Workflow Templates

Automated Gmail Labeling and Brainstorming This template can be used to automatically label your incoming Gmail messages with AI and to build a knowledge graph from the emails tagged with a specific label to brainstorm new ideas based on them. You can also get notified about the emails with the most important labels via Telegram as well as receive new ideas as you are building a knowledge graph of incoming messages. The idea generation is based on the InfraNodus knowledge graph content gap detection algorithm, which builds a network from your content and then finds a blind spot and uses AI to generate an interesting research question or idea that can be used to bridge this gap. Why it works so well? Think of all the business emails you receive that bypass the spam filters. Probably, they are personalized to you already. Now imagine if you build a knowledge graph from them for over a month. You will then have a ideation device based on your interests and marketing profile. Now, if you identify the gaps inside and generate interesting research questions based on them, you will come up with new interesting ideas that will be relevant (because they touch on the topics that matter to you), but novel, because they bridge them in new ways. What is it useful for? Automate Gmail incoming message labeling** with the new Classifier n8n node — much more advanced than the default Gmail labeling rules. Get notified via Telegram (or a messenger of your choice) about the most important messages and be sure not to miss anything important. Keep the messages with a certain label saved into knowledge graph for brainstorming and ideation. Every time a new message of this category comes in, it's added into the graph, changing its structure, a new idea is generated. So instead of looking at each specific offer, you now use them to generate insights for you. How it works Step 1: This template can is triggered automatically when a new Gmail message arrives. Note: you need to connect your Gmail account here in this node Step 2: We use the new n8n AI Classifier Node to classify your email based on its content. You might need to update to n8n 1.94 version to make it work. Note: we like to use Gemini AI for that classifier as it's the same company as Gmail, so should be safe with data Step 3: After classifying the message, we label the message with the appropriate label. Note: you need to create the labels before in your Gmail account Step 4: For a certain category (e.g. "Business" you format the message and save it into your InfraNodus graph. *Note: specify your InfraNodus API here and choose the name of the graph. It will use the InfraNodus HTTP graphAndEntries endpoint and save your data to an InfraNodus graph. By default, we save the text knowledge graph using the contextSettings parameters (it will only build a text graph of the content), but you can take an alternative setting from this InfraNodus HTTP node's settings and create a social knowledge graph, that will also show email senders in the graph itself.* Step 5 (optional): Generate an interesting insight question with the graphAndAdvice endpoint) of InfraNodus. Step 6 (optional): Then send this insight via Telegram to a chat. Step 7 (optional): Link some important labels to the second Telegram notification node, so you receive important messages for specified labels. Step 8 (optional): Send a Telegram notification We use Telegram, because it takes only 30 seconds to set up a bot with an API (send /newbot to @botfather, unlike Discord or Slack, which is long and cumbersome to set up. You can also attach a Gmail send node and generate an email instead. How to use You need an InfraNodus GraphRAG API account and key to use this workflow. Create an InfraNodus account or log in. Get the API key at https://infranodus.com/api-access and create a Bearer authorization key for the InfraNodus HTTP nodes. Add this Authorization code in Steps 4 and 5 of the workflow. Come up with the name of the graph and change it in the HTTP InfraNodus nodes in the steps 4 and 5 and also in the Telegram node in Step 6 that sends a link to the graph. For additional text processing / idea generation settings you can use in the HTTP InfraNodus nodes, see the InfraNodus access points page. For example, in Step 4 you can change the text processing settings to build a social knowledge graph (settings are available in the Node's Notes section) and in Step 5 you can change the requestMode from question to idea to receive business ideas instead. Authorize your Gmail account for Steps 2, 3, 7 and 8 Gmail nodes. The easiest way to set it up is to open a free Google Console API account and to create an OAuth access point for n8n. You can then reuse it with other Google services like Google Sheets, Drive, etc. So it's a useful thing to have in general. Set up the Gemini AI API key using the instructions in the Step 2 Gemini AI classification node. Set up the Telegram node bot for the Step 8. It takes only 30 seconds: just go to @botfather and type in /newbot and you'll have an API key ready. To get the conversation ID, follow the n8n / Telegram instructions in the node itself. Once everything is ready, try to run the default automated workflow to test if everything works well. Requirements An InfraNodus account and API key An Google Cloud API OAuth client and key for Gmail access A Gemini AI API key A Telegram bot API key n8n version 1.94 and higher (for Text Classification AI node to work) Customizing this workflow Check our other n8n workflows at https://n8n.io/creators/infranodus/ for useful content gap analysis, expert panel, and marketing, and research workflows that utilize GraphRAG for better AI generation. Finally, check out https://infranodus.com to learn more about our network analysis technology used to build knowledge graphs from text. For support, please, contact https://support.noduslabs.com

AI-Powered SEO Keyword Research Workflow with n8n > automates comprehensive keyword research for content creation Table of Contents Introduction Workflow Architecture NocoDB Integration Data Flow Core Components Setup Requirements Possible Improvements Introduction This n8n workflow automates SEO keyword research using AI and data-driven analytics. It combines OpenAI's language models with DataForSEO's analytics to generate comprehensive keyword strategies for content creation. The workflow is triggered by a webhook from NocoDB, processes the input data through multiple stages, and returns a detailed content brief with optimized keywords. Workflow Architecture The workflow follows a structured process: Input Collection: Receives data via webhook from NocoDB Topic Expansion: Generates keywords using AI Keyword Metrics Analysis: Gathers search volume, CPC, and difficulty metrics Competitor Analysis: Analyzes competitor content for ranking keywords Final Strategy Creation: Combines all data to generate a comprehensive keyword strategy Output Storage: Saves results back to NocoDB and sends notifications NocoDB Integration Database Structure The workflow integrates with two tables in NocoDB: Input Table Schema This table collects the input parameters for the keyword research: | Field Name | Type | Description | | --------------- | ------------- | --------------------------------------------------------------------------- | | ID | Auto Number | Unique identifier | | Primary Topic | Text | The main keyword/topic to research | | Competitor URLs | Text | Comma-separated list of competitor websites | | Target Audience | Single Select | Description of the target audience (Solopreneurs, Marketing Managers, etc.) | | Content Type | Single Select | Type of content (Blog, Product page, etc.) | | Location | Single Select | Target geographic location | | Language | Single Select | Target language for keywords | | Status | Single Select | Workflow status (Pending, Started, Done) | | Start Research | Checkbox | Active Workflow when you set this to true | Output Table Schema This table stores the generated keyword strategy: | Field Name | Type | Description | | ------------------ | ----------- | ------------------------------------------------ | | ID | Auto Number | Unique identifier | | primary_topic_used | Text | The topic that was researched | | report_content | Long Text | The complete keyword strategy in Markdown format | | generatedAt | Datetime | Automatically generated by NocoDb | Webhook Settings NocoDB Webhook Settings Data Flow The workflow handles data in the following sequence: Webhook Trigger: Receives input from NocoDB when a new keyword research request is created Field Extraction: Extracts primary topic, competitor URLs, audience, and other parameters AI Topic Expansion: Uses OpenAI to generate related keywords, categorized by type and intent Keyword Analysis: Sends primary keywords to DataForSEO to get search volume, CPC, and difficulty Competitor Research: Analyzes competitor pages to identify their keyword rankings Strategy Generation: Combines all data to create a comprehensive keyword strategy Storage & Notification: Saves the strategy to NocoDB and sends a notification to Slack Core Components 1. Topic Expansion This component uses OpenAI and a structured output parser to generate: 20 primary keywords 30 long-tail keywords with search intent 15 question-based keywords 10 related topics 2. DataForSEO Integration Two API endpoints are used: Search Volume & CPC**: Gets monthly search volume and cost-per-click data Keyword Difficulty**: Evaluates how difficult it would be to rank for each keyword 3. Competitor Analysis This component: Analyzes competitor URLs to identify which keywords they rank for Identifies content gaps or opportunities Determines the search intent their content targets 4. Final Keyword Strategy The AI-generated strategy includes: Top 10 primary keywords with metrics 15 long-tail opportunities with low competition 5 question-based keywords to address in content Content structure recommendations 3 potential content titles optimized for SEO Setup Requirements To use this workflow, you'll need: n8n Instance: Either cloud or self-hosted NocoDB Account: For data input and storage API Keys: OpenAI API key DataForSEO API credentials Slack API token (for notifications) Database Setup: Create the required tables in NocoDB as described above Possible Improvements The workflow could be enhanced with the following improvements: Enhanced Keyword Strategy Add topic clustering to group related keywords Enhance the final output with more specific content structure suggestions Include word count recommendations for each content section Additional Data Sources Integrate Google Search Console data for existing content optimization Add Google Trends data to identify rising topics Include sentiment analysis for different keyword groups Improved Competitor Analysis Analyze content length and structure from top-ranking pages Identify common backlink sources for competitor content Extract content headings to better understand content organization Automation Enhancements Add scheduling capabilities to run updates on existing content Implement content performance tracking over time Create alert thresholds for changes in keyword difficulty or search volume Example Output Here is an example Output the Workflow generated based on the following inputs. Inputs: Primary Topic: AI Automation Competitor URLs: n8n.io, zapier.com, make.com Target Audience: Small Business Owners Content Type: Landing Page Location: United States Language: English Output: Final Keyword Strategy The workflow provides a powerful automation for content marketers and SEO specialists to develop data-driven keyword strategies with minimal manual effort. > Original Workflow: AI-Powered SEO Keyword Research Automation - The vibe Marketer

Automatic lead enrichment in Slack: monitors your Slack channel for new lead emails posted there, extracts each company’s name or domain, sends it to the Extruct API for data enrichment, then posts back a structured Slack card with company name, website, LinkedIn profile, number of employees, industry, recent news, and key contacts. Who’s it for: Sales teams, SDRs, and marketing ops who capture new lead information in Slack and want instant enrichment without leaving the channel. How it works: When a lead email is posted into your designated Slack channel, the workflow: Monitors for any new message containing a company name or domain. Extracts that company identifier. Sends it to Extruct API for research. Waits for enrichment to complete. Posts back into the same Slack thread a formatted card with: Company name Website LinkedIn profile Number of employees Industry Recent news Key contacts How to set up: Sign up for Extruct AI Copy the Extruct table ID Create & install your Slack app Configure n8n credentials & channel Activate & use Requirements: Extruct account & API token Extruct table template Slack workspace with permission to install apps

Who is this for? This workflow is designed for n8n users and developers who need to automate the documentation process of their n8n workflows. It's particularly useful for teams looking to streamline their documentation efforts and ensure consistency across their workflow documentation. What problem is this workflow solving? / Use case The primary problem this workflow addresses is the manual and time-consuming process of creating documentation for n8n workflows. It automates the generation of concise, clear, and comprehensive documentation directly from the workflow's JSON, making it easier for both technical and non-technical users to understand what the workflow does and how it operates. What this workflow does Upon receiving a form submission with the workflow title and JSON, this workflow automatically generates documentation that includes: A brief introduction to the workflow. The trigger mechanism (webhook URLs for test and production environments, or cron schedules). Setup requirements, including necessary credentials and external dependencies. Setup Credentials Setup: Ensure you have OpenAI API credentials configured in n8n to use the GPT model for generating documentation text. Form Submission: Users must submit the form with the workflow title and JSON. The form is accessible via: Test URL: domain/form-test/{webhookId} Production URL: domain/form/{webhookId} How to customize this workflow to your needs Modify Trigger URLs**: Adjust the webhook or form URLs based on your domain and specific n8n setup. Customize Documentation Template**: Edit the OpenAI node's prompt to change the structure or details of the generated documentation. Extend Functionality**: Add nodes to integrate with other systems (e.g., automatically publishing the documentation to a wiki or sending it via email). This workflow simplifies the documentation process, making it accessible and manageable for teams of all sizes and technical abilities. By automating documentation, it ensures that all workflows are properly documented, enhancing understanding and efficiency within teams.

Fetches workflow definitions from within n8n, selecting only the ones that have one or more (configurable) assigned tags and then: Derives a suitable backup filename by reducing the workflow name to a string with alphanumeric characters and no-spaces Note: This isn't bulletproof, but works as long as workflow names aren't too crazy. Determines which workflows need to be backed up based on whether each one: has been modified. (Note: Even repositioning a node counts.) ...or... is new. (Note: Renaming counts as this.) Commits JSON copies of each workflow, as necessary, to a Gitlab repository with a generated, date-stamped commit message. Setup Credentials Create a Gitlab Credentials item and assign it to all Gitlab nodes. Create an n8n Credentials item and assign it to the n8n node Note: This was tested with http://localhost:5678/api/v1 but should work with any reachable n8n instance and API key. Modify these values in the "Globals" Node gitlab_owner - {{your gitlab account}} gitlab_project - {{ your gitlab project name }} gitlab_workflow_path - {{ subdirectory in the project where backup files should be saved/committed }} tags_to_match_for_backup - {{tag(s) to match for backup selection}} *ALERT: According to the n8n node's Filters -> tags field annotations, and API documentation, this supports a CSV list of multiple tags (e.g. tag1,tag2), but the API behavior requires workflows to have all-of the listed tags, not any-of them.* See: https://github.com/n8n-io/n8n/issues/10348 TL/DR - Don't expect a multiple tag list to be more inclusive. Possible workaround: To match more than one tag value, duplicate the n8n node into multiple single-tag matches, or split and iterate multiple values, and merge the results. Possible Enhancements Make the branch ("Reference") for all the gitlab nodes configurable. Fixed on all as "main" in the template. Add an n8n node to generate an audit and store the output in gitlab along with the backups. Extend the workflow at the end to create a Gitlab release/tag whenever any backup files are actually updated or created.

This n8n template demonstrates how you can automate community moderation using human-in-the-loop functionality for Discord. The use-case is for detecting and dealing with spam messages in a predefined and consistent way. Human-in-the-loop allows for a balance between overly aggressive bots and time and effort from the moderation team. How it works A scheduled trigger is used to scan the most recent messages in a Discord Channel. Messages are tagged via the "Remove Duplicates" node so they don't get processed again in the future. Messages are grouped by user to allow for minimising of number of notifications sent. An AI text classifier node is then used to detect for spam in each user's message. When detected, a notification is sent to a moderation channel using the Send-and-wait mode for Discord. This notification comes with an n8n form and dropdown list of predefined actions to take in dealing with the spam messages. Once sent the workflow waits until a response is received. Once a moderator selects an action, the workflow continues and carries out a predefined moderation action. How to use Depending on how busy your community is and subject to spammers, you may need to increase the scheduled interval. Add as many or few moderation actions as required. Remember to activate the workflow to get it started. Requirements Discord channel for messages to moderate OpenAI for text classification Customising this template It is possible to cover multiple channels. Add as many as your community needs. Not using Discord. The template can also work in slack or other services which offer the same bot functionality.

🎥 Bulk TikTok Video Download Without Watermark to Google Drive This workflow automates the process of downloading TikTok videos and uploading them to Google Drive. It reads TikTok URLs from a Google Sheet, downloads the video using the TikTok Video Downloader — a tool for downloading TikTok videos without watermark in HD quality — uploads it to Drive, makes it public, and updates the same sheet with the Drive link. 🔧 What It Does ✅ Manually triggered when ready to run. 📄 Reads TikTok URLs from a Google Sheet. 🔁 Loops through each URL one at a time. 🌐 Fetches video download links using the TikTok Video Downloader — a reliable TikTok video downloader without watermark. ⬇️ Downloads each video in high-definition (HD) format using the direct media link. ☁️ Uploads the video to Google Drive. 🔓 Sets public sharing permission for the video. ✏️ Updates the original Google Sheet with the public Drive URL. 📋 Google Sheet Example Make sure your sheet has at least these columns: | url | drive_link (to be auto-filled) | |-------------------------------------|--------------------------------| | https://www.tiktok.com/@user1... | (blank initially) | | https://www.tiktok.com/@user2... | (blank initially) | > The workflow reads from url and fills in drive_link after upload. 🧩 Nodes Used | Node Name | Type | Purpose | |------------------------------|-------------------|-------------------------------------------------------| | When clicking ‘Execute’ | Manual Trigger | Starts the workflow manually | | Get Data From Google Sheets | Google Sheets | Fetches rows (TikTok URLs) | | Loop Over Items | Split In Batches | Iterates over each row | | Call TikTok Downloader | HTTP Request | Gets video download link from TikTok Video Downloader | | Wait | Wait | Optional delay to prevent overload | | Download File | HTTP Request | Downloads HD video using media link | | Upload File In Google Drive | Google Drive | Uploads the video to Google Drive | | Set Public Permission | Google Drive | Makes the uploaded file publicly accessible | | Update Row In Google Sheet | Google Sheets | Adds Drive link to the same row | | Sleep | Wait | Small delay between each iteration | 📝 Requirements ✅ Google API credentials (Service Account) with access to: Google Sheets Google Drive 🔐 RapidAPI Key for TikTok Video Downloader – a TikTok video downloader without watermark (HD supported) 🗂 A Google Sheet with a url column containing TikTok video URLs 🧩 Challenges Solved | ❗ Challenge | ✅ Solution | |-------------|-------------| | TikTok video URLs often have watermarks and low quality | Used TikTok Video Downloader API for HD + no watermark download links | | No easy way to bulk download and organize TikToks | Automated fetching, downloading, and uploading using n8n + Google Drive | | Manual video saving and re-uploading to Drive is time-consuming | Eliminated all manual steps with a fully automated workflow | | Tracking which videos are already processed | Automatically updates the Google Sheet row with the final Drive link | | Drive files are private by default | Automatically sets public sharing permission on uploaded videos | | Risk of API rate limits or throttling | Added Wait nodes and batch processing to avoid overload | 🎁 Benefits | 🌟 Benefit | 💬 Description | |------------|----------------| | 🚀 Saves Time | Fully automates a previously manual workflow | | 🎥 High Quality Content | Videos downloaded are HD + watermark-free — ready for reuse or archives | | 🔁 Reusable Setup | Can process unlimited TikTok URLs via the Google Sheet | | 📊 Organized Output | Keeps track of source URL and uploaded Drive link in a single sheet | | 🔐 Secure but Shareable | Drive links are auto-shared publicly while remaining under your control | | 🔄 Scalable | Can be run daily, weekly, or triggered by new rows — completely scalable | | 💸 Cost-Effective | No need for paid tools or manual freelancers — runs on n8n + free APIs | 💡 Use Cases Content curation from TikTok Archiving user-submitted TikToks Automating social-to-cloud workflows Bulk migration of video content Saving TikTok videos in HD without watermark for sharing or archiving 📌 Tips Replace manual trigger with Cron for full automation. Use the TikTok Video Downloader responsibly — check API limits. Store metadata (e.g., uploader, hashtags) in additional Google Sheet columns. This tool helps ensure you're always downloading high-quality TikTok videos without watermark.

This n8n workflow template uses uProc's "Get Email by Domain, Firstname and Lastname" tool to discover a professional email address, and then sends that email to a Telegram channel. > ⚠️ Note: You must set up your *uProc credentials (Email + API Key)* from the *Integration settings* before running this workflow. 🚀 What It Does Uses user-provided data: first name, last name, and company domain Calls uProc to discover the most likely email address for that person Sends the discovered email and confidence level to a Telegram group 🛠️ Step-by-Step Setup Add uProc Credentials Go to the uProc integration page and copy your email and API key. Add them as credentials in your n8n instance. Set Tool Parameters Use the Set node to define: firstname: First name of the person lastname: Last name of the person domain: Their company domain Replace the Set Node (Optional) You can dynamically fetch the firstname, lastname, and domain from other sources like: Google Sheets MySQL or Postgres Webhook or Form submissions Run the Workflow Trigger the flow manually or integrate it with a larger automation. 🔍 uProc Parameters Explained domain**: The company domain (e.g., uproc.io) firstname**: First name of the person lastname** (in parameter: language): Last name of the person mode**: verify: Verifies email in real-time with mail server guess: Guesses based on company format (e.g., firstname.lastname@domain.com) 📦 uProc Response Fields email: Discovered email address confidence: Indicates if the result is verified or risky (e.g., catch-all) score: Reliability score from 0 (unreliable) to 99 (highly reliable) 📬 Notification via Telegram After discovering the email, the result is sent to a specified Telegram channel with this format: User Miquel Colomer has next email on uproc.io: contact@uproc.io (verified - 99) Clicking the email allows you to send a message directly to the recipient. 🔐 Credentials Used uProc API** – For discovering email addresses Telegram API** – To send messages to a specific group/channel ✨ Customization Tips Loop over a list of people**: Replace the set node with a data source that contains multiple people. Filter by score or confidence** before sending. Add additional outputs**: You can send the data via Email, Slack, or save it to a database. Trigger automatically**: Combine with a webhook or time-based trigger for automation. ❓Questions? Template created by Miquel Colomer and n8nhackers.com. Need help customizing or deploying? Contact us for consulting and support.

This n8n template shows you how to create an MCP server out of your existing n8n workflows. With this, any MCP client connected can get more done with powerful end-to-end workflows rather than just simple tools. Designing agent tools for outcome rather than utility has been a long recommended practice of mine and it applies well when it comes to building MCP servers; In gist, agents to be making the least amount of calls possible to complete a task. This is why n8n can be a great fit for MCP servers! This template connects your agent/MCP client (like Claude Desktop) to your existing workflows by allowing the AI to discover, manage and run these workflows indirectly. How it works An MCP trigger is used and attaches 4 custom workflow tools to discover and manage existing workflows to use and 1 custom workflow tool to execute them. We'll introduce an idea of "available" workflows which the agent is allowed to use. This will help limit and avoid some issues when trying to use every workflow such as clashes or non-production. The n8n node is a core node which taps into your n8n instance API and is able to retrieve all workflows or filter by tag. For our example, we've tagged the workflows we want to use with "mcp" and these are exposed through the tool "search workflows". Redis is used as our main memory for keeping track of which workflows are "available". The tools we have are "add Workflow", "remove workflow" and "list workflows". The agent should be able to manage this autonomously. Our approach to allow the agent to execute workflows is to use the Subworkflow trigger. The tricky part is figuring out the input schema for each but was eventually solved by pulling this information out of the workflow's template JSON and adding it as part of the "available" workflow's description. To pass parameters through the Subworkflow trigger, we can do so via the passthrough method - which is that incoming data is used when parameters are not explicitly set within the node. When running, the agent will not see the "available" workflows immediately but will need to discover them via "list" and "search". The human will need to make the agent aware that these workflows will be preferred when answering queries or completing tasks. How to use First, decide which workflows will be made visible to the MCP server. This example uses the tag of "mcp" but you can all workflows or filter in other ways. Next, ensure these workflows have Subworkflow triggers with input schema set. This is how the MCP server will run them. Set the MCP server to "active" which turns on production mode and makes available to production URL. Use this production URL in your MCP client. For Claude Desktop, see the instructions here - https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-langchain.mcptrigger/#integrating-with-claude-desktop. There is a small learning curve which will shape how you communicate with this MCP server so be patient and test. The MCP server will work better if there is a focused goal in mind ie. Research and report, rather than just a collection of unrelated tools. Requirements N8N API key to filter for selected workflows. N8N workflows with Subworkflow triggers! Redis for memory and tracking the "available" workflows. MCP Client or Agent for usage such as Claude Desktop - https://claude.ai/download Customising this workflow If your targeted workflows do not use the subworkflow trigger, it is possible to amend the executeTool to use HTTP requests for webhooks. Managing available workflows helps if you have many workflows where some may be too similar for the agent. If this isn't a problem for you however, feel free to remove the concept of "available" and let the agent discover and use all workflows!

Description This automation template is designed for content creators, social media managers, and influencers who want to streamline their video publishing workflow. It automatically detects new videos uploaded to a specific Google Drive folder, generates AI-powered descriptions based on video audio content, and simultaneously publishes them across Instagram, TikTok, and YouTube while tracking everything in Airtable. Note: This workflow uses upload-post.com API (free trial no credit card required) for multi-platform video distribution and requires API tokens for each service. The AI-generated descriptions are created using OpenAI's transcription and chat models to analyze video audio content.* Who Is This For? Content Creators & Influencers:** Automatically publish your videos across all major social platforms without manual work. Social Media Managers:** Maintain consistent posting schedules across multiple platforms with AI-generated, platform-optimized descriptions. Marketing Teams:** Scale video content distribution with automated workflows that include tracking and status monitoring. Video Producers:** Focus on creating content while the system handles the tedious task of multi-platform publishing and description generation. What Problem Does This Workflow Solve? Publishing the same video content across Instagram, TikTok, and YouTube is time-consuming and repetitive. You need to manually upload each video, write unique descriptions, and track publication status. This workflow addresses these challenges by: Automated Video Distribution:** Detects new videos in Google Drive and automatically uploads them to all three platforms simultaneously. AI-Powered Content Generation:** Uses OpenAI to transcribe video audio and generate engaging, platform-appropriate descriptions automatically. Centralized Tracking:** Maintains detailed records in Airtable including upload status, URLs, and metadata for each platform. Error Monitoring:** Provides real-time error notifications via Telegram to ensure you're always aware of any issues. How It Works Video Upload Detection: The workflow monitors a specific Google Drive folder for new video uploads using automated triggers. Content Analysis: Downloads the video, extracts audio, and uses OpenAI to transcribe and generate compelling descriptions. Airtable Integration: Creates and updates records to track video metadata, descriptions, and publication status. Multi-Platform Publishing: Simultaneously uploads the video to Instagram, TikTok, and YouTube using the upload-post.com API. Status Tracking: Updates Airtable records with publication status and platform-specific URLs for each successful upload. Setup Google Drive Configuration: Set up the Google Drive trigger to monitor your specific folder Configure OAuth2 credentials for Google Drive access OpenAI Integration: Add your OpenAI API key to enable audio transcription and description generation Airtable Setup: Create an Airtable base with fields for Video Name, Description, Platform Status, URLs, and Upload Date Add your Airtable API token and configure base/table IDs in the "Set Variables" node Upload-Post.com Account: Create an account at upload-post.com to get your API token Configure the token in the HTTP request nodes for each platform Set your user ID in the variables section Platform Accounts: Ensure your Instagram, TikTok, and YouTube accounts are connected to upload-post.com Error Notifications: (Optional) Configure Telegram bot credentials for error notifications Requirements Accounts:** Google Drive, OpenAI, Airtable, upload-post.com, Telegram (optional) API Keys & Credentials:** Google Drive OAuth2, OpenAI API Key, Airtable API Token, upload-post.com API Token Platform Setup:** Instagram, TikTok, and YouTube accounts connected to upload-post.com Transform your video publishing workflow from hours of manual work to a fully automated system that handles everything from content analysis to multi-platform distribution and tracking.

Who is this for? This template is designed for internal support teams, product specialists, and knowledge managers in technology companies who want to automate ingestion of product documentation and enable AI-driven, retrieval-augmented question answering via WhatsApp. What problem is this workflow solving? Support agents often spend too much time manually searching through lengthy documentation, leading to inconsistent or delayed answers. This solution automates importing, chunking, and indexing product manuals, then uses retrieval-augmented generation (RAG) to answer user queries accurately and quickly with AI via WhatsApp messaging. What these workflows do Workflow 1: Document Ingestion & Indexing Manually triggered to import product documentation from Google Docs. Automatically splits large documents into chunks for efficient searching. Generates vector embeddings for each chunk using OpenAI embeddings. Inserts the embedded chunks and metadata into a MongoDB Atlas vector store, enabling fast semantic search. Workflow 2: AI-Powered Query & Response via WhatsApp Listens for incoming WhatsApp user messages, supporting various types: Text messages: Plain text queries from users. Audio messages: Voice notes transcribed into text for processing. Image messages: Photos or screenshots analyzed to provide contextual answers. Document messages: PDFs, spreadsheets, or other files parsed for relevant content. Converts incoming queries to vector embeddings and performs similarity search on the MongoDB vector store. Uses OpenAI’s GPT-4o-mini model with retrieval-augmented generation to produce concise, context-aware answers. Maintains conversation context across multiple turns using a memory buffer node. Routes different message types to appropriate processing nodes to maximize answer quality. **Setup Setting up vector embeddings** 1- Authenticate Google Docs and connect your Google Docs URL containing the product documentation you want to index. 2- Authenticate MongoDB Atlas and connect the collection where you want to store the vector embeddings. Create a search index on this collection to support vector similarity queries. 3- Ensure the index name matches the one configured in n8n (data_index). See the example MongoDB search index template below for reference. Setting up chat 1- Authenticate the WhatsApp node with your Meta account credentials to enable message receiving and sending. 2- Connect the MongoDB collection containing embedded product documentation to the MongoDB Vector Search node used for similarity queries. 3- Set up the system prompt in the Knowledge Base Agent node to reflect your company’s tone, answering style, and any business rules, ensuring it references the connected MongoDB collection for context retrieval. Make sure Both MongoDB nodes (in ingestion and chat workflows) are connected to the same collection with: An embedding field storing vector data, Relevant metadata fields (e.g., document ID, source), and The same vector index name configured (e.g., data_index).

Want to see it in action? Watch the full breakdown here: 📺 Video Link Template Description This n8n workflow empowers you to query structured financial data from Google Sheets or CSV files using AI-generated SQL. Unlike traditional vector database solutions that falter with numerical queries, this template leverages PostgreSQL for efficient data storage and an AI agent to dynamically create optimized SQL queries from natural language inputs. What It Does Retrieves data from Google Sheets or CSV files Infers the data schema and builds a PostgreSQL table Populates the table with your data Uses an AI agent to translate natural language questions into SQL queries Returns precise numerical results quickly and efficiently Why Use This? No SQL knowledge required—the AI generates queries for you Bypasses the inefficiencies and costs of vector database approaches Scales effortlessly without overwhelming the language model Fully free and open-source Setup Requirements Pre-Conditions PostgreSQL Database**: A running PostgreSQL instance (no specific extensions required beyond standard installation). Google Sheets Access**: A publicly accessible or shared Google Sheet URL with structured data (e.g., financial records). Need a starting point? Use this Sample Google Sheet Template. n8n Instance**: A working n8n setup with access to the Google Drive and PostgreSQL nodes. Step-by-Step Instructions Add Your Google Sheets URL Open the "Google Drive Trigger" node. Replace the placeholder URL with your Google Sheet’s link. Verify the sheet name matches your data source. Configure PostgreSQL Update the "PostgreSQL" nodes with your database credentials (host, database, user, password). The workflow automatically creates and populates the table based on your data schema. Run the Workflow Execute the workflow manually to set up the database. Once initialized, use the AI agent by asking questions like: "How much did I sell last week?" "What were the total sales for Product X in February?" (Optional) Automate Updates Add a "Schedule Trigger" node to sync your Google Sheets data with PostgreSQL on a regular basis. How It Works Schema Detection**: The workflow analyzes your Google Sheets or CSV data to infer its structure and create an appropriate PostgreSQL table. AI-Powered Queries**: An optimized AI agent converts your natural language questions into precise SQL queries, ensuring accurate results. Efficient Retrieval**: By using PostgreSQL instead of vector-based methods, this template avoids common pitfalls like slow performance or inaccurate numerical outputs. Tips for Success Ensure your Google Sheet or CSV has consistent column headers for smooth schema detection. Test with simple questions first to verify the AI agent’s query generation. Check out the n8n Template Submission Guidelines for more best practices.