+5
Create a REST API for PDF Digital Signatures with Webhooks
Published 11 days ago
Created by
Ferenc Erb
Template description
Overview
An automation workflow that creates a complete REST API for digitally signing PDF documents using n8n webhooks. This service demonstrates how to implement secure document signing functionality through standardized API endpoints with file upload and download capabilities.
Use Case
This workflow is designed for developers and automation specialists who need to implement digital document signing. It's particularly useful for:
- Integrating PDF signing capabilities into existing document workflows
- API-based automation of signature processes
- Creating proof-of-concept implementations for document verification systems
- Learning n8n's webhook capabilities and file handling techniques
- Testing PDF signing in development environments before production implementation
What This Workflow Does
API-Based Document Management
- Exposes RESTful webhook endpoints for all document operations
- Handles multipart/form-data uploads for PDF documents
- Processes JSON payloads for signing configuration
- Provides download functionality for completed documents
Digital Certificate Handling
- Uploads existing PFX/PKCS#12 digital certificates
- Generates new certificates with customizable attributes
- Securely manages certificate storage and access
- Associates certificates with signing operations
Cryptographic PDF Signing
- Applies digital signatures using industry-standard cryptographic methods
- Embeds signature information within PDF document structure
- Validates document integrity through cryptographic verification
- Preserves original document while adding signature elements
Webhook Integration System
- Routes different API methods to appropriate handlers
- Validates request payloads and file content
- Manages authentication through webhook paths
- Returns structured responses for integration with other systems
Technical Architecture
Components
- API Gateway: n8n webhook nodes that receive external requests
- Request Router: Switch nodes that direct operations based on method parameters
- Document Processor: Function nodes for PDF manipulation and verification
- Certificate Manager: Specialized nodes for cryptographic key operations
- Storage Interface: File operation nodes for document persistence
- Response Formatter: Nodes that structure API responses
Integration Flow
Client Request → Webhook Endpoint → Method Router →
Processing Engine → Digital Signing → Storage →
Response Generation → Client Response
Setup Instructions
Prerequisites
- n8n installation (minimum version 0.214.0)
- Node.js 14 or higher
- Required environment variable:
NODE_FUNCTION_ALLOW_EXTERNAL: "node-forge,@signpdf/signpdf,@signpdf/signer-p12,@signpdf/placeholder-plain"
Configuration Steps
-
Import Workflow
- Import the workflow JSON into your n8n instance
- Activate the workflow to enable the webhooks
-
Configure Storage
- Set the storage path variables in the workflow
- Ensure proper permissions on the storage directories
-
Test API Endpoints
- Use the included test scripts to verify functionality
- Test PDF upload, certificate generation, and signing
-
Integration
- Document the webhook URLs for integration with other systems
- Configure error handling according to your requirements
Testing Methods
Test the workflow functionality using various HTTP requests and JSON data:
- Upload PDF documents to the document processing endpoint
- Upload or generate digital certificates
- Execute PDF signing operations
- Download signed documents from the download endpoint
Webhook Endpoints
The workflow exposes two primary webhook endpoints that form a complete API for PDF digital signing operations:
1. Document Processing Endpoint (/webhook/docu-digi-sign)
This endpoint handles all document and certificate operations:
Method: Upload PDF
- HTTP:
POST - Content-Type: multipart/form-data
- Parameters: method, uploadType, fileName, fileData
Method: Upload Certificate
- HTTP:
POST - Content-Type: multipart/form-data
- Parameters: method, uploadType, fileName, fileData
Method: Generate Certificate
- HTTP:
POST - Content-Type: application/json
- Parameters: method, subjectCN, issuerCN, serialNumber, validFrom, validTo, password
Method: Sign PDF
- HTTP:
POST - Content-Type: application/json
- Parameters: method, inputPdf, pfxFile, pfxPassword
2. Document Download Endpoint (/webhook/docu-download)
This endpoint handles the retrieval of processed documents:
Method: Download Signed PDF
- HTTP:
GET - Content-Type: application/json
- Parameters: method, fileType, fileName
Key Workflow Sections
The workflow is organized into logical sections with clear responsibilities:
- Request Processing: Parses incoming webhook data
- Method Routing: Directs requests to appropriate handlers
- Document Management: Handles file operations and storage
- Cryptographic Operations: Manages signing and certificate functions
- Response Formatting: Structures and returns results
Share Template
More Sales workflow templates
+10
Building Your First WhatsApp Chatbot
This n8n template builds a simple WhatsApp chabot acting as a Sales Agent. The Agent is backed by a product catalog vector store to better answer user's questions.
This template is intended to help introduce n8n users interested in building with WhatsApp.
How it works
This template is in 2 parts: creating the product catalog vector store and building the WhatsApp AI chatbot.
A product brochure is imported via HTTP request node and its text contents extracted.
The text contents are then uploaded to the in-memory vector store to build a knowledgebase for the chatbot.
A WhatsApp trigger is used to capture messages from customers where non-text messages are filtered out.
The customer's message is sent to the AI Agent which queries the product catalogue using the vector store tool.
The Agent's response is sent back to the user via the WhatsApp node.
How to use
Once you've setup and configured your WhatsApp account and credentials
First, populate the vector store by clicking the "Test Workflow" button.
Next, activate the workflow to enable the WhatsApp chatbot.
Message your designated WhatsApp number and you should receive a message from the AI sales agent.
Tweak datasource and behaviour as required.
Requirements
WhatsApp Business Account
OpenAI for LLM
Customising this workflow
Upgrade the vector store to Qdrant for persistance and production use-cases.
Handle different WhatsApp message types for a more rich and engaging experience for customers.
Jimleuk
+8
Scrape business emails from Google Maps without the use of any third party APIs
Who is this template for?
This workflow template is designed for sales, marketing, and business development professionals who want a cost-effective and efficient way to generate leads. By leveraging n8n core nodes, it scrapes business emails from Google Maps without relying on third-party APIs or paid services, ensuring there are no additional costs involved.
Ideal for small business owners, freelancers, and agencies, this template automates the process of collecting contact information for targeted outreach, making it a powerful tool for anyone looking to scale their lead generation efforts without incurring extra expenses.
You can watch the video tutorial here: https://youtu.be/HaiO-UeiKBA
How it works
This template streamlines email scraping from Google Maps using only n8n core nodes, ensuring a completely free and self-contained solution. Here’s how it operates:
Input Queries
You provide a list of queries, each consisting of keywords related to the type of business you want to target and the specific region or subregion you’re interested in.
Iterates through Queries
The workflow processes each query one at a time. For each query, it triggers a sub-workflow dedicated to handling the scraping tasks.
Scrapes Google Maps for URLs
Using these queries, the workflow scrapes Google Maps to collect URLs of business listings matching the provided criteria.
Fetches HTML Content
The workflow then fetches the HTML pages of the collected URLs for further processing.
Extracts Emails
Using a Code Node with custom JavaScript, the workflow runs regular expressions on the HTML content to extract business email addresses.
Setup
Add Queries:
Open the first node, "Run Workflow" and input a list of queries, each containing the business keywords and the target region.
Configure the Google Sheets Node:
Open the Google Sheets node and select a document and specific sheet where the scraped results will be saved.
Run the workflow:
Click on "Test workflow" and watch your Google Sheets document gradually receive business email addresses.
Customize as Needed:
You can adjust the regular expressions in the Code Node to refine the email extraction logic or add logic to extract other kinds of information.
Akram Kadri
+5
Automated Web Scraping: email a CSV, save to Google Sheets & Microsoft Excel
How it works:
The workflow starts by sending a request to a website to retrieve its HTML content.
It then parses the HTML extracting the relevant information
The extracted data is storted and converted into a CSV file.
The CSV file is attached to an email and sent to your specified address.
The data is simultaneously saved to both Google Sheets and Microsoft Excel for further analysis or use.
Set-up steps:
Change the website to scrape in the "Fetch website content" node
Configure Microsoft Azure credentials with Microsoft Graph permissions (required for the Save to Microsoft Excel 365 node)
Configure Google Cloud credentials with access to Google Drive, Google Sheets and Gmail APIs (the latter is required for the Send CSV via e-mail node).
Mihai Farcas
+4
OpenAI GPT-3: Company Enrichment from website content
Enrich your company lists with OpenAI GPT-3 ↓
You’ll get valuable information such as:
Market (B2B or B2C)
Industry
Target Audience
Value Proposition
This will help you to:
add more personalization to your outreach
make informed decisions about which accounts to target
I've made the process easy with an n8n workflow.
Here is what it does:
Retrieve website URLs from Google Sheets
Extract the content for each website
Analyze it with GPT-3
Update Google Sheets with GPT-3 data
Lucas Perret
+7
AI web researcher for sales
Who is this for?
This workflow is for all sales reps and lead generation manager who need to prepare their prospecting activities, and find relevant information to personalize their outreach.
Use Case
This workflow allows you to do account research with the web using AI.
It has the potential to replace manual work done by sales rep when preparing their prospecting activities by searching complex information available online.
What this workflow does
The advanced AI module has 2 capabilities:
Research Google using SerpAPI
Visit and get website content using a sub-workflow
From an unstructured input like a domain or a company name.
It will return the following properties:
domain
company Linkedin Url
cheapest plan
has free trial
has entreprise plan
has API
market (B2B or B2C)
The strength of n8n here is that you can adapt this workflow to research whatever information you need.
You just have to precise it in the prompt and to precise the output format in the "Strutured Output Parser" module.
Detailed instructions + video guide can be found by following this link.
Lucas Perret
+2
Gmail AI Auto-Responder: Create Draft Replies to incoming emails
This workflow automatically generates draft replies in Gmail.
It's designed for anyone who manages a high volume of emails or often face writer's block when crafting responses.
Since it doesn't send the generated message directly, you're still in charge of editing and approving emails before they go out.
How It Works:
Email Trigger: activates when new emails reach the Gmail inbox
Assessment: uses OpenAI gpt-4o and a JSON parser to determine if a response is necessary.
Reply Generation: crafts a reply with OpenAI GPT-4 Turbo
Draft Integration: after converting the text to html, it places the draft into the Gmail thread as a reply to the first message
Set Up Overview (~10 minutes):
OAuth Configuration (follow n8n instructions here):
Setup Google OAuth in Google Cloud console. Make sure to add Gmail API with the modify scope.
Add Google OAuth credentials in n8n. Make sure to add the n8n redirect URI to the Google Cloud Console consent screen settings.
OpenAI Configuration: add OpenAI API Key in the credentials
Tweaking the prompt: edit the system prompt in the "Generate email reply" node to suit your needs
Detailed Walkthrough
Check out this blog post where I go into more details on how I built this workflow.
Reach out to me here if you need help building automations for your business.
Nicolas Chourrout
More Product workflow templates
+10
Create a Branded AI-Powered Website Chatbot
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.
> Note: Widget includes a "Powered By" affiliate link
Wayne Simpson
Chat with Postgresql Database
Who is this template for?
This workflow template is designed for any professionals seeking relevent data from database using natural language.
How it works
Each time user ask's question using the n8n chat interface, the workflow runs.
Then the message is processed by AI Agent using relevent tools - Execute SQL Query, Get DB Schema and Tables List and Get Table Definition, if required. Agent uses these tool to form and run sql query which are necessary to answer the questions.
Once AI Agent has the data, it uses it to form answer and returns it to the user.
Set up instructions
Complete the Set up credentials step when you first open the workflow. You'll need a Postgresql Credentials, and OpenAI api key.
Template was created in n8n v1.77.0
KumoHQ
+13
AI Agent To Chat With Files In Supabase Storage
Video Guide
I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n.
Youtube Link
Who is this for?
This workflow is ideal for researchers, analysts, business owners, or anyone managing a large collection of documents. It's particularly beneficial for those who need quick contextual information retrieval from text-heavy files stored in Supabase, without needing additional services like Google Drive.
What problem does this workflow solve?
Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files.
What this workflow does
The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include:
Fetching and comparing files to avoid duplicate processing.
Handling file downloads and extracting content based on the file type.
Converting documents into vectorized data for contextual information retrieval.
Storing and querying vectorized data from a Supabase vector store.
File Extraction and Processing: Automates handling of multiple file formats (e.g., PDFs, text files), and extracts document content.
Vectorized Embeddings Creation: Generates embeddings for processed data to enable AI-driven interactions.
Dynamic Data Querying: Allows users to query their document repository conversationally using a chatbot.
Setup
N8N Workflow
Fetch File List from Supabase:
Use Supabase to retrieve the stored file list from a specified bucket.
Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing.
Compare and Filter Files:
Aggregate the files retrieved from storage and compare them to the existing list in the Supabase files table.
Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled.
Handle File Downloads:
Download new files using detailed storage configurations for public/private access.
Adjust the storage settings and GET requests to match your Supabase setup.
File Type Processing:
Use a Switch node to target specific file types (e.g., PDFs or text files).
Employ relevant tools to process the content:
For PDFs, extract embedded content.
For text files, directly process the text data.
Content Chunking:
Break large text data into smaller chunks using the Text Splitter node.
Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks.
Vector Embedding Creation:
Generate vectorized embeddings for the processed content using OpenAI's embedding tools.
Ensure metadata, such as file ID, is included for easy data retrieval.
Store Vectorized Data:
Save the vectorized information into a dedicated Supabase vector store.
Use the default schema and table provided by Supabase for seamless setup.
AI Chatbot Integration:
Add a chatbot node to handle user input and retrieve relevant document chunks.
Use metadata like file ID for targeted queries, especially when multiple documents are involved.
Testing
Upload sample files to your Supabase bucket.
Verify if files are processed and stored successfully in the vector store.
Ask simple conversational questions about your documents using the chatbot (e.g., "What does Chapter 1 say about the Roman Empire?").
Test for accuracy and contextual relevance of retrieved results.
Mark Shcherbakov
+7
Build Your First AI Data Analyst Chatbot
Enhance your data analysis by connecting an AI Agent to your dataset, using n8n tools.
This template teaches you how to build an AI Data Analyst Chatbot that is capable of pulling data from your sources, using tools like Google Sheets or databases. It's designed to be easy and efficient, making it a good starting point for AI-driven data analysis.
You can easily replace the current Google Sheets tools for databases like Postgres or MySQL.
How It Works
The core of the workflow is the AI Agent. It's connected to different data retrieval tools, to get data from Google Sheets (or your preferred database) in many different ways.
Once the data is retrieved, the Calculator tool allows the AI to perform mathematical operations, making your data analysis precise.
Who is this template for
Data Analysts & Researchers:** Pull data from different sources and perform quick calculations.
Developers & AI Enthusiasts:** Learn to build your first AI Agent with easy dataset access.
Business Owners:** Streamline your data analysis with AI insights and automate repetitive tasks.
Automation Experts:** Enhance your automation skills by integrating AI with your existing databases.
How to Set Up
You can find detailed instructions in the workflow itself.
Check out my other templates
👉 https://n8n.io/creators/solomon/
Solomon
+5
🚀 Boost your customer service with this WhatsApp Business bot!
This n8n workflow demonstrates how to automate customer interactions and appointment management via WhatsApp Business bot.
After submitting a Google Form, the user receives a notification via WhatsApp. These notifications are sent via a template message.
In case user sends a message to the bot, the text and user data is stored in Google Sheets.
To reply back to the user, fill in the ReplyText column and change the Status to 'Ready'. In a few seconds n8n will fetch the unsent replies and deliver them one by one via WhatsApp Business node.
Customize this workflow to fit your specific needs, connect different online services and enhance your customer communication! 🎉
Setup Instructions
To get this workflow up and running, you'll need to:
👇 Create a WhatsApp template message on the Meta Business portal.
Obtain an Access Token and WhatsApp Business Account ID from the Meta Developers Portal. This is needed for the WhatsApp Business Node to send messages.
Set up a WhatsApp Trigger node with App ID and App Secret from the Meta Developers Portal.
Right after that copy the WhatsApp Trigger URL and add it as a Callback URL in the Meta Developers Portal. This trigger is needed to receive incoming messages and their status updates.
Connect your Google Sheets account for data storage and management. Check out the documentation page.
⚠️ Important Notes
WhatsApp allows automatic custom text messages only within 24 hours of the last user message. Outside with time frame only approved template messages can be sent.
The workflow uses a Google Sheet to manage form submissions, incoming messages and prepare responses. You can replace these nodes and connect the WhatsApp bot with other systems.
Eduard
+7
✨ Vision-Based AI Agent Scraper - with Google Sheets, ScrapingBee, and Gemini
Important Notes:
Check Legal Regulations:
This workflow involves scraping, so ensure you comply with the legal regulations in your country before getting started. Better safe than sorry!
Workflow Description:
😮💨 Tired of struggling with XPath, CSS selectors, or DOM specificity when scraping ?
This AI-powered solution is here to simplify your workflow! With a vision-based AI Agent, you can extract data effortlessly without worrying about how the DOM is structured.
This workflow leverages a vision-based AI Agent, integrated with Google Sheets, ScrapingBee, and the Gemini-1.5-Pro model, to extract structured data from webpages. The AI Agent primarily uses screenshots for data extraction but switches to HTML scraping when necessary, ensuring high accuracy.
Key Features:
Google Sheets Integration**: Manage URLs to scrape and store structured results.
ScrapingBee**: Capture full-page screenshots and retrieve HTML data for fallback extraction.
AI-Powered Data Parsing**: Use Gemini-1.5-Pro for vision-based scraping and a Structured Output Parser to format extracted data into JSON.
Token Efficiency**: HTML is converted to Markdown to optimize processing costs.
This template is designed for e-commerce scraping but can be customized for various use cases.
Dataki
More IT Ops workflow templates
Chat with Postgresql Database
Who is this template for?
This workflow template is designed for any professionals seeking relevent data from database using natural language.
How it works
Each time user ask's question using the n8n chat interface, the workflow runs.
Then the message is processed by AI Agent using relevent tools - Execute SQL Query, Get DB Schema and Tables List and Get Table Definition, if required. Agent uses these tool to form and run sql query which are necessary to answer the questions.
Once AI Agent has the data, it uses it to form answer and returns it to the user.
Set up instructions
Complete the Set up credentials step when you first open the workflow. You'll need a Postgresql Credentials, and OpenAI api key.
Template was created in n8n v1.77.0
KumoHQ
+13
AI Agent To Chat With Files In Supabase Storage
Video Guide
I prepared a detailed guide explaining how to set up and implement this scenario, enabling you to chat with your documents stored in Supabase using n8n.
Youtube Link
Who is this for?
This workflow is ideal for researchers, analysts, business owners, or anyone managing a large collection of documents. It's particularly beneficial for those who need quick contextual information retrieval from text-heavy files stored in Supabase, without needing additional services like Google Drive.
What problem does this workflow solve?
Manually retrieving and analyzing specific information from large document repositories is time-consuming and inefficient. This workflow automates the process by vectorizing documents and enabling AI-powered interactions, making it easy to query and retrieve context-based information from uploaded files.
What this workflow does
The workflow integrates Supabase with an AI-powered chatbot to process, store, and query text and PDF files. The steps include:
Fetching and comparing files to avoid duplicate processing.
Handling file downloads and extracting content based on the file type.
Converting documents into vectorized data for contextual information retrieval.
Storing and querying vectorized data from a Supabase vector store.
File Extraction and Processing: Automates handling of multiple file formats (e.g., PDFs, text files), and extracts document content.
Vectorized Embeddings Creation: Generates embeddings for processed data to enable AI-driven interactions.
Dynamic Data Querying: Allows users to query their document repository conversationally using a chatbot.
Setup
N8N Workflow
Fetch File List from Supabase:
Use Supabase to retrieve the stored file list from a specified bucket.
Add logic to manage empty folder placeholders returned by Supabase, avoiding incorrect processing.
Compare and Filter Files:
Aggregate the files retrieved from storage and compare them to the existing list in the Supabase files table.
Exclude duplicates and skip placeholder files to ensure only unprocessed files are handled.
Handle File Downloads:
Download new files using detailed storage configurations for public/private access.
Adjust the storage settings and GET requests to match your Supabase setup.
File Type Processing:
Use a Switch node to target specific file types (e.g., PDFs or text files).
Employ relevant tools to process the content:
For PDFs, extract embedded content.
For text files, directly process the text data.
Content Chunking:
Break large text data into smaller chunks using the Text Splitter node.
Define chunk size (default: 500 tokens) and overlap to retain necessary context across chunks.
Vector Embedding Creation:
Generate vectorized embeddings for the processed content using OpenAI's embedding tools.
Ensure metadata, such as file ID, is included for easy data retrieval.
Store Vectorized Data:
Save the vectorized information into a dedicated Supabase vector store.
Use the default schema and table provided by Supabase for seamless setup.
AI Chatbot Integration:
Add a chatbot node to handle user input and retrieve relevant document chunks.
Use metadata like file ID for targeted queries, especially when multiple documents are involved.
Testing
Upload sample files to your Supabase bucket.
Verify if files are processed and stored successfully in the vector store.
Ask simple conversational questions about your documents using the chatbot (e.g., "What does Chapter 1 say about the Roman Empire?").
Test for accuracy and contextual relevance of retrieved results.
Mark Shcherbakov
+6
Notion AI Assistant Generator
This n8n workflow template lets teams easily generate a custom AI chat assistant based on the schema of any Notion database. Simply provide the Notion database URL, and the workflow downloads the schema and creates a tailored AI assistant designed to interact with that specific database structure.
Set Up
Watch this quick set up video 👇
Key Features
Instant Assistant Generation**: Enter a Notion database URL, and the workflow produces an AI assistant configured to the database schema.
Advanced Querying**: The assistant performs flexible queries, filtering records by multiple fields (e.g., tags, names). It can also search inside Notion pages to pull relevant content from specific blocks.
Schema Awareness**: Understands and interacts with various Notion column types like text, dates, and tags for accurate responses.
Reference Links**: Each query returns direct links to the exact Notion pages that inform the assistant’s response, promoting transparency and easy access.
Self-Validation**: The workflow has logic to check the generated assistant, and if any errors are detected, it reruns the agent to fix them.
Ideal for
Product Managers**: Easily access and query product data across Notion databases.
Support Teams**: Quickly search through knowledge bases for precise information to enhance support accuracy.
Operations Teams**: Streamline access to HR, finance, or logistics data for fast, efficient retrieval.
Data Teams**: Automate large dataset queries across multiple properties and records.
How It Works
This AI assistant leverages two HTTP request tools—one for querying the Notion database and another for retrieving data within individual pages. It’s powered by the Anthropic LLM (or can be swapped for GPT-4) and always provides reference links for added transparency.
Max Tkacz
+3
Notion knowledge base AI assistant
Who is this for
This workflow is perfect for teams and individuals who manage extensive data in Notion and need a quick, AI-powered way to interact with their databases. If you're looking to streamline your knowledge management, automate searches, and get faster insights from your Notion databases, this workflow is for you. It’s ideal for support teams, project managers, or anyone who needs to query specific data across multiple records or within individual pages of their Notion setup.
Check out the Notion template this Assistant is set up to use: https://www.notion.so/templates/knowledge-base-ai-assistant-with-n8n
How it works
The Notion Database Assistant uses an AI Agent built with Retrieval-Augmented Generation (RAG) to query this Knowledge Base style Notion database. The assistant can search across multiple properties like tags or question and retrieves content from inside individual Notion pages for additional context.
Key features include:
Querying the database with flexible filters.
Searching within individual Notion pages and extracting relevant blocks.
Providing a reference link to the exact Notion pages used to inform its responses, ensuring transparency and easy verification.
This assistant uses two HTTP request tools—one for querying the Notion database and another for pulling data from within specific pages. It streamlines knowledge retrieval, offering a conversational, AI-driven way to interact with large datasets.
Set up
Find basic set up instructions inside the workflow itself or watch a quickstart video 👇
Max Tkacz
+3
Backup n8n workflows to Google Drive
Temporary solution using the undocumented REST API for backups using Google drive.
Please note that there are issues with this workflow. It does not support versioning, so please know that it will create multiple copies of the workflows so if you run this daily it will make the folder grow quickly. Once I figure out how to version in Gdrive I'll update it here.
Angel Menendez
+8
Advanced Telegram Bot, Ticketing System, LiveChat, User Management, Broadcasting
A robust n8n workflow designed to enhance Telegram bot functionality for user management and broadcasting. It facilitates automatic support ticket creation, efficient user data storage in Redis, and a sophisticated system for message forwarding and broadcasting.
How It Works
Telegram Bot Setup: Initiate the workflow with a Telegram bot configured for handling different chat types (private, supergroup, channel).
User Data Management: Formats and updates user data, storing it in a Redis database for efficient retrieval and management.
Support Ticket Creation: Automatically generates chat tickets for user messages and saves the corresponding topic IDs in Redis.
Message Forwarding: Forwards new messages to the appropriate chat thread, or creates a new thread if none exists.
Support Forum Management: Handles messages within a support forum, differentiating between various chat types and user statuses.
Broadcasting System: Implements a broadcasting mechanism that sends channel posts to all previous bot users, with a system to filter out blocked users.
Blocked User Management: Identifies and manages blocked users, preventing them from receiving broadcasted messages.
Versatile Channel Handling: Ensures that messages from verified channels are properly managed and broadcasted to relevant users.
Set Up Steps
Estimated Time**: Around 30 minutes.
Requirements**: A Telegram bot, a Redis database, and Telegram group/channel IDs are necessary.
Configuration**: Input the Telegram bot token and relevant group/channel IDs. Configure message handling and user data processing according to your needs.
Detailed Instructions**: Sticky notes within the workflow provide extensive setup information and guidance.
Live Demo Workflow
Bot: Telegram Bot Link (Click here)
Support Group: Telegram Group Link (Click here)
Broadcasting Channel: Telegram Channel Link (Click here)
Keywords: n8n workflow, Telegram bot, chat ticket system, Redis database, message broadcasting, user data management, support forum automation
Nskha
