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Intersystems.ipynb

@@ -0,0 +1,1782 @@
+{
+ "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": "code",
+   "execution_count": 2,
+   "id": "95c000ff-bf0c-4489-8643-93a238db41dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ['OPENAI_API_KEY']=\"sk-proj-CG2E98bSWs53X2eWO0Z4T3BlbkFJLm7H1vfkbua0zP548CKQ\""
+   ]
+  },
+  {
+   "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": {
+      "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": 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": [
+       "<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>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",
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+       "        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",
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+       "       -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",
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+       "         6.99447095e-02,  5.04214764e-02,  3.58386151e-02,\n",
+       "         5.29594952e-03, -1.40875215e-02,  1.99297220e-02,\n",
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+       "        -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": [
+       "<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>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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

database.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
    "outputs": [],
    "source": [
     "# Load knowledge graph\n",
-    "entity_embeddings = pd.read_csv('./data/entity_embeddings.csv', index_col=0)\n",
+    "entity_embeddings = pd.read_csv('./entity_embeddings.csv', index_col=0)\n",
     "entity_embeddings[\"embedding\"] = entity_embeddings[\"embedding\"].apply(\n",
     "    lambda x: x[1:-1])\n",
     "\n",
@@ -103,7 +103,7 @@
    "source": [
     "# Load clinical trials\n",
     "\n",
-    "relation_embeddings = pd.read_csv('./data/relation_embeddings.csv', index_col=0)\n",
+    "relation_embeddings = pd.read_csv('./relation_embeddings.csv', index_col=0)\n",
     "relation_embeddings[\"embedding\"] = relation_embeddings[\"embedding\"].apply(\n",
     "    lambda x: x[1:-1])\n",
     "\n",
@@ -126,7 +126,7 @@
     "    with conn.begin():\n",
     "        for index, row in relation_embeddings.iterrows():\n",
     "            sql = text(\"\"\"\n",
-    "                INSERT INTO Test.ClinicalTrials \n",
+    "                INSERT INTO Test.RelationEmbeddings \n",
     "                (embedding, label, uri) \n",
     "                VALUES (TO_VECTOR(:embedding), :label, :uri)\n",
     "            \"\"\")\n",
@@ -134,7 +134,7 @@
     "                'embedding': str(row['embedding']),\n",
     "                'label': row['label'], \n",
     "                'uri': row['uri']\n",
-    "            })\n"
+    "            })"
    ]
   },
   {