       "        -4.38152434e-04,  4.08602282e-02, -2.29470823e-02,\n",
       "        -1.89938806e-02, -1.52037974e-04,  1.05516789e-02,\n",
       "         2.08601039e-02, -6.98119551e-02,  3.66246551e-02,\n",
       "        -1.26779894e-03, -4.03217562e-02, -5.35424761e-02,\n",
       "         6.51817098e-02,  4.29646857e-02,  2.56071109e-02,\n",
       "        -3.28080021e-02,  1.20534413e-02,  3.56224040e-03,\n",
       "        -1.01593453e-02, -1.96505673e-04,  4.33485657e-02,\n",
       "        -4.25680764e-02,  9.73126665e-03,  3.76882474e-03,\n",
       "        -1.40319867e-02, -3.63940969e-02, -3.09983976e-02,\n",
       "        -4.19548260e-33,  7.11604580e-02,  4.78382297e-02,\n",
       "         1.89297704e-03, -1.60731785e-02,  2.53787991e-02,\n",
       "        -3.15741785e-02, -4.27713171e-02, -7.53164338e-03,\n",
       "         1.68679946e-03,  1.92391127e-02, -2.20667192e-04,\n",
       "         1.32907527e-02,  5.99487219e-03,  2.75156219e-02,\n",
       "        -5.06000873e-03, -3.58465910e-02,  8.20948277e-03,\n",
       "        -2.11624149e-02, -7.07996823e-03, -4.23992332e-03,\n",
       "        -1.09853260e-01, -3.66037302e-02,  3.55480015e-02,\n",
       "         4.23291475e-02,  1.48312682e-02,  5.68749309e-02,\n",
       "         3.57767567e-02,  1.40728084e-02, -4.00471613e-02,\n",
       "         1.01988176e-02,  2.83056553e-02, -1.55737845e-03,\n",
       "         1.24238459e-02,  1.20237898e-02, -7.69484974e-03,\n",
       "        -3.30727436e-02, -1.45808076e-02,  3.43246050e-02,\n",
       "         3.21143419e-02, -4.96741422e-02, -5.27968369e-02,\n",
       "         2.51889303e-02, -1.11904610e-02,  5.64832352e-02,\n",
       "         2.77636852e-02,  5.90689071e-02, -2.61273161e-02,\n",
       "        -6.95008039e-02, -3.15576978e-02, -5.62214339e-03,\n",
       "        -7.93884136e-03, -3.62196900e-02, -8.26047733e-03,\n",
       "         8.05249214e-02, -4.16241921e-02, -2.01846119e-02,\n",
       "        -2.52235290e-02, -3.88054736e-02, -2.00710595e-02,\n",
       "         1.50789914e-03, -5.51338419e-02, -8.35673045e-03,\n",
       "        -1.61523875e-02, -8.79513845e-02, -5.28004877e-02,\n",
       "        -2.88654189e-03, -1.11697149e-02,  7.10910782e-02,\n",
       "         4.44932319e-02,  8.69598426e-03, -1.14432694e-02,\n",
       "         4.47212979e-02,  2.70624813e-02, -3.86100151e-02,\n",
       "        -3.07358261e-02,  2.75634117e-02,  1.48464069e-02,\n",
       "        -1.00845508e-02,  6.45884350e-02,  4.28387662e-03,\n",
       "         8.05836394e-02, -1.69498641e-02,  4.44465503e-02,\n",
       "        -2.09145956e-02, -3.37407738e-02,  3.85780074e-02,\n",
       "        -7.44559616e-02,  1.17512364e-02,  1.01964204e-02,\n",
       "        -3.02421930e-03,  4.80608828e-02, -1.49494391e-02,\n",
       "         2.54592765e-02, -1.46158040e-02,  5.46646416e-02,\n",
       "         1.43051194e-03,  2.99116820e-02,  2.24273186e-02,\n",
       "        -5.79927117e-03, -1.33864526e-02, -2.52460372e-02,\n",
       "        -2.69225910e-02,  1.64003875e-02,  1.20901112e-02,\n",
       "         3.38429734e-02, -2.11539529e-02,  7.17787817e-02,\n",
       "        -7.78904185e-02, -4.04084288e-02,  4.90567498e-02,\n",
       "        -2.61603445e-02,  1.97753590e-02,  4.97209951e-02,\n",
       "        -4.88655381e-02, -4.52128090e-02,  3.63065898e-02,\n",
       "         2.68440694e-02,  3.29160057e-02, -8.24410375e-03,\n",
       "        -1.33646047e-02, -6.22822754e-02, -1.13362661e-02,\n",
       "        -3.79339382e-02, -6.56360280e-05, -1.08087100e-02,\n",
       "         2.67575700e-02,  1.33866509e-02,  5.89998253e-02,\n",
       "        -2.54666172e-02, -3.05371322e-02, -1.53249800e-02,\n",
       "        -9.87035502e-03,  1.95337094e-07, -1.76476724e-02,\n",
       "         5.71432859e-02, -2.49180794e-02,  5.85253723e-02,\n",
       "         4.49808314e-02, -5.99673577e-02, -9.97425616e-03,\n",
       "         4.07801419e-02,  4.13940698e-02,  2.55707726e-02,\n",
       "         2.18985360e-02, -3.04434425e-03, -3.77355106e-02,\n",
       "        -6.24866784e-02, -1.17468778e-02, -4.82194684e-02,\n",
       "        -7.78659210e-02, -1.48841189e-02, -1.75396129e-02,\n",
       "        -2.48471629e-02,  8.05181568e-04, -4.85844910e-03,\n",
       "        -5.16015477e-03,  7.53483502e-03, -9.46175400e-03,\n",
       "        -2.39896346e-02, -3.14654633e-02,  1.50111094e-02,\n",
       "        -1.22348899e-02,  3.00448518e-02,  3.55701670e-02,\n",
       "         3.08971256e-02,  1.72299352e-02,  5.93419448e-02,\n",
       "        -5.74274361e-02, -8.16087723e-02, -4.80572283e-02,\n",
       "        -2.68838424e-02, -1.96331330e-02, -9.15831141e-03,\n",
       "         1.07509056e-02,  2.35639680e-02, -2.62569580e-02,\n",
       "         9.21937004e-02,  1.37132118e-02, -1.19096776e-02,\n",
       "        -4.09874134e-02,  3.37628126e-02, -4.64820908e-03,\n",
       "        -2.50304434e-02,  6.25852346e-02, -1.24449311e-02,\n",
       "         3.82654071e-02, -2.35330854e-02,  8.68125912e-03,\n",
       "         5.08641489e-02,  2.53822445e-03,  5.25634140e-02,\n",
       "         1.14882430e-02,  5.01894541e-02, -3.55215147e-02,\n",
       "        -3.31749097e-02, -3.02003417e-03, -5.36288768e-02,\n",
       "        -2.80938316e-02, -7.51279444e-02, -4.71623316e-02,\n",
       "         9.56887701e-35,  2.55127084e-02, -1.44770980e-04,\n",
       "         1.96710341e-02, -1.33620016e-02, -1.51910949e-02,\n",
       "        -3.28495577e-02, -1.52465852e-03, -2.65272055e-02,\n",
       "        -4.35708016e-02, -1.75950192e-02, -2.20594816e-02]], dtype=float32)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "svec= np.array(vec).reshape(1,-1)\n",
    "print(svec.shape)\n",
    "svec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "fef30d70-6958-4259-abb6-09f8c1870a2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.7731663  0.79433584]] [[330 331]]\n"
     ]
    }
   ],
   "source": [
    "distances, I= index.search(svec, k=2)\n",
    "print(distances, I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "eb00598c-9799-4697-b2a3-356bb5aae0f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>desease_condition</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>['alzheimer disease', 'dementia', 'brain disea...</td>\n",
       "      <td>nct_id: NCT02164643\\nsummary: A Multicenter na...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>['alzheimer disease', 'dementia', 'brain disea...</td>\n",
       "      <td>nct_id: NCT02164643\\nsummary: A Multicenter na...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     desease_condition  \\\n",
       "330  ['alzheimer disease', 'dementia', 'brain disea...   \n",
       "331  ['alzheimer disease', 'dementia', 'brain disea...   \n",
       "\n",
       "                                                  text  \n",
       "330  nct_id: NCT02164643\\nsummary: A Multicenter na...  \n",
       "331  nct_id: NCT02164643\\nsummary: A Multicenter na...  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2= df.iloc[I[0]]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "af5bf8e2-43b6-47af-affa-5111789371ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nct_id: NCT02164643\\nsummary: A Multicenter national longitudinal cohort study including at least 800 individuals consecutively recruited from French Research Memory Centers and followed-up over 24 month and included in Memento.\\nintervention_type: Drug\\nintervention_name: Florbetapir (18F)\\nintervention_description: nan\\nkeywords: [\"Alzheimer\\'s disease\", \\'Mild Cognitive Impairment\\']'"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.iloc[1].text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3899f81-e120-475c-97ed-080cb7f46510",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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