Snapshot
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| Attribute | Detail |
|---|---|
License | MIT license |
Retrieval model | Tree index with LLM reasoning |
Local mode | Text-based PDFs; indexing runs on your machine and storage is local |
Cloud mode | Managed indexing and cloud storage for text-based, scanned, and image-rich documents |
Evaluate PageIndex
Where it fits
The tree-based workflow is designed to make retrieval traceable to explicit references rather than relying on vector similarity alone.
The SDK separates the model used to build the index from the model used to search it, so teams can choose them independently.
Cloud adds parsing, OCR, image understanding, tree-index construction, and managed storage while keeping chat and retrieval compatible with the application’s model.
Trade-offs
Local mode is positioned for text-heavy PDFs. Workflows involving scans, images, folders, metadata, block-level citations, or an MCP server point to the Cloud offering, so deployment choice affects both document coverage and operational ownership.
Cost considerations
The repository is MIT licensed. For local indexing, PageIndex’s benchmark says building a tree locally runs about $0.001 per page with gpt-5.6-luna as the index model; this is a one-time indexing workload, and later questions reuse the index. Local SDK examples use an LLM API key, while the Cloud example requires a PageIndex API key and uses a compatible chat model. Actual cost therefore depends on the selected model and whether indexing and storage are run locally or in PageIndex Cloud.
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
209 GitHub commits 90d40.3k GitHub stars0 vulnerabilities across 1 package
See all signals from 3 sources
PyPI · pageindex@0.5.3
Sources
This review is based on PageIndex’s official GitHub repository and website captures dated 2026-10-27.