Workflow Templates

This project presents an intelligent email management system powered by advanced artificial intelligence. It utilizes Google's Gemini 2.0 AI model to automatically categorize incoming emails into queries, project updates, and feedback, and generates context-specific responses in real time. Approach: The system processes emails promptly, ensuring consistent and timely communication. In addition to crafting automated replies, it streamlines workflow efficiency by sending calendar invitations for meetings without manual intervention. Results: The Smart Email Auto-Responder enhances email management by marking emails as read, applying appropriate labels, and systematically logging correspondence. This significantly reduces manual workload while improving client engagement and operational productivity.

Title RAG AI Agent for Documents in Google Drive → Pinecone → OpenAI Chat (n8n workflow) Short Description This n8n workflow implements a Retrieval-Augmented Generation (RAG) pipeline + AI agent, allowing users to drop documents into a Google Drive folder and then ask questions about them via a chatbot. New files are indexed automatically to a Pinecone vector store using OpenAI embeddings; the AI agent loads relevant chunks at query time and answers using context plus memory. Why this workflow matters / what problem it solves Large language models (LLMs) are powerful, but they lack up-to-date, domain-specific knowledge. RAG augments the LLM with relevant external documents, reducing hallucination and enabling precise answers. (Pinecone) This workflow automates the ingestion, embedding, storage, retrieval, and chat logic — with minimal manual work. It’s modular: you can swap data sources, vector DBs, or LLMs (with some adjustments). It leverages the built-in AI Agent node in n8n to tie all the parts together. (n8n) How to get the required credentials | Service | Purpose in Workflow | Setup Link | What you need / steps | | ------------------------- | ------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | | Google Drive (OAuth2) | Trigger new file events & download the file | https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/ | Create a Google Cloud OAuth app, grant it Drive scopes, get client ID & secret, configure redirect URI, paste into n8n credentials. | | Pinecone | Vector database for embeddings | https://docs.n8n.io/integrations/builtin/credentials/pinecone/ | Sign up at Pinecone, in dashboard create an index, get API key + environment, paste into n8n credential. | | OpenAI | Embeddings + chat model | https://docs.n8n.io/integrations/builtin/credentials/openai/ | Log in to OpenAI, generate a secret API key, paste into n8n credentials. | You’ll configure these under n8n → Credentials → New Credential, matching credential names referenced in your workflow nodes. Detailed Walkthrough: How the Workflow Works Here’s a step-by-step of what happens inside your workflow (matching your JSON): 1. Google Drive Trigger Watches a specified folder in Google Drive. Whenever a new file appears (fileCreated event), the workflow is triggered (polling every minute). You must set the folder ID (in “folderToWatch”) to the Drive folder you want to monitor. 2. Download File Takes the file ID from the trigger and downloads the file content (binary). 3. Indexing Path: Embeddings + Storage (This path only runs when new files arrive) The file is sent to the Default Data Loader node (via the Recursive Character Text Splitter) to break it into chunks with overlap (so context is preserved). Each chunk is fed into Embeddings OpenAI to convert text into embedding vectors. Then Pinecone Vector Store (insert mode) ingests the vector + text metadata into your Pinecone index. This ensures your vector store stays up-to-date with files you drop into Drive. 4. Chat / Query Path (Triggered by user chat via webhook) When a chat message arrives via When Chat Message Received, it gets passed into the AI Agent node. Before generation, the AI Agent calls the Pinecone Vector Store1 set in “retrieve-as-tool” mode, which runs a vector-based retrieval using the user query embedding. The relevant text chunks are pulled as tools/context. The OpenAI Chat Model node is linked as the language model for the agent. Simple Memory** node provides conversational memory (keeping history across messages). The agent combines retrieved context + memory + user input and instructs the model to produce a response. 