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b023f0c0f2
This creates a new kind of text splitter for markdown files. The user can supply a set of headers that they want to split the file on. We define a new text splitter class, `MarkdownHeaderTextSplitter`, that does a few things: (1) For each line, it determines the associated set of user-specified headers (2) It groups lines with common headers into splits See notebook for example usage and test cases.
325 lines
11 KiB
Plaintext
325 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "70e9b619",
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"metadata": {},
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"source": [
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"# MarkdownHeaderTextSplitter\n",
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"\n",
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"The objective is to split a markdown file by a specified set of headers.\n",
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" \n",
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"**Given this example:**\n",
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"\n",
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"# Foo\n",
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"\n",
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"## Bar\n",
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"\n",
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"Hi this is Jim \n",
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"Hi this is Joe\n",
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"\n",
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"## Baz\n",
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"\n",
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"Hi this is Molly\n",
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" \n",
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"**Written as:**\n",
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"\n",
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"```\n",
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"md = '# Foo\\n\\n ## Bar\\n\\nHi this is Jim \\nHi this is Joe\\n\\n ## Baz\\n\\n Hi this is Molly' \n",
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"```\n",
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"\n",
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"**If we want to split on specified headers:**\n",
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"```\n",
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"[(\"#\", \"Header 1\"),(\"##\", \"Header 2\")]\n",
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"```\n",
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"\n",
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"**Then we expect:** \n",
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"```\n",
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"{'content': 'Hi this is Jim \\nHi this is Joe', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar'}}\n",
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"{'content': 'Hi this is Molly', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Baz'}}\n",
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"```\n",
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"\n",
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"**Options:**\n",
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" \n",
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"This also includes `return_each_line` in case a user want to perform other types of aggregation. \n",
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"\n",
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"If `return_each_line=True`, each line and associated header metadata are returned. "
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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": "19c044f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.text_splitter import MarkdownHeaderTextSplitter"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ec8d8053",
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"metadata": {},
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"source": [
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"`Test case 1`"
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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": "5cd0a66c",
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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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"{'content': 'Hi this is Jim', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar'}}\n",
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"{'content': 'Hi this is Joe', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar'}}\n",
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"{'content': 'Hi this is Molly', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Baz'}}\n"
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]
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}
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],
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"source": [
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"# Doc\n",
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"markdown_document = '# Foo\\n\\n ## Bar\\n\\nHi this is Jim\\n\\nHi this is Joe\\n\\n ## Baz\\n\\n Hi this is Molly' \n",
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" \n",
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"# Test case 1\n",
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"headers_to_split_on = [\n",
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" (\"#\", \"Header 1\"),\n",
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" (\"##\", \"Header 2\"),\n",
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"]\n",
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"\n",
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"markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on,return_each_line=True)\n",
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"\n",
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"chunked_docs = markdown_splitter.split_text(markdown_document)\n",
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"for chunk in chunked_docs:\n",
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" print(chunk)"
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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": "67d25a1c",
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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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"{'content': 'Hi this is Jim \\nHi this is Joe', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar'}}\n",
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"{'content': 'Hi this is Molly', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Baz'}}\n"
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]
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}
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],
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"source": [
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"markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on,return_each_line=False)\n",
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"chunked_docs = markdown_splitter.split_text(markdown_document)\n",
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"for chunk in chunked_docs:\n",
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" print(chunk)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f1f74dfa",
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"metadata": {},
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"source": [
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"`Test case 2`"
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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": "2183c96a",
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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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"{'content': 'Text under H3.', 'metadata': {'Header 1': 'H1', 'Header 2': 'H2', 'Header 3': 'H3'}}\n",
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"{'content': 'Text under H2_2.', 'metadata': {'Header 1': 'H1_2', 'Header 2': 'H2_2'}}\n"
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]
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}
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],
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"source": [
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"headers_to_split_on = [\n",
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" (\"#\", \"Header 1\"),\n",
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" (\"##\", \"Header 2\"),\n",
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" (\"###\", \"Header 3\"),\n",
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"]\n",
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"markdown_document = '# H1\\n\\n## H2\\n\\n### H3\\n\\nText under H3.\\n\\n# H1_2\\n\\n## H2_2\\n\\nText under H2_2.'\n",
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"markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on,return_each_line=False)\n",
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"chunked_docs = markdown_splitter.split_text(markdown_document)\n",
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"for chunk in chunked_docs:\n",
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" print(chunk)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "add24254",
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"metadata": {},
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"source": [
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"`Test case 3`"
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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": 6,
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"id": "c3f4690f",
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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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"{'content': 'Hi this is Jim \\nHi this is Joe', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar'}}\n",
