BranchesTags

Latest commit

History

12 Commits

Folders and files

NameName
Last commit message
Last commit date
.gitignore
.gitignore
OpenAI (2).ipynb
OpenAI (2).ipynb
README.md
README.md
requirements.txt
requirements.txt

Repository files navigation

LangChain & Google Gemini Integration 🤖

This repository explores the practical application of Google’s Gemini 2.5 Flash model within the LangChain ecosystem. It serves as a foundational resource for developers looking to move beyond simple API calls toward building structured, chain-based AI workflows.

Project Overview

The core objective is to demonstrate how to wrap Google's generative models in LangChain's modular architecture. By using prompt templates and chains, the project shows how to create reproducible and scalable AI interactions for tasks such as information retrieval and general-purpose assistance.

Key Features

  • Gemini LLM Orchestration: Configuration and invocation of the gemini-2.5-flash model using ChatGoogleGenerativeAI.
  • Dynamic Prompt Engineering: Creation of PromptTemplates to handle variable-based inputs for consistent model responses.
  • Chain Execution: Implementation of the LangChain Expression Language (LCEL) to pipe templates directly into the model for streamlined execution.
  • External Context Awareness: Foundational setup for integrating external data sources into the model's reasoning loop.

Tech Stack

  • Language: Python
  • LLM: Google Gemini 2.5 Flash
  • Framework: LangChain (langchain-google-genai)
  • Environment: Jupyter Notebook / Google Colab

Setup & Installation

1. Environment Configuration

It is recommended to use a dedicated environment to manage the specific API dependencies:

# Create and activate the environment
git clone https://github.com/Joe-Naz01/OpenAI.git
cd OpenAI
conda create -n gemini_langchain python=3.10 -y
conda activate gemini_langchain
# Install required packages
pip install -r requirements.txt
jupyter notebook

About

This notebook provides a hands-on guide to building AI-powered applications with LangChain and Google Gemini. It covers initializing chat models, designing dynamic prompt templates, and orchestrating execution chains to automate natural language tasks.

Topics

artificial-intelligencedata-sciencegeminigemini-apilangchainlanggraphlcelmachine-learningprompt-engineeringpython3

