+2

Share YouTube Videos with AI Summaries on Discord

Boost engagement on your Discord server by automatically sharing new YouTube videos along with AI generated summaries of their content. This workflow is ideal for content creators and community managers looking to provide value and spark interest through summarized content, making it easier for community members to decide if a video is of interest to them. Watch this video tutorial to learn more about the template. How it works RSS Feed Trigger**: Monitors your YouTube channel for new uploads using the RSS feed. Video Captions Retrieval**: Fetches video captions using the YouTube API to get detailed content data. AI Summary Generation**: Uses an AI model to generate concise summaries from the video captions, highlighting key points. Discord Notification**: Posts video announcements along with their AI generated summaries to a specified Discord channel using a webhook. Set up steps Configure YouTube RSS Feed: Set up the RSS feed node to detect new video uploads. Add your YouTube channel ID to the URL in the first node: https://www.youtube.com/feeds/videos.xml?channel_id=YOUR_CHANNEL_ID. Connect OpenAI Account: To enable AI summary generation, connect your OpenAI account in n8n. Set Up Discord Webhook: Create a webhook in your Discord server and configure it in the Discord node. Design the Message: Format the Discord message as you like to include the video title, link, and the AI generated summary. Example This template empowers you to maintain a highly engaging Discord community, ensuring members receive not only regular updates but also valuable insights into each video's content without needing to watch immediately.

Mike Russell

+10

Building Your First WhatsApp Chatbot

This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions. This template is intended to help introduce n8n users interested in building with WhatsApp. How it works This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot. A product brochure is imported via HTTP request node and its text contents extracted. The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot. A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out. The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool. The Agent's response is sent back to the user via the WhatsApp node. How to use Once you've setup and configured your WhatsApp account and credentials First, populate the vector store by clicking the "Test Workflow" button. Next, activate the workflow to enable the WhatsApp chatbot. Message your designated WhatsApp number and you should receive a message from the AI sales agent. Tweak datasource and behaviour as required. Requirements WhatsApp Business Account OpenAI for LLM Customising this workflow Upgrade the vector store to Qdrant for persistance and production use-cases. Handle different WhatsApp message types for a more rich and engaging experience for customers.

Jimleuk

+9

Actioning Your Meeting Next Steps using Transcripts and AI

This n8n workflow demonstrates how you can summarise and automate post-meeting actions from video transcripts fed into an AI Agent. Save time between meetings by allowing AI handle the chores of organising follow-up meetings and invites. How it works This workflow scans for the calendar for client or team meetings which were held online. * Attempts will be made to fetch any recorded transcripts which are then sent to the AI agent. The AI agent summarises and identifies if any follow-on meetings are required. If found, the Agent will use its Calendar Tool to to create the event for the time, date and place for the next meeting as well as add known attendees. Requirements Google Calendar and the ability to fetch Meeting Transcripts (There is a special OAuth permission for this action!) OpenAI account for access to the LLM. Customising the workflow This example only books follow-on meetings but could be extended to generate reports or send emails.

Jimleuk

+8

Parse DMARC reports, save them in database and notify on DKIM or SPF error

Who is it for If you are a postmaster or you manage email server, you can set up DKIM and SPF records to ensure that spoofing your email address is hard. On your domain you can also set up DMARC record to receive XML reports from email providers (rua tag). Those reports contain data if email they received passed DKIM and SPF verifications. Since DMARC email is public, you will receive a lot of emails from email providers, not only if DKIM/SPF fail. There is no need for it - you probably only need to know if SPF/DKIM failed. So this script is intended to automatically parse all DMARC reports that come from email providers, but ONLY send you notification if SPF or DKIM failed - meaning that either someone tries to spoof your email or your DKIM/SPF is improperly set up. How it works script monitors postmaster email for DMARC reprots (rua) unpacks report and parses XML into JSON maps JSON and formats fields for MySQL/MariaDB input inputs into database sends notification on DKIM or SPF failure Remember to set up email input mailbox notification channels for slack for email

Łukasz

+9

Generate SQL queries from schema only - AI-powered

This workflow is a modification of the previous template on how to create an SQL agent with LangChain and SQLite. The key difference – the agent has access only to the database schema, not to the actual data. To achieve this, SQL queries are made outside the AI Agent node, and the results are never passed back to the agent. This approach allows the agent to generate SQL queries based on the structure of tables and their relationships, without having to access the actual data. This makes the process more secure and efficient, especially in cases where data confidentiality is crucial. 🚀 Setup To get started with this workflow, you’ll need to set up a free MySQL server and import your database (check Step 1 and 2 in this tutorial). Of course, you can switch MySQL to another SQL database such as PostgreSQL, the principle remains the same. The key is to download the schema once and save it locally to avoid repeated remote connections. Run the top part of the workflow once to download and store the MySQL chinook database schema file on the server. With this approach, we avoid the need to repeatedly connect to a remote db4free database and fetch the schema every time. As a result, we reach greater processing speed and efficiency. 🗣️ Chat with your data Start a chat: send a message in the chat window. The workflow loads the locally saved MySQL database schema, without having the ability to touch the actual data. The file contains the full structure of your MySQL database for analysis. The Langchain AI Agent receives the schema, your input and begins to work. The AI Agent generates SQL queries and brief comments based solely on the schema and the user’s message. An IF node checks whether the AI Agent has generated a query. When: Yes: the AI Agent passes the SQL query to the next MySQL node for execution. No: You get a direct answer from the Agent without further action. The workflow formats the results of the SQL query, ensuring they are convenient to read and easy to understand. Once formatted, you get both the Agent answer and the query result in the chat window. 🌟 Example queries Try these sample queries to see the schema-driven AI Agent in action: Would you please list me all customers from Germany? What are the music genres in the database? What tables are available in the database? Please describe the relationships between tables. - In this example, the AI Agent does not need to create the SQL query. And if you prefer to keep the data private, you can manually execute the generated SQL query in your own environment using any database client or tool you trust 🗄️ 💭 The AI Agent memory node does not store the actual data as we run SQL-queries outside the agent. It contains the database schema, user questions and the initial Agent reply. Actual SQL query results are passed to the chat window, but the values are not stored in the Agent memory.

Yulia

+2

Creators Hub: Generate Dynamic SVG Stats with daily updates

n8n Creators Template: Creator Profile Stats Updater This n8n workflow template is designed to automate the process of updating a creator's profile statistics, including total workflows, complex workflows, approved workflows, pending workflows, total nodes, and total views. It utilizes various nodes to fetch data, process it, and update a SVG file hosted on GitHub to reflect the latest stats. Workflow Overview Schedule Trigger: Triggers the workflow execution at specified intervals. Config: Sets up configuration details like creator username, colors for text, icons, border, and card. Get Workflows: Fetches workflows associated with the creator from the n8n API. Workflows Data: Processes the fetched data to calculate various statistics. Get User: Fetches user details from the n8n API. Download Image: Downloads the creator's profile image. Extract From File: Extracts binary data from the downloaded image file. SVG: Generates an SVG file with updated stats and visual representation. GitHub: Commits the updated SVG file to the specified GitHub repository. Final: Prepares the final data set for further processing or output. Sticky Note: Provides a visual note or reminder within the workflow editor. Embed & Live Preview Since it's a .SVG format you can host it anywhere. treat it like normal image so you can embed it with any site, forum, page that support posting images. here's example code for markdown: Here's the result Or served through CDN & Cache Setup Instructions GitHub Credentials: Ensure you have GitHub credentials set up in your n8n instance to allow the workflow to commit changes to your repository. Configure Trigger: Adjust the Schedule Trigger node to set the desired execution intervals for the workflow. Set Configuration: Customize the Config node with your GitHub username and preferred aesthetic options for the SVG. Deploy Workflow: Import the workflow into your n8n instance and deploy it. Customization Options Text and Icon Colors**: Customize the colors used in the SVG by modifying the respective fields in the Config node. Profile Image Size**: Adjust the image size in the Download Image node URL if needed. Commit Messages**: Modify the commit messages in the GitHub nodes to suit your version control conventions [I've used $now funaction to include current time in message which will gives allways a diffrent commit value]. Requirements n8n (Self-hosted or Cloud version compatible with 2024 releases and up) GitHub account and repository Basic understanding of n8n workflow configuration Support and Contributions For support, please refer to the n8n community forum or the official n8n documentation. Contributions to the template can be made you're allowed to reuse this workflow and reshare with edit (like new design/colors etc..) under your name.

