Workflow Templates

Using the Systeme API can be challenging due to its pagination settings and low rate limit. This requires a bit more knowledge about API requests than a beginner might have. This template provides preconfigured HTTP Request nodes to help you work more efficiently. Pagination settings, item limits, and rate limits are all configured for you, making it easier to get started. How to configure Systeme.io credentials The Systeme API uses the Header Auth method. So create a Header Auth credential in your n8n with the name "X-API-Key". . Check out my other templates 👉 https://n8n.io/creators/solomon/

This workflow allows to scrape Google Maps data in an efficient way using SerpAPI. You'll get all data from Gmaps at a cheaper cost than Google Maps API. Add as input, your Google Maps search URL and you'll get a list of places with many data points such as: phone number website rating reviews address And much more. Full guide to implement the workflow is here: https://lempire.notion.site/Scrape-Google-Maps-places-with-n8n-b7f1785c3d474e858b7ee61ad4c21136?pvs=4

AI-Powered Tech Radar Advisor This project is built on top of the famous open source ThoughtWorks Tech Radar. You can use this template to build your own AI-Powered Tech Radar Advisor for your company or group of companies. Target Audience This template is perfect for: Tech Audit & Governance Leaders:** Those seeking to build a tech landscape AI platform portal. Tech Leaders & Architects:** Those aiming to provide modern AI platforms that help others understand the rationale behind strategic technology adoption. Product Managers:** Professionals looking to align product innovation with the company's current tech trends. IT & Engineering Teams:** Teams that need to aggregate, analyze, and visualize technology data from multiple sources efficiently. Digital Transformation Experts:** Innovators aiming to leverage AI for actionable insights and strategic recommendations. Data Analysts & Scientists:** Individuals who want to combine structured SQL analysis with advanced semantic search using vector databases. Developers:** Those interested in integrating RAG chatbot functionality with conversation storage. 1. Description Tech Constellation is an AI-powered Tech Radar solution designed to help organizations visualize and steer their technology adoption strategy. It seamlessly ingests data from a Tech Radar Google Sheet—converting it into both a MySQL database and a vector index—to consolidate your tech landscape in one place. The platform integrates an interactive AI chat interface powered by four specialized agents: AI Agent Router:** Analyzes and routes user queries to the most suitable processing agent. SQL Agent:** Executes precise SQL queries on structured data. RAG Agent:** Leverages semantic, vector-based search for in-depth insights. Output Guardrail Agent:** Validates responses to ensure they remain on-topic and accurate. This powerful template is perfect for technology leaders, product managers, engineering teams, and digital transformation experts looking to make data-driven decisions aligned with strategic initiatives across groups of parent-child companies. 2. Features Data Ingestion A Google Sheet containing tech radar data is used as the primary source. The data is ingested and converted into a MySQL database. Simultaneously, the data is indexed into a vector database for semantic (vector-based) search. Interactive AI Chat Chat Integration:** An AI-powered chat interface allows users to ask questions about the tech radar. Customizable AI Agents:** AI Agent Router: Determines the query type and routes it to the appropriate agent. SQL Agent: Processes queries using SQL on structured data. RAG Agent: Performs vector-based searches on document-like data. Output Guardrail Agent: Validates queries and ensures that the responses remain on-topic and accurate. Usage Examples Tell me, is TechnologyABC adopted or on hold, and why? List all the tools that are considered part of the strategic direction for company3 but are not adopted. Project Links & Additional Details GitHub Repository (Frontend Interface Source Code):** github.com/dragonjump/techconstellation Try It:** https://scaler.my

This workflow is designed to process PDF documents using Mistral's OCR capabilities, store the extracted text in a Qdrant vector database, and enable Retrieval-Augmented Generation (RAG) for answering questions. Here’s how it functions: Once configured, the workflow automates document ingestion, vectorization, and intelligent querying, enabling powerful RAG applications. Benefits End-to-End Automation** No manual interaction is needed: documents are read, processed, and made queryable with minimal setup. Scalable and Modular** The workflow uses subflows and batching, making it easy to scale and customize. Multi-Model Support** Combines Mistral for OCR, OpenAI for embeddings, and Gemini for intelligent answering—taking advantage of the strengths of each. Real-Time Q\&A** With RAG integration, users can query document content through natural language and receive accurate responses grounded in the PDF data. Light or Full Mode** Users can choose to index full page content or only summarized text, optimizing for either performance or richness. How It Works PDF Processing with Mistral OCR: The workflow starts by uploading a PDF file to Mistral's API, which performs OCR to extract text and metadata. The extracted content is split into manageable chunks (e.g., pages or sections) for further processing. Vector Storage in Qdrant: The extracted text is converted into embeddings using OpenAI's embedding model. These embeddings are stored in a Qdrant vector database, enabling efficient similarity searches for RAG. Question-Answering with RAG: When a user submits a question via a chat interface, the workflow retrieves relevant text chunks from Qdrant using vector similarity. A language model (Google Gemini) generates answers based on the retrieved context, providing accurate and context-aware responses. Optional Summarization: The workflow includes an optional summarization step using Google Gemini to condense the extracted text for faster processing or lighter RAG usage. Set Up Steps To deploy this workflow in n8n, follow these steps: Configure Qdrant Database: Replace QDRANTURL and COLLECTION in the "Create collection" and "Refresh collection" nodes with your Qdrant instance details. Ensure the Qdrant collection is configured with the correct vector size (e.g., 1536 for OpenAI embeddings) and distance metric (e.g., Cosine). Set Up Credentials: Add credentials for: Mistral Cloud API (for OCR processing). OpenAI API (for embeddings). Google Gemini API (for chat and summarization). Google Drive (if sourcing PDFs from Drive). Qdrant API (for vector storage). PDF Source Configuration: If using Google Drive, specify the folder ID in the "Search PDFs" node. Alternatively, modify the workflow to accept PDFs from other sources (e.g., direct uploads or external APIs). Customize Text Processing: Adjust chunk size and overlap in the "Token Splitter" node to optimize for your document type. Choose between raw text or summarized content for RAG by toggling between the "Set page" and "Summarization Chain" nodes. Test the RAG: Trigger the workflow manually or via a chat message to verify OCR, embedding, and Qdrant storage. Use the "Question and Answer Chain" node to test query responses. Optional Sub-Workflows: The workflow supports execution as a sub-workflow for batch processing (e.g., handling multiple PDFs). Need help customizing? Contact me for consulting and support or add me on Linkedin.

💡 What it is for This workflow helps to automatically discover undocumented API endpoints by analysing JavaScript files from the website's HTML code. When building automation for platforms without public APIs, we face a significant technical barrier. In a perfect world, every service would offer well-documented APIs with clear endpoints and authentication methods. But the reality is different. Before we resort to complex web scraping, let's analyse the architecture of the platform and check whether it makes internal API calls. We will examine JavaScript files embedded in the HTML source code to find and extract potential API endpoints. ⚙️Key Features To discover hidden API endpoints, we can apply two major approaches: 1. Predefined regex extraction: manually insert a fixed regex with the necessary conditions to extract endpoints. Unlike LLM, which creates a custom regex for each JS file, we provide a generic expression to capture all URL strings. We do not want to accidentally miss important API endpoints. 2. AI-supported extraction: ask LLMs to examine the structure of the JavaScript code. The 1st model will: capture potential API endpoints create a detailed description of each identified endpoint with methods and query parameters the 2nd LLM connected to the AI Agent will generate a regex for each JS file individually based on the output of the 1st model. In addition to pure endpoint extraction, we supplement our analysis with: AI regex validation:** the AI Agent calls a validation tool to iteratively improve its regex based on the reference data. Results comparison:** side-by-side analysis of API endpoints extracted with a predefined regex against AI-supported results. ✅Requirements: OpenRouter API access: for AI-powered analysis (Gemini + Claude models by default) Minimal setup: simply configure the target URL and run Platforms: JS files must be accessible and have embedded standard API endpoints patterns (/api/, /v1/, etc.) 💪Use Cases 📚 API documentation: create complete endpoint descriptions for internal APIs 🚀 Automation & integration projects: find the APIs you need when official documentation is missing 🛠 Web scraping projects: discover data access patterns 🔍 Security research: map attack surfaces and explore unprotected endpoints 🎉Extracted the endpoints, what now? To execute API requests, we often need additional information such as query parameters or JSON body data: One way to find out exactly how the request is being made on the platform is to navigate to the Network tab in the Dev Tools console while interacting with the platform. Look for anything that resembles API requests and review the request/response headers, payload and query parameters. Alternatively, you can also check the JS file and the page source code for the required values. ✨Inspiration As a guitarist who also builds workflows, I wanted to automate communication with the booking platform I use in my music project. While trying to connect to the platform from n8n, I ran into a challenge: no public APIs. Fortunately, I found out that the platform I work with was built as a modern web app with client-side JavaScript that contained information about the API structure. This led me to the topic of hidden API endpoints and eventually to this workflow. It is part of my music booking project which I presented at the n8n Community Meetup in Berlin on 22 May 2025.

Enhance your data analysis by connecting an AI Agent to your dataset, using n8n tools. This template teaches you how to build an AI Data Analyst Chatbot that is capable of pulling data from your sources, using tools like Google Sheets or databases. It's designed to be easy and efficient, making it a good starting point for AI-driven data analysis. You can easily replace the current Google Sheets tools for databases like Postgres or MySQL. How It Works The core of the workflow is the AI Agent. It's connected to different data retrieval tools, to get data from Google Sheets (or your preferred database) in many different ways. Once the data is retrieved, the Calculator tool allows the AI to perform mathematical operations, making your data analysis precise. Who is this template for Data Analysts & Researchers:** Pull data from different sources and perform quick calculations. Developers & AI Enthusiasts:** Learn to build your first AI Agent with easy dataset access. Business Owners:** Streamline your data analysis with AI insights and automate repetitive tasks. Automation Experts:** Enhance your automation skills by integrating AI with your existing databases. How to Set Up You can find detailed instructions in the workflow itself. Check out my other templates 👉 https://n8n.io/creators/solomon/

Automate Colombian Cashflow Data Extraction to Google Sheets with AI Who’s it for This workflow is designed for finance professionals, accountants, small business owners in Colombia, or anyone needing to automate the extraction of invoice data and its entry into Google Sheets. It's particularly useful for handling Colombian tax and legal specifics. How it works / What it does This workflow automates the process of extracting critical data from invoices and receipts (PDFs and JPEGs) and organizing it in a Google Sheet: Triggers:** The workflow initiates when a new file is created or an existing file is updated in a designated Google Drive folder. File Handling:** It first downloads the detected file. Routing:** A "Switch" node intelligently routes the file based on its extension – one path for PDFs and another for JPEGs. Data Extraction:** For PDF files, it directly extracts all text content from the document. For JPEG image files, it utilizes an AI Agent (Azure OpenAI) to process the image and extract its textual content. AI-Powered Reasoning:** Two "Reasoning Agent" nodes (Azure OpenAI Chat Models) act as a specialized "Colombian Tax and Legal Extraction Agent". They parse the extracted text from invoices to pull out structured data in JSON format, including: Vendor name. Modification date. Line items with detailed description, sub_total, iva_value, total_amount, category, and sub_category. Specific Colombian tax fields like Retefuente and ReteICA. The number of items generated. Output Parsing:** A "Structured Output Parser" node ensures that the AI's output strictly adheres to a predefined JSON schema, guaranteeing consistent data formatting. Data Preparation:** "Edit Field" nodes ensure the AI's extracted data is in a valid format. Item Splitting:** "Split data" nodes separate the 'items' array from the AI's output, allowing each individual line item from the invoice to be processed as a separate entry for the Google Sheet. Google Sheet Integration:** Finally, "Fill Template" nodes append the fully processed invoice data (per line item) into your designated Google Sheet. How to set up Google Drive Credentials: Ensure you have configured your Google Drive OAuth2 API credentials in n8n. Azure OpenAI Credentials: Set up your Azure OpenAI API credentials, ensuring access to models like gpt-4o. Or you can simply use your traditional OpenAI or others LLMs. Google Sheets Credentials: Configure your Google Sheets OAuth2 API credentials. Google Drive Folder ID: In the "1a. Updated file trigger" and "1b. Created file trigger" nodes, update the folderToWatch parameter with your specific Google Drive Folder ID. Google Sheet ID and Sheet Name: In the "8. Fill Template" and "8. Fill Template1" nodes, update the documentId and sheetName parameters with your specific Google Sheet ID and the name of the sheet where data should be appended. Requirements An active n8n instance. A Google Drive account for file uploads. A Google Sheets account for data storage. An Azure OpenAI account with access to chat models (e.g., gpt-4o) for the "Azure OpenAI Chat Model" nodes and "Extract Data Agent". How to customize the workflow AI Extraction Prompts:** Modify the prompt instructions in the "5. Reasoning Agent" and "5. Reasoning Agent1" nodes if you need to extract different data points or change the output format. Google Sheet Column Mappings:** Adjust the columns mapping in the "8. Fill Template" and "8. Fill Template1" nodes to match your specific Google Sheet headers and data requirements. File Types:** Extend the "3. Route" node to handle additional file types (e.g., DOCX, PNG) by adding new conditions and corresponding extraction nodes.

AI Recipe Generator from Pantry Items using FatSecret API This workflow creates an intelligent WhatsApp cooking assistant that transforms pantry ingredients into personalized recipe suggestions using AI and the FatSecret Recipes API What Makes This Different: AI-Powered Recipe Discovery** - Uses Google Gemini AI to understand user intent and dietary preferences Smart Ingredient Analysis** - Automatically extracts ingredients, dietary restrictions, and cooking constraints FatSecret API Integration** - Leverages comprehensive recipe database with nutritional information WhatsApp Native Experience** - Seamless chat interface for recipe discovery Contextual Memory** - Remembers conversation context for better user experience Intelligent Parameter Mapping** - AI automatically maps user requests to API parameters Key Benefits of AI-Driven Architecture: Natural Language Understanding** - Users can describe what they have in plain English Personalized Recommendations** - Considers dietary restrictions, time constraints, and preferences Eliminates Manual Search** - No need to manually input specific ingredients or filters Scalable Recipe Database** - Access to thousands of recipes through FatSecret API Conversational Interface** - Natural chat flow instead of form-based inputs Smart Context Management** - Remembers previous requests for better follow-up suggestions Who's it for This template is designed for food delivery services, meal planning apps, nutritionists, cooking enthusiasts, and businesses looking to provide intelligent recipe recommendations. It's perfect for companies who want to engage customers through WhatsApp with personalized cooking assistance, helping users discover new recipes based on available ingredients and preferences. How it works / What it does This workflow creates an intelligent WhatsApp cooking assistant that transforms simple ingredient lists into personalized recipe suggestions. The system: Receives WhatsApp messages through webhook triggers Analyzes user input using Google Gemini AI to extract ingredients, dietary needs, and preferences Maps user requests to FatSecret API parameters automatically Searches recipe database based on extracted criteria (ingredients, calories, time, cuisine, etc.) Processes API results to format recipe suggestions with images and nutritional info Maintains conversation context using memory buffer for better user experience Sends formatted responses back to users via WhatsApp Key Innovation: AI-Powered Parameter Extraction - Unlike traditional recipe apps that require users to fill out forms or select from predefined options, this system understands natural language requests and automatically maps them to the appropriate API parameters, making recipe discovery as simple as texting a friend. How to set up 1. Configure WhatsApp Business API Set up WhatsApp Business API credentials Configure webhook endpoints for message reception Set up phone number ID and recipient handling Ensure proper message sending permissions 2. Configure Google Gemini AI Set up Google Gemini (PaLM) API credentials Ensure proper API access and quota limits Configure the AI model for recipe-related conversations Test the AI's understanding of cooking terminology 3. Configure FatSecret API Set up FatSecret OAuth2 API credentials Ensure access to the Recipes Search v3 endpoint Configure proper authentication and rate limiting Test API connectivity and response handling 4. Set up Memory Management Configure the memory buffer for conversation context Set appropriate session key mapping for user identification Adjust context window length based on expected conversation depth Test memory persistence across multiple messages 5. Test the Integration Send test messages through WhatsApp to verify end-to-end functionality Test various ingredient combinations and dietary restrictions Verify recipe suggestions are relevant and properly formatted Check that context memory works across multiple interactions Requirements WhatsApp Business API** account with webhook capabilities Google Gemini AI** API access for natural language processing FatSecret API** credentials for recipe database access n8n instance** with proper webhook and HTTP request capabilities Active internet connection** for real-time API interactions How to customize the workflow Modify Recipe Search Parameters Adjust the number of results returned (currently set to 5) Add more filtering options (cuisine types, cooking methods, difficulty levels) Implement pagination for browsing through more recipe options Add sorting preferences (newest, oldest, calorie-based, popularity) Enhance AI Capabilities Train the AI on specific dietary restrictions or cuisine preferences Add support for multiple languages Implement recipe rating and review integration Add nutritional goal tracking and meal planning features Expand Recipe Sources Integrate with additional recipe APIs (Spoonacular, Edamam, etc.) Add support for user-generated recipes Implement recipe bookmarking and favorites Add shopping list generation from selected recipes Improve User Experience Add recipe step-by-step instructions Implement cooking timer and progress tracking Add recipe sharing capabilities Implement user preference learning over time Business Features Add recipe monetization options Implement affiliate marketing for ingredients Add restaurant delivery integration Implement meal kit subscription services Key Features Natural language processing** - Understands cooking requests in plain English Intelligent parameter mapping** - AI automatically extracts search criteria Comprehensive recipe database** - Access to thousands of recipes via FatSecret API WhatsApp native interface** - Seamless chat experience for recipe discovery Contextual memory** - Remembers conversation history for better recommendations Dietary restriction support** - Handles allergies, preferences, and special diets Nutritional information** - Provides calorie counts and macro details Image integration** - Shows recipe photos when available Technical Architecture Highlights AI-Powered Processing Google Gemini integration** - Advanced natural language understanding Smart parameter extraction** - Automatic mapping of user requests to API calls Contextual memory** - Conversation history management for better user experience Intelligent fallbacks** - Graceful handling of unclear or incomplete requests API Integration Excellence FatSecret Recipes API** - Comprehensive recipe database with nutritional data OAuth2 authentication** - Secure and reliable API access Parameter optimization** - Efficient API calls with relevant search criteria Response processing** - Clean formatting of recipe suggestions WhatsApp Integration Webhook-based triggers** - Real-time message reception Message formatting** - Clean, readable recipe presentations User identification** - Proper session management for multiple users Error handling** - Graceful fallbacks for failed operations Performance Optimizations Efficient API calls** - Single request per user message Memory management** - Optimized conversation context storage Response caching** - Reduced API calls for repeated requests Scalable architecture** - Handles multiple concurrent users Use Cases Food delivery platforms** requiring recipe recommendation engines Meal planning services** needing ingredient-based recipe discovery Nutrition and wellness apps** requiring dietary-specific suggestions Cooking schools** offering personalized recipe guidance Grocery stores** helping customers plan meals around available ingredients Restaurant chains** providing recipe inspiration for home cooking Health coaches** offering personalized meal suggestions Social cooking communities** sharing recipe ideas and inspiration Business Value Customer Engagement** - Interactive recipe discovery increases user retention Personalization** - AI-driven recommendations improve user satisfaction Operational Efficiency** - Automated recipe suggestions reduce manual support Revenue Generation** - Recipe recommendations can drive ingredient sales Brand Differentiation** - AI-powered cooking assistant sets services apart Data Insights** - User preferences provide valuable market intelligence Scalability** - Handles multiple users simultaneously without performance degradation This template revolutionizes recipe discovery by combining the power of AI natural language processing with comprehensive recipe databases, creating an intuitive WhatsApp experience that makes cooking inspiration as simple as having a conversation with a knowledgeable chef friend.

Get a personalized list of garage sales happening today, based on your current location, directly in Telegram each morning! This n8n workflow integrates Home Assistant and Brocabrac.frto: Automatically detect your location every day Scrape and parse garage sale listings from Brocabrac Filter for high-quality and nearby events Send a neatly formatted message to your Telegram account Perfect for treasure hunters and second-hand enthusiasts who want to stay in the loop with zero effort!

🧠 Business Card Scanner – Automate Contact Extraction This workflow automates the process of extracting contact details from business cards (PDF or image) and saving them directly into an n8n Data Table. No more manual data entry — just upload a card and let AI do the rest. ⚙️ How It Works Upload the business card via a web form (PDF or image). The uploaded file is converted to Base64 for processing. The Base64 data is sent to the Mistral OCR API, which extracts text from the image. The OCR output is parsed into JSON. An AI Agent (OpenAI GPT-4o-mini) interprets the extracted text and converts it into structured business card information (e.g., name, company, email, phone). The Structured Output Parser validates and aligns the data with a predefined schema. The workflow upserts (inserts or updates) the contact details into an n8n Data Table named business cards, using the email address as the unique identifier. ✅ Result: Seamless digitization of business cards into structured, searchable contact data. 🧩 Prerequisites Before importing the workflow, make sure you have the following: n8n Instance with access to the Data Table feature OpenAI Platform account and API key (configured in n8n) Mistral AI account and API key (configured in n8n) 🛠️ Setup Steps Import the Workflow Download and import the JSON file into your n8n instance. Create a Data Table Name it business_cards (or adjust the workflow accordingly). Add the following fields: firstname name company jobdescription phone mobil email street postcode place web Configure API Credentials Mistral OCR API → Add your API key under HTTP Bearer Auth. OpenAI API → Add your API key under OpenAI Credentials. Model: gpt-4o-mini (recommended for speed and low cost). Activate the Web Form Trigger Enable the trigger node to make the business card upload form accessible via a public URL. Test the Workflow Upload a sample business card. Confirm that extracted contact data automatically appears in your Data Table. 💡 Example JSON Output { "firstname": "Anna", "name": "Müller", "company": "NextGen Tech GmbH", "jobdescription": "Head of Marketing", "email": "anna.mueller@nextgen.tech", "phone": "+49 821 1234567", "mobil": "+49 170 9876543", "street": "Schillerstraße 12", "postcode": "86150", "place": "Augsburg", "web": "https://nextgen.tech" }

Automated B2B Lead Generation from Google Maps to Google Sheets using BrowserAct This n8n template automates local lead generation by scraping Google Maps for businesses, saving them to Google Sheets, and notifying you in real-time via Telegram. This workflow is perfect for sales teams, marketing agencies, and local B2B services looking to build targeted lead lists automatically. Self-Hosted Only This Workflow uses a community contribution and is designed and tested for self-hosted n8n instances only. How it works The workflow is triggered manually. You can set the Location, Bussines_Category, and number of leads (Extracted_Data) in the first BrowserAct node. A BrowserAct node ("Run a workflow task") initiates the scraping job on Google Maps using your specified criteria. A second BrowserAct node ("Get details of a workflow task") pauses the workflow and waits for the scraping task to be 100% complete. A Code node takes the raw JSON string output from the scraper and correctly parses it, splitting the data into individual items (one for each business). A Google Sheets node appends or updates each lead into your spreadsheet, matching on the "Name" column to prevent duplicate entries. Finally, a Telegram node sends a message with the new lead's details to your specified chat, providing instant notification. Requirements BrowserAct** API account for web scraping BrowserAct* "Google Maps Local Lead Finder*" Template BrowserAct** n8n Community Node -> (n8n Nodes BrowserAct) Google Sheets** credentials for saving leads Telegram** credentials for sending notifications Need Help? How to Find Your BrowseAct API Key & Workflow ID How to Connect n8n to Browseract How to Use & Customize BrowserAct Templates How to Use the BrowserAct N8N Community Node Workflow Guidance and Showcase AUTOMATE Local Lead Generation: Google Maps to Sheets & Telegram with n8n

This workflow automates the process of analyzing a contract submitted via a web form. It extracts the text from an uploaded PDF, uses AI to identify potential red flags, and sends a summary report to a Telegram chat. Prerequisites Before you can use this workflow, you'll need a few things set up. 1. JotForm Form You need to create a form in JotForm with at least two specific fields: Email Address**: A standard field to collect the user's email. File Upload**: This field will be used to upload the contract or NDA. Make sure to configure it to allow .pdf files. 2. API Keys and IDs JotForm API Key**: You can generate this from your JotForm account settings under the "API" section. Gemini API Key**: You'll need an API key from Google AI Studio to use the Gemini model. Telegram Bot Token**: Create a new bot by talking to the @BotFather on Telegram. It will give you a unique token. Telegram Chat ID**: This is the ID of the user, group, or channel you want the bot to send messages to. You can get this by using a bot like @userinfobot. Node-by-Node Explanation Here is a breakdown of what each node in the workflow does, in the order they execute. 1. JotForm Trigger What it does**: This node kicks off the entire workflow. It actively listens for new submissions on the specific JotForm you select. How it works**: When someone fills out your form and hits "Submit," JotForm sends the submission data (including the email and a link to the uploaded file) to this node. 2. Grab Attachment Details (HTTP Request) What it does**: The initial data from JotForm doesn't contain a direct download link for the file. This node takes the submissionID from the trigger and makes a request to the JotForm API to get the full details of that submission. How it works**: It constructs a URL using the submissionID and your JotForm API key to fetch the submission data, which includes the proper download URL for the uploaded contract. 3. Grab the Attached Contract (HTTP Request) What it does**: Now that it has the direct download link, this node fetches the actual PDF file. How it works**: It uses the file URL obtained from the previous node to download the contract. The node is set to expect a "file" as the response, so it saves the PDF data in binary format for the next step. 4. Extract Text from PDF File What it does**: This node takes the binary PDF data from the previous step and extracts all the readable text from it. How it works**: It processes the PDF and outputs plain text, stripping away any formatting or images. This raw text is now ready to be analyzed by the AI. 5. AI Agent (with Google Gemini Chat Model) What it does**: This is the core analysis engine of the workflow. It takes the extracted text from the PDF and uses a powerful prompt to analyze it. The "Google Gemini Chat Model" node is connected as its "brain." How it works**: It sends the contract text to the Gemini model. The prompt instructs Gemini to act as an expert contract analyst. It specifically asks the AI to identify major red flags and hidden/unfair clauses. It also tells the AI to format the output as a clean report using Telegram's MarkdownV2 style and to keep the response under 1500 characters. 6. Send a text message (Telegram) What it does**: This is the final step. It takes the formatted analysis report generated by the AI Agent and sends it to your specified Telegram chat. How it works**: It connects to your Telegram bot using your Bot Token and sends the AI's output ($json.output) to the Chat ID you've provided. Because the AI was instructed to format the text in MarkdownV2, the message will appear well-structured in Telegram with bolding and bullet points.