Automates the process of generating, storing, and publishing engaging LinkedIn posts derived from books (PDFs) using AI…
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 Scheduled Publishing Every day: Pulls an unpublished post Publishes it to LinkedIn Updates the post's status and timestamp in the Google Sheet ⚙️ Setup Guide 📂 Google Drive Create a folder for book PDFs Connect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. An n8n automation workflow template by Mohamed Abdelwahab.
Mohamed Abdelwahab
web
free
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