# templates

name: Daily Birthday Reminders from Google Contacts to Slack
author_name: WeblineIndia
author_username: weblineindia
categories: []
created_at: 2025-01-15T08:32:01.026Z
updated_at: 2025-01-15T08:32:01.026Z
url: https://n8n.io/workflows/2731
scraped_at: 2025-12-24 13:02:46
description:
  Ensure you never miss a birthday with this automated workflow designed by WeblineIndia. It retrieves your Google Contacts, identifies birthdays happening today, and sends personalized reminders directly to a designated Slack channel. This daily automation keeps your team informed and makes birthday celebrations effortless.

  Steps

  Set Daily Schedule (Cron Node)
  Configure a Cron node to trigger the workflow daily at a specific time (e.g., 8 AM).
  This ensures the workflow runs consistently every day to check for birthdays.

  Retrieve Contacts (Google Contacts - Get Contact Node)
  Use the Google Contacts (Get Contact) node to fetch your contact list.
  Ensure your contacts have birthday details stored for accurate filtering.

  Filter Birthdays (IF Node)
  Add an IF Node to compare the current date with each contact’s birthday.
  Only contacts whose birthdays match today’s date will move to the next step.

  Send Birthday Notifications to Slack (Slack - Send Message Node)
  Use the Slack node to send a personalized birthday message to your chosen Slack channel (e.g., #general or #birthdays).
  Customize the message to include the contact’s name, e.g., "🎉 Today is John Doe's birthday! Let’s celebrate!"
  Configure the node to target a specific Slack channel for seamless notifications.

  Activate Workflow
  Save and activate the workflow.

  From now on, the workflow will automatically check for birthdays daily and send timely reminders to your Slack team.

  Outcome
  This hassle-free automation keeps your team engaged and ensures no birthday goes unnoticed. Celebrate special days of your contacts effortlessly and maintain meaningful connections.

  About WeblineIndia
  This workflow showcases our commitment to delivering innovative automation solutions that enhance productivity and foster better relationships. Let us help you build the AI automation tools that make a difference.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.googleContacts","n8n-nodes-base.if","n8n-nodes-base.slack","n8n-nodes-base.filter","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  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


name: CSV to JSON Converter with Error Handling and Slack Notifications
author_name: n8n Team
author_username: n8n-team
categories: []
created_at: 2025-01-15T11:45:23.218Z
updated_at: 2025-01-15T11:45:23.218Z
url: https://n8n.io/workflows/2732
scraped_at: 2025-12-24 13:02:46
description:
  Who this template is for
  This template is for developers or teams who need to convert CSV data into JSON format through an API endpoint, with support for both file uploads and raw CSV text input.

  Use case
  Converting CSV files or raw CSV text data into JSON format via a webhook endpoint, with error handling and notifications. This is particularly useful when you need to transform CSV data into JSON as part of a larger automation or integration process.

  How this workflow works

  Receives POST requests through a webhook endpoint at 
  /tool/csv-to-json
  Uses a Switch node to handle different input types:
      File uploads (binary data)
      Plain text CSV data
      JSON format data
  Processes the CSV data:
      For files: Uses the Extract From File node
      For raw text: Converts the text to CSV using a custom Code node that handles both comma and semicolon delimiters
  Aggregates the processed data and returns:
      Success response (200): Converted JSON data
      Error response (500): Error message with details
  In case of errors, sends notifications to a Slack error channel with execution details and a link to debug

  Set up steps

  Configure the webhook endpoint by deploying the workflow
  Set up Slack integration for error notifications:
      Update the Slack channel ID (currently set to "C0832GBAEN4")
      Configure OAuth2 authentication for Slack
  Test the endpoint using either:
      CURL for file uploads:
          
                  bash
          Copy
          curl -X POST "https://yoururl.com/webhook-test/tool/csv-to-json" \
               -H "Content-Type: text/csv" \
               --data-binary @path/to/your/file.csv
          
                  
      Or send raw CSV data as text/plain content type

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.extractFromFile","n8n-nodes-base.respondToWebhook","n8n-nodes-base.respondToWebhook","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.switch","n8n-nodes-base.slack","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.webhook","n8n-nodes-base.aggregate","n8n-nodes-base.aggregate","n8n-nodes-base.respondToWebhook"]

workflow_json_compressed:
  [encoded data, 4212 characters]


name: Line Message API : Push Message & Reply
author_name: darrell_tw
author_username: darrelltw
categories: []
created_at: 2025-01-15T17:08:04.179Z
updated_at: 2025-01-15T17:08:04.179Z
url: https://n8n.io/workflows/2733
scraped_at: 2025-12-24 13:02:46
description:
  Workflow Description
  This workflow demonstrates how to use the LINE Messaging API to handle two scenarios:
  Replying to a user's message using a reply token.
  Sending a push message to a specific LINE user using their user ID.

  Key Features
  Webhook Integration: Receives and processes incoming messages from LINE using a webhook.
  Conditional Logic: Checks if the received event type is a message and handles it accordingly.
  Reply Message: Automatically responds to the user's message using the LINE reply token.
  Push Message: Sends a test message to a specific LINE user using their unique user ID.

  Pre-Configuration
  To simplify the setup process, create a Header Auth credential in n8n:
  Name**: Authorization
  Value**: Bearer {line token}  
  This will authenticate all API requests to the LINE Messaging API.
  Node Configurations

  1.1. Webhook from LINE Message
  Purpose**: Captures incoming events from the LINE Messaging API.
  Configuration**:
    HTTP Method: POST
    Path: {n8n-webhook-page}

  1.2. If Condition
  Purpose**: Checks if the received event type is message.
  Configuration**:
    Condition:
      {{ $json.body.events[0].type }} equals "message"

  1.3. Line: Reply with Token
  Purpose**: Replies to the user's message using the LINE reply token.
  Configuration**:
    Method: POST
    URL: https://api.line.me/v2/bot/message/reply
    JSON Body:
          {
        "replyToken": "{{ $('Webhook from Line Message').item.json.body.events[0].replyToken }}",
        "messages": [
          {
            "type": "text",
            "text": "收到您的訊息 : {{ $('Webhook from Line Message').item.json.body.events[0].message.text }}"
          }
        ]
      }
  2.1. Manual Trigger: Test Workflow
  Purpose**: Triggers the workflow for testing the push message functionality.
  Configuration**: No additional setup required.

  2.2. Edit Fields
  Purpose**: Prepares the unique LINE user ID for the push message.
  Configuration**:
    Field:
      line_uid: Uxxxxxxxxxxxx

  2.3. Line: Push Message
  Purpose**: Sends a test message to a specific LINE user.
  Configuration**:
    Method: POST
    URL: https://api.line.me/v2/bot/message/push
    JSON Body:
          {
        "to": "{{ $json.line_uid }}",
        "messages": [
          {
            "type": "text",
            "text": "推播測試"
          }
        ]
      }
  工作流程描述
  此工作流程展示如何使用 LINE Messaging API 處理兩種情境：
  使用 reply token 回應使用者的訊息。
  使用使用者的 user ID 發送 推播訊息。

  主要功能
  Webhook 整合：透過 Webhook 接收並處理來自 LINE 的訊息。
  條件邏輯：檢查接收到的事件類型是否為訊息並進行處理。
  回應訊息：使用 LINE 的 reply token 自動回覆使用者的訊息。
  推播訊息：使用 LINE User ID 向指定用戶發送測試訊息。

  預先設定
  為簡化設定流程，請在 n8n 中建立 Header Auth 憑證：
  名稱**：Authorization
  值**：Bearer {line token}  
  此設定將用於認證所有 LINE Messaging API 的請求。
  節點設定

  1.1. Webhook from LINE Message
  用途**：接收來自 LINE Messaging API 的事件。
  設定**：
    HTTP 方法：POST
    路徑：{n8n-webhook-page}

  1.2. If 條件判斷
  用途**：檢查接收到的事件類型是否為 message。
  設定**：
    條件：
      {{ $json.body.events[0].type }} 等於 "message"

  1.3. Line: Reply with Token
  用途**：使用 LINE reply token 回應使用者訊息。
  設定**：
    方法：POST
    URL：https://api.line.me/v2/bot/message/reply
    JSON 主體：
          {
        "replyToken": "{{ $('Webhook from Line Message').item.json.body.events[0].replyToken }}",
        "messages": [
          {
            "type": "text",
            "text": "收到您的訊息 : {{ $('Webhook from Line Message').item.json.body.events[0].message.text }}"
          }
        ]
      }
  2.1. 手動觸發：測試工作流程
  用途**：測試推播訊息功能。
  設定**：無需額外設定。

  2.2. Edit Fields
  用途**：準備推播訊息所需的 LINE 使用者 ID。
  設定**：
    欄位：
      line_uid：Uxxxxxxxxxxxx

  2.3. Line: 推播訊息
  用途**：向特定 LINE 使用者發送測試訊息。
  設定**：
    方法：POST
    URL：https://api.line.me/v2/bot/message/push
    JSON 主體：
          {
        "to": "{{ $json.line_uid }}",
        "messages": [
          {
            "type": "text",
            "text": "推播測試"
          }
        ]
      }
      

nodes_used:
  ["n8n-nodes-base.httpRequest","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Stock Technical Analysis with Google Gemini
author_name: Udit Rawat
author_username: ailistmaster
categories: []
created_at: 2025-01-16T08:21:40.374Z
updated_at: 2025-01-16T08:21:40.374Z
url: https://n8n.io/workflows/2735
scraped_at: 2025-12-24 13:02:46
description:
  The purpose of this workflow, "Sell: Stock Vision," is to create an AI-powered technical analysis agent capable of analyzing financial charts for equity stocks and cryptocurrencies. This workflow provides users with actionable insights into market trends, price movements, candlestick patterns, and technical indicators to support informed trading decisions.

  How It Works

  Integration with TradingView:** The workflow uses the Chart-Img.com API to fetch detailed financial charts for the specified stock or cryptocurrency.
  AI-Powered Analysis:** The workflow employs advanced AI models, including Google's Gemini Chat Model, to analyze the retrieved charts for candlestick patterns, support/resistance levels, and technical indicators like MACD and RSI.
  News and Sentiment Analysis:** By integrating with SerpAPI, the workflow gathers relevant news about the stock or cryptocurrency to evaluate its potential impact on market movements.
  Comprehensive Insights:** It provides detailed trading strategies, including buy/sell recommendations, stop-loss levels, and risk-reward evaluations.
  Continuous Memory:** The AI agent uses buffer memory to retain context for enhanced analysis and continuity.

  Use Case
  This workflow is perfect for traders and analysts who need reliable and AI-powered market analysis to make informed trading decisions efficiently.

  Tutorial

  Obtain API keys for Chart-Img.com and SerpAPI.
  Configure the workflow in your n8n instance by inputting the required API keys and connecting the nodes.
  Trigger the workflow by providing the stock or cryptocurrency symbol, and let the agent do the rest!

  https://youtu.be/9fR4qNMT5LM

nodes_used:
  ["n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.toolSerpApi"]

workflow_json_compressed:
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name: Summarize YouTube Videos from Transcript
author_name: Ahmed Alnaqa
author_username: ahmedalnaqa
categories: []
created_at: 2025-01-16T12:37:51.928Z
updated_at: 2025-01-16T12:37:51.928Z
url: https://n8n.io/workflows/2736
scraped_at: 2025-12-24 13:02:43
description:
  Who is this template for?  
  This workflow template is designed for content creators, researchers, educators, and professionals who need quick, accurate summaries of YouTube videos. It’s ideal for those looking to save time, extract key insights, or repurpose video content into concise formats for reports, studies, or social media.  

  What does it do?  
  The workflow automates the process of summarizing YouTube videos by extracting the transcript, analyzing the content, and generating a concise summary. It leverages AI tools to ensure accuracy and relevance, making it easier to digest lengthy videos in seconds.  

  Why is it useful?  
  This template saves hours of manual effort by automating video summarization, enabling users to focus on analyzing or sharing insights rather than watching entire videos. It’s particularly useful for staying updated with trends, conducting research, or creating content efficiently.  

  How does it work?  
  The workflow integrates with YouTube’s Transcript API powered by Apify Actor to fetch video transcripts, process the text using AI-powered summarization tools, and deliver a clear, concise summary.

  Setup Instructions

  You need an Apify account and an API key to connect with the Actor. Follow the steps below:

  Create a Free Account.
  Choose the appropriate Actor from the Apify search.  
  Under the Integration tab, click on “Use API endpoints.”  
  Select the API that best suits your needs.  

nodes_used:
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name: Ultimate Content Generator for WordPress
author_name: Alex Kim
author_username: alexk1919
categories: []
created_at: 2025-01-17T03:25:32.265Z
updated_at: 2025-01-17T03:25:32.265Z
url: https://n8n.io/workflows/2737
scraped_at: 2025-12-24 13:02:45
description:
  Overview  
  This workflow automates the end-to-end process of creating, optimizing, and publishing content on WordPress. 

  It integrates AI-powered tools, Airtable, and WordPress plugins to generate high-quality, on-brand posts effortlessly. 

  Perfect for content creators, marketers, and business owners looking to save time and scale their content strategy.  

  Features  

  Content Creation:  
  AI-Powered Content:** Generates SEO-friendly blog posts with structured headings, relevant keywords, and meta descriptions.  
  Custom Prompts:** Tailor the AI-generated content to match your brand’s tone and voice.  

  SEO Optimization:  
  RankMath Plugin Integration:** Updates RankMath SEO with focus keywords and meta descriptions, ensuring your content is search-engine optimized.  

  Content Management:  
  Airtable Integration:** Organizes content ideas, drafts, and publishing schedules in one place. Easily scalable for teams or solo creators.  

  Visuals:  
  Branded Featured Images:** Automatically generates on-brand images for every post.  

  Publishing:  
  Effortless Formatting:** Adapts content to fit your WordPress theme and schedules it for publication.  

  Workflow Steps  
  Trigger:  
     Initiated manually or on a schedule.  
  Content Management:  
     Retrieves and organizes ideas from Airtable.  
  Content Generation:  
     Generates AI-driven blog content tailored to your audience.  
  SEO Optimization:  
     Automatically updates RankMath with SEO details.  
  Featured Image Creation:  
     Produces on-brand images for the post.  
  Publishing:  
     Formats and schedules the post on WordPress.  

  Prerequisites  

  API Keys:  
  OpenAI  
  Airtable  
  WordPress REST API  
  RankMath SEO Plugin  

  Custom Code:  
  Add a small update to your WordPress theme’s functions.php file to enable seamless automation.  

  Customization  
  Replace Airtable with another content management system if preferred.  
  Adjust AI prompts to reflect different tones, styles, or industries.  
  Add integrations for additional plugins, analytics, or storage services.  

  Usage  
  Import the workflow into your n8n instance.  
  Configure API credentials for WordPress, Airtable, OpenAI, and RankMath.  
  Update your functions.php file with the provided code snippet.  
  Customize prompts and Airtable structure for your content needs.  
  Trigger the workflow manually or set it on a schedule.  

  Notes  
  Experiment with Airtable views or add filters for more granular control over your content pipeline.  
  Extend the workflow to include social media posting or analytics tracking.  
  For questions, refer to n8n documentation or reach out to the creator.  

  Tools Used  
  Airtable  
  OpenAI GPT  
  WordPress REST API  
  RankMath SEO Plugin  

  Feel free to adapt and extend this workflow to meet your specific needs! 🎉  

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name: Transform Image to Lego Style Using Line and Dall-E
author_name: Boriwat Chanruang
author_username: boriwatopal
categories: []
created_at: 2025-01-17T03:33:28.196Z
updated_at: 2025-01-17T03:33:28.196Z
url: https://n8n.io/workflows/2738
scraped_at: 2025-12-24 13:02:46
description:
  Who is this for?
  This workflow is designed for:
  Content creators**, artists, or hobbyists looking to experiment with AI-generated art.
  Small business owners* or *marketers** using LEGO-style designs for branding or promotions.
  Developers* or *AI enthusiasts** wanting to automate image transformations through messaging platforms like LINE.

  What problem is this workflow solving?
  Simplifies the process of creating custom AI-generated LEGO-style images.
  Automates the manual effort of transforming user-uploaded images into AI-generated artwork.
  Bridges the gap between messaging platforms (LINE) and advanced AI tools (DALL·E).
  Provides a seamless system for users to upload an image and receive an AI-transformed output without technical expertise.

  What this workflow does
  Image Upload via LINE:
     Users send an image to the LINE chatbot.
  AI-Powered Prompt Creation:
     GPT generates a prompt to describe the uploaded image for LEGO-style conversion.
  AI Image Generation:
     DALL·E 3 processes the prompt and creates a LEGO-style isometric image.
  Image Delivery:
     The generated image is returned to the user in LINE.

  Setup

  Prerequisites
  LINE Developer Account** with API credentials.
  Access to OpenAI API with DALL·E and GPT-4 capabilities.
  A configured n8n instance to run this workflow.

  Steps
  Environment Setup:
     Add your LINE API Token and OpenAI credentials as environment variables (LINE_API_TOKEN, OPENAI_API_KEY) in n8n.
  Configure LINE Webhook:
     Point the LINE webhook to your n8n instance.
  Connect OpenAI:
     Set up OpenAI API credentials in the workflow nodes for GPT-4 and DALL·E.
  Test Workflow:
     Upload a sample image in LINE and ensure it returns the LEGO-style AI image.

  How to customize this workflow to your needs
  Localization**:
    Modify response messages in LINE to fit your audience's language and tone.
  Integration**:
    Add nodes to send notifications through other platforms like Slack or email.
  Image Style**:
    Replace the LEGO-style image prompt with other artistic styles or themes.

  Advanced Use Cases
  Art Contests:
     Users upload images and receive AI-enhanced outputs for community voting or branding.
  Marketing Campaigns:
     Quickly generate creative visual content for ads and promotions using customer-submitted photos.
  Education:
     Use the workflow to teach students about AI-generated art and automation through a hands-on approach.

  Tips for Optimization
  Error Handling**:
    Add fallback nodes to handle invalid images or API errors gracefully.
  Logging**:
    Implement a logging mechanism to track requests and outputs for debugging and analytics.
  Scalability**:
    Use queue-based systems or cloud scaling to handle large volumes of image requests.

  Enhancements
  Add sticky notes in n8n to provide inline instructions for configuring each node.
  Create a tutorial video or documentation for first-time users to set up and customize the workflow.
  Include advanced filters to allow users to select from multiple styles beyond LEGO (e.g., pixel art, watercolor).

  This workflow enables seamless interaction between messaging platforms and advanced AI capabilities, making it highly versatile for various creative and business applications.

nodes_used:
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name: Convert Addresses to LatLong with Google Sheets and Google Maps API
author_name: Boriwat Chanruang
author_username: boriwatopal
categories: []
created_at: 2025-01-17T03:43:26.331Z
updated_at: 2025-01-17T03:43:26.331Z
url: https://n8n.io/workflows/2739
scraped_at: 2025-12-24 13:02:46
description:
  Template Detail
  This template automates the process of converting a list of addresses into their latitude and longitude (LatLong) coordinates using Google Sheets and the Google Maps API. It's designed for businesses, developers, and analysts who need accurate geolocation data for use cases like delivery routing, event planning, or market analysis.

  What the Template Does
  Fetch Address Data: Retrieves addresses from a Google Sheet.
  Google Maps API Integration: Sends each address to the Google Maps API and retrieves the corresponding LatLong coordinates.
  Update Google Sheets: Automatically updates the same Google Sheet with the LatLong data for each address.

  Enhancements
  Google Sheets Template: Provide a pre-configured Google Sheets template that users can copy. Example link: Google Sheets Template. 

  Columns required:
     Address: Column to input addresses.
     LatLong: Column for the latitude and longitude results.

  Updated Workflow Structure

  Trigger:
     A manual trigger node starts the workflow.
  Retrieve Data from Google Sheets:
     Fetch addresses from a Google Sheet.
  Send to Google Maps API:
     For each address, retrieve the LatLong coordinates directly via the Google Maps API.
  Update Google Sheets:
     Write the LatLong results back into the Google Sheet.

  Steps to Use
  Prepare Google Sheet:
     Copy the provided Google Sheets template and add your addresses to the Address column.
  Configure Google Cloud API:
     Enable the Maps API for your Google Cloud project.
     Generate an API key with the required permissions.
  Run the Workflow:
     Start the workflow in n8n; it will process the addresses automatically.
     Updated LatLong data will appear in the corresponding Google Sheet.
  Review the Results:
     Use the enriched LatLong data for mapping or analysis.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets"]

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name: Basic Automatic Gmail Email Labelling with OpenAI and Gmail API
author_name: Martijn Kerver
author_username: mkc
categories: []
created_at: 2025-01-17T08:51:32.434Z
updated_at: 2025-01-17T08:51:32.434Z
url: https://n8n.io/workflows/2740
scraped_at: 2025-12-24 13:02:44
description:
  Description
  This workflow automates email categorization in Gmail using the Gmail API and OpenAI's language model. It periodically checks for new emails, reads their content, and categorizes them based on existing Gmail labels. If no matching label is found, the workflow creates a new label and assigns it to the email.

  Key Features
  Polling for Emails**: The workflow triggers every 5 minutes to check for new emails using the Gmail Trigger node.
  Reading Labels**: Existing Gmail labels are fetched to determine the most relevant match for email categorization.
  Dynamic Labeling**: If no existing label matches, a new label is created dynamically based on the email's content.
  OpenAI Integration**: The workflow uses OpenAI's Chat model to analyze email content and suggest or create appropriate labels.
  Email Categorization**: Labels are applied to emails, ensuring they are organized in Gmail's structure. The workflow also removes less relevant emails (e.g., ads) from the inbox.

  Nodes in Use
  Gmail Trigger: Polls Gmail every 5 minutes for new emails.
  Gmail - Read Labels: Fetches all existing Gmail labels.
  Gmail - Get Message: Retrieves the full content of a specific email.
  Gmail - Add Label to Message: Assigns a chosen label to the email.
  Gmail - Create Label: Creates a new label if necessary.
  OpenAI Chat Model: Analyzes email content for categorization.
  Memory Buffer: Retains context for email analysis across multiple iterations.
  Wait Node: Adds a buffer period to manage email processing.

  Prerequisites
  Gmail API Setup**: Ensure Gmail OAuth2 credentials are configured in n8n.
  OpenAI API Key**: Configure OpenAI credentials for email analysis.
  Labeling Standards**: Maintain a consistent Gmail label structure for better organization.

  Instructions
  Add your Gmail API credentials to the Gmail nodes.
  Add your OpenAI API credentials to the OpenAI Chat Model node.
  Activate the workflow. It will start polling for new emails every 5 minutes.
  Monitor and refine the categorization logic if necessary to ensure alignment with Gmail's organizational needs.

  Use Case
  Ideal for individuals or teams handling high email volumes who want to maintain an organized inbox and automate repetitive categorization tasks.

  Note: You can improve the prompt to get better results from the agent by giving it more personal rules on how to categorize.

nodes_used:
  ["n8n-nodes-base.gmailTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","n8n-nodes-base.gmailTool","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.wait","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: AI-Powered RAG Workflow For Stock Earnings Report Analysis
author_name: Mihai Farcas
author_username: mihailtd
categories: []
created_at: 2025-01-17T09:45:18.164Z
updated_at: 2025-01-17T09:45:18.164Z
url: https://n8n.io/workflows/2741
scraped_at: 2025-12-24 13:02:43
description:
  This n8n workflow creates a financial analysis tool that generates reports on a company's quarterly earnings using the capabilities of OpenAI GPT-4o-mini, Google's Gemini AI and Pinecone's vector search. By analyzing PDFs of any company's earnings reports from their Investor Relations page, this workflow can answer complex financial questions and automatically compile findings into a structured Google Doc.

  How it works:
  Data loading and indexing
  Fetches links to PDF earnings document from a Google Sheet containing a list of file links.
  Downloads the PDFs from Google Drive.
  Parses the PDFs, splits the text into chunks, and generates embeddings using the Embeddings Google AI node (text-embedding-004 model).
  Stores the embeddings and corresponding text chunks in a Pinecone vector database for semantic search.

  Report generation with AI agent
  Utilizes an AI Agent node with a specifically crafted system prompt. The agent orchestrates the entire process.
  The agent uses a Vector Store Tool to access and retrieve information from the Pinecone database.
  Report delivery
  Saves the generated report as a Google Doc in a specified Google Drive location.

  Set up steps
  Google Cloud Project & Vertex AI API:
  	Create a Google Cloud project.
  	Enable the Vertex AI API for your project.
  Google AI API key:
  	Obtain a Google AI API key from Google AI Studio.
  Pinecone account and API key:
  	Create a free account on the Pinecone website.
  	Obtain your API key from your Pinecone dashboard.
  	Create an index named company-earnings in your Pinecone project.
  Google Drive - download and save financial documents:
  	Go to a company you want to analize and download their quarterly earnings PDFs
  	Save the PDFs in Google Drive
  	Create a Google Sheet that stores a list of file URLs pointing to the PDFs you downloaded and saved to Google Drive
  Configure credentials in your n8n environment for:
  	Google Sheets OAuth2
  	Google Drive OAuth2
  	Google Docs OAuth2
  	Google Gemini(PaLM) Api (using your Google AI API key)
  	Pinecone API (using your Pinecone API key)
  Import and configure the workflow:
  	Import this workflow into your n8n instance.
  	Update the List Of Files To Load (Google Sheets) node to point to your Google Sheet.
  	Update the Download File From Google Drive to point to the column where the file URLs are
  	Update the Save Report to Google Docs node to point to your Google Doc where you want the report saved.

nodes_used:
  ["@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.splitInBatches","n8n-nodes-base.manualTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.vectorStorePinecone","n8n-nodes-base.googleDocs","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","n8n-nodes-base.googleSheets","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Post Hourly Crypto Market Summaries via Coingecko to X and to Email
author_name: Badr
author_username: b4dr
categories: []
created_at: 2025-01-17T11:18:56.845Z
updated_at: 2025-01-17T11:18:56.845Z
url: https://n8n.io/workflows/2746
scraped_at: 2025-12-24 13:02:47
description:
  Description  
  This workflow, delivers real-time cryptocurrency market updates (default: Bitcoin) by fetching data from the CoinGecko API. It formats the information into a visually engaging message and shares it on X (formerly Twitter) and via email. The workflow is set to trigger hourly but is fully customizable to suit different schedules or cryptocurrencies.

  Key Features  
  Real-Time Market Data Retrieval:** Fetches up-to-date cryptocurrency market data using CoinGecko API.  
  AI-Driven Message Formatting:** Converts raw data into a structured, engaging message.  
  Social Media Integration:** Posts updates to X with OAuth2 authentication.  
  Email Notifications:** Sends updates via email to specified recipients.  
  Flexible Customization:** Adaptable for tracking multiple cryptocurrencies or adding new notification channels.  

  Workflow Steps  
  Trigger Workflow: Default is hourly, but the interval can be customized.  
  Fetch Cryptocurrency Data: Retrieves market data using the CoinGecko API.  
  Format the Message: Converts the data into a readable and visually appealing format.  
  Post Update on X: Shares the message on X (formerly Twitter).  
  Send Email Notifications: Sends the update to a specified email address.  
  Optional Notifications: Expand to additional channels like Slack or Telegram.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.twitter","n8n-nodes-base.gmail"]

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name: 🎨 Interactive Image Editor with FLUX.1 Fill Tool for Inpainting
author_name: Eduard
author_username: eduard
categories: []
created_at: 2025-01-17T12:45:15.799Z
updated_at: 2025-01-17T12:45:15.799Z
url: https://n8n.io/workflows/2747
scraped_at: 2025-12-24 13:02:43
description:
  &gt; Like this template? Connect with Eduard via LinkedIn.

  This workflow is a prototype of an AI-powered image editing interface, similar to Photoshop's Generative Fill feature, but running entirely in the browser. It provides a web-based editor that allows users to:

  Select areas in images using an adjustable brush tool
  Input text prompts to guide the AI generation
  Compare original and generated images side by side
  Iterate on edits with different prompts and settings
  Save or reuse generated images

  &gt; 🎨 Perfect for product catalog management, seasonal content updates, and creative image editing tasks!

  📋 Requirements
  FLUX API Access: You'll need API credentials from FLUX to use this workflow.
  Configure the HTTP Header Auth credential in n8n with your FLUX API key


  🔧 Key Components
  FLUX Fill API for AI-powered image generation
  Konva.js for canvas manipulation
  img-comparison-slider for result visualization
  Custom CSS/JS for editor functionality


  Simple Editor Interface
     HTML page with an editor is served on the Webhook call
     Adjustable brush selection tool
     Provides several mock examples and allows uploading custom images
     Basic prompt and FLUX model parameter controls

  Image Processing Pipeline
     Handles image and mask separately
     Processes FLUX Fill API requests
     Delivers results back to the editor

  Result Viewer
     Split-screen comparison of original and generated images
     Interactive slider for before/after comparison
     Options to save or continue editing
     Support for multiple iteration cycles

  🎯 Use Cases
  This prototype is particularly useful for:

  Testing AI-powered image editing concepts
  Quick product visualization experiments
  Exploring creative image variations
  Demonstrating inpainting capabilities

  &gt; 💡 Pro Tip: Save masks for frequently edited areas to quickly generate variations with different prompts!

  The workflow can be extended to integrate with various data sources and can be customized for specific business needs.

nodes_used:
  ["n8n-nodes-base.merge","n8n-nodes-base.respondToWebhook","n8n-nodes-base.wait","n8n-nodes-base.if","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.webhook","n8n-nodes-base.html","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.noOp"]

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name: Proxmox AI Agent with n8n and Generative AI Integration
author_name: Amjid Ali
author_username: amjid
categories: []
created_at: 2025-01-18T14:13:14.250Z
updated_at: 2025-01-18T14:13:14.250Z
url: https://n8n.io/workflows/2749
scraped_at: 2025-12-24 13:02:42
description:
  Proxmox AI Agent with n8n and Generative AI Integration

  This template automates IT operations on a Proxmox Virtual Environment (VE) using an AI-powered conversational agent built with n8n. By integrating Proxmox APIs and generative AI models (e.g., Google Gemini), the workflow converts natural language commands into API calls, enabling seamless management of your Proxmox nodes, VMs, and clusters.
  Buy My Book:  
  Mastering n8n on Amazon

  Full Courses & Tutorials:  
  http://lms.syncbricks.com

  Watch Video on Youtube

  How It Works  
  Trigger Mechanism  
     The workflow can be triggered through multiple channels like chat (Telegram, email, or n8n's built-in chat).  
     Interact with the AI agent conversationally.

  AI-Powered Parsing  
     A connected AI model (Google Gemini or other compatible models like OpenAI or Claude) processes your natural language input to determine the required Proxmox API operation.

  API Call Generation  
     The AI parses the input and generates structured JSON output, which includes:  
       response_type: The HTTP method (GET, POST, PUT, DELETE).  
       url: The Proxmox API endpoint to execute.  
       details: Any required payload parameters for the API call.

  Proxmox API Execution  
     The structured output is used to make HTTP requests to the Proxmox VE API. The workflow supports various operations, such as:  
       Retrieving cluster or node information.  
       Creating, deleting, starting, or stopping VMs.  
       Migrating VMs between nodes.  
       Updating or resizing VM configurations.

  Response Formatting  
     The workflow formats API responses into a user-friendly summary. For example:  
       Success messages for operations (e.g., "VM started successfully").  
       Error messages with missing parameter details.

  Extensibility  
     You can enhance the workflow by connecting additional triggers, external services, or AI models. It supports:  
       Telegram/Slack integration for real-time notifications.  
       Backup and restore workflows.  
       Cloud monitoring extensions.

  Key Features  
  Multi-Channel Input**: Use chat, email, or custom triggers to communicate with the AI agent.  
  Low-Code Automation**: Easily customize the workflow to suit your Proxmox environment.  
  Generative AI Integration**: Supports advanced AI models for precise command interpretation.  
  Proxmox API Compatibility**: Fully adheres to Proxmox API specifications for secure and reliable operations.  
  Error Handling**: Detects and informs you of missing or invalid parameters in your requests.  

  Example Use Cases  
  Create a Virtual Machine  
     Input: "Create a VM with 4 cores, 8GB RAM, and 50GB disk on psb1."  
     Action: Sends a POST request to Proxmox to create the VM with specified configurations.

  Start a VM  
     Input: "Start VM 105 on node psb2."  
     Action: Executes a POST request to start the specified VM.

  Retrieve Node Details  
     Input: "Show the memory usage of psb3."  
     Action: Sends a GET request and returns the node's resource utilization.

  Migrate a VM  
     Input: "Migrate VM 202 from psb1 to psb3."  
     Action: Executes a POST request to move the VM with optional online migration.

  Pre-Requisites  
  Proxmox API Configuration  
     Enable the Proxmox API and generate API keys in the Proxmox Data Center.  
     Use the Authorization header with the format:  
       PVEAPIToken=&lt;user&gt;@&lt;realm&gt;!&lt;token-id&gt;=&lt;token-value&gt;

  n8n Setup  
     Add Proxmox API credentials in n8n using Header Auth.  
     Connect a generative AI model (e.g., Google Gemini) via the relevant credential type.

  Access the Workflow  
     Import this template into your n8n instance.  
     Replace placeholder credentials with your Proxmox and AI service details.

  Additional Notes  
  This template is designed for Proxmox 7.x and above.  
  For advanced features like backup, VM snapshots, and detailed node monitoring, you can extend this workflow.  
  Always test with a non-production Proxmox environment before deploying in live systems.

  Start with n8n  

  Learn n8n with Amjid   
  Get n8n Book 

  What is Proxmox

nodes_used:
  ["n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.toolHttpRequest","@n8n/n8n-nodes-langchain.outputParserAutofixing","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.telegramTrigger","n8n-nodes-base.gmailTrigger","n8n-nodes-base.webhook","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.code","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.merge","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.switch","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 10532 characters]


name: 🤖 Telegram Messaging Agent for Text/Audio/Images
author_name: Joseph LePage
author_username: joe
categories: []
created_at: 2025-01-18T18:10:46.426Z
updated_at: 2025-01-18T18:10:46.426Z
url: https://n8n.io/workflows/2751
scraped_at: 2025-12-24 13:02:41
description:
  🤖 This n8n workflow creates an intelligent Telegram bot that processes multiple types of messages and provides automated responses using AI capabilities. The bot serves as a personal assistant that can handle text, voice messages, and images through a sophisticated processing pipeline.

  Core Components

  Message Reception and Validation 📥
  🔄 Implements webhook-based message reception for real-time processing
  🔐 Features a robust user validation system that verifies sender credentials
  🔀 Supports both testing and production webhook endpoints for development flexibility

  Message Processing Pipeline ⚡
  🔄 Uses a smart router to detect and categorize incoming message types
  📝 Processes three main message formats:
    💬 Text messages
    🎤 Voice recordings
    📸 Images with captions

  AI Integration 🧠
  🤖 Leverages OpenAI's GPT-4 for message classification and processing
  🗣️ Incorporates voice transcription capabilities for audio messages
  👁️ Features image analysis using GPT-4 Vision API for processing visual content

  Technical Architecture

  Webhook Management 🔌
  🌐 Maintains separate endpoints for testing and production environments
  📊 Implements automatic webhook status monitoring
  ⚡ Provides real-time webhook configuration updates

  Error Handling ⚠️
  🔍 Features comprehensive error detection and reporting
  🔄 Implements fallback mechanisms for unprocessable messages
  💬 Provides user feedback for failed operations

  Message Classification System 📋
  🏷️ Categorizes incoming messages into tasks and general conversation
  🔀 Implements separate processing paths for different message types
  🧩 Maintains context awareness across message processing

  Security Features

  User Authentication 🔒
  ✅ Validates user credentials against predefined parameters
  👤 Implements first name, last name, and user ID verification
  🚫 Restricts access to authorized users only

  Response System

  Intelligent Responses 💡
  🤖 Generates contextual responses based on message classification

nodes_used:
  ["n8n-nodes-base.if","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote","n8n-nodes-base.webhook","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.telegram","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.telegram","n8n-nodes-base.convertToFile","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.textClassifier","@n8n/n8n-nodes-langchain.textClassifier","n8n-nodes-base.set","n8n-nodes-base.telegram","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.switch"]

workflow_json_compressed:
  [encoded data, 6132 characters]


name: HR & IT Helpdesk Chatbot with Audio Transcription
author_name: Felipe Braga
author_username: occult
categories: []
created_at: 2025-01-19T21:41:25.066Z
updated_at: 2025-01-19T21:41:25.066Z
url: https://n8n.io/workflows/2752
scraped_at: 2025-12-24 13:02:42
description:
  An intelligent chatbot that assists employees by answering common HR or IT questions, supporting both text and audio messages. This unique feature ensures employees can conveniently ask questions via voice messages, which are transcribed and processed just like text queries.

  How It Works
  Message Capture: When an employee sends a message to the chatbot in WhatsApp or Telegram (text or audio), the chatbot captures the input.
  Audio Transcription: For audio messages, the chatbot transcribes the content into text using an AI-powered transcription service (e.g., Whisper, Google Cloud Speech-to-Text).
  Query Processing:
     The transcribed text (or directly entered text) is sent to an AI service (e.g., OpenAI) to generate embeddings.
     These embeddings are used to search a vector database (e.g., Supabase or Qdrant) containing the company’s internal HR and IT documentation.
     The most relevant data is retrieved and sent back to the AI service to compose a concise and helpful response.
  Response Delivery: The chatbot sends the final response back to the employee, whether the input was text or audio.

  Set Up Steps
  Estimated Time**: 20–25 minutes
  Prerequisites**:
    Create an account with an AI provider (e.g., OpenAI).
    Connect WhatsApp or Telegram credentials in n8n.
    Set up a transcription service (e.g., Whisper or Google Cloud Speech-to-Text).
    Configure a vector database (e.g., Supabase or Qdrant) and add your internal HR and IT documentation.
    Import the workflow template into n8n and update environment variables for your credentials.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.extractFromFile","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStorePGVector","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","n8n-nodes-base.telegramTrigger","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.telegram","n8n-nodes-base.telegram","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryPostgresChat","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.vectorStorePGVector","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.embeddingsOpenAi","n8n-nodes-base.telegram","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 6396 characters]


name: RAG Chatbot for Company Documents using Google Drive and Gemini
author_name: Mihai Farcas
author_username: mihailtd
categories: []
created_at: 2025-01-20T00:06:27.623Z
updated_at: 2025-01-20T00:06:27.623Z
url: https://n8n.io/workflows/2753
scraped_at: 2025-12-24 13:02:41
description:
  This workflow implements a Retrieval Augmented Generation (RAG) chatbot that answers employee questions based on company documents stored in Google Drive. It automatically indexes new or updated documents in a Pinecone vector database, allowing the chatbot to provide accurate and up-to-date information. The workflow uses Google's Gemini AI for both embeddings and response generation.

  How it works
  The workflow uses two Google Drive Trigger nodes: one for detecting new files added to a specified Google Drive folder, and another for detecting file updates in that same folder.
  Automated Indexing: When a new or updated document is detected
  The Google Drive node downloads the file.
  The Default Data Loader node loads the document content.
  The Recursive Character Text Splitter node breaks the document into smaller text chunks.
  The Embeddings Google Gemini node generates embeddings for each text chunk using the text-embedding-004 model.
  The Pinecone Vector Store node indexes the text chunks and their embeddings in a specified Pinecone index.
  7.The Chat Trigger node receives user questions through a chat interface. The user's question is passed to an AI Agent node.
  The AI Agent node uses a Vector Store Tool node, linked to a Pinecone Vector Store node in query mode, to retrieve relevant text chunks from Pinecone based on the user's question.
  The AI Agent sends the retrieved information and the user's question to the Google Gemini Chat Model (gemini-pro).
  The Google Gemini Chat Model generates a comprehensive and informative answer based on the retrieved documents.
  A Window Buffer Memory node connected to the AI Agent provides short-term memory, allowing for more natural and context-aware conversations.

  Set up steps

  Google Cloud Project and Vertex AI API:
  Create a Google Cloud project.
  Enable the Vertex AI API for your project.
  Google AI API Key:
  Obtain a Google AI API key from Google AI Studio.
  Pinecone Account:
  Create a free account on the Pinecone website.
  Obtain your API key from your Pinecone dashboard.
  Create an index named company-files in your Pinecone project.
  Google Drive:
  Create a dedicated folder in your Google Drive where company documents will be stored.
  Credentials in n8n: Configure credentials in your n8n environment for:
  Google Drive OAuth2
  Google Gemini(PaLM) Api (using your Google AI API key)
  Pinecone API (using your Pinecone API key)
  Import the Workflow:
  Import this workflow into your n8n instance.
  Configure the Workflow:
  Update both Google Drive Trigger nodes to watch the specific folder you created in your Google Drive.
  Configure the Pinecone Vector Store nodes to use your company-files index.

nodes_used:
  ["@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.vectorStorePinecone","@n8n/n8n-nodes-langchain.embeddingsGoogleGemini","n8n-nodes-base.googleDrive","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDriveTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","@n8n/n8n-nodes-langchain.lmChatGoogleGemini","n8n-nodes-base.stickyNote"]

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name: Summarize the New Documents from Google Drive and Save Summary in Google Sheet
author_name: WeblineIndia
author_username: weblineindia
categories: []
created_at: 2025-01-20T04:56:30.203Z
updated_at: 2025-01-20T04:56:30.203Z
url: https://n8n.io/workflows/2754
scraped_at: 2025-12-24 13:02:45
description:
  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.

nodes_used:
  ["n8n-nodes-base.googleDocs","@n8n/n8n-nodes-langchain.toolWikipedia","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.googleSheets","n8n-nodes-base.stickyNote","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Jotform to KlickTipp Integration - Webinar registration
author_name: KlickTipp
author_username: KlickTipp
categories: []
created_at: 2025-01-20T09:28:25.362Z
updated_at: 2025-01-20T09:28:25.362Z
url: https://n8n.io/workflows/2755
scraped_at: 2025-12-24 13:02:47
description:
  Community Node Disclaimer: This workflow uses KlickTipp community nodes.

  How It Works:  

  Jotform Webinar Registry Integration: This workflow streamlines the process of handling webinar registrations submitted via JotForm. It ensures the data is correctly formatted and seamlessly integrates with KlickTipp.  
  Data Transformation: Input data is validated and transformed to meet KlickTipp’s API requirements, including formatting phone numbers, converting dates, and validating URLs.  

  Key Features  

  JotForm Trigger:  
  Captures new form submissions, including participant details and webinar preferences.  

  Data Processing:  
  Standardizes and validates input fields  
  Converts phone numbers to numeric-only format with international prefixes.  
  Transforms dates into UNIX timestamps.  
  Validates LinkedIn URLs and applies fallback URLs if validation fails.  
  Scales numerical fields, such as work experience, for specific use cases.  

  Subscriber Management in KlickTipp:  
  Adds or updates participants as subscribers in KlickTipp.  
  Includes custom field mappings, such as:  
  	Personal information (name, email, phone number).  
  	Webinar details (chosen webinar, start date/time).  
  	Preferences (reminder intervals, questions for presenters).
  Tags contacts for segmentation: Adds fixed and dynamic tags to contacts.
    

  Error Handling:  
  Validates critical fields like phone numbers, URLs, and dates to prevent incorrect data submissions.  

  Setup Instructions  

  Install and Configure Nodes:  
  Set up the JotForm and KlickTipp nodes in your n8n instance.  
  Authenticate your JotForm and KlickTipp accounts.  

  Custom Field Preparation in KlickTipp:  
  Create the necessary custom fields to match the data structure:  

  | Field Name                          | Field Type   |
  |-----------------------------------------|------------------|
  | Jotform \| URL Linkedin                 | URL              |
  | Jotform \| Workexperience in Years      | Decimal Number   |
  | Jotform \| Webinar start timestamp      | Date & Time      |
  | Jotform \| Questions/Notes              | Text             |
  | Jotform \| Webinar                      | Text             |
  | Jotform \| Reminder                     | Text             |


  After creating fields, allow 10-15 minutes for them to sync. If fields don’t appear, reconnect your KlickTipp credentials.  

  Field Mapping and Adjustments:  
  Verify and customize field assignments in the workflow to align with your specific form and subscriber list setup.  

  Workflow Logic  

  Trigger via JotForm Submission: A new form submission from JotForm initiates the workflow  
  Data Transformation: Processes raw form data to ensure compatibility with KlickTipp’s API.  
  Add to KlickTipp Subscriber List: Adds participants to the designated KlickTipp list, including webinar-specific details.  
  Get all tags from KlickTipp and create a list:
  Fetches all existing Tags and turns them into an array

  Define tags to dynamically set for contacts: Definiton of variables that are received from the form submission and should be converted into tags

  Merge tags of both lists: Checks whether the list of existing tags in KlickTipp contains the tags which should be dynamically set based on the form submission

  Tag creation and tagging contacts: Creates new tags if it previously did not exist and then tags the contact



  Benefits  

  Efficient lead generation: Contacts from forms are automatically imported into KlickTipp and can be used immediately, saving time and increasing the conversion rate.  
  Automated processes: Experts can start workflows directly, such as welcome emails or course admissions, reducing administrative effort.  
  Error-free data management: The template ensures precise data mapping, avoids manual corrections and reinforces a professional appearance.  

  Testing and Deployment:  

  Test the workflow by filling the form on Jotform and verifying data updates in KlickTipp.  

  Notes:  

  Customization: Update field mappings within the KlickTipp nodes to align with your account setup. This ensures accurate data syncing.  

  Resources:  
  Jotform KlickTipp Knowledge Base help article
  Use KlickTipp Community Node in n8n
  Automate Workflows: KlickTipp Integration in n8n

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workflow_json_compressed:
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name: AI-Powered Candidate Shortlisting Automation for ERPNext
author_name: Amjid Ali
author_username: amjid
categories: []
created_at: 2025-01-20T19:58:00.264Z
updated_at: 2025-01-20T19:58:00.264Z
url: https://n8n.io/workflows/2757
scraped_at: 2025-12-24 13:02:46
description:
  Template Guide for Employee Shortlisting AI Agent Automation

  Overview
  This template automates the process of shortlisting job applicants using ERPNext, n8n, and AI-powered decision-making tools like Google Gemini and OpenAI. It reduces manual effort, ensures fast evaluations, and provides justifiable decisions about applicants. This is ideal for businesses aiming to streamline their recruitment process while maintaining accuracy and professionalism.

  YouTube Tutorial:** For a full walkthrough of this template, visit:  

    Integrate AI in ERPNext: Automate Recruitment Job Applicant Shortlisting in Seconds!

  What Does This Template Do?
  Webhook Integration with ERPNext: Automatically triggers the workflow when a job application is created in ERPNext.
  Resume Validation: Ensures resumes are attached and correctly processes various file formats like PDF and DOC.
  AI-Powered Evaluation: Uses AI to compare resumes against job descriptions and provides a:
     Fit Level (Strong, Moderate, or Weak)
     Score (0–100)
     Justification for the decision.
  Automated Decision Making: Based on AI-generated scores:
     Candidates with a score of 80 or higher are Accepted.
     Candidates below 80 are Rejected.
     Applications missing required fields or attachments are put On Hold.
  ERPNext Integration: Updates applicant records in ERPNext, including custom fields such as justification, fit level, and scores.
  Notifications: Notifies candidates via email, WhatsApp, or SMS about their application status.

  Step-by-Step Guide

  Step 1: Set Up ERPNext Webhook
  Go to Webhooks in ERPNext.
  Create a webhook for the Job Applicant DocType.
  Set the trigger to Insert.
  Pin and test the webhook to ensure proper data flow.

  Step 2: Import the Template into n8n
  Open your n8n instance.
  Import the provided workflow template.
  Check all nodes for proper configuration.

  Step 3: Configure Credentials
  Add your ERPNext API credentials to the ERPNext nodes.
  Add credentials for AI services like OpenAI or Google Gemini.
  Configure additional services like WhatsApp or email if you plan to use them for notifications.

  Step 4: Test Resume Validation
  Test how the workflow handles different file types (e.g., PDF, DOC, JPG).
  Ensure resumes without the proper format or attachment are flagged and rejected.

  Step 5: AI Evaluation
  The AI model (Google Gemini or OpenAI) will evaluate resumes against job descriptions.
  Customize the AI prompt to suit your job evaluation needs.
  The output will include a Fit Level, Score, Rating, and Justification.

  Step 6: Decision Automation
  The workflow automatically categorizes applicants:
    Accepted for scores ≥ 80.
    Rejected for scores &lt; 80.
    On Hold if essential fields or attachments are missing.

  Step 7: Update ERPNext Records
  The workflow updates the Job Applicant record in ERPNext with:
    Status (Accepted, Rejected, On Hold)
    AI-generated Fit Level, Score, Rating, and Justification.

  Step 8: Notify Candidates
  Configure notification nodes (email, WhatsApp, or SMS).
  Inform candidates about their application status and include feedback if required.

  How It Works
  Trigger: The workflow starts when a job application is submitted in ERPNext.
  Validation: Checks if the resume is attached and in the correct format.
  AI Evaluation: Compares the resume with the job description and generates a decision.
  ERPNext Update: Updates the applicant's record with the decision and justification.
  Notification: Sends a personalized notification to the candidate.

  Dos and Don’ts

  Dos:
  Customize Prompts:** Tailor the AI prompt to match your specific job evaluation requirements.
  Test the Workflow:** Run sample data to ensure the process works as intended.
  Secure Your Credentials:** Keep your API credentials safe and do not share them publicly.
  Optimize for Different Formats:** Ensure the workflow can handle all types of resumes you expect.

  Don’ts:
  Avoid Manual Intervention:** Let the workflow handle most of the tasks to ensure efficiency.
  Do Not Skip Testing:** Always test the workflow with various scenarios to avoid errors.
  Do Not Overlook Notifications:** Ensure candidates are notified promptly to maintain professionalism.

  Customization Options
  Add logic for more file types (e.g., scanned images using OCR).
  Enhance the AI prompts to analyze more complex resume data.
  Integrate additional tools like Slack or Trello for recruitment tracking.

  Resources
  GET n8n Now  

  N8N COURSE  
  n8n Book  

  YouTube Tutorial:** For a full walkthrough of this template, visit:  
    SyncBricks YouTube Channel
  Detailed Guides and Courses:** Learn more about ERPNext and AI-driven automation at:  
    SyncBricks LMS

  Support
  If you encounter issues or want to explore more possibilities with AI-driven automation, feel free to reach out:  
  Email:** amjid@amjidali.com  
  Website:** ERPNext and Other Courses  
  LinkedIn:** Amjid Ali

  Let me know if you'd like further details or modifications to the guide!

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workflow_json_compressed:
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name: AI-Driven Lead Management and Inquiry Automation with ERPNext & n8n
author_name: Amjid Ali
author_username: amjid
categories: []
created_at: 2025-01-20T20:28:59.526Z
updated_at: 2025-01-20T20:28:59.526Z
url: https://n8n.io/workflows/2758
scraped_at: 2025-12-24 13:02:45
description:
  Overview
  This workflow template automates lead management and customer inquiry processing by integrating ERPNext, AI agents, and email notifications. It streamlines the process of capturing leads, analyzing inquiries, and generating actionable responses. The workflow uses ERPNext to capture inquiries, analyzes them with AI, and notifies the appropriate team or individual, all while maintaining a professional approach.

  What This Template Does
  ERPNext Webhook Integration:
     Captures leads and inquiries through ERPNext webhooks.
     Triggers the workflow when a new lead is created.

  AI-Powered Inquiry Analysis:
     Uses AI to extract key details from lead notes (e.g., customer name, organization, inquiry summary).
     Classifies inquiries as valid or invalid based on relevance to products, services, or solutions.

  Contact Assignment:
     Matches inquiries to the appropriate contact(s) using a Google Sheets database or ERPNext contact information.
     Handles multiple contacts if required.

  Email Notifications:
     Generates professional email notifications for valid inquiries.
     Sends emails to the appropriate contact(s) with inquiry details and action steps.

  Invalid Lead Handling:
     Identifies invalid inquiries (e.g., unrelated to products or services) and flags them for follow-up or dismissal.

  Custom Email Formatting:
     Converts plain text into professionally formatted HTML emails.
     Ensures that communication is clear, concise, and visually appealing.

  How It Works

  Step 1: Capture Lead Data
  Webhook in ERPNext:**
    Create a webhook in ERPNext for the "Lead" DocType.
    Set the trigger to on_insert to capture new leads in real-time.

  Lead Details:**
    The workflow fetches lead details, including notes, contact information, and the source of the lead.

  Step 2: Validate and Analyze Inquiry
  AI Agent for Analysis:**
    An AI agent analyzes the lead notes to extract key details and classify the inquiry as valid or invalid.
    The analysis includes checking the relevance of the inquiry to products, services, or solutions offered by the company.

  Invalid Leads:**
    If the inquiry is invalid, the workflow flags it and stops further processing.

  Step 3: Assign Contact(s)
  Google Sheets Integration:**
    Uses a Google Sheets database to map products, services, or solutions to responsible contacts.
    Ensures that inquiries are directed to the right person or team.

  Multiple Contacts:**
    Handles cases where multiple contacts are responsible for a particular product or service.

  Step 4: Generate and Send Email Notifications
  AI-Generated Emails:**
    The workflow generates a professional email summarizing the inquiry.
    Emails include details like customer name, organization, inquiry summary, and action steps.

  Custom HTML Formatting:**
    Emails are converted to HTML for a polished and professional appearance.

  Send Notifications:**
    Sends email notifications through Microsoft Outlook or another configured email client.
    Optionally, notifies via WhatsApp or SMS for urgent inquiries.

  Step 5: Post-Inquiry Actions
  ERPNext Record Updates:**
    Updates the lead record in ERPNext with relevant details, including inquiry status and contact information.

  Setup Instructions

  Prerequisites
  ERPNext:
     A configured ERPNext instance with lead data and a webhook for the "Lead" DocType.
  Google Sheets:
     A sheet mapping products, services, or solutions to responsible contacts.
  AI Integration:
     Credentials for OpenAI or other supported AI platforms.
  Email Client:
     Credentials for Microsoft Outlook or another email client.

  Step-by-Step Setup
  ERPNext Configuration:
     Create a webhook for the "Lead" DocType in ERPNext.
     Test the webhook with sample data to ensure proper integration.

  Workflow Import:
     Import the workflow template into n8n.
     Configure nodes with your API credentials for ERPNext, Google Sheets, and AI tools.

  Google Sheets Integration:
     Prepare a Google Sheet with columns for product, service, or solution and the responsible contact(s).
     Link the sheet to the workflow.

  AI Agent Configuration:
     Customize the AI agent’s prompts to align with your business’s products and services.
     Adjust criteria for valid and invalid inquiries as needed.

  Email Setup:
     Configure the email client node with your email service credentials.
     Customize the email template for your organization.

  Testing:
     Run the workflow with sample leads to validate the entire process.
     Check email notifications, contact assignments, and record updates in ERPNext.

  Dos and Don’ts

  Dos:
  Test Thoroughly:** Test the workflow with various scenarios before deploying in production.
  Secure Credentials:** Keep API and email credentials secure to avoid unauthorized access.
  Customize Prompts:** Tailor AI prompts to match your business needs and language style.
  Use Professional Email Templates:** Ensure emails are clear and well-formatted.

  Don’ts:
  Skip Validation:** Always validate inquiry data to avoid sending irrelevant notifications.
  Overload the Workflow:** Avoid adding unnecessary nodes that can slow down processing.
  Ignore Errors:** Monitor logs and address errors promptly for a smooth workflow.

  Resources
  GET n8n Now  

  N8N COURSE  
  n8n Book  

  YouTube Tutorial:**  
    Watch the full step-by-step tutorial on setting up this workflow:  
    SyncBricks YouTube Channel

  Courses and Training:**  
    Learn more about ERPNext and AI automation through my comprehensive courses:  
    SyncBricks LMS

  Support and Contact:**  
    Email: amjid@amjidali.com  
    Website: SyncBricks  
    LinkedIn: Amjid Ali

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workflow_json_compressed:
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name: Extract and process information directly from PDF using Claude and Gemini
author_name: Agent Studio
author_username: agentstudio
categories: []
created_at: 2025-01-21T07:44:36.708Z
updated_at: 2025-01-21T07:44:36.708Z
url: https://n8n.io/workflows/2764
scraped_at: 2025-12-24 13:02:44
description:
  Overview
  This workflow helps you compare Claude 3.5 Sonnet and Gemini 2.0 Flash when extracting data from a PDF
  This workflow extracts and processes the data within a PDF in one single step, instead of calling an OCR and then an LLM”

  How it works
  The initial 2 steps download the PDF and convert it to base64.
  This base64 string is then sent to both Claude 3.5 Sonnet and Gemini 2.0 Flash to extract information.
  This workflow is made to let you compare results, latency, and cost (in their dedicated dashboard).

  How to use it
  Set up your Google Drive if not already done
  Select a document on your Google Drive
  Modify the prompt in "Define Prompt" to extract the information you need and transform it as wanted.
  Get a Claude API key and/or Gemini API key
  Note that you can deactivate one of the 2 API calls if you don't want to try both
  Test the Workflow

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name: Automated Hugging Face Paper Summary Fetching & Categorization Workflow
author_name: quentin
author_username: quentin-royal
categories: []
created_at: 2025-01-21T09:33:22.365Z
updated_at: 2025-01-21T09:33:22.365Z
url: https://n8n.io/workflows/2765
scraped_at: 2025-12-24 13:02:47
description:
  How the Automated Workflow Works  
  Scheduled Fetching from Hugging Face ⏰  
  The workflow triggers every weekday at 8 AM, automatically fetching the latest papers from Hugging Face for easy access.  

  Duplication Check to Avoid Redundant Entries 🔍  
  It ensures the paper's summary is not already stored in your Notion workspace, preventing duplicate records and keeping your database organized.  

  Content Analysis with OpenAI 🧠  
  Using OpenAI's powerful capabilities, the workflow analyzes the fetched paper summary, extracts key insights, and categorizes the content for easier understanding.  

  Data Storage and Notification Integration 📥🔔  
  Once the summary is processed, it's automatically stored in your Notion workspace, and a notification containing the paper details is sent to your designated Slack channel for quick reference.

  Set Up Your Automated Workflow  
  Create Your n8n Account 📝  
  Start by registering for an n8n account and logging into the n8n cloud service.  

  Connect OpenAI, Notion, and Slack 🔗  
  Link your OpenAI, Notion, and Slack accounts by entering the appropriate tokens. This step will take approximately 10–15 minutes to complete.  

  Import the Workflow Template 📥  
  Import the provided workflow template into your n8n instance to streamline the setup process.  

  Activate the Workflow for Daily Summaries 🚀  
  After importing, simply enable the workflow, and you’re all set to receive daily paper summaries automatically.  

  Setup Time ⏳: Approximately 15–20 minutes.

  Why Use This Automated Workflow?  
  This automated workflow not only saves you time by fetching and categorizing the latest research papers but also helps streamline your Notion workspace and Slack notifications, allowing you to stay organized and efficient without manual intervention.

  Results Presentation

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.if","n8n-nodes-base.splitInBatches","n8n-nodes-base.splitOut","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.notion","n8n-nodes-base.httpRequest","n8n-nodes-base.html","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.notion","n8n-nodes-base.slack"]

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name: Extract personal data with self-hosted LLM Mistral NeMo
author_name: Yulia
author_username: yulia
categories: []
created_at: 2025-01-21T10:08:30.509Z
updated_at: 2025-01-21T10:08:30.509Z
url: https://n8n.io/workflows/2766
scraped_at: 2025-12-24 13:02:45
description:
  This workflow shows how to use a self-hosted Large Language Model (LLM) with n8n's LangChain integration to extract personal information from user input. This is particularly useful for enterprise environments where data privacy is crucial, as it allows sensitive information to be processed locally.

  📖 For a detailed explanation and more insights on using open-source LLMs with n8n, take a look at our comprehensive guide on open-source LLMs.

  🔑 Key Features

  Local LLM
     Connect Ollama to run Mistral NeMo LLM locally
     Provide a foundation for compliant data processing, keeping sensitive information on-premises

  Data extraction
     Convert unstructured text to a consistent JSON format
     Adjust the JSON schema to meet your specific data extraction needs.

  Error handling
     Implement auto-fixing for LLM outputs
     Include error output for further processing

  ⚙️ Setup and сonfiguration

  Prerequisites

  n8n AI Starter Kit installed

  Configuration steps

  Add the Basic LLM Chain node with system prompts.
  Set up the Ollama Chat Model with optimized parameters.
  Define the JSON schema in the Structured Output Parser node.

  🔍 Further resources
  Run LLMs locally with n8n
  Video tutorial on using local AI with n8n

  Apply the power of self-hosted LLMs in your n8n workflows while maintaining control over your data processing pipeline!

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOllama","@n8n/n8n-nodes-langchain.outputParserAutofixing","@n8n/n8n-nodes-langchain.outputParserStructured","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: 🤖🔍 The Ultimate Free AI-Powered Researcher with Tavily Web Search & Extract
author_name: Joseph LePage
author_username: joe
categories: []
created_at: 2025-01-21T16:42:02.076Z
updated_at: 2025-01-21T16:42:02.076Z
url: https://n8n.io/workflows/2768
scraped_at: 2025-12-24 13:02:43
description:
  🔍 This n8n workflow integrates Tavily's search and extract APIs with AI summarization capabilities to process web content efficiently.

  Quick Setup
  Get your Tavily API key from https://app.tavily.com/home
  Replace tvly-YOUR_API_KEY in the "Tavily API Key" node
  Connect your OpenAI credentials to the "OpenAI Chat Model" node
  Deploy the workflow and start the chat trigger

  Core Features
  Search & Extract 🎯
  Intelligent web searching with relevance filtering
  Automated content extraction from top results
  AI-powered content summarization in markdown format

  User Interaction 💬
  Chat-based search topic input
  Real-time processing pipeline
  Structured markdown output

  The workflow demonstrates practical implementation of Tavily's API endpoints while handling the complete process from search to summarization in a single automated pipeline.

nodes_used:
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name: Gravity Forms to KlickTipp Integration - Feedback form
author_name: KlickTipp
author_username: KlickTipp
categories: []
created_at: 2025-01-21T17:03:13.432Z
updated_at: 2025-01-21T17:03:13.432Z
url: https://n8n.io/workflows/2769
scraped_at: 2025-12-24 13:02:47
description:
  Community Node Disclaimer: This workflow uses KlickTipp community nodes.

  How It Works  

  Gravity Forms Customer Feedback Form Integration: This workflow streamlines the process of handling customer feedback submitted via Gravity Forms. It ensures the data is correctly formatted and seamlessly integrates with KlickTipp.  

  Data Transformation: Input data is validated and transformed to meet KlickTipp’s API requirements, including formatting phone numbers and converting dates.  

  Key Features  

  Gravity Forms Trigger  
  Captures new form submissions from Gravity Forms via a webhook and initiates the workflow.  

  Data Processing and Transformation  
  Formats and validates essential data:  
    Converts phone numbers to numeric-only format with international prefixes.  
    Transforms dates (e.g., birthdays) to UNIX timestamps.  
    Calculates and scales numeric responses (e.g., webinar ratings).  
    Parses webinar selections into timestamps for structured scheduling.  

  Subscriber Management in KlickTipp  
  Adds or updates contacts in a KlickTipp subscriber list.  
  Includes custom field mappings such as:  
    Personal details (name, email, birthday, phone number).  
    Feedback and preferences (e.g., webinar ratings, chosen sessions).  
    Structured answers from form responses.
  Tags contacts for segmentation: Adds fixed and dynamic tags to contacts.
    

  Error Handling  
  Ensures invalid or empty data is handled gracefully, preventing workflow interruptions.  

  Setup Instructions  

  Install and Configure Nodes:  
  Set up the Webhook, Set, and KlickTipp nodes in your n8n instance.  
  Authenticate your Gravity Forms and KlickTipp accounts.  

  Prepare Custom Fields in KlickTipp:  
  Create fields in KlickTipp to align with the form submission data, such as:  

  | Field Name                               | Field Type   |
  |----------------------------------------------|------------------|
  | Gravityforms \| URL Linkedin                 | URL              |
  | Gravityforms \| Course/webinar start timestamp | Date & Time      |
  | Gravityforms \| Course/webinar rating        | Decimal Number   |
  | Gravityforms \| Feedback                     | Text             |
  | Gravityforms \| Contact permission           | Text             |

  After creating fields, allow 10-15 minutes for them to sync. If fields don’t appear, reconnect your KlickTipp credentials.  

  Field Mapping and Adjustments:  
  Verify and customize field assignments in the workflow to align with your specific form and subscriber list setup.  

  Workflow Logic  

  Trigger via Gravity Forms Submission:  
     The workflow begins when a new form submission is received through the webhook.  

  Transform Data for KlickTipp:  
     Formats and validates raw form data for compatibility with KlickTipp’s API.  

  Add to KlickTipp Subscriber List:  
     Adds processed data as a new subscriber or updates an existing one.  

  Get all tags from KlickTipp and create a list: Fetches all existing Tags and turns them into an array

  Define tags to dynamically set for contacts: Definiton of variables that are received from the form submission and should be converted into tags

  Merge tags of both lists:
  Checks whether the list of existing tags in KlickTipp contains the tags which should be dynamically set based on the form submission

  Tag creation and tagging contacts: Creates new tags if it previously did not exist and then tags the contact


  Benefits  

  Efficient lead generation: Contacts from forms are automatically imported into KlickTipp and can be used immediately, saving time and increasing the conversion rate.  
  Automated processes: Experts can start workflows directly, such as welcome emails or course admissions, reducing administrative effort.  
  Error-free data management: The template ensures precise data mapping, avoids manual corrections and reinforces a professional appearance.  

  Testing and Deployment  

  Test the workflow by filling the form on Gravity Forms and verifying data updates in KlickTipp.  

  Notes  

  Customization: Update field mappings within the KlickTipp nodes to align with your account setup. This ensures accurate data syncing.  

  Resources:  
  Gravity Forms KlickTipp Knowledge Base help article
  Use KlickTipp Community Node in n8n
  Automate Workflows: KlickTipp Integration in n8n

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name: AI Data Extraction with Dynamic Prompts and Airtable
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-01-21T18:38:26.344Z
updated_at: 2025-01-21T18:38:26.344Z
url: https://n8n.io/workflows/2771
scraped_at: 2025-12-24 13:02:45
description:
  This n8n template introduces the Dynamic Prompts Ai workflow pattern which are incredible for certain types of data extraction tasks where attributes are unknown or need to remain flexible.

  The general idea behind this pattern is that the prompts for requested attributes to be extracted live outside the template and so can be changed at any time - without needing to edit the template. This seriously cuts down on maintainance requirements and is reusable for any number of tables at little cost.

  Check out the video demo I did for n8n Studio here: https://www.youtube.com/watch?v=_fNAD1u8BZw

  Check out the example Airtable here: https://airtable.com/appAyH3GCBJ56cfXl/shrXzR1Tj99kuQbyL

  Looking for the Baserow Version? https://n8n.io/workflows/2780-ai-data-extraction-with-dynamic-prompts-and-baserow/

  How it works
  Given we have an "input" field for context and a number of fields for the data we want to extract, this template will run in the background to react to any changes to either the "input" or fields and automatically update the rows accordingly.
  The key is that Airtable fields have a special property called the "field description". In this pattern, we use this property to allow the user to store a simple prompt describing the data that should exist in the column.
  Our n8n template reads these column descriptions aka "prompts" to use as instructions to perform tasks on the "input".
  In this template, the "input" is a PDF of a resume/CV and the columns are attributes a HR person would want to extract from it - such as full name, address, last position, years of experience etc.

  How to use
  First publish this template and ensure it's accessible via webhook URL.
  You then have to run the "create airtable webhooks" mini-flow to configure your Airtable to send change events to the n8n template. This mini-flow exists in the template but you'll have to update the IDs.

  Check the template for more instructions.

  Requirements
  Airtable for Tables/Database
  OpenAI for LLM and extraction. Feel free to choose another LLM if preferred.

  Customising this workflow
  If you're not using files, you can replace the "input" field with anything you like. For example, the "input" could be single line text.

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name: Realtime Notion Todoist 2-way Sync with Redis
author_name: Mario
author_username: octionic
categories: []
created_at: 2025-01-21T22:51:02.952Z
updated_at: 2025-01-21T22:51:02.952Z
url: https://n8n.io/workflows/2772
scraped_at: 2025-12-24 13:02:45
description:
  Purpose

  This solution enables you to manage all your Notion and Todoist tasks from different workspaces as well as your calendar events in a single place.

  All tasks can be managed in Todoist and additionally Fantastical can be used to manage scheduled tasks & events all together.

  Demo & Explanation

  How it works

  The realtime sync consists of two workflows, both triggered by a registered webhook from either Notion or Todoist
  To avoid overwrites by lately arriving webhook calls, every time the current task is retrieved from both sides.
  Redis is used to prevent from endless loops, since an update in one system triggers another webhook call again. Using the ID of the task, the trigger is being locked down for 15 seconds.
  Depending on the detected changes, the other side is updated accordingly.
  Generally Notion is treaded as the main source. Using an "Obsolete" Status, it is guaranteed, that tasks never get deleted entirely by accident.
  The Todoist ID is stored in the Notion task, so they stay linked together
  An additional full sync workflow daily fixes inconsistencies, if any of them occurred, since webhooks cannot be trusted entirely.
  Since Todoist requires a more complex setup, a tiny workflow helps with activating the webhook.
  Another tiny workflow helps generating a global config, which is used by all workflows for mapping purposes.

  Mapping (Notion &gt;&gt; Todoist)

  Name: Task Name
  Priority: Priority (1: do first, 2: urgent, 3: important, 4: unset)
  Due: Date
  Status: Section (Done: completed, Obsolete: deleted)
  &lt;page_link&gt;: Description (read-only)
  Todoist ID: &lt;task_id&gt;

  Current limitations

  Changes on the same task cannot be made simultaneously in both systems within a 15-20 second time frame
  Subtasks are not linked automatically to their parent yet
  Recurring tasks are not supported yet
  Tasks names do not support URL’s yet

  Prerequisites

  Notion
  A database must already exist (get a basic template here) with the following properties (case matters!):
    Text: "Name"
    Status: "Status", containing at least the options "Backlog", "In progress", "Done", "Obsolete"
    Select: "Priority", containing the options "do first", "urgent", "important"
    Date: "Due"
    Checkbox: "Focus"
    Text: "Todoist ID"
  Todoist
  A project must already exist with the same sections like defined as Status in Notion (except Done and Obsolete)
  Redis
  Create a Free Redis Cloud instance or self-host

  Setup



  The setup involves quite a lot of steps, yet many of them can be automated for business internal purposes.

  Just follow the video or do the following steps:
  Setup credentials for Notion (access token), Todoist (access token) and Redis - you can also create empty credentials and populate these later during further setup
  Clone this workflow by clicking the "Use workflow" button and then choosing your n8n instance - otherwise you need to map the credentials of many nodes.
  Follow the instructions described within the bundle of sticky notes on the top left of the workflow

  How to use

  You can apply changes (create, update, delete) to tasks both in Notion and Todoist which then get synced over within a couple of seconds (this is handled by the differential realtime sync)
  The daily running full sync, resolves possible discrepancies in Todoist and sends a summary via email, if anything needed to be updated. In case that contains an unintended change, you can jump to the Task from the email directly to fix it manually.

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name: HR Job Posting and Evaluation with AI
author_name: Francis Njenga
author_username: gatura
categories: []
created_at: 2025-01-22T08:13:09.656Z
updated_at: 2025-01-22T08:13:09.656Z
url: https://n8n.io/workflows/2773
scraped_at: 2025-12-24 13:02:44
description:
  Workflow Documentation: HR Job Posting and Evaluation with AI

  Detailed Description
  The HR Job Posting and Evaluation with AI workflow is designed to streamline and enhance recruitment for technical roles, such as Automation Specialists. By automating key stages in the hiring process, this workflow ensures a seamless experience for both candidates and HR teams. From collecting applications to evaluating candidates using AI and scheduling interviews, this workflow provides an end-to-end solution for recruitment challenges.

  Who is this for?
  This workflow is ideal for:
  HR Professionals**: Managing multiple job postings and candidates efficiently.
  Recruitment Teams**: Handling large volumes of applications for technical positions.
  Hiring Managers**: Ensuring structured and objective candidate evaluations.

  What problem does this workflow solve?
  Time-Consuming Processes**: Automates repetitive tasks like data entry, CV management, and scheduling.
  Fair Candidate Evaluation**: Leverages AI to provide objective insights based on resumes and job descriptions.
  Streamlined Communication**: Ensures timely and personalized candidate interactions, improving their experience.

  What this workflow does
  This workflow automates the following steps:
  Form Submission: Collects candidate information via a structured application form.
  Data Storage: Stores applicant details in Airtable for centralized tracking.
  CV Management: Automatically uploads resumes to Google Drive for easy access and organization.
  AI-Powered Candidate Evaluation: Scores candidates based on their resumes and job descriptions using OpenAI, providing actionable insights.
  Interview Scheduling: Automates scheduling based on candidate and interviewer availability.
  Communication: Sends customized emails to candidates for interview invitations and feedback.

  Setup
  Prerequisites
  To use this workflow, you’ll need:
  n8n Account**: To create and run the workflow.
  Airtable Account**: For managing applicant data.
  Google Drive Account**: For storing candidate CVs.
  OpenAI API Key**: For AI-powered candidate scoring.
  SMTP Email Account**: For sending candidate communications.

  Setup Process
  Airtable Configuration:
     Create a base in Airtable with tables for Applicants and Job Positions.
  Google Drive Setup:
     Create a folder for CV storage and ensure you have write permissions.
  Integrate Airtable in n8n:
     Use the Airtable API key to connect Airtable to n8n.
  Integrate Google Drive in n8n:
     Authorize Google Drive to enable CV storage automation.
  OpenAI Integration:
     Add your OpenAI API key to n8n for candidate scoring.
  Email Configuration:
     Set up your SMTP email account in n8n for sending notifications and invitations.

  How to customize this workflow
  Tailor the workflow to fit your unique recruitment needs:

  Edit Job Descriptions:
     Adjust the form parameters to match the specific role and qualifications.
  Refine AI Evaluation Criteria:
     Modify OpenAI prompts to reflect the skills and competencies for the desired position.
  Personalize Email Templates:
     Update email content to match your organization’s tone and branding.
  Add New Features:
     Incorporate additional steps like feedback collection or integration with other HR tools.

  Conclusion
  The HR Job Posting and Evaluation with AI workflow simplifies and automates the recruitment process, enabling HR teams to focus on engaging with candidates rather than handling administrative tasks. With its powerful integrations and customization options, this workflow helps organizations hire efficiently while improving the candidate experience.

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name: Typeform to KlickTipp Integration - Quiz
author_name: KlickTipp
author_username: KlickTipp
categories: []
created_at: 2025-01-22T09:29:58.300Z
updated_at: 2025-01-22T09:29:58.300Z
url: https://n8n.io/workflows/2774
scraped_at: 2025-12-24 13:02:47
description:
  Community Node Disclaimer: This workflow uses KlickTipp community nodes.

  How It Works:  

  Typeform Quiz Integration: This workflow streamlines the process of handling quiz answers submitted via Typeform. It ensures the data is correctly formatted and seamlessly integrates with KlickTipp.  
  Data Transformation: Input data is validated and transformed to meet KlickTipp’s API requirements, including formatting phone numbers and converting dates.

  Key Features

  Typeform Trigger:  
  Captures new quiz submissions from Typeform, including user details and quiz responses.

  Data Processing and Transformation:  
  Formats phone numbers to numeric-only format with international prefixes.  
  Converts dates (e.g., birthdays) to UNIX timestamps.  
  Maps multiple-choice quiz answers to string values for API compatibility.  
  Scales numeric quiz responses for tailored use cases.  

  Subscriber Management in KlickTipp:  
  Adds participants as subscribers to a designated KlickTipp list, with custom field mappings for:  
    Personal details (e.g., name, email, phone number, birthday).  
    Quiz responses (e.g., intended usage of KlickTipp, company location, and team size).
   Tags contacts for segmentation: Adds fixed and dynamic tags to contacts.  

  Error Handling:  
  Handles empty or malformed data gracefully, ensuring clean submissions to KlickTipp.  

  Setup Instructions  

  Install and Configure Nodes:  
  Set up the Typeform and KlickTipp nodes in your n8n instance.  
  Authenticate your Typeform and KlickTipp accounts.  

  Prepare Custom Fields in KlickTipp:  
  Create custom fields to store quiz answers and personal details, such as:  

   | Field Name                                     | Field Type   |
  |----------------------------------------------------|------------------|
  | Typeform \| URL Linkedin                           | URL              |
  | Typeform \| Question 1 KlickTipp benefits          | Text             |
  | Typeform \| Question 2 KlickTipp headquarter       | Text             |
  | Typeform \| Question 3 Members CHT                 | Decimal Number   |

  After creating fields, allow 10-15 minutes for them to sync. If fields don’t appear, reconnect your KlickTipp credentials.  

  Field Mapping and Adjustments:  
  Verify and customize field assignments in the workflow to align with your specific form and subscriber list setup.  

  Workflow Logic  

  Trigger via Typeform Submission:  
     The workflow initiates upon receiving a new quiz submission.  
  Transform Data for KlickTipp:  
     Converts and validates data from Typeform to match KlickTipp’s API requirements.  
  Add to KlickTipp Subscriber List:  
     Submits the cleaned data to KlickTipp, including all relevant quiz answers.  

  Get all tags from KlickTipp and create a list: Fetches all existing Tags and turns them into an array

  Define tags to dynamically set for contacts: Definiton of variables that are received from the form submission and should be converted into tags

  Merge tags of both lists: Checks whether the list of existing tags in KlickTipp contains the tags which should be dynamically set based on the form submission

  Tag creation and tagging contacts: Creates new tags if it previously did not exist and then tags the contact


  Benefits  

  Efficient lead generation: Contacts from forms are automatically imported into KlickTipp and can be used immediately, saving time and increasing the conversion rate.  
  Automated processes: Experts can start workflows directly, such as welcome emails or course admissions, reducing administrative effort.  
  Error-free data management: The template ensures precise data mapping, avoids manual corrections and reinforces a professional appearance.  

  Testing and Deployment  

  Test the workflow by filling the form on Typeform and verifying data updates in KlickTipp.  

  Notes  

  Customization: Update field mappings within the KlickTipp nodes to align with your account setup. This ensures accurate data syncing.  

  Resources:  
  Typeform KlickTipp Knowledge Base help article
  Use KlickTipp Community Node in n8n
  Automate Workflows: KlickTipp Integration in n8n

nodes_used:
  ["n8n-nodes-base.set","n8n-nodes-klicktipp.klicktipp","n8n-nodes-base.typeformTrigger","n8n-nodes-base.stickyNote","n8n-nodes-klicktipp.klicktipp","n8n-nodes-base.merge","n8n-nodes-base.set","n8n-nodes-base.splitOut","n8n-nodes-base.if","n8n-nodes-klicktipp.klicktipp","n8n-nodes-base.aggregate","n8n-nodes-klicktipp.klicktipp","n8n-nodes-base.aggregate","n8n-nodes-klicktipp.klicktipp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Sync New Files From Google Drive with Airtable
author_name: WeblineIndia
author_username: weblineindia
categories: []
created_at: 2025-01-22T14:01:41.722Z
updated_at: 2025-01-22T14:01:41.722Z
url: https://n8n.io/workflows/2776
scraped_at: 2025-12-24 13:02:47
description:
  This workflow automatically fetches newly uploaded files from a specific folder in Google Drive, shares them via email with specified recipients, and logs the file details (name, ID, created time, modified time) into Airtable for easy tracking. It streamlines the process of file sharing and management while keeping track of important metadata in a central place.
  Step-by-Step Instructions

  Google Drive Node (Fetch New File)
  Action: 
  This node fetches newly uploaded files from the specific folder you’ve mentioned in your Google Drive.

  Configuration:
  Set the folder ID in the Google Drive node where the files are uploaded.
  Use the “New File in Folder” trigger to automatically detect new files added to the folder.

  Send Email Node (Share File via Email)
  Action: 
  After detecting the new file, this node shares the file via email with the recipient you specify.

  Configuration:
  Set the recipient's email address.
  Include the file URL from the Google Drive node in the email body, allowing easy access to the file.
  Add the file name as part of the email subject or body to notify the recipient about the new file.

  Airtable Node (Store File Metadata)
  Action: 
  This node stores the file’s metadata, such as name, ID, creation time, modification time, and the email address to which it was sent, in your Airtable database.

  Configuration:
  Set up Airtable with a table.
  Map the output from the Google Drive node to store the file metadata, and use the email address from the email node for tracking.

  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.

nodes_used:
  ["n8n-nodes-base.googleDrive","n8n-nodes-base.airtable","n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: 🐋DeepSeek V3 Chat & R1 Reasoning Quick Start
author_name: Joseph LePage
author_username: joe
categories: []
created_at: 2025-01-22T23:27:28.003Z
updated_at: 2025-01-22T23:27:28.003Z
url: https://n8n.io/workflows/2777
scraped_at: 2025-12-24 13:02:42
description:
  This n8n workflow demonstrates multiple ways to harness DeepSeek's AI models in your automation pipeline! 🌟

  Core Features

  Multiple Integration Methods 🔌
  Local deployment using Ollama for DeepSeek-R1
  Direct API integration with DeepSeek Chat V3
  Conversational agent with memory buffer
  HTTP request implementation with both raw and JSON formats

  Model Options 🧠
  DeepSeek Chat V3 for general conversation
  DeepSeek-R1 for advanced reasoning
  Memory-enabled agent for persistent context

  Quick Setup 🛠️

  API Configuration
  Base URL: https://api.deepseek.com
  Get your API key from platform.deepseek.com/api_keys

  Local Setup 💻
  Install Ollama for local deployment
  Set up DeepSeek-R1 via Ollama
  Configure local credentials in n8n

  Implementation Details 🔧

  Conversational Agent
  Window Buffer Memory for context
  Customizable system messages
  Built-in error handling with retries

  API Endpoints 🌐
  Chat completions for V3 and R1 models
  OpenAI API format compatibles

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOllama","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: AI News Research Team: 24/7 Newsletter Automation with Citations with Perplexity
author_name: Derek Cheung
author_username: derekcheungsa
categories: []
created_at: 2025-01-23T01:34:15.762Z
updated_at: 2025-01-23T01:34:15.762Z
url: https://n8n.io/workflows/2778
scraped_at: 2025-12-24 13:02:45
description:
  Purpose of workflow:

  This AI-powered workflow automatically generates detailed, well-researched newsletters by monitoring and analyzing specified news topics (like Bitcoin, Nvidia, etc.). It uses a team of AI research agents to gather, analyze, and compile information with automatic citations, saving significant time in newsletter creation.

  How it works:

  Multi-agent system:
  Research Leader: Analyzes topics and creates content outline
  Project Planner: Breaks down research into specific tasks
  Research Assistants: Conduct detailed research on assigned subtopics
  Editor: Combines research and polishes final output

  Key features:
  Automated daily monitoring of specified news topics
  Real-time information gathering using Perplexity AI
  Auto-citation functionality for source verification
  Flexible time window filtering (day/week/month)
  Options for detailed or simple reports
  Direct email delivery of completed newsletters

  Step-by-step setup:
  Perplexity API Setup:
     Create account at perplexity.ai
     Navigate to API tab
     Generate API key
     Set up credentials with 'Bearer' authentication

  Workflow Configuration:
     Connect Google Sheets containing news monitoring topics
     Configure schedule trigger for daily execution
     Set up email delivery settings
     Define report type preferences (detailed/simple)
     Specify time window for news gathering

  Integration:
     Connect with newsletter tools like Kit
     Import generated content as starting point
     Edit and customize as needed before publishing

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.markdown","@n8n/n8n-nodes-langchain.chainLlm","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.set","n8n-nodes-base.merge","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.outputParserStructured","n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.gmail","n8n-nodes-base.switch","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.gmail","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.googleSheets","n8n-nodes-base.splitInBatches","n8n-nodes-base.stickyNote"]

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name: Remove Personally Identifiable Information (PII) from CSV Files with OpenAI
author_name: Artur
author_username: arlusm1
categories: []
created_at: 2025-01-23T12:56:20.092Z
updated_at: 2025-01-23T12:56:20.092Z
url: https://n8n.io/workflows/2779
scraped_at: 2025-12-24 13:02:47
description:
  What this workflow does
  Monitors Google Drive: The workflow triggers whenever a new CSV file is uploaded.
  Uses AI to Identify PII Columns: The OpenAI node analyzes the data and identifies PII-containing columns (e.g., name, email, phone).
  Removes PII: The workflow filters out these columns from the dataset.
  Uploads Cleaned File: The sanitized file is renamed and re-uploaded to Google Drive, ensuring the original data remains intact.

  How to customize this workflow to your needs
  Adjust PII Identification: Modify the prompt in the OpenAI node to align with your specific data compliance requirements.
  Include/Exclude File Types: Adjust the Google Drive Trigger settings to monitor specific file types (e.g., CSV only).
  Output Destination: Change the folder in Google Drive where the sanitized file is uploaded.

  Setup
  Prerequisites:
  A Google Drive account.
  An OpenAI API key.
  Workflow Configuration:
  Configure the Google Drive Trigger to monitor a folder for new files.
  Configure the OpenAI Node to connect with your API
  Set the Google Drive Upload folder to a different location than the Trigger folder to prevent workflow loops.

nodes_used:
  ["n8n-nodes-base.googleDriveTrigger","n8n-nodes-base.googleDrive","n8n-nodes-base.extractFromFile","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.merge","n8n-nodes-base.googleDrive","n8n-nodes-base.splitOut","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.stickyNote"]

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name: AI Data Extraction with Dynamic Prompts and Baserow
author_name: Jimleuk
author_username: jimleuk
categories: []
created_at: 2025-01-23T13:34:03.455Z
updated_at: 2025-01-23T13:34:03.455Z
url: https://n8n.io/workflows/2780
scraped_at: 2025-12-24 13:02:46
description:
  This n8n template introduces the Dynamic Prompts AI workflow pattern which are incredible for certain types of data extraction tasks where attributes are unknown or need to remain flexible.

  The general idea behind this pattern is that the prompts for requested attributes to be extracted live outside the template and so can be changed at any time - without needing to edit the template. This seriously cuts down on maintainance requirements and is reusable for any number of tables at little cost.

  Check out the n8n Studio Episode here: https://www.youtube.com/watch?v=_fNAD1u8BZw

  Community post here: https://community.n8n.io/t/dynamic-prompts-with-n8n-baserow-and-airtable/72052


  Looking for the Airtable Version? https://n8n.io/workflows/2771-ai-data-extraction-with-dynamic-prompts-and-airtable/

  How it works
  Given we have an "input" field for context and a number of fields for the data we want to extract, this template will run in the background to react to any changes to either the "input" or fields and automatically update the rows accordingly.
  The key is that Baserow fields have a special property called the "field description". In this pattern, we use this property to allow the user to store a simple prompt describing the data that should exist in the column.
  Our n8n template reads these column descriptions aka "prompts" to use as instructions to perform tasks on the "input".
  In this template, the "input" is a PDF of a resume/CV and the columns are attributes a HR person would want to extract from it - such as full name, address, last position, years of experience etc.

  How to use
  First publish this template and ensure it's accessible via webhook URL.
  You then have to complete the "create Baserow webhooks" steps to configure your baserow to send change events to the n8n template. Baserow webhooks are created in the Baserow web interface.

  Check the template for more instructions.

  Requirements
  Baserow for Tables/Database
  OpenAI for LLM and extraction. Feel free to choose another LLM if preferred.

  Customising this workflow
  If you're not using files, you can replace the "input" field with anything you like. For example, the "input" could be single line text.

nodes_used:
  ["n8n-nodes-base.webhook","n8n-nodes-base.switch","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.extractFromFile","n8n-nodes-base.httpRequest","n8n-nodes-base.set","n8n-nodes-base.splitInBatches","n8n-nodes-base.noOp","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.httpRequest","n8n-nodes-base.splitOut","n8n-nodes-base.code","n8n-nodes-base.splitInBatches","n8n-nodes-base.noOp","n8n-nodes-base.httpRequest","n8n-nodes-base.extractFromFile","n8n-nodes-base.httpRequest","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.chainLlm","n8n-nodes-base.filter","n8n-nodes-base.filter","n8n-nodes-base.noOp","n8n-nodes-base.noOp","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
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name: Automated End-to-End Fine-Tuning of OpenAI Models with Google Drive Integration
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-01-23T14:01:48.509Z
updated_at: 2025-01-23T14:01:48.509Z
url: https://n8n.io/workflows/2781
scraped_at: 2025-12-24 13:02:46
description:
  1. How it Works  
  This n8n workflow automates fine-tuning OpenAI models through these key steps:  
  Manual Trigger**:  
    Starts with the "When clicking ‘Test workflow’" event to initiate the process.  
    Downloads a .jsonl file from Google Drive   
  Upload to OpenAI**:  
    Uploads the .jsonl file to OpenAI via the "Upload File" node (with purpose "fine-tune").  
  Create Fine-tuning Job**:  
    Sends a POST request to the endpoint https://api.openai.com/v1/fine_tuning/jobs with:  
          {  
        "training_file": "{{ $json.id }}",  
        "model": "gpt-4o-mini-2024-07-18"  
      }  
        OpenAI automatically starts training the model based on the provided file.  
  Interaction with the Trained Model**:  
    An "AI Agent" uses the custom model (e.g., ft:gpt-4o-mini-2024-07-18:n3w-italia::XXXX7B) to respond to chat messages.  

  2. Set up Steps  
  To configure the workflow:  
  Prepare the Training File:  
     Create a .jsonl file following the specified syntax (e.g., travel assistant Q/A examples).  
     Upload it to Google Drive and update the ID in the "Google Drive" node.  

  Configure Credentials:  
     Google Drive: Connect an account via OAuth2 (googleDriveOAuth2Api).  
     OpenAI: Add your API key in the "OpenAI Chat Model" and "Upload File" nodes.  

  Customize the Model:  
     In the "OpenAI Chat Model" node, specify the name of your fine-tuned model (e.g., ft:gpt-4o-mini-...).  
     Update the HTTP request body (Create Fine-tuning Job) if needed (e.g., a different base model).  

  Start the Workflow:  
     Use the manual trigger ("Test workflow") to begin the upload and training process.  
     Test the model via the "Chat Trigger" (chat messages).  

  Integrated Documentation:  
     Follow the instructions in the Sticky Notes to:  
       Properly format the .jsonl (Step 1).  
       Monitor progress on OpenAI (Step 2, link: https://platform.openai.com/finetune/).  

  Note: Ensure the .jsonl file adheres to OpenAI’s required structure and that credentials are valid.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.agent","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.httpRequest"]

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name: Build an OpenAI Assistant with Google Drive Integration
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-01-23T14:21:30.480Z
updated_at: 2025-01-23T14:21:30.480Z
url: https://n8n.io/workflows/2782
scraped_at: 2025-12-24 13:02:45
description:
  Workflow Overview  
  This workflow automates the creation and management of a custom OpenAI Assistant for a travel agency ("Travel with us"), leveraging Google Drive for document storage.

  How It Works  

  1. Create the OpenAI Assistant  
  Node**: OpenAI  
    Action: Creates a custom assistant named "Travel with us" Assistant using the gpt-4o-mini model.  
    Instructions:  
      Respond only using the provided document (e.g., agency-specific info).  
      Stay friendly, brief, and focused on travel-related queries.  
      Ignore irrelevant questions politely.  
    Credentials: Requires OpenAI API key.  

  2. Upload Agency Document  
  Google Drive Node**:  
    Action: Downloads a Google Doc as a PDF.   
  OpenAI2 Node**:  
    Action: Uploads the PDF to OpenAI with purpose: "assistants".  
    Output: Generates a file_id.  

  3. Update the Assistant with the Document  
  OpenAI Node**:  
    Action: Updates the assistant to include the uploaded file.

  4. Chat Interaction  
  Chat Trigger**:  
    Activates when a message is received ("When chat message received").  
  OpenAI Assistant Node**:  
    Action: Uses the updated assistant to respond to user queries.  
    Memory: Window Buffer Memory retains chat context for coherent conversations.  

  Set Up Steps  
  Prepare the Document:  
     Store your travel agency guide in Google Drive (e.g., as a Google Doc).  
     Update the Google Drive node with your document’s ID.  

  Configure Credentials:  
     Google Drive: Connect via OAuth2 (googleDriveOAuth2Api).  
     OpenAI: Add your API key to all OpenAI nodes.  

  Customize the Assistant:  
     Modify the instructions in the OpenAI node to reflect your agency’s needs.  
     Ensure the document includes FAQs, policies, and travel info.  

  Test the Workflow:  
     Trigger manually ("Test workflow") to create the assistant and upload the file.  
     Send a chat message (e.g., "What are your travel packages?") to test responses.  

  Dependencies  
  Google Drive Account**: To store and retrieve the agency document.  
  OpenAI API Access**: For assistant creation and file uploads.  

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin.

nodes_used:
  ["n8n-nodes-base.manualTrigger","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.openAi"]

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name: AI marketing report (Google Analytics & Ads, Meta Ads), sent via email/Telegram
author_name: Friedemann Schuetz
author_username: freddy-schuetz
categories: []
created_at: 2025-01-23T14:40:20.555Z
updated_at: 2025-01-23T14:40:20.555Z
url: https://n8n.io/workflows/2783
scraped_at: 2025-12-24 13:02:42
description:
  What this workflow does
  This workflow retrieves Online Marketing data (Google Analytics for several domains, Google Ads, Meta Ads) from the last 7 days and the same period in the previous year. The data is then prepared by AI as a table, analyzed and provided with a small summary.
  The summary is then sent by email to a desired address and, shortened and summarized again, sent to a Telegram account.

  This workflow has the following sequence:

  time trigger (e.g. every Monday at 7 a.m.)
  retrieval of Online Marketing data from the last 7 days (via sub workflows)
  assignment and summary of the data
  retrieval of Online Marketing data from the same time period of the previous year
  allocation and summary of the data
  preparation in tabular form and brief analysis by AI.
  sending the report as an email
  preparation in short form by AI for Telegram (optional)
  sending as Telegram message.

  Requirements
  The following accesses are required for the workflow:
  Google Analytics (via Google Analytics API): Documentation
  Google Ads (via HTTP Request -&gt; Google Ads API):Documentation
  Meta Ads (via Facebook Graph API): Documentation
  AI API access (e.g. via OpenAI, Anthropic, Google or Ollama)
  SMTP access data (for sending the mail)
  Telegram access data (optional for sending as Telegram message): Documentation

  You must set up the individual sub-workflows as separate workflows. Then set the “Execute workflow trigger” here. Then select the corresponding sub-workflow in the AI Agent Tools.
  You can select the number of domains yourself. If the data queries are not required, simply delete the corresponding tool (e.g. “Analytics_Domain_5).

  Feel free to contact me via LinkedIn, if you have any questions!

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","n8n-nodes-base.executeWorkflowTrigger","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.googleAnalytics","n8n-nodes-base.googleAnalytics","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.code","n8n-nodes-base.httpRequest","n8n-nodes-base.httpRequest","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.facebookGraphApi","n8n-nodes-base.summarize","n8n-nodes-base.set","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.code","@n8n/n8n-nodes-langchain.openAi","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.code","n8n-nodes-base.code","n8n-nodes-base.set","n8n-nodes-base.summarize","n8n-nodes-base.emailSend","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.openAi","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.telegram","n8n-nodes-base.stickyNote"]

workflow_json_compressed:
  [encoded data, 13224 characters]


name: Personal Shopper Chatbot for WooCommerce with RAG using Google Drive and openAI
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-01-23T14:55:58.691Z
updated_at: 2025-01-23T14:55:58.691Z
url: https://n8n.io/workflows/2784
scraped_at: 2025-12-24 13:02:44
description:
  This workflow combines OpenAI, Retrieval-Augmented Generation (RAG), and WooCommerce to create an intelligent personal shopping assistant. It handles two scenarios:  
  Product Search: Extracts user intent (keywords, price ranges, SKUs) and fetches matching products from WooCommerce.  
  General Inquiries: Answers store-related questions (e.g., opening hours, policies) using RAG and documents stored in Google Drive.  

  How It Works  

  1. Chat Interaction & Intent Detection  
  Chat Trigger**:  
    Starts when a user sends a message ("When chat message received").  
  Information Extractor**:  
    Uses OpenAI to analyze the message and determine if the user is searching for a product or asking a general question.  
    Extracts:  
      search (true/false).  
      keyword, priceRange, SKU, category (if product-related).  
    Example:  
          {  
        "search": true,  
        "keyword": "red handbags",  
        "priceRange": { "min": 50, "max": 100 },  
        "SKU": "BAG123",  
        "category": "women's accessories"  
      }  
  2. Product Search (WooCommerce Integration)  
  AI Agent**:  
    If search: true, routes the request to the personal_shopper tool.  
    WooCommerce Node:  
      Queries the WooCommerce store using extracted parameters (keyword, priceRange, SKU).  
      Filters products in stock (stockStatus: "instock").  
      Returns matching products (e.g., "red handbags under €100").  

  3. General Inquiries (RAG System)  
  RAG Tool**:  
    If search: false, uses the Qdrant Vector Store to retrieve store information from documents.  
    Google Drive Integration:  
      Documents (e.g., store policies, FAQs) are stored in Google Drive.  
      Downloaded, split into chunks, and embedded into Qdrant for semantic search.  
    OpenAI Chat Model: Generates answers based on retrieved documents (e.g., "Our store opens at 9 AM").  

  Set Up Steps  

  1. Configure the RAG System  
  Google Drive Setup**:  
    Upload store documents .  
    Update the Google Drive2 node with your folder ID.  
  Qdrant Vector Database**:  
    Clean the collection (update Qdrant Vector Store node with your URL).  
    Use Embeddings OpenAI to convert documents into vectors.  

  2. Configure OpenAI & WooCommerce  
  OpenAI Credentials**:  
    Add your API key to all OpenAI nodes (OpenAI Chat Model, Embeddings OpenAI, etc.).  
  WooCommerce Integration**:  
    Connect your WooCommerce store (credentials in the personal_shopper node).  
    Ensure product data is synced and accessible.  

  3. Customize the AI Agent  
  Intent Detection**:  
    Modify the Information Extractor’s system prompt to align with your store’s terminology.  
  RAG Responses**:  
    Update the tool description to reflect your store’s documents.  

  Notes  
  This template is ideal for e-commerce businesses needing a hybrid assistant for product discovery and customer support.

  Need help customizing?  
  Contact me for consulting and support or add me on Linkedin. 

nodes_used:
  ["@n8n/n8n-nodes-langchain.chatTrigger","@n8n/n8n-nodes-langchain.memoryBufferWindow","@n8n/n8n-nodes-langchain.toolCalculator","n8n-nodes-base.set","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolVectorStore","@n8n/n8n-nodes-langchain.vectorStoreQdrant","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.wooCommerceTool","@n8n/n8n-nodes-langchain.informationExtractor","@n8n/n8n-nodes-langchain.lmChatOpenAi","n8n-nodes-base.googleDrive","n8n-nodes-base.googleDrive","@n8n/n8n-nodes-langchain.embeddingsOpenAi","@n8n/n8n-nodes-langchain.documentDefaultDataLoader","@n8n/n8n-nodes-langchain.textSplitterTokenSplitter","n8n-nodes-base.manualTrigger","n8n-nodes-base.httpRequest","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.vectorStoreQdrant","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent"]

workflow_json_compressed:
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name: RSS Feed News Processing and Distribution Workflow
author_name: PollupAI
author_username: Pollup
categories: []
created_at: 2025-01-23T16:39:37.327Z
updated_at: 2025-01-23T16:39:37.327Z
url: https://n8n.io/workflows/2785
scraped_at: 2025-12-24 13:02:46
description:
  Who is this for?

  This workflow is designed for professionals and teams who need to monitor multiple RSS feeds, filter the latest content, and distribute actionable updates as a Trello comment. Ideal for content managers, marketers, and team leads managing news or content pipelines.

  What problem is this workflow solving?

  Manually monitoring RSS feeds and keeping track of the latest content can be time-consuming. This workflow automates the aggregation, filtering, and distribution of news, ensuring that only relevant and timely updates are shared with your team or audience.

  What this workflow does:
  Aggregates RSS Feeds: Pulls data from up to three RSS feeds simultaneously.
  Filters Content: Filters articles based on their publication date (default: last 7 days).
  Organizes and Sorts: Sorts filtered articles by date for clarity.
  Formats Updates: Transforms news items into Markdown format for better readability.
  Publishes and Notifies: Posts comments to Trello cards and sends an email to a moderator to check the comment.

  Setup:
  Connect your RSS feeds by configuring the RSS Read nodes.
  Link your Trello and Gmail accounts for seamless integration.
  Adjust the schedule trigger to set how often the workflow should run (e.g., daily, weekly).
  Test the workflow to ensure all connections and configurations are correct.

  How to customize this workflow to your needs:
  Change the Number of RSS Feeds: Add or remove RSS Read nodes and update the merge configuration accordingly.
  Adjust the Date Filter: Modify the date logic in the “Filter by date” node to include more or fewer days.
  Limit the Number of Articles: Adjust the limit in the “Limit news to x” node.
  Custom Formatting: Update the Transform node to format the news items differently.
  Alternative Notifications: Replace Trello and Gmail with other integrations, such as Slack or Microsoft Teams.

  This workflow ensures your team stays informed with minimal effort and delivers content updates in an organized and professional manner.

nodes_used:
  ["n8n-nodes-base.scheduleTrigger","n8n-nodes-base.rssFeedRead","n8n-nodes-base.set","n8n-nodes-base.filter","n8n-nodes-base.sort","n8n-nodes-base.limit","n8n-nodes-base.code","n8n-nodes-base.trello","n8n-nodes-base.gmail","n8n-nodes-base.merge","n8n-nodes-base.rssFeedRead","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.rssFeedRead","n8n-nodes-base.stickyNote"]

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name: Create a Branded AI-Powered Website Chatbot
author_name: Wayne Simpson
author_username: nocodecreative
categories: []
created_at: 2025-01-23T17:44:16.860Z
updated_at: 2025-01-23T17:44:16.860Z
url: https://n8n.io/workflows/2786
scraped_at: 2025-12-24 13:02:41
description:
  Create a Branded AI Website Chatbot

  Engage website visitors with an intelligent chat widget powered by OpenAI. This template includes:

  💬 Natural conversation handling
  📅 Microsoft Outlook calendar integration
  📝 Lead capture and information gathering
  🔄 Human handoff capabilities

  Simply add a JavaScript snippet to your website and configure the workflow to match your needs. Follow our detailed setup guide to get started in minutes.



  &gt; Note: Widget includes a "Powered By" affiliate link

nodes_used:
  ["@n8n/n8n-nodes-langchain.memoryBufferWindow","n8n-nodes-base.respondToWebhook","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.toolHttpRequest","n8n-nodes-base.executeWorkflowTrigger","n8n-nodes-base.set","n8n-nodes-base.code","n8n-nodes-base.httpRequest","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.toolWorkflow","@n8n/n8n-nodes-langchain.chatTrigger","n8n-nodes-base.switch","n8n-nodes-base.set","n8n-nodes-base.microsoftOutlook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.if","n8n-nodes-base.respondToWebhook","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote"]

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name: Backup n8n Workflows to Bitbucket
author_name: Gareth B. Davies
author_username: garethbdavies
categories: []
created_at: 2025-01-24T00:25:33.966Z
updated_at: 2025-01-24T00:25:33.966Z
url: https://n8n.io/workflows/2787
scraped_at: 2025-12-24 13:02:47
description:
  An automated backup solution designed for self-hosted n8n users to automatically backup their workflows to Bitbucket, leveraging Bitbucket's free private repository offering.

  Perfect for maintaining version control of your n8n workflows without additional costs.

  How it works:
  Runs on a regular schedule to check all workflows in your n8n instance
  Compares each workflow with its version in Bitbucket
  Only uploads workflows that are new or have changed
  Uses basic rate limiting to stay within Bitbucket's API limits
  Formats filenames for easy tracking and includes timestamps in commit messages
  Handles errors gracefully with automatic retries

  Set up steps (10-15 minutes):
  Create a free Bitbucket account and private repository
  Create a Bitbucket App Password with repository write access
  Add Bitbucket credentials to n8n (using your username and app password)
  Set up n8n API access (generate API key in your n8n instance)
  Configure your Bitbucket workspace and repository names in the Set node
  Optional: Adjust the backup schedule (default: 2 AM daily)

  Perfect for n8n self-hosters who want:
  Version control for their workflows
  Automated daily backups
  Free private repository storage
  Easy workflow recovery
  Change tracking over time

  The workflow includes basic error handling and rate limiting to ensure reliable backups even with larger numbers of workflows. Adjust your timing based on https://support.atlassian.com/bitbucket-cloud/docs/api-request-limits/.

nodes_used:
  ["n8n-nodes-base.code","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.n8n","n8n-nodes-base.splitInBatches","n8n-nodes-base.httpRequest","n8n-nodes-base.if","n8n-nodes-base.httpRequest","n8n-nodes-base.wait","n8n-nodes-base.set"]

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name: Daily meetings summarization with Gemini AI
author_name: Johnny Rafael
author_username: johnnyrafael
categories: []
created_at: 2025-01-24T03:54:33.086Z
updated_at: 2025-01-24T03:54:33.086Z
url: https://n8n.io/workflows/2789
scraped_at: 2025-12-24 13:02:46
description:
  This workflow implements the Gemini AI chat model to summarize your daily meetings and send the summary to a Slack channel daily at 9 AM (or any other time you choose).

  It automatically retrieves your Google Calendar events and feeds them to the model.

  The workflow uses Google’s Gemini AI for response generation.

  How it works
  The workflow uses a Scheduled Trigger Node as the main trigger.
  The AI Agent Node uses the Google Calendar action to retrieve relevant meeting data.
  The AI Agent sends the retrieved information to the Google Gemini Chat Model (gemini-flash).
  The Google Gemini Chat Model generates a summary and informative response based on today’s meetings.

  ++Setup Steps++
  Google Cloud Project and Vertex AI API:

  	Create a Google Cloud project.
  	Enable the Vertex AI API for your project.

  Google AI API Key:
  	Obtain a Google AI API key from Google AI Studio.
  Credentials in n8n:
  	Configure credentials in your n8n environment for:
  		Google Gemini (PaLM) API (using your Google AI API key).
  Import the Workflow:
  	Import this workflow into your n8n instance.
  Configure the Workflow:
  	Update both Slack and Gemini nodes with your credentials.

nodes_used:
  ["n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.slack","n8n-nodes-base.googleCalendarTool","@n8n/n8n-nodes-langchain.agent","n8n-nodes-base.scheduleTrigger","n8n-nodes-base.stickyNote","@n8n/n8n-nodes-langchain.lmChatGoogleGemini"]

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name: AI Fitness Coach Strava Data Analysis and Personalized Training Insights
author_name: Amjid Ali
author_username: amjid
categories: []
created_at: 2025-01-24T09:02:16.267Z
updated_at: 2025-01-24T09:02:16.267Z
url: https://n8n.io/workflows/2790
scraped_at: 2025-12-24 13:02:44
description:
  Detailed Title
  "Triathlon Coach AI Workflow: Strava Data Analysis and Personalized Training Insights using n8n"

  Description
  This n8n workflow enables you to build an AI-driven virtual triathlon coach that seamlessly integrates with Strava to analyze activity data and provide athletes with actionable training insights. The workflow processes data from activities like swimming, cycling, and running, delivers personalized feedback, and sends motivational and performance improvement advice via email or WhatsApp.

  Workflow Details

  Trigger: Strava Activity Updates
  Node:** Strava Trigger
  Purpose:** Captures updates from Strava whenever an activity is recorded or modified. The data includes metrics like distance, pace, elevation, heart rate, and more.
  Integration:** Uses Strava API for real-time synchronization.

  Step 1: Data Preprocessing
  Node:** Code
  Purpose:** Combines and flattens the raw Strava activity data into a structured format for easier processing in subsequent nodes.
  Logic:** A recursive function flattens JSON input to create a clean and readable structure.

  Step 2: AI Analysis with Google Gemini
  Node:** Google Gemini Chat Model
  Purpose:** Leverages Google Gemini's advanced language model to analyze the activity data. 
  Functionality:**
    Identifies key performance metrics.
    Provides feedback and insights specific to the type of activity (e.g., running, swimming, or cycling).
    Offers tailored recommendations and motivational advice.

  Step 3: Generate Structured Output
  Node:** Structure Output
  Purpose:** Processes the AI-generated response to create a structured format, such as headings, paragraphs, and bullet lists.
  Output:** Formats the response for clear communication.

  Step 4: Convert to HTML
  Node:** Convert to HTML
  Purpose:** Converts the structured output into an HTML format suitable for email or other presentation methods.
  Output:** Ensures the response is visually appealing and easy to understand.

  Step 5: Send Email with Training Insights
  Node:** Send Email
  Purpose:** Sends a detailed email to the athlete with performance insights, training recommendations, and motivational messages.
  Integration:** Utilizes Gmail or SMTP for secure and efficient email delivery.

  Optional Step: WhatsApp Notifications
  Node:** WhatsApp Business Cloud
  Purpose:** Sends a summary of the activity analysis and key recommendations via WhatsApp for instant access.
  Integration:** Connects to WhatsApp Business Cloud for automated messaging.

  Additional Notes
  Customization:  
     You can modify the AI prompt to adapt the recommendations to the athlete's specific goals or fitness levels.
     The workflow is flexible and can accommodate additional nodes for more advanced analysis or output formats.

  Scalability:  
     Ideal for individual athletes or coaches managing multiple athletes.  
     Can be expanded to include additional metrics or insights based on user preferences.

  Performance Metrics Handled:  
     Swimming: SWOLF, stroke count, pace.  
     Cycling: Cadence, power zones, elevation.  
     Running: Pacing, stride length, heart rate zones.

  Implementation Steps
  Set Up Strava API Key:
     Log in to Strava Developers to generate your API key.
     Integrate the API key into the Strava Trigger node.

  Configure Google Gemini Integration:
     Use your Google Gemini (PaLM) API credentials in the Google Gemini Chat Model node.

  Customize Email and WhatsApp Messaging:
     Update the Send Email and WhatsApp Business Cloud nodes with the recipient’s details.

  Automate Execution:
     Deploy the workflow and use n8n's scheduling features or cron jobs for periodic execution.

  GET n8n Now  

  N8N COURSE  
  n8n Book  

  Developer Notes
  Author:** Amjid Ali  
  improvements.
  Resources:**
  See in Action: Syncbricks Youtube  
    PayPal: Support the Developer  
    Courses : SyncBricks LMS

  By using this workflow, triathletes and coaches can elevate training to the next level with AI-powered insights and actionable recommendations.

nodes_used:
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name: Scrape Trustpilot Reviews with DeepSeek, Analyze Sentiment with OpenAI
author_name: Davide
author_username: n3witalia
categories: []
created_at: 2025-01-24T14:51:16.882Z
updated_at: 2025-01-24T14:51:16.882Z
url: https://n8n.io/workflows/2792
scraped_at: 2025-12-24 13:02:44
description:
  Workflow Overview  
  This workflow automates the process of scraping Trustpilot reviews, extracting key details, analyzing sentiment, and saving the results to Google Sheets. It uses OpenAI for sentiment analysis and HTML parsing for review extraction.

  How It Works  

  1. Scrape Trustpilot Reviews   
  HTTP Request**:  
    Fetches review pages from Trustpilot (https://it.trustpilot.com/review/{{company_id}}).  
    Paginates through pages (up to max_page limit).  
  HTML Parsing**:  
    Extracts review URLs using CSS selectors
    Splits the URLs into individual review links.  

  2. Extract Review Details   
  Information Extractor**:  
    Uses DeepSeek to extract structured data from the review:  
      Author: Name of the reviewer.  
      Rating: Numeric rating (1-5).  
      Date: Review date in YYYY-MM-DD format.  
      Title: Review title.  
      Text: Full review text.  
      Total Reviews: Number of reviews by the user.  
      Country: Reviewer’s country (2-letter code).  

  3. Sentiment Analysis  
  Sentiment Analysis Node**:  
    Uses OpenAI to classify the review text as Positive, Neutral, or Negative.  
    Example output:  
          {  
        "category": "Positive",  
        "confidence": 0.95  
      }  
  4. Save to Google Sheets  
  Google Sheets Node**:  
    Appends or updates the extracted data to a Google Sheet 

  Set Up Steps  

  1. Configure Trustpilot Scraping  
  Edit Fields1 Node**:  
    Set company_id to the Trustpilot company name 
    Set max_page to limit the number of pages scraped.  

  2. Configure Google Sheets  
  Google Sheets Node**:  
    Update the documentId with your Google Sheet ID 
    Ensure the sheet has the required columns (Id, Data, Nome, etc.).  

  3. Configure OpenAI  
  OpenAI Chat Model Node**:  
    Add your OpenAI API key.  
  Sentiment Analysis Node**:  
    Ensure the categories match your desired sentiment labels (Positive, Neutral, Negative).  

  Key Components  
  Nodes**:  
    HTTP Request/HTML: Scrape and parse Trustpilot reviews.  
    Information Extractor: Extract structured review data using DeepSeek.  
    Sentiment Analysis: Classify review sentiment.  
    Google Sheets: Save and update review data.  
  Credentials**:  
    OpenAI API key. 
    DeepSeek API key.  
    Google Sheets OAuth2.  

nodes_used:
  ["n8n-nodes-base.splitOut","@n8n/n8n-nodes-langchain.informationExtractor","n8n-nodes-base.if","n8n-nodes-base.manualTrigger","n8n-nodes-base.limit","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.stickyNote","n8n-nodes-base.set","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.googleSheets","n8n-nodes-base.googleSheets","n8n-nodes-base.httpRequest","n8n-nodes-base.html","n8n-nodes-base.googleSheets","@n8n/n8n-nodes-langchain.sentimentAnalysis","@n8n/n8n-nodes-langchain.lmChatOpenAi","@n8n/n8n-nodes-langchain.lmChatOpenAi"]

workflow_json_compressed:
  [encoded data, 4784 characters]


