Index Google Drive Docs to Pinecone with OpenAI Embeddings for RAG

This workflow automates the indexing of documents from a specific Google Drive folder into a Pinecone vector database using OpenAI embeddings. It triggers on new file uploads, searches and downloads files, loops over each one, loads and parses the content, splits text into chunks (e.g., 600 chars with 60 overlap), generates embeddings, and upserts them into Pinecone with custom metadata.

Ideal for building dynamic knowledge bases, it keeps your vector store updated effortlessly, enabling retrieval-augmented generation (RAG) pipelines, semantic search, and AI-powered Q&A systems over internal docs like SOPs, course materials, or technical guides.

Benefits include zero manual effort for maintaining fresh data, scalable batch processing, and production-grade integration of Google Drive, OpenAI, and Pinecone. Perfect for AI agents, chatbots, or search tools, saving hours on data preparation and enhancing accuracy in LLM applications.

Setup requires connecting credentials for Google Drive, OpenAI API, and Pinecone index/namespace, making it intermediate-level for teams leveraging vector databases.