Workflow Templates

Who is this for? This workflow is perfect for: Businesses and teams who need an automated solution to organize, analyze, and retrieve insights from their internal documents. Researchers who want to quickly analyze and query large collections of research papers, reports, or datasets. Customer support teams looking to streamline access to product documentation and support resources. Legal and compliance professionals needing to reference and query legal documents with confidence. AI enthusiasts and developers wanting to implement Retrieval-Augmented Generation (RAG) systems without starting from scratch. What problem is this workflow solving? Manually organizing, processing, and searching through documents can be time-consuming, error-prone, and inefficient. This workflow solves that by: Automating document processing** from Google Drive, supporting multiple formats like PDFs, CSVs, and Google Docs. Extracting, chunking, and enhancing document text**, preserving context and improving AI comprehension. Storing vector embeddings** in a secure, scalable Supabase vector database, enabling semantic search and retrieval. Providing an interactive AI chat interface** that allows users to ask natural language questions and get precise, document-based answers. This means teams can quickly access relevant insights from their document repositories—boosting productivity and ensuring accurate information retrieval. Key Features 🚀 End-to-End Document Processing: From Google Drive upload detection to vector embedding and storage. 🔍 Semantic Search & Retrieval: Users can ask complex, natural-language questions and receive contextually relevant answers. 🤖 AI-Powered Summaries & Metadata: Automatically generates document titles and summaries using Google Gemini AI. 📝 Smart Chunking & Contextual Enhancement: Breaks documents into smart chunks with overlap, preserving context and table integrity. 🔐 Secure & Scalable Vector Database: Stores and retrieves embeddings in a Supabase vector store for fast, reliable searches. 💬 Conversational AI Interface: Uses OpenAI to power natural, accurate, and cost-effective AI chat interactions. How does this workflow work? Monitors Google Drive for new files Extracts text from PDFs and CSVs (or Google Docs auto-converted) Splits text into context-preserving chunks Enhances chunk quality and stores embeddings in Supabase Enables natural language search and AI-powered chat interactions with the stored documents Typical Use Cases 📚 Corporate Knowledge Base 🔬 Research Paper Analysis 📞 Customer Support Document Query ⚖️ Legal Document Review and Analysis 🔍 Internal Team Documentation Search Why You’ll Love It This workflow lets you build a scalable, searchable, and AI-powered document system—without needing to write complex code or manage multiple systems. With this, you can: Stay organized with automated document processing. Deliver faster, more accurate answers to user queries. Reduce manual work and improve productivity. Gain a competitive edge with cutting-edge AI search capabilities. Setup Requirements An n8n instance with Google Drive, Supabase, OpenAI, and Gemini credentials configured. Access to a Supabase vector store for storing document embeddings. Configurable chunk size, overlap, and processing limits (default: 1000 characters per chunk, 20 chunks max).

This workflow automates the entire process of creating SEO-optimized meta titles and descriptions. It analyzes your webpage, spies on top-ranking competitors for the same keywords, and then uses a multi-step AI process to generate compelling, length-constrained meta tags. 🤖 How It Works This workflow operates in a three-phase process for each URL you provide: Phase 1: Self-Analysis When you add a URL to a Google Sheet with the status "New", the workflow scrapes your page's content. The first AI then performs a deep analysis to identify the page's primary keyword, semantic keyword cluster, search intent, and target audience. Phase 2: Competitor Intelligence The workflow takes your primary keyword and performs a live Google search. A custom code block intelligently filters the search results to identify true competitors. A second AI analyzes their meta titles and descriptions to find common patterns and successful strategies. Phase 3: Master Generation & Update The final AI synthesizes all gathered intelligence—your page's data and the competitor's winning patterns—to generate a new, optimized meta title and description. It then writes this new data back to your Google Sheet and updates the status to "Generated". ⚙️ Setup Instructions You should be able to set up this workflow in about 10-15 minutes ⏱️. 🔑 Prerequisites You will need the following accounts and API keys: A Google Account with access to Google Sheets. A Google AI / Gemini API key. A SerpApi key for Google search data. A ScrapingDog API key for reliable website scraping. 🛠️ Configuration Google Sheet Setup: Create a new Google Sheet. The workflow requires the following columns: URL, Status, Current Meta Title, Current Meta Description, Generated Meta Title, Generated Meta Description, and Ranking Factor. Add Credentials: Google Sheets Nodes: Connect your Google account credentials to the Google Sheets Trigger & Google Sheets nodes. Google Gemini Nodes: Add your Google Gemini API key to the credentials for all three Google Gemini Chat Model nodes. Scrape Website Node: In this HTTP Request node, go to Query Parameters and replace <your-api-key> with your ScrapingDog API key. Googl SERP Node: In this HTTP Request node, go to Query Parameters and replace <your-api-key> with your SerpApi API key. Configure Google Sheets Nodes: Copy the Document ID from your Google Sheet's URL. Paste this ID into the "Document ID" field in the following nodes: Google Sheets Trigger, Get row(s) in sheet1, and Update row in sheet. In each of those nodes, select the correct sheet name from the "Sheet Name" dropdown. ✅ Activate Workflow Save and activate the workflow. To run it, simply add a new row to your Google Sheet containing the URL you want to process and set the "Status" column to New.

AI powered Automated Crypto Insights with Chart-img and BrowserAI Tired of paying for costly crypto updates? Or reading long analyses? This n8n workflow automates the delivery of personalized crypto insights, using Chart-img for capturing coin graphs of BTC, ETH, SOL, and XRP as base64 images, and BrowserAI for web scraping and information gathering of news and articles. This setup ensures thorough market coverage and timely updates, without breaking the bank. Overview Designed for crypto enthusiasts, traders, and analysts, this workflow automates the process of collecting and distributing valuable crypto information. It’s perfect for anyone wanting consistent and accurate updates conveniently. Setup Instructions Pre-conditions Chart-img Account: Register for a Chart-img account and obtain an API key here. BrowserAI Account: Sign up for BrowserAI and get your API key from your BrowserAI dashboard. Step-by-Step Setup 🗓️ Schedule and Date Calculation Triggers twice daily at 8AM and 8PM to ensure up-to-date insights, and can be changed to your like. Calculates yesterday’s date dynamically for accurate data retrieval. 📊 Coin Graph Capture with Chart-img Uses Chart-img API to capture 24-hour graphs for BTC, ETH, SOL, and XRP. Converts images to base64 strings for easy integration into analysis. 🌐 Web Scraping with BrowserAI Creates tasks in BrowserAI to gather the latest crypto news and insights. Automates data extraction for comprehensive market analysis. ⌛ Monitor and Complete Tasks Incorporates status checks to ensure BrowserAI tasks complete successfully before proceeding. ✏️ Analyze and Synthesize Information Combines graph data with web-scraped insights for an enriched summary. Uses AI to generate simple, informative descriptions under 60 words to not overload you. 📩 Deliver Insights Efficiently Sends the compiled analysis to your Telegram, with easy options to switch to WhatsApp, email, or any other communication channel. Customization Guidance Content Personalization:** Customize the datasets and keywords for tailored updates. Modify Schedule:** Adjust triggering times according to your needs using n8n’s scheduling options. This workflow delivers a seamless and cost-effective approach to staying informed about crypto market trends, combining the latest technology for superior insights. ++WARNING:++ This template is intended for personal use only and does not constitute financial advice. Any actions taken using this tool are solely the user's responsibility.

