Automated book summarization with DeepSeek AI, Qdrant Vector DB & Google Drive
📚 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!
Problem 🛠️
⏳ Reading full books or papers to extract core ideas can take hours. 🧠 Manually summarizing or searching inside long documents is inefficient and overwhelming.
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 🧹
🔥 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
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 Creatednode to monitorbook_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
How It Works 🌟
- 📂 A file is uploaded to Google Drive
- ⬇️ File is downloaded
- 🧱 It's processed, split into chunks, and sent to Qdrant using Cohere embeddings
- ❓ A Q&A chain with vector retriever performs information extraction
- 🧠 A DeepSeek AI Agent analyzes and summarizes the book
- 📤 The summary is saved to your Drive
- 🧽 The Qdrant vector collection is deleted (clean-up)
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
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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Automated Book Summarization with DeepSeek AI and Qdrant Vector DB
This n8n workflow automates the process of summarizing books from new files added to a specified Google Drive folder. It leverages DeepSeek AI for summarization and Qdrant as a vector database for efficient retrieval and context management.
What it does
This workflow streamlines the book summarization process through the following steps:
- Monitors Google Drive: It triggers whenever a new file is added to a designated Google Drive folder.
- Loads Document Content: It reads the content of the newly added document.
- Splits Document into Chunks: The document's text is broken down into smaller, manageable chunks using a Recursive Character Text Splitter.
- Generates Embeddings: Cohere Embeddings are created for each text chunk.
- Stores Embeddings in Qdrant: These embeddings are then stored in a Qdrant Vector Store, making the document searchable and retrievable by similarity.
- Retrieves Relevant Information: A Vector Store Retriever fetches relevant text chunks from Qdrant based on the summarization query.
- Summarizes with DeepSeek AI: An AI Agent, powered by the DeepSeek Chat Model, uses a Question and Answer Chain to generate a comprehensive summary of the book.
- Extracts Key Information: An Information Extractor node is used to pull out specific, structured data from the generated summary.
Prerequisites/Requirements
To use this workflow, you will need:
- n8n Instance: A running n8n instance.
- Google Drive Account: Configured with credentials in n8n to monitor a specific folder.
- DeepSeek AI API Key: For the DeepSeek Chat Model.
- Cohere API Key: For generating embeddings.
- Qdrant Instance: A running Qdrant Vector Database instance, accessible by n8n.
- Langchain Nodes: Ensure the
@n8n/n8n-nodes-langchainpackage is installed in your n8n instance.
Setup/Usage
- Import the Workflow: Download the provided JSON and import it into your n8n instance.
- Configure Credentials:
- Google Drive: Set up your Google Drive OAuth2 credentials and select the folder you wish to monitor for new book files in the "Google Drive Trigger" node.
- DeepSeek AI: Provide your DeepSeek AI API Key in the "DeepSeek Chat Model" node.
- Cohere: Provide your Cohere API Key in the "Embeddings Cohere" node.
- Qdrant: Configure your Qdrant credentials (e.g., host, API key) in the "Qdrant Vector Store" node.
- Activate the Workflow: Once all credentials are set and configurations are complete, activate the workflow.
Now, whenever a new book file (e.g., PDF, TXT, DOCX) is added to your specified Google Drive folder, the workflow will automatically process it, generate a summary using DeepSeek AI, and extract key information.
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