Who this is for? The Structured Data Extract & Data Mining workflow is crafted for researchers, content analysts, SEO strategists, and AI developers who need to transform semi-structured web data (like markdown content or scraped HTML) into actionable structured datasets. It is ideal for: Content Analysts** - Organizing and mining large volumes of markdown or HTML content. SEO & Trend Researchers** - Exploring topics by location and category. AI Engineers & NLP Developers** - Looking to automate insight extraction from unstructured inputs. Growth Marketers** - Tracking topic-level trends for strategic campaigns. Automation Specialists** - Streamlining workflows from scrape to storage. What problem is this workflow solving? Extracting insights from markdown or HTML documents typically requires manual review, formatting, and parsing. This becomes unscalable when dealing with large datasets or when real-time response is needed. Additionally, trend and topic extraction usually involves external tools, custom scripts, and inconsistent formatting. This workflow solves: Automatic text extraction from markdown or structured content.
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WHandle WhatsApp support chats with OpenRouter, Pinecone, and Gemini
Quick overview This template implements a WhatsApp support suite that logs inbound events to a dashboard API, routes conversations through an OpenRouter-powered AI agent with Pinecone RAG and memory, exposes a webhook for human outbound replies, and provides a webhook to summarize recent chats for handoff. How it works Triggers on WhatsApp Cloud API events and routes status updates (sent/delivered/read) to a dashboard API endpoint for storage. For inbound messages, looks up the contact in the dashboard API and normalizes the message into a consistent schema (sender, type, timestamp, and best-effort content). If the message contains media (image/video/audio/document), fetches the WhatsApp media URL, downloads the file, uploads it to the dashboard’s media endpoint, and attaches the resulting media URL and MIME type. Stores inbound messages and reactions in the dashboard API, then checks via the cases endpoint whether the sender already has an open case. If the inbound message is text and no open case is found, queries Pinecone as a tool (using Google Gemini embeddings), uses an OpenRouter chat model with conversation memory to draft a reply or create a new case via the dashboard API,
AI: Summarize podcast episode and enhance using Wikipedia
The workflow automates the process of creating a summarized and enriched podcast digest, which is then sent via email. Note that to use this template, you need to be on n8n version 1.19.4 or later.
Confirm and log WhatsApp restaurant orders with Groq and Google Sheets
Quick Overview This workflow triggers on incoming WhatsApp orders, uses Groq to extract items and quantities, validates them against a live Google Sheets menu, logs the result to an orders sheet, and replies to the customer with either a clarification request or a priced confirmation (with optional VIP owner alerts). How it works Triggers when a new WhatsApp message is received on your WhatsApp Business Cloud number. Sends the message text to Groq (Llama 3.3) to extract a structured list of ordered items and quantities. Reads the latest menu (item name, price, availability) from a Google Sheets “Menu” tab. Matches extracted items to the menu, checks availability, calculates line subtotals and the total price, and compiles any validation issues. If there are issues, replies to the customer on WhatsApp asking for a corrected order and appends a “needs_clarification” entry to the Google Sheets “Orders” tab. If the order is valid, appends a “confirmed” entry to the Google Sheets “Orders” tab and sends a WhatsApp confirmation with the item summary and total. If the confirmed total meets or exceeds the VIP threshold, sends a separate WhatsApp notification to the owner number. Setup Conne