VectifyAI / PageIndex

# PageIndex: Vectorless, Reasoning-based RAG
 Reasoning-based RAG  ◦  No Vector DB, No Chunking  ◦  Context-Aware Retrieval  ◦  Reads Like a Human 
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 Updates 
- [Aug '26] 🔥 **PageIndex SDK**: **local** and **cloud** modes — one client to index, retrieve, and chat on your machine (with your own LLM API key) or on PageIndex Cloud.
- [Aug '26] ⚡ **PageIndex Flash**: fast tree index generation for text-based PDFs, now the default indexing method in PageIndex SDK local mode.
- Scale PageIndex to Millions of Documents: *PageIndex File System* is a file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document.
- PageIndex App: a human-like document analysis agent for long professional documents. 
# What is PageIndex?
Are you frustrated with vector database retrieval accuracy for long and complex documents? Vector-based RAG retrieves by semantic **similarity**. But **similarity ≠ relevance** — what retrieval actually needs is relevance, and relevance requires **reasoning**. On professional documents that demand contextual understanding, domain expertise, and multi-step reasoning, similarity search misses what is relevant but not similar, and returns what is similar but not relevant.
Inspired by AlphaGo, **PageIndex** replaces the vector index with a **hierarchical tree index** and lets an LLM **reason** its way through it, the way a human expert turns to and reads the right section of a long report. Retrieval happens in two steps:
1. **Index**: generate a **tree-structure index** for each document
2. **Retrieve**: agentically **search that tree** with LLM reasoning
### 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 . 
### Compare with Vector RAG
| | Vector RAG | **PageIndex** |
|---|---|---|
| **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. |
It is ideal for financial reports, legal documents, regulatory filings, technical manuals, medical literature, academic textbooks, and any other long, complex professional document.
# Quickstart
 [code] 
 [code] 
### Model Recommendations
- **`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.
**Have a Claude subscription?** PageIndex can use your subscription to call Claude models through Claude Code: usage is covered by your plan, no API