PageIndex vs RAG: Why Traditional Retrieval is Broken (PageIndex AI Tutorial)
What if the way we’ve been building AI document chatbots is fundamentally flawed? In this video, we explore PageIndex AI, a revolutionary approach to document QA that achieves 98.7% accuracy without using vector databases, chunking, or embeddings.
We break down the core debate of PageIndex vs RAG—explaining why traditional Retrieval Augmented Generation often fails on structured documents like financial reports and legal contracts. You'll learn how PageIndex RAG utilizes a "Reasoning Tree" to navigate documents like a human expert, providing complete transparency and an audit trail for every …
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Chapters (16)
The Problem: Why RAG is Broken
1:12
How Traditional RAG Works (And why it fails)
2:06
Problem 1: Arbitrary Chunking vs. Context
2:33
Problem 2: Similarity is NOT Relevance
2:56
Problem 3: The Black Box Issue
3:54
What is PageIndex AI?
4:23
The Architecture: Building a Reasoning Tree
5:04
Step 1: Tree Search (Cognitive Navigation)
5:37
Step 2: Grounded Answer Generation
7:00
Why PageIndex Beats Chunking for Financial Reports
8:12
Benchmarks: 98.7% Accuracy on FinanceBench
9:13
Hands-on Tutorial: Setting up the Pipeline
9:53
Code Walkthrough: Installing PageIndex Library
10:41
Uploading Documents & Indexing
11:19
Visualizing the AI Reasoning Tree
12:41
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