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.

🧠 Overview

This workflow: Watches a Google Drive folder for new or updated book PDFs. Extracts and embeds the content using OpenAI. Stores the data in a Pinecone vector database. Uses a LangChain agent to generate post ideas. Creates concise LinkedIn posts with hook, insight, CTA. Updates a Google Sheet and posts to LinkedIn.

🛠 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 Scheduled Publishing Every day: Pulls an unpublished post Publishes it to LinkedIn Updates the post's status and timestamp in the Google Sheet

⚙️ 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

🔁 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]

🧪 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": "..." }, ... ]

✅ 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?" }

📆 Schedule Control

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

📝 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