Introducing PageIndex Flash
PageIndex Flash runs entirely on your own machine and is designed for text-based PDFs. It builds a hierarchical tree index from a PDF's own layout, indexing a 1,000-page document in minutes for about a dollar.
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PageIndex Flash runs entirely on your own machine and is designed for text-based PDFs. It builds a hierarchical tree index from a PDF's own layout, indexing a 1,000-page document in minutes for about a dollar.
PageIndex File System is a file-level tree layer that sits above your documents and scales the same PageIndex tree search from a single document to millions of documents in one index.
We built what Andrej Karpathy described, and solved the hard part. OpenKB is an open-source CLI that compiles raw documents into a structured, interlinked wiki, powered by PageIndex for long PDFs.
We argue that context blindness — the inability of vector-based retrieval to condition on full conversational and reasoning context — is a fundamental limitation of vector RAG, and outline a paradigm shift from semantic similarity to context-dependent relevance classification.
VentureBeat covers PageIndex, the vectorless, reasoning-based RAG framework that uses tree search over document structure to reach 98.7% accuracy on FinanceBench, where vector-based retrieval typically fails.
How PageIndex’s vectorless, reasoning-based RAG overcomes the challenges of traditional vector RAG in long, complex technical manuals.
We benchmarked PageIndex Chat against ChatGPT 5.1 on real-world long documents. PageIndex achieved 100% accuracy compared to ChatGPT 5.1's 59-82%, with faster response times and page-level traceability.
Experience the power of reasoning-based RAG with PageIndex Chat - our new conversational interface for intelligent document understanding.
PageIndex is a vectorless, reasoning-based retrieval framework that simulates how human experts extract knowledge from complex documents. Instead of relying on vector similarity search, it builds a tree-structured index from documents and enables LLMs to perform agentic reasoning over that…
The same bet behind both PageIndex and Claude Code: skip the vector DB and let the LLM itself drive retrieval. We explore the rise of agentic retrieval over vector indexing and PageIndex's agentic, vectorless RAG framework.
We introduce Mafin2.5, which is built based on PageIndex, with a 98.7% accuracy rate on the finance industry question-answering benchmark.
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