🔍 Build a PDF Q&A system with LlamaIndex, OpenAI embeddings & Pinecone vector DB
⚡ 637 views · 🔍 AI RAG & Knowledge Retrieval
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 Contentfunction 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.
🔗 Nodes Used
HTTP Request, Google Drive, Google Drive Trigger, Embeddings OpenAI, Recursive Character Text Splitter, Pinecone Vector Store
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File