forked from Archives/langchain
c998569c8f
#docs: text splitters improvements
Changes are only in the Jupyter notebooks.
- added links to the source packages and a short description of these
packages
- removed " Text Splitters" suffixes from the TOC elements (they made
the list of the text splitters messy)
- moved text splitters, based on the length function into a separate
list. They can be mixed with any classes from the "Text Splitters", so
it is a different classification.
## Who can review?
@hwchase17 - project lead
@eyurtsev
@vowelparrot
NOTE: please, check out the results of the `Python code` text splitter
example (text_splitters/examples/python.ipynb). It looks suboptimal.
154 lines
3.9 KiB
Plaintext
154 lines
3.9 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "80f6cd99",
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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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">[Markdown](https://en.wikipedia.org/wiki/Markdown) is a lightweight markup language for creating formatted text using a plain-text editor.\n",
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"\n",
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"`MarkdownTextSplitter` splits text along Markdown headings, code blocks, or horizontal rules. It's implemented as a simple subclass of `RecursiveCharacterSplitter` with Markdown-specific separators. See the source code to see the Markdown syntax expected by default.\n",
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"\n",
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"1. How the text is split: by list of `markdown` specific separators\n",
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"2. How the chunk size is measured: by number of characters"
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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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"id": "96d64839",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain.text_splitter import MarkdownTextSplitter"
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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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"id": "cfb0da17",
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"metadata": {
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"tags": []
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},
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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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"markdown_splitter = MarkdownTextSplitter(chunk_size=100, chunk_overlap=0)"
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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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"id": "d59a4fe8",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"docs = markdown_splitter.create_documents([markdown_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": 4,
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"id": "cbb2e100",
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"metadata": {
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"tags": []
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},
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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\\n\\n⚡ Building applications with LLMs through composability ⚡', metadata={}),\n",
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" Document(page_content=\"Quick Install\\n\\n```bash\\n# Hopefully this code block isn't split\\npip install langchain\", metadata={}),\n",
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" Document(page_content='As an open source project in a rapidly developing field, we are extremely open to contributions.', 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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"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": 5,
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"id": "91b56e7e-b285-4ca4-a786-149544e0e3c6",
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['# 🦜️🔗 LangChain\\n\\n⚡ Building applications with LLMs through composability ⚡',\n",
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" \"Quick Install\\n\\n```bash\\n# Hopefully this code block isn't split\\npip install langchain\",\n",
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" 'As an open source project in a rapidly developing field, we are extremely open to contributions.']"
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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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"markdown_splitter.split_text(markdown_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": null,
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"id": "9bee7858-9175-4d99-bd30-68f2dece8601",
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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": "3.10.6"
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},
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"vscode": {
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"interpreter": {
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"hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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