Workflow Templates

This is the core AI agent used for isra36.com. Don't trust complex AI-generated SQL queries without double-checking them in a safe environment. That's where isra36 comes in. It automatically creates a test environment with the necessary data, generates code for your task, runs it to double-check for correctness, and handles errors if necessary. If you enable auto-fixing, isra36 will detect and fix issues on its own. If not, it will ask for your permission before making changes during debugging. In the end, you get thoroughly verified code along with full details about the environment it ran in. Setup It is an embedded chat for the website, but you can pin input data and run it on your own n8n instance. Input data sessionId: uuid\_v4. Required to handle ongoing conversations and to create table names (used as a prefix). threadId: string | nullable. If aiProvider is openai, conversation history is managed on OpenAI’s side. This is not needed in the first request—it will start a new conversation. For ongoing conversations, you must provide this value. You can get it from the OpenAIMainBrain node output after the first run. If you want to start a new conversation, just leave it as null. apiKey: string. Your API key for the selected aiProvider. aiProvider: string. Currently supported values: openai, openrouter. model: string. The AI model key (e.g., gpt-4.1, o3-mini, or any supported model key from OpenRouter). autoErrorFixing: boolean. If true, it will automatically fix errors encountered when running code in the environment. If false, it will ask for your permission before attempting a fix. chatInput: string. The user's prompt or message. currentDbSchemaWithData: string. A JSON representation of the database schema with sample data. Used to inform the AI about the current database structure during an ongoing conversation. Please use the '[]' value in the first request. Example string for filled db structure : '{"users":[{"id":1,"name":"John Doe","email":"john.d@example.com"},{"id":2,"name":"Jane Smith","email":"jane.s@example.com"}],"products":[{"product_id":101,"product_name":"Laptop","price":999.99}]}' Make sure to fill in your credentials: Your OpenAI or OpenRouter API key Access to a local PostgreSQL database for test execution You can view your generated tables using your preferred PostgreSQL GUI. We recommend DBeaver. Alternatively, you can activate the “Deactivated DB Visualization” nodes below. To use them, connect each to the most recent successful Set node and manually adjust the output. However, the easiest and most efficient method is to use a GUI. Workflow Explanation We store all input values in the localVariables node. Please use this node to get the necessary data. OpenAI has a built-in assistant that manages chat history on their side. For OpenRouter, we handle chat history locally. That’s why we use separate nodes like ifOpenAi and isOpenAi. Note that if logic can also be used inside nodes. The AutoErrorFixing loop will run only a limited number of times, as defined by the isMaxAutoErrorReached node. This prevents infinite loops. The Execute_AI_result node connects to the PostgreSQL test database used to execute queries. Guidance on customization This setup is built for PostgreSQL, but it can be adapted to any programming language, and the logic can be extended to any programming framework. To customize the logic for other programming languages: Change instruction parameter in localVariables node. Replace the Execute_AI_result PostgreSQL node with another executable node. For example, you can use the HTTP Request node. Update the GenerateErrorPrompt node's prompt parameter to generate code specific to your target language or framework. Any workflows built on top of this must credit the original author and be released under an open-source license.

This template monitors Google Drive folder for new files, extracts text from PDFs, images, text files, CSVs, and Google Docs., reads images with meta/llama-3.2-11b-vision-instruct, structures the result with nvidia/llama-3.3-nemotron-super-49b-v1.5, logs everything to Google Sheets, and sends a Telegram notification when processing finishes. ✨ What This Template Does Watches a specific Google Drive folder for new files with Google Drive Trigger. 📂 Downloads each new file with Google Drive before processing. ⬇️ Routes PDFs, images, text files, CSVs, and Google Docs through the correct extraction branch. 🔀 Extracts image text with meta/llama-3.2-11b-vision-instruct 🖼️ Structures extracted content into JSON fields with nvidia/llama-3.3-nemotron-super-49b-v1.5 through NVIDIA NIM. 🤖 Appends the final result to Google Sheets in Extract_Log. 📊 Sends a Telegram notification when processing is complete. 📬 Key Benefits Turns a Drive folder into a reusable intake point for mixed file types. ⏱️ Creates a searchable audit trail in Google Sheets for every processed file. 📚 Sends a lightweight Telegram notification without requiring Telegram as the input channel. ✅ Keeps the extraction and structuring logic reusable for internal ops or client delivery workflows. 🔁 Makes it easier to test multimodal document processing with free-tier NVIDIA NIM models. 💡 Features Google Drive Trigger configured for new files in a specific folder. 📥 Google Drive download step for binary file access before extraction. ⚙️ File-type routing with Switch and normalization with Code nodes. 🧠 Native n8n Extract from File nodes for PDF, TXT, and CSV parsing. 📄 NVIDIA NIM HTTP Request nodes for image OCR and structured JSON generation. 🤖 Google Sheets append logging with a fixed Extract_Log tab schema. 📈 Plain-text Telegram completion notifications with a fixed destination chat ID. 📨 Requirements n8n instance with access to Google Drive Trigger, Google Drive, Google Docs, Google Sheets, HTTP Request, Telegram, and Extract from File nodes. 🧰 Google Drive OAuth2 credential with access to the watched folder. 🔐 Google Docs OAuth2 credential with access to any Google Docs files you want to process. 📘 Google Sheets OAuth2 credential and a sheet with an Extract_Log tab. 📊 Telegram bot credential plus a valid destination chat ID for notifications. 🤝 NVIDIA NIM API key stored as an HTTP Header Auth credential. 🔑 A folder ID and Google Sheet ID added to the provided placeholders before activation. 🛠️ Target Audience Operations teams monitoring a shared Drive folder for inbound files. 🗂️ Founders and solo operators who want document extraction. 👤 Agencies building reusable back-office workflows for receipts, notes, and uploaded files. 🏢 Analysts who want structured text output logged into Google Sheets automatically. 📋 Automation builders testing file-driven multimodal extraction with Drive as the source. 🧪 Step-by-Step Setup Instructions Import the workflow and read every sticky note on the canvas before editing any nodes. 📝 Connect your Google Drive, Google Docs, Google Sheets, Telegram, and NVIDIA NIM credentials. 🔐 Replace REPLACE_WITH_GOOGLE_DRIVE_FOLDER_ID, REPLACE_WITH_GOOGLE_SHEET_ID, and REPLACE_WITH_TELEGRAM_CHAT_ID in the marked nodes. 📌 Create the Extract_Log tab with the required headers shown in the sticky notes. 📑 Test one file at a time in this order: PDF, TXT, CSV, image, then Google Docs file. 🧪 Confirm that each test adds one clean row to Google Sheets and sends one Telegram notification. ✅ Activate the workflow only after every supported path works end to end. 🚀 Built by Cordexa Technologies https://cordexa.tech | cordexatech@gmail.com

AI Email Assistant - Smart Email Processing & Response 🤖 A sophisticated n8n workflow that transforms your email management with AI-powered classification, automatic responses, and intelligent organization. 🎯 What This Workflow Does This advanced AI email assistant automatically: Analyzes** incoming emails using intelligent classification Categorizes** messages by priority, urgency, and type Generates** context-aware draft responses in your voice Organizes** emails with smart labeling and filing Alerts** you to urgent messages instantly Manages** attachments with cloud storage integration Perfect for busy professionals, customer service teams, and anyone drowning in email! ✨ Key Features 🧠 Intelligent Email Analysis Context-Aware Processing**: Understands email threads and conversation history Smart Classification**: Automatically categorizes by priority, urgency, and required actions Multi-Criteria Assessment**: Evaluates response needs, follow-up requirements, team involvement Dynamic Label Management**: Syncs with your Gmail labels for consistent organization 📝 AI-Powered Response Generation Professional Draft Creation**: Generates contextually appropriate responses Tone Matching**: Mirrors the formality and style of incoming emails Multiple Response Options**: Provides alternatives for complex inquiries Customizable Voice**: Adapts to your business communication style 🔔 Smart Notification System Urgent Email Alerts**: Instant notifications for high-priority messages Telegram/Slack Integration**: Get alerts where you work Smart Filtering**: Only notifies when truly urgent Quick Action Links**: Direct links to Gmail for immediate response 📎 Advanced Attachment Management Automatic Cloud Upload**: Saves attachments to Google Drive Smart File Naming**: Organized by date, sender, and content Duplicate Detection**: Prevents redundant uploads File Type Filtering**: Optional filtering for security 🏷️ Intelligent Organization Auto-Labeling**: Applies relevant Gmail labels automatically Progress Tracking**: Marks emails as "processed" or "digested" Priority Indicators**: Visual priority levels in your inbox Category-Based Sorting**: Groups similar emails together 🛠️ Setup Instructions Prerequisites n8n instance (cloud or self-hosted) Gmail account with API access OpenAI API key (or compatible AI service) Google Drive account (for attachments) Telegram bot (optional, for alerts) Step 1: Import the Workflow Download AI_Email_Assistant_Community_Template.json In n8n, navigate to Templates → Import from File Select the downloaded JSON file The workflow will import as inactive Step 2: Configure Credentials Gmail Setup: Create Gmail OAuth2 credentials in n8n Configure the following nodes: Email_Trigger Get Conversation Thread Get Latest Message Content Create Draft Response Assign Classification Label Mark as Processed Get All Gmail Labels Test connections to ensure proper authentication AI Model Setup: Configure the AI Language Model node Options include: OpenAI (GPT-4, GPT-3.5-turbo) Anthropic Claude (recommended) Local LLMs via Ollama Add your API credentials Test the connection Google Drive Setup (Optional): Create Google Drive OAuth2 credentials Configure nodes: Upload to Google Drive Check Existing Attachments Replace YOUR_GOOGLE_DRIVE_FOLDER_ID with your folder ID Create a dedicated folder for email attachments Telegram Alerts (Optional): Create a Telegram bot via @BotFather Get your chat ID Configure the Send Urgent Alert node Replace YOUR_TELEGRAM_CHAT_ID with your actual chat ID Step 3: Customize AI Instructions Email Classification (AI Email Classifier node): Review the classification criteria in the system message Adjust urgency keywords for your business Modify priority levels based on your needs Customize category definitions Response Generation (AI Response Generator node): Update the response guidelines Replace [YOUR NAME] with your actual name Adjust tone and style preferences Add company-specific response templates Step 4: Configure Gmail Labels Create Custom Labels in Gmail: High Priority Medium Priority Low Priority Needs Response Urgent Follow Up Required Processed (or use existing labels) Update Label IDs: Run the workflow once to get label IDs Replace YOUR_PROCESSED_LABEL_ID in the "Mark as Processed" node Update any hardcoded label references Step 5: Test and Deploy Testing Process: Send yourself a test email Monitor the workflow execution Verify classification accuracy Check draft response quality Confirm labeling works correctly Test urgent alert functionality Fine-Tuning: Adjust AI prompts based on test results Refine classification criteria Update response templates Modify notification preferences Go Live: Activate the workflow Monitor initial performance Adjust settings as needed 📊 Email Classification System Priority Levels High**: Urgent matters requiring immediate attention Medium**: Important but not time-critical Low**: Routine or informational messages Classification Categories toReply**: Direct questions or requests requiring response urgent**: Immediate business impact or crisis situations dateRelated**: Time-sensitive events or deadlines attachmentsToUpload**: Financial docs or important files requiresFollowUp**: Multi-step processes or ongoing projects forwardToTeam**: Cross-departmental or collaborative items Response Generation Guidelines Professional Tone**: Business casual, warm but professional Context Awareness**: Considers email thread history Structured Responses**: Clear paragraphs with actionable next steps Placeholder System**: Uses [PLACEHOLDER] for missing information Alternative Options**: Provides multiple response choices for complex inquiries 🔧 Advanced Customization File Type Filtering // In Get Specific File Types node, modify: if (mimeType === 'application/pdf' || mimeType === 'text/xml' || mimeType === 'image/jpeg') { // Process file } Custom Urgency Keywords Update the AI classifier prompt with your business-specific urgent terms: Keywords: "URGENT", "EMERGENCY", "CRITICAL", "ASAP", "IMMEDIATE" Custom terms: "CLIENT ESCALATION", "SYSTEM DOWN", "LEGAL DEADLINE" Response Templates Customize the response generator with your company voice: Greeting style: "Hi [Name]" vs "Dear [Name]" Closing: "Best Regards" vs "Thank you" vs "Cheers" Company-specific phrases and terminology Integration Options CRM Systems**: Add nodes to create tasks in your CRM Project Management**: Auto-create tickets in Jira, Asana, etc. Calendar Integration**: Schedule follow-ups automatically Slack/Teams**: Alternative notification channels 🚨 Troubleshooting Common Issues 1. Gmail Authentication Errors Verify OAuth2 credentials are active Check Gmail API quotas Ensure proper scopes are configured 2. AI Classification Inconsistency Review and refine classification prompts Add more specific examples Adjust confidence thresholds 3. Response Generation Problems Validate AI model configuration Check API key and quotas Test with simpler email examples 4. Attachment Upload Failures Verify Google Drive permissions Check folder ID configuration Ensure sufficient storage space 5. Missing Notifications Test Telegram bot configuration Verify chat ID is correct Check urgency classification logic Performance Optimization Rate Limiting**: Gmail has API quotas - monitor usage Batch Processing**: Workflow processes one email at a time Error Handling**: Built-in retry logic for reliability Resource Management**: Monitor AI API costs and usage 📈 Best Practices 1. Email Management Regular Monitoring**: Review classifications weekly Label Hygiene**: Keep Gmail labels organized Feedback Loop**: Manually correct misclassifications Archive Strategy**: Set up auto-archiving for processed emails 2. AI Optimization Prompt Engineering**: Continuously refine AI instructions Example Training**: Add specific examples for your business Context Limits**: Monitor token usage and costs Model Selection**: Choose appropriate AI model for your needs 3. Security Considerations Credential Management**: Regularly rotate API keys Data Privacy**: Review what data is sent to AI services Access Control**: Limit workflow access to authorized users Audit Logging**: Monitor workflow executions 4. Workflow Maintenance Regular Updates**: Keep n8n and node versions current Backup Strategy**: Export workflow configurations regularly Documentation**: Keep setup notes and customizations documented Testing**: Test major changes in development environment first 🤝 Contributing to the Community This workflow template demonstrates: Comprehensive AI Integration**: Multiple AI touchpoints working together Production-Ready Architecture**: Error handling, retry logic, and monitoring Extensive Documentation**: Clear setup and customization guidance Flexible Configuration**: Adaptable to different business needs Best Practice Examples**: Security, performance, and maintenance considerations 📄 License & Support This workflow is provided free to the n8n community under MIT License. Community Resources: n8n Community Forum for questions GitHub Issues for bug reports Documentation updates welcome Professional Support: For enterprise deployments or custom modifications, consider: n8n Cloud for managed hosting Professional services for complex integrations Custom AI model training for specific use cases Transform your email workflow today! 🚀 This AI Email Assistant reduces email processing time by up to 90% while ensuring no important message goes unnoticed. Perfect for busy professionals who want to stay responsive without being overwhelmed by their inbox.

Overview This intelligent chatbot workflow enables natural language conversations with your documents, supporting multiple file formats including PDFs, Word documents, Excel spreadsheets, and text files. Built with advanced RAG (Retrieval-Augmented Generation) technology, this chatbot can understand, analyze, and answer questions about your document content with contextual accuracy and intelligent responses. How It Works Intelligent Document Processing & Conversation Pipeline: Multi-Format Document Ingestion**: Automatically processes and indexes various document formats (PDF, DOCX, XLSX, TXT, etc.) Smart Content Chunking**: Breaks down documents into meaningful segments while preserving context and relationships Vector Database Storage**: Creates searchable embeddings for fast and accurate information retrieval Contextual Conversation Engine**: Uses AI to understand user queries and retrieve relevant document sections Natural Language Responses**: Generates human-like responses with citations and source references Multi-Turn Conversations**: Maintains conversation history and context across multiple interactions Real-Time Processing**: Instant responses with live document updates and dynamic content refresh Setup Instructions Estimated Setup Time: 15-20 minutes Prerequisites n8n instance (v0.200.0 or higher recommended) OpenAI/Gemini API key for embeddings and chat completion Vector database service (optional: Pinecone, Weaviate, or Qdrant) File storage service (optional: Google Drive, Dropbox, AWS S3) Web server for chatbot interface (optional) Configuration Steps Configure Document Input Sources Set up file upload webhook for direct document submission Configure cloud storage watchers for automatic document processing Add support for multiple file formats and size limits Set up document validation and security checks Setup Document Processing Pipeline Configure text extraction engines for different file types Set up intelligent chunking parameters (chunk size, overlap, boundaries) Add metadata extraction for document categorization Configure OCR for scanned documents (optional) Configure Vector Database Set up your chosen vector database credentials Configure embedding model settings (Gemini models/text-embedding-004 recommended) Set up collection/index structure for document storage Configure search parameters and similarity thresholds Setup AI Chat Engine Add your AI service API credentials (Gemini, Claude, etc.) Configure conversation prompts and system instructions Set up context window management and token optimization Add response formatting and citation rules Configure Chat Interface Set up webhook endpoints for chat API Configure session management and conversation history Add authentication and rate limiting (optional) Set up real-time updates and streaming responses Setup Monitoring & Analytics Configure conversation logging and analytics Set up performance monitoring for response times Add usage tracking and cost monitoring Configure error handling and failover mechanisms Use Cases Business & Enterprise Knowledge Base Queries**: Ask questions about company policies, procedures, and documentation Contract Analysis**: Query legal documents, contracts, and compliance materials Training Materials**: Interactive learning with training manuals and educational content Financial Reports**: Analyze and discuss financial statements, budgets, and forecasts Research & Academia Research Paper Analysis**: Discuss findings, methodologies, and citations from academic papers Literature Reviews**: Compare and contrast multiple research documents Thesis Support**: Get insights from reference materials and research data Grant Proposals**: Analyze requirements and optimize proposal content Legal & Compliance Legal Document Review**: Query contracts, agreements, and legal texts Regulatory Compliance**: Understand compliance requirements from regulatory documents Case Law Research**: Analyze legal precedents and court decisions Policy Analysis**: Interpret organizational policies and procedures Technical Documentation API Documentation**: Interactive queries about technical specifications User Manuals**: Get help and guidance from product documentation Code Documentation**: Understand codebases and technical implementations Troubleshooting Guides**: Interactive problem-solving with technical guides Personal Productivity Document Summarization**: Get quick summaries of long documents Information Extraction**: Find specific data points across multiple documents Content Research**: Research topics across your personal document library Meeting Notes**: Query and analyze meeting transcripts and notes Key Features Advanced Document Processing Multi-Format Support**: PDF, DOCX, XLSX, TXT, PPTX, and more Intelligent Chunking**: Context-aware document segmentation Metadata Extraction**: Automatic categorization and tagging OCR Integration**: Process scanned documents and images with text Intelligent Conversation Contextual Understanding**: Maintains conversation context and document relationships Source Attribution**: Provides citations and references for all answers Multi-Document Queries**: Compare and analyze across multiple documents Follow-up Questions**: Natural conversation flow with clarifying questions Performance & Scalability Fast Retrieval**: Vector-based semantic search for instant responses Scalable Architecture**: Handle large document collections efficiently Batch Processing**: Process multiple documents simultaneously Caching System**: Optimized response times with intelligent caching Security & Privacy Document Encryption**: Secure storage and transmission of sensitive documents Access Control**: User-based permissions and document access restrictions Audit Logging**: Complete conversation and access audit trails Data Retention**: Configurable data retention and deletion policies Technical Architecture Document Processing Flow File Upload → Format Detection → Text Extraction → Content Chunking Metadata Extraction → Embedding Generation → Vector Storage → Index Creation Conversation Flow User Query → Intent Analysis → Vector Search → Context Retrieval Response Generation → Source Attribution → Answer Formatting → Delivery Supported File Formats Documents**: PDF, DOC, DOCX, RTF, TXT, MD Spreadsheets**: XLS, XLSX, CSV Presentations**: PPT, PPTX Images**: PNG, JPG (with OCR) Archives**: ZIP (auto-extracts supported formats) Web**: HTML, XML Integration Options Chat Interfaces Web Widget**: Embeddable chat widget for websites API Endpoints**: RESTful API for custom integrations Slack/Teams**: Direct integration with team collaboration tools Mobile Apps**: API-first design for mobile application integration Data Sources Cloud Storage**: Google Drive, Dropbox, OneDrive, AWS S3 Document Systems**: SharePoint, Confluence, Notion Email**: Process attachments from email systems CRM/ERP**: Integration with business systems Performance Specifications Response Time**: < 3 seconds for typical queries Document Capacity**: Supports collections of 10,000+ documents Concurrent Users**: Scales to handle multiple simultaneous conversations Accuracy**: >90% relevance for domain-specific queries Advanced Configuration Options Customization Custom Prompts**: Tailor AI behavior for specific use cases Branding**: Customize chat interface with your company branding Language Support**: Multi-language document processing and responses Domain Expertise**: Fine-tune for specific industries or domains Analytics & Monitoring Usage Analytics**: Track popular queries and document usage Performance Metrics**: Monitor response times and accuracy User Feedback**: Collect ratings and improve responses A/B Testing**: Test different configurations and prompts Troubleshooting & Support Common Issues Slow Responses**: Check vector database performance and API limits Inaccurate Answers**: Review chunking strategy and embedding quality Format Errors**: Verify document formats and processing capabilities Memory Issues**: Monitor token usage and context window limits Optimization Tips Use clear, specific questions for best results Ensure documents are well-formatted with proper headers Regular vector database maintenance for optimal performance Monitor API usage to optimize costs and performance

AI-Powered Academic Assignment Generator This n8n workflow template automates the complete academic assignment generation process from student queries to professional document delivery. Students submit assignment requests via Telegram, and the workflow generates comprehensive, plagiarism-free academic content using Google Gemini AI, formats it into professional PDF documents, and delivers downloadable links while maintaining complete records. What does this workflow do? 📱 Telegram Integration**: Receives structured assignment requests from students 🤖 AI Content Generation**: Creates comprehensive academic answers (500+ words per question) 📄 Professional Formatting**: Generates university-standard HTML/PDF documents ☁️ Cloud Storage**: Automatically stores files in organized Google Drive folders 📊 Record Keeping**: Maintains complete assignment database in Google Sheets 🔄 End-to-End Automation**: Complete pipeline from query to document delivery How it works The workflow processes student assignment requests through 16 interconnected nodes, handling everything from input parsing to final document delivery: Input → AI Processing → Document Generation → Storage & Delivery Setup Requirements Credentials needed: Telegram Bot Token** (for receiving/sending messages) Google Gemini API Key** (for AI content generation) Google Sheets API** (for record keeping) Google Drive API** (for file storage) PDFCrowd API** (for PDF conversion) Pre-setup steps: Create a Telegram bot and obtain the bot token Set up Google Drive folder structure for file organization Create Google Sheets template with proper column headers Configure API rate limits and usage quotas Workflow Breakdown 🔌 Input Processing Nodes Student Query Intake Bot (Telegram Trigger) Student Query Intake Bot (Telegram Trigger) Listens for incoming student messages with assignment details Monitors specific chat ID for authorized users Triggers workflow when structured assignment requests are received Structured Data Parser (Code Node) Extracts student information using regex patterns Parses: Name, Faculty, Department, Level, Course, Registration Number Automatically sets current date and handles missing data Outputs clean JSON structure for AI processing 🤖 AI Processing Nodes Student Assignment Auto-Composer (LangChain Agent) Main AI orchestrator for assignment generation Uses structured prompts for consistent academic formatting Generates 500-word answers per question with APA citations Ensures plagiarism-free, original academic content Generator Model (Google Gemini Chat) Primary AI model for high-quality content generation Handles complex academic writing and formatting requirements Fallback Model Generator (Google Gemini - Gemma) Backup AI model ensuring workflow reliability Activates when primary model encounters issues Structured Output Parser (LangChain) Validates AI-generated content against JSON schema Enforces required field compliance and format consistency Auto-fixes common formatting issues 🔧 Processing & Error Handling Error Handler (Code Node) Handles text processing errors and data type issues Converts non-string values and provides error recovery Ensures workflow continuity even with problematic data Wait Node Introduces strategic 2-second delay for processing stability Allows AI processing to complete before next steps 📊 Data Management Nodes Edit Fields (Set Node) Maps AI output to Google Sheets column structure Ensures data consistency for database storage Long Essay Record Sheet (Google Sheets) Stores complete assignment records with metadata Maintains comprehensive student assignment database Uses Name field as unique identifier for record updates 📄 Document Generation Nodes Static HTML Builder (LangChain Agent) Converts structured data into professional HTML documents Applies academic formatting: Times New Roman, 12pt, double-spaced Creates university-standard document structure HTTP Request (PDF Conversion) Converts HTML to high-quality PDF using PDFCrowd API Maintains academic formatting and professional appearance Uses student name for file identification ☁️ Storage & Delivery Nodes Upload File (Google Drive) Stores generated PDFs in organized Drive folders Creates shareable links for easy access Maintains systematic file organization Send Text Message (Telegram) Delivers Google Drive download link to student Completes the automation cycle with instant access Input Format Students should format their Telegram messages as follows: Name: John Doe Faculty: Engineering Department: Computer Science Level: 200L Course: CSC 201 - Data Structures Reg number: 2024001234 Question: Explain the concept of Big O notation Compare different sorting algorithms Discuss the applications of binary trees Features ✨ Intelligent Processing Smart Input Parsing**: Handles unstructured text inputs automatically Multi-Question Support**: Processes complex assignment requirements Data Validation**: Ensures complete and accurate information capture 🎓 Academic Excellence University Standards**: Professional formatting and citation styles Original Content**: Plagiarism-free AI-generated assignments Comprehensive Answers**: 500+ words per question with detailed explanations 🛡️ Reliability & Error Handling Fallback Systems**: Multiple AI models for continuous operation Error Recovery**: Automatic handling of processing issues Data Integrity**: Schema validation and field verification Use Cases This workflow template is perfect for: 📚 Educational Institutions**: Automate student assignment processing and grading assistance 👨‍🎓 Academic Support Services**: Provide structured learning assistance and content generation 🏫 Online Learning Platforms**: Integrate assignment automation into educational systems 📝 Content Creation Services**: Generate academic-quality content for educational purposes 🤖 AI Learning Projects**: Implement complex AI workflows with multiple service integrations Output Examples Generated Assignment Features: Professional formatting** with Times New Roman, 12pt font, double-spacing Complete academic structure** including headers, student information, questions, and references Comprehensive answers** averaging 500+ words per question with detailed explanations Proper citations** in APA format with authentic academic references PDF delivery** through shareable Google Drive links Database Records: Complete student information tracking Assignment question and answer storage Timestamp and metadata preservation Easy retrieval and analysis capabilities Performance & Reliability Processing Time: 2-3 minutes per assignment Success Rate: >95% with fallback mechanisms Content Quality: University-standard academic writing Scalability: Handles multiple concurrent requests Error Recovery: Automatic retry and alternative processing paths Customization Options Easily configurable elements: Chat IDs**: Modify for different Telegram groups or users AI Models**: Switch between different Google Gemini models Document Formatting**: Adjust academic standards and styling Storage Locations**: Configure Google Drive folders and naming conventions Database Fields**: Modify Google Sheets columns and data structure Advanced customizations: Add support for different document formats (Word, LaTeX) Integrate additional AI providers (OpenAI, Claude, etc.) Implement grading and feedback mechanisms Add multi-language support Create batch processing capabilities Getting Started Import the workflow into your n8n instance Configure credentials for all required services Set up Telegram bot and obtain necessary permissions Create Google Drive folders and Google Sheets template AI-Powered Academic Assignment Test with sample data to ensure proper functionality Deploy and monitor for production use Tags academic education ai telegram google-sheets pdf-generation automation langchain assignment student-support

🛠️ Gotify Tool MCP Server Complete MCP server exposing all Gotify Tool operations to AI agents. Zero configuration needed - all 3 operations pre-built. ⚡ Quick Setup Need help? Want access to more workflows and even live Q&A sessions with a top verified n8n creator.. All 100% free? Join the community Import this workflow into your n8n instance Activate the workflow to start your MCP server Copy the webhook URL from the MCP trigger node Connect AI agents using the MCP URL 🔧 How it Works • MCP Trigger: Serves as your server endpoint for AI agent requests • Tool Nodes: Pre-configured for every Gotify Tool operation • AI Expressions: Automatically populate parameters via $fromAI() placeholders • Native Integration: Uses official n8n Gotify Tool tool with full error handling 📋 Available Operations (3 total) Every possible Gotify Tool operation is included: 💬 Message (3 operations) • Create a message • Delete a message • Get many messages 🤖 AI Integration Parameter Handling: AI agents automatically provide values for: • Resource IDs and identifiers • Search queries and filters • Content and data payloads • Configuration options Response Format: Native Gotify Tool API responses with full data structure Error Handling: Built-in n8n error management and retry logic 💡 Usage Examples Connect this MCP server to any AI agent or workflow: • Claude Desktop: Add MCP server URL to configuration • Custom AI Apps: Use MCP URL as tool endpoint • Other n8n Workflows: Call MCP tools from any workflow • API Integration: Direct HTTP calls to MCP endpoints ✨ Benefits • Complete Coverage: Every Gotify Tool operation available • Zero Setup: No parameter mapping or configuration needed • AI-Ready: Built-in $fromAI() expressions for all parameters • Production Ready: Native n8n error handling and logging • Extensible: Easily modify or add custom logic > 🆓 Free for community use! Ready to deploy in under 2 minutes.

How it Works: Trigger: The workflow is triggered by a webhook, initiated by an Airtable automation. This automation sends the Book or Chapter record ID and the desired action (e.g., "Generate Book Details," "Generate Chapters," "Generate Chapter Research," "Generate Chapter Content"). Action Routing: A "Switch" node directs the workflow based on the action query parameter received from the webhook. This determines which part of the book creation process will be executed. Data Retrieval: The workflow fetches the relevant book or chapter data from Airtable using the provided recordId. AI Processing: Book Details Generation: If the action is "Generate Book Details," an AI Agent (powered by a Large Language Model (LLM) like Google Gemini and the Perplexity search tool) researches the book idea. It focuses on crafting a compelling book description, identifying the target audience, and conducting general book research to maximize bestseller potential. The research brief is then saved back to Airtable. Chapter Generation: If the action is "Generate Chapters," an LLM generates 7-10 chapter titles and descriptions based on the book idea and previous research. A structured output parser ensures the chapter data is in the correct format. The chapters are then split into individual items and saved as separate records in the "Chapter" table in Airtable, linked to the main book record. Chapter Research Generation: If the action is "Generate Chapter Research," another AI Agent conducts in-depth research on a specific chapter, using the Perplexity search tool multiple times. It focuses on finding stories, case studies, historical events, and expert perspectives to make the chapter engaging and credible. The research is saved back to the "Chapter" record in Airtable. Chapter Content Generation: If the action is "Generate Chapter Content," an LLM writes the full content of the chapter, using the research gathered in the previous step, the overall book research, and the chapter description. The generated content is saved back to the "Chapter" record in Airtable. Airtable Updates: In each of the AI processing steps, the workflow updates the corresponding Airtable record (either "Book" or "Chapter") with the generated results (research, chapter details, or content) and sets the "Action" field back to "Idle." Set Up Steps: Airtable Setup (Estimated time: 10-15 minutes): Copy the Airtable base blueprint: https://airtable.com/appfkz4KUlKvOjtbp/shra78TlDfqLRdSfT. This will create the "Book" and "Chapter" tables with the necessary fields. In the "Book" table, create three Airtable Automations: Trigger: When a record matches conditions -> Action is Generate Book Details Action: Run a script. Use the following script: let autoRoute = input.config(); await fetch(autoRoute.webhookUrl + "?recordId=" + autoRoute.recordId + "&action=" + autoRoute.action); In the script action's configuration, add three "Input variables": webhookUrl (map it to your n8n webhook URL, obtained in the next step) recordId (map it to the Airtable record ID) action (map it to Action) Repeat this process to create two more automations in the "Book" table, identical except triggered when Action is Generate Chapters, respectively. In the "Chapter" table, create two Airtable Automations: Trigger: When a record matches conditions -> Action is Generate Chapter Research Action: Run a script (use the same script as above, with the same input variables). Create a second automation, identical except triggered when Action is Generate Chapter Content. n8n Setup (Estimated time: 15-20 minutes): Import the provided JSON workflow into n8n. Webhook Node: Copy the "Test URL" from the Webhook node. This is the webhookUrl you'll use in the Airtable automations. Important: Once you've tested and are ready to go live, switch to the "Production URL." Airtable Nodes: Configure all Airtable nodes (there are eight). You'll need to connect your Airtable account using OAuth 2. Select the correct Base ("Book Agency \[v1] Cobuild" or whatever you named it) and Table ("Book" or "Chapter") for each node. The field mappings are already defined in the template, but double-check them. LLM Nodes (Google Gemini & OpenAI): Connect your Google Gemini and OpenAI accounts to the respective LLM nodes. You'll need API keys for both. You may also configure different LLM Models. Perplexity Nodes Connect your Perplexity AI API to the Perplexity nodes. You'll need API keys for that. Activate the workflow. Testing (Estimated Time: 5-10 minutes): Go to your Airtable "Book" table. Create a New Record. Fill in the "Idea" field with a book concept. Change the "Action" field to "Generate Book Details". The Airtable automation should trigger, sending a request to your n8n webhook. Monitor the n8n execution log to see the workflow in action. Check the Airtable record to see if the "Research" field is populated. Repeat the testing for Generate Chapters, Generate Chapter Research and Generate Chapter Content.

Complete AI support system using website data (RAG pipeline) This template provides a full end-to-end Retrieval-Augmented Generation (RAG) system using n8n. It includes two connected workflows: A data ingestion pipeline that crawls a website and stores its content in a vector database. A customer support chatbot that retrieves this knowledge and answers user queries in real time. Together, these workflows allow you to turn any public website into an intelligent AI-powered support assistant grounded in real business data. Use cases AI customer support chatbot for your website Internal company knowledge assistant Product FAQ automation Helpdesk or IT support bot AI receptionist for services Semantic search over company content How it works Ingestion workflow Discover all URLs from a website sitemap. Filter and normalize the URLs. Fetch each page and extract readable text. Clean HTML into plain text. Split text into overlapping chunks. Generate embeddings using OpenAI. Store vectors in Pinecone with metadata. Chatbot workflow A user sends a message via chat webhook. The agent queries Pinecone for relevant knowledge. Retrieved content is passed to OpenAI. OpenAI generates a grounded response. Short-term memory maintains conversation context. How to use Step 1 – Run ingestion Set your target website URL. Add Firecrawl, OpenAI, and Pinecone credentials. Create a Pinecone index. Execute the ingestion workflow. Wait until all pages are indexed. Step 2 – Run chatbot Deploy the chatbot workflow. Set the same Pinecone index and namespace. Copy the chat webhook URL. Connect it to a website, chat widget, or WhatsApp bot. Start chatting with your AI assistant. Requirements Firecrawl account OpenAI API key Pinecone account and index Public website to crawl Optional: frontend chat interface Good to know The chatbot never answers from memory for business data. All company knowledge comes from Pinecone. If Pinecone returns nothing, the bot fails safely. HTML cleaning is basic and can be replaced with: Mozilla Readability Jina Reader Unstructured Chunk size and overlap affect retrieval quality. Pinecone can be replaced with: Qdrant Weaviate Supabase Vector Chroma Customising this workflow You can extend this system by: Adding PDF or document loaders Scheduling ingestion daily or weekly Connecting CRM or ticketing systems Adding appointment booking tools Switching to local or open-source models Adding multilingual support Storing raw content in a database Adding feedback or logging What this n8n template demonstrates Real-world RAG architecture Web crawling pipelines Text chunking strategies Vector database integration AI agent orchestration Memory-controlled conversations Production-grade AI support systems End-to-end AI infrastructure with n8n Architecture overview This template follows a modern AI system design: Website → Ingestion → Embeddings → Pinecone → Retrieval → OpenAI → User It separates: Data preparation (offline) Knowledge storage Runtime inference This makes the system scalable, maintainable, and safe for production use. Need a custom setup? If you want a similar AI system built for your business (custom data sources, CRM integration, WhatsApp bots, booking systems, dashboards, or private deployments), feel free to reach out at dinakars2003@gmail.com. I help companies design and deploy production-ready AI workflows.

How it works: Accesses a target website, searches for new PDFs, and downloads them automatically. Extracts content from each PDF and sends it to an AI for summarization. Delivers the AI-generated summary directly to a Discord channel. Marks processed URLs in Google Sheets to avoid duplicates. Set up steps: Configure the website URL in the HTTP Request node. Connect to Google Cloud API (enable Drive & Sheets) and link your spreadsheet. Set up an OpenRouter API key and choose your preferred AI model. Create a Discord webhook for notifications.

The workflow performs tasks that would normally require human intervention on Google News links, transforming the RSS feeds into data that can be used by an automated system like n8n, thus creating a solid foundation for further applications Who is this for? This workflow is ideal for developers, journalists, and content aggregators who need to extract and clean Google News URLs from its RSS feed. What problem does this workflow solve? Google News RSS provides encoded URLs that contain additional tracking parameters. This workflow decodes those URLs and provides clean, direct links to news articles, making them easier to process, share, and analyze. What this workflow does Fetch Google News RSS – Retrieves news articles from Google News based on predefined parameters (language, country). Limit results – Reduces the number of requests to avoid excessive API usage. Extract encoded content – Retrieves the encoded news URLs. Decode the URLs – Uses a decoding mechanism to extract clean links. Remove unwanted characters – Cleans up the decoded URLs to ensure they are properly formatted. Aggregate results – Outputs a final list of clean, readable URLs. Setup Modify RSS parameters (hl, gl) to match your target region. Adjust the result limit to control the number of processed articles. How to customize this workflow To customize this workflow, you can add an HTTP Request node to retrieve the article's text, an HTML Extract node to process the text, an AI node to generate new content, and a WordPress node to publish it Another option is to use an AI Agent node to classify articles by category based on the title or through HTML Extract. You can then save the classified articles using a Google Sheets node, organizing them by category and creating an high quality editorial plan This workflow efficiently processes Google News RSS, removes unnecessary encoding, and delivers clean, shareable URLs. 🚀

The Scrape and Analyze Amazon Product Info with Decodo + OpenAI workflow automates the process of extracting product information from an Amazon product page and transforming it into meaningful insights. The workflow then uses OpenAI to generate descriptive summaries, competitive positioning insights, and structured analytical output based on the extracted information. Disclaimer Please note - This workflow is only available on n8n self-hosted as it’s making use of the community node for the Decodo Web Scraping Who this is for? This workflow is ideal for: E-commerce product researchers Marketplace sellers (Amazon, Flipkart, Shopify, etc.) Competitive intelligence teams Product comparison bloggers and reviewers Pricing and product analytics engineers Automation builders needing AI-powered product insights What problem is this workflow solving? Manually extracting Amazon product details, ads, pricing, reviews, and competitive signals is: Time-consuming Requires switching across tools Difficult to analyze at scale Not structured for reporting Hard to compare products objectively This workflow automates: Web scraping of Amazon product pages Extraction of product features and ad listings AI-generated product summaries Competitive positioning analysis Generation of structured product insight output Export to Google Sheets for tracking and reporting What this workflow does This workflow performs an end-to-end product intelligence pipeline, including: Data Collection Scrapes an Amazon product page using Decodo Retrieves product details and advertisement placements Data Extraction Extracts: Product specs Key feature descriptions Ads data Supplemental metadata AI-Driven Analysis Generates: Descriptive product summary Competitive positioning insights Structured product insight schema Data Consolidation Merges descriptive, analytical, and structured outputs Export & Persistence Aggregates results Writes final dataset to Google Sheets for: tracking comparison reporting product research archives Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n instance** Decodo API credentials** OpenAI API credentials** Make sure to install the Decodo Community Node. Required Credentials Decodo API Go to Credentials Add Decodo API Enter API key Save as: Decodo Credentials account OpenAI API Go to Credentials Select OpenAI Enter API key Save as: OpenAi account Google Sheets Add Google Sheets OAuth Authorize via Google Save as desired account Inputs to configure Modify in Set the Input Fields node: product_url = https://www.amazon.in/Sony-DualSense-Controller-Grey-PlayStation/dp/B0BQXZ11B8 How to customize this workflow to your needs You can easily adapt this workflow for various use cases. Change the product being analyzed Modify: product_url Change AI model In OpenAI nodes: Replace gpt-4.1-mini Use Gemini, Claude, Mistral, Groq (if supported) Customize the insight schema Edit Product Insights node to include: sustainability markers sentiment extraction pricing bands safety compliance brand comparisons Expand data extraction You may extract: product reviews FAQs Q&A seller information delivery and logistics signals Change output destination Replace Google Sheets with: PostgreSQL MySQL Notion Slack Airtable Webhook delivery CSV export Turn it into a batch processor Loop over: multiple ASINs category listings search results pages Summary This workflow provides a complete automated product intelligence engine, combining Decodo’s scraping capabilities with OpenAI’s analytical reasoning to transform Amazon product pages into structured insights, competitive analysis, and summarized evaluations automatically stored for reporting and comparison.

Here's the corrected English text: Dropbox Folder Monitoring Workflow As we don't have (yet?) a Dropbox node "Watching new files" or "Watching folder", I created this central workflow to do it. How it works Triggered by Dropbox webhook I respond immediately to Dropbox to avoid webhook disabling Then I add/duplicate one branch per monitored folder, according to my needs In my case, I need to monitor several folders, like "vocal notes to process", "transcriptions to LinkedIn posts" or "quotes to add". This workflow shows 2 types of folder monitoring: Way #1: Each file in the monitored folder calls a sub-workflow Way #2: We get all files from the monitored folder and compare them to a database. If the file is not listed in DB, i supposed it's new one. Way #1 - We get all files from the monitored folder I set a variable folder_to_watch to indicate which folder to monitor. This step is here just to be homogeneous and allow setting the folder path only once in this branch. I list the folder files We keep only files (exclude folders) Then I call the specialized sub-workflow Way #2 - We want only new files from the monitored folder I set a variable folder_to_watch to indicate which folder to monitor I list the folder files and keep only files Meanwhile, I query my DB to get known files about this folder (I send the query to NocoDB (folder_to_watch,eq,{{ $json.folder_to_watch }})) Now I can exclude old files and keep only new ones by merging (I compare from Dropbox file id - as the file could be renamed by the user) I add the new file in DB to be sure to recognize it next time - I save the JSON Dropbox data: { "id":"{{ $json.id }}", "name":"{{ $json.name }}", "lastModifiedClient": "{{ $json.lastModifiedClient }}", "lastModifiedServer": "{{ $json.lastModifiedServer }}", "rev": "{{ $json.rev }}", "contentSize": {{ $json.contentSize }}, "type": "{{ $json.type }}", "contentHash": "{{ $json.contentHash }}", "pathLower": "{{ $json.pathLower }}", "pathDisplay": "{{ $json.pathDisplay }}", "isDownloadable": {{ $json.isDownloadable }} } And now I can call my sub-workflow :) My DB Columns details: folder_to_watch data (json/text) timestamp file_id (Dropbox file ID, to ease future searches) My vision: I have only one workflow in my n8n that monitors Dropbox folders/files This workflow calls the required sub-workflow specialized for the tasks required I will have as many branches as I have folders to monitor (if I have 5 different folders to watch, I will get 5 branches and 5 sub-workflows)