README

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

- [Oct '26] 🔥 Page Search API: find the pages that answer each question in about a second, and send your LLM only those. Cut 97% token cost on document question answering; more accurate and faster than vector search (benchmark).
- [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

bash
pip install -U pageindex



python
import os
from pageindex import PageIndexClient

os.environ["OPENAI_API_KEY"] = "your-openai-key"

client = PageIndexClient(
    index="gpt-6-luna",                  model to build the tree index
    chat="gpt-6-sol",                    model to

Top issues

Discussions

Latest release

- Cheaper than vector RAG: for both index and query.
- Faster: trees are created from layout stats.
- Unified SDK: vectorless RAG. Local or cloud, for index and chat.
- Runs on your Claude subscription.

Index, to chat, to agent integration, one client.
Local mode needs no server, no vector DB, no PageIndex API key.

python
client = PageIndexClient()
client.submit_document("report.pdf")
client.chat("What does the report conclude?")


 Highlights

- Flash engine: the local default (mode="standard" keeps the classic LLM pipeline). Embedded bookmarks are consumed when trustworthy, and tree optimi