langchain/docs/modules/indexes/text_splitters/examples/tiktoken_splitter.ipynb

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   "cell_type": "markdown",
   "id": "53049ff5",
   "metadata": {},
   "source": [
    "# TiktokenText Splitter\n",
    "\n",
    "1. How the text is split: by `tiktoken` tokens\n",
    "2. How the chunk size is measured: by `tiktoken` tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8c73186a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is a long document we can split up.\n",
    "with open('../../../state_of_the_union.txt') as f:\n",
    "    state_of_the_union = f.read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1a118b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.text_splitter import TokenTextSplitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ef37c5d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_splitter = TokenTextSplitter(chunk_size=10, chunk_overlap=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5750228a",
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Madam Speaker, Madam Vice President, our\n"
     ]
    }
   ],
   "source": [
    "texts = text_splitter.split_text(state_of_the_union)\n",
    "print(texts[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a87dc30",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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