embed.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "from tqdm import tqdm\n",
    "\n",
    "import voyageai\n",
    "\n",
    "vo = voyageai.Client('')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<voyageai.object.embeddings.EmbeddingsObject object at 0x1584f4f40>\n"
     ]
    }
   ],
   "source": [
    "\n",
    "texts = [\n",
    "    \"test1\",\n",
    "    \"test2\",\n",
    "]\n",
    "\n",
    "result = vo.embed(texts, model=\"voyage-large-2-instruct\", input_type=\"document\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'embeddings', 'total_tokens', 'update']\n",
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      "332\n",
      "<bound method EmbeddingsObject.update of <voyageai.object.embeddings.EmbeddingsObject object at 0x13d53ef40>>\n"
     ]
    }
   ],
   "source": [
    "print(dir(result)) # 'embeddings', 'total_tokens', 'update'\n",
    "print(result.embeddings)\n",
    "print(result.embeddings[0])\n",
    "print(result.total_tokens)\n",
    "print(result.update)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: def Mathlib.Tactic.applyTheConstructor\n",
      "\n",
      "-- import Mathlib.Tactic.Use\n",
      "def Mathlib.Tactic.applyTheConstructor\n",
      ": Lean.MVarId → Lean.MetaM (List Lean.MVarId × List Lean.MVarId × List Lean.MVarId)\n",
      "\n",
      "/- When the goal `mvarId` is an inductive datatype with a single constructor,\n",
      "this applies that constructor, then returns metavariables for the non-parameter explicit arguments\n",
      "along with metavariables for the parameters and implicit arguments.\n",
      "\n",
      "The first list of returned metavariables correspond to the arguments that `⟨x,y,...⟩` notation uses.\n",
      "The second list corresponds to everything else: the parameters and implicit arguments.\n",
      "The third list consists of those implicit arguments that are instance implicits, which one can\n",
      "try to synthesize. The third list is a sublist of the second list.\n",
      "\n",
      "Returns metavariables for all arguments whether or not the metavariables are assigned. -/\n"
     ]
    }
   ],
   "source": [
    "def preprocess(theorem):\n",
    "    '''\n",
    "    returns (name, theorem document)\n",
    "    '''\n",
    "    module, name, typ, comment = theorem.split('--ast--\\n')\n",
    "    module, name, typ, comment = module.strip(), name.strip(), typ.strip(), comment.strip()\n",
    "    if comment != '':\n",
    "        comment = f'/- {comment} -/'\n",
    "    return name, f'''\n",
    "-- {module}\n",
    "{name}\n",
    ": {typ}\n",
    "\n",
    "{comment}\n",
    "'''.strip()\n",
    "\n",
    "def preprocess_names(theorem):\n",
    "    module, name, typ, comment = theorem.split('--ast--\\n')\n",
    "    return name.strip()\n",
    "\n",
    "theorems = [preprocess(theorem) for theorem in open('ast.txt').read().split('----ast----\\n')[1:]]\n",
    "names = [preprocess_names(theorem) for theorem in open('ast.txt').read().split('----ast----\\n')[1:]]\n",
    "print('name:', theorems[0][0])\n",
    "print()\n",
    "print(theorems[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "280019"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted token usage: 39253676\n"
     ]
    }
   ],
   "source": [
    "tokenized = vo.tokenize(theorems, model=\"voyage-large-2-instruct\")\n",
    "print('predicted token usage:', sum([len(x) for x in tokenized]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "embed.npz not found, creating new embed from scratch\n",
      "embed = {} <class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "embed = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2801/2801 [29:23<00:00,  1.59it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding done. Saving...\n"
     ]
    }
   ],
   "source": [
    "batch_size = 100\n",
    "for i in tqdm(range(0, len(theorems), batch_size)):\n",
    "    # skip already embedded theorems\n",
    "    batch = [\n",
    "        (name, theorem)\n",
    "        for name, theorem in theorems[i:i+batch_size] \n",
    "        if name not in embed\n",
    "    ]\n",
    "    if len(batch) == 0:\n",
    "        continue\n",
    "    # obey the 300 rpm limit. (Each batch with 100 theorems counts as 1 request)\n",
    "    time.sleep(0.1)\n",
    "    result = vo.embed(\n",
    "        [theorem for name, theorem in batch], \n",
    "        model=\"voyage-large-2-instruct\", \n",
    "        input_type=\"document\"\n",
    "    )\n",
    "    for i, (name, _) in enumerate(batch):\n",
    "        embed[name] = np.array(result.embeddings[i])\n",
    "\n",
    "print('Embedding done. Saving...')\n",
    "np.savez('embed.npz', embed=embed)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "embed_arr = np.array([embed[name] for name in names])\n",
    "np.save('embed_arr.npy', embed_arr)"
   ]
  }
 ],
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