RAG Without Vectors: How PageIndex Retrieves by Reasoning

The article "RAG Without Vectors: How PageIndex Retrieves by Reasoning" discusses a novel approach to Retrieval-Augmented Generation (RAG) systems, specifically focusing on an AI agent called PageIndex. Unlike traditional RAG methods that rely heavily on vector embeddings for information retrieval, PageIndex operates without vectors and instead uses reasoning to navigate through the hierarchical structure of documents.

Key Points:

1. PageIndex Overview:

   • Purpose: To provide a more efficient and cost-effective way to retrieve information from structured documents.
   • Mechanism: Instead of embedding text into vector space for similarity searches, PageIndex builds an index that represents the document's hierarchy (sections, subsections, etc.) and uses this structure to reason about where relevant content might be located.

2. How It Works:

   • Step 1: Indexing Documents:

     • The system takes a document and creates a hierarchical tree-like structure representing its sections and subsections.
     • This index is built once, allowing for efficient reuse when querying the same document multiple times without additional costs.

   • Step 2: Query Processing:

     • When a query comes in, PageIndex uses reasoning to navigate through the

Read the full article at MarkTechPost

---

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.