Workflow Templates

6 nodes5 views

Summarize the New Documents from Google Drive and Save Summary in Google Sheet

by WeblineIndia

This workflow is created by AI developers at WeblineIndia. It streamlines the process of managing content by automatically identifying and fetching the most recently added Google Doc file from your Google Drive. It extracts the content of the document for processing and leverages an AI model to generate a concise and meaningful summary of the extracted text. The summarized content is then stored in a designated Google Sheet, alongside relevant details like the document name and the date it was added, providing an organized and easily accessible reference for future use. This automation simplifies document handling, enhances productivity, and ensures seamless data management. Steps : Fetch the Most Recent Document from Google Drive Action:** Use the Google Drive Node. Details:** List files, filter by date to fetch the most recently added .doc file, and retrieve its file ID and metadata. Extract Content from the Document Action:** Use the Google Docs Node. Details:** Set the operation to "Get Content," pass the file ID, and extract the document's text content. Summarize the Document Using an AI Model Action:** Use an AI Model Node (e.g., OpenAI, ChatGPT). Details:** Provide the extracted text to the AI model, use a prompt to generate a summary, and capture the result. Store the Summarized Content in Google Sheets Action:** Use the Google Sheets Node. Details:** Append a new row to the target sheet with details such as the original document name, summary, and date added. About WeblineIndia WeblineIndia specializes in delivering innovative and custom AI solutions to simplify and automate business processes. If you need any help, please reach out to us.

🤖AI & Machine Learning

5 nodes6057 views

Extract Timesheet Data with Mistral OCR & Gmail Human Verification

by Rajeet Nair

📖 Description 🔹 How it works This workflow introduces an AI + Human-in-the-Loop pipeline for employee timesheet management. It combines the power of Google Drive, AI (OCR + LLM), and Gmail with a human review step to ensure accuracy and compliance. AI-Powered File Discovery Scans a Google Drive folder for new or updated timesheet files (PDF, Word, Excel, Images). AI Data Extraction Uses OCR and LLM (Mistral) to intelligently read and extract structured data. Supports multiple formats: PDF, Word (DOC/DOCX), Excel (XLS/XLSX), and Image files (JPG, PNG, scanned documents). Creates clean JSON with file details and timesheet logs (date, hours worked, tasks, notes). Smart Data Formatting Converts AI output into a clear HTML summary table for easy review. Flags potential anomalies (missing hours, duplicate dates, irregular entries). Human-in-the-Loop Verification Sends an approval email via Gmail containing: File metadata AI-generated HTML summary JSON attachment of raw extracted data HR/Managers review the summary and approve/reject before final actions occur. Post-Approval Automation (optional) Approved records can be saved in a separate Google Drive folder. Employees or HR receive confirmation emails. ⚙️ Set up steps Connect Credentials Add Google Drive and Gmail credentials in n8n. Configure Mistral (or any LLM) API credentials. Configure Google Drive In the “Search files and folders” node, replace the folderId with your company’s timesheet folder ID. Customize Extraction Schema Sticky notes explain how JSON output is structured. Adapt it for your organization’s needs (e.g., overtime, project codes). Set Up Human Verification Emails Update Gmail node recipients to your HR or approval team. Customize the email body (AI summary + JSON file attached). Activate & Test Enable the workflow. Upload a sample timesheet to trigger the AI + human verification loop. ⚡ Result: A robust AI + Human-in-the-Loop workflow that reduces repetitive data entry, prevents payroll errors, and gives HR full confidence before final approval.

6 nodes4 views

🎯 Precision Prospecting: Automate LinkedIn Lead Gen with Bright Data

by Miquel Colomer

🎯 Precision Prospecting: Automate LinkedIn Lead Gen with n8n & Bright Data 📝 Overview This workflow turns n8n into an AI-powered prospector, automatically searching Google for LinkedIn profiles, scraping profile data via Bright Data, and summarizing key details. Ideal for sales and recruitment teams seeking targeted lead lists without manual research. 🎥 Workflow in Action Want to see this workflow in action? You have a chat window output below: 🔑 Key Features AI Chat Trigger**: Start prospecting via conversational prompts. Contextual Memory**: Retains the last 20 messages for coherent dialogue. Automated Google Search**: Generates site-restricted queries and fetches the top result. Bright Data Scraping**: Synchronously scrapes LinkedIn profile details by URL. Intelligent Filtering**: Extracts only valid LinkedIn profile links. Limit Control**: Returns a single, most relevant profile per request. LLM Summary**: Uses GPT-4o-mini to interpret and present scraped data. 🚀 How It Works (Step-by-Step) Prerequisites: n8n ≥ v1.0 with community nodes: install n8n-nodes-brightdata (not verified community node). API credentials: OpenAI, Bright Data (web unlocker zone “web\_unlocker1”). Webhook endpoint for chat trigger. Node Configuration: When chat message received (chatTrigger): Fires on user prompt. Simple Memory1 (memoryBufferWindow): Stores the last 20 chat messages. AI Prospector Agent (agent): Orchestrates search logic. Get 1 Google Result (brightData): Performs a Google search with site:linkedin.com/in. Get Links from Body (html): Extracts all `` hrefs from the search result page. Extract Links (splitOut): Splits out individual link entries. Filter only LinkedIn Profiles (filter): Ensures the URL contains “linkedin.com/” and starts with “https\://”. Limit (limit): Restricts output to the first valid profile URL. Search LinkedIn URI (toolWorkflow): Passes the URL to a secondary workflow to fetch the first link. Get LinkedIn Profile Data (brightDataTool): Scrapes the profile JSON. OpenAI Chat Model (lmChatOpenAi): Summarizes and formats the scraped data. Workflow Logic: User asks for a person by company & name, company & position, or LinkedIn URL. Agent builds a Google query (e.g., site:linkedin.com/in bright data cmo) and calls “Get 1 Google Result.” Extracted links are filtered and limited to the top valid profile. If user provided a direct LinkedIn URL, Agent skips search and scrapes immediately. Scraped profile JSON is passed to GPT-4o-mini to generate a concise summary. Testing & Optimization: Trigger via Execute Workflow for dry runs. Inspect intermediate node outputs in n8n’s Execution panel. Adjust maxIterations or memory window length for performance. Tune Bright Data zone or country settings to optimize scraping speed. Deployment & Monitoring: Activate the workflow and expose its webhook URL. Use n8n’s built-in Alerts or external monitoring (e.g., Slack notifications) on failures. Rotate credentials via n8n’s Credential Vault when needed. Version-control workflow via duplicates or Git-backed n8n instances. ✅ Pre-requisites OpenAI Account**: API key for GPT-4o-mini. Bright Data Account**: Zone “web\_unlocker1” and dataset gd_l1viktl72bvl7bjuj0. n8n Version**: v1.0+ with community nodes installed. Permissions**: Webhook access, Credential Vault read/write. 👤 Who Is This For? Sales teams automating outbound LinkedIn prospecting. Recruiters sourcing candidates without manual scraping. Marketing ops looking to enrich CRM with accurate profile data. 📈 Benefits & Use Cases Efficiency**: Reduces hours of manual search and data entry to seconds. Accuracy**: Filters out non-LinkedIn links and ensures high-quality results. Scalability**: Handle multiple prospect requests concurrently via chat or API. Integration**: Easily hook into CRMs or email sequencers downstream. Workflow created and verified by Miquel Colomer https://www.linkedin.com/in/miquelcolomersalas/ and N8nHackers https://n8nhackers.com

5 nodes988 views

Route User Requests to Specialized Agents with GPT-4o Mini

by Dhrumil Patel

This n8n workflow template is designed to route user input to specialized agents (like a Reminder Agent, Email Agent, etc.) using a structured output from a language model. Here's a complete description of what it does and how each part works: 🔁 Workflow Purpose: This template receives a user's request via Webhook, processes it using an LLM, extracts structured data like the agent name and user query, and routes the input to the appropriate sub-workflow (agent) based on the specified agent type. 🧩 Workflow Breakdown: 1. Webhook (Trigger) Node: Webhook Purpose: Accepts a POST request from any frontend or API source. It contains the raw user input. 2. GPT Model (LLM Inference) Node: GPT 4o Mini Purpose: Interprets the user input and determines: Which agent should handle it (e.g., "Reminder Agent", "Email Agent", etc.) The actual user request (in structured format) 3. Auto-Fixing Output Parser Node: Auto-fixing Output Parser Purpose: Ensures that the output from the LLM matches the expected structure. If there's a mismatch, it automatically corrects it using a re-prompt. 4. Structured Output Parser Node: Structured Output Parser Purpose: Converts the language model's response into a strict JSON structure with keys like: "Agent Name" "user input" "sessionID" 5. Agent Router Node: Switch ("Agent Route") Purpose: Based on "Agent Name", it routes the input to one of the following sub-workflows: 📅 Reminder Agent 📧 Email Agent 🧾 Document Agent 🤝 Meeting Agent 6. Sub-Workflow Call (Execute Workflow) Each agent is implemented as a separate n8n workflow: The input is forwarded to the selected agent. For example, if "Agent Name" is "Reminder Agent", the workflow "Reminder Agent" is called with "user input". 7. Webhook Response After the sub-agent workflow finishes, a Respond to Webhook node sends the output back as an HTTP response. ✅ Key Features: Fully modular and extensible LLM-driven routing using OpenRouter GPT-4o Auto-corrects structured output errors Clean separation of concerns (agent logic is decoupled in sub-workflows) Easily add more agents by updating the switch logic 📦 Use Case Examples: User says: “Remind me to call my mom tomorrow.” → Routed to Reminder Agent User says: “Send an email to the HR team.” → Routed to Email Agent User says: “Schedule a meeting with John next week.” → Routed to Meeting Agent

5 nodes341 views

Redesign T-Shirt Mockups to Print-Ready Files with GPT-4 Vision & Imagen 4

by Thong

🧠 What This Workflow Does This n8n workflow allows you to upload a T-shirt mockup design (even if it's rough or outdated), and automatically turns it into a refined, print-ready artwork using the power of AI. It starts with an image of a T-shirt design, analyzes it using OpenAI's vision model, and then generates a cleaner, upgraded prompt to be used with OpenAI’s image generation API (gpt-image-1). The final output is a new T-shirt graphic optimized for printing on solid black background, with no visible shirt or mockup framing. ⚙️ How It Works User Sends a T-shirt Mockup Image Link The workflow begins when the user drops an image link (T-shirt mockup) into a chat interface or input trigger. AI Analyzes the Image (OpenAI Vision) Using OpenAI’s GPT-4 vision capabilities, the workflow extracts the key design elements from the image: Characters, text, layout Graphic style, composition Visual tone and focus AI Agent Creates a Refined Prompt The extracted details are passed to an AI agent that: Preserves the original layout and message Enhances the visual composition and typography Removes mockup elements like shirt collar, sleeves, shadows. Locks the artwork on a pure black background only Outputs a clean, artistic, JSON-safe one-line prompt for generation Text Escaping for API Compatibility A JavaScript function node escapes the prompt (quotes, slashes, line breaks) to make it safe for use in downstream JSON requests. Image Generation via GPT-Image-1 API or IMAGEN 4 from GOOGLE The final prompt is sent to OpenAI’s gpt-image-1 to generate a brand-new artwork — ideal for direct printing on a black T-shirt. ⚠️ Cost Notice for gpt-image-1 Usage This workflow uses OpenAI's gpt-image-1 model to generate high-quality T-shirt artwork from refined prompts. Please note that this model is a paid service, and each image generation request may cost approximately $0.25 per design, depending on resolution and usage. We strongly recommend users to review their OpenAI API usage plan and be mindful of costs when running this workflow, especially if generating in bulk or integrating into larger automation flows. You can monitor your usage at: https://platform.openai.com/docs/models/gpt-image-1 (Optional) You can send the result to Telegram, upload to Notion, or store it in your design system. ✅ Key Features Works from any uploaded mockup image Converts design concepts into print-ready artwork prompts Avoids outputting shirt models, collars, or product mockups Optimized for solid black background with no distractions Modular and easy to connect with file delivery or approval flows 🚀 How to Use Import the .json workflow into n8n Configure your OpenAI credentials for both vision and image APIs Trigger the flow by sending an image url of a T-shirt mockup Let the workflow generate and return a brand-new design from that concept

📝Content Management

5 nodes4464 views

Automated Event Discovery with Bright Data & n8n

by Yaron Been

Description This workflow automatically discovers and collects information about upcoming events in your area or industry. It saves you time by eliminating the need to manually check multiple event websites and provides a centralized database of relevant events. Overview This workflow automatically scrapes websites for upcoming events in your area or industry and compiles them into a structured format. It uses Bright Data to access event listing websites and extract event details like dates, locations, and descriptions. Tools Used n8n:** The automation platform that orchestrates the workflow. Bright Data:** For scraping event websites without being blocked. Calendar/Database:** For storing and organizing event information. 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. Set Up Data Storage: Configure where you want to store the event data. Customize: Specify locations, event types, and date ranges to monitor. Use Cases Event Planners:** Stay updated on competing or complementary events. Community Managers:** Discover local events to share with your community. Marketing Teams:** Find industry events for networking opportunities. 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 #events #eventdiscovery #brightdata #webscraping #eventfinder #localevents #eventcalendar #eventplanning #n8nworkflow #workflow #nocode #eventautomation #eventscraping #eventtracking #upcomingEvents #eventmarketing #eventmanagement #eventdatabase #communityevents #eventnotifications #eventorganizer #eventtech #eventindustry #eventcollection

4 nodes76 views

Smart Email Responder Workflow using AI

by Ankur Parag Kulkarni

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.

6 nodes3218 views

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"

8 nodes2 views

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

by Oneclick AI Squad

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

4 nodes150 views

AI-Powered Email to Jira Ticket Creation with Llama 3.2

by Oneclick AI Squad

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.

6 nodes150 views

Airline Web Check-in Data Extraction with Ollama AI, Google Sheets & Postgres Vector DB

by Oneclick AI Squad

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.