🧠 Who is this for? Startup founders designing creative growth strategies Marketing teams seeking low-cost, high-impact campaigns Consultants and agencies needing fast guerrilla plans Creators exploring AI-powered content and campaigns ❓ What problem does this workflow solve? Building a full guerrilla marketing strategy usually takes hours of brainstorming, validation, and formatting. This template does all of that in minutes using a swarm of AI agents, from idea generation to KPIs, and even kills bad ideas before you waste time on them. ⚙️ What this workflow does Starts with a chat input where you describe your business or idea A “Swarm Intelligence” loop: One AI agent generates guerrilla ideas Another agent critically validates the idea and gives honest feedback If the idea is weak, it asks for a new one If accepted, the swarm continues with 16 AI specialists generating: 🎯 Objectives 🧍‍♂️ Personas 🎤 Messaging 🧨 Tactics 📢 Channels 🧮 Budget 📊 KPIs 📋 Risk plan and more Merges all chapters into a final Markdown file Lets you download the campaign in seconds 🛠️ Setup Import the workflow to your n8n instance (Optional) Configure your LLM (OpenAI or Ollama) in the “OpenAI Chat Model” node Type your business idea (e.g., “Luxury dog collar brand for Instagram dads”) Wait for flow completion Download the final marketing plan file 🤖 LLM Flexibility (Choose Your Model) Supports any LLM via LangChain: Ollama (LLaMA 3.1, Mistral, DeepSeek) OpenAI (GPT-4, GPT-3.5) To switch models, just replace the “Language Model” node, no other logic needs updating 📌 Notes Output is professional and ready-to-pitch Built-in pessimistic validator filters out bad ideas before wasting time 📩 Need help? Email: sinamirshafiee@gmail.com Happy to support setup or customization!

Who is this for? Content creators, digital marketers, and social media managers who want to automate the creation of short-form videos for platforms like TikTok, YouTube Shorts, and Instagram Reels without extensive video editing skills. What problem does this workflow solve? Creating engaging short-form videos consistently is time-consuming and requires multiple tools and skills. This workflow automates the entire process from ideation to publishing, significantly reducing the manual effort needed while maintaining content quality. What this workflow does This all-in-one solution transforms ideas into fully produced short-form videos through a 5-step process: Generate video captions from ideas stored in a Google Sheet Create AI-generated images using Flux and the OpenAI API Convert images to videos using Kling's API Add voice-overs to your content with Eleven Labs Complete the video production with Creatomate by adding templates, transitions, and combining all elements The workflow handles everything from sourcing content ideas to rendering the final video, and even notifies you on Discord when videos are ready. Setup (Est. time: 20-30 minutes) Before getting started, you'll need: n8n installation (tested on version 1.81.4) OpenAI API Key (free trial credits available) PiAPI (free trial credits available) Eleven Labs (free account) Creatomate API Key (free trial credits available) Google Sheets API enabled in Google Cloud Console Google Drive API enabled in Google Cloud Console OAuth 2.0 Client ID and Client Secret from your Google Cloud Console Credentials How to customize this workflow to your needs Adjust the Google Sheet structure to include additional data like video length, duration, style, etc. Modify the prompt templates for each AI service to match your brand voice and content style Update the Creatomate template to reflect your visual branding Configure notification preferences in Discord to manage your workflow This workflow combines multiple AI technologies to create a seamless content production pipeline, saving you hours of work per video and allowing you to focus on strategy rather than production.

An intelligent IT support agent that uses Azure AI Search for knowledge retrieval, Microsoft Entra ID integration for user management, and Jira for ticket creation. The agent can answer questions using internal documentation and perform administrative tasks like password resets. How It Works The workflow operates in three main sections: Agent Chat Interface: A chat trigger receives user messages and routes them to an AI agent powered by Google Gemini. The agent maintains conversation context using buffer memory and has access to multiple tools for different tasks. Knowledge Management: Users can upload documentation files (.txt, .md) through a form trigger. These documents are processed, converted to embeddings using OpenAI's API, and stored in an Azure AI Search index with vector search capabilities. Administrative Tools: The agent can query Microsoft Entra ID to find users, reset passwords, and create Jira tickets when issues need escalation. It uses semantic search to find relevant internal documentation before responding to user queries. The workflow includes a separate setup section that creates the Azure AI Search service and index with proper vector search configuration, semantic search capabilities, and the required field schema. Prerequisites To use this template, you'll need: n8n cloud or self-hosted instance Azure subscription with permissions to create AI Search services Microsoft Entra ID (Azure AD) access with user management permissions OpenAI API account for embeddings Google Gemini API access Jira Software Cloud instance Basic understanding of Azure resource management Setup Instructions Import the template into n8n. Configure credentials: Add Google Gemini API credentials Add OpenAI API credentials for embeddings Add Microsoft Azure OAuth2 credentials with appropriate permissions Add Microsoft Entra ID OAuth2 credentials Add Jira Software Cloud API credentials Update workflow parameters: Open the "Set Common Fields" nodes Replace <azure subscription id> with your Azure subscription ID Replace <azure resource group> with your target resource group name Replace <azure region> with your preferred Azure region Replace <azure ai search service name> with your desired service name Replace <azure ai search index name> with your desired index name Update the Jira project ID in the "Create Jira Ticket" node Set up Azure infrastructure: Run the manual trigger "When clicking 'Test workflow'" to create the Azure AI Search service and index This creates the vector search index with semantic search configuration Configure the vector store webhook: Update the "Invoke Query Vector Store Webhook" node URL with your actual webhook endpoint The webhook URL should point to the "Semantic Search" webhook in the same workflow Upload knowledge base: Use the "On Knowledge Upload" form to upload your internal documentation Supported formats: .txt and .md files Documents will be automatically embedded and indexed Test the setup: Use the chat interface to verify the agent responds appropriately Test knowledge retrieval with questions about uploaded documentation Verify Entra ID integration and Jira ticket creation Security Considerations Use least-privilege access for all API credentials Microsoft Entra ID credentials should have limited user management permissions Azure credentials need Search Service Contributor and Search Index Data Contributor roles OpenAI API key should have usage limits configured Jira credentials should be restricted to specific projects Consider implementing rate limiting on the chat interface Review password reset policies and ensure force password change is enabled Validate all user inputs before processing administrative requests Extending the Template You could enhance this template by: Adding support for additional file formats (PDF, DOCX) in the knowledge upload Implementing role-based access control for different administrative functions Adding integration with other ITSM tools beyond Jira Creating automated escalation rules based on query complexity Adding analytics and reporting for support interactions Implementing multi-language support for international organizations Adding approval workflows for sensitive administrative actions Integrating with Microsoft Teams or Slack for notifications

Automates the process of generating, storing, and publishing engaging LinkedIn posts derived from books (PDFs) using AI and vector search. 🧠 Overview This workflow: Watches a Google Drive folder for new or updated book PDFs. Extracts and embeds the content using OpenAI. Stores the data in a Pinecone vector database. Uses a LangChain agent to generate post ideas. Creates concise LinkedIn posts with hook, insight, CTA. Updates a Google Sheet and posts to LinkedIn. 🛠 Workflow Breakdown 📥 1. Google Drive Trigger Trigger:** Watches a folder for new or updated PDF files. Action:** Downloads the updated PDF. 📄 2. Extract and Embed Content Extract from File:** Parses PDF to extract text. Text Splitter:** Breaks text into chunks. Embeddings (OpenAI):** Converts chunks into vector embeddings. Pinecone Vector Store:** Saves the embeddings with the book name as namespace. 🧠 3. Post Idea Generation (LangChain Agent) Uses a prompt to: Search Pinecone DB Extract insights Format into 5 LinkedIn post ideas with: Hook Insight CTA Memory buffer** and structured output parser are used for clean AI interaction. ✍️ 4. Post Creation Each idea is: Split Rewritten with a GPT model prompt to match LinkedIn tone Styled for under 600 characters Includes emojis, hashtags, and tone guidelines 📊 5. Google Sheet Integration Saves all generated posts to a Google Sheet. Marks status: "published" or "no". 🔁 6. Scheduled Publishing Every day: Pulls an unpublished post Publishes it to LinkedIn Updates the post's status and timestamp in the Google Sheet ⚙️ Setup Guide 📂 Google Drive Create a folder for book PDFs Connect your Google Drive account to n8n Provide access token with file read permission 📊 Google Sheets Create a Google Sheet with columns: bookname, hook, insight, cta, postContent, published, date Add credentials in n8n with read/write permission 🧠 Pinecone Set up a Pinecone project and index (linkdenpost) Namespace will be auto-named using the book filename 🔑 API Credentials Required OpenAI API** (for embeddings and post generation) Pinecone API** (for vector storage and retrieval) LinkedIn OAuth2** (to publish posts) Google Drive & Sheets** credentials 🔁 Flow Summary graph TD A[Google Drive Trigger] --> B[Download PDF] B --> C[Extract Text] C --> D[Text Splitter] D --> E[Create Embeddings] E --> F[Pinecone Vector Store] F --> G[LangChain Agent] G --> H[Structured Output (5 Post Ideas)] H --> I[Split Ideas] I --> J[Format as LinkedIn Post (GPT)] J --> K[Store in Google Sheet] L[Schedule Trigger] --> M[Get Unpublished Post] M --> N[Post to LinkedIn] N --> O[Mark as Published] 🧪 Prompt Example (Used in LangChain Agent) You are a content strategist. Search the Pinecone vector DB containing a book. Generate 5 unique LinkedIn post ideas with: A Hook (curiosity driven) Insight (summary < 100 words) CTA ("Agree or disagree?", etc.) Respond in structured JSON: [ { "Hook": "...", "Insight": "...", "CTA": "..." }, ... ] ✅ Output Sample { "Hook": "Why your lab's results might be invalid 😱", "Insight": "ISO/IEC 17025 stresses that labs must plan and address risks to impartiality and validity.", "CTA": "Does your lab audit for these risks?" } 📆 Schedule Control Uses Schedule Trigger to post daily at a set time. Ensures automation with LinkedIn and accurate Google Sheet syncing. 📝 Notes Posts remain professional and concise for a LinkedIn audience Works with any PDF book Supports multi-book pipelines You can filter and tag books by filename or folder for segmenting post styles

How it works This workflow acts like your own personal AI assistant, automatically fetching and summarizing the most relevant Security, Privacy, and Compliance news from curated RSS feeds. It processes only the latest articles (past 24 hours), organizes them by category, summarizes key insights using AI, and delivers a clean HTML digest straight to your inbox—saving you time every day. Key Highlights Handles three independent tracks: Security, Privacy, and Compliance Processes content from customizable RSS sources (add/remove easily) Filters fresh articles, removes duplicates, and sorts by recency Uses AI to summarize and format insights in a digestible format Sends polished HTML digests via Gmail—one per category Fully modular and extensible—adapt it to your needs Personalization You can easily tailor the workflow: 🎯 Customize feeds: Add or remove sources in the following Code nodes: Fetch Security RSS, Fetch Privacy Feeds, and Fetch Compliance Feeds 🔧 Modify logic: Adjust filters, sorting, formatting, or even AI prompts as needed 🧠 Bring your own LLM: Works with Gemini, but easily swappable for other LLM APIs Setup Instructions Requires Gmail and LLM (e.g., Gemini) credentials Prebuilt with placeholders for RSS feeds and email output Designed to be readable, maintainable, and fully adaptable

Stay ahead of trends by automating your content research. This workflow fetches trending keywords from Google Trends RSS, extracts key insights from top articles, and saves structured summaries in Google Sheets—helping you build a data-driven editorial plan effortlessly. How it works Fetch Google Trends RSS – The workflow retrieves trending keywords along with three related article links. Extract & Process Content – It fetches the content of these articles, cleans the HTML, and generates a concise summary using Jina AI. Store in Google Sheets – The processed insights, including the trending keyword and summary, are saved in a pre-configured Google Sheet. Setup Steps Prepare a Google Sheet – Ensure you have a Google Sheet ready to store the extracted data. Configure API Access – Set up Google Sheets API and any required authentication. Get Jina.ai API key Adjust Workflow Settings – A dedicated configuration node allows you to fine-tune how data is processed and stored. Customization Modify the RSS source to focus on specific Google Trends regions or categories. Adjust the content processing logic to refine how article summaries are created. Expand the workflow to integrate with CMS (e.g., WordPress) for automated content planning. This workflow is ideal for content strategists, SEO professionals, and news publishers who want to quickly identify and act on trending topics without manual research. 🚀 Google Sheets Fields Copy and paste these column headers into your Google Sheet: | Column Name | Description | |------------------------|-------------| | status | Initial status of the keyword (e.g., "idea") | | trending_keyword | Trending keyword extracted from Google Trends | | approx_traffic | Estimated traffic for the trending keyword | | pubDate | Date when the keyword was fetched | | news_item_url1 | URL of the first related news article | | news_item_title1 | Title of the first news article | | news_item_url2 | URL of the second related news article | | news_item_title2 | Title of the second news article | | news_item_url3 | URL of the third related news article | | news_item_title3 | Title of the third news article | | news_item_picture1 | Image URL from the first news article | | news_item_source1 | Source of the first news article | | news_item_picture2 | Image URL from the second news article | | news_item_source2 | Source of the second news article | | news_item_picture3 | Image URL from the third news article | | news_item_source3 | Source of the third news article | | abstract | AI-generated summary of the articles (limited to 49,999 characters) | Instructions Open Google Sheets and create a new spreadsheet. Copy the column names from the table above. Paste them into the first row of your Google Sheet.

🔧 Workflow Summary This system automates LinkedIn lead generation and enrichment in six clear stages: 1. Lead Collection (via Apollo.io) Automatically pulls leads based on keywords, roles, or industries using Apollo’s API. Captures name, job title, company, and LinkedIn profile URL. You can kick off the workflow via form, webhook, WhatsApp, Telegram, or any other custom trigger that passes search parameters. 2. LinkedIn Username Extraction Extracts usernames from LinkedIn profile URLs using a script step. These usernames are required for further enrichment using RapidAPI. 3. Email Retrieval (via Apollo.io User ID) Fetches verified work email using the Apollo User ID. Email validity is double-checked using www.mails.so filtering out undeliverable or inactive emails by checking MX records and deliverability. 4. Profile Summary (via LinkedIn API on RapidAPI) Enriches lead data by pulling bio/summary details to understand their background and expertise. 5. Activity Insights (Posts & Reposts) Collects recent posts or reposts to help craft personalised messages based on what they’re currently engaging with. 6. Leads Sheet Update All data is written into a Google Sheet. New columns are populated dynamically without erasing existing data. ⸻ ✅ Smart Retry Logic Each workflow is equipped with a fail-safe system: Tracks status per row: ✅ done, ❌ failed, ⏳ pending Failed rows are automatically retried after a custom delay (e.g., 2 weeks). Ensures minimal drop-offs and complete data coverage. 📊 Google Sheets Setup Make a copy of the following: Template 1: Apollo Leads Scraper & Enrichment Template 2: Final Enriched Leads The system appends data (like emails, bios, activity) step by step. 🔐 API Credentials Needed 1. Apollo API Sign up and generate API key at Apollo Developer Portal Be sure to enable the “Master API Key” toggle so the same key works for all endpoints. 2. LinkedIn Data API (via RapidAPI) Subscribe at RapidAPI - LinkedIn Data Use your key in the x-rapidapi-key header. 3. Mails.so API Get your API Key from mails.so dashboard 🛠️ Troubleshooting – LinkedIn Lead Machine ✅ Common Mistakes & Fixes 1. API Keys Not Working Make sure API keys for Apollo, RapidAPI, and mails.so are correct. Apollo “Master API Key” must be enabled. Keys should be saved as Generic Credentials in n8n. 2. Leads Not Found Check if the search query (keyword/job title) is too narrow. Apollo might return empty results if the filters are incorrect. 3. LinkedIn URLs Missing or Invalid Ensure Apollo is returning valid LinkedIn URLs. Improper URLs will cause username extraction and enrichment steps to fail. 4. Emails Not Coming Through Apollo may not have verified emails for all leads. mails.so might reject invalid or expired email addresses. 5. Google Sheet Not Updating Make sure the Google Sheet is shared with the right Google account (linked to n8n). Check if the column names match and data isn’t blocked due to formatting. 6. Status Columns Not Changing Each row must have done, failed, or pending in the status column. If the status doesn’t update, the retry logic won’t trigger. 7. RapidAPI Not Returning Data Double-check if username is present and valid. Make sure the RapidAPI plan is active and within limits. 8. Workflow Not Running Check if the trigger node (form, webhook, etc.) is connected and active. Make sure you’re passing the required inputs (keyword, role, etc.). Need Help? Contact www.KrupalPatel.com for support and custom workflow development

How it works This workflow automates the process of checking for and applying updates to a self-hosted n8n instance running on Docker. It runs on a schedule, checks for new versions, summarizes the release notes with AI, and asks for your approval via Telegram before updating. Scheduled Check: The workflow runs hourly, triggered by a Schedule node. Version Discovery: It first confirms it's running in a Docker environment. It uses SSH to connect to the host machine and inspects the running n8n container to find its current version tag (e.g., latest or next). It then queries the Docker Hub API to compare the image digest (a unique ID for an image version) of the running version against the latest available version for that tag. Update Detection: If the digests do not match, it means a new image has been pushed for your version tag (e.g., a new latest image is available), and an update is needed. AI-Powered Release Notes: It fetches the official release notes for the new version from the GitHub API. An AI model (LLM) summarizes these technical notes into a concise, human-readable overview of the key features and fixes. Manual Approval: It sends a message to a Telegram chat with the AI-generated summary and two buttons: "✅ Update" and "❌ Ignore". The workflow then pauses and waits for your response. Execute Update: If you approve the update, the workflow uses SSH to run a docker compose command on your server, which pulls the new image, stops the old containers, and starts the new ones. Set up steps Setup time: ~5-10 minutes SSH Credentials: Go to Credentials and create a new SSH credential with the username, host, and password/private key for the server where your n8n Docker instance is running. Select this credential in the Get n8n Current Version and Update Docker nodes. Telegram Bot Credentials: Create a Telegram Bot and get its API token. Go to Credentials and create a new Telegram credential with your bot's token. Select this credential in the Send a text message node. AI Model Credentials: Ensure you have credentials for an AI provider (like Google AI, OpenAI, etc.) set up. Select your desired credential in the Google Gemini Chat Model node (or replace it with your preferred LLM node). Configure Paths and Commands: Open the Docker Path node. Set the docker_path to the absolute path of your docker-compose.yml file on the server (e.g., /root/n8n). If you use workers, adjust the worker_command to include the correct --scale argument for your setup. If not, you can leave it blank. Set Your Chat ID: Open the Approve Update Telegram node and enter your personal Telegram Chat ID in the Chat ID field. This ensures the approval message is sent to you. Activate the workflow. It will now check for updates every hour. To enable fully automatic updates (without manual approval): Delete the nodes from Get n8n Releases to Approved ? and connect the Needs Update ? node directly to the Update Docker node.

Overview Advanced AI-powered stock analysis workflow that combines multi-timeframe technical analysis with real-time news sentiment to generate actionable BUY/SELL/HOLD recommendations. Uses sophisticated algorithms to process price data, news sentiment, and market context for informed trading decisions. Core Features Multi-Timeframe Technical Analysis 4-Hour Charts** - Intraday trend analysis and entry timing Daily Charts** - Primary trend identification and key levels Weekly Charts** - Long-term context and major trend direction Moving Average Analysis** - 5, 10, and 20-period trend indicators Support/Resistance Levels** - Dynamic price level identification Volume Analysis** - Trading activity and momentum confirmation AI-Powered News Sentiment Analysis Real-Time News Processing** - Latest market-moving headlines Sentiment Scoring** - Numerical sentiment rating (-1 to +1 scale) Impact Assessment** - News relevance to stock performance Multi-Source Analysis** - Comprehensive news coverage evaluation Context-Aware Processing** - Financial market-specific sentiment analysis Intelligent Recommendation Engine Professional Trading Logic** - Multi-timeframe alignment analysis Risk/Reward Calculations** - Minimum 1:2 ratio requirements Entry/Exit Price Targets** - Specific actionable price levels Stop-Loss Recommendations** - Risk management guidelines Confidence Scoring** - Recommendation strength assessment Technical Capabilities Data Sources & APIs TwelveData API** - Professional-grade price and volume data NewsAPI Integration** - Comprehensive news coverage Perplexity AI** - Additional sentiment context and analysis Chart-Img API** - Visual chart generation for analysis Real-Time Processing** - Live market data integration AI Models & Analysis GPT-4 Integration** - Advanced natural language processing Custom Sentiment Engine** - Financial market-tuned sentiment analysis Multi-Model Approach** - Cross-validation of recommendations Algorithmic Trading Logic** - Professional-grade decision frameworks Visual Analysis Tools Interactive Charts** - TradingView-style chart