Workflow Templates

⚠️ Note: This template requires a community node and works only on self-hosted n8n installations. It uses the Typhoon OCR Python package and custom command execution. Make sure to install required dependencies locally. Who is this for? This template is for developers, operations teams, and automation builders in Thailand (or any Thai-speaking environment) who regularly process PDFs or scanned documents in Thai and want to extract structured text into a Google Sheet. It is ideal for: Local government document processing Thai-language enterprise paperwork AI automation pipelines requiring Thai OCR What problem does this solve? Typhoon OCR is one of the most accurate OCR tools for Thai text. However, integrating it into an end-to-end workflow usually requires manual scripting and data wrangling. This template solves that by: Running Typhoon OCR on PDF files Using AI to extract structured data fields Automatically storing results in Google Sheets What this workflow does Trigger: Run manually or from any automation source Read Files: Load local PDF files from a doc/ folder Execute Command: Run Typhoon OCR on each file using a Python command LLM Extraction: Send the OCR markdown to an AI model (e.g., GPT-4 or OpenRouter) to extract fields Code Node: Parse the LLM output as JSON Google Sheets: Append structured data into a spreadsheet Setup 1. Install Requirements Python 3.10+ typhoon-ocr: pip install typhoon-ocr Install Poppler and add to system PATH (needed for pdftoppm, pdfinfo) 2. Create folders Create a folder called doc in the same directory where n8n runs (or mount it via Docker) 3. Google Sheet Create a Google Sheet with the following column headers: | book\_id | date | subject | detail | signed\_by | signed\_by2 | contact | download\_url | | -------- | ---- | ------- | ------ | ---------- | ----------- | ------- | ------------- | You can use this example Google Sheet as a reference. 4. API Key Export your TYPHOON_OCR_API_KEY and OPENAI_API_KEY in your environment (or set inside the command string in Execute Command node). How to customize this workflow Replace the LLM provider in the Basic LLM Chain node (currently supports OpenRouter) Change output fields to match your data structure (adjust the prompt and Google Sheet headers) Add trigger nodes (e.g., Dropbox Upload, Webhook) to automate input About Typhoon OCR Typhoon is a multilingual LLM and toolkit optimized for Thai NLP. It includes typhoon-ocr, a Python OCR library designed for Thai-centric documents. It is open-source, highly accurate, and works well in automation pipelines. Perfect for government paperwork, PDF reports, and multilingual documents in Southeast Asia.

Who is this for? Content creators, social media managers, digital marketers, and researchers who need to download original TikTok videos without watermarks for analysis, repurposing, or archiving purposes. What problem does this workflow solve? Downloading TikTok videos without watermarks typically requires using questionable third-party websites that may have limitations, ads, or privacy concerns. This workflow provides a clean, automated solution that can be integrated into your own systems and processes. What this workflow does This workflow automates the process of downloading TikTok videos without watermarks in three simple steps: Fetch the TikTok video page by providing the video URL Extract the raw video URL from the page's HTML data Download the original video file without watermark (Optional) Upload to Google Drive with public sharing link generation The workflow uses web scraping techniques to extract the original video source directly from TikTok's own servers, maintaining the highest possible quality without any added watermarks or branding. Setup (Est. time: 5-10 minutes) Before getting started, you'll need: n8n installation The URL of a TikTok you want to download (Optional) Google Drive API enabled in Google Cloud Console with OAuth Client ID and Client Secret credentials if you want to use the upload feature How to customize this workflow to your needs Replace the example TikTok URL with your desired video links Modify the file naming convention for downloaded videos Integrate with other nodes to process videos after downloading Create a webhook to trigger the workflow from external applications Set up a schedule to regularly download videos from specific accounts This workflow can be extended to support various use cases like trending content analysis, competitor research, creating compilation videos, or building a content library for inspiration. It provides a foundation that can be customized to fit into larger automated workflows for content creation and social media management.

This n8n template demonstrates how to create a comprehensive voice-powered restaurant assistant that handles table reservations, food orders, and restaurant information requests through natural language processing. The system uses VAPI for voice interaction and PostgreSQL for data management, making it perfect for restaurants looking to automate customer service with voice AI technology. Good to know Voice processing requires active VAPI subscription with per-minute billing Database operations are handled in real-time with immediate confirmations The system can handle multiple simultaneous voice requests All customer data is stored securely in PostgreSQL with proper indexing How it works Table Booking & Order Handling Workflow Voice requests are captured through VAPI triggers when customers make booking or ordering requests The system processes natural language commands and extracts relevant details (party size, time, food items) Customer data is immediately saved to the bookings and orders tables in PostgreSQL Voice confirmations are sent back through VAPI with booking details and estimated wait times All transactions are logged with timestamps for restaurant management tracking Restaurant Info Provider Workflow Info requests trigger when customers ask about hours, menu, location, or services Restaurant details are retrieved from the restaurant_info table containing current information Wait nodes ensure proper data loading before voice response generation Structured restaurant information is delivered via VAPI in natural, conversational format Database Schema Bookings Table booking_id (PRIMARY KEY) - Unique identifier for each reservation customer_name - Customer's full name phone_number - Contact number for confirmation party_size - Number of guests booking_date - Requested reservation date booking_time - Requested time slot special_requests - Dietary restrictions or special occasions status - Booking status (confirmed, pending, cancelled) created_at - Timestamp of booking creation Orders Table order_id (PRIMARY KEY) - Unique order identifier customer_name - Customer's name phone_number - Contact for order updates order_items - JSON array of food items and quantities total_amount - Calculated order total order_type - Delivery, pickup, or dine-in special_instructions - Cooking preferences or allergies status - Order status (received, preparing, ready, delivered) created_at - Order timestamp Restaurant_Info Table info_id (PRIMARY KEY) - Information entry identifier category - Type of info (hours, menu, location, contact) title - Information title description - Detailed information content is_active - Whether info is currently valid updated_at - Last modification timestamp How to use The manual trigger can be replaced with webhook triggers for integration with existing restaurant systems Import the workflow into your n8n instance and configure VAPI credentials Set up PostgreSQL database with the required tables using the schema provided above Configure restaurant information in the restaurant_info table Test voice commands such as "Book a table for 4 people at 7 PM" or "What are your opening hours?" Customize voice responses in VAPI nodes to match your restaurant's tone and branding The system can handle multiple concurrent voice requests and scales with your restaurant's needs Requirements VAPI account for voice processing and natural language understanding PostgreSQL database for storing booking, order, and restaurant information n8n instance with database and VAPI integrations enabled Customising this workflow Voice AI automation can be adapted for various restaurant types - from quick service to fine dining establishments Try popular use-cases such as multi-location booking management, dietary restriction handling, or integration with existing POS systems The workflow can be extended to include payment processing, SMS notifications, and third-party delivery platform integration

Supercharge Your Security Operations for Free Stop wasting time manually investigating suspicious IP addresses. This workflow template is your launchpad to automating real-time IP cybersecurity analysis using the NixGuard platform, which you can use for free. This is the first of a two-part system designed to integrate seamlessly into your existing security stack, especially with Wazuh. It calls our main workflow, Automate IP Reputation Checks and Get AI Risk Summaries from NixGuard, to do the heavy lifting. What This Workflow Unlocks for You Free AI-Powered Risk Summaries:** Don't just get data; get answers. NixGuard provides a clear, human-readable summary of why an IP is considered risky. Automated IP Reputation Checks:** Programmatically check any IP against a vast array of threat intelligence sources. A Foundation for Your SOC Automation:** Use the results to trigger your incident response process. The template includes a pre-built example of how to send a detailed alert to Slack, which you can easily adapt for Jira, TheHive, or any other tool. How the Two-Workflow System Works This "Dispatcher" workflow is designed for flexibility. It holds your API key and input, then calls the main analysis workflow. This allows you to easily create multiple triggers (e.g., one for Slack bots, one for webhooks) without duplicating the core logic. Critical Setup Instructions Get the Main Workflow: First, add the main analysis engine to your n8n instance from the community page: NixGuard Analysis Workflow. Add Your Free API Key: In this workflow, click the blue Set API Key & Initial Prompt node. Paste your free NixGuard API key into the apiKey value field. Connect The Workflows: Click the purple Execute NixGuard & Wazuh Workflow node. In the parameters, use the dropdown to select the main analysis workflow you added in Step 1. Ready to automate your threat intelligence? Get your free API key and learn more at; 🔗 Learn more about NixGuard: [thenex.world](thenex.world )🔗 Get started with a free security subscription: thenex.world/security/subscribe Tags: Free, IP Analysis, NixGuard, Wazuh, Security, Automation, AI, Cybersecurity, Threat Intelligence, SOC, Incident Response, IP Reputation, DevSecOps, API

Who this is for? Extract Amazon Best Seller Electronic Info is an automated workflow that extracts best seller data from Amazon's Electronics section using Bright Data Web Unlocker, transform it into structured JSON using Google Gemini's LLM, and forwards a fully structured JSON response to a specified webhook for downstream use. This workflow is tailored for: eCommerce Analysts** Who need to monitor Amazon best-seller trends in the Electronics category and track changes in real-time or on a schedule. Product Intelligence Teams** Who want structured insights on competitor offerings, including rankings, prices, ratings, and promotions. AI-powered Chatbot Developers** Who are building assistants capable of answering product-related queries with fresh, structured data from Amazon. Growth Hackers & Marketers** Looking to automate competitive research and surface trending product data to inform pricing strategies. Data Aggregators and Price Trackers** Who need reliable and smart scraping of Amazon data enriched with AI-driven parsing. What problem is this workflow solving? Keeping up with Amazon's best sellers in Electronics is a time-consuming, error-prone task when done manually.This workflow automates the process, ensuring:​ Automating Data Extraction from Amazon Best Sellers using Bright Data, ensuring reliable access to real-time, structured data. Enhancing Raw Data with Google Gemini, turning product lists into structured JSON using the Google Gemini LLM. Sending Results to a Webhook, enabling seamless integration into dashboards, databases, or chatbots. What this workflow does The workflow performs the following steps:​ Extracts Amazon Best Seller Electronics page info using Bright Data's Web Unlocker API. Processes the unstructured content using Google Gemini's Flash Exp model to extract structured product data. Sends the structured information to a webhook endpoint. Setup Sign up at Bright Data. Navigate to Proxies & Scraping and create a new Web Unlocker zone by selecting Web Unlocker API under Scraping Solutions. In n8n, configure the Header Auth account under Credentials (Generic Auth Type: Header Authentication). The Value field should be set with the Bearer XXXXXXXXXXXXXX. The XXXXXXXXXXXXXX should be replaced by the Web Unlocker Token. In n8n, configure the Google Gemini(PaLM) Api account with the Google Gemini API key (or access through Vertex AI or proxy). Update the Amazon URL with the Bright Data zone by navigating to the Amazon URL with the Bright Data Zone node. Update the Webhook HTTP Request node with the Webhook endpoint of your choice. How to customize this workflow to your needs This workflow is built to be flexible - whether you're a market researcher, e-commerce entrepreneur, or data analyst. Here's how you can adapt it to fit your specific use case: Change the Amazon Category** Update the Amazon URL with the topic of your interest such as Computers & Accessories, Home Audio, etc. Customize the Gemini Prompt** Update the Gemini prompt to get different styles of output — comparison tables, summaries, feature highlights, etc. Send Output to Other Destinations** Replace the Webhook URL to forward output to: Google Sheets Airtable Slack or Discord Custom API endpoints

