Documentation - RipAI

• Route: `/docs`
• URL: https://rippdf.com/docs
• Source file: `src/pages/Docs.jsx`

Page Summary

Developer documentation for RipAI. Learn how to install, configure, and integrate RipAI into your AI workflows.

Key Headings

• H1: RipAI + Pinecone: End-to-End Vector Ingestion Guide
• H3: 1. Prerequisites and Environment Setup
• H3: 2. Configure Your Pinecone Index
• H3: 3. Batch Ingestion Script
• H3: 4. Retrieval Test Script

Page Content Extract

• Getting Started
• Installation Guide
• Ingesting to Pinecone
• RipAI Loader
• PDF Convertibility Score
• RipAI + Pinecone: End-to-End Vector Ingestion Guide
• RipAI Data Packs are decoupled, pre-chunked, and ready to ingest into Pinecone for production AI retrieval.
• 1. Prerequisites and Environment Setup
• Install Python 3.8+.
• Create a Pinecone account (Starter tier works).
• Install required libraries:
• Expected RipAI Data Pack structure:
• 2. Configure Your Pinecone Index
• Open Pinecone dashboard and create an API key.
• Create an index with settings that match your embedding model.
• rippdf-index
• Capacity Mode:
• 3. Batch Ingestion Script
• ingest_rippdf.py
• next to your
• folder and paste this script:
• Before running, replace
• YOUR_PINECONE_API_KEY_HERE
• with your real Pinecone API key.
• Run ingestion:
• 4. Retrieval Test Script
• After ingestion, create
• test_retrieval.py
• to verify your query-to-chunk matching:
• Make sure this script also uses your real Pinecone API key in
• PINECONE_API_KEY
• Run retrieval test:
• If retrieval returns high scores plus clean markdown chunks, your RipAI Data Packs are successfully integrated for production AI pipelines.

Canonical References

• https://rippdf.com/ai/docs.md
• https://rippdf.com/ai/product.md