5. Connections / Flow Logic The Embeddings OpenAI node’s output is wired into Pinecone Vector Store (insert) and also into Pinecone Vector Store1 (so the same embeddings can be used for retrieval). The AI Agent has tool access to Pinecone retrieval and memory. The Download File node triggers the insert path. The When chat message triggers the agent path. Similar Workflows / Inspirations & Comparisons To help understand how your workflow fits into what’s already out there, here are a few analogues: n8n Blog: “Build a custom knowledge RAG chatbot”** — they show a workflow that ingests documents from external sources, indexes them in Pinecone, and responds to queries via n8n + LLM. (n8n Blog) Index Documents from Google Drive to Pinecone** — this is nearly identical for the ingestion part: trigger on Drive, split, embed, upload. (n8n) Build & Query RAG System with Google Drive, OpenAI, Pinecone** — shows the full RAG + chat logic, same pattern. (n8n) Chat with GitHub API Documentation (RAG)** — demonstrates converting API spec into chunks, embedding, retrieving, and chatting. (n8n) Community tutorials & forums** talk about using the AI Agent node with tools like Pinecone, and how the RAG part is often built as a sub-workflow feeding an agent. (n8n Community) What sets your workflow apart is your explicit combination: Google Drive → automatic ingestion → chat agent with tool integration + memory. Many templates show either ingestion or chat, but fewer show them combined cleanly with n8n’s AI Agent. Suggested Published Description (you can paste/adjust) > RAG AI Agent for Google Drive Documents (n8n workflow) > > This workflow turns a Google Drive folder into a live, queryable knowledge base. Drop PDF, docx, or text files into the folder → new documents are automatically indexed into a Pinecone vector store using OpenAI embeddings → you can ask questions via a webhook chat interface and the AI agent will retrieve relevant text, combine it with memory, and answer in context. > > Credentials needed > > * Google Drive OAuth2 (see: https://docs.n8n.io/integrations/builtin/credentials/google/oauth-generic/) > * Pinecone (see: https://docs.n8n.io/integrations/builtin/credentials/pinecone/) > * OpenAI (see: https://docs.n8n.io/integrations/builtin/credentials/openai/) > > How it works > > 1. Drive trigger picks up new files > 2. Download, split, embed, insert into Pinecone > 3. Chat webhook triggers AI Agent > 4. Agent retrieves relevant chunks + memory > 5. Agent uses OpenAI model to craft answer > > This is built on the core RAG pattern (ingest → retrieve → generate) and enhanced by n8n’s AI Agent node for clean tool integration. > > Inspiration & context > This approach follows best practices from existing n8n RAG tutorials and templates, such as the “Index Documents from Google Drive to Pinecone” ingestion workflow and “Build & Query RAG System” templates. (n8n) > > You're free to swap out the data source (e.g. Dropbox, S3) or vector DB (e.g. Qdrant) as long as you adjust the relevant nodes. If you like, I can generate a polished Markdown README for you (with badges, diagrams, instructions) ready for GitHub/n8n community publishing. Do you want me to build that? [1]: https://www.pinecone.io/learn/retrieval-augmented-generation/?utm_source=chatgpt.com "Retrieval-Augmented Generation (RAG) - Pinecone" [2]: https://n8n.io/integrations/agent/?utm_source=chatgpt.com "AI Agent integrations | Workflow automation with n8n" [3]: https://blog.n8n.io/rag-chatbot/?utm_source=chatgpt.com "Build a Custom Knowledge RAG Chatbot using n8n" [4]: https://n8n.io/workflows/4552-index-documents-from-google-drive-to-pinecone-with-openai-embeddings-for-rag/?utm_source=chatgpt.com "Index Documents from Google Drive to Pinecone with OpenAI ... - N8N" [5]: https://n8n.io/workflows/4501-build-and-query-rag-system-with-google-drive-openai-gpt-4o-mini-and-pinecone/?utm_source=chatgpt.com "Build & Query RAG System with Google Drive, OpenAI GPT-4o-mini ..." [6]: https://n8n.io/workflows/2705-chat-with-github-api-documentation-rag-powered-chatbot-with-pinecone-and-openai/?utm_source=chatgpt.com "Chat with GitHub API Documentation: RAG-Powered Chatbot ... - N8N"