generation Technical Indicators** - Visual representation of analysis Dark Theme Support** - Professional trading interface Multiple Timeframes** - Comprehensive visual analysis Use Cases & Applications Individual Traders Day Trading Signals** - Short-term entry/exit recommendations Swing Trading Analysis** - Multi-day position guidance Risk Management** - Stop-loss and position sizing advice Market Timing** - Optimal entry point identification Investment Research Due Diligence** - Comprehensive stock analysis Sentiment Monitoring** - News impact assessment Technical Screening** - Multi-criteria stock evaluation Portfolio Optimization** - Individual stock recommendations Automated Trading Systems Signal Generation** - Systematic buy/sell/hold alerts Risk Controls** - Automated stop-loss calculations Multi-Asset Analysis** - Scalable across stock universe Backtesting Support** - Historical recommendation validation Financial Advisors & Analysts Client Reporting** - Professional analysis documentation Research Automation** - Streamlined analysis workflow Decision Support** - Data-driven recommendation framework Market Commentary** - AI-generated insights and rationale Key Benefits Professional-Grade Analysis Institutional Quality** - Bank-level analytical frameworks Multi-Dimensional** - Technical + fundamental + sentiment analysis Real-Time Processing** - Live market data integration Objective Decision Making** - Removes emotional bias from analysis Time Efficiency Instant Analysis** - Seconds vs hours of manual research Automated Processing** - Continuous market monitoring Scalable Operations** - Analyze multiple stocks simultaneously 24/7 Availability** - Round-the-clock market analysis Risk Management Built-in Stop Losses** - Automatic risk level calculation Position Sizing** - Risk-appropriate recommendation sizing Multi-Timeframe Validation** - Reduces false signals Conservative Approach** - Defaults to HOLD when uncertain Setup Requirements API Keys Needed TwelveData API - Free tier available at twelvedata.com NewsAPI Key - Free tier available at newsapi.org OpenAI API - For GPT-4 analysis capabilities Perplexity API - Additional sentiment analysis Chart-Img API - Optional chart visualization (chart-img.com) Configuration Steps API Integration - Add your API keys to respective nodes Symbol Format - Supports company names or stock symbols Risk Parameters - Customize stop-loss and target calculations Notification Setup - Configure alert delivery methods Testing & Validation - Verify API connections and data flow Advanced Features Natural Language Processing Company Name Recognition** - Automatic symbol conversion Context Understanding** - Market-aware news interpretation Multi-Language Support** - Global news source analysis Entity Extraction** - Key information identification Error Handling & Reliability API Failure Recovery** - Graceful degradation strategies Data Validation** - Input/output quality checks Rate Limit Management** - Automatic throttling controls Backup Data Sources** - Redundant information feeds Customization Options Timeframe Selection** - Adjustable analysis periods Risk Tolerance** - Configurable risk/reward ratios Sentiment Weighting** - Balance technical vs fundamental analysis Alert Thresholds** - Custom trigger conditions Important Disclaimers This tool provides educational and informational analysis only. All trading decisions should: Consider your personal risk tolerance and financial situation Be validated with additional research and professional advice Account for market volatility and potential losses Follow proper risk management principles Performance Optimization Speed Enhancements Parallel Processing** - Simultaneous data retrieval Caching Strategies** - Reduced API call frequency Efficient Algorithms** - Optimized calculation methods Memory Management** - Scalable resource usage Accuracy Improvements Multi-Source Validation** - Cross-reference data points Historical Backtesting** - Performance validation Continuous Learning** - Algorithm refinement Market Adaptation** - Evolving analysis criteria Transform your investment research with AI-powered analysis that combines the speed of automation with the depth of professional-grade financial analysis.