n8n Google Drive to PGVector Embeddings (OpenAI + LangChain)

Automatically turn your Google Drive documents into PGVector embeddings—no manual indexing required

This n8n workflow watches a Google Drive folder for new files, extracts their text, generates OpenAI text-embedding-3-small embeddings with LangChain, and stores them in a Postgres + PGVector database for semantic search and RAG use cases. Once embedded, files are moved to a separate “vectorized” folder to prevent duplication.

What this workflow does

• Monitors a Google Drive folder for new content (manual trigger or scheduled run; default is 3 AM daily).
• Supports multiple file types: PDF, TXT, and JSON.
• Extracts and splits text using a LangChain Text Splitter approach (implemented via batching and file extraction steps).
• Generates embeddings using OpenAI with the text-embedding-3-small model.
• Stores vectors in Postgres with PGVector so your data becomes query-ready for semantic retrieval.
• Prevents re-processing by moving successfully processed files to a dedicated “vectorized” folder.

Use cases

• RAG AI agents that need ongoing knowledge ingestion from private documents
• Semantic search across internal PDFs, notes, and JSON content
• Automated document pipelines for indexing, classification, or downstream AI workflows
• Knowledge ingestion for AI assistants without manual chunking or embedding jobs

Technical details

• Integrations: Google Drive OAuth2 (Search Folder, Download File, Move File)
• AI/embeddings: OpenAI Embeddings (text-embedding-3-small) + LangChain Text Splitter
• Vector storage: Postgres with PGVector (Postgres PGVector Store node)
• n8n workflow behavior: Manual trigger or Scheduled trigger, batch splitting, file extraction, and deduplication via folder move