This n8n template allows you to instantly fetch a random dog image from the Dog CEO API by simply sending a webhook request. It's a fun and simple way to integrate random dog photos into your projects, whether for websites, applications, or playful automations. 🔧 How it works Trigger Webhook: This node acts as the entry point for the workflow. It listens for any incoming POST request. No specific data is required in the webhook body, as the workflow fetches a random image. Fetch Random Dog Image: This node makes an HTTP GET request to https://dog.ceo/api/breeds/image/random. The API responds with a JSON object containing the URL of a random dog image. Respond with Image URL: This node sends the URL of the random dog image back to the service that initiated the webhook. 👤 Who is it for? This workflow is ideal for: Developers: Quickly integrate random dog images into web applications, bots, or prototypes. Content Creators: Get fresh, random dog photos for social media, blogs, or presentations. Learning n8n: A straightforward example of using a webhook to trigger an API call and return data. Anyone who loves dogs! 📑 Data Structure When you trigger the webhook, you can send an empty POST request body. The workflow will return a JSON response similar to this (the message URL will vary): { "message": "https://images.dog.ceo/breeds/hound-walker/n02089867_2626.jpg", "status": "success" } ⚙️ Setup Instructions Import Workflow: In your n8n editor, click "Import from JSON" and paste the provided workflow JSON. Configure Webhook Path: Double-click the Trigger Webhook node. In the 'Path' field, set a unique and descriptive path (e.g., /get-dog-image). Activate Workflow: Save and activate the workflow. 📝 Tips Download the Image: Instead of just returning the URL, you can download the image and then process it. Insert another HTTP Request node after Fetch Random Dog Image to download the image binary. Set the HTTP Request node's 'Response Format' to 'Binary'. Use the expression ={{ $json.message }} for the URL. Save to Cloud Storage: After downloading the image (as described above), you can save it to various cloud storage services: Google Drive: Add a Google Drive node. Connect it to the output of the image download node. Configure it to upload the binary data to a specific folder. Amazon S3: Add an AWS S3 node. Configure it to upload the binary data, specifying your bucket and desired filename. Dropbox: Use the Dropbox node to upload the image file. Send as a Message: Share the dog image directly in a chat or email: Slack/Discord/Telegram: Use the respective integration node to send the image URL or the downloaded image as an attachment. Email: Attach the downloaded image to an email using an Email or Gmail node. Display on a Web Page: If you're embedding this into a web application, you can simply use the returned URL in an tag to display the image. Error Handling: You can add an Error Trigger node to catch any issues during the image fetching process (e.g., if the Dog CEO API is down) and send notifications.

✅💬Build Your Own WhatsApp Fact-Checking Bot with AI Tired of misinformation spreading on WhatsApp? 🤨 This workflow transforms your n8n instance into a powerful, automated fact-checking bot\! Send any news, claim, or question to a designated WhatsApp number, and this bot will use AI to research it, provide a verdict, and send back a summary with direct source links. Fight fake news with the power of automation and AI\! 🚀 How it works ⚙️ This workflow uses a simple but powerful three-step process: 📬 WhatsApp Gateway (Webhook node): This is the front door. The workflow starts when the Webhook node receives an incoming message from a user via a Twilio WhatsApp number. 🕵️ The Digital Detective (Perplexity node): The user's message is sent to the Perplexity node. Here, a powerful AI model, instructed by a custom system prompt, analyzes the claim, scours the web for reliable information, and generates a verdict (e.g., ✅ Likely True, ❌ Likely False). 📲 WhatsApp Reply (Twilio node): The final, formatted response, complete with the verdict, a simple summary, and source citations, is sent back to the original user via the Twilio node. Setup Guide 🛠️ Follow these steps carefully to get your fact-checking bot up and running. Prerequisites A Twilio Account with an active phone number or access to the WhatsApp Sandbox. A Perplexity AI Account to get an API key. 1\. Configure Credentials You'll need to add API keys for both Perplexity and Twilio to your n8n instance. Perplexity AI: Go to your Perplexity AI API Settings. Generate and copy your API Key. In n8n, go to Credentials \& New, search for "Perplexity," and add your key. Twilio: Go to your Twilio Console Dashboard. Find and copy your Account SID and Auth Token. In n8n, go to Credentials \& New, search for "Twilio," and add your credentials. 2\. Set Up the Webhook and Tunnel To allow Twilio's cloud service to communicate with your n8n instance, you need a public URL. The n8n tunnel is perfect for this. Start the n8n Tunnel: If you are running n8n locally, you'll need to expose it to the web. Open your terminal and run: n8n start --tunnel Copy Your Webhook URL: Once the tunnel is active, open your n8n workflow. In the Receive Whatsapp Messages (Webhook) node, you will see two URLs: Test and Production. Copy the Test/Production URL. This is the public URL that Twilio will use. 3\. Configure Your Twilio WhatsApp Sandbox Go to the Twilio Console and navigate to Messaging \& Try it out \& Send a WhatsApp message. Select the Sandbox Settings tab. In the section "WHEN A MESSAGE COMES IN," paste your n8n Production Webhook URL. Make sure the method is set to HTTP POST. Click Save. How to Use Your Bot 🚀 Activate the Sandbox: To start, you (and any other users) must send a WhatsApp message with the join code (e.g., join given-word) to your Twilio Sandbox number. Twilio provides this phrase on the same Sandbox page. Fact-Check Away\! Once joined, simply send any claim or question to the Twilio number. For example: Did Elon Musk discover a new planet? Within moments, the workflow will trigger, and you'll receive a formatted reply with the verdict and sources right in your chat\! Further Reading & Resources 🔗 n8n Tunnel Documentation Twilio for WhatsApp Quickstart Perplexity AI API Documentation

The Prompt converter workflow tackles the challenge of turning your natural language video ideas into perfectly formatted JSON prompts tailored for Veo 3 video generation. By leveraging Langchain AI nodes and Google Gemini, this workflow automates and refines your input to help you create high-quality videos faster and with more precision—think of it as your personal video prompt translator that speaks fluent cinematic! 💡 Why Use Prompt Converter? Save time: Automate converting complex video prompts into structured JSON, cutting manual formatting headaches and boosting productivity. Avoid guesswork: Eliminate unclear video prompt details by generating detailed, cinematic descriptions that align perfectly with Veo 3 specs. Improve output quality: Optimize every parameter for Veo 3's video generation model to get realistic and stunning results every time. Gain a creative edge: Turn vague ideas into vivid video concepts with AI-powered enhancement—your video project's secret weapon. ⚡ Perfect For Video creators: Content developers wanting quick, precise video prompt formatting without coding hassles. AI enthusiasts: Developers and hobbyists exploring Langchain and Google Gemini for media generation. Marketing teams: Professionals creating video ads or visuals who need consistent prompt structuring that saves time. 🔧 How It Works ⏱ Trigger: User submits a free text prompt via message or webhook. 📎 Process: The text goes through an AI model that understands and reworks it into detailed JSON parameters tailored for Veo 3. 🤖 Smart Logic: Langchain nodes parse and optimize the prompt with cinematic details, set reasonable defaults, and structure the data precisely. 💌 Output: The refined JSON prompt is sent to Google Gemini for video generation with optimized settings. 🔐 Quick Setup Import the JSON file to your n8n instances Add credentials: Azure OpenAI, Gemini API, OpenRouter API Customize: Adjust prompt templates or default parameters in the Prompt converter node Test: Run your workflow with sample text prompts to see videos come to life 🧩 You'll Need Active n8n instances Azure OpenAI API Gemini API Key OpenRouter API (alternative AI option) 🛠️ Level Up Ideas Add integration with video hosting platforms to auto-upload generated videos 🧠 Nodes Used Prompt Input** (Chat Trigger) OpenAI** (Azure OpenAI GPT model) Alternative** (OpenRouter API) Prompt converter** (Langchain chain LLM for JSON conversion) JSON parser** (structured output extraction) Generate a video** (Google Gemini video generation) Made by: Khaisa Studio Tags: video generation, AI, Langchain, automation, Google Gemini Category: Video Production Need custom work? Contact me

✨ What It Does Mello is a Claude-powered Slack assistant that helps you stay on top of unread messages across all your channels. It: Summarizes conversations contextually using Claude AI. Generates reply suggestions and sends them as private (ephemeral) Slack messages. Lets you respond instantly with one-click AI-suggested replies. Perfect for busy teams, founders, and anyone looking to reduce Slack noise and save hours each week. 🔧 Setup Instructions Create a Slack App Go to Slack API → Your Apps Click Create New App and set it up for your workspace Under OAuth & Permissions, add: Bot Token Scopes: commands, chat:write, channels:history, users:read User Token Scopes: channels:history, chat:write Enable Interactivity, and point the Request URL to your n8n webhook (e.g. /slash-summarize) Add Claude API Get an API key from Claude (Anthropic) In n8n, set up the Claude API credential (or switch to OpenAI) Import This Workflow Go to your n8n instance, click Import, and paste this template Update any placeholders (Slack app, Claude key, webhook URLs) Follow the inline sticky notes for guidance Test It Type /summarize in any Slack channel Mello will fetch unread messages, summarize them, and show reply buttons in a private message ⏱ Setup time: ~10 minutes 🛠 Workflow Highlights Slash command trigger (/summarize) Slack API integration to fetch messages Claude AI for contextual summaries Reply suggestions with smart buttons Private Slack delivery (ephemeral messages) Designed to be easily extended (e.g. add support for OpenAI, custom storage) 🔒 Note This is a lite preview of the full Mello workflow. ✅ The full version includes: Slack reply buttons with thread context Full OAuth flow with token storage MongoDB integration Custom Claude/OpenAI configuration Hosted version with onboarding, branding & support 💡 Want access to the complete version? 📩 Email nina@baloon.dev

Guidebook: How the Website ChatBot Template Works Chapter 1: Introduction & Objectives This guidebook provides a comprehensive walkthrough of the Website ChatBot developed using n8n and OpenAI. The chatbot is designed to qualify real estate leads and encourage site visits for the Alcove New Kolkata Sangam project through personalized, intelligent conversations. Chapter 2: Tools Required 1. n8n Workflow Automation Tool An open-source workflow builder to automate data flows between services. 2. OpenAI Account with GPT-4o-mini Access For generating AI-based chatbot responses. 3. Web Chat Widget Frontend integration that sends messages via webhook to the chatbot. Chapter 3: Workflow Breakdown Step 1: Webhook Receives POST requests from the chat widget. Endpoint: /webhook/chatbot-webhook Step 2: Set User Message Extracts message from the JSON body. Stores it as user_message. Step 3: Memory Setup Uses session ID to track conversation across messages. Step 4: OpenAI Chat Model GPT-4o-mini processes queries using the defined agent prompt. Step 5: AI Agent (Khusboo) Persona of a pre-sales agent. Uses AIDA + BANT + SPIN + PAS frameworks. Shares videos, responds in Hinglish, schedules site visits. Step 6: Respond to Webhook Formats the chatbot's reply into a JSON response. Chapter 4: Strategy & Psychology Behind Responses | Framework | Purpose | | --------- | ---------------------------------------------------- | | AIDA | Capture attention, interest, desire, action | | BANT | Qualify Budget, Authority, Need, Timing | | SPIN | Understand user's Situation, Problems, Implications | | PAS | Tackle objections using Problem, Agitation, Solution | The chatbot aims to qualify leads and gently move them toward booking a site visit without pushing or over-informing. Chapter 5: Setup Instructions A. n8n Workflow Setup Import the JSON workflow. Ensure OpenAI credentials are set up. Enable webhook at /webhook/chatbot-webhook. B. Frontend Widget Integration Send message as POST to the webhook with structure: { "message": "Looking for 2 BHK", "session_id": "user123" } Chapter 6: Testing & Troubleshooting Test via Postman Send sample request to verify AI response. Common Issues | Issue | Fix | | ---------------- | ----------------------------------- | | No response | Check webhook URL or credentials | | Repeated replies | Ensure memory node is active | | Wrong language | Check system message language rules | Chapter 7: Sample Conversations User: Hi, I’m looking for a home near the Ganga. Bot: Namaste! Main Khusboo hoon, Alcove New Kolkata Sangam se. Aapka naam kya hai? User: Rajat. Bot: Great Rajat! Kya aap apne family ke saath shift hone ka plan kar rahe ho? ... (continues using frameworks) Chapter 8: FAQs & Maintenance Tips Q: Can I update the AI agent persona? A: Yes, by modifying the system message inside the AI Agent node. Q: How do I share new videos or links? A: Add them in the sharingVideos or UserRequests section in the system message. Q: How to scale this for multiple projects? A: Duplicate the workflow and update the aboutProject and links accordingly. End of Guidebook.