Resources

Readme
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

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
Latest commit
History
12 Commits
Folders and files
NameName
Last commit message
Last commit date
.gitignore
.gitignore
OpenAI (2).ipynb
OpenAI (2).ipynb
README.md
README.md
requirements.txt
requirements.txt
Repository files navigation
LangChain & Google Gemini Integration 🤖
This repository explores the practical application of Google’s Gemini 2.5 Flash model within the LangChain ecosystem. It serves as a foundational resource for developers looking to move beyond simple API calls toward building structured, chain-based AI workflows.
Project Overview
The core objective is to demonstrate how to wrap Google's generative models in LangChain's modular architecture. By using prompt templates and chains, the project shows how to create reproducible and scalable AI interactions for tasks such as information retrieval and general-purpose assistance.
Key Features
  • Gemini LLM Orchestration: Configuration and invocation of the gemini-2.5-flash model using ChatGoogleGenerativeAI.
  • Dynamic Prompt Engineering: Creation of PromptTemplates to handle variable-based inputs for consistent model responses.
  • Chain Execution: Implementation of the LangChain Expression Language (LCEL) to pipe templates directly into the model for streamlined execution.
  • External Context Awareness: Foundational setup for integrating external data sources into the model's reasoning loop.
Tech Stack
  • Language: Python
  • LLM: Google Gemini 2.5 Flash
  • Framework: LangChain (langchain-google-genai)
  • Environment: Jupyter Notebook / Google Colab
Setup & Installation
1. Environment Configuration
It is recommended to use a dedicated environment to manage the specific API dependencies:
# Create and activate the environment
git clone https://github.com/Joe-Naz01/OpenAI.git
cd OpenAI
conda create -n gemini_langchain python=3.10 -y
conda activate gemini_langchain
# Install required packages
pip install -r requirements.txt
jupyter notebook
About
This notebook provides a hands-on guide to building AI-powered applications with LangChain and Google Gemini. It covers initializing chat models, designing dynamic prompt templates, and orchestrating execution chains to automate natural language tasks.
Topics
artificial-intelligencedata-sciencegeminigemini-apilangchainlanggraphlcelmachine-learningprompt-engineeringpython3
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
12 Commits
Folders and files
NameName
Last commit message
Last commit date
.gitignore
.gitignore
OpenAI (2).ipynb
OpenAI (2).ipynb
README.md
README.md
requirements.txt
requirements.txt
Repository files navigation
LangChain & Google Gemini Integration 🤖
This repository explores the practical application of Google’s Gemini 2.5 Flash model within the LangChain ecosystem. It serves as a foundational resource for developers looking to move beyond simple API calls toward building structured, chain-based AI workflows.
Project Overview
The core objective is to demonstrate how to wrap Google's generative models in LangChain's modular architecture. By using prompt templates and chains, the project shows how to create reproducible and scalable AI interactions for tasks such as information retrieval and general-purpose assistance.
Key Features
  • Gemini LLM Orchestration: Configuration and invocation of the gemini-2.5-flash model using ChatGoogleGenerativeAI.
  • Dynamic Prompt Engineering: Creation of PromptTemplates to handle variable-based inputs for consistent model responses.
  • Chain Execution: Implementation of the LangChain Expression Language (LCEL) to pipe templates directly into the model for streamlined execution.
  • External Context Awareness: Foundational setup for integrating external data sources into the model's reasoning loop.
Tech Stack
  • Language: Python
  • LLM: Google Gemini 2.5 Flash
  • Framework: LangChain (langchain-google-genai)
  • Environment: Jupyter Notebook / Google Colab
Setup & Installation
1. Environment Configuration
It is recommended to use a dedicated environment to manage the specific API dependencies:
# Create and activate the environment
git clone https://github.com/Joe-Naz01/OpenAI.git
cd OpenAI
conda create -n gemini_langchain python=3.10 -y
conda activate gemini_langchain
# Install required packages
pip install -r requirements.txt
jupyter notebook
About
This notebook provides a hands-on guide to building AI-powered applications with LangChain and Google Gemini. It covers initializing chat models, designing dynamic prompt templates, and orchestrating execution chains to automate natural language tasks.
Topics
artificial-intelligencedata-sciencegeminigemini-apilangchainlanggraphlcelmachine-learningprompt-engineeringpython3
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
12 Commits
Folders and files
NameName
Last commit message
Last commit date
.gitignore
.gitignore
OpenAI (2).ipynb
OpenAI (2).ipynb
README.md
README.md
requirements.txt
requirements.txt
Repository files navigation
LangChain & Google Gemini Integration 🤖
This repository explores the practical application of Google’s Gemini 2.5 Flash model within the LangChain ecosystem. It serves as a foundational resource for developers looking to move beyond simple API calls toward building structured, chain-based AI workflows.
Project Overview
The core objective is to demonstrate how to wrap Google's generative models in LangChain's modular architecture. By using prompt templates and chains, the project shows how to create reproducible and scalable AI interactions for tasks such as information retrieval and general-purpose assistance.
