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Introduction

md2chunks is a Python project designed for context-enriched markdown chunking, particularly useful for Retrieval-Augmented Generation (RAG) tasks. It processes markdown files, splits them into manageable chunks, and enriches them with context to facilitate efficient information retrieval and processing.

Features

  • Markdown Processing: Converts markdown files to structured text.
  • Text Splitting: Splits text into chunks based on token count, with special handling for URLs, decimals, and abbreviations.
  • Context Enrichment: Adds context to each chunk to maintain the hierarchical structure of the original document.
  • Logging: Provides detailed logging for debugging and monitoring.

Setup

This environment is setup using UV. Board the UV train, life is easier.

  1. Install UV
  2. Build Virtual Environment: uv sync
  3. source .venv/bin/activate

Note: You can alternatively add the following alias to your .zshrc or .bashrc:

alias activate="source .venv/bin/activate"

That way, all you have to do is run: 3. activate

Usage

  1. In src/settings.py enter your Markdown directory path in MD_DIR_PATH and add your markdown files inside it.
  2. Create a folder to store processed markdown files (so that original files remain intact) and provide that path in the PROCESSED_DIR_PATH inside src/settings.py Note: This is an intermediate file and is only useful for debugging purposes.
  3. Run python main.py

Note: main.py only returns the chunks to a variable and quits the program. You are free to extend it your usecase. Incase you want to visualise the chunks, refer to visualisation.ipynb. To look at the chunks run the notebook instead of step 5. 6. logs can be found inside the logs folder 7. Post use run deactivate

Acknowledgements

The idea of TextNodes in src/nodes.py is inspired from LlamaIndex

License

Please refer to LICENSE

About

Context Enriched Markdown Chunking for RAG

Resources

Readme
MIT license
Activity
Custom properties

Stars

3 stars

Watchers

2 watching

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
md2chunks is a Python project designed for context-enriched markdown chunking, particularly useful for Retrieval-Augmented Generation (RAG) tasks. It processes markdown files, splits them into manageable chunks, and enriches them with context to facilitate efficient information retrieval and processing.
Features
  • Markdown Processing: Converts markdown files to structured text.
  • Text Splitting: Splits text into chunks based on token count, with special handling for URLs, decimals, and abbreviations.
  • Context Enrichment: Adds context to each chunk to maintain the hierarchical structure of the original document.
  • Logging: Provides detailed logging for debugging and monitoring.
Setup
This environment is setup using UV. Board the UV train, life is easier.
  1. Install UV
  2. Build Virtual Environment: uv sync
  3. source .venv/bin/activate
Note: You can alternatively add the following alias to your .zshrc or .bashrc:
alias activate="source .venv/bin/activate"

That way, all you have to do is run:
3. activate
Usage
  1. In src/settings.py enter your Markdown directory path in MD_DIR_PATH and add your markdown files inside it.
  2. Create a folder to store processed markdown files (so that original files remain intact) and provide that path in the PROCESSED_DIR_PATH inside src/settings.py
Note: This is an intermediate file and is only useful for debugging purposes.
  3. Run python main.py
Note: main.py only returns the chunks to a variable and quits the program. You are free to extend it your usecase.
Incase you want to visualise the chunks, refer to visualisation.ipynb. To look at the chunks run the notebook instead of step 5.
6. logs can be found inside the logs folder
7. Post use run deactivate
Acknowledgements
The idea of TextNodes in src/nodes.py is inspired from LlamaIndex
License
Please refer to LICENSE
About
Context Enriched Markdown Chunking for RAG
Resources
Readme
MIT license
Activity
Custom properties
Stars
3 stars
Watchers
2 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
md2chunks is a Python project designed for context-enriched markdown chunking, particularly useful for Retrieval-Augmented Generation (RAG) tasks. It processes markdown files, splits them into manageable chunks, and enriches them with context to facilitate efficient information retrieval and processing.
Features
  • Markdown Processing: Converts markdown files to structured text.
  • Text Splitting: Splits text into chunks based on token count, with special handling for URLs, decimals, and abbreviations.
  • Context Enrichment: Adds context to each chunk to maintain the hierarchical structure of the original document.
  • Logging: Provides detailed logging for debugging and monitoring.
Setup
This environment is setup using UV. Board the UV train, life is easier.
  1. Install UV
  2. Build Virtual Environment: uv sync
  3. source .venv/bin/activate
Note: You can alternatively add the following alias to your .zshrc or .bashrc:
alias activate="source .venv/bin/activate"

