GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
PageIndex is a Python-based tool for indexing documents for retrieval-augmented generation tasks, emphasizing reasoning-based retrieval without heavy reliance on vector databases.
Good fit when
- Use PageIndex when you need to index documents for retrieval-augmented generation tasks that require reasoning-based retrieval.
- Use it when you want to minimize costs and time for indexing documents, as it costs about $0.001 per page and scales predictably with document length.
Avoid when
- Do not use PageIndex if your application heavily relies on vector databases for document retrieval, as PageIndex is designed to work without them.
- Avoid PageIndex if you need to handle documents larger than the context window of the model you are using, as PageIndex may not be able to process them.
Observed Sep 18, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 18, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 18, 2026
- Security (OSV)
- 36 low (36 low)
- As of Sep 18, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install PageIndexHow it fits your stack(9)
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Overview
PageIndex is a tool for indexing documents for retrieval-augmented generation (RAG) tasks, focusing on reasoning-based retrieval without heavy reliance on vector databases.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Sep 18, 2026
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README
Local indexing cost and time Building a tree locally runs about $0.001 per page with 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...
For agents
This page has a .md twin and JSON over the API.