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 hook, insight, CTA.
6. Updates a 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  • Pulls an unpublished post
  • Publishes 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