How do you hit 98.7% accuracy with RAG on complex documents?
How do you hit 98.7% accuracy with RAG on complex documents?
Traditional RAG often fails because semantic similarity isn't true relevance. For tough domains, you need a system that reasons.
This is where 𝗣𝗮𝗴𝗲𝗜𝗻𝗱𝗲𝘅 shines.
It's a new, vectorless RAG framework that mimics how a human expert analyzes a document.
Instead of chunking and embedding, 𝗣𝗮𝗴𝗲𝗜𝗻𝗱𝗲𝘅 builds a "table-of-contents" tree and uses tree search to find the most relevant information.
Why is this better?
- 𝗛𝘂𝗺𝗮𝗻-𝗹𝗶𝗸𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: It simulates how experts navigate complex information.
- 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀: The reasoning is traceable and interpretable. No more "vibe retrieval."
- 𝗡𝗼 𝗔𝗿𝗯𝗶𝘁𝗿𝗮𝗿𝘆 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: It respects the document's natural structure.
- 𝗡𝗼 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕: The system relies purely on LLM reasoning and document hierarchy.
- This approach achieved state-of-the-art results on the FinanceBench benchmark.
Check it out: github(.)com/VectifyAI/PageIndex
Labels:
News