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RAG Without Vectors: How PageIndex Retrieves by Reasoning

MarkTechPost / 4/26/2026

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Key Points

  • The article argues that most RAG systems fail at retrieval because they depend on vector similarity, which is only a weak stand-in for true relevance.
  • It highlights the need for relevance that is explicitly grounded in reasoning rather than just “closest embedding” matches.
  • It focuses on long, professional documents (e.g., financial reports, research papers, and legal texts) where reasoning-based retrieval may be more effective.
  • It introduces “PageIndex” as an approach for retrieving answers through reasoning without relying on vector embeddings.
  • Overall, the piece positions reasoning-based retrieval as a more reliable way to support accurate RAG responses in complex documents.

Retrieval is where most RAG systems quietly break. Traditional pipelines rely on vector similarity—embedding queries and document chunks into the same space and fetching the “closest” matches. But similarity is a weak proxy for what we actually need: relevance grounded in reasoning. In long, professional documents—like financial reports, research papers, or legal texts—the right answer […]

The post RAG Without Vectors: How PageIndex Retrieves by Reasoning appeared first on MarkTechPost.

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