📥 Transform Google Drive documents into vector embeddings

Automatically convert documents from Google Drive into vector embeddings using OpenAI, LangChain, and PGVector — fully automated through n8n. --- ⚙️ What It Does This workflow monitors a Google Drive folder for new files, supports multiple file types (PDF, TXT, JSON), and processes them into vector embeddings using OpenAI’s text-embedding-3-small model. These embeddings are stored in a Postgres database using the PGVector extension, making them query-ready for semantic search or RAG-based AI agents. After successful processing, files are moved to a separate “vectorized” folder to avoid duplication. --- 💡 Use Cases - Powering Retrieval-Augmented Generation (RAG) AI agents - Semantic search across private documents - AI assistant knowledge ingestion - Automated document pipelines for indexing or classification --- 🧠 Workflow Highlights - Trigger Options: Manual or Scheduled (3 AM daily by default) - Supported File Types: PDF, TXT, JSON - Embedding Stack: LangChain Text Splitter, OpenAI Embeddings, PGVector - Deduplication: Files are moved after processing - License: CC BY-SA 4.0 - Author: AlexK1919 --- 🛠 What You’ll Need - Google Drive OAuth2 credentials (connected to Search Folder, Download File, and Move File nodes) - OpenAI API Key (used in the Embeddings OpenAI node) - Postgres + PGVector database (connected in the Postgres PGVector Store node) --- 🔧 Step-by-Step Setup Instructions 1. Create Google OAuth2 credentials in n8n and connect them to all Google Drive nodes. 2. Set your source folder ID in the Search Folder node — this is where incoming files are placed. 3. Set your processed folder ID in the Move File node — files will be moved here after vectorization. 4. Ensure you have a PGVector-enabled Postgres instance and input the table name and collection in the Postgres PGVector Store node. 5. Add your OpenAI credentials to the Embeddings OpenAI node and select text-embedding-3-small. 6. Optional: Activate the Schedule Trigger node to run daily or configure your own schedule. 7. Run manually by triggering When clicking ‘Test workflow’ for on-demand ingestion. --- 🧩 Customization Tips Want to support more file types or enhance the pipeline? - Add new extractors: Use Extract from File with other formats like DOCX, Markdown, or HTML. - Refine logic by file type: The Switch node routes files to the correct extraction method based on MIME type (application/pdf, text/plain, application/json). - Pre-process with OCR: Add an OCR step before extraction to handle scanned PDFs or images. - Add filters: Enhance the Search Folder or Switch node logic to skip specific files or folders. --- 📄 License This workflow is available under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. You are free to use, adapt, and share this workflow for non-commercial purposes under the terms of this license. Full license details: