Automated Vector Indexing of Google Drive Documents

This n8n workflow streamlines the process of automatically indexing documents stored in a specific Google Drive folder into a Pinecone vector database, enabling advanced search and retrieval capabilities. When a new file is uploaded to the monitored folder, the workflow triggers and begins by fetching all files within the folder. Each file is downloaded, parsed, and split into manageable chunks suitable for embedding. Using OpenAI’s embedding model, the content is transformed into vector representations, which are then upserted into a Pinecone index under a designated namespace. This setup is ideal for semantic search, document Q&A systems, or retrieval-augmented generation (RAG), ensuring that your knowledge base remains current without manual intervention. The process includes essential steps like file detection, retrieval, chunking, embedding generation, and vector storage, making it a robust solution for maintaining a dynamic document index.