{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from usearch.index import Index\n",
    "\n",
    "LM_MODEL = \"all-MiniLM-L6-v2\"\n",
    "DB_NAME = \"kuberian.db\"\n",
    "\n",
    "model = SentenceTransformer(LM_MODEL)\n",
    "conn = sqlite3.connect(DB_NAME)\n",
    "cur = conn.cursor()\n",
    "\n",
    "index = Index(\n",
    "    ndim=384, \n",
    "    metric='cos',\n",
    "    dtype='f16')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35000\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import clear_output\n",
    "for i, row in enumerate(cur.execute(\"SELECT function_id, summary FROM function_analyses\")):\n",
    "    id, summary = row\n",
    "    vec = model.encode(summary)\n",
    "    index.add(id, vec)\n",
    "    if i%100 == 0:\n",
    "        print(i)\n",
    "        clear_output(wait=True)\n",
    "index.save(\"./kuberian.usearch\")"
   ]
  }
 ],
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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