OpenAI Workflows
630 Ergebnisse — ausschließlich quellenverknüpfte n8n-Referenzen
SBuild an MCP Server with Airtable
Who is this for? This template is designed for anyone who wants to integrate MCP with their AI Agents using Airtable. Whether you're a developer, a data analyst, or an automation enthusiast, if you're looking to leverage the power of MCP and Airtable in your n8n workflows, this template is for you. What problem is this workflow solving? This template caters to MCP beginners seeking a hands-on example and developers looking to integrate Airtable MCP service. When integrating MCP with Airtable, manually updating AI Agents after changes to Airtable data on the MCP Server is time-consuming and error-prone. This template automates the process, enabling the AI Agent to instantly recognize changes made to Airtable on the MCP Server. In data management, for example, it ensures that record updates or additions in Airtable are automatically detected by the AI Agent. With detailed steps, it simplifies the integration process for all users. What this workflow does This workflow focuses on integrating MCP with Airtable within n8n. Specifically, it allows you to build an MCP Server and Client using Airtable nodes in n8n. Any changes made to the Airtable Base/Table on the MCP Server are automatic
Automate Support Ticket Triage and Resolution with JIRA and AI
This n8n template automates triaging of newly opened support tickets and issue resolution via JIRA. If your organisation deals with a large number of support requests daily, automating triaging is a great use-case for introducing AI to your support teams. Extending the idea, we can also get AI to give a first attempt at resolving the issue intelligently. How it works A scheduled trigger picks up newly opened JIRA support tickets from the queue and discards any seen before. An AI agent analyses the open ticket to add labels, priority on the seriousness of the issue and simplifies the description for better readability and understanding for human support. Next, the agent attempts to address and resolve the issue by finding similar issues (by tags) which have been resolved. Each similar issue has its comments analysed and summarised to identify the actual resolution and facts. These summarises are then used as context for the AI agent to suggest a fix to the open ticket. How to use Simply connect your JIRA instance to the workflow and activate to start watching for open tickets. Depending on frequency, you may need to increase for decrease the intervals. Define labels to use in the ag
AI Customer Support Assistant · WhatsApp Ready · Works for Any Business
AI Customer-Support Assistant that auto-maps any business site, answers WhatsApp in real time, and lets you earn or save thousands by replacing pricey SaaS chat tools. ⚡ What the workflow does Live “AI employee”* - the bot crawls pages on demand (products, policies, FAQs) so you *never** upload documents or fine-tune a model. No-code setup** - Drop in API keys, paste your domain, publish the webhook—ready in \~15 min. Chat memory** - each conversation turn is written to Supabase/Postgres and automatically replayed into the next prompt, letting the assistant remember context so follow-up questions feel natural and coherent even across long sessions. WhatsApp ready** - Free-form replies inside the 24-hour service window, automatically switches to a template when required (recommended by Meta). 🚀 Why you’ll love it | Benefit | Impact | | ------------------------- | --------------------------------------------------------------------- | | Zero content training | Point the AI Agent at any domain → go live. | | Save or earn money | Replace pricey SaaS chat tools or sell white-label bots to clients. | | Channel-agnostic | Ships with WhatsApp; swap one node for Telegram, Slack, or web cha
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🚀 YouTube Comment Sentiment Analyzer with Google Sheets & OpenAI
🚀 YouTube Comment Sentiment Analyzer with Google Sheets & OpenAI Who Should Use This? Influencers, marketers, and data teams who need instant insights into audience sentiment—without manual exports or scattered tools. The Challenge Manual exports** from YouTube Studio Time-consuming** sentiment tagging Data scattered** across multiple platforms Our workflow automates everything: from fetching comments to logging analysis—so you can focus on insights, not spreadsheets. What You’ll Get Dynamic Input Read a list of YouTube URLs from your Google Sheet. Full Comment Harvest Pull all top-level comments (handles pagination 100/page). Deep Sentiment Scan Classify each comment as Positive, Neutral, or Negative using OpenAI. Smart Formatting Capture metadata (author, likes, timestamp) alongside sentiment. Seamless Storage Append or update rows in your Google Sheet—ready for reporting. Easy Setup Prepare Google Sheet Create a sheet with a video_urls column (full YouTube links). Add and authorize a Google Sheets Oauth or service-account credential in n8n. Enable YouTube API Activate Data API v3 in Google Cloud, grab an API key, and save as an HTTP credential in n8n. Configure OpenAI Enter you
Automate Etsy Data Mining with Bright Data Scrape & Google Gemini
Who this is for? The Automate Etsy Data Mining with Bright Data Scrape & Google Gemini workflow is designed for eCommerce analysts, product researchers, and AI developers seeking to extract actionable insights from Etsy listings at scale. It is ideal for: eCommerce Entrepreneurs** - Researching product demand and competition. Market Analysts** - Tracking pricing, reviews, and trends across Etsy categories. Product Managers** - Identifying niche opportunities and design inspirations. Data Scientists & AI Engineers** - Automating product intelligence pipelines. Growth Hackers** - Leveraging Etsy insights to refine product-market fit. What problem is this workflow solving? Manually browsing Etsy to analyze product listings, pricing, reviews, and seller activity is slow, inconsistent, and unscalable. Scraping Etsy requires unlocking JavaScript-heavy content and structuring noisy data for analysis. This workflow solves: Automated and scalable scraping of Etsy product listings using Bright Data’s infrastructure. A fully paginated data structured Estry production data extraction via the Google Gemini LLM. Enables faster decision-making for product research and competitive analysis via the
Build a Document QA System with RAG using Milvus, Cohere, and OpenAI for Google Drive
Template Description This template creates a powerful Retrieval Augmented Generation (RAG) AI agent workflow in n8n. It monitors a specified Google Drive folder for new PDF files, extracts their content, generates vector embeddings using Cohere, and stores these embeddings in a Milvus vector database. Subsequently, it enables a RAG agent that can retrieve relevant information from the Milvus database based on user queries and generate responses using OpenAI, enhanced by the retrieved context. Functionality The workflow automates the process of ingesting documents into a vector database for use with a RAG system. Watch New Files: Triggers when a new file (specifically targeting PDFs) is added to a designated Google Drive folder. Download New: Downloads the newly added file from Google Drive. Extract from File: Extracts text content from the downloaded PDF file. Default Data Loader / Set Chunks: Processes the extracted text, splitting it into manageable chunks for embedding. Embeddings Cohere: Generates vector embeddings for each text chunk using the Cohere API. Insert into Milvus: Inserts the generated vector embeddings and associated metadata into a Milvus vector database. When cha
WDiscover & Enrich Decision-Makers with Apollo and Human Verification
🧩 What This Workflow Does This workflow automates the process of identifying and enriching decision-maker contacts from a list of companies. By integrating with Apollo's APIs and Google Sheets, it streamlines lead generation, ensures data accuracy through human verification, and maintains an organized leads database. 📚 Use Case Ideal for sales and marketing teams aiming to: Automate the discovery of key decision-makers (e.g., CEOs, CTOs). Enrich contact information with LinkedIn profiles, emails, and phone numbers. Maintain an up-to-date leads database with minimal manual intervention. Receive weekly summaries of newly verified leads. 🧪 Setup 1. Google Sheets Preparation: Use the following pre-configured Google Sheet: Company Decision Maker Discovery Sheet. This spreadsheet includes the necessary tabs and columns: Companies, Contacts, and Contacts (Verified). It also contains a custom onEdit Apps Script function that automatically updates the Status column to Pending whenever the Domain field is modified. To review or modify the script, navigate to Extensions > Apps Script within the Google Sheet. 2. Credentials Setup: Configure the following credentials in your n8n instance:
SAI Agent Web Search using SearchAPI & LLM
🤖 AI Agent Web Search using SearchApi & LLM Who is this for? This workflow is ideal for anyone conducting online research, including students, researchers, content creators, and professionals looking for accurate, up-to-date, and verifiable information. It also serves as an excellent foundation for building more sophisticated AI-driven applications. What problem does this workflow solve? / Use case This workflow automates web searches by enabling an AI agent to efficiently retrieve and summarize external, verifiable information, ensuring accuracy through source citations. What this workflow does Connects an AI agent node to SearchApi.io as an integrated search tool. Empowers the AI agent to perform real-time web searches using various SearchApi engines (e.g., Google, Bing). Allows the AI agent to dynamically determine search parameters based on user interaction, delivering contextually relevant results. Ensures responses include clearly cited sources for validation and further exploration. Setup Install the SearchApi community node: Open Settings → Community Nodes inside your self‑hosted n8n instance. Fill npm Package Name with @searchapi/n8n-nodes-searchapi. Accept the risk promp
Generate YouTube Video Summaries with SearchAPI Transcripts and LLM
