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

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

Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

Use, adapt, and share – even commercially – as long as you give proper credit and share alike.