Description This workflow automates the extraction of structured data from invoices or similar documents using Docsumo's API. Users can upload a PDF via an n8n form trigger, which is then sent to Docsumo for processing and structured parsing. The workflow fetches key document metadata and all line items, reconstructs each invoice row with combined header and item details, and finally exports all results as an Excel file. Ideal for automating invoice data entry, reporting, or integrating with accounting systems. How It Works A user uploads a PDF document using the integrated n8n form trigger. The workflow securely sends the document to Docsumo via REST API. After uploading, it checks and retrieves the parsed document results. Header information and table line items are extracted and mapped into structured records. The complete result is exported as an Excel (.xls) file. Setup Steps Docsumo Account: Register and obtain your API key from Docsumo. n8n Credentials Manager: Add your Docsumo API key as an HTTP header credential (never hardcode the key in the workflow). Workflow Configuration: In the HTTP Request nodes, set the authentication to your saved Docsumo credentials. Update the file type or document type in the request (e.g., "type": "invoice") as needed for your use case. Testing: Enable the workflow and use the built-in form to upload a sample invoice for extraction. Features Supports PDF uploads via n8n’s built-in form or via API/webhook extension. Sends files directly to Docsumo for document data extraction using secure credentials. Extracts invoice-level metadata (number, date, vendor, totals) and full line item tables. Consolidates all data in easy-to-use Excel format for download or integration. Modular node structure, easily extensible for further automation. Prerequisites Docsumo account with API access enabled. n8n instance with form, HTTP Request, Code, and Excel/Convert to File nodes. Working Docsumo API Key stored securely in n8n’s credential manager. Example Use Cases | Scenario | Benefit | |---------------------|-----------------------------------------| | Invoice Automation | Extract line items and metadata rapidly | | Receipts Processing | Parse and digitize business receipts | | Bulk Bill Imports | Batch process bills for analytics | Notes Credentials Security:** Do not store your API key directly in HTTP Request nodes; always use n8n credentials manager. Sticky Notes:** The workflow includes sticky notes for setup, input, API call, extraction, and output steps to assist template users. Custom Columns:** You can customize header or line item extraction by editing the Code node as needed.

Daily Company News Bot This n8n template demonstrates how to use Free FinnHub API to retrieve the company news from a list stock tickers and post messages in Slack channel with a pre-scheduled time. How it works We firstly define the list of stock tickers you are interested Loop over items to call FinnHub API to get the latest company news for the ticker Then we format the company news as a markdown text content which could be sent to Slack Post a new message in Slack channel Wait for 5 seconds, then move to the next ticker How to use Simply setup a scheduler trigger to automatically trigger the workflow Requirements FinnHub API Key Slack channel webhook Need Help? Contact me via My Blog or ask in the Forum! Happy Hacking!