That way, all you have to do is run:
3. activate
Usage
  1. In src/settings.py enter your Markdown directory path in MD_DIR_PATH and add your markdown files inside it.
  2. Create a folder to store processed markdown files (so that original files remain intact) and provide that path in the PROCESSED_DIR_PATH inside src/settings.py
Note: This is an intermediate file and is only useful for debugging purposes.
  3. Run python main.py
Note: main.py only returns the chunks to a variable and quits the program. You are free to extend it your usecase.
Incase you want to visualise the chunks, refer to visualisation.ipynb. To look at the chunks run the notebook instead of step 5.
6. logs can be found inside the logs folder
7. Post use run deactivate
Acknowledgements
The idea of TextNodes in src/nodes.py is inspired from LlamaIndex
License
Please refer to LICENSE
About
Context Enriched Markdown Chunking for RAG
Resources
Readme
MIT license
Activity
Custom properties
Stars
3 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
md2chunks is a Python project designed for context-enriched markdown chunking, particularly useful for Retrieval-Augmented Generation (RAG) tasks. It processes markdown files, splits them into manageable chunks, and enriches them with context to facilitate efficient information retrieval and processing.
Features
  • Markdown Processing: Converts markdown files to structured text.
  • Text Splitting: Splits text into chunks based on token count, with special handling for URLs, decimals, and abbreviations.
  • Context Enrichment: Adds context to each chunk to maintain the hierarchical structure of the original document.
  • Logging: Provides detailed logging for debugging and monitoring.
Setup
This environment is setup using UV. Board the UV train, life is easier.
  1. Install UV
  2. Build Virtual Environment: uv sync
  3. source .venv/bin/activate
Note: You can alternatively add the following alias to your .zshrc or .bashrc:
alias activate="source .venv/bin/activate"

That way, all you have to do is run:
3. activate
Usage
  1. In src/settings.py enter your Markdown directory path in MD_DIR_PATH and add your markdown files inside it.
  2. Create a folder to store processed markdown files (so that original files remain intact) and provide that path in the PROCESSED_DIR_PATH inside src/settings.py
Note: This is an intermediate file and is only useful for debugging purposes.
  3. Run python main.py
Note: main.py only returns the chunks to a variable and quits the program. You are free to extend it your usecase.
Incase you want to visualise the chunks, refer to visualisation.ipynb. To look at the chunks run the notebook instead of step 5.
6. logs can be found inside the logs folder
7. Post use run deactivate
Acknowledgements
The idea of TextNodes in src/nodes.py is inspired from LlamaIndex
License
Please refer to LICENSE
About
Context Enriched Markdown Chunking for RAG
Resources
Readme
MIT license
Activity
Custom properties
Stars
3 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
md2chunks is a Python project designed for context-enriched markdown chunking, particularly useful for Retrieval-Augmented Generation (RAG) tasks. It processes markdown files, splits them into manageable chunks, and enriches them with context to facilitate efficient information retrieval and processing.
Features
  • Markdown Processing: Converts markdown files to structured text.
  • Text Splitting: Splits text into chunks based on token count, with special handling for URLs, decimals, and abbreviations.
  • Context Enrichment: Adds context to each chunk to maintain the hierarchical structure of the original document.
  • Logging: Provides detailed logging for debugging and monitoring.
Setup
This environment is setup using UV. Board the UV train, life is easier.
  1. Install UV
  2. Build Virtual Environment: uv sync
  3. source .venv/bin/activate
Note: You can alternatively add the following alias to your .zshrc or .bashrc:
alias activate="source .venv/bin/activate"

That way, all you have to do is run:
3. activate
Usage
  1. In src/settings.py enter your Markdown directory path in MD_DIR_PATH and add your markdown files inside it.
  2. Create a folder to store processed markdown files (so that original files remain intact) and provide that path in the PROCESSED_DIR_PATH inside src/settings.py
Note: This is an intermediate file and is only useful for debugging purposes.
  3. Run python main.py
Note: main.py only returns the chunks to a variable and quits the program. You are free to extend it your usecase.
Incase you want to visualise the chunks, refer to visualisation.ipynb. To look at the chunks run the notebook instead of step 5.
6. logs can be found inside the logs folder
7. Post use run deactivate
Acknowledgements
The idea of TextNodes in src/nodes.py is inspired from LlamaIndex
License
Please refer to LICENSE
About
Context Enriched Markdown Chunking for RAG
Resources
Readme
MIT license
Activity
Custom properties
Stars
3 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
md2chunks is a Python project designed for context-enriched markdown chunking, particularly useful for Retrieval-Augmented Generation (RAG) tasks. It processes markdown files, splits them into manageable chunks, and enriches them with context to facilitate efficient information retrieval and processing.
Features
  • Markdown Processing: Converts markdown files to structured text.
  • Text Splitting: Splits text into chunks based on token count, with special handling for URLs, decimals, and abbreviations.
  • Context Enrichment: Adds context to each chunk to maintain the hierarchical structure of the original document.
  • Logging: Provides detailed logging for debugging and monitoring.
Setup
This environment is setup using UV. Board the UV train, life is easier.
  1. Install UV
  2. Build Virtual Environment: uv sync
  3. source .venv/bin/activate
Note: You can alternatively add the following alias to your .zshrc or .bashrc:
alias activate="source .venv/bin/activate"

