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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Vector Search "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import os, pandas as pd\n",
"from sqlalchemy import create_engine, text"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"username = 'demo'\n",
"password = 'demo'\n",
"hostname = os.getenv('IRIS_HOSTNAME', 'localhost')\n",
"port = '1972' \n",
"namespace = 'USER'\n",
"CONNECTION_STRING = f\"iris://{username}:{password}@{hostname}:{port}/{namespace}\"\n",
"\n",
"engine = create_engine(CONNECTION_STRING)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load knowledge graph\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",
"len_label = entity_embeddings['label'].str.len().max()\n",
"len_uri = entity_embeddings['uri'].str.len().max()\n",
"# TODO: set varchar length dynamically as above\n",
"with engine.connect() as conn:\n",
" with conn.begin(): \n",
" result = conn.execute(text('DROP TABLE IF EXISTS Test.EntityEmbeddings'))\n",
" sql = f\"\"\"\n",
" CREATE TABLE Test.EntityEmbeddings (\n",
" embedding VECTOR(DOUBLE, 50),\n",
" label VARCHAR(143),\n",
" uri VARCHAR(38)\n",
" )\n",
" \"\"\"\n",
" result = conn.execute(text(sql))\n",
"\n",
"with engine.connect() as conn:\n",
" with conn.begin():\n",
" for index, row in entity_embeddings.iterrows():\n",
" sql = text(\"\"\"\n",
" INSERT INTO Test.EntityEmbeddings \n",
" (embedding, label, uri) \n",
" VALUES (TO_VECTOR(:embedding), :label, :uri)\n",
" \"\"\")\n",
" conn.execute(sql, {\n",
" 'embedding': str(row['embedding']),\n",
" 'label': row['label'], \n",
" 'uri': row['uri']\n",
" })\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Calculate distance between entities\n",
"with engine.connect() as conn:\n",
" with conn.begin():\n",
" sql = f\"\"\"\n",
" SELECT TOP 10 e1.uri AS uri1, e2.uri AS uri2, e1.label AS label1, e2.label AS label2,\n",
" VECTOR_COSINE(e1.embedding, e2.embedding) AS distance\n",
" FROM Test.EntityEmbeddings e1, Test.EntityEmbeddings e2\n",
" WHERE e1.uri = 'http://identifiers.org/medgen/C0002395'\n",
" ORDER BY distance DESC\n",
" \"\"\"\n",
" result = conn.execute(text(sql))\n",
" data = result.fetchall()\n",
" display(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load clinical trials\n",
"\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",
"len_label = relation_embeddings['label'].str.len().max()\n",
"len_uri = relation_embeddings['uri'].str.len().max()\n",
"# TODO: set varchar length dynamically as above\n",
"with engine.connect() as conn:\n",
" with conn.begin():# Load \n",
" result = conn.execute(text('DROP TABLE IF EXISTS Test.RelationEmbeddings'))\n",
" sql = f\"\"\"\n",
" CREATE TABLE Test.RelationEmbeddings (\n",
" embedding VECTOR(DOUBLE, 50),\n",
" label VARCHAR(10),\n",
" uri VARCHAR(38)\n",
" )\n",
" \"\"\"\n",
" result = conn.execute(text(sql))\n",
"\n",
"with engine.connect() as conn:\n",
" with conn.begin():\n",
" for index, row in relation_embeddings.iterrows():\n",
" sql = text(\"\"\"\n",
" INSERT INTO Test.RelationEmbeddings \n",
" (embedding, label, uri) \n",
" VALUES (TO_VECTOR(:embedding), :label, :uri)\n",
" \"\"\")\n",
" conn.execute(sql, {\n",
" 'embedding': str(row['embedding']),\n",
" 'label': row['label'], \n",
" 'uri': row['uri']\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load knowledge graph\n",
"clinical_trials = pd.read_csv(\"clinical_trials_embeddings.csv\")\n",
"clinical_trials[\"embeddings\"] = clinical_trials[\"embeddings\"].apply(lambda x: x[1:-1])\n",
"display(clinical_trials.head())\n",
"\n",
"# TODO: set varchar length dynamically as above\n",
"with engine.connect() as conn:\n",
" with conn.begin():\n",
" result = conn.execute(text(\"DROP TABLE IF EXISTS Test.ClinicalTrials\"))\n",
" sql = f\"\"\"\n",
" CREATE TABLE Test.ClinicalTrials (\n",
" nct_id VARCHAR(11) PRIMARY KEY,\n",
" diseases TEXT,\n",
" embedding VECTOR(DOUBLE, 768)\n",
" )\n",
" \"\"\"\n",
" result = conn.execute(text(sql))\n",
"\n",
"with engine.connect() as conn:\n",
" with conn.begin():\n",
" for index, row in clinical_trials.iterrows():\n",
"\n",
" sql = text(\n",
" \"\"\"\n",
" INSERT INTO Test.ClinicalTrials \n",
" (nct_id, diseases, embedding)\n",
" VALUES (:nct_id, :diseases, TO_VECTOR(:embedding))\n",
" \"\"\"\n",
" )\n",
" conn.execute(\n",
" sql,\n",
" {\n",
" \"nct_id\": row[\"nct_id\"],\n",
" \"diseases\": row[\"desease_condition\"],\n",
" \"embedding\": str(row[\"embeddings\"]),\n",
" },\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# %%\n",
"import pandas as pd\n",
"import rdflib\n",
"\n",
"# Load the disease descriptions from MGDEF.RRF\n",
"df_disease_descriptions = pd.read_csv(\"MGDEF.RRF\", sep=\"|\", header=0)\n",
"# Rename the column '#CUI' to 'CUI'\n",
"df_disease_descriptions.rename(columns={\"#CUI\": \"CUI\"}, inplace=True)\n",
"# Remove the last column, it's empty\n",
"df_disease_descriptions = df_disease_descriptions.iloc[:, :-1]\n",
"# Filter out the rows where the SUPPRESS field is equal to 'Y'\n",
"df_disease_descriptions = df_disease_descriptions[df_disease_descriptions[\"SUPPRESS\"] != \"Y\"]\n",
"# Some of the rows include a \\n character, so we need to remove the rows where the CUI field contains spaces or doesn't start with 'C'\n",
"df_disease_descriptions = df_disease_descriptions[df_disease_descriptions[\"CUI\"].str.startswith(\"C\") & ~df_disease_descriptions[\"CUI\"].str.contains(\" \")]\n",
"# Remove the rows where the DEF field is empty\n",
"df_disease_descriptions = df_disease_descriptions[df_disease_descriptions[\"DEF\"].notnull()]\n",
"df_disease_descriptions['uri'] = df_disease_descriptions['CUI'].apply(lambda x: f'http://identifiers.org/medgen/{x}')\n",
"\n",
"with engine.connect() as conn:\n",
" with conn.begin(): \n",
" result = conn.execute(text('DROP TABLE IF EXISTS Test.DiseaseDescriptions'))\n",
" sql = f\"\"\"\n",
" CREATE TABLE Test.DiseaseDescriptions (\n",
" uri VARCHAR(50),\n",
" description TEXT\n",
" )\n",
" \"\"\"\n",
" result = conn.execute(text(sql))\n",
"\n",
"with engine.connect() as conn:\n",
" with conn.begin():\n",
" for index, row in df_disease_descriptions.iterrows():\n",
" print(row['DEF'])\n",
" print(row['uri'])\n",
" sql = text(\"\"\"\n",
" INSERT INTO Test.DiseaseDescriptions \n",
" (uri, description) \n",
" VALUES ( :uri, :description)\n",
" \"\"\")\n",
" conn.execute(sql, {\n",
" 'uri': row['uri'],\n",
" 'description': row['DEF'], \n",
" })"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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