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Chat with news articles using AI analysis in Telegram with vector search

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Chat with news articles using AI analysis in Telegram with vector search preview
Open on n8n.io

Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

Overview This workflow allows users to send any newspaper or article link to a Telegram bot. The workflow then: 1. Validates the URL 2. Scrapes the webpage (title, description, full text, images...

Best for

  • Document Extraction automation workflows
  • AI RAG automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.telegram, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.splitout, n8n-nodes-base.httprequest, n8n-nodes-base.noop, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.documentdefaultdataloader

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Mehedi Ahamed.

Original n8n.io source

1.1 Workflow description

Title
Chat with news articles using AI analysis in Telegram with vector search
Workflow name
Chat with news articles using AI analysis in Telegram with vector search

πŸ“Œ Overview

This workflow allows users to send any newspaper or article link to a Telegram bot. The workflow then:

  1. Validates the URL
  2. Scrapes the webpage (title, description, full text, images, OG metadata)
  3. Processes it using a Vision-Language Model (VLM)
  4. Generates structured summaries & highlights
  5. Downloads images (if available)
  6. Sends a formatted report + document back to Telegram
  7. Stores the summary in a vector database
  8. Allows users to chat with the article using semantic search

Perfect for: βœ” News researchers βœ” Students βœ” Journalists βœ” Telegram-based AI assistants βœ” 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.

πŸ“€ Outputs

You receive:

βœ” Telegram message summary βœ” Downloadable summary .txt file βœ” Extracted images (if available) βœ” Chat-based Q&A agent to explore the newspaper details


πŸš€ Use Cases

  • News summarization bots
  • Media intelligence agents
  • Educational news explorers
  • Topic-based daily digest creators

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Send a document

Type / Role
n8n-nodes-base.telegram - telegram
Config choices
Version 1.2

Block 2 - Structured Output Parser1

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.3

Block 3 - VLM Agent2

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 4 - VLM Agent3

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.2

Block 5 - Split Out

Type / Role
n8n-nodes-base.splitOut - splitOut
Config choices
Version 1

Block 6 - HTTP Request1

Type / Role
n8n-nodes-base.httpRequest - httpRequest
Config choices
Version 4.2

Block 7 - No Operation, do nothing1

Type / Role
n8n-nodes-base.noOp - noOp
Config choices
Version 1

Block 8 - Embeddings OpenAI

Type / Role
@n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices
Version 1.2

Block 9 - Default Data Loader

Type / Role
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices
Version 1.1

Block 10 - Insert Data to Store

Type / Role
@n8n/n8n-nodes-langchain.vectorStoreInMemory - vectorStoreInMemory
Config choices
Version 1.2

Block 11 - Query Data Tool

Type / Role
@n8n/n8n-nodes-langchain.vectorStoreInMemory - vectorStoreInMemory
Config choices
Version 1.2

Block 12 - AI Agent

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 2

Block 13 - Sticky Note2

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 14 - Sticky Note3

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 15 - Code

Type / Role
n8n-nodes-base.code - code
Config choices
Version 2

Block 16 - Sticky Note

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 17 - Check Whether URL

Type / Role
n8n-nodes-base.if - if
Config choices
Version 2.2

Block 18 - Listen to Telegram for Link

Type / Role
n8n-nodes-base.telegramTrigger - telegramTrigger
Config choices
Version 1.2

Block 19 - Rename Link Field

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 20 - Sticky Note1

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 21 - VLM Run Highlighter

Type / Role
@n8n/n8n-nodes-langchain.agent - agent
Config choices
Version 1.7

Block 22 - Sticky Note4

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 23 - Check URLs Validity

Type / Role
n8n-nodes-base.if - if
Config choices
Version 2.2

Block 24 - Covert to Text File

Type / Role
n8n-nodes-base.convertToFile - convertToFile
Config choices
Version 1.1

Showing the first 24 of 30 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow Chat with news articles using AI analysis in Telegram with vector search
Complexity advanced
Nodes 30
Categories Document Extraction, AI RAG
Author Mehedi Ahamed
Published 19 Nov 2025

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/10996/10996.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Chat with news articles using AI analysis in Telegram with vector search do?

Overview This workflow allows users to send any newspaper or article link to a Telegram bot. The workflow then: 1. Validates the URL 2. Scrapes the webpage (title, description, full text, images...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Document Extraction, AI RAG use case.