Workflow Templates

Detailed Title "Triathlon Coach AI Workflow: Strava Data Analysis and Personalized Training Insights using n8n" Description This n8n workflow enables you to build an AI-driven virtual triathlon coach that seamlessly integrates with Strava to analyze activity data and provide athletes with actionable training insights. The workflow processes data from activities like swimming, cycling, and running, delivers personalized feedback, and sends motivational and performance improvement advice via email or WhatsApp. Workflow Details Trigger: Strava Activity Updates Node:** Strava Trigger Purpose:** Captures updates from Strava whenever an activity is recorded or modified. The data includes metrics like distance, pace, elevation, heart rate, and more. Integration:** Uses Strava API for real-time synchronization. Step 1: Data Preprocessing Node:** Code Purpose:** Combines and flattens the raw Strava activity data into a structured format for easier processing in subsequent nodes. Logic:** A recursive function flattens JSON input to create a clean and readable structure. Step 2: AI Analysis with Google Gemini Node:** Google Gemini Chat Model Purpose:** Leverages Google Gemini's advanced language model to analyze the activity data. Functionality:** Identifies key performance metrics. Provides feedback and insights specific to the type of activity (e.g., running, swimming, or cycling). Offers tailored recommendations and motivational advice. Step 3: Generate Structured Output Node:** Structure Output Purpose:** Processes the AI-generated response to create a structured format, such as headings, paragraphs, and bullet lists. Output:** Formats the response for clear communication. Step 4: Convert to HTML Node:** Convert to HTML Purpose:** Converts the structured output into an HTML format suitable for email or other presentation methods. Output:** Ensures the response is visually appealing and easy to understand. Step 5: Send Email with Training Insights Node:** Send Email Purpose:** Sends a detailed email to the athlete with performance insights, training recommendations, and motivational messages. Integration:** Utilizes Gmail or SMTP for secure and efficient email delivery. Optional Step: WhatsApp Notifications Node:** WhatsApp Business Cloud Purpose:** Sends a summary of the activity analysis and key recommendations via WhatsApp for instant access. Integration:** Connects to WhatsApp Business Cloud for automated messaging. Additional Notes Customization: You can modify the AI prompt to adapt the recommendations to the athlete's specific goals or fitness levels. The workflow is flexible and can accommodate additional nodes for more advanced analysis or output formats. Scalability: Ideal for individual athletes or coaches managing multiple athletes. Can be expanded to include additional metrics or insights based on user preferences. Performance Metrics Handled: Swimming: SWOLF, stroke count, pace. Cycling: Cadence, power zones, elevation. Running: Pacing, stride length, heart rate zones. Implementation Steps Set Up Strava API Key: Log in to Strava Developers to generate your API key. Integrate the API key into the Strava Trigger node. Configure Google Gemini Integration: Use your Google Gemini (PaLM) API credentials in the Google Gemini Chat Model node. Customize Email and WhatsApp Messaging: Update the Send Email and WhatsApp Business Cloud nodes with the recipient’s details. Automate Execution: Deploy the workflow and use n8n's scheduling features or cron jobs for periodic execution. GET n8n Now N8N COURSE n8n Book Developer Notes Author:** Amjid Ali improvements. Resources:** See in Action: Syncbricks Youtube PayPal: Support the Developer Courses : SyncBricks LMS By using this workflow, triathletes and coaches can elevate training to the next level with AI-powered insights and actionable recommendations.

Description This workflow automatically discovers and collects information about upcoming events in your area or industry. It saves you time by eliminating the need to manually check multiple event websites and provides a centralized database of relevant events. Overview This workflow automatically scrapes websites for upcoming events in your area or industry and compiles them into a structured format. It uses Bright Data to access event listing websites and extract event details like dates, locations, and descriptions. Tools Used n8n:** The automation platform that orchestrates the workflow. Bright Data:** For scraping event websites without being blocked. Calendar/Database:** For storing and organizing event information. How to Install Import the Workflow: Download the .json file and import it into your n8n instance. Configure Bright Data: Add your Bright Data credentials to the Bright Data node. Set Up Data Storage: Configure where you want to store the event data. Customize: Specify locations, event types, and date ranges to monitor. Use Cases Event Planners:** Stay updated on competing or complementary events. Community Managers:** Discover local events to share with your community. Marketing Teams:** Find industry events for networking opportunities. Connect with Me Website:** https://www.nofluff.online YouTube:** https://www.youtube.com/@YaronBeen/videos LinkedIn:** https://www.linkedin.com/in/yaronbeen/ Get Bright Data:** https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #events #eventdiscovery #brightdata #webscraping #eventfinder #localevents #eventcalendar #eventplanning #n8nworkflow #workflow #nocode #eventautomation #eventscraping #eventtracking #upcomingEvents #eventmarketing #eventmanagement #eventdatabase #communityevents #eventnotifications #eventorganizer #eventtech #eventindustry #eventcollection

How It Works Form Submission: The workflow starts with the On form submission node, which triggers when a user submits a contact form. The form collects the user's name, email, and message. Text Classification: The Text Classifier node uses an AI model (GPT-4) to classify the submitted message into one of the predefined categories: Request Quote: For quote requests. Product info: For general product inquiries. General problem: For issues or problems related to products. Order: For questions about placed orders. Other: For any messages that don’t fit the above categories. Email Routing: Based on the classification, the workflow routes the message to the appropriate department via email: Prod. Dep.: For product-related inquiries. Quote Dep.: For quote requests. Gen. Dep.: For general problems. Order Dep.: For order-related questions. Other Dep.: For all other inquiries. Each email includes the user's name, email, message, and the classified category. Data Logging: The workflow logs the form submission and classification results into a Google Sheets document. Each department has its own sheet where the data is appended, including: User’s name, email, and message. Submission date and time. Assigned category. Email recipient details. AI Model Integration: The OpenAI node provides the AI model (GPT-4) used by the Text Classifier to classify the messages. The model is instructed to classify the text into one of the predefined categories without additional explanations. Set Up Steps Configure the Form Trigger: Set up the On form submission node to collect user inputs (name, email, and message) and trigger the workflow. Set Up the Text Classifier: Configure the Text Classifier node to use the OpenAI model (GPT-4) for text classification. Define the categories and their descriptions (e.g., "Request Quote", "Product info", etc.). Set the fallback category to "Other" for unclassifiable messages. Configure Email Sending: Set up the Email Send nodes for each department (Prod. Dep., Quote Dep., Gen. Dep., Order Dep., Other Dep.). Configure the email subject, body, and reply-to address using the form data and classification results. Ensure SMTP credentials are correctly configured for sending emails. Set Up Google Sheets Integration: Configure the Google Sheets nodes to append data to the appropriate sheets for each department. Map the form data (name, email, message, date, category, and recipient) to the corresponding columns in the Google Sheets document. Test the Workflow: Submit a test form to ensure the workflow correctly classifies the message, sends the email to the right department, and logs the data in Google Sheets. Verify that the OpenAI model is classifying messages accurately. Activate the Workflow: Once tested, activate the workflow to automate the process of handling contact form submissions. Key Features Automated Classification**: Uses AI to classify messages into relevant categories, reducing manual effort. Email Routing**: Sends emails to the appropriate department based on the classification. Data Logging**: Logs all form submissions and classification results in Google Sheets for tracking and analysis. Scalability**: Easily adaptable to additional categories or departments by modifying the workflow. This workflow is ideal for eCommerce businesses or customer support teams looking to automate and streamline the handling of contact form submissions. Need help customizing? Contact me for consulting and support or add me on Linkedin.

How it Works This workflow automates the process of handling job applications by extracting relevant information from submitted CVs, analyzing the candidate's qualifications against a predefined profile, and storing the results in a Google Sheet. Here’s how it operates: Data Collection and Extraction: The workflow begins with a form submission (On form submission node), which triggers the extraction of data from the uploaded CV file using the Extract from File node. Two informationExtractor nodes (Qualifications and Personal Data) are used to parse specific details such as educational background, work history, skills, city, birthdate, and telephone number from the text content of the CV. Processing and Evaluation: A Merge node combines the extracted personal and qualification data into a single output. This merged data is then passed through a Summarization Chain that generates a concise summary of the candidate’s profile. An HR Expert chain evaluates the candidate against a desired profile (Profile Wanted), assigning a score and providing considerations for hiring. Finally, all collected and processed data including the evaluation results are appended to a Google Sheets document via the Google Sheets node for further review or reporting purposes [[9]]. Set Up Steps To replicate this workflow within your own n8n environment, follow these steps: Configuration: Begin by setting up an n8n instance if you haven't already; you can sign up directly on their website or self-host the application. Import the provided JSON configuration into your n8n workspace. Ensure that all necessary credentials (e.g., Google Drive, Google Sheets, OpenAI API keys) are correctly configured under the Credentials section since some nodes require external service integrations like Google APIs and OpenAI for language processing tasks. Customization: Adjust the parameters of each node according to your specific requirements. For example, modify the fields in the formTrigger node to match what kind of information you wish to collect from applicants. Customize the prompts given to AI models in nodes like Qualifications, Summarization Chain, and HR Expert so they align with the type of analyses you want performed on the candidates' profiles. Update the destination settings in the Google Sheets node to point towards your own spreadsheet where you would like the final outputs recorded. Need help customizing? Contact me for consulting and support or add me on Linkedin.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This workflow automatically scrapes and summarizes the latest industry news, delivering a curated digest to your team. Stay informed without sifting through countless articles. Overview Bright Data scrapes top news sites, blogs, and press release feeds relevant to your sector. OpenAI summarizes each article and tags it by topic. The daily digest is compiled into Markdown and sent via Slack and email, while full summaries are archived in Notion. Tools Used n8n** – Automation framework Bright Data** – Scrapes news sources reliably OpenAI** – Generates concise summaries and tags Slack & Gmail** – Distributes daily digest Notion** – Stores detailed article notes How to Install Import the Workflow into n8n. Configure Bright Data credentials. Set Up OpenAI API key. Authorize Slack, Gmail, and Notion. Customize Source List & Keywords in the Set node. Use Cases Executive Briefings**: Keep leadership updated. Product Teams**: Track competitor announcements. Marketing**: Identify content trends quickly. Investors**: Monitor sector developments. Connect with Me Website**: https://www.nofluff.online YouTube**: https://www.youtube.com/@YaronBeen/videos LinkedIn**: https://www.linkedin.com/in/yaronbeen/ Get Bright Data**: https://get.brightdata.com/1tndi4600b25 (Using this link supports my free workflows with a small commission) #n8n #automation #industrynews #webscraping #brightdata #openai #newsdigest #n8nworkflow #nocode

This workflow is for automating and centralizing your bookmarking process using AI-powered tagging and seamless integration between your Android device and a self-hosted Read Deck platform (https://readeck.org/en/). This workflow eliminates manual entry, organizes links with smart AI-generated tags, and ensures your bookmarks are always accessible, searchable, and secure. How It Works 📱 Android Shortcut Integration Use the HTTP Shortcuts app to create a 1-tap trigger that sends URLs and titles from your Android phone directly to n8n. 🤖 AI-Powered Tagging & Processing Leverage ChatGPT-4 to analyze content context and auto-generate relevant tags (e.g., “Tech Tutorials,” “Productivity Tools”). Extract clean titles and URLs from messy shared data (even from apps like Twitter or Reddit). 🔗 Readeck Integration Automatically save processed bookmarks to your self-hosted Readeck-like platform with structured metadata (title, URL, tags). ⚡ Silent Automation It runs in the background—no pop-ups or interruptions. 🔒 Pro Security Optional authentication (API tokens, headers) to protect your data. Use Case Perfect for researchers, content creators, or anyone drowning in tabs who wants to: Save articles, videos, or social posts in one click. Organize bookmarks with AI-generated tags. Build a personal knowledge base that’s always accessible. Tutorial 1️⃣ Set Up Android Shortcut Install "HTTP Shortcuts" and configure it to send data to your n8n webhook. Enable “Share Menu” to trigger bookmarks from any app. 2️⃣ Configure n8n Workflow Import the template and add your Read Deck API token (or similar service). 3️⃣ Test & Scale Share a link from your phone—watch it appear in Read Deck instantly! Add error handling or notifications for advanced use. Note: For self-hosted platforms, ensure your instance is publicly accessible (or use a VPN). Why Choose This Workflow? Zero Manual Entry: Save hours of copying/pasting. AI Organization: Say goodbye to chaotic bookmark folders. Privacy First: Host your data on your terms. Transform your bookmarking chaos into a streamlined system—try “Save: Bookmark” today! 🚀

This n8n template empowers IT support teams by automating document ingestion and instant query resolution through a conversational AI. It integrates Google Drive, Pinecone, and a Chat AI agent (using Google Gemini/OpenRouter) to transform static support documents into an interactive, searchable knowledge base. With two interlinked workflows—one for processing support documents and one for handling chat queries—employees receive fast, context-aware answers directly from your support documentation. Overview Document Ingestion Workflow Google Drive Trigger:** Monitors a specified folder for new file uploads (e.g., updated support documents). File Download & Extraction:** Automatically downloads new files and extracts text content. Data Cleaning & Text Splitting:** Utilizes a Code node to remove line breaks, trim extra spaces, and strip special characters, while a text splitter segments the content into manageable chunks. Embedding & Storage:** Generates text embeddings using Google Gemini and stores them in a Pinecone vector store for rapid similarity search. Chat Query Workflow Chat Trigger:** Initiates when an employee sends a support query. Vector Search & Context Retrieval:** Retrieves the top relevant document segments from Pinecone based on similarity scores. Prompt Construction:** A Code node combines the retrieved document snippets with the user’s query into a detailed prompt. AI Agent Response:** The constructed prompt is sent to an AI agent (using OpenRouter Chat Model) to generate a clear, step-by-step solution. Key Benefits & Use Case Imagine a large organization where every IT support document—from troubleshooting guides to system configurations—is stored in a single Google Drive folder. When an employee encounters an issue (e.g., “How do I reset my VPN credentials?”), they simply type the query into a chat interface. Instantly, the workflow retrieves the most relevant context from the ingested documents and provides a detailed, actionable answer. This process reduces resolution times, enhances support consistency, and significantly lightens the load on IT staff. Prerequisites A valid Google Drive account with access to the designated folder. A Pinecone account for storing and retrieving text embeddings. Google Gemini* (or *OpenRouter**) credentials to power the Chat AI agent. An operational n8n instance configured with the necessary nodes and credentials. Workflow Details 1 Document Ingestion Workflow Google Drive Trigger Node:** Listens for file creation events in the specified folder. Google Drive Download Node:** Downloads the newly added file. Extract from File Node:** Extracts text content from the downloaded file. Code Node (Data Cleaning):** Cleans the extracted text by removing line breaks, trimming spaces, and eliminating special characters. Recursive Text Splitter Node:** Segments the cleaned text into manageable chunks. Pinecone Vector Store Node:** Generates embeddings (via Google Gemini) and uploads the chunks to Pinecone. 2 Chat Query Workflow Chat Trigger Node:** Receives incoming user queries. Pinecone Vector Store Node (Query):** Searches for relevant document chunks based on the query. Code Node (Context Builder):** Sorts the retrieved documents by relevance and constructs a prompt merging the context with the query. AI Agent Node:** Sends the prompt to the Chat AI agent, which returns a detailed answer. How to Use Import the Template: Import the template into your n8n instance. Configure the Google Drive Trigger: Set the folder ID (e.g., 1RQvAHIw8cQbtwI9ZvdVV0k0x6TM6H12P) and connect your Google Drive credentials. Set Up Pinecone Nodes: Enter your Pinecone index details and credentials. Configure the Chat AI Agent: Provide your Google Gemini (or OpenRouter) API credentials. Test the Workflows: Validate the document ingestion workflow by uploading a sample support document. Validate the chat query workflow by sending a test query and verifying the returned support information. Additional Notes Ensure all credentials (Google Drive, Pinecone, and Chat AI) are correctly set up and tested before deploying the workflows in production. The template is fully customizable. Adjust the text cleaning, splitting parameters, or the number of document chunks retrieved based on your support documentation's size and structure. This template not only enhances IT support efficiency but also offers a scalable solution for managing and leveraging growing volumes of support content.

Track NFT market trends, collections, and trades in real time—directly from Telegram! This master workflow integrates the OpenSea API, GPT-4o-mini AI, and Telegram, allowing users to request natural-language NFT analytics and receive structured insights instantly. Whether you're an NFT trader, collector, or market analyst, this Telegram-native assistant brings you on-demand market intelligence—powered by OpenSea and AI. > ⚠️ Important: This workflow requires three sub-workflows to function properly. These must be downloaded and published in your n8n instance. 🧩 Required Sub-Workflows To activate this template, download and publish the following workflows: Analyze NFT Market Trends with AI-Powered OpenSea Analytics Agent Tool Get Real-time NFT Insights with OpenSea AI-Powered NFT Agent Tool Get Real-time NFT Marketplace Insights with OpenSea Marketplace Agent Tool 📌 You can also find these by visiting my Creator profile: 👉 https://n8n.io/creators/don-the-gem-dealer/ How It Works A Telegram bot receives a message (e.g., “Top sales for Azuki”). The AI router in this workflow determines which agent should process the request: Marketplace Agent → Listings, offers, and orders Analytics Agent → Sales volume, price trends, wallet behavior NFT Agent → Metadata, traits, ownership info The selected agent queries the OpenSea API using your API key. The response is processed using GPT-4o-mini, formatted, and sent back via Telegram. What You Can Do with This Agent 🔹 Discover undervalued NFTs based on trait rarity and price 🔹 Track market trends for any collection in real time 🔹 Compare collection performance by volume, sales, and listings 🔹 Analyze flipping trends and whale activity across wallets 🔹 Retrieve NFT ownership and metadata instantly 🔹 View trait-specific offers for insight into rarity-driven demand Example Queries You Can Use ✅ "What are the cheapest NFTs in the Pudgy Penguins collection?" ✅ "Get sales volume for Azuki and CloneX over the last 30 days." ✅ "Who owns Bored Ape #456?" ✅ "Show the best current offers for Moonbirds." Set Up Steps Create a Telegram Bot Use @BotFather to create your bot and get the API token. Get an OpenSea API Key Apply for your API key via the OpenSea Developer Portal. Configure n8n Credentials Add your Telegram Bot and OpenSea API Key under Credentials in n8n. Download Required Sub-Workflows Install and publish the following workflows: Analytics Agent Tool NFT Agent Tool Marketplace Agent Tool Deploy & Test Chat with your Telegram bot. Try: "Compare BAYC and Azuki volume" or "Show listings for Doodles." ✅ Final Notes > If your queries don’t respond correctly, make sure all three sub-workflows are installed and published, not just saved. 🚀 Dominate the NFT market with AI-powered OpenSea intelligence—right from your Telegram inbox!

Get deep insights into NFT market trends, sales data, and collection statistics—all powered by AI and OpenSea! This workflow connects GPT-4o-mini, OpenSea API, and n8n automation to provide real-time analytics on NFT collections, wallet transactions, and market trends. It is ideal for NFT traders, collectors, and investors looking to make informed decisions based on structured data. How It Works Receives user queries via Telegram, webhooks, or another connected interface. Determines the correct API tool based on the request (e.g., collection stats, wallet transactions, event tracking). Retrieves data from OpenSea API (requires API key). Processes the information using an AI-powered analytics agent. Returns structured insights in an easy-to-read format for quick decision-making. What You Can Do with This Agent 🔹 Retrieve NFT Collection Stats → Get floor price, volume, sales data, and market cap. 🔹 Track Wallet Activity → Analyze transactions for a given wallet address. 🔹 Monitor NFT Market Trends → Track historical sales, listings, bids, and transfers. 🔹 Compare Collection Performance → View side-by-side market data for different NFT projects. 🔹 Analyze NFT Transaction History → Check real-time ownership changes for any NFT. 🔹 Identify Market Shifts → Detect sudden spikes in demand, price changes, and whale movements. Example Queries You Can Use ✅ "Get stats for the Bored Ape Yacht Club collection." ✅ "Show me all NFT sales from the last 24 hours." ✅ "Fetch all NFT transfers for wallet 0x123...abc on Ethereum." ✅ "Compare the last 3 months of sales volume for Azuki and CloneX." ✅ "Track the top 10 wallets making the most NFT purchases this week." Available API Tools & Endpoints 1️⃣ Get Collection Stats → /api/v2/collections/{collection_slug}/stats (Retrieve NFT collection-wide market data) 2️⃣ Get Events → /api/v2/events (Fetch global NFT sales, transfers, listings, bids, redemptions) 3️⃣ Get Events by Account → /api/v2/events/accounts/{address} (Track transactions by wallet) 4️⃣ Get Events by Collection → /api/v2/events/collection/{collection_slug} (Get sales activity for a collection) 5️⃣ Get Events by NFT → /api/v2/events/chain/{chain}/contract/{address}/nfts/{identifier} (Retrieve historical transactions for a specific NFT) Set Up Steps Get an OpenSea API Key Sign up at OpenSea API and request an API key. Configure API Credentials in n8n Add your OpenSea API key under HTTP Header Authentication. Connect the Workflow to Telegram, Slack, or Database (Optional) Use n8n integrations to send alerts to Telegram, Slack, or save results to Google Sheets, Notion, etc. Deploy and Test Send a query (e.g., "Azuki latest sales") and receive instant NFT market insights! Stay ahead in the NFT market—get real-time analytics with OpenSea’s AI-powered analytics agent!

This n8n workflow automates the transformation of press releases into polished articles. It converts the content of an email and its attachments (PDF or Word documents) into an AI-written article/blog post. What does it do? This workflow assists editors and journalists in managing incoming press-releases from governments, companies, NGOs, or individuals. The result is a draft article that can easily be reviewed by the editor, who receives it in a reply email containing both the original input and the output, plus an AI-generated self-assessment. This self-assessment represents an additional feedback loop where the AI compares the input with the output to evaluate the quality and accuracy of its transformation. How does it work? Triggered by incoming emails in Google, it first filters attachments, retaining only Word and PDF files while removing other formats like JPGs. The workflow then follows one of three paths: If no attachments remain, it processes the inline email message directly. For PDF attachments, it uses an extractor to obtain the document content. For Word attachments, it extracts the text content by a http request. In each case, the extracted content is then passed to an AI agent that converts the press release into a well-structured article according to predefined prompts. A separate AI evaluation step provides a self-assessment by comparing the output with the original input to ensure quality and accuracy. Finally, the workflow generates a reply email to the sender containing three components: the original input, the AI-generated article, and the self-assessment. This streamlined process helps editors and journalists efficiently manage incoming press releases, delivering draft articles that require minimal additional editing." How to set it up 1. Configure Gmail Connection: Create or use an existing Gmail address Connect it through the n8n credentials manager Configure polling frequency according to your needs Set the trigger event to "Message Received" Optional: Filter incoming emails by specifying authorized senders Enable the "Download Attachments" option 2. Set Up AI Integration: Create an OpenAI account if you don't have one Create a new AI assistant or use an existing one Customize the assistant with specific instructions, style guidelines, or response templates Configure your API credentials in n8n to enable the connection 3. Configure Google Drive Integration: Connect your Google Drive credentials in n8n Set the operation mode to "Upload" Configure the input data field name as "data" -Set the file naming format to dynamic: {{ $json.fileName }} 4. Configure HTTP Request Node: Set request method to "POST" Enter the appropriate Google API endpoint URL Include all required authorization headers Structure the request body according to API specifications Ensure proper error handling for API responses 5. Configure HTTP Request Node 2: Set request method to "GET" Enter the appropriate Google API endpoint URL Include all required authorization headers Configure query parameters as needed Implement response validation and error handling 6. Configure Self-Assessment Node: Set operation to "Message a Model" Select an appropriate AI model (e.g., GPT-4, Claude) Configure the following prompt in the Message field: Please analyze and compare the following input and output content: (for example) Original Input: {{ $('HTTP Request3').item.json.data }} {{ $('Gmail Trigger').item.json.text }} Generated Output: {{ $json.output }} Provide a detailed self-assessment that evaluates: Content accuracy and completeness Structure and readability improvements Tone and style appropriateness Any information that may have been omitted or misrepresented Overall quality of the transformation 7. Configure Reply Email Node: Set operation to "Send" and select your Gmail account Configure the "To" field to respond to the original sender: {{ $('Gmail Trigger').item.json.from }} Set an appropriate subject line: RE: {{ $('Gmail Trigger').item.json.subject }} Structure the email body with clear sections using the following template: handlebars EDITED ARTICLE* {{ $('AI Article Writer 2').item.json.output }} SELF-ASSESSMENT* Rating: 1 (poor) to 5 (excellent) {{ $json.message.content }} ORIGINAL MESSAGE* {{ $('Gmail Trigger').item.json.text }} ATTACHMENT CONTENT* {{ $('HTTP Request3').item.json.data }} Note: Adjust the template fields according to the input source (PDF, Word document, or inline message). For inline messages, you may not need the "ATTACHMENT CONTENT" section.

Tags: Supply Chain, Logistics, Control Tower Context Hey! I’m Samir, a Supply Chain Engineer and Data Scientist from Paris, and the founder of LogiGreen Consulting. We design tools to help companies improve their logistics processes using data analytics, AI, and automation—to reduce costs and minimize environmental impact. > Let’s use N8N to build smarter and more sustainable supply chains! 📬 For business inquiries, you can add me on LinkedIn Who is this template for? This workflow template is designed for logistics operations that need a monitoring solution for their distribution chains. Connected to your Transportation Management Systems, this AI agent can answer any question about the shipments handled by your distribution teams. How does it work? The workflow is connected to a Google BigQuery table that stores outbound order data (customer deliveries). Here’s what the AI agent does: 🤔 Receives a user question via chat. 🧠 Understands the request and generates the correct SQL query. ✅ Executes the SQL query using a BigQuery node. 💬 Responds to the user in plain English. Thanks to the chat memory, users can ask follow-up questions to dive deeper into the data. What do I need to get started? This workflow requires no advanced programming skills. You’ll need: A Google BigQuery account with an SQL table storing transactional records. An OpenAI API key (GPT-4o) for the chat model. Next Steps Follow the sticky notes in the workflow to configure each node and start using AI to support your supply chain operations. 🎥 Watch My Tutorial 🚀 Curious how N8N can transform your logistics operations? Notes The chat trigger can easily be replaced with Teams, Telegram, or Slack for a better user experience. You can also connect this to a customer chat window using a webhook. This workflow was built using N8N version 1.82.1 Submitted: March 24, 2025

🧠 This workflow is designed for one purpose only, to bulk-upload structured JSON articles from an FTP server into a Qdrant vector database for use in LLM-powered semantic search, RAG systems, or AI assistants. The JSON files are pre-cleaned and contain metadata and rich text chunks, ready for vectorization. This workflow handles Downloading from FTP Parsing & splitting Embedding with OpenAI-embedding Storing in Qdrant for future querying JSON structure format for blog articles { "id": "article_001", "title": "reseguider", "language": "sv", "tags": ["london", "resa", "info"], "source": "alltomlondon.se", "url": "https://...", "embedded_at": "2025-04-08T15:27:00Z", "chunks": [ { "chunk_id": "article_001_01", "section_title": "Introduktion", "text": "Välkommen till London..." }, ... ] } 🧰 Benefits ✅ Automated Vector Loading Handles FTP → JSON → Qdrant in a hands-free pipeline. ✅ Clean Embedding Input Supports pre-validated chunks with metadata: titles, tags, language, and article ID. ✅ AI-Ready Format Perfect for Retrieval-Augmented Generation (RAG), semantic search, or assistant memory. ✅ Flexible Architecture Modular and swappable: FTP can be replaced with GDrive/Notion/S3, and embeddings can switch to local models like Ollama. ✅ Community Friendly This template helps others adopt best practices for vector DB feeding and LLM integration.