🎥 Summarize YouTube Videos using SearchApi & LLM Who is this for? This workflow is ideal for content creators, students, digital marketers, educators, and researchers who want to quickly summarize YouTube videos. What problem does this workflow solve? Manually extracting important information from lengthy YouTube videos can be tedious and prone to errors. This workflow streamlines the process by automatically fetching video transcripts using SearchApi.io and producing concise, informative summaries through a summarization chain powered by any LLM provider. This allows users to quickly access crucial information without the need for manual transcription or detailed viewing. What this workflow does Fetches the complete transcript of a YouTube video using SearchApi. Combines the retrieved transcript into a single, continuous text. Utilizes a Summarization Chain with an LLM (e.g., OpenRouter models) to create a concise summary of the video content. Setup Install the SearchApi community node: Open Settings → Community Nodes inside your self‑hosted n8n instance. Fill npm Package Name with @searchapi/n8n-nodes-searchapi. Accept the risk prompt, and hit Install. It should now appear as a
Dynamically switch between LLMs for AI Agents using LangChain Code
Dynamically switch between LLMs for AI Agents using LangChain Code Purpose This example workflow demonstrates a way to connect multiple LLMs to a single AI Agent/LangChain Node and programmatically use one – or in this case loop through them. What it does This AI workflow takes in customer complaints and generates a response that is being validated before returned. If the answer was not satisfactory, the response will be generated again with a more capable model. How it works A LangChain Code Node allows multiple LLMs to be connected to a single Basic LLM Chain. On every call only one LLM is actually being connected to the Basic LLM Chain, which is determined by the index defined in a previous Node. The AI output is later validated by a Sentiment Analysis Node If the result was not satisfactory, it loops back to the beginning and executes the same query with the next available LLM The loop ends either when the result passed the requirements or when all LLMs have been used before. Setup Clone the workflow and select the belonging credentials. You'll need an OpenAI Account, alternatively you can swap the LLM nodes with ones from a different provider like Anthropic after the import. H
CWMulti-Platform Social Media Publisher with Blotato, GPT-4 Mini & Airtable
How it works • Automates multi-platform social media posting (Instagram, YouTube, TikTok, etc.) using AI-generated content • Integrates Airtable, n8n, and Blotato for full content scheduling and publishing • Supports both image and video uploads with dynamic text and account routing Set up steps • Takes ~15–30 minutes to set up depending on how many platforms you connect • Requires Airtable personal access token and Blotato API key • Uses sticky notes throughout the workflow to explain config, tokens, and troubleshooting clearly
WAutomated PR Code Reviews with GitHub, GPT-4, and Google Sheets Best Practices
AI-Agent Code Review for GitHub Pull Requests Description: This n8n workflow automates the process of reviewing code changes in GitHub pull requests using an OpenAI-powered agent. It connects your GitHub repo, extracts modified files, analyzes diffs, and uses an AI agent to generate a code review based on your internal code best practices (fed from a Google Sheet). It ends by posting the review as a comment on the PR and tagging it with a visual label like ✅ Reviewed by AI. 🔧 What It Does Triggered on PR creation Extracts code diffs from the PR Formats and feeds them into an OpenAI prompt Enriches the prompt using a Google Sheet of Swift best practices Posts an AI-generated review as a comment on the PR Applies a PR label to visually mark reviewed PRs ✅ Prerequisites Before deploying this workflow, ensure you have the following: n8n Instance (Self-hosted or Cloud) GitHub Repository with PR activity OpenAI API Key** for GPT-4o, GPT-4-turbo, or GPT-3.5 GitHub OAuth App** (or PAT) connected to n8n to post comments and access PR diffs (Optional) Google Sheets API credentials if using the code best practices lookup node. ⚙️ Setup Instructions 1. Import the Workflow in n8n, click on Wor
Create a Session-Based Telegram Chatbot with GPT-4o-mini and Google Sheets
How It Works This workflow creates an AI-powered Telegram chatbot with session management, allowing users to: Start new conversations** (/new). Check current sessions** (/current). Resume past sessions** (/resume). Get summaries** (/summary). Ask questions** (/question). Key Components: Session Management**: Uses Google Sheets to track active/expired sessions (storing SESSION IDs and STATE). /new creates a session; /resume reactivates past ones. AI Processing**: OpenAI GPT-4 generates responses with contextual memory (via Simple Memory node). Summarization: Condenses past conversations when requested. Data Logging**: All interactions (prompts/responses) are saved to Google Sheets for audit and retrieval. User Interaction**: Telegram commands trigger specific actions (e.g., /question [query] fetches answers from session history). Main Advantages 1. Multi-session Handling Each user can create, manage, and switch between multiple sessions independently, perfect for organizing different conversations without confusion. 2. Persistent Memory Conversations are stored in Google Sheets, ensuring that chat history and session states are preserved even if the server or n8n instance restarts.
Optimize Amazon Ads with GPT-4o for Bid, Budget & Keyword Recommendations
Overview This template is designed for Amazon sellers and advertisers who want to automate their campaign performance analysis and bidding strategy. It solves the common challenge of manually reviewing Sponsored Products reports and guessing how to adjust keywords, placements, and budgets. By combining Amazon Advertising reports with OpenAI's GPT-4o, this workflow delivers real-time, personalized optimization instructions — automatically. Features 📥 Automatically downloads Sponsored Products reports from Google Drive 🧠 Uses AI to analyze campaign, keyword, placement, targeting, and budget performance 📊 Supports both .csv and .xlsx report formats 🔁 Handles multiple ASINs and scales easily across ad accounts 📧 Sends structured optimization recommendations to your inbox via Gmail 🗂 Built-in logic to normalize filenames and correctly map reports 🧹 Includes error handling and formatting cleanup for AI-ready input Requirements To use this workflow, you’ll need: An Amazon Ads account with access to Sponsored Products reports A Google Drive folder where Amazon Ads reports are delivered (manually or via Gmail automation) A Gmail account (for sending summaries) An OpenAI API key with
WGenerate & Enrich LinkedIn Leads with Apollo.io, LinkedIn API, Mail.so & GPT-3.5
Note: Now includes an Apify alternative for Rapid API (Some users can't create new accounts on Rapid API, so I have added an alternative for you. But immediately you are able to get access to Rapid API, please use that option, it returns more detailed data). *Scroll to bottom for APify setup guide* This n8n workflow automates LinkedIn lead generation, enrichment, and activity analysis using Apollo.io, RapidAPI, Google Sheets and Mail.so. Perfect for sales teams, founders, B2B marketers, and cold outreach pros who want personalized lead insights to drive better conversion rates. ⚙️ How This Workflow Works The workflow is broken down into several key steps, each designed to help you build and enrich a valuable list of LinkedIn leads: 1. 🔑 Lead Discovery (Keyword Search via Apollo) Pulls leads using Apollo.io's API based on keywords, industries, or job titles. Saves lead name, title, company, and LinkedIn URL to your Google Sheet. You can replace the trigger node from the form node to a webhook, whatsapp, telegram, etc, any way for you to send over your query variables over to initiate the workflow. 2. 🧠 Username Extraction (from LinkedIn URL) Extracts the LinkedIn username from pro
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Automated Stock Analysis Reports with Technical & News Sentiment using GPT-4o
Stock Analysis Agent (Hebrew, RTL, GPT-4o) Overview Get comprehensive stock analysis with this AI-powered workflow that provides actionable insights for your investment decisions. On a weekly basis, this workflow: Analyzes stock data from multiple sources (Chart-img, Twelve Data API, Alphavantage) Performs technical analysis using advanced indicators (RSI, MACD, Bollinger Bands, Resistance and Support Levels) Scans financial news from Alpha Vantage to capture market sentiment Uses OpenAI's GPT-4o to identify patterns, trends, and trading opportunities Generates a fully styled, responsive HTML email (with proper RTL layout) in Hebrew Sends detailed recommendations directly to your inbox Perfect for investors, traders, and financial analysts who want data-driven stock insights - combining technical indicators with news sentiment for more informed decisions. Setup Instructions Estimated setup time: 15 minutes Required credentials: OpenAI API Key Chart-img API Key (free tier) Twelve Data API Key (free tier) Alpha Vantage API Key (free tier) SMTP credentials (for email delivery) Steps: Import this template into your n8n instance. Add your API keys under credentials. Configure the SMTP E
Assign Requests Using AI and Send Reminders Based On NocoDB Kanban Board Status
Who is it for? This is automation for support project manager, which helps not only to keep developres informed but also automatically keep clients in the loop - especially useful if you are managing SLA-like agreement. It is actually simple incident management board using free Kanban board, that is extended in functionality via N8N. How It Works? Script has two entry points. The first one is incident form. When incident details are provided, automation gets incident definitions from database and pushes both information to AI. AI comparse definitions with client request, refines incident priority and pushed it in NocoDB database. Second is schedule trigger, which is responsible for regular notificaitons on task status. If task is not picked up or delivered in proper time, then emails or slack messages are being sent both to client and responsible developer. How to set up? Clone automation Create (samples below) two NocoDB tables: one with definitions and second that servers as Kanban board (mind column naming!) Set up email and slack connection You should be ready to go Different incident naming If your incident level naming is different, you need to update few nodes and few column
