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

1. Telegram Trigger

Listens for messages from the user. Detects if the message contains a valid link.

2. 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.

3. VLM Run – Highlighter

A Vision-Language Model analyzes the scraped content and outputs:

{ "news_summary": { "headline": "", "source_url": "", "published_date": "", "key_points": "", "summary": "", "extracted_images_url": "" } }

4. Image Validation & Download

Checks if image URLs are valid. Downloads them (if any). Sends them to Telegram as documents.

5. Summary File Generation

Converts VLM output into a .txt report. Sends the report back to the user.

6. 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