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: pip install -U pageindex now ships local mode: index, retrieve, and chat entirely on your machine with your own LLM key, or point the same client at PageIndex Cloud with an API key.
• [Aug '26] ⚡ PageIndex Flash: tree structure generation from PDFs in seconds, with structure extracted heuristically from the document's own layout info instead of built by an LLM.
• 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
Unit	fixed-size chunks	natural sections
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

pip install -U pageindex

import os
from pageindex import PageIndexClient

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

client = PageIndexClient(
    index="gpt-5.6-luna",               # model to build the tree index
    chat="gpt-5.6-sol",                 # model to search the tree
)
doc_id = client.submit_document("report.pdf")["doc_id"]

answer = client.chat("What was the 2023 operating margin?", doc_id=doc_id)
print(answer)

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.

Use PageIndex through the SDK client →

Configure other models, streaming, multi-document search, citations, and more.

Integrate PageIndex with your own agent →

Drop PageIndex tools into the OpenAI Agents SDK, the Claude Agent SDK, or any other framework.

Benchmarks

Local indexing cost and time

Building a tree locally runs about $0.001 per page with gpt-5.6-luna as the index model, so a 1,000-page textbook costs a little over a dollar and a few minutes, once, and every later question reuses it. PageIndex is designed not to rely heavily on the model used at index time, so in our experiments a basic model does not hurt quality.

Indexing time also scales predictably with document length. In the same local setup, the benchmark documents (9 to 1,098 pages) finished in roughly 13 seconds to 4.5 minutes.

Query cost and accuracy

PageIndex-OSS-Benchmark measures exactly the setup in the quickstart above (PageIndexClient() in local mode, flash indexing, no OCR) on 62 lookup questions over 34 PDFs (1,945 pages) drawn from MMLongBench-Doc-V2. Every question's answer is a fact stated in running text, so a wrong answer is a retrieval or reading failure, not a reasoning one.

Full results, data, and the runner are in the benchmark repo.

Cost per query vs. native PDF input

The alternative to retrieval is handing the model the whole PDF on every question. That cost grows with the document; PageIndex's does not, because it reads only the nodes its reasoning reaches. On documents where both routes return the same answer, native PDF input costs 2.1× more at 52 pages and 16.6× more at 420 (gpt-5.6-sol, prompt caching excluded) — and at 805 pages the document no longer fits in the context window at all.

Leading accuracy on FinanceBench

PageIndex reached a state-of-the-art 98.7% accuracy on FinanceBench (financial document QA benchmark), vastly outperforming vector-based RAG.

Explore the full FinanceBench evaluation results and the blog post.

PageIndex Cloud

The open-source version is ideal for text-heavy PDFs and local workflows. With PageIndex Cloud, document indexing and storage run in the cloud: PageIndex handles parsing, OCR, image understanding, tree-index construction, and managed storage for you. The chat and retrieval layer remains compatible with your model, so you can search the cloud-hosted index using the model provider your application already uses.

Moving indexing and storage from Local to Cloud only requires a PageIndex API key:

import os
from pageindex import PageIndexClient

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

client = PageIndexClient(
    index="cloud",                       # build and store the index in PageIndex Cloud
    chat="gpt-5.6-sol",                  # use your preferred compatible model for chat
)
doc_id = client.submit_document("report.pdf", wait=True)["doc_id"]
print(client.chat("What was the 2023 operating margin?", doc_id=doc_id))

Capability	Local (this repo)	Cloud (get an API key)
Best for	text-heavy PDFs and local workflows	scanned, image-heavy, and large document collections
Indexing	runs locally	runs in PageIndex Cloud, with production OCR and image understanding
Storage	local	managed in PageIndex Cloud
Chat model	your model	your model, or the managed chat included with your key
Citations	page-level	line-level
Image understanding	—	✅
Multi-document scale	manual	PageIndex File System
MCP server	—	✅

More About PageIndex Cloud

• Scale PageIndex to Millions of Documents: PageIndex File System is a Cloud-only, file-level tree indexing layer that lets PageIndex reason over an entire corpus, not just a single document.

Ready to Try It?

• Get a PageIndex API key
• Read the PageIndex Cloud documentation

For dedicated deployment (VPC or on-premises), contact us or book a demo.

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Please cite this work as:

Mingtian Zhang, Yu Tang and PageIndex Team,
"PageIndex: Next-Generation Vectorless, Reasoning-based RAG",
PageIndex Blog, Sep 2025.

Or use the BibTeX citation.

@article{zhang2025pageindex,
  author = {Mingtian Zhang and Yu Tang and PageIndex Team},
  title = {PageIndex: Next-Generation Vectorless, Reasoning-based RAG},
  journal = {PageIndex Blog},
  year = {2025},
  month = {September},
  note = {https://pageindex.ai/blog/pageindex-intro},
}

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