Build your own N8N Workflows MCP Server
This n8n template shows you how to create an MCP server out of your existing n8n workflows. With this, any MCP client connected can get more done with powerful end-to-end workflows rather than just simple tools. Designing agent tools for outcome rather than utility has been a long recommended practice of mine and it applies well when it comes to building MCP servers; In gist, agents to be making the least amount of calls possible to complete a task. This is why n8n can be a great fit for MCP servers! This template connects your agent/MCP client (like Claude Desktop) to your existing workflows by allowing the AI to discover, manage and run these workflows indirectly. How it works An MCP trigger is used and attaches 4 custom workflow tools to discover and manage existing workflows to use and 1 custom workflow tool to execute them. We'll introduce an idea of "available" workflows which the agent is allowed to use. This will help limit and avoid some issues when trying to use every workflow such as clashes or non-production. The n8n node is a core node which taps into your n8n instance API and is able to retrieve all workflows or filter by tag. For our example, we've tagged the workflo
Update Hubspot engagement by parsing inbox mail with AI
Who is this for? This workflow is designed for Customer Success Managers (CSM), sales, support, or marketing teams using HubSpot CRM who want to automate customer engagement tracking when new emails arrive. It’s ideal for businesses looking to streamline CRM updates without manual data entry. Problem Solved / Use Case Manually logging email interactions in HubSpot is time-consuming. This workflow automatically parses incoming emails, checks if the sender exists in HubSpot, and either: Creates a new contact + logs the email as an engagement (if the sender is new). Logs the email as an engagement for an existing contact. What This Workflow Does Triggers when a new email arrives in a connected IMAP inbox. Parses the email using AI (OpenAI) to extract structured data. Searches HubSpot for the sender’s email address. Updates HubSpot: Creates a contact (if missing) and logs the email as an engagement. Or logs the engagement for an existing contact. Setup Configure Email Account: Replace the default IMAP node with your email provider HubSpot Credentials: Add your HubSpot API key in the HubSpot nodes. OpenAI Integration: Ensure your OpenAI API key is set for email parsing. Customization Ti
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Chat with Your Email History using Telegram, Mistral and Pgvector for RAG
Who is this for? Everyone! Did you dream of asking an AI "what hotel did I stay in for holidays last summer?" or "what were my marks last semester like?". Dream no more, as vector similarity searches and this workflow are the foundations to make it possible (as long as the information appears in your e-mails 😅). 100% Local and Open Source! This workflow is designed to use locally-hosted open source. Ollama as LLM provider, nomic-embed-text as the embeddings model, and pgvector as the vector database engine, on top of Postgres. Structured AND Vectorized This workflow combines structured and semantic search on your e-mail. No need for enterprise setups! Leverage the convenience of n8n and open source to get a bleeding edge solution. Setup You will need a PGVector database with embeddings for all your email. Use my other template Gmail to Vector Embeddings with PGVector and Ollama to set it up in a breeze! Make a copy of my Email Assistant: Convert Natural Language to SQL Queries with Phi4-mini and PostgreSQL, you will need it for structured searches. Install this template and modify the Call the SQL composer Workflow step, to point at your copy of the SQL workflow. Adjust the rest o
Real-time Crypto News & Sentiment Analysis via Telegram with GPT-4o
Stay on top of the latest crypto news and market sentiment instantly, all inside Telegram! This workflow aggregates articles from the top crypto news sources, filters for your topic of interest, and summarizes key news and market sentiment using GPT-4o AI. Ideal for crypto traders, investors, analysts, and market watchers needing fast, intelligent news briefings. > 💬 Just type a coin name (e.g., "Bitcoin", "Solana", "DeFi") into your Telegram AI Agent—and get a smart news digest. How It Works Telegram Bot Trigger User sends a keyword (e.g., "Ethereum") of questions to the Telegram AI Agent. Keyword Extraction (AI-Powered) An AI agent identifies the main topic for better targeting. News Aggregation Pulls articles from 9 major crypto news RSS feeds: Cointelegraph Bitcoin Magazine CoinDesk Bitcoinist NewsBTC CryptoPotato 99Bitcoins CryptoBriefing Crypto.news Filtering Finds and merges articles relevant to the user's keyword. AI Summarization GPT-4o generates a 3-part summary: News Summary Market Sentiment Analysis List of Article Links Telegram Response Sends a structured, easy-to-read digest back to the user. 🔍 What You Can Do with This Workflow 🔹 Summarize breaking news for an
Extract & Classify Invoices & Receipts with Gmail, OpenAI and Google Drive
Who is it for? Anyone who wants to automatically aggregate their invoices or receipts. Main beneficiaries: small business owners and freelancers. How it works Creates a folder in Google Drive for uploading invoices and receipts. Responds (Webhook response) with URL to the created folder. Gets all emails with attachments from a Gmail mailbox. (Optional) Filters emails, e.g. exclude emails sent to specific address. Filters only PDF attachments. Classifies all PDF attachment contents with an AI model (is it a receipt or an invoice?). Uploads receipts and invoices to the created Google Drive folder and optionally sends an email with the attachments to, e.g., your accountant. Pre-conditions/Requirements Gmail and Google Drive accounts A Google Cloud OAuth 2.0 Client ID or a service account with Google Drive and Gmail APIs enabled OpenAI API account and API key Set up steps Provide credentials for the nodes: Gmail, Google Drive, OpenAI. Configure parameters in the "Configure" node. Most importantly: "sendInvoicesTo" for the email address where invoices/receipts should be sent. It uses a Webhook node trigger. It expects a body with a schema such as: { "name": "getInvoicesAndReceiptsFromEm
Analyze Client Transcripts & Route Feedback with GPT-4o Mini, HubSpot, and Gmail
Who is this for? This workflow is designed for Customer Satisfaction Managers (CSM), sales professionals, and operations managers who need to automate the analysis of client transcripts, save summarized notes to HubSpot, and route relevant feedback to the appropriate departments via email. What problem is this workflow solving? / Use Case Manually processing client conversations, extracting key insights, and distributing them to the right teams is time-consuming and error-prone. This workflow automates: Transcript analysis** using AI (OpenAI) to identify relevant content. HubSpot integration** to log meeting notes against client records. Email routing** to ensure feedback reaches the correct departments (e.g., support, sales, product, admin). What this workflow does Input Transcript: Accepts a client conversation transcript (e.g., from emails, calls, or chats). HubSpot Sync: Searches for the client’s HubSpot ID using their email. Uploads a summarized version of the conversation as meeting notes. AI-Powered Routing: Uses an OpenAI model to analyze the transcript and categorize content by department. Triggers emails (via Gmail) to route feedback to the relevant teams. Form Completion
WAutomated Generation of AI Advertising Photos for Product Marketing
How it works This workflow automates the transformation of standard product images into professional product photography featuring human models It uses AI to analyze product images, create tailored photography prompts, and generate high-quality enhanced versions Set up steps You'll need an OpenAI API key and access to gpt-image-1 (verify your organization) Set up a Google Sheets spreadsheet with columns: Image-URL, Prompt, Output Create a Google Drive folder to store the generated images Requirements: OpenAI API access (for image generation and analysis) Google Sheets and Google Drive accounts Basic product images (URLs) as input The spreadsheet must contain a column named "Image-URL" with links to the product images This workflow automatically: Reads product image URLs from your Google Sheet Downloads the images for processing Analyzes each image to understand what product it contains Creates specialized photography prompts ensuring each product is shown with a human model Generates professional product photography using OpenAI's image generation capabilities Uploads results to Google Drive and updates your spreadsheet with links Extra: You can also use the included simple image g