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Chat Google Docs

A chat application that allows users to ask queries based on the contents of a Google Doc using natural language. It combines Retrieval-Augmented Generation (RAG) with Google OAuth to provide personalized access to both public and private Google Docs.

It uses Google Docs as a content source, Vespa.ai for vector + keyword hybrid search, and DeepSeek AI as the LLM for generating responses.

Features

  • Chat-based interface for querying document content
  • Authenticates users via Google OAuth to access private Google Docs
  • Fetches content from public or private Google Docs
  • Chunks the document into sections
  • Embeds chunks using an embedding model
  • Indexes them into Vespa.ai
  • Performs hybrid search (BM25 + vector similarity)
  • Uses DeepSeek AI to generate contextual and accurate responses from top-ranked results

How It Works

  1. User logs in using Google OAuth

  2. The app fetches content from a public or private Google Doc (accessible to the logged-in user)

  3. The document is chunked into manageable sections

  4. Embeddings are generated for each chunk

  5. Chunks are stored in a Vespa index

  6. When the user sends a query:

    • A hybrid search is performed in Vespa (BM25 + vector similarity)
    • The top-k results are passed along with the query to DeepSeek AI
    • The model returns a final response, which is shown in the chat

Architecture

RAG Chat Assistant Architecture

Setup Instructions

1. Clone the Repository

2. Install Dependencies

npm install

3. Setup Environment Variables

Copy .env.sample to .env.local:

cp .env.sample .env.local

Then fill in the required keys

Running Vespa Locally

4. Install Vespa CLI

Follow the official guide: https://docs.vespa.ai/en/vespa-cli.html

5. Start Vespa using Docker

docker compose up

Wait for Vespa to fully start up (may take a few minutes).

6. Set Vespa CLI Target to Local

vespa config set target local

7. Deploy Vespa App Schema

vespa deploy --target local src/vespa

▶️ Start the App

npm run dev

Once running, users can sign in with their Google account and start chatting with their own private or public Google Docs using natural language.

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, '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
Chat Google Docs
A chat application that allows users to ask queries based on the contents of a Google Doc using natural language. It combines Retrieval-Augmented Generation (RAG) with Google OAuth to provide personalized access to both public and private Google Docs.
It uses Google Docs as a content source, Vespa.ai for vector + keyword hybrid search, and DeepSeek AI as the LLM for generating responses.
Features
  • Chat-based interface for querying document content
  • Authenticates users via Google OAuth to access private Google Docs
  • Fetches content from public or private Google Docs
  • Chunks the document into sections
  • Embeds chunks using an embedding model
  • Indexes them into Vespa.ai
  • Performs hybrid search (BM25 + vector similarity)
  • Uses DeepSeek AI to generate contextual and accurate responses from top-ranked results
How It Works
  1. User logs in using Google OAuth
  2. The app fetches content from a public or private Google Doc (accessible to the logged-in user)
  3. The document is chunked into manageable sections
  4. Embeddings are generated for each chunk
  5. Chunks are stored in a Vespa index
  6. When the user sends a query:
    • A hybrid search is performed in Vespa (BM25 + vector similarity)
    • The top-k results are passed along with the query to DeepSeek AI
    • The model returns a final response, which is shown in the chat
Architecture
RAG Chat Assistant Architecture
Setup Instructions
1. Clone the Repository
2. Install Dependencies
npm install
3. Setup Environment Variables
Copy .env.sample to .env.local:
cp .env.sample .env.local
Then fill in the required keys
Running Vespa Locally
4. Install Vespa CLI
Follow the official guide:
https://docs.vespa.ai/en/vespa-cli.html
5. Start Vespa using Docker
docker compose up
Wait for Vespa to fully start up (may take a few minutes).
6. Set Vespa CLI Target to Local
vespa config set target local
7. Deploy Vespa App Schema
vespa deploy --target local src/vespa
▶️ Start the App
npm run dev
Once running, users can sign in with their Google account and start chatting with their own private or public Google Docs using natural language.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, '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
Chat Google Docs
A chat application that allows users to ask queries based on the contents of a Google Doc using natural language. It combines Retrieval-Augmented Generation (RAG) with Google OAuth to provide personalized access to both public and private Google Docs.
It uses Google Docs as a content source, Vespa.ai for vector + keyword hybrid search, and DeepSeek AI as the LLM for generating responses.
Features
  • Chat-based interface for querying document content
  • Authenticates users via Google OAuth to access private Google Docs
  • Fetches content from public or private Google Docs
  • Chunks the document into sections
  • Embeds chunks using an embedding model
  • Indexes them into Vespa.ai
  • Performs hybrid search (BM25 + vector similarity)
  • Uses DeepSeek AI to generate contextual and accurate responses from top-ranked results
How It Works
  1. User logs in using Google OAuth
  2. The app fetches content from a public or private Google Doc (accessible to the logged-in user)
  3. The document is chunked into manageable sections
  4. Embeddings are generated for each chunk
  5. Chunks are stored in a Vespa index
  6. When the user sends a query:
    • A hybrid search is performed in Vespa (BM25 + vector similarity)
    • The top-k results are passed along with the query to DeepSeek AI
    • The model returns a final response, which is shown in the chat
Architecture
RAG Chat Assistant Architecture
Setup Instructions
1. Clone the Repository
2. Install Dependencies
npm install
3. Setup Environment Variables
Copy .env.sample to .env.local:
cp .env.sample .env.local
Then fill in the required keys
Running Vespa Locally
4. Install Vespa CLI
Follow the official guide:
https://docs.vespa.ai/en/vespa-cli.html
5. Start Vespa using Docker
docker compose up
Wait for Vespa to fully start up (may take a few minutes).
6. Set Vespa CLI Target to Local
vespa config set target local
7. Deploy Vespa App Schema
vespa deploy --target local src/vespa
▶️ Start the App
npm run dev
Once running, users can sign in with their Google account and start chatting with their own private or public Google Docs using natural language.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 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
Chat Google Docs
A chat application that allows users to ask queries based on the contents of a Google Doc using natural language. It combines Retrieval-Augmented Generation (RAG) with Google OAuth to provide personalized access to both public and private Google Docs.
It uses Google Docs as a content source, Vespa.ai for vector + keyword hybrid search, and DeepSeek AI as the LLM for generating responses.
Features
  • Chat-based interface for querying document content
  • Authenticates users via Google OAuth to access private Google Docs
  • Fetches content from public or private Google Docs
  • Chunks the document into sections
  • Embeds chunks using an embedding model
  • Indexes them into Vespa.ai
  • Performs hybrid search (BM25 + vector similarity)
  • Uses DeepSeek AI to generate contextual and accurate responses from top-ranked results
How It Works
  1. User logs in using Google OAuth
  2. The app fetches content from a public or private Google Doc (accessible to the logged-in user)
  3. The document is chunked into manageable sections
  4. Embeddings are generated for each chunk
  5. Chunks are stored in a Vespa index
  6. When the user sends a query:
    • A hybrid search is performed in Vespa (BM25 + vector similarity)
    • The top-k results are passed along with the query to DeepSeek AI
    • The model returns a final response, which is shown in the chat
Architecture
RAG Chat Assistant Architecture
Setup Instructions
1. Clone the Repository
2. Install Dependencies
npm install
3. Setup Environment Variables
Copy .env.sample to .env.local:
cp .env.sample .env.local
Then fill in the required keys
Running Vespa Locally
4. Install Vespa CLI
Follow the official guide:
https://docs.vespa.ai/en/vespa-cli.html
5. Start Vespa using Docker
docker compose up
Wait for Vespa to fully start up (may take a few minutes).
6. Set Vespa CLI Target to Local
vespa config set target local
7. Deploy Vespa App Schema
vespa deploy --target local src/vespa
▶️ Start the App
npm run dev
Once running, users can sign in with their Google account and start chatting with their own private or public Google Docs using natural language.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 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
Chat Google Docs
A chat application that allows users to ask queries based on the contents of a Google Doc using natural language. It combines Retrieval-Augmented Generation (RAG) with Google OAuth to provide personalized access to both public and private Google Docs.
It uses Google Docs as a content source, Vespa.ai for vector + keyword hybrid search, and DeepSeek AI as the LLM for generating responses.
Features
  • Chat-based interface for querying document content
  • Authenticates users via Google OAuth to access private Google Docs
  • Fetches content from public or private Google Docs
  • Chunks the document into sections
  • Embeds chunks using an embedding model
  • Indexes them into Vespa.ai
  • Performs hybrid search (BM25 + vector similarity)
  • Uses DeepSeek AI to generate contextual and accurate responses from top-ranked results
How It Works
  1. User logs in using Google OAuth
  2. The app fetches content from a public or private Google Doc (accessible to the logged-in user)
  3. The document is chunked into manageable sections
  4. Embeddings are generated for each chunk
  5. Chunks are stored in a Vespa index
  6. When the user sends a query:
    • A hybrid search is performed in Vespa (BM25 + vector similarity)
    • The top-k results are passed along with the query to DeepSeek AI
    • The model returns a final response, which is shown in the chat
Architecture
RAG Chat Assistant Architecture
Setup Instructions
1. Clone the Repository
2. Install Dependencies
npm install
3. Setup Environment Variables
Copy .env.sample to .env.local:
cp .env.sample .env.local
Then fill in the required keys
Running Vespa Locally
4. Install Vespa CLI
Follow the official guide:
https://docs.vespa.ai/en/vespa-cli.html
5. Start Vespa using Docker
docker compose up
Wait for Vespa to fully start up (may take a few minutes).
6. Set Vespa CLI Target to Local
vespa config set target local
7. Deploy Vespa App Schema
vespa deploy --target local src/vespa
▶️ Start the App
npm run dev
Once running, users can sign in with their Google account and start chatting with their own private or public Google Docs using natural language.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 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
Chat Google Docs
A chat application that allows users to ask queries based on the contents of a Google Doc using natural language. It combines Retrieval-Augmented Generation (RAG) with Google OAuth to provide personalized access to both public and private Google Docs.
It uses Google Docs as a content source, Vespa.ai for vector + keyword hybrid search, and DeepSeek AI as the LLM for generating responses.
Features
  • Chat-based interface for querying document content
  • Authenticates users via Google OAuth to access private Google Docs
  • Fetches content from public or private Google Docs
  • Chunks the document into sections
  • Embeds chunks using an embedding model
  • Indexes them into Vespa.ai
  • Performs hybrid search (BM25 + vector similarity)
  • Uses DeepSeek AI to generate contextual and accurate responses from top-ranked results
How It Works
  1. User logs in using Google OAuth
  2. The app fetches content from a public or private Google Doc (accessible to the logged-in user)
  3. The document is chunked into manageable sections
  4. Embeddings are generated for each chunk
  5. Chunks are stored in a Vespa index
  6. When the user sends a query:
    • A hybrid search is performed in Vespa (BM25 + vector similarity)
    • The top-k results are passed along with the query to DeepSeek AI
    • The model returns a final response, which is shown in the chat
Architecture
RAG Chat Assistant Architecture
Setup Instructions
1. Clone the Repository
2. Install Dependencies
npm install
3. Setup Environment Variables
Copy .env.sample to .env.local:
cp .env.sample .env.local
Then fill in the required keys
Running Vespa Locally
4. Install Vespa CLI
Follow the official guide:
https://docs.vespa.ai/en/vespa-cli.html
5. Start Vespa using Docker
docker compose up
Wait for Vespa to fully start up (may take a few minutes).
6. Set Vespa CLI Target to Local
vespa config set target local
7. Deploy Vespa App Schema
vespa deploy --target local src/vespa
▶️ Start the App
npm run dev
Once running, users can sign in with their Google account and start chatting with their own private or public Google Docs using natural language.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 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
Chat Google Docs
A chat application that allows users to ask queries based on the contents of a Google Doc using natural language. It combines Retrieval-Augmented Generation (RAG) with Google OAuth to provide personalized access to both public and private Google Docs.
It uses Google Docs as a content source, Vespa.ai for vector + keyword hybrid search, and DeepSeek AI as the LLM for generating responses.
Features
  • Chat-based interface for querying document content
  • Authenticates users via Google OAuth to access private Google Docs
  • Fetches content from public or private Google Docs
  • Chunks the document into sections
  • Embeds chunks using an embedding model
  • Indexes them into Vespa.ai
  • Performs hybrid search (BM25 + vector similarity)
  • Uses DeepSeek AI to generate contextual and accurate responses from top-ranked results
How It Works
  1. User logs in using Google OAuth
  2. The app fetches content from a public or private Google Doc (accessible to the logged-in user)
  3. The document is chunked into manageable sections
  4. Embeddings are generated for each chunk
  5. Chunks are stored in a Vespa index
  6. When the user sends a query:
    • A hybrid search is performed in Vespa (BM25 + vector similarity)
    • The top-k results are passed along with the query to DeepSeek AI
    • The model returns a final response, which is shown in the chat
Architecture
RAG Chat Assistant Architecture
Setup Instructions
1. Clone the Repository
2. Install Dependencies
npm install
3. Setup Environment Variables
Copy .env.sample to .env.local:
cp .env.sample .env.local
Then fill in the required keys
Running Vespa Locally
4. Install Vespa CLI
Follow the official guide:
https://docs.vespa.ai/en/vespa-cli.html
5. Start Vespa using Docker
docker compose up
Wait for Vespa to fully start up (may take a few minutes).
6. Set Vespa CLI Target to Local
vespa config set target local
7. Deploy Vespa App Schema
vespa deploy --target local src/vespa
▶️ Start the App
npm run dev
Once running, users can sign in with their Google account and start chatting with their own private or public Google Docs using natural language.
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
0 stars
Watchers
0 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); }
})();
})();

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Chat Google Docs
A chat application that allows users to ask queries based on the contents of a Google Doc using natural language. It combines Retrieval-Augmented Generation (RAG) with Google OAuth to provide personalized access to both public and private Google Docs.
It uses Google Docs as a content source, Vespa.ai for vector + keyword hybrid search, and DeepSeek AI as the LLM for generating responses.
Features
  • Chat-based interface for querying document content
  • Authenticates users via Google OAuth to access private Google Docs
  • Fetches content from public or private Google Docs
  • Chunks the document into sections
  • Embeds chunks using an embedding model
  • Indexes them into Vespa.ai
  • Performs hybrid search (BM25 + vector similarity)
  • Uses DeepSeek AI to generate contextual and accurate responses from top-ranked results
How It Works
  1. User logs in using Google OAuth
  2. The app fetches content from a public or private Google Doc (accessible to the logged-in user)
  3. The document is chunked into manageable sections
  4. Embeddings are generated for each chunk
  5. Chunks are stored in a Vespa index
  6. When the user sends a query:
    • A hybrid search is performed in Vespa (BM25 + vector similarity)
    • The top-k results are passed along with the query to DeepSeek AI
    • The model returns a final response, which is shown in the chat
Architecture
RAG Chat Assistant Architecture
Setup Instructions
1. Clone the Repository
2. Install Dependencies
npm install
3. Setup Environment Variables
Copy .env.sample to .env.local:
cp .env.sample .env.local
Then fill in the required keys
Running Vespa Locally
4. Install Vespa CLI
Follow the official guide:
https://docs.vespa.ai/en/vespa-cli.html
5. Start Vespa using Docker
docker compose up
Wait for Vespa to fully start up (may take a few minutes).
6. Set Vespa CLI Target to Local
vespa config set target local
7. Deploy Vespa App Schema
vespa deploy --target local src/vespa
▶️ Start the App
npm run dev
Once running, users can sign in with their Google account and start chatting with their own private or public Google Docs using natural language.
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