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414 lines
13 KiB
Plaintext
414 lines
13 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# CodeTextSplitter\n",
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"\n",
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"CodeTextSplitter allows you to split your code with multiple language support. Import enum `Language` and specify the language. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.text_splitter import (\n",
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" RecursiveCharacterTextSplitter,\n",
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" Language,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['cpp',\n",
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" 'go',\n",
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" 'java',\n",
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" 'js',\n",
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" 'php',\n",
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" 'proto',\n",
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" 'python',\n",
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" 'rst',\n",
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" 'ruby',\n",
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" 'rust',\n",
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" 'scala',\n",
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" 'swift',\n",
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" 'markdown',\n",
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" 'latex',\n",
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" 'html']"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Full list of support languages\n",
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"[e.value for e in Language]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['\\nclass ', '\\ndef ', '\\n\\tdef ', '\\n\\n', '\\n', ' ', '']"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# You can also see the separators used for a given language\n",
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"RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Python\n",
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"\n",
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"Here's an example using the PythonTextSplitter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='def hello_world():\\n print(\"Hello, World!\")', metadata={}),\n",
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" Document(page_content='# Call the function\\nhello_world()', metadata={})]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"PYTHON_CODE = \"\"\"\n",
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"def hello_world():\n",
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" print(\"Hello, World!\")\n",
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"\n",
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"# Call the function\n",
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"hello_world()\n",
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"\"\"\"\n",
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"python_splitter = RecursiveCharacterTextSplitter.from_language(\n",
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" language=Language.PYTHON, chunk_size=50, chunk_overlap=0\n",
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")\n",
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"python_docs = python_splitter.create_documents([PYTHON_CODE])\n",
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"python_docs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## JS\n",
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"Here's an example using the JS text splitter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='function helloWorld() {\\n console.log(\"Hello, World!\");\\n}', metadata={}),\n",
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" Document(page_content='// Call the function\\nhelloWorld();', metadata={})]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"JS_CODE = \"\"\"\n",
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"function helloWorld() {\n",
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" console.log(\"Hello, World!\");\n",
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"}\n",
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"\n",
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"// Call the function\n",
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"helloWorld();\n",
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"\"\"\"\n",
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"\n",
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"js_splitter = RecursiveCharacterTextSplitter.from_language(\n",
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" language=Language.JS, chunk_size=60, chunk_overlap=0\n",
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")\n",
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"js_docs = js_splitter.create_documents([JS_CODE])\n",
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"js_docs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Markdown\n",
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"\n",
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"Here's an example using the Markdown text splitter."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"markdown_text = \"\"\"\n",
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"# 🦜️🔗 LangChain\n",
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"\n",
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"⚡ Building applications with LLMs through composability ⚡\n",
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"\n",
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"## Quick Install\n",
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"\n",
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"```bash\n",
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"# Hopefully this code block isn't split\n",
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"pip install langchain\n",
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"```\n",
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"\n",
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"As an open source project in a rapidly developing field, we are extremely open to contributions.\n",
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"\"\"\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='# 🦜️🔗 LangChain', metadata={}),\n",
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" Document(page_content='⚡ Building applications with LLMs through composability ⚡', metadata={}),\n",
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" Document(page_content='## Quick Install', metadata={}),\n",
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" Document(page_content=\"```bash\\n# Hopefully this code block isn't split\", metadata={}),\n",
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" Document(page_content='pip install langchain', metadata={}),\n",
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" Document(page_content='```', metadata={}),\n",
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" Document(page_content='As an open source project in a rapidly developing field, we', metadata={}),\n",
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" Document(page_content='are extremely open to contributions.', metadata={})]"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"md_splitter = RecursiveCharacterTextSplitter.from_language(\n",
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" language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0\n",
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")\n",
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"md_docs = md_splitter.create_documents([markdown_text])\n",
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"md_docs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Latex\n",
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"\n",
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"Here's an example on Latex text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"latex_text = \"\"\"\n",
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"\\documentclass{article}\n",
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"\n",
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"\\begin{document}\n",
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"\n",
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"\\maketitle\n",
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"\n",
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"\\section{Introduction}\n",
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"Large language models (LLMs) are a type of machine learning model that can be trained on vast amounts of text data to generate human-like language. In recent years, LLMs have made significant advances in a variety of natural language processing tasks, including language translation, text generation, and sentiment analysis.\n",
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"\n",
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"\\subsection{History of LLMs}\n",
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"The earliest LLMs were developed in the 1980s and 1990s, but they were limited by the amount of data that could be processed and the computational power available at the time. In the past decade, however, advances in hardware and software have made it possible to train LLMs on massive datasets, leading to significant improvements in performance.\n",
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"\n",
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"\\subsection{Applications of LLMs}\n",
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"LLMs have many applications in industry, including chatbots, content creation, and virtual assistants. They can also be used in academia for research in linguistics, psychology, and computational linguistics.\n",
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"\n",
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"\\end{document}\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='\\\\documentclass{article}\\n\\n\\x08egin{document}\\n\\n\\\\maketitle', metadata={}),\n",
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" Document(page_content='\\\\section{Introduction}', metadata={}),\n",
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" Document(page_content='Large language models (LLMs) are a type of machine learning', metadata={}),\n",
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" Document(page_content='model that can be trained on vast amounts of text data to', metadata={}),\n",
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" Document(page_content='generate human-like language. In recent years, LLMs have', metadata={}),\n",
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" Document(page_content='made significant advances in a variety of natural language', metadata={}),\n",
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" Document(page_content='processing tasks, including language translation, text', metadata={}),\n",
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" Document(page_content='generation, and sentiment analysis.', metadata={}),\n",
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" Document(page_content='\\\\subsection{History of LLMs}', metadata={}),\n",
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" Document(page_content='The earliest LLMs were developed in the 1980s and 1990s,', metadata={}),\n",
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" Document(page_content='but they were limited by the amount of data that could be', metadata={}),\n",
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" Document(page_content='processed and the computational power available at the', metadata={}),\n",
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" Document(page_content='time. In the past decade, however, advances in hardware and', metadata={}),\n",
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" Document(page_content='software have made it possible to train LLMs on massive', metadata={}),\n",
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" Document(page_content='datasets, leading to significant improvements in', metadata={}),\n",
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" Document(page_content='performance.', metadata={}),\n",
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" Document(page_content='\\\\subsection{Applications of LLMs}', metadata={}),\n",
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" Document(page_content='LLMs have many applications in industry, including', metadata={}),\n",
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" Document(page_content='chatbots, content creation, and virtual assistants. They', metadata={}),\n",
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" Document(page_content='can also be used in academia for research in linguistics,', metadata={}),\n",
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" Document(page_content='psychology, and computational linguistics.', metadata={}),\n",
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" Document(page_content='\\\\end{document}', metadata={})]"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"latex_splitter = RecursiveCharacterTextSplitter.from_language(\n",
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" language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0\n",
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")\n",
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"latex_docs = latex_splitter.create_documents([latex_text])\n",
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"latex_docs"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## HTML\n",
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"\n",
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"Here's an example using an HTML text splitter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"html_text = \"\"\"\n",
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"<!DOCTYPE html>\n",
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"<html>\n",
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" <head>\n",
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" <title>🦜️🔗 LangChain</title>\n",
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" <style>\n",
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" body {\n",
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" font-family: Arial, sans-serif;\n",
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" }\n",
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" h1 {\n",
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" color: darkblue;\n",
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" }\n",
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" </style>\n",
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" </head>\n",
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" <body>\n",
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" <div>\n",
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" <h1>🦜️🔗 LangChain</h1>\n",
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" <p>⚡ Building applications with LLMs through composability ⚡</p>\n",
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" </div>\n",
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" <div>\n",
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" As an open source project in a rapidly developing field, we are extremely open to contributions.\n",
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" </div>\n",
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" </body>\n",
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"</html>\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[Document(page_content='<!DOCTYPE html>\\n<html>\\n <head>', metadata={}),\n",
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" Document(page_content='<title>🦜️🔗 LangChain</title>\\n <style>', metadata={}),\n",
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" Document(page_content='body {', metadata={}),\n",
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" Document(page_content='font-family: Arial, sans-serif;', metadata={}),\n",
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" Document(page_content='}\\n h1 {', metadata={}),\n",
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" Document(page_content='color: darkblue;\\n }', metadata={}),\n",
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" Document(page_content='</style>\\n </head>\\n <body>\\n <div>', metadata={}),\n",
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" Document(page_content='<h1>🦜️🔗 LangChain</h1>', metadata={}),\n",
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" Document(page_content='<p>⚡ Building applications with LLMs through', metadata={}),\n",
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" Document(page_content='composability ⚡</p>', metadata={}),\n",
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" Document(page_content='</div>\\n <div>', metadata={}),\n",
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" Document(page_content='As an open source project in a rapidly', metadata={}),\n",
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" Document(page_content='developing field, we are extremely open to contributions.', metadata={}),\n",
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" Document(page_content='</div>\\n </body>\\n</html>', metadata={})]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"html_splitter = RecursiveCharacterTextSplitter.from_language(\n",
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" language=Language.MARKDOWN, chunk_size=60, chunk_overlap=0\n",
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")\n",
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"html_docs = html_splitter.create_documents([html_text])\n",
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"html_docs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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