Workflow Templates

How to use Get a .csv file with your contacts (you can download this from any contact manager app) Set API key for Google Drive API, and Notion (you need to create a "connection" in Notion) Create Database for your contacts in Notion Choose which properties to extract from the .csv and pass it in to the Notion database. Right now, it transfer 4 pieces of information: full name, email, phone, and company.

Generate & Publish Professional COE Blogs with Gemini AI and Google Drive

by Oneclick AI Squad

💬Communication & Collaboration

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

intermediate

4 nodes149 views

Call Center Analytics with Dual-AI Verification using DeepSeek Models

by Omar Akoudad

The workflow is well-designed for CRM analysis with a robust quality control mechanism. The dual-AI approach ensures reliable results, while the webhook integration makes it production-ready for real-time CRM data processing. Dual-AI Architecture: Uses DeepSeek Reasoner for analysis and DeepSeek Chat for verification. Flexible Input: Supports both manual testing and production webhook integration. Quality Assurance: Built-in verification system to ensure report accuracy. Comprehensive Analysis: Covers lead conversion, upsell metrics, agent ranking, and more. Professional Output: Generates structured markdown reports with actionable insights

Text-to-Image Generation with Flux AI, Google Drive Storage & Sheets Logging

by Sk developer

🎨 AI Image Generator with Flux AI Generate realistic, high-quality images from text prompts using the Flux AI Text-to-Image Generator API via RapidAPI, and seamlessly store the results in Google Drive and log them in Google Sheets — all automated using n8n. 🧠 What This Workflow Does This no-code automation enables you to: 🖋️ Enter a custom text prompt using a web form. 🖼️ Generate a photorealistic image using Flux AI’s Text-to-Image Generator via RapidAPI. ☁️ Upload the image to Google Drive. 📊 Log the prompt and result in a Google Sheet. ⚠️ Capture and log errors in a fallback sheet. 💡 Use Case Ideal for: Digital artists and marketers Social media managers Brand mockup creators Rapid concept prototyping All without writing a single line of code. ✅ Benefits No-code automation** for AI-generated images Cloud storage** and structured logging Error handling** built-in Fast content creation** for design, branding, or concept testing Powered by* the Flux AI Text-to-Image Generator API via *RapidAPI** 🧩 Node-by-Node Breakdown 1. 📝 On Form Submission Accepts user input for a creative text prompt. 🔍 Example: “A silver can with vapor and blue lightning background.” 💡 Benefit: No technical knowledge needed. 2. 🌐 HTTP Request — Flux AI API Sends the prompt to the Flux AI Text-to-Image Generator API via RapidAPI. 📦 Returns an image encoded in base64. 💡 Benefit: Seamless integration with cutting-edge image generation. 3. 🧪 Code Node — Base64 Decoder Converts the base64 image to a binary .jpg file. 💡 Benefit: Readies the image for upload/download/sharing. 4. 📁 Google Drive Uploads the generated image to your Google Drive folder. 💡 Benefit: Secure, sharable cloud storage. 5. 📊 Google Sheets — Success Log Appends a row with the original prompt, filename, and generation date. 💡 Benefit: Tracks history of all generated images. 6. ⚠️ IF Node — Error Detection Checks if the image generation failed. 💡 Benefit: Prevents workflow from halting and routes to error logging. 7. 📉 Google Sheets — Error Log Logs failed prompts and error messages. 💡 Benefit: Helps identify what went wrong (e.g. malformed prompt). 🛠️ Challenges Solved | Problem | How This Workflow Fixes It | |--------|-----------------------------| | Manual prompt-based image generation is slow | Fully automated with Flux AI | | No storage pipeline for generated images | Integrated with Google Drive | | No audit trail for prompts/images | Logged into Google Sheets | | Errors go unnoticed in image generation | Built-in error check and logging | | Users lack API access or dev experience | Friendly web form UI | 🔗 API Spotlight This workflow is powered by the Flux AI Text-to-Image Generator API — available exclusively on RapidAPI. Why use this API? Ultra-fast text-to-image rendering High-resolution results Developer-friendly and cost-effective Great for branding, mockups, and visuals We’ve integrated this API to make advanced image generation accessible with just a prompt — no AI or dev experience required.

Send trending "Show HN" to email

by Jan Oberhauser

💬Communication & Collaboration

Triggers every day at 1pm Gets the current content from Hacker News Gets all the different submission items Extracts the rank, title and url Checks if it is a "Show HN" submission Combines the items into a simple email text Sends an email with the email text

2 nodes1573 views

Create ticket on specific customer messages in Telegram

by tanaypant

This is a workflow where a support channel on Telegram is being used to gather customer feedback. Depending on certain keywords in the customer's message, this workflow creates a ticket with a tag in your Freshdesk instance. The customer is then sent a message on Telegram and an item is created on Monday.com for tracking.

🤖AI & Machine Learning

3 nodes7038 views

Tiny tiny Rss (aka tt-rss) Feed to Mastodon

by dev

Every 10 minutes look at your published news in your Tiny tiny RSS public feed and make a toot on your mastodon. You'll need: Your mastondon URL instance Your mastondon access token Your Tiny Tiny RSS public published feed URL

Receive updates of the position of the ISS every minute

by Harshil Agrawal

This workflow demonstrates the use of static data in n8n. The workflow is built on the concept of polling. Cron node: The Cron node triggers the workflow every minute. You can configure the time based on your use-case. HTTP Request node: This node makes an HTTP Request to an API that returns the position of the ISS. Set node: In the Set node we set the information that we need in the workflow. Since we only need the timestamp, latitude, and longitude we set this in the node. If you need other information, you can set them in this node. Function node: The Function node, checks if the incoming data is similar to the data that was returned in the previous execution or not. If the data is different the Function node returns this new node, otherwise, it returns a message 'No New Items'. The data is also stored as static data with the workflow. Based on your use-case, you can build the workflow further. For example, you can use it send updates to Mattermost or Slack

Build a Document QA System with Google Drive, Pinecone, and OpenAI RAG

by Abdullahi Ahmed

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"

Execute multiple Command Lines based on Text File Inputs

by jason

This workflow takes a text file as input. It pulls the information from the text file and used it as a parameter to execute a command for each text line. This workflow references a file /home/n8n/filelist.txt in the Read Binary File node which will need to be changed to work properly. You can also edit the Execute Command node to modify what happens for each of these lines of text. Note: This workflow requires the Execute Command node which is only available on the on-premise version of n8n.

🤖AI & Machine Learning

0 nodes3066 views

Load data into spreadsheet or database

by Max Tkacz

💬Communication & Collaboration

This workflow is a generic example of how to load data from your workflow into a destination that stores tabular data. For example, a Google Sheets or Airtable sheet, a .CSV file, or any relational database like MySQL. Generally, you need to ensure that you send well-formatted data into the Spreadsheet or Database node. You can use the Set or Function node to transform data into the correct format for your destination. Key concepts Spreadsheets and databases have columns, like "Name" and "Email". The data you send into a Spreadsheet/ Database node needs to match these column names for each row of data that you want to insert. Data points need to be represented as key-value pairs. Specifically, each item of data needs to have a JSON key for each column in the sheet. For a spreadsheet with "Name" and "Email" columns, it would look like: {"Name" : "Karla", "Email" : "karla@email.com"} Before appending or inserting data to a spreadsheet or database, you might need to transform it into the correct format. You can preprocess the data with a Set or Function node. The Set node allows you to perform simple transforms when the data you want to load into spreadsheet rows is already represented as items. Use the Function node when you need to map nested data (like arrays) inside a single item to their own top-level items (Example in community forums). Spreadsheet and database nodes in n8n perform their configured action (like Append, Create Row, Write to File) on each item of input data. Workflow walkthrough