Nskha

Convert image to text using GROQ LLaVA V1.5 7B

What this template does This template uses GROQ LLAVA V1.5 7B API that offers fast inference for multimodal models with vision capabilities for understanding and interpreting visual data from images. . The users send a image and get a description of the image from the model. Setup Open the Telegram app and search for the BotFather user (@BotFather) Start a chat with the BotFather Type /newbot to create a new bot Follow the prompts to name your bot and get a unique API token Save your access token and username Once you set your bot, you can send the image, and get the descriptions.

felipe biava cataneo

+2

Convert an XML file to JSON via webhook call

Who this template is for This template is for everyone who needs to work with XML data a lot and wants to convert it to JSON instead. Use case Many products still work with XML files as their main language. Unfortunately, not every software still supports XML, as many switched to more modern storing languages such as JSON. This workflow is designed to handle the conversion of XML data to JSON format via a webhook call, with error handling and Slack notifications integrated into the process. How this workflow works Triggering the workflow: This workflow initiates upon receiving an HTTP POST request at the webhook endpoint specified in the "POST" node. The endpoint, designated as , can be accessed externally by sending a POST request to that URL. Data routing and processing: Upon receiving the POST request, the Switch node routes the workflow's path based on conditions determined by the content type of the incoming data or any encountered errors. The Extract From File and Edit Fields (Set) nodes manage XML input processing, adapting their actions according to the data's content type. XML to JSON conversion: The XML data extracted from the input is passed through the "XML" node, which performs the conversion process, transforming it into JSON format. Response handling: If the XML-to-JSON conversion is successful, a success response is sent back with a status of "OK" and the converted JSON data. If there are any errors during the XML-to-JSON conversion process, an error response is sent back with a status of "error" and an error message. Error handling: in case of an error during processing, the workflow sends a notification to a Slack channel designated for error reporting. Set up steps Set up your own in the Webhook node. While building or testing a workflow, use a test webhook URL. When your workflow is ready, switch to using the production webhook URL. Set credentials for Slack.

n8n Team

+3

Transfer credentials to other n8n instances using a Multi-Form

Purpose This workflow allows you to transfer credentials from one n8n instance to another. How it works A multi-form setup guides you through the entire process You get to choose one of your predefined (in the Settings node) remote instances first Then all credentials of the current instance are being retrieved using the Execute Command node On the next form page you can select one of the credentials by their name and initiate the transfer Finally the credential is being created on the remote instance using the n8n API. A final form ending indicates if that action succeeded or not. Setup Select your credentials in the nodes which require those Configure your remote instance(s) in the Settings node Every instance is defined as object with the keys name, apiKey and baseUrl. Those instances are then wrapped inside an array. You can find an example described within a note on the workflow canvas. How to use Grab the (production) URL of the Form from the first node Open the URL and follow the instructions given in the multi-form Disclaimer Please note, that this workflow can only run on self-hosted n8n instances, since it requires the Execute Command Node. Security: Beware, that all credentials are being decrypted and processed within the workflow. Also the API keys to other n8n instances are stored within the workflow. This solution is primarily meant for transferring data between testing environments. For production use consider the n8n enterprise edition which provides a reliable way to manage credentials across different environments.

Mario

+5

Save your workflows into a Gitlab repository

This template is inspired by Save your workflows into a GitHub repository by hikerspath and Back Up Your n8n Workflows To Github by jon-n8n. Basic Retrieve all workflows from an n8n instance and save it on a gitlab project. If the workflow exist, il will only save the changes. Flow What the workflow does : Sets custom parameters Gets workflows Iterates through each workflow one by one Get the file from Gitlab if exists Compare the files as objects (not as strings) Return a status on the workflow Create, Edit or ignore the file depending on the status Return a list of status for each workflow Configuration Select a credential in each Gitlab nodes. Edit the data in node "Globals" : repo.owner : slug of the user or team owning the repo repo.name : slug of the repository repo.branch : branch to commit on repo.path : from root of the repository. Should end with / Comments Error on gitlab nodes will not stop the run but will list the current workflow as error in the results Some fields are ignored to determined if there is changes : updatedAt : should be ignored if only ignores fields are changed globals : it's running information, no need to follow the changes