That way, all you have to do is run:
3. activate
Usage
  1. In src/settings.py enter your Markdown directory path in MD_DIR_PATH and add your markdown files inside it.
  2. Create a folder to store processed markdown files (so that original files remain intact) and provide that path in the PROCESSED_DIR_PATH inside src/settings.py
Note: This is an intermediate file and is only useful for debugging purposes.
  3. Run python main.py
Note: main.py only returns the chunks to a variable and quits the program. You are free to extend it your usecase.
Incase you want to visualise the chunks, refer to visualisation.ipynb. To look at the chunks run the notebook instead of step 5.
6. logs can be found inside the logs folder
7. Post use run deactivate
Acknowledgements
The idea of TextNodes in src/nodes.py is inspired from LlamaIndex
License
Please refer to LICENSE
About
Context Enriched Markdown Chunking for RAG
Resources
Readme
MIT license
Activity
Custom properties
Stars
3 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Introduction
md2chunks is a Python project designed for context-enriched markdown chunking, particularly useful for Retrieval-Augmented Generation (RAG) tasks. It processes markdown files, splits them into manageable chunks, and enriches them with context to facilitate efficient information retrieval and processing.
Features
  • Markdown Processing: Converts markdown files to structured text.
  • Text Splitting: Splits text into chunks based on token count, with special handling for URLs, decimals, and abbreviations.
  • Context Enrichment: Adds context to each chunk to maintain the hierarchical structure of the original document.
  • Logging: Provides detailed logging for debugging and monitoring.
Setup
This environment is setup using UV. Board the UV train, life is easier.
  1. Install UV
  2. Build Virtual Environment: uv sync
  3. source .venv/bin/activate
Note: You can alternatively add the following alias to your .zshrc or .bashrc:
alias activate="source .venv/bin/activate"

That way, all you have to do is run:
3. activate
Usage
  1. In src/settings.py enter your Markdown directory path in MD_DIR_PATH and add your markdown files inside it.
  2. Create a folder to store processed markdown files (so that original files remain intact) and provide that path in the PROCESSED_DIR_PATH inside src/settings.py
Note: This is an intermediate file and is only useful for debugging purposes.
  3. Run python main.py
Note: main.py only returns the chunks to a variable and quits the program. You are free to extend it your usecase.
Incase you want to visualise the chunks, refer to visualisation.ipynb. To look at the chunks run the notebook instead of step 5.
6. logs can be found inside the logs folder
7. Post use run deactivate
Acknowledgements
The idea of TextNodes in src/nodes.py is inspired from LlamaIndex
License
Please refer to LICENSE
About
Context Enriched Markdown Chunking for RAG
Resources
Readme
MIT license
Activity
Custom properties
Stars
3 stars
Watchers
2 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Introduction
md2chunks is a Python project designed for context-enriched markdown chunking, particularly useful for Retrieval-Augmented Generation (RAG) tasks. It processes markdown files, splits them into manageable chunks, and enriches them with context to facilitate efficient information retrieval and processing.
Features
  • Markdown Processing: Converts markdown files to structured text.
  • Text Splitting: Splits text into chunks based on token count, with special handling for URLs, decimals, and abbreviations.
  • Context Enrichment: Adds context to each chunk to maintain the hierarchical structure of the original document.
  • Logging: Provides detailed logging for debugging and monitoring.
Setup
This environment is setup using UV. Board the UV train, life is easier.
  1. Install UV
  2. Build Virtual Environment: uv sync
  3. source .venv/bin/activate
Note: You can alternatively add the following alias to your .zshrc or .bashrc:
alias activate="source .venv/bin/activate"

That way, all you have to do is run:
3. activate
Usage
  1. In src/settings.py enter your Markdown directory path in MD_DIR_PATH and add your markdown files inside it.
  2. Create a folder to store processed markdown files (so that original files remain intact) and provide that path in the PROCESSED_DIR_PATH inside src/settings.py
Note: This is an intermediate file and is only useful for debugging purposes.
  3. Run python main.py
Note: main.py only returns the chunks to a variable and quits the program. You are free to extend it your usecase.
Incase you want to visualise the chunks, refer to visualisation.ipynb. To look at the chunks run the notebook instead of step 5.
6. logs can be found inside the logs folder
7. Post use run deactivate
Acknowledgements
The idea of TextNodes in src/nodes.py is inspired from LlamaIndex
License
Please refer to LICENSE
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Context Enriched Markdown Chunking for RAG
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