📥 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 privat

Help me set up the n8n workflow "📥 Transform Google Drive Documents into Vector Embeddings" (https://n8n.io/workflows/3647). It uses: Google Drive, Embeddings OpenAI, Recursive Character Text Splitter, Default Data Loader, Postgres PGVector Store. Import the template JSON into my n8n instance, list every credential I need to create, and walk me through testing it.