Build a PDF Q&A system with LlamaIndex, OpenAI embeddings & Pinecone vector DB

Parse, Normalize, Extract, and Store PDF Content for RAG in Pinecone This workflow automates a full RAG pipeline for structured documents (like insurance policies). What it does - Watches a Google Drive folder for new PDFs - Uploads to LlamaIndex Cloud for parsing → returns clean Markdown - Normalizes text (removes headers, footers, page numbers, formatting artifacts) - Splits text into chunks (~1200 chars with 150 overlap) - Generates embeddings with OpenAI - Stores vectors in Pinecone with metadata - Connects a Chat Agent that retrieves answers from Pinecone Who’s it for - Developers building chatbots or Q&A systems for structured docs - Teams working with insurance, compliance, or legal PDFs - Anyone who needs to normalize & store documents for semantic search Requirements - Google Drive connected (for source PDFs) - LlamaIndex Cloud account (parsing API key) - Pinecone account (vector DB) - OpenAI account (LLM and embeddings) How to use and customize Update the folder name in google drive trigger node. Place a pdf file in the same folder in google drive. Customize the Normalized Content function node to adjust regex for headers/footers specific to your documents. Adjust chunk size or metadata namespace in the Pinecone node to fit your project needs. ---