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"{'content': 'Hi this is Lance', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar', 'Header 3': 'Boo'}}\n",
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"{'content': 'Hi this is Molly', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Baz'}}\n"
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]
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}
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],
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"source": [
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"markdown_document = '# Foo\\n\\n ## Bar\\n\\nHi this is Jim\\n\\nHi this is Joe\\n\\n ### Boo \\n\\n Hi this is Lance \\n\\n ## Baz\\n\\n Hi this is Molly' \n",
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" \n",
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"headers_to_split_on = [\n",
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" (\"#\", \"Header 1\"),\n",
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" (\"##\", \"Header 2\"),\n",
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" (\"###\", \"Header 3\"),\n",
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"]\n",
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"markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on,return_each_line=False)\n",
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"chunked_docs = markdown_splitter.split_text(markdown_document)\n",
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"for chunk in chunked_docs:\n",
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" print(chunk)"
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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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"id": "20907fb7",
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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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"{'content': 'Hi this is Jim', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar'}}\n",
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"{'content': 'Hi this is Joe', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar'}}\n",
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"{'content': 'Hi this is Lance', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar', 'Header 3': 'Boo'}}\n",
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"{'content': 'Hi this is Molly', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Baz'}}\n"
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]
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}
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],
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"source": [
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"markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on,return_each_line=True)\n",
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"chunked_docs = markdown_splitter.split_text(markdown_document)\n",
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"for chunk in chunked_docs:\n",
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" print(chunk)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9c448431",
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"metadata": {},
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"source": [
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"`Test case 4`"
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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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"id": "9858ea51",
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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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"{'content': 'Hi this is Jim \\nHi this is Joe', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar'}}\n",
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"{'content': 'Hi this is Lance', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar', 'Header 3': 'Boo'}}\n",
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"{'content': 'Hi this is John', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Bar', 'Header 3': 'Boo', 'Header 4': 'Bim'}}\n",
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"{'content': 'Hi this is Molly', 'metadata': {'Header 1': 'Foo', 'Header 2': 'Baz'}}\n"
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]
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}
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],
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"source": [
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"markdown_document = '# Foo\\n\\n ## Bar\\n\\nHi this is Jim\\n\\nHi this is Joe\\n\\n ### Boo \\n\\n Hi this is Lance \\n\\n #### Bim \\n\\n Hi this is John \\n\\n ## Baz\\n\\n Hi this is Molly'\n",
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" \n",
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"headers_to_split_on = [\n",
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" (\"#\", \"Header 1\"),\n",
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" (\"##\", \"Header 2\"),\n",
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" (\"###\", \"Header 3\"),\n",
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" (\"####\", \"Header 4\"),\n",
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"]\n",
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" \n",
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"markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on,return_each_line=False)\n",
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"chunked_docs = markdown_splitter.split_text(markdown_document)\n",
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"for chunk in chunked_docs:\n",
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" print(chunk)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bba6eb9e",
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"metadata": {},
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"source": [
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"`Test case 5`"
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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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"id": "8af8f9a2",
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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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"{'content': 'Markdown[9] is a lightweight markup language for creating formatted text using a plain-text editor. John Gruber created Markdown in 2004 as a markup language that is appealing to human readers in its source code form.[9] \\nMarkdown is widely used in blogging, instant messaging, online forums, collaborative software, documentation pages, and readme files.', 'metadata': {'Header 1': 'Intro', 'Header 2': 'History'}}\n",
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"{'content': 'As Markdown popularity grew rapidly, many Markdown implementations appeared, driven mostly by the need for \\nadditional features such as tables, footnotes, definition lists,[note 1] and Markdown inside HTML blocks.', 'metadata': {'Header 1': 'Intro', 'Header 2': 'Rise and divergence'}}\n",
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"{'content': 'From 2012, a group of people, including Jeff Atwood and John MacFarlane, launched what Atwood characterised as a standardisation effort.', 'metadata': {'Header 1': 'Intro', 'Header 2': 'Rise and divergence', 'Header 4': 'Standardization'}}\n",
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"{'content': 'Implementations of Markdown are available for over a dozen programming languages.', 'metadata': {'Header 1': 'Intro', 'Header 2': 'Implementations'}}\n"
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]
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}
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],
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"source": [
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"markdown_document = '# Intro \\n\\n ## History \\n\\n Markdown[9] is a lightweight markup language for creating formatted text using a plain-text editor. John Gruber created Markdown in 2004 as a markup language that is appealing to human readers in its source code form.[9] \\n\\n Markdown is widely used in blogging, instant messaging, online forums, collaborative software, documentation pages, and readme files. \\n\\n ## Rise and divergence \\n\\n As Markdown popularity grew rapidly, many Markdown implementations appeared, driven mostly by the need for \\n\\n additional features such as tables, footnotes, definition lists,[note 1] and Markdown inside HTML blocks. \\n\\n #### Standardization \\n\\n From 2012, a group of people, including Jeff Atwood and John MacFarlane, launched what Atwood characterised as a standardisation effort. \\n\\n ## Implementations \\n\\n Implementations of Markdown are available for over a dozen programming languages.'\n",
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" \n",
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"headers_to_split_on = [\n",
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" (\"#\", \"Header 1\"),\n",
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" (\"##\", \"Header 2\"),\n",
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" (\"###\", \"Header 3\"),\n",
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" (\"####\", \"Header 4\"),\n",
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"]\n",
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" \n",
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"markdown_splitter = MarkdownHeaderTextSplitter(headers_to_split_on=headers_to_split_on,return_each_line=False)\n",
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"chunked_docs = markdown_splitter.split_text(markdown_document)\n",
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"for chunk in chunked_docs:\n",
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" print(chunk)"
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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": "3.9.16"
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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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