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.