This n8n workflow transforms simple chat requests into professional Center of Excellence (COE) blog posts using AI, then automatically publishes them to Google Drive. What Is This? An intelligent blog creation system that takes a topic from chat and produces executive-level blog posts. Uses three AI stages to ensure high-quality, professional content suitable for business consumption. Why Use This? Professional Content**: Creates sophisticated blogs with business insights Save Time**: Topic to published blog in 2-3 minutes No Writing Skills Needed**: AI handles all the writing and formatting Auto-Publishing**: Creates and shares Google Docs automatically Easy to Use**: Just chat your topic, get your blog How It Works 1. Blog Request & Planning Start Blog Request**: Chat interface receives your blog topic Create Blog Outline**: AI generates structured outline with sections AI Brain for Outline**: Powers the initial content planning 2. Content Review & Writing Review & Fix Outline**: AI improves outline structure and flow Write Full Blog Post**: Transforms outline into complete professional blog AI Brain for Review/Writing**: Advanced AI models handle content creation 3. Publish & Share Blog Clean Up Text Format**: Removes formatting issues for clean presentation Save Blog to Google Drive**: Creates properly formatted Google Doc Email Blog to Stakeholder**: Shares with specified team members Make Blog Public**: Creates shareable public link Send Blog Link to User**: Returns document URL via chat How to Use Start Chat: Access the chat interface and type your blog topic Wait: AI automatically processes (2-3 minutes) Get Your Blog: Receive Google Drive link to your published blog Good to Know Processing Time**: 2-3 minutes per blog Content Quality**: Uses advanced Gemini AI models for professional output Auto-Formatting**: Creates clean Google Docs ready for sharing Instant Sharing**: Stakeholders get email notifications automatically Public Access**: Generates shareable links for broader distribution Customizing This Workflow Content Style Modify AI prompts to match your company's writing tone Adjust content evaluation criteria for different audiences Change blog structure templates Publishing & Sharing Update stakeholder email addresses Change Google Drive folder destinations Modify sharing permissions (public/private) Add more distribution channels AI Enhancement Switch between different AI models for speed vs quality Add more review stages for specialized content Include company-specific knowledge sources

This AI-powered workflow reads emails, understands the request using an LLM, and creates structured Jira issues. Key Insights Poll for new emails every 5 minutes; ensure Gmail/IMAP is properly configured. AI analysis requires a reliable LLM model (e.g., Chat Model or AI Tool). Workflow Process Trigger the workflow with the Check for New Emails Gmail Trigger node. Fetch full email content using the Fetch Full Email Content get message node. Analyze email content with the Analyze Email & Extract Tasks node using AI. Parse the AI-generated JSON output into tasks with the Parse JSON Output from AI node. Create the main Jira issue with the Jira - Create Main Issue create: issue node. Split subtasks from JSON and create them with the Split Subtasks JSON Items and Create Subtasks create: issue nodes. Usage Guide Import the workflow into n8n and configure Gmail and Jira credentials. Test with a sample email to ensure ticket creation and subtask assignment. Prerequisites Gmail/IMAP credentials for email polling Jira API credentials with issue creation permissions Customization Options Adjust the Analyze Email & Extract Tasks node to refine AI task extraction or modify the polling frequency in the trigger node.

Overview This workflow retrieves airline web check-in URLs from Google Sheets, scrapes their content, employs an LLM to generate structured JSON data, refreshes the sheet, creates embeddings, and saves them in a Postgres vector DB for future semantic searches or question-answering. Quick Notes Verify that Google Sheets has accurate URLs for scraping. Ensure the Postgres vector DB is set up correctly for embedding storage. Process Flow Start the workflow with the Chat Trigger - Start node. Retrieve airline check-in URLs using the Fetch Airline URLs node. Scrape webpage data with the Scrape Airline Webpage node. Extract JSON data using the Extract info with LLM node with a Chat Model. Pause for a response with the Wait for Response node. Update Google Sheets with the Store Extracted Data node. Create embeddings with the Generate Embeddings node and store in Postgres vector DB with the Save to Vector DB node. Break down long text with the Split Long Text node and delay the next batch with the Wait Before Next Batch node. Getting Started Import the workflow into n8n and set up Google Sheets and Postgres vector DB credentials. Run a test with a sample URL to confirm scraping and embedding storage. Tailored Adjustments Tweak the Extract info with LLM node to adjust JSON output or modify the Fetch Airline URLs node to pull from different sheet fields.

The template allows to make Dropcontact batch requests up to 250 requests every 10 minutes (1500/hour). Valuable if high volume email enrichment is expected. Dropcontact will look for email & basic email qualification if first_name, last_name, company_name is provided. +++++++++++++++++++++++++++++++++++++++++ Step 1: Node "Profiles Query" Connect your own source (Airtable, Google Sheets, Supabase,...) the template is using Postgres by default. Note I: Be careful your source is only returning a maximum of 250 items. Note II: The next node uses the next variables, make sure you can map these from your source file: first_name last_name website (company_name would work too) full_name (see note) Note III: This template is using the Dropcontact Batch API, which works in a POST & GET setup. Not a GET request only to retrieve data, as Dropcontact needs to process the batch data load properly. +++++++++++++++++++++++++++++++++++++++++ Step 2: Node "Data Transformation" Will transform the input variables in the proper json format. This json format is expected from the Dropcontact API to make a batch request. "full_name" is being used as a custom identifier to update the returned email to the proper contact in your source database. To make things easy, use a unique identiefer in the full_name variable. +++++++++++++++++++++++++++++++++++++++++ Step3: Node: "Bulk Dropcontact Requests". Enter your Dropcontact credentials in the node: Bulk Dropcontact Requests. +++++++++++++++++++++++++++++++++++++++++ Step4: Connect your output source by mapping the data you like to use. +++++++++++++++++++++++++++++++++++++++++ Step5: Node: "Slack" (OPTIONAL) Connect your slack account, if an error occur, you will be notified. TIP: Try to run the workflow with a batch of 10 (not 250) as it might need to run initially before you will be able to map the data to your final destination. Once the data fields are properly mapped, adjust back to 250.

Description This workflow automatically collects and organizes research papers from academic databases and journals into Google Sheets. It helps researchers and students save time by eliminating manual searches across multiple academic sources and centralizing research materials. Overview This workflow automatically scrapes research papers from academic databases and journals, then organizes them in Google Sheets. It uses Bright Data to access academic sources and extracts key information like titles, authors, abstracts, and citations. Tools Used n8n:** The automation platform that orchestrates the workflow. Bright Data:** For scraping academic websites and research databases without getting blocked. Google Sheets:** For organizing and storing research paper data. How to Install Import the Workflow: Download the .json file and import it into your n8n instance. Configure Bright Data: Add your Bright Data credentials to the Bright Data node. Connect Google Sheets: Authenticate your Google account. Customize: Specify research topics, journals, or authors to track. Use Cases Academic Researchers:** Stay updated on new papers in your field. Students:** Collect research for literature reviews and dissertations. Research Teams:** Collaborate on literature databases. Connect with Me Website:** https://www.nofluff.online YouTube:** https://www.youtube.com/@YaronBeen/videos LinkedIn:** https://www.linkedin.com/in/yaronbeen/ Get Bright Data:** https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #research #academicpapers #brightdata #googlesheets #researchpapers #academicresearch #literaturesearch #scholarlyarticles #n8nworkflow #workflow #nocode #researchautomation #academicscraping #researchtools #papertracking #academicjournals #researchdatabase #literaturereview #academicwriting #datascraping #researchorganization #scholarlyresearch #citationmanagement #academicproductivity

Overview This workflow automates customer support ticket processing using AI-powered analysis, classification, and intelligent routing. It processes incoming tickets from email or webhook, translates messages when needed, analyzes sentiment and urgency, and routes tickets to auto-reply or escalation flows. The system also updates CRM platforms and logs observability metrics for monitoring. This enables faster response times, improved customer experience, and scalable support operations. How It Works Input Sources Receives tickets via: IMAP Email Trigger Webhook endpoint Workflow Configuration Defines: CRM/Helpdesk API endpoint Escalation webhook URL Observability logging endpoint Data Cleaning & Normalization Extracts and cleans HTML content Normalizes ticket data: Ticket ID User email Message content Timestamp Source channel Language Detection & Translation Detects the original language Translates message into English if needed Returns confidence score AI Support Intelligence Classifies ticket into: Sentiment (positive/neutral/negative) Urgency (low → critical) Category (billing, bug, technical, etc.) Generates: Short summary Churn risk score (0–1) Recommended action path Decision Routing Routes tickets based on AI output: Auto Reply → Generate response Escalate / Critical → Send to team Auto Reply Flow AI Reply Generation Drafts professional response using ticket context Keeps tone empathetic and actionable CRM/Helpdesk Update Sends structured ticket data to CRM: Priority Category Sentiment Churn risk Draft reply Escalation Flow Escalation Handling Sends high-priority tickets to support team Includes full ticket context and analysis Observability & Monitoring Metrics Logging Tracks: Response time Escalation status Category & urgency Sentiment & churn risk Sends data to observability endpoint (optional) Setup Instructions Email / Webhook Setup Configure IMAP credentials OR webhook endpoint (support-ticket) AI Model Setup Add Anthropic or OpenAI credentials Connect models to: Translation agent Intelligence agent Reply generator CRM / Helpdesk Integration Set API endpoint URL Configure headers and authentication Escalation Setup Add webhook URL for team notifications (Slack, internal API, etc.) Observability (Optional) Configure logging endpoint for metrics tracking Customize Prompts Adjust system messages for: Translation Classification Reply generation Use Cases AI-powered customer support automation SaaS support ticket triaging Multi-language support systems Helpdesk automation with CRM integration Customer churn risk detection workflows Requirements Anthropic or OpenAI API key Email (IMAP) or webhook source CRM/helpdesk system API Optional observability/logging service n8n instance Key Features Multi-channel ticket ingestion (email + webhook) Automatic language detection and translation AI-based sentiment, urgency, and category classification Intelligent routing (auto-reply vs escalation) AI-generated support replies CRM integration for structured ticket updates Observability and performance tracking Summary A powerful AI-driven support automation workflow that processes, analyzes, and routes customer tickets intelligently. It reduces manual workload, improves response speed, and enables scalable, data-driven support operations.

Description Turn incoming Gmail messages into structured Zendesk tickets, enriched by Azure OpenAI, and log key details to Google Sheets for tracking. Ideal for IT Support teams needing fast, consistent intake and documentation. ⚡ What This Template Does Fetches new emails via Gmail Trigger. ✉️ Normalizes Gmail data and formats it for downstream steps. Enriches and structures content with Azure OpenAI Chat Model and Output Parsers. Creates Zendesk tickets from the processed data. 🎫 Appends or updates logs in Google Sheets for auditing and reporting. 📊 Key Benefits Saves time by automating ticket creation and logging. ⏱️ Improves ticket quality with AI-driven normalization and structure. Ensures consistent records in Google Sheets for easy reporting. Reduces manual errors in IT Support intake. ✅ Features Gmail-triggered intake flow for new messages. AI enrichment using Azure OpenAI Chat Model with parsing and memory tooling. Zendesk ticket creation (create: ticket) with structured fields. Google Sheets logging (appendOrUpdate: sheet). Modular design with Execute Workflow nodes for reuse and scaling. Requirements n8n instance (Cloud or self-hosted). Gmail credentials configured in n8n for the Gmail Trigger. Zendesk credentials with permission to create tickets. Google Sheets credentials with access to the target spreadsheet (append/update enabled). Azure OpenAI credentials configured for the Azure OpenAI Chat Model and associated parsing. Target Audience IT Support and Helpdesk teams handling email-based requests. 🛠️ Operations teams standardizing inbound email workflows. Agencies and MSPs offering managed support intake. Internal automation teams centralizing ticket capture and logging. Step-by-Step Setup Instructions Connect Gmail credentials in n8n and select the inbox/label for the Gmail Trigger. Add Zendesk credentials and confirm ticket creation permissions. Configure Google Sheets credentials and select the target sheet for logs. Add Azure OpenAI credentials to the Azure OpenAI Chat Model node and verify parsing steps. Import the workflow, assign credentials to each node, update any placeholders, and run a test. Rename the final email/logging nodes descriptively (e.g., “Log to Support Sheet”) and schedule if needed.

Description Process new resumes from Google Drive, extract structured candidate data with AI, save to Google Sheets, and auto-create a ClickUp hiring task. Gain a centralized, searchable candidate database and instant task kickoff—no manual data entry. 🚀 What This Template Does Watches a Google Drive folder for new resume PDFs and triggers the workflow. 📂 Downloads the file and converts the PDF to clean, readable text. 📄 Analyzes resume text with an AI Resume Analyzer to extract structured candidate info (name, email, phone, experience, skills, education). 🤖 Cleans and validates the AI JSON output for reliability. 🧹 Appends or updates a candidate row in Google Sheets and creates a ClickUp hiring task. ✅ Key Benefits Save hours with end-to-end, hands-off resume processing. ⏱️ Never miss a candidate—every upload triggers automatically. 🔔 Keep a single source of truth in Sheets, always up-to-date. 📊 Kickstart hiring instantly with auto-created ClickUp tasks. 🗂 Works with varied resume formats using AI extraction. 🧠 Features Google Drive “Watch for New Resumes” trigger (every minute). ⏲ PDF-to-text extraction optimized for text-based PDFs. 📘 AI-powered resume parsing into standardized JSON fields. 🧩 JSON cleanup and validation for safe storage. 🧰 Google Sheets append-or-update for a central candidate database. 📑 ClickUp task creation with candidate-specific titles and assignment. 🎯 Requirements n8n instance (cloud or self-hosted); recommended n8n version 1.106.3 or higher. 🔧 Google Drive access to a dedicated resumes folder (PDF resumes recommended). 📂 Google Sheets credential with edit access to the candidate database sheet. 📈 ClickUp workspace/project access to create tasks for hiring. 📌 AI service credentials for the Resume Analyzer step (add in n8n Credentials). 🤖 Target Audience HR and Talent Acquisition teams needing faster screening. 👥 Recruiters and staffing agencies handling high volumes. 🏢 Startups and ops teams standardizing candidate intake. 🚀 No-code/low-code builders automating hiring workflows. 🧩 Step-by-Step Setup Instructions Connect Google Drive, Google Sheets, ClickUp, and your AI service in n8n Credentials. 🔐 Set the Google Drive “watched” folder (e.g., Resume_store). 📁 Import the workflow, assign credentials to all nodes, and map your Sheets columns. 🗂️ Adjust the ClickUp task details (title pattern, assignee, list). 📝 Run once with a sample PDF to test, then enable scheduling (every 1 minute). ▶️ Optionally rename the email/task nodes for clarity (e.g., “Create Hiring Task in ClickUp”). ✍️

This n8n template empowers IT support teams by automating document ingestion and instant query resolution through a conversational AI. It integrates Google Drive, Pinecone, and a Chat AI agent (using Google Gemini/OpenRouter) to transform static support documents into an interactive, searchable knowledge base. With two interlinked workflows—one for processing support documents and one for handling chat queries—employees receive fast, context-aware answers directly from your support documentation. Overview Document Ingestion Workflow Google Drive Trigger:** Monitors a specified folder for new file uploads (e.g., updated support documents). File Download & Extraction:** Automatically downloads new files and extracts text content. Data Cleaning & Text Splitting:** Utilizes a Code node to remove line breaks, trim extra spaces, and strip special characters, while a text splitter segments the content into manageable chunks. Embedding & Storage:** Generates text embeddings using Google Gemini and stores them in a Pinecone vector store for rapid similarity search. Chat Query Workflow Chat Trigger:** Initiates when an employee sends a support query. Vector Search & Context Retrieval:** Retrieves the top relevant document segments from Pinecone based on similarity scores. Prompt Construction:** A Code node combines the retrieved document snippets with the user’s query into a detailed prompt. AI Agent Response:** The constructed prompt is sent to an AI agent (using OpenRouter Chat Model) to generate a clear, step-by-step solution. Key Benefits & Use Case Imagine a large organization where every IT support document—from troubleshooting guides to system configurations—is stored in a single Google Drive folder. When an employee encounters an issue (e.g., “How do I reset my VPN credentials?”), they simply type the query into a chat interface. Instantly, the workflow retrieves the most relevant context from the ingested documents and provides a detailed, actionable answer. This process reduces resolution times, enhances support consistency, and significantly lightens the load on IT staff. Prerequisites A valid Google Drive account with access to the designated folder. A Pinecone account for storing and retrieving text embeddings. Google Gemini* (or *OpenRouter**) credentials to power the Chat AI agent. An operational n8n instance configured with the necessary nodes and credentials. Workflow Details 1 Document Ingestion Workflow Google Drive Trigger Node:** Listens for file creation events in the specified folder. Google Drive Download Node:** Downloads the newly added file. Extract from File Node:** Extracts text content from the downloaded file. Code Node (Data Cleaning):** Cleans the extracted text by removing line breaks, trimming spaces, and eliminating special characters. Recursive Text Splitter Node:** Segments the cleaned text into manageable chunks. Pinecone Vector Store Node:** Generates embeddings (via Google Gemini) and uploads the chunks to Pinecone. 2 Chat Query Workflow Chat Trigger Node:** Receives incoming user queries. Pinecone Vector Store Node (Query):** Searches for relevant document chunks based on the query. Code Node (Context Builder):** Sorts the retrieved documents by relevance and constructs a prompt merging the context with the query. AI Agent Node:** Sends the prompt to the Chat AI agent, which returns a detailed answer. How to Use Import the Template: Import the template into your n8n instance. Configure the Google Drive Trigger: Set the folder ID (e.g., 1RQvAHIw8cQbtwI9ZvdVV0k0x6TM6H12P) and connect your Google Drive credentials. Set Up Pinecone Nodes: Enter your Pinecone index details and credentials. Configure the Chat AI Agent: Provide your Google Gemini (or OpenRouter) API credentials. Test the Workflows: Validate the document ingestion workflow by uploading a sample support document. Validate the chat query workflow by sending a test query and verifying the returned support information. Additional Notes Ensure all credentials (Google Drive, Pinecone, and Chat AI) are correctly set up and tested before deploying the workflows in production. The template is fully customizable. Adjust the text cleaning, splitting parameters, or the number of document chunks retrieved based on your support documentation's size and structure. This template not only enhances IT support efficiency but also offers a scalable solution for managing and leveraging growing volumes of support content.

Overview & Setup This n8n template demonstrates how to automatically generate authentic User-Generated Content (UGC) style marketing videos for eCommerce products using AI. Simply upload a product image, and the workflow creates multiple realistic influencer-style video ads complete with scripts, personas, and video generation. Use cases Generate multiple UGC video variations for A/B testing Create authentic-looking product demonstrations for social media Produce influencer-style content without hiring creators Quickly test different marketing angles for new products Scale video content creation for eCommerce catalogs Good to know Sora 2 video generation takes approximately 2-3 minutes per 12-second video Each video generation costs approximately $0.50-1.00 USD (check OpenAI pricing for current rates) The workflow generates multiple video variations from a single product image Videos are automatically uploaded to Google Drive upon completion Generated videos are in 720x1280 (9:16) format optimized for social media How it works Product Analysis: OpenAI's vision API analyzes the uploaded product image to understand its features, benefits, and target audience Persona Creation: The system generates a detailed profile of the ideal influencer/creator who would authentically promote this product Script Generation: Gemini 2.5 Pro creates multiple authentic UGC video scripts (12 seconds each) with frame-by-frame breakdowns, natural dialogue, and camera movements Frame Generation: For each script, Gemini generates a custom first frame that adapts the product image to match UGC aesthetic and aspect ratio Video Production: Sora 2 API generates the actual video using the script and custom first frame as reference Status Monitoring: The workflow polls the video generation status every 15 seconds until completion Upload & Storage: Completed videos are automatically uploaded to Google Drive with organized naming How to use Click the form trigger URL to access the submission form Upload your product image (works best with clean product shots on white/neutral backgrounds) Enter the product name Submit the form and wait for the workflow to complete Find your generated UGC videos in the specified Google Drive folder Each run produces multiple video variations you can test Requirements OpenAI API** account with Sora 2 access for video generation and GPT-4 Vision Google Gemini API** account for script generation and image adaptation Google Drive** account for video storage Sufficient API credits for video generation (budget accordingly) Customizing this workflow Adjust the video duration in the generate_video node (currently set to 12 seconds) Modify the persona prompt in analyze_product node to target different audience demographics Change the script style in set_build_video_prompts node for different UGC aesthetics (excited discovery, casual recommendation, etc.) Update the Google Drive folder in upload_video node to organize videos by campaign Add additional processing nodes for video editing, subtitle generation, or thumbnail creation Modify the aspect ratio in resize_image node for different platforms (1:1 for Instagram feed, 16:9 for YouTube, etc.)