Julien DEL RIO

+24

Scale Deal Flow with a Pitch Deck AI Vision, Chatbot and QDrant Vector Store

Are you a popular tech startup accelerator (named after a particular higher order function) overwhelmed with 1000s of pitch decks on a daily basis? Wish you could filter through them quickly using AI but the decks are unparseable through conventional means? Then you're in luck! This n8n template uses Multimodal LLMs to parse and extract valuable data from even the most overly designed pitch decks in quick fashion. Not only that, it'll also create the foundations of a RAG chatbot at the end so you or your colleagues can drill down into the details if needed. With this template, you'll scale your capacity to find interesting companies you'd otherwise miss! Requires n8n v1.62.1+ How It Works Airtable is used as the pitch deck database and PDF decks are downloaded from it. An AI Vision model is used to transcribe each page of the pitch deck into markdown. An Information Extractor is used to generate a report from the transcribed markdown and update required information back into pitch deck database. The transcribed markdown is also uploaded to a vector store to build an AI chatbot which can be used to ask questions on the pitch deck. Check out the sample Airtable here: https://airtable.com/appCkqc2jc3MoVqDO/shrS21vGqlnqzzNUc How To Use This template depends on the availability of the Airtable - make a duplicate of the airtable (link) and its columns before running the workflow. When a new pitchdeck is received, enter the company name into the Name column and upload the pdf into the File column. Leave all other columns blank. If you have the Airtable trigger active, the execution should start immediately once the file is uploaded. Otherwise, click the manual test trigger to start the workflow. When manually triggered, all "new" pitch decks will be handled by the workflow as separate executions. Requirements OpenAI for LLM Airtable For Database and Interface Qdrant for Vector Store Customising This Workflow Extend this starter template by adding more AI agents to validate claims made in the pitch deck eg. Linkedin Profiles, Page visits, Reviews etc.

Jimleuk

+6

Reconcile Rent Payments with Local Excel Spreadsheet and OpenAI

This n8n workflow is designed to work on the local network and assists with reconciling downloaded bank statements with internal tenant records to quickly highlight any issues with payments such as missed or late payments or those of incorrect amounts. This assistant can then generate a report to quick flag attention to ensure remedial action is taken. How it works The workflow monitors a local network drive to watch for new bank statements that are added. This bank statement is then imported into the n8n workflow, its contents extracted and sent to the AI Agent. The AI Agent analyses the line items to identify the dates and any incoming payments from tenants. The AI agent then uses an locally-hosted Excel ("XLSX") spreadsheet to get both tenant records and property records. From this data, it can determine for each active tenant when payment is due, the amount and the tenancy duration. Comparing to the bank statement, the AI Agent can now report on where tenants have missed their payments, made late payments or are paying the incorrect amounts. The final report is generated and logged in the same XLSX for a human to check and action. Requirements A self-hosted version of n8n is required. OpenAI account for the AI model Customising this workflow If you organisation has a Slack or Teams account, consider sending reports to a channel for increased productivity. Email may be a good choice too. Want to go fully local? A version of this workflow is available which uses Ollama instead. You can download this template here: https://drive.google.com/file/d/1YRKjfakpInm23F_g8AHupKPBN-fphWgK/view?usp=sharing

Jimleuk

+8

Extract spending history from gmail to google sheet

How it works Fetch transaction notification emails (including attachments) Clean up data Let AI (Basic LLM Chain node) generate bookkeeping item Send to Google sheet Details The example fetch email from Gmail lables, suggested using filters to automatically orgianize email into the labels Data will send to "raw data" sheet Example google sheet: https://docs.google.com/spreadsheets/d/1_IhdHj8bxtsfH2MRqKuU2LzJuzm4DaeKSw46eFcyYts/edit?gid=1617968863#gid=1617968863

hana

+17

Breakdown Documents into Study Notes using Templating MistralAI and Qdrant

This n8n workflow takes in a document such as a research paper, marketing or sales deck or company filings, and breaks them down into 3 templates: study guide, briefing doc and timeline. These templates are designed to help a student, associate or clerk quickly summarise, learn and understand the contents to be more productive. Study guide - a short quiz of questions and answered generated by the AI Agent using the contents of the document. Briefing Doc - key information and insights are extracted by the AI into a digestable form. Timeline - key events, durations and people are identified and listed into a simple to understand timeline by the AI How it works A local file trigger watches a local network directory for new documents. New documents are imported into the workflow, its contents extracted and vectorised into a Qdrant vector store to build a mini-knowledgebase. The document then passes through a series of template generating prompts where the AI will perform "research" on the knowledgebase to generate the template contents. Generated study guide, briefing and timeline documents are exported to a designated folder for the user. Requirements Self-hosted version of n8n. Qdrant instance for knowledgebase. Mistral.ai account for embeddings and AI model. Customising your workflow Try adding your own templates or adjusting the existing templates to suit your unique use-case. Anything is quite possible and limited only by your imagination! Want to go fully local? A version of this workflow is available which uses Ollama instead. You can download this template here: https://drive.google.com/file/d/1VV5R2nW-IhVcFP_k8uEks4LsLRZrHSNG/view?usp=sharing

Jimleuk

+10

ERP AI chatbot for Odoo sales module with OpenAI

Who is this for? This workflow is for everyone who wants to have easier access to their Odoo sales data without complex queries. Use Case To have a clear overview of your sales data in Odoo you typically needs to extract data from it manually to analyse it. This workflow uses OpenAI's language models to create an intelligent chatbot that provides conversational access to your Odoo sales opportunity data. How it works Creates a summary of all Odoo sales opportunities using OpenAI Uses that summary as context for the OpenAI chat model Keeps the summary up to date using a schedule trigger Set up steps: Configure the Odoo credentials Configure OpenAI credentials Toggle "Make Chat Publicly Available" from the Chat Trigger node.

Mihai Farcas

+16

Build a Tax Code Assistant with Qdrant, Mistral.ai and OpenAI

This n8n workflows builds another example of creating a knowledgebase assistant but demonstrates how a more deliberate and targeted approach to ingesting the data can produce much better results for your chatbot. In this example, a government tax code policy document is used. Whilst we could split the document into chunks by content length, we often lose the context of chapters and sections which may be required by the user. Our approach then is to first split the document into chapters and sections before importing into our vector store. Additionally, using metadata correctly is key to allow filtering and scoped queries. Example Human: "Tell me about what the tax code says about cargo for intentional commerce?" AI: "Section 11.25 of the Texas Property Tax Code pertains to "MARINE CARGO CONTAINERS USED EXCLUSIVELY IN INTERNATIONAL COMMERCE." In this section, a person who is a citizen of a foreign country or an en..." How it works The tax code policy document is downloaded as a zip file from the government website and its pages are extracted as separate chapters. Each chapter is then parsed and split into its sections using data manipulation expressions. Each section is then inserted into our Qdrant vector store tagged with its source, chapter and section numbers as metadata. When our AI Agent needs to retrieve data from our vector store, we use a custom workflow tool to perform the query to Qdrant. Because we're relying on Qdrant's advanced filtering capabilities, we perform the search using the Qdrant API rather than the Qdrant node. When the AI Agent, needs to pull full wording or extracts, we can use Qdrant's scroll API and metadata filtering to do so. This makes Qdrant behave like a key-value store for our document. Requirements A Qdrant instance is required for the vector store and specifically for it's filtering functionality. Mistral.ai account for Embeddings and AI models. Customising this workflow Depending on your use-case, consider returning actual PDF pages (or links) to the user for the extra confirmation and to build trust. Not using Mistral? You are able to replace but note to match the distance and dimension size of Qdrant collection to your chosen embedding model.

Jimleuk

+5

📂 Automatically Update Stock Portfolio from OneDrive to Excel

Seamlessly Sync and Update Data from a csv in OneDrive to Excel with n8n This workflow is perfect for users who need a reliable, automated way to transfer and organize data from OneDrive into Excel—especially for tasks like portfolio tracking, inventory management, and record-keeping. By monitoring your OneDrive folder for new CSV files, it performs data cleaning, transformation, and real-time updates in an Excel sheet, ensuring only new or changed records are added. How it Works Automated Monitoring: Every minute, the workflow scans a designated OneDrive folder for new files. File Verification: It checks if the detected file is in CSV format; if not, the process stops with an error message. Data Extraction and Cleaning: CSV data is loaded, and irrelevant headers are removed before mapping to specified columns in Excel. Excel Update: The workflow maps data to your Excel sheet, updating only new or modified entries based on a unique identifier ("Ticker/ISIN"). Cleanup: To keep your OneDrive organized, processed files are deleted after updating Excel. Setup Steps Connect OneDrive and Excel Accounts: Link your Microsoft OneDrive and Excel accounts in n8n. Designate Folder and Worksheet: Specify the OneDrive folder for monitoring and the Excel sheet for data updates. Configure Sync Frequency and CSV Validation: Set the monitoring frequency to every minute and ensure the workflow identifies CSV files accurately. Once configured, this workflow offers a hands-free, efficient solution to keep your OneDrive and Excel data synchronized effortlessly.

Louis

+14

Building RAG Chatbot for Movie Recommendations with Qdrant and Open AI

Create a recommendation tool without hallucinations based on RAG with the Qdrant Vector database. This example is based on movie recommendations on the IMDB-top1000 dataset. You can provide your wishes and your "big no's" to the chatbot, for example: "A movie about wizards but not Harry Potter", and get top-3 recommendations. How it works a video with the full design process Upload IMDB-1000 dataset to Qdrant Vector Store, embedding movie descriptions with OpenAI; Set up an AI agent with a chat. This agent will call a workflow tool to get movie recommendations based on a request written in the chat; Create a workflow which calls Qdrant's Recommendation API to retrieve top-3 recommendations of movies based on your positive and negative examples. Set Up Steps You'll need to create a free tier Qdrant Cluster (Qdrant can also be used locally; it's open-sourced) and set up API credentials You'll OpenAI credentials You'll need GitHub credentials & to upload the IMDB Kaggle dataset to your GitHub.

Jenny

+7

Automate Your RFP Process with OpenAI Assistants

This n8n workflow demonstrates how to automate oftern time-consuming form filling tasks in the early stages of the tendering process; the Request for Proposal document or "RFP". It does this by utilising a company's knowledgebase to generating question-and-answer pairs using Large Language Models. How it works A buyer's RFP is submitted to the workflow as a digital document that can be parsed. Our first AI agent scans and extracts all questions from the document into list form. The supplier sets up an OpenAI assistant prior loaded with company brand, marketing and technical documents. The workflow loops through each of the buyer's questions and poses these to the OpenAI assistant. The assistant's answers are captured until all questions are satisified and are then exported into a new document for review. A sales team member is then able to use this document to respond quickly to the RFP before their competitors. Example Webhook Request curl --location 'https://<n8n_webhook_url>' \ --form 'id="RFP001"' \ --form 'title="BlueChip Travel and StarBus Web Services"' \ --form 'reply_to="[email protected]"' \ --form 'data=@"k9pnbALxX/RFP Questionnaire.pdf"' Requirements An OpenAI account to use AI services. Customising the workflow OpenAI assistants is only one approach to hosting a company knowledgebase for AI to use. Exploring different solutions such as building your own RAG-powered database can sometimes yield better results in terms of control of how the data is managed and cost.

Jimleuk

+6

Multi-Agent PDF-to-Blog Content Generation

Purpose of workflow: The purpose of this workflow is to automatically transform complex, lengthy PDFs into concise, insightful blog posts. This process aims to make dense information more accessible and understandable to a broader audience. How it works: Multi-agent team: Working together to transform PDF to insightful blog Content Analyst: Extracts key information and structure from the PDF. Project Planner: Organizes the workflow and ensures efficient processing. Writers: Multiple agents that craft engaging content based on the analyzed information. Editor: Polishes the final output for accuracy, reliability, and readability. Step by step User inputs parameters via a n8n form (tone, word count, sections, PDF file). System extracts text from the PDF. Content Analyst AI agent analyzes the extracted text, identifying main topics and key points. Project Planner AI agent organizes the workflow, creating a structure for the blog post. Multiple Writer AI agents (based on the number of sections specified) each write a portion of the blog post. Editor AI agent combines all sections and polishes the final draft. Generates a title for the blog post. Final content is uploaded to a publishing Ghost platform

Derek Cheung

+10

Extract data from resume and create PDF with Gotenberg

With this workflow you can extract data from resume documents uploaded via a Telegram bot. Workflow transform readable content of PDF resume into structured data, using AI nodes and returns PDF with formatted, plain HTML. You can modify this workflow to perform other actions with structured data (e.g. insert it into database or create other, well-formatted documents). Functionality of this workflow was presented during the n8n community call on March 7, 2024 - recording of presentation available here. ⚠️ Workflow made for demo purposes. If you want to use it in real life, please make sure necessary measures for personal data protection are set. How it works? User uploads readable PDF resume document into Telegram bot. After authentication based on chat ID parameter, workflow extracts text from the PDF and transfers it into AI chain with connected sub-nodes: OpenAI Chat Model and Structured Output (JSON) Parser. Then, each extracted section (employment history, projects etc.) is formatted into desired HTML structure. Finally, the document is converted into new, structured PDF using Gotenberg. 💡 This workflow requires installed Gotenberg. If you are not familiar with this software, please have a look on my YouTube tutorial. You can also replace call to Gotenberg with other PDF generation service (such as PDFMonkey or ApiTemplate). Set up steps Create Telegram bot and add its credentials in n8n. Set your chat ID parameter in Auth node. Adjust JSON schema in Structured Output Parser according to your needs. Optionally: replace HTTP call to Gotenberg with PDF generation service of your choice. If you like this workflow, please subscribe to my YouTube channel and/or my newsletter.

Oskar

Send a message with an inline embedded image with Gmail

The built-in Gmail node doesn't yet support embedding images within the body of the email, but you can pull this off using the HTTP node, and this template shows you how. Requirements A Gmail account How it works The workflow downloads an image, converts it into the format that the Gmail API expects (base64), packages it into a multipart MIME email and uses the HTTP node to send it.

David Roberts

+12

Import workflows and map their credentials using a Multi-Form

Purpose This workflow allows you to import any workflow from a file or another n8n instance and map the credentials easily. How it works A multi-form setup guides you through the entire process At the beginning you have two options: Upload a workflow file (JSON) Copy workflow from a remote n8n instance If you choose the second option, you get to choose one of your predefined (in the Settings node) remote instances first, then it retrieves a list of all the workflows using the n8n API which you then can choose a workflow from. Now both initial options come together - the workflow file is being processed In parallel all credentials of the current instance are being retrieved using the Execute Command node The next form page enables a mapping of all the credentials used in the workflow. The matching happens between the names (because one workflow can contain different credentials of the same type) of the original credentials and the ones available on the current instance. Every option then shows all available credentials of the same type. In addition the user has always the choice to create a new credential on the fly. For every option which was set to create a new credential, an empty credential is being created on the current instance using the n8n API. An emoji is being appended to the name, which indicates that it needs to be populated. Finally the workflow gets updated with the new credential ID’s and created on the current instance using the n8n API. Then the user gets a message, if the process has succeeded or not. Setup Select your credentials in the nodes which require those Configure your remote instance(s) in the Settings node. (You can skip this step, if you only want to use the File Upload feature) Every instance is defined as object with the keys name, apiKey and baseUrl. Those instances are then wrapped inside an array. You can find an example described within a note on the workflow canvas. How to use Grab the (production) URL of the Form from the first node Open the URL and follow the instructions given in the multi-form Disclaimer Security: Beware, that all credentials are being decrypted and processed within the workflow. Also the API keys to other n8n instances are stored within the workflow. This solution is primarily meant for transferring data between testing environments. For production use consider the n8n enterprise edition which provides a reliable way to deploy workflows between different environments without the need of manual credential mapping.

Mario

+5

📄🌐PDF2Blog - Create Blog Post on Ghost CRM from PDF Document

From PDF to Powerful Blog Posts: AI-Powered Content Transformation Turn complex documents into engaging digital content that drives results. This n8n Workflow uses AI to transforms lengthy PDFs into compelling blog posts that attract and retain readers while you focus on strategic initiatives. Time-Saving Innovation 🚅Lightning-Fast Processing Transform lengthy documents into polished blog content in under 1 minute, eliminating hours of manual work. Our system handles the heavy lifting, delivering up to a 95% reduction in content production time. 📱Intelligent Analysis The AI engine identifies and extracts key insights, organizing information for maximum impact. Each document undergoes comprehensive analysis to ensure no valuable content is overlooked. Advanced Content Optimization ✍️Dynamic Writing Styles Possible Adjust the prompt for multiple tone options: Professional for corporate communications Conversational for engaging blogs Thought leadership for industry authority 📊SEO-Ready Content Potential Adjust the prompt to automatically optimized for search engines, incorporating relevant keywords and semantic structure to improve visibility and drive organic traffic. Ideal Applications 🤼Content Marketing Teams Scale content production without sacrificing quality or consistency. Perfect for teams looking to maintain a robust publishing schedule while maximizing resource efficiency. 🏫Academic Communication Help researchers and institutions share complex findings with broader audiences through accessible, engaging content that maintains academic integrity. 🧑‍💻Digital Publishers Streamline the content transformation process while ensuring each piece meets modern digital standards and reader expectations. Transform your content strategy with an intelligent system that delivers consistent, high-quality results while dramatically reducing production time.

Joseph LePage

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📄🌐PDF2Blog - Create Blog Post on Ghost CRM from PDF Document

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