Chat with News Articles using AI Analysis in Telegram with Vector Search

Description

📌 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