🎬 Generate LinkedIn posts using Telegram, Supabase vector DB and OpenAI RAG

⚡ 26 views · 🎬 Content Creation & Video

💡 Pro Tip — If you’re pulling LinkedIn data through HTTP requests or dealing with API restrictions, there’s a community node called ScraperNode that handles this natively. It has dedicated scrapers for profiles, companies, jobs, and people search — you just pass a URL and get structured data back.

Description

Overview

AI-powered n8n workflow that creates viral LinkedIn posts by learning from successful content. Features two modules: (1) Telegram-based scraper that builds a vector database of viral LinkedIn posts, and (2) Web form that generates optimized posts using multi-agent AI with RAG (Retrieval-Augmented Generation) from your curated viral content library.

Key Capabilities:

• Scrapes LinkedIn post content via Telegram bot
• Stores posts in Supabase vector database with OpenAI embeddings
• 3-agent system analyzes hooks, structures outlines, and generates posts
• RAG integration retrieves similar viral posts for pattern matching
• Auto-publishes to LinkedIn or provides formatted output

How It Works

Module 1: Viral Post Collection (Telegram Bot)

Step 1: URL Validation

• User sends LinkedIn post URL to Telegram bot
• Workflow validates URL contains “linkedin.com”
• Shows typing indicator for better UX

Step 2: Content Scraping

• HTTP request fetches post HTML
• CSS selector extracts main commentary: [data-test-id="main-feed-activity-card__commentary"]
• Handles scraping failures with error messages

Step 3: Vector Storage

• Converts post text to OpenAI embeddings (text-embedding-ada-002)
• Stores in Supabase linkedin_post table with vector indexing
• Sends success confirmation via Telegram

Module 2: AI Post Generation (Web Form)

Stage 1: Hook Analysis Agent

• Input: User-provided hook text
• Process: AI extracts topic, niche/industry, emotional tone, and 3-5 key points
• Output: Structured JSON with analyzed elements
• Models: GPT-4o-mini or Gemini 2.5-flash (dual fallback)

Stage 2: Post Structure Agent

• Input: Analyzed hook data
• Process: Creates 5-section outline (Hook, Problem, Value/Lesson, Solution, CTA)
• Output: Structured framework for final post
• Models: GPT-4o-mini or Gemini 2.5-flash

Stage 3: Post Generator Agent (RAG)

• Input: Post structure + topic
• RAG Process:
  • Queries Supabase vector store for 5 most similar viral posts
  • Analyzes patterns: hooks, storytelling, CTAs, engagement metrics
  • Identifies optimal length, formatting, and emotional triggers
• Output: Complete LinkedIn post applying viral patterns
• Models: GPT-4o-mini or Gemini 2.5-flash with GPT-5-NANO for structured output

Stage 4: Publication

• Auto-publishes to LinkedIn via API
• Or returns formatted post text for manual posting

How To Use

Setup

1. Configure Supabase Vector Database

• Create Supabase project
• Create table: linkedin_post with vector column (1536 dimensions for OpenAI embeddings)
• Enable vector extension: CREATE EXTENSION vector;
• Update credentials in “Upload Document” and “Supabase Vector Store” nodes

2. Set Up Telegram Bot (Module 1)

• Create bot via @BotFather
• Get bot token and update “On Telegram Message” credentials
• Start bot and get your chat ID
• Activate workflow

3. Configure OpenAI API

• Add API key to “Embeddings” nodes (both modules)
• Configure language model credentials (GPT-4o-mini, GPT-5-NANO)

4. Set Up LinkedIn API (Optional for Module 2)

• Create LinkedIn app with member permissions
• Configure OAuth2 credentials in “Create a post” node
• Or remove node to get text output only

5. Access Web Form

• Get form URL from “LinkedIn Form” webhook
• Bookmark for easy access

🔗 Nodes Used

HTTP Request, Telegram, Telegram Trigger, LinkedIn, AI Agent, Embeddings OpenAI

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup