📥 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.

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⚙️ 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.

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💡 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