Mehedi Ahamed
Templates by Mehedi Ahamed
Chat with news articles using AI analysis in Telegram with vector search
📌 Overview This workflow allows users to send any newspaper or article link to a Telegram bot. The workflow then: Validates the URL Scrapes the webpage (title, description, full text, images, OG metadata) Processes it using a Vision-Language Model (VLM) Generates structured summaries & highlights Downloads images (if available) Sends a formatted report + document back to Telegram Stores the summary in a vector database Allows users to chat with the article using semantic search Perfect for: ✔ News researchers ✔ Students ✔ Journalists ✔ Telegram-based AI assistants ✔ Automated media monitoring --- 🧠 What the Workflow Does Telegram Trigger Listens for messages from the user. Detects if the message contains a valid link. URL Scraper A custom n8n Code node fetches the webpage and extracts: Meta description paragraph text All image sources Open Graph metadata (og:title, og:image) Returns everything as structured JSON. VLM Run – Highlighter A Vision-Language Model analyzes the scraped content and outputs: json { "news_summary": { "headline": "", "source_url": "", "published_date": "", "key_points": "", "summary": "", "extractedimagesurl": "" } } Image Validation & Download Checks if image URLs are valid. Downloads them (if any). Sends them to Telegram as documents. Summary File Generation Converts VLM output into a .txt report. Sends the report back to the user. Vector Store + Q&A Agent Converts the summary into embeddings. Stores the vector in an in-memory store. Provides the user with a chat interface: Ask anything about the newspaper article. The AI agent retrieves information using the vector store. --- 📤 Outputs You receive: ✔ Telegram message summary ✔ Downloadable summary .txt file ✔ Extracted images (if available) ✔ Chat-based Q&A agent to explore the newspaper details --- 🚀 Use Cases News summarization bots Media intelligence agents Educational news explorers Topic-based daily digest creators
By Mehedi AhamedAutomate X-ray analysis with VLM Orion and distribute to Gmail, Telegram & Drive
📌 Overview This workflow provides an automated pipeline for processing medical X-ray images using VLM Run (model: vlmrun-orion-1:auto), and distributing the AI-generated analysis to multiple channels—email, Telegram, and Google Drive. --- ⚙️ How It Works Upload X-Ray Image A Form Trigger allows the user to upload an X-ray file. Once the image is submitted, the workflow immediately starts processing. --- Automated X-Ray Analysis The uploaded X-ray image is sent to VLM Run (vlmrun-orion-1:auto) via an OpenAI-compatible endpoint. The model returns: A text-based interpretation or description A disease-highlighted output image (if detected) A URL reference pointing to the annotated result image stored in Google Cloud --- Extract Artifact From artifact reference, download file using artifact node. --- Generate Report File The Convert to File node transforms the analysis text into a shareable .txt report. This file is used both for email and Drive storage. --- Send Notifications to Gmail & Telegram The workflow automatically: 📧 Emails the doctor (or configured staff email): The diagnostic description The generated report file The annotated X-ray image 📨 Sends a Telegram message containing: The same report The disease-highlighted X-ray image This ensures instant notification and cross-platform availability. --- Upload to Google Drive The final step uses Google Drive OAuth2 to store: The report file The annotated medical image These files are uploaded to a designated Drive folder for archiving and future reference. --- 🧩 Key Features ✔️ Automated X-ray processing using VLM Run ✔️ Structured extraction of annotated medical images ✔️ Multi-channel notification (Email + Telegram) ✔️ Centralized archive via Google Drive ✔️ Zero manual intervention after upload ✔️ Works with OpenAI-compatible VLM endpoints --- 🔧 Requirements VLM Run API Credentials Required to call vlm-agent-1 for image analysis. Gmail OAuth2 Credentials Needed to automatically email the diagnostic report. Telegram Bot Token Sends analysis results to a Telegram chat or group. Google Drive OAuth2 Stores reports and annotated images in Google Drive. --- 📎 Notes This workflow automates image handling and communication. All AI-generated content must be reviewed by a qualified medical professional before any clinical use.
By Mehedi AhamedProcess Image Auto segmentation & Detection with VLM Run, Drive-Telegram Sharing
This workflow automates image processing using VLM Run, extracting signed URLs, downloading results, and distributing them via multiple channels (Google Drive & Telegram). ✅ Key Features Upload image files through a Form Trigger. Process images through two VLM Run agents simultaneously: Segmentation Agent → extracts objects. Detection Agent → generates bounding boxes. Webhooks capture completed results asynchronously. Arifact Node download the images. Downloaded images are automatically: Uploaded to Google Drive Sent to a Telegram chat ⚙️ Workflow Flow User Uploads File → Form Trigger node VLM Run Agents → Segmentation & Detection agents Webhook Nodes → Receive processed results Artifact Node → Download the artifacts Distribution Nodes → Upload to Google Drive & Telegram 📌 Notes Ensure Google Drive OAuth2 credentials have upload permissions. Telegram Bot token and chat ID must be configured correctly. Workflow allows multi-channel sharing of images automatically. --- ⚠️ Community Node Disclaimer > This workflow uses VLM Run node
By Mehedi Ahamed