Generate LinkedIn Posts from Books using OpenAI, LangChain & Pinecone Vector Search

Automates the process of generating, storing, and publishing engaging LinkedIn posts derived from books (PDFs) using AI and vector search.

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🧠 Overview

This workflow:

1. Watches a Google Drive folder for new or updated book PDFs.
2. Extracts and embeds the content using OpenAI.
3. Stores the data in a Pinecone vector database.
4. Uses a LangChain agent to generate post ideas.
5. Creates concise LinkedIn posts with hooks, insights, CTAs.
6. Updates to Google Sheet and posts to LinkedIn.

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🛠 Workflow Breakdown

📥 1. Google Drive Trigger

• Trigger: Watches a folder for new or updated PDF files.
• Action: Downloads the updated PDF.

📄 2. Extract and Embed Content

• Extract from File: Parses PDF to extract text.
• Text Splitter: Breaks text into chunks.
• Embeddings (OpenAI): Converts chunks into vector embeddings.
• Pinecone Vector Store: Saves the embeddings with the book name as namespace.

🧠 3. Post Idea Generation (LangChain Agent)

• Uses a prompt to:
  • Search Pinecone DB
  • Extract insights
  • Format into 5 LinkedIn post ideas with:
    • Hook
    • Insight
    • CTA
• Memory buffer and structured output parser are used for clean AI interaction.

✍️ 4. Post Creation

• Each idea is:
  • Split
  • Rewritten with a GPT model prompt to match LinkedIn tone
  • Styled for under 600 characters
  • Includes emojis, hashtags, and tone guidelines

📊 5. Google Sheet Integration

• Saves all generated posts to a Google Sheet .
• Marks status: "published" or "no" .

🔁 6. Scheduled Publishing

• Every day:
  • Pulls an unpublished post
  • Publish it to LinkedIn
  • Updates the post's status and timestamp in the Google Sheet

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⚙️ Setup Guide

📂 Google Drive

• Create a folder for book PDFs
• Connect your Google Drive account to n8n
• Provide access token with file read permission

📊 Google Sheets

• Create a Google Sheet with columns:
  • bookname , hook , insight , cta , postContent , published , date
• Add credentials in n8n with read/write permission

🧠 Pinecone

• Set up a Pinecone project and index ( linkdenpost )
• Namespace will be auto-named using the book filename

🔑 API Credentials Required

• OpenAI API (for embeddings and post generation)
• Pinecone API (for vector storage and retrieval)
• LinkedIn OAuth2 (to publish posts)
• Google Drive & Sheets credentials

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🔁 Flow Summary

 graph TD
 A[Google Drive Trigger] --> B[Download PDF]
 B --> C[Extract Text]
 C --> D[Text Splitter]
 D --> E[Create Embeddings]
 E --> F[Pinecone Vector Store]
 F --> G[LangChain Agent]
 G --> H[Structured Output (5 Post Ideas)]
 H --> I[Split Ideas]
 I --> J[Format as LinkedIn Post (GPT)]
 J --> K[Store in Google Sheet]
 L[Schedule Trigger] --> M[Get Unpublished Post]
 M --> N[Post to LinkedIn]
 N --> O[Mark as Published]

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🧪 Prompt Example (Used in LangChain Agent)

 You are a content strategist. Search the Pinecone vector DB containing a book. Generate 5 unique LinkedIn post ideas with:
 - A Hook (curiosity driven)
 - Insight (summary < 100 words) 
- CTA ("Agree or disagree?", etc.)

 Respond in structured JSON:
 [
 { "Hook": "...", "Insight": "...", "CTA": "..." },
 ...
 ]

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✅ Output Sample

 {
 "Hook": "Why your lab's results might be invalid 😱",
 "Insight": "ISO/IEC 17025 stresses that labs must plan and address risks to impartiality and validity.",
 "CTA": "Does your lab audit for these risks?"
 }

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📆 Schedule Control

• Uses Schedule Trigger to post daily at a set time.
• Ensures automation with LinkedIn and accurate Google Sheet syncing.

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📝 Notes

• Posts remain professional and concise for a LinkedIn audience
• Works with any PDF book
• Supports multi-book pipelines
• You can filter and tag books by filename or folder for segmenting post styles