📚 AI Book Summarizer with Vector Search – n8n Automation

Overview

This n8n workflow automates the process of summarizing uploaded books from Google Drive using vector databases and LLMs. It uses Cohere for embeddings , Qdrant for storage and retrieval , and DeepSeek or your preferred LLM for summarization and Q&A. Designed for researchers, students, and productivity enthusiasts!

Result Example

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Problem 🛠️

⏳ Reading full books or papers to extract core ideas can take hours.
🧠 Manually summarizing or searching inside long documents is inefficient and overwhelming.

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Solution ✅

Use this workflow to:

• Upload a book to Google Drive 📥
• Auto-split and embed the content into Qdrant 🔍
• Summarize it using DeepSeek or another LLM 🤖
• Store the final summary back to Google Drive 📤
• Clean up the vector store afterward 🧹

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🔥 Result

⚡ Instant AI-generated book summary
💡 Ability to perform semantic search and question-answering
📁 Summary saved back to your cloud
🧠 Enhanced productivity for learning and research

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Setup ⚙️ (4–8 minutes)

1. Google Drive Setup

• 🔗 Connect Google Drive credentials
• 📁 Create an input folder (e.g., book_uploads)
• 📁 Create an output folder (e.g., book_summaries)
• ⚡ Trigger: Use File Created node to monitor book_uploads
• 📥 Summary will be saved in book_summaries

2. LLM & Embeddings Setup

• 🔑 Create and test API keys for:
  • DeepSeek/OpenAI for summarization
  • Cohere for embeddings
  • Qdrant for vector storage
• 🧪 Ensure all credentials are added in n8n

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How It Works 🌟

1. 📂 A file is uploaded to Google Drive
2. ⬇️ File is downloaded
3. 🧱 It's processed, split into chunks, and sent to Qdrant using Cohere embeddings
4. ❓ A Q &A chain with vector retriever performs information extraction
5. 🧠 A DeepSeek AI Agent analyzes and summarizes the book
6. 📤 The summary is saved to your Drive
7. 🧽 The Qdrant vector collection is deleted (clean-up)

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What’s Included 📦

• ✅ Google Drive integration (input/output)
• ✅ File chunking and embedding using Cohere
• ✅ Vector storage with Qdrant
• ✅ Q&A with vector retrieval
• ✅ Summarization via DeepSeek or other LLM
• ✅ Clean-up for minimal storage overhead

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Customization 🎨

You can tailor it to your use case:

• 🧑‍🏫 Adjust summarization prompt for study notes or executive summaries
• 🌍 Add translation node for multilingual support
• 🔍 Enable long-term memory by skipping vector deletion
• 📨 Send summaries to Notion, Slack, or Email
• 🧩 Use other LLM providers (OpenAI, Claude, Gemini, etc.)

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🌐 Explore more workflows

❤️ Buy more workflows at: adamcrafts
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