Key Features
  • Gemini LLM Orchestration: Configuration and invocation of the gemini-2.5-flash model using ChatGoogleGenerativeAI.
  • Dynamic Prompt Engineering: Creation of PromptTemplates to handle variable-based inputs for consistent model responses.
  • Chain Execution: Implementation of the LangChain Expression Language (LCEL) to pipe templates directly into the model for streamlined execution.
  • External Context Awareness: Foundational setup for integrating external data sources into the model's reasoning loop.
Tech Stack
  • Language: Python
  • LLM: Google Gemini 2.5 Flash
  • Framework: LangChain (langchain-google-genai)
  • Environment: Jupyter Notebook / Google Colab
Setup & Installation
1. Environment Configuration
It is recommended to use a dedicated environment to manage the specific API dependencies:
# Create and activate the environment
git clone https://github.com/Joe-Naz01/OpenAI.git
cd OpenAI
conda create -n gemini_langchain python=3.10 -y
conda activate gemini_langchain
# Install required packages
pip install -r requirements.txt
jupyter notebook
About
This notebook provides a hands-on guide to building AI-powered applications with LangChain and Google Gemini. It covers initializing chat models, designing dynamic prompt templates, and orchestrating execution chains to automate natural language tasks.
Topics
artificial-intelligencedata-sciencegeminigemini-apilangchainlanggraphlcelmachine-learningprompt-engineeringpython3
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
12 Commits
Folders and files
NameName
Last commit message
Last commit date
.gitignore
.gitignore
OpenAI (2).ipynb
OpenAI (2).ipynb
README.md
README.md
requirements.txt
requirements.txt
Repository files navigation
LangChain & Google Gemini Integration 🤖
This repository explores the practical application of Google’s Gemini 2.5 Flash model within the LangChain ecosystem. It serves as a foundational resource for developers looking to move beyond simple API calls toward building structured, chain-based AI workflows.
Project Overview
The core objective is to demonstrate how to wrap Google's generative models in LangChain's modular architecture. By using prompt templates and chains, the project shows how to create reproducible and scalable AI interactions for tasks such as information retrieval and general-purpose assistance.
Key Features
  • Gemini LLM Orchestration: Configuration and invocation of the gemini-2.5-flash model using ChatGoogleGenerativeAI.
  • Dynamic Prompt Engineering: Creation of PromptTemplates to handle variable-based inputs for consistent model responses.
  • Chain Execution: Implementation of the LangChain Expression Language (LCEL) to pipe templates directly into the model for streamlined execution.
  • External Context Awareness: Foundational setup for integrating external data sources into the model's reasoning loop.
Tech Stack
  • Language: Python
  • LLM: Google Gemini 2.5 Flash
  • Framework: LangChain (langchain-google-genai)
  • Environment: Jupyter Notebook / Google Colab
Setup & Installation
1. Environment Configuration
It is recommended to use a dedicated environment to manage the specific API dependencies:
# Create and activate the environment
git clone https://github.com/Joe-Naz01/OpenAI.git
cd OpenAI
conda create -n gemini_langchain python=3.10 -y
conda activate gemini_langchain
# Install required packages
pip install -r requirements.txt
jupyter notebook
About
This notebook provides a hands-on guide to building AI-powered applications with LangChain and Google Gemini. It covers initializing chat models, designing dynamic prompt templates, and orchestrating execution chains to automate natural language tasks.
Topics
artificial-intelligencedata-sciencegeminigemini-apilangchainlanggraphlcelmachine-learningprompt-engineeringpython3
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
12 Commits
Folders and files
NameName
Last commit message
Last commit date
.gitignore
.gitignore
OpenAI (2).ipynb
OpenAI (2).ipynb
README.md
README.md
requirements.txt
requirements.txt
Repository files navigation
LangChain & Google Gemini Integration 🤖
This repository explores the practical application of Google’s Gemini 2.5 Flash model within the LangChain ecosystem. It serves as a foundational resource for developers looking to move beyond simple API calls toward building structured, chain-based AI workflows.
Project Overview
The core objective is to demonstrate how to wrap Google's generative models in LangChain's modular architecture. By using prompt templates and chains, the project shows how to create reproducible and scalable AI interactions for tasks such as information retrieval and general-purpose assistance.
Key Features
  • Gemini LLM Orchestration: Configuration and invocation of the gemini-2.5-flash model using ChatGoogleGenerativeAI.
  • Dynamic Prompt Engineering: Creation of PromptTemplates to handle variable-based inputs for consistent model responses.
  • Chain Execution: Implementation of the LangChain Expression Language (LCEL) to pipe templates directly into the model for streamlined execution.
  • External Context Awareness: Foundational setup for integrating external data sources into the model's reasoning loop.
Tech Stack
  • Language: Python
  • LLM: Google Gemini 2.5 Flash
  • Framework: LangChain (langchain-google-genai)
  • Environment: Jupyter Notebook / Google Colab
Setup & Installation
1. Environment Configuration
It is recommended to use a dedicated environment to manage the specific API dependencies:
# Create and activate the environment
git clone https://github.com/Joe-Naz01/OpenAI.git
cd OpenAI
conda create -n gemini_langchain python=3.10 -y
conda activate gemini_langchain
# Install required packages
pip install -r requirements.txt
jupyter notebook
About
This notebook provides a hands-on guide to building AI-powered applications with LangChain and Google Gemini. It covers initializing chat models, designing dynamic prompt templates, and orchestrating execution chains to automate natural language tasks.
Topics
artificial-intelligencedata-sciencegeminigemini-apilangchainlanggraphlcelmachine-learningprompt-engineeringpython3
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
12 Commits
Folders and files
NameName
Last commit message
Last commit date
.gitignore
.gitignore
OpenAI (2).ipynb
OpenAI (2).ipynb
README.md
README.md
requirements.txt
requirements.txt
Repository files navigation
LangChain & Google Gemini Integration 🤖
This repository explores the practical application of Google’s Gemini 2.5 Flash model within the LangChain ecosystem. It serves as a foundational resource for developers looking to move beyond simple API calls toward building structured, chain-based AI workflows.
Project Overview
The core objective is to demonstrate how to wrap Google's generative models in LangChain's modular architecture. By using prompt templates and chains, the project shows how to create reproducible and scalable AI interactions for tasks such as information retrieval and general-purpose assistance.
Key Features
  • Gemini LLM Orchestration: Configuration and invocation of the gemini-2.5-flash model using ChatGoogleGenerativeAI.
  • Dynamic Prompt Engineering: Creation of PromptTemplates to handle variable-based inputs for consistent model responses.
  • Chain Execution: Implementation of the LangChain Expression Language (LCEL) to pipe templates directly into the model for streamlined execution.
  • External Context Awareness: Foundational setup for integrating external data sources into the model's reasoning loop.
Tech Stack
  • Language: Python
  • LLM: Google Gemini 2.5 Flash
  • Framework: LangChain (langchain-google-genai)
  • Environment: Jupyter Notebook / Google Colab
Setup & Installation
1. Environment Configuration
It is recommended to use a dedicated environment to manage the specific API dependencies:
# Create and activate the environment
git clone https://github.com/Joe-Naz01/OpenAI.git
cd OpenAI
conda create -n gemini_langchain python=3.10 -y
conda activate gemini_langchain
# Install required packages
pip install -r requirements.txt
jupyter notebook
About
This notebook provides a hands-on guide to building AI-powered applications with LangChain and Google Gemini. It covers initializing chat models, designing dynamic prompt templates, and orchestrating execution chains to automate natural language tasks.
Topics
artificial-intelligencedata-sciencegeminigemini-apilangchainlanggraphlcelmachine-learningprompt-engineeringpython3
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
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
Latest commit
History
12 Commits
Folders and files
NameName
Last commit message
Last commit date
.gitignore
.gitignore
OpenAI (2).ipynb
OpenAI (2).ipynb
README.md
README.md
requirements.txt
requirements.txt
Repository files navigation
LangChain & Google Gemini Integration 🤖
This repository explores the practical application of Google’s Gemini 2.5 Flash model within the LangChain ecosystem. It serves as a foundational resource for developers looking to move beyond simple API calls toward building structured, chain-based AI workflows.
Project Overview
The core objective is to demonstrate how to wrap Google's generative models in LangChain's modular architecture. By using prompt templates and chains, the project shows how to create reproducible and scalable AI interactions for tasks such as information retrieval and general-purpose assistance.
Key Features
  • Gemini LLM Orchestration: Configuration and invocation of the gemini-2.5-flash model using ChatGoogleGenerativeAI.
  • Dynamic Prompt Engineering: Creation of PromptTemplates to handle variable-based inputs for consistent model responses.
  • Chain Execution: Implementation of the LangChain Expression Language (LCEL) to pipe templates directly into the model for streamlined execution.
  • External Context Awareness: Foundational setup for integrating external data sources into the model's reasoning loop.
Tech Stack
  • Language: Python
  • LLM: Google Gemini 2.5 Flash
  • Framework: LangChain (langchain-google-genai)
  • Environment: Jupyter Notebook / Google Colab
Setup & Installation
1. Environment Configuration
It is recommended to use a dedicated environment to manage the specific API dependencies:
# Create and activate the environment
git clone https://github.com/Joe-Naz01/OpenAI.git
cd OpenAI
conda create -n gemini_langchain python=3.10 -y
conda activate gemini_langchain
# Install required packages
pip install -r requirements.txt
jupyter notebook
About
This notebook provides a hands-on guide to building AI-powered applications with LangChain and Google Gemini. It covers initializing chat models, designing dynamic prompt templates, and orchestrating execution chains to automate natural language tasks.
Topics
artificial-intelligencedata-sciencegeminigemini-apilangchainlanggraphlcelmachine-learningprompt-engineeringpython3
Resources
Readme
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages