Source link : https://tech365.info/this-tree-search-framework-hits-98-7-on-paperwork-the-place-vector-search-fails/
A brand new open-source framework referred to as PageIndex solves one of many outdated issues of retrieval-augmented era (RAG): dealing with very lengthy paperwork.
The traditional RAG workflow (chunk paperwork, calculate embeddings, retailer them in a vector database, and retrieve the highest matches primarily based on semantic similarity) works nicely for primary duties similar to Q&A over small paperwork.
PageIndex abandons the usual “chunk-and-embed” technique totally and treats doc retrieval not as a search downside, however as a navigation downside.
However as enterprises attempt to transfer RAG into high-stakes workflows — auditing monetary statements, analyzing authorized contracts, navigating pharmaceutical protocols — they’re hitting an accuracy barrier that chunk optimization can’t resolve.
AlphaGo for paperwork
PageIndex addresses these limitations by borrowing an idea from game-playing AI slightly than search engines like google and yahoo: tree search.
When people want to seek out particular data in a dense textbook or a protracted annual report, they don’t scan each paragraph linearly. They seek the advice of the desk of contents to establish the related chapter, then the part, and eventually the particular web page. PageIndex forces the LLM to copy this human conduct.
As a substitute of pre-calculating vectors, the framework builds a “Global Index” of the doc’s construction, making a tree the place nodes signify chapters, sections, and subsections….
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Author : tech365
Publish date : 2026-01-31 00:25:00
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