PageIndex

PageIndex is built on a clear critique of vector RAG: chunking loses context and similarity search is brittle. Instead of embeddings, it creates a tree-structured index of your document hierarchy. An LLM agent navigates that tree using the full conversation history, so answers come with a visible path back to source text. That's a meaningful difference if you've ever traced a hallucinated answer to a bad chunk. Strengths: The traceability is the headline—each answer shows which pages and sections it used. Vision-based reading bypasses OCR errors, which helps with scanned PDFs. The File System layer (May 2026) scales to millions of documents by building trees on demand. OpenKB (April 2026) is an open-source LLM knowledge base you can self-host. The 2026 SDK update brings reasoning-based RAG local—works with your own LLM, no vector DB. Weaknesses: Because retrieval decisions run through an LLM, you pay more in latency and cost than vector search. Pricing is undisclosed; you must talk to sales. The ecosystem is young—docs and community are still maturing. If you need sub-100ms responses or have a simple keyword-search use case, this is overkill. Where it fits: Developers who want to avoid vector DB complexity, researchers parsing long reports, enterprises needing auditable AI for compliance. Where it doesn't: real-time apps needing low latency, or teams deeply invested in vector stacks. Bottom line: If explainability and verifiability are non-negotiable, PageIndex is credible and open-source friendly. Pilot it on a real document set to judge cost and quality before committing.