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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.
130 lines
3.6 KiB
Plaintext
130 lines
3.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ea2973ac",
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"metadata": {},
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"source": [
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"# NLTK\n",
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"\n",
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">[The Natural Language Toolkit](https://en.wikipedia.org/wiki/Natural_Language_Toolkit), or more commonly [NLTK](https://www.nltk.org/), is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language.\n",
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"\n",
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"Rather than just splitting on \"\\n\\n\", we can use `NLTK` to split based on [NLTK tokenizers](https://www.nltk.org/api/nltk.tokenize.html).\n",
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"\n",
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"1. How the text is split: by `NLTK` tokenizer.\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": null,
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"id": "b6af9886-7d53-4aab-84f6-303c4cce7882",
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"metadata": {},
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"outputs": [],
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"source": [
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"#pip install nltk"
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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": "aed17ddf",
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"metadata": {},
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"outputs": [],
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"source": [
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"# This is a long document we can split up.\n",
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"with open('../../../state_of_the_union.txt') as f:\n",
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" state_of_the_union = f.read()"
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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": "20fa9c23",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.text_splitter import NLTKTextSplitter\n",
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"text_splitter = NLTKTextSplitter(chunk_size=1000)"
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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": "5ea10835",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Madam Speaker, Madam Vice President, our First Lady and Second Gentleman.\n",
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"\n",
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"Members of Congress and the Cabinet.\n",
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"\n",
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"Justices of the Supreme Court.\n",
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"\n",
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"My fellow Americans.\n",
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"\n",
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"Last year COVID-19 kept us apart.\n",
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"\n",
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"This year we are finally together again.\n",
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"\n",
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"Tonight, we meet as Democrats Republicans and Independents.\n",
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"\n",
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"But most importantly as Americans.\n",
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"\n",
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"With a duty to one another to the American people to the Constitution.\n",
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"\n",
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"And with an unwavering resolve that freedom will always triumph over tyranny.\n",
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"\n",
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"Six days ago, Russia’s Vladimir Putin sought to shake the foundations of the free world thinking he could make it bend to his menacing ways.\n",
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"\n",
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"But he badly miscalculated.\n",
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"\n",
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"He thought he could roll into Ukraine and the world would roll over.\n",
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"\n",
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"Instead he met a wall of strength he never imagined.\n",
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"\n",
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"He met the Ukrainian people.\n",
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"\n",
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"From President Zelenskyy to every Ukrainian, their fearlessness, their courage, their determination, inspires the world.\n",
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"\n",
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"Groups of citizens blocking tanks with their bodies.\n"
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]
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}
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],
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"source": [
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"texts = text_splitter.split_text(state_of_the_union)\n",
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"print(texts[0])"
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]
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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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