Workflow Templates

Automated Research Report Generation with OpenAI, Wikipedia, Google Search, Gmail/Telegram and PDF Output Description What Problem Does This Solve? 🛠️ This workflow automates the process of generating professional research reports for researchers, students, and professionals. It eliminates manual research and report formatting by aggregating data, generating content with AI, and delivering the report as a PDF via Gmail or Telegram. Target audience: Researchers, students, educators, and professionals needing quick, formatted research reports. What Does It Do? 🌟 Aggregates research data from Wikipedia, Google Search, and SerpApi. Refines user queries and generates structured content using OpenAI. Converts the content into a professional HTML report, then to PDF. Sends the PDF report via Gmail or Telegram. Key Features 📋 Real-time data aggregation from multiple sources. AI-driven content generation with OpenAI. Automated HTML-to-PDF conversion for professional reports. Flexible delivery via Gmail or Telegram. Error handling for robust execution. Setup Instructions Prerequisites ⚙️ n8n Instance**: Self-hosted or cloud n8n instance. API Credentials**: OpenAI API: API key with GPT model access, stored in n8n credentials. SerpApi (Google Search): API key from SerpApi, stored in n8n credentials (do not hardcode in nodes). Gmail API: Credentials from Google Cloud Console with Gmail scope. Telegram API: Bot token from BotFather on Telegram. Installation Steps 📦 Import the Workflow: Copy the workflow JSON from the "Template Code" section below. Import it into n8n via "Import from File" or "Import from URL". Configure Credentials: Add API credentials in n8n’s Credentials section for OpenAI, SerpApi, Gmail, and Telegram. Assign credentials to respective nodes. For example: In the SerpApi Google Search node, use n8n credentials for SerpApi: api_key={{ $credentials.SerpApiKey }}. In the Send Research PDF on Gmail node, use Gmail credentials. In the Send PDF to Telegram node, use Telegram bot credentials. Set Up Nodes: OpenAI Nodes (Research AI Agent, OpenAI Chat Model, OpenAI Chat Middle Memory): Update the model (e.g., gpt-4o) and prompt as needed. Input Validation (Input Validation node): Ensure your input query format matches the expected structure (e.g., topic: "AI ethics"). Delivery Options (Send Research PDF on Gmail, Send PDF to Telegram): Configure recipient email or Telegram chat ID. Test the Workflow: Run the workflow by clicking the "Test Workflow" node. Verify that the research report PDF is generated and sent via Gmail or Telegram. How It Works High-Level Steps 🔍 Query Refinement**: Refines the input query for better research. Aggregate Data**: Fetches data from Wikipedia, Google Search, and SerpApi. Generate Report**: Uses OpenAI to create a structured report. Convert to PDF**: Converts the report to HTML, then PDF. Deliver Report**: Sends the PDF via Gmail or Telegram. Detailed descriptions are available in the sticky notes within the workflow screenshot above. Node Names and Actions Research and Report Generation Test Workflow: Triggers the workflow for testing. Input Validation: Validates the input query. Query Refiner: Refines the query for better results. Research AI Agent: Coordinates research using OpenAI. OpenAI Chat Model: Generates content for the report. Structured Output Parser: Parses OpenAI output into structured data. OpenAI Chat Middle Memory: Retains context during research. Wikipedia Google Search: Fetches data from Wikipedia. SerpApi Google Search: Fetches data via SerpApi. Merge Split Items: Merges data from multiple sources. Aggregate: Aggregates all research data. Generate PDF HTML: Creates an HTML report. Convert HTML to PDF: Converts HTML to PDF. Download PDF: Downloads the PDF file. Send PDF to Telegram: Sends the PDF via Telegram. Send Research PDF on Gmail: Sends the PDF via Gmail. Customization Tips Expand Data Sources** 📡: Add more sources (e.g., academic databases) by adding nodes to Merge Split Items. Change Report Style** ✍️: Update the Generate PDF HTML node to modify the HTML template (e.g., adjust styling or sections). Alternative Delivery** 📧: Add nodes to send the PDF via other platforms (e.g., Slack). Adjust AI Model** 🧠: Modify the OpenAI Chat Model node to use a different model (e.g., gpt-3.5-turbo).

How it works Continuous monitoring**: Real-time surveillance of supplier performance, financial health, and operational status Risk scoring**: AI-powered assessment of supplier risks across multiple dimensions (financial, operational, geopolitical) Automated alerts**: Instant notifications when supplier risk levels exceed predefined thresholds Contingency activation**: Automatic triggering of backup suppliers and alternative sourcing plans Set up steps Supplier database**: Connect your ERP/procurement system with complete supplier information Financial data sources**: Integrate with credit monitoring services (Dun & Bradstreet, Experian) News monitoring**: Configure news APIs for real-time supplier-related news tracking Performance metrics**: Set up KPIs tracking (delivery times, quality scores, compliance) Alert systems**: Configure Slack, Teams, or email notifications for risk alerts Backup protocols**: Define alternative supplier activation procedures Key Features 🔍 360° supplier visibility**: Complete view of supplier ecosystem health and performance ⚡ Real-time risk detection**: Immediate identification of potential supply chain disruptions 📊 Predictive analytics**: Forecasting potential supplier issues before they impact operations 🚨 Automated escalation**: Risk-based alert system with appropriate stakeholder notifications 📈 Performance benchmarking**: Continuous comparison against industry standards and peers 🔄 Contingency management**: Automated backup supplier activation and procurement rerouting 🌍 Geopolitical monitoring**: Tracking of regulatory changes and political risks by region 💰 Cost impact analysis**: Financial impact assessment of supplier disruptions Risk categories monitored Financial stability**: Credit scores, payment delays, bankruptcy indicators Operational performance**: Delivery reliability, quality metrics, capacity utilization Compliance status**: Regulatory adherence, certifications, audit results Geopolitical risks**: Political instability, trade restrictions, regulatory changes Environmental factors**: Natural disasters, climate risks, sustainability metrics Cyber security**: Security breaches, data protection compliance Automated responses Low risk (0-30)**: Routine monitoring and performance tracking Medium risk (31-60)**: Enhanced monitoring with supplier engagement High risk (61-80)**: Immediate supplier contact and mitigation planning Critical risk (81-100)**: Emergency protocols and backup supplier activation Integration capabilities ERP systems**: SAP, Oracle, Microsoft Dynamics for procurement data Risk platforms**: Resilinc, Riskmethods, Prewave for specialized risk intelligence Financial services**: Credit monitoring and financial health assessment News APIs**: Real-time news monitoring and sentiment analysis Communication tools**: Slack, Teams, email for stakeholder notifications This workflow provides comprehensive supply chain visibility and proactive risk management, enabling companies to maintain operational continuity while minimizing disruption costs.

How It Works – Your AI Marketing Team in Action This automation acts as your AI-powered content and image marketing assistant inside Telegram. With just a voice note or text message, it can: 🧠 Understand your request – Whether you send a message or speak into Telegram, it transcribes and processes your input using GPT-4. 🎨 Create and edit content – Based on what you say, it can generate: ✍️ Blog posts 💼 LinkedIn posts 🎬 Faceless videos 🖼️ AI-generated images 🪄 Edits to existing images 🔎 Searches through your image database 💬 Replies directly in Telegram – It sends you back the result—whether that’s a post, image, or video link—without leaving the app. 🧩 Built using LangChain agent logic – It intelligently chooses the right tool from a suite of sub-workflows like "Create Image", "Blog Post", or "Video" using agent reasoning. 🛠️ Setup Steps – Get Started in Minutes! ⌛ Time Estimate: ~15–30 minutes (faster if you're familiar with n8n) 🔗 1. Import the Template Pack 📥 Download and install these workflows into your n8n: Create Image, Edit Image, Search Images Blog Post, LinkedIn Post, Video 🔐 2. Add Required Credentials Telegram Bot 🤖 OpenRouter AI 🧠 Tavily API (for smart research) 📚 ElevenLabs 🎙️ (for voice in videos) PiAPI & Runway 🎞️ (for faceless videos) 🧩 3. Link the Tools to the Agent Node – Make sure the "Marketing Team Agent" is connected to each of the content creation tools as shown in the workflow. 📎 4. Download Templates & Logs 🧾 Google Sheets Log Template (to track output) 🖼️ Creatomate Template (optional for enhanced image control – shared in Skool group) 📌 Pro Tip: All detailed step-by-step setup instructions are included as sticky notes inside the n8n canvas. Just follow along!

SEO Blog Generator with GPT-4o, Perplexity, and Telegram Integration This workflow helps you automatically generate SEO-optimized blog posts using Perplexity.ai, OpenAI GPT-4o, and optionally Telegram for interaction. 🚀 Features 🧠 Topic research via Perplexity sub-workflow ✍️ AI-written blog post generated with GPT-4o 📊 Structured output with metadata: title, slug, meta description 📩 Integration with Telegram to trigger workflows or receive outputs (optional) ⚙️ Requirements ✅ OpenAI API Key (GPT-4o or GPT-3.5) ✅ Perplexity API Key (with access to /chat/completions) ✅ (Optional) Telegram Bot Token and webhook setup 🛠 Setup Instructions Credentials: Add your OpenAI credentials (openAiApi) Add your Perplexity credentials under httpHeaderAuth Optional: Setup Telegram credentials under telegramApi Inputs: Use the Form Trigger or Telegram input node to send a Research Query Subworkflow: Make sure to import and activate the subworkflow Perplexity_Searcher to fetch recent search results Customization: Edit prompt texts inside the Blog Content Generator and Metadata Generator to change writing style or target industry Add or remove output nodes like Google Sheets, Notion, etc. 📦 Output Format The final blog post includes: ✅ Blog content (1500-2000 words) ✅ Metadata: title, slug, and meta description ✅ Extracted summary in JSON ✅ Delivered to Telegram (if connected) Need help? Reach out on the n8n community forum

Build a Weekly AI Trend Alerter with arXiv and Weaviate Ditch the endless scroll for AI trends. Meet Archi, your personal AI research assistant that hits you up once a week with everyone you need to know. 🧑🏽‍🔬 This workflow scrapes AI and machine learning article abstracts from arXiv, enriches them with topic categories using a LLM, and embeds them in a Weaviate vector store. The vector store is then used as a tool for agentic RAG to write a concise, easy-to-read summary of the week in AI research. The final output is a short, weekly email sent to the address of your choice that summarizes key AI research trends and future research directions, with links directly to the most interesting and impactful arXiv papers of the week. Who it's for This workflow is for anyone who can't keep up with all the latest AI advances. Coding skills are not required. How it works This is a contiguous workflow that can be summarized in two main parts: a data pipeline that fetches and embeds articles in Weaviate, and an agentic workflow that generates a weekly email summary. Part 1: Automatically fetch newly published articles on a weekly basis Fetch article abstracts (and metadata) from arXiv's free API Pre-process abstract data Enrich each article with a primary topic, secondary topics, and estimated potential impact of the research using a LLM Post-process data Insert data and embeddings into Weaviate Part 2: Use an AI Agent and Weaviate to generate a weekly summary email Add Weaviate as a Tool to an AI agent node Query Weaviate, agentically, to generate a report on the most important research trends of the week Post-process data Send the summary via email Prerequisites An existing Weaviate cluster. You can view instructions for setting up a local cluster with Docker here or a Weaviate Cloud cluster here. API keys to generate embeddings and power chat models. We use a combination of OpenRouter and OpenAI models. Feel free to switch out the models as you like. An email address with STMP privileges. This is the address the email will come from. In this demo we use a personal Gmail address. You can create a new credential to link a STMP Account using these instructions. Self-hosted n8n instance. See this video for how to get set up in just three minutes. How to run the workflow Go through the prerequisites, creating a Weaviate cluster (can be local or cloud), downloading self-hosted n8n, creating STMP privileges for your email account, and adding your API keys and other credentials. Select the embedding and chat models you'd like to use. Enter the email addresses you want to send the email from and to. Let it rip. Workflow output The output for this workflow is a weekly email that summarizes key research trends and future research directions based on AI and ML papers published on arXiv. Here's an example of a summary email: Hey there, Here's a quick rundown of the key trends in Machine Learning research from the past week. * Key Research Trends This Week* This week saw significant advancements in retrieval-augmented systems, foundation models for specialized domains, and techniques balancing efficiency with performance. Advanced RAG Architectures**: Researchers are developing sophisticated RAG frameworks that go beyond simple document retrieval, with AdaPCR introducing passage combination retrieval and UrbanMind proposing a framework for urban intelligence with multilevel optimization. Foundation Models for Tabular Data**: The Real-TabPFN shows that targeted continued pre-training on real-world datasets can significantly boost the performance of foundation models for tabular data, outperforming models trained on broader, potentially noisier datasets. Efficiency-Focused Techniques**: Researchers are developing resourceful methods that maintain performance without expensive computations, like logit reweighting for topic-focused summarization and strategic querying for privacy-preserving personalization. * Future Research Directions* Based on current trends, we expect to see the following developments in the near future: Explainable RAG Systems**: Following the source attribution work in RAG systems, we can expect more research into making complex retrieval systems transparent and explainable for users. Cross-Domain and Cross-Modal Fusion**: The promising performance of vision-language and code-specialized LLMs in retrieval tasks points toward unified retrievers capable of handling text, code, images, and multimodal content. Data-Centric Synthetic Generation**: As shown by work on synthetic relational tabular data, we'll likely see more sophisticated approaches to generating high-quality synthetic data for pre-training foundation models in specialized domains. This week highlights how researchers are making AI more efficient, explainable, and applicable to specialized domains. Look out for more developments in RAG systems, tabular foundation models, and privacy-preserving AI techniques in the coming weeks. Until next week, Archi Want to make it better? Feel free to tweak, build on, or completely reconfigure this workflow. If you come up with something cool, let us know and we might just share it with our community! 💚

How it works Regulatory monitoring**: Continuously tracks changes in laws, regulations, and compliance requirements across multiple jurisdictions Contract analysis**: AI-powered review of existing contracts to identify compliance gaps and risks Automated alerts**: Real-time notifications when regulatory changes affect your contracts or business operations Compliance reporting**: Generates audit-ready reports and documentation for regulatory compliance Set up steps Legal databases**: Connect to legal research platforms (Westlaw, LexisNexis, EUR-Lex) Contract repository**: Integrate with your contract management system or document storage Regulatory feeds**: Configure government and regulatory body RSS feeds and APIs AI legal analysis**: Set up OpenAI or specialized legal AI for contract analysis Compliance calendar**: Integrate with calendar systems for deadline tracking Audit trail**: Configure logging and documentation systems for compliance records Key Features 🔍 Multi-jurisdiction monitoring**: Tracks regulatory changes across different countries and regions 📊 Risk assessment**: Automatically scores compliance risks and potential impact ⚡ Real-time alerts**: Instant notifications when regulations affecting your business change 📋 Gap analysis**: Identifies discrepancies between current contracts and new requirements 🤖 AI-powered analysis**: Uses natural language processing to understand legal text 📈 Compliance dashboard**: Visual overview of compliance status across all contracts 🔄 Automated remediation**: Suggests contract amendments and compliance actions 📱 Mobile notifications**: Critical compliance alerts on mobile devices Compliance areas monitored Data protection**: GDPR, CCPA, and other privacy regulations Financial services**: Banking regulations, securities law, anti-money laundering Healthcare**: HIPAA, medical device regulations, pharmaceutical compliance Employment law**: Labor regulations, workplace safety, discrimination laws Environmental**: ESG requirements, environmental protection regulations Industry-specific**: Sector-specific regulations and standards Contract types supported Vendor agreements**: Supplier contracts and service agreements Employment contracts**: Employee agreements and contractor terms Data processing agreements**: Privacy and data handling contracts Customer agreements**: Terms of service and customer contracts Partnership agreements**: Joint ventures and strategic partnerships Licensing agreements**: Software licenses and intellectual property Automated responses Low risk (0-30)**: Routine monitoring and documentation Medium risk (31-60)**: Enhanced review and stakeholder notification High risk (61-80)**: Immediate legal review and action planning Critical risk (81-100)**: Emergency legal intervention and compliance measures Integration capabilities Legal research**: Westlaw, LexisNexis, Bloomberg Law Document management**: SharePoint, Google Drive, Dropbox Contract systems**: DocuSign, PandaDoc, ContractWorks Communication tools**: Slack, Teams, email for legal team alerts Calendar systems**: Outlook, Google Calendar for compliance deadlines This workflow ensures continuous legal compliance by monitoring regulatory changes and automatically assessing their impact on your contracts and business operations.

This n8n template helps you build a full AI-powered LinkedIn content generator with just a few clicks. Paired with the free WeWeb UI template, it becomes a ready-to-use web app where users can: Add their own OpenAI API key Customize the prompt and define 6 content topics Edit the AI-generated topics Choose when to generate LinkedIn posts, complete with hashtags and an optional image Who This Is For Perfect for marketers, indie hackers, and solopreneurs who want to build their personal brand on LinkedIn while staying in control of what gets posted. 🧠 What Makes This Different Unlike most AI agents, you stay fully in control: You define the tone and focus via the prompt. You choose which topics to keep or modify. You decide when to generate a post. You can build on top of this and create your own SaaS product. It’s also modular and extendable—hook it up to your backend, add user login, or feed AI improvements based on user input. ⚙️ How It Works Triggering Events: The app includes 3 pre-configured triggers, ready to be hooked into your WeWeb frontend. Just update the webhook URLs after duplicating the n8n workflow. Topic Generation: A call is made to OpenAI (GPT-4) to generate topic ideas based on your prompt. Post Creation: Once topics are approved or edited, GPT-4 writes full posts with suggested hashtags. Image Generation (Optional): If enabled, a DALL·E call generates a relevant image. Everything Stays Local: All data and images are handled locally, no cloud storage setup needed. 🧪 Requirements & Setup No fancy infrastructure required. Here’s what helps you get started: Free WeWeb account** (recommended) to use the frontend UI template OpenAI account** with API access (for GPT-4 and DALL·E) n8n account** (self-hosted or cloud) to run the backend workflow The template is completely free to use. Since each user adds their own OpenAI API key, you don't need to worry about usage costs or rate limits on your end. 🔧 Want to Go Further? This setup is beginner-friendly, but developers can: Add user accounts Save post history Feed user feedback back into the prompt logic Launch their own branded version as a SaaS

Keep your Hostinger VPS servers secure with automated backups! This n8n (self-hosted) workflow for is designed to create daily snapshots and send server metrics effortlessly, ensuring you always have an up-to-date recovery copy. Key Features: ✅ Automated Snapshots: Daily execution with zero manual intervention. ✅ Smart Replacement: Hostinger allows only 1 snapshot per VPS—the workflow automatically replaces the previous one. ✅ Notifications: Alerts via WhatsApp (Evolution API) or other configurable channels for execution confirmation. Quick Setup: Prerequisites: Install the Community Node n8n-nodes-hostinger-api and n8n-nodes-evolution-api in your n8n instance. Generate a Hostinger API Key in their dashboard: hpanel.hostinger.com/profile/api. Workflow Configuration: Add the Hostinger API credential in the first node and reuse it across the workflow. Customize the schedule (e.g., daily at 2 AM) and notification method (Evolution API for WhatsApp, email, etc.). Important Note: Hostinger overwrites the previous snapshot with each new execution, keeping only the latest version. VPS Metrics avaliables (send in messages): 🔹Status: snapshot status 🔹Date: snapshot date time 🔹Server: server name 🔹IP: external server IP ⚙️ Métrics: 🔹 Number of vCPUs 🔹 Ram usage / avaliable 🔹 Hard Disk usage / avaliable 🔹 Operational Sys and version 🔹 Uptime time (days, hours)

🖼️ Text-to-Image Generator using n8n + Flux AI This n8n workflow automates image generation from text prompts using the Text-to-Image Flux AI API. It reads prompts from Google Sheets, generates images via API, uploads them to Google Drive, and logs the outcome. 🌟 Key Features Integrates with Text-to-Image Flux AI on RapidAPI Converts base64 image data to downloadable files Stores images on Google Drive Updates logs and errors back into Google Sheets Skips prompts already processed 📄 Google Sheet Column Structure Your source Google Sheet should include the following columns: | Column Name | Description | |-------------------|--------------------------------------------------| | Prompt | The text prompt to generate an image from | | drive path | (Optional) File path or URL of saved image | | Generated Date | Date/time the image was generated | | Base64 | Base64 string or error message (for logging) | Only rows with a non-empty Prompt and empty drive path will be processed. 📌 Use Case Perfect for: Bulk AI image generation for content marketing Creative automation with prompt-based image creation Building image assets based on structured datasets Any workflow where prompts are tracked via Google Sheets Uses the Text-to-Image Flux AI API to generate high-quality images on demand. 🔧 Workflow Summary | Step | Node | Description | |------|------|-------------| | 1 | Manual Trigger | Manually start the workflow | | 2 | Google Sheets2 | Reads prompts from Google Sheets | | 3 | Loop Over Items | Processes rows one by one | | 4 | If2 | Skips rows that already have images | | 5 | HTTP Request1 | Calls Text-to-Image Flux AI via RapidAPI | | 6 | Code1 | Converts base64 image to binary file | | 7 | Google Drive1 | Uploads the image file to a Drive folder | | 8 | Google Sheets1 | Logs base64 result and timestamp back | | 9 | If1 | Handles errors from the API | | 10 | Google Sheets4 | Logs errors to the sheet | | 11 | Wait | Adds delay between batches to prevent rate-limiting | 🚀 RapidAPI: Text-to-Image Flux AI This flow is powered by Text-to-Image Flux AI. Be sure to: Sign up at RapidAPI and subscribe to the API. Copy your API Key. Replace "your key" in the HTTP Request1 node’s x-rapidapi-key header. You can test the API directly here before connecting it to n8n. ✅ Tips for Setup Ensure you’ve set up a Google Service Account with access to both Sheets and Drive. Fill only the Prompt column — leave drive path and Base64 empty for new prompts. Monitor your RapidAPI dashboard for usage and quota. Create your free n8n account and set up the workflow in just a few minutes using the link below: 👉 Start Automating with n8n Save time, stay consistent, and grow your LinkedIn presence effortlessly!

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=<user>@<realm>!<token-id>=<token-value> 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

Overview This template is designed for Amazon sellers and advertisers who want to automate their campaign performance analysis and bidding strategy. It solves the common challenge of manually reviewing Sponsored Products reports and guessing how to adjust keywords, placements, and budgets. By combining Amazon Advertising reports with OpenAI's GPT-4o, this workflow delivers real-time, personalized optimization instructions — automatically. Features 📥 Automatically downloads Sponsored Products reports from Google Drive 🧠 Uses AI to analyze campaign, keyword, placement, targeting, and budget performance 📊 Supports both .csv and .xlsx report formats 🔁 Handles multiple ASINs and scales easily across ad accounts 📧 Sends structured optimization recommendations to your inbox via Gmail 🗂 Built-in logic to normalize filenames and correctly map reports 🧹 Includes error handling and formatting cleanup for AI-ready input Requirements To use this workflow, you’ll need: An Amazon Ads account with access to Sponsored Products reports A Google Drive folder where Amazon Ads reports are delivered (manually or via Gmail automation) A Gmail account (for sending summaries) An OpenAI API key with access to GPT-4o Optional: a developer account for the Amazon Ads API to fully automate report generation in the future Setup Instructions 📂 Connect your Amazon Ads reports folder in the Google Drive node 🔐 Add your credentials to the OpenAI and Gmail nodes 📝 Schedule five reports in the Amazon Ads Console: Search Term Report → Detailed Targeting Report → Detailed Campaign Report → Summary Placement Report → Summary Budget Report → Summary Use “Last 30 Days”, “Daily”, and .xlsx or .csv format 🔁 (Optional) Automate report ingestion using Gmail + Drive workflows 🧪 Test with one account, then replicate across additional ad accounts as needed ⏱️ Setup time: 15–30 minutes 📌 All field-specific guidance is included in workflow notes`

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Who is this for? IT teams and support organizations looking to automate Level 1 support with AI-powered assistance while maintaining proper ticket management workflows. What problem does this solve? Eliminates repetitive manual support tasks by providing instant, context-aware assistance that references organizational knowledge and creates structured tickets when needed. What this workflow does RAG Pipeline**: Processes PDF/CSV documents into searchable vector database Intelligent Slack Bot**: This AI-helpdesk assistant handles support requests with thread-aware conversations Vector Knowledge Search**: Searches embedded knowledge base articles and historical case data JIRA Integration**: Creates, searches, and manages support tickets automatically Emoji Reactions**: Users can trigger actions (create tickets, escalate) via emoji reactions Requirements Required Accounts: n8n Cloud or self-hosted instance Slack workspace with admin access Supabase account (vector database) JIRA Cloud instance OpenAI API key Technical Prerequisites: Basic n8n workflow knowledge Slack app creation experience Understanding of vector databases Setup Steps 1. Slack App Configuration Create new Slack app with Bot Token Scopes: app_mentions:read, channels:history, channels:read, groups:history, groups:read, im:history, im:read, mpim:history, mpim:read, users:read Configure Event Subscriptions: app_mention, message.channels, message.groups, reaction_added Set Request URL to your n8n Slack Trigger webhook 2. Supabase Vector Database Setup Create new Supabase project Enable pgvector extension Create documents table with vector column (1536 dimensions for OpenAI embeddings) Configure RLS policies for secure access 3. JIRA Configuration Generate API token from JIRA Cloud Create helpdesk project with appropriate issue types Note project ID and issue type IDs for workflow configuration 4. n8n Workflow Configuration Import workflow and configure credentials Update Slack channel IDs in trigger nodes Set OpenAI API key in all OpenAI nodes Configure Supabase connection in vector store nodes Update JIRA project settings in MCP server nodes 5. Knowledge Base Data Format Supported file formats: PDF, CSV CSV Structure: Structure your data with columns, but not limited to, Ticket#, Issue Description, Issue Summary, Resolution Provided, Case Status, Contact User PDF Content: Technical documentation, troubleshooting guides, policy documents Upload documents via the form trigger to automatically embed in vector database. Customization Options AI Agent Behavior Modify system prompt in AIHelpdesk Agent node Adjust conversation memory window (default: 20 messages) Change AI model (GPT-4o, GPT-3.5-turbo, etc.) Reaction Mappings Customize emoji-to-action mappings in Reaction Handler code Add new reaction types for department-specific workflows Configure escalation rules and priority levels JIRA Integration Customize ticket templates and fields Add auto-assignment rules based on issue type Configure SLA and priority mappings Vector Search Adjust similarity thresholds for knowledge retrieval Modify search result limits and relevance scoring Add metadata filtering for departmental knowledge bases Advanced Features Thread-aware conversation memory Automatic bot loop prevention Context-preserving ticket creation Multi-modal file processing (PDF + CSV) Scalable MCP architecture for tool integration Use Cases Level 1 IT Support**: Automate common troubleshooting workflows Employee Onboarding**: Answer policy and procedure questions Internal Help Desk**: Route and track internal service requests Knowledge Management**: Make organizational knowledge searchable and actionable Template includes Complete Slack integration with thread support RAG pipeline for document processing Vector similarity search implementation JIRA ticket lifecycle management Emoji reaction-based user interactions Comprehensive error handling and validation