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.

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