VectifyAI/PageIndex

Quickstart

pip install -U pageindex

Compare with Vector RAG

It is ideal for financial reports, legal documents, regulatory filings, technical manuals, medical literature, academic textbooks, and any other long, complex professional document.

• Index — vector index — tree index
• Retrieval — semantic similarity search — LLM reasoning over the tree
• Result — opaque, “vibe retrieval” — traceable to explicit references
• Context — query embedding only — full context: conversation history, domain knowledge, etc.

Model Recommendations

Have a Claude subscription? PageIndex can use your subscription to call Claude models through Claude Code: usage is covered by your plan, no API key needed. Install with pip install -U "pageindex[claude]", then prefix model names with claude-code/, e.g., index="claude-code/haiku", chat="claude-code/opus" (see example).

• index=: a basic model is sufficient. The tree structure itself is extracted from the document layout without an LLM; the index model only summarizes and refines it, which a basic model does well.
• chat=: use the best model you can afford. The chat model searches the tree to retrieve information. See Query cost and accuracy.

TL;DR

PageIndex is a vectorless, reasoning-based RAG engine that mirrors how humans read, delivering traceable, explainable, and context-aware retrieval, with no vector DBs or chunking.