Build a PDF Q&A System with LlamaIndex, OpenAI Embeddings & Pinecone Vector DB
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Alok Kumar
Description
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.