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li2doc

Python app that generates a summarized document from multiple files of different types (pdf, doc, csv, html, etc...).

This project implements a Retrieval-Augmented Generation (RAG) pipeline, using LlamaIndex.

About (Flowchart)

  • The system first retrieves relevant text passages from a data chunks derivated from defined documents (files/).
  • The retrieved passages are then used as input for a language generation model, which generates a summary or response that incorporates information and ideas from the original passages.
  • This generated response is intended to be more coherent, informative, and relevant to the user's query than a traditional generation-only approach.
  • The flowchart ends with the successful creation and saving of the PDF file, allowing users to easily review and reference their responses in a convenient format.

flowchart

Requirements

  • Python ==3.12
  • Node >12

Setup

Install Python Dependencies

pip install -r requirements.txt

Install Node Dependencies

npm install

Install Ollama

Download

Manual install instructions

curl -fsSL https://ollama.com/install.sh | sh

Usage

npm start

This will start the python script, load data from ./files and show the program output.

Server

Run the nitro server using the following command

npm run serve:mac

Scripts

Please update the .env file!

Download

Download a model using the following command

npm run model:download

Load

Load a model using the following command

npm run model:load

Prompt it

Send a prompt to a model using the following command

npm run model:prompt "Type here the content of the prompt..."

Authors

  • jraleman

About

No description, website, or topics provided.

Resources

Readme
Activity
Custom properties

Stars

1 star

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
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;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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BranchesTags
Repository files navigation
li2doc
Python app that generates a summarized document from multiple files of different types (pdf, doc, csv, html, etc...).
This project implements a Retrieval-Augmented Generation (RAG) pipeline, using LlamaIndex.
About (Flowchart)
  • The system first retrieves relevant text passages from a data chunks derivated from defined documents (files/).
  • The retrieved passages are then used as input for a language generation model, which generates a summary or response that incorporates information and ideas from the original passages.
  • This generated response is intended to be more coherent, informative, and relevant to the user's query than a traditional generation-only approach.
  • The flowchart ends with the successful creation and saving of the PDF file, allowing users to easily review and reference their responses in a convenient format.
flowchart
Requirements
  • Python ==3.12
  • Node >12
Setup
Install Python Dependencies
pip install -r requirements.txt
Install Node Dependencies
npm install
Install Ollama
Download
Manual install instructions
curl -fsSL https://ollama.com/install.sh | sh
Usage
npm start
This will start the python script, load data from ./files and show the program output.
Server
Run the nitro server using the following command
npm run serve:mac
Scripts
Please update the .env file!
Download
Download a model using the following command
npm run model:download
Load
Load a model using the following command
npm run model:load
Prompt it
Send a prompt to a model using the following command
npm run model:prompt "Type here the content of the prompt..."
Authors
  • jraleman
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} 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
li2doc
Python app that generates a summarized document from multiple files of different types (pdf, doc, csv, html, etc...).
This project implements a Retrieval-Augmented Generation (RAG) pipeline, using LlamaIndex.
About (Flowchart)
  • The system first retrieves relevant text passages from a data chunks derivated from defined documents (files/).
  • The retrieved passages are then used as input for a language generation model, which generates a summary or response that incorporates information and ideas from the original passages.
  • This generated response is intended to be more coherent, informative, and relevant to the user's query than a traditional generation-only approach.
  • The flowchart ends with the successful creation and saving of the PDF file, allowing users to easily review and reference their responses in a convenient format.
flowchart
Requirements
  • Python ==3.12
  • Node >12
Setup
Install Python Dependencies
pip install -r requirements.txt
Install Node Dependencies
npm install
Install Ollama
Download
Manual install instructions
curl -fsSL https://ollama.com/install.sh | sh
Usage
npm start
This will start the python script, load data from ./files and show the program output.
Server
Run the nitro server using the following command
npm run serve:mac
Scripts
Please update the .env file!
Download
Download a model using the following command
npm run model:download
Load
Load a model using the following command
npm run model:load
Prompt it
Send a prompt to a model using the following command
npm run model:prompt "Type here the content of the prompt..."
Authors
  • jraleman
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
li2doc
Python app that generates a summarized document from multiple files of different types (pdf, doc, csv, html, etc...).
This project implements a Retrieval-Augmented Generation (RAG) pipeline, using LlamaIndex.
About (Flowchart)
  • The system first retrieves relevant text passages from a data chunks derivated from defined documents (files/).
  • The retrieved passages are then used as input for a language generation model, which generates a summary or response that incorporates information and ideas from the original passages.
  • This generated response is intended to be more coherent, informative, and relevant to the user's query than a traditional generation-only approach.
  • The flowchart ends with the successful creation and saving of the PDF file, allowing users to easily review and reference their responses in a convenient format.
flowchart
Requirements
  • Python ==3.12
  • Node >12
Setup
Install Python Dependencies
pip install -r requirements.txt
Install Node Dependencies
npm install
Install Ollama
Download
Manual install instructions
curl -fsSL https://ollama.com/install.sh | sh
Usage
npm start
This will start the python script, load data from ./files and show the program output.
Server
Run the nitro server using the following command
npm run serve:mac
Scripts
Please update the .env file!
Download
Download a model using the following command
npm run model:download
Load
Load a model using the following command
npm run model:load
Prompt it
Send a prompt to a model using the following command
npm run model:prompt "Type here the content of the prompt..."
Authors
  • jraleman
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} 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
li2doc
Python app that generates a summarized document from multiple files of different types (pdf, doc, csv, html, etc...).
This project implements a Retrieval-Augmented Generation (RAG) pipeline, using LlamaIndex.
About (Flowchart)
  • The system first retrieves relevant text passages from a data chunks derivated from defined documents (files/).
  • The retrieved passages are then used as input for a language generation model, which generates a summary or response that incorporates information and ideas from the original passages.
  • This generated response is intended to be more coherent, informative, and relevant to the user's query than a traditional generation-only approach.
  • The flowchart ends with the successful creation and saving of the PDF file, allowing users to easily review and reference their responses in a convenient format.
flowchart
Requirements
  • Python ==3.12
  • Node >12
Setup
Install Python Dependencies
pip install -r requirements.txt
Install Node Dependencies
npm install
Install Ollama
Download
Manual install instructions
curl -fsSL https://ollama.com/install.sh | sh
Usage
npm start
This will start the python script, load data from ./files and show the program output.
Server
Run the nitro server using the following command
npm run serve:mac
Scripts
Please update the .env file!
Download
Download a model using the following command
npm run model:download
Load
Load a model using the following command
npm run model:load
Prompt it
Send a prompt to a model using the following command
npm run model:prompt "Type here the content of the prompt..."
Authors
  • jraleman
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} 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
li2doc
Python app that generates a summarized document from multiple files of different types (pdf, doc, csv, html, etc...).
This project implements a Retrieval-Augmented Generation (RAG) pipeline, using LlamaIndex.
About (Flowchart)
  • The system first retrieves relevant text passages from a data chunks derivated from defined documents (files/).
  • The retrieved passages are then used as input for a language generation model, which generates a summary or response that incorporates information and ideas from the original passages.
  • This generated response is intended to be more coherent, informative, and relevant to the user's query than a traditional generation-only approach.
  • The flowchart ends with the successful creation and saving of the PDF file, allowing users to easily review and reference their responses in a convenient format.
flowchart
Requirements
  • Python ==3.12
  • Node >12
Setup
Install Python Dependencies
pip install -r requirements.txt
Install Node Dependencies
npm install
Install Ollama
Download
Manual install instructions
curl -fsSL https://ollama.com/install.sh | sh
Usage
npm start
This will start the python script, load data from ./files and show the program output.
Server
Run the nitro server using the following command
npm run serve:mac
Scripts
Please update the .env file!
Download
Download a model using the following command
npm run model:download
Load
Load a model using the following command
npm run model:load
Prompt it
Send a prompt to a model using the following command
npm run model:prompt "Type here the content of the prompt..."
Authors
  • jraleman
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
li2doc
Python app that generates a summarized document from multiple files of different types (pdf, doc, csv, html, etc...).
This project implements a Retrieval-Augmented Generation (RAG) pipeline, using LlamaIndex.
About (Flowchart)
  • The system first retrieves relevant text passages from a data chunks derivated from defined documents (files/).
  • The retrieved passages are then used as input for a language generation model, which generates a summary or response that incorporates information and ideas from the original passages.
  • This generated response is intended to be more coherent, informative, and relevant to the user's query than a traditional generation-only approach.
  • The flowchart ends with the successful creation and saving of the PDF file, allowing users to easily review and reference their responses in a convenient format.
flowchart
Requirements
  • Python ==3.12
  • Node >12
Setup
Install Python Dependencies
pip install -r requirements.txt
Install Node Dependencies
npm install
Install Ollama
Download
Manual install instructions
curl -fsSL https://ollama.com/install.sh | sh
Usage
npm start
This will start the python script, load data from ./files and show the program output.
Server
Run the nitro server using the following command
npm run serve:mac
Scripts
Please update the .env file!
Download
Download a model using the following command
npm run model:download
Load
Load a model using the following command
npm run model:load
Prompt it
Send a prompt to a model using the following command
npm run model:prompt "Type here the content of the prompt..."
Authors
  • jraleman
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

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

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BranchesTags
Repository files navigation
li2doc
Python app that generates a summarized document from multiple files of different types (pdf, doc, csv, html, etc...).
This project implements a Retrieval-Augmented Generation (RAG) pipeline, using LlamaIndex.
About (Flowchart)
  • The system first retrieves relevant text passages from a data chunks derivated from defined documents (files/).
  • The retrieved passages are then used as input for a language generation model, which generates a summary or response that incorporates information and ideas from the original passages.
  • This generated response is intended to be more coherent, informative, and relevant to the user's query than a traditional generation-only approach.
  • The flowchart ends with the successful creation and saving of the PDF file, allowing users to easily review and reference their responses in a convenient format.
flowchart
Requirements
  • Python ==3.12
  • Node >12
Setup
Install Python Dependencies
pip install -r requirements.txt
Install Node Dependencies
npm install
Install Ollama
Download
Manual install instructions
curl -fsSL https://ollama.com/install.sh | sh
Usage
npm start
This will start the python script, load data from ./files and show the program output.
Server
Run the nitro server using the following command
npm run serve:mac
Scripts
Please update the .env file!
Download
Download a model using the following command
npm run model:download
Load
Load a model using the following command
npm run model:load
Prompt it
Send a prompt to a model using the following command
npm run model:prompt "Type here the content of the prompt..."
Authors
  • jraleman
About
No description, website, or topics provided.
Resources
Readme
